From 357152c6b2ced5cafadbdcb5864841811fd35da2 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 10 Apr 2023 21:14:40 +0900 Subject: [PATCH 01/71] create load meg data --- configs/config_GOD.yaml | 59 ++++ data/GOD/montage.csv | 174 +++++++++++ {speech_decoding => meg_decoding}/__init__.py | 0 .../dataclass/brennan2018.py | 0 meg_decoding/dataclass/god.py | 47 +++ .../dataclass/gwilliams2022.py | 0 meg_decoding/matlab_utils/create_montage.py | 36 +++ meg_decoding/matlab_utils/load_meg.py | 66 ++++ {speech_decoding => meg_decoding}/models.py | 0 .../utils/bcolors.py | 0 .../utils/get_dataloaders.py | 0 .../utils/layout.py | 4 + .../utils/loss.py | 0 .../utils/preproc_utils.py | 0 .../utils/reproducibility.py | 0 .../utils/wav2vec_util.py | 0 train_wowandb.py | 281 ++++++++++++++++++ 17 files changed, 667 insertions(+) create mode 100644 configs/config_GOD.yaml create mode 100644 data/GOD/montage.csv rename {speech_decoding => meg_decoding}/__init__.py (100%) rename {speech_decoding => meg_decoding}/dataclass/brennan2018.py (100%) create mode 100644 meg_decoding/dataclass/god.py rename {speech_decoding => meg_decoding}/dataclass/gwilliams2022.py (100%) create mode 100644 meg_decoding/matlab_utils/create_montage.py create mode 100644 meg_decoding/matlab_utils/load_meg.py rename {speech_decoding => meg_decoding}/models.py (100%) rename {speech_decoding => meg_decoding}/utils/bcolors.py (100%) rename {speech_decoding => meg_decoding}/utils/get_dataloaders.py (100%) rename {speech_decoding => meg_decoding}/utils/layout.py (91%) rename {speech_decoding => meg_decoding}/utils/loss.py (100%) rename {speech_decoding => meg_decoding}/utils/preproc_utils.py (100%) rename {speech_decoding => meg_decoding}/utils/reproducibility.py (100%) rename {speech_decoding => meg_decoding}/utils/wav2vec_util.py (100%) create mode 100644 train_wowandb.py diff --git a/configs/config_GOD.yaml b/configs/config_GOD.yaml new file mode 100644 index 0000000..e53e944 --- /dev/null +++ b/configs/config_GOD.yaml @@ -0,0 +1,59 @@ +# ==== Dataset ==== # +dataset: GOD # for Brennan2018, override with Brennan2018 in CLI +rebuild_dataset: False + +# ==== Training ==== # +use_wandb: False +wandb: + project: speech_decoding + entity: sensho + run_name: word-onsets +use_sampler: True # applicable to Gwilliams only +reproducible: False # NOTE: do we need it at all? +split_ratio: 0.8 # train. FIXME for valid +split_mode: shallow # sentence, shallow, deep +num_workers: 6 +batch_size: 64 +updates: 1200 +lr: 3e-4 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +D1: 270 +D2: 320 +F: 512 # NOTE: because if you set last4layers=False, then it's set to 1024 in the dataset class +K: 32 +d_drop: 0.1 # for spatial attention, drop channels within d_drop of a randomly selected channel + +init_temperature: 5.1 +wav2vec_model: facebook/wav2vec2-large-xlsr-53 # (HuggingFace) # xlsr_53_56k (FAIR) + +# == Data pre-processing parameters === # +preprocs: + audio_resample_rate: 16000 # before wav2vec + lowpass_filter_width: 128 + brain_resample_rate: 120 # Hz + brain_filter_low: 1.0 # Hz + brain_filter_high: 60 # Hz + seq_len_sec: 3 # segment length in seconds + baseline_len_sec: 0.5 # baseline period in seconds + shift_brain: True # whether to shift M/EEG into the future relative to audio + shift_len: 150 # if True, by how many ms + last4layers: True # if True, the brain_encoder's emsize will be 1024, not 512 + subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + clamp: True + clamp_lim: 20 + +memory_efficient: True + +# ==== +num_subjects: 2 + +# ==== Logging ==== # +hydra: + job: + chdir: True diff --git a/data/GOD/montage.csv b/data/GOD/montage.csv new file mode 100644 index 0000000..c0cfd66 --- /dev/null +++ b/data/GOD/montage.csv @@ -0,0 +1,174 @@ +0.1586078281800682,0.028752821774559574,0.08398781179726497 +0.14947005676845906,0.05460706272001638,0.08589998605562807 +0.13257387170845222,0.07985556576812787,0.09175225882306513 +0.15646545313852744,-0.004832937661943781,0.1075151459471555 +0.1422890408553612,0.05175590621979743,0.11356716278602455 +0.12512716181666245,0.0746990045923511,0.11986631737426412 +0.11329546861975057,0.06732781797120801,0.14281748627502944 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speech_decoding/dataclass/brennan2018.py rename to meg_decoding/dataclass/brennan2018.py diff --git a/meg_decoding/dataclass/god.py b/meg_decoding/dataclass/god.py new file mode 100644 index 0000000..dc55d66 --- /dev/null +++ b/meg_decoding/dataclass/god.py @@ -0,0 +1,47 @@ +import os, sys +from re import sub +import torch +import torchaudio +import torchaudio.functional as F +from torch.utils.data import Sampler, Dataset +import numpy as np +import pandas as pd +import glob +import json +from natsort import natsorted +import scipy.io +import mne, mne_bids +from tqdm import tqdm +import ast +from typing import Union +from termcolor import cprint +from pprint import pprint +from einops import rearrange +from sklearn.preprocessing import RobustScaler, StandardScaler + +from speech_decoding.utils.wav2vec_util import load_wav2vec_model, getW2VLastFourLayersAvg + +mne.set_log_level(verbose="WARNING") + + +class GODDatasetBase(Dataset): + def __init__(self, args, preprocess_pipleine:list=[]): + pass + + def __len__(self): + return len(self.Y) + + def __getitem__(self, i): # NOTE: i is id of a speech segment + + i_in_task, task = self.segment_to_task(i) + + key_no_task = np.random.choice(list(self.X.keys())) + X = self.X[key_no_task][task] # ( 208, ~100000 ) + onset = self.meg_onsets[key_no_task][task][i_in_task] # scalar + + # NOTE: Extract MEG segment. Doing this to save memory from overlapping segments + X = X[:, onset : onset + self.seq_len_samp] # ( 208, 360 ) + + subject_idx = np.where(self.valid_subjects == key_no_task.split("_")[0])[0][0] + + return X, self.Y[i], subject_idx \ No newline at end of file diff --git a/speech_decoding/dataclass/gwilliams2022.py b/meg_decoding/dataclass/gwilliams2022.py similarity index 100% rename from speech_decoding/dataclass/gwilliams2022.py rename to meg_decoding/dataclass/gwilliams2022.py diff --git a/meg_decoding/matlab_utils/create_montage.py b/meg_decoding/matlab_utils/create_montage.py new file mode 100644 index 0000000..c14b9b0 --- /dev/null +++ b/meg_decoding/matlab_utils/create_montage.py @@ -0,0 +1,36 @@ +import scipy.io +import scipy +import numpy as np +import os +import csv + +def create_montage(filename:str, savefile:str): + data = scipy.io.loadmat(filename) + channel_infos = data['Channel'][0] + n_ch = len(channel_infos) + assert n_ch == 203, 'tell Yang-san' + montage_list = [] + for i in range(n_ch): + loc_array = channel_infos[i][4] + if len(loc_array) > 0: + assert loc_array.ndim == 2, 'MEG montage file requires 3x8 corrdinates for cube.' + montage_list.append(list(np.mean(loc_array, axis=1))) + + with open(savefile, 'w') as f: + writer = csv.writer(f) + writer.writerows(montage_list) + print('montage file is saved as ', savefile) + + + + + +if __name__ == '__main__': + DATAROOT = '/work/project/MEG_GOD/GOD_dataset/' + + filename = os.path.join(DATAROOT, 'channel_ricoh_acc1.mat') + savefile = './data/GOD/montage.csv' + + create_montage(filename, savefile) + + diff --git a/meg_decoding/matlab_utils/load_meg.py b/meg_decoding/matlab_utils/load_meg.py new file mode 100644 index 0000000..9393835 --- /dev/null +++ b/meg_decoding/matlab_utils/load_meg.py @@ -0,0 +1,66 @@ +import scipy.io +import scipy +import numpy as np +import os +import csv +import numpy as np +from hydra import initialize, compose + + +def get_meg_data(args, meg_filepath:str, label_filepath:str, trigger_filepath:str): + data = scipy.io.loadmat(meg_filepath) + """ + dict_keys(['__header__', '__version__', '__globals__', 'F', 'Std', + 'Comment', 'ChannelFlag', 'Time', 'DataType', 'Device', 'nAvg', + 'Leff', 'Events', 'ColormapType', 'DisplayUnits', 'History']) + """ + MEG_Data = data['F'] # 203 x 391000 = ch x time sample + assert len(MEG_Data) == 203 + if args.fs is None: + sampling_rate = MEG_Data.shape[1] / data['Time'] # 1000 Hz + else: + sampling_rate = args.fs + + # Event_Data = data['Events'][0] + # assert Event_Data[1][0][0] == 'visual', '{}'.format(Event_Data[0][1][0][0]) + # onset_timing = Event_Data[1][3][0] # trial + # assert len(onset_timing) == 600 # GOD is 600 + + Label_Data = scipy.io.loadmat(label_filepath) + """ + dict_keys(['__header__', '__version__', '__globals__', 'vec_image', 'vec_index']) + """ + image_features = Label_Data['vec_image'] + assert image_features.shape == (600, 512) # n_samples x image_feature_dims + labels = Label_Data['vec_index'][0] # index of image + assert len(labels) == 600 # n_samples + + Trigger_Data = scipy.io.loadmat(trigger_filepath) + """ + dict_keys(['__header__', '__version__', '__globals__', 'trigger']) + """ + triggers = Trigger_Data['trigger'][0] + assert len(triggers) == 600 # stimulus onset timing + + + + import pdb; pdb.set_trace() + +if __name__ == '__main__': + DATAROOT = '/work/project/MEG_GOD/GOD_dataset/' + + with initialize(version_base=None, config_path="../../../configs/"): + args = compose(config_name='analysis') + processed_meg_path_pattern = os.path.join(DATAROOT, '{sub}/mat/{name}') + label_path_pattern = os.path.join(DATAROOT, '{sub}/labels/{name}') + trigger_meg_path_pattern = os.path.join(DATAROOT, '{sub}/trigger/{name}') + + sub_list = list(args.subjects.keys()) + split = 'train' + + for sub in sub_list: + for meg_name, label_name, trigger_name in zip(args.subjects[sub][split]['mat'], args.subjects[sub][split]['labels'], args.subjects[sub][split]['trigger']): + processed_meg_path = processed_meg_path_pattern.format(sub=sub, name=meg_name) + label_path = label_path_pattern.format(sub=sub, name=label_name) + trigger_path = trigger_meg_path_pattern.format(sub=sub, name=trigger_name) + get_meg_data(args, processed_meg_path, label_path, trigger_path) \ No newline at end of file diff --git a/speech_decoding/models.py b/meg_decoding/models.py similarity index 100% rename from speech_decoding/models.py rename to meg_decoding/models.py diff --git a/speech_decoding/utils/bcolors.py b/meg_decoding/utils/bcolors.py similarity index 100% rename from speech_decoding/utils/bcolors.py rename to meg_decoding/utils/bcolors.py diff --git a/speech_decoding/utils/get_dataloaders.py b/meg_decoding/utils/get_dataloaders.py similarity index 100% rename from speech_decoding/utils/get_dataloaders.py rename to meg_decoding/utils/get_dataloaders.py diff --git a/speech_decoding/utils/layout.py b/meg_decoding/utils/layout.py similarity index 91% rename from speech_decoding/utils/layout.py rename to meg_decoding/utils/layout.py index 90edc71..2c2651a 100644 --- a/speech_decoding/utils/layout.py +++ b/meg_decoding/utils/layout.py @@ -31,6 +31,10 @@ def ch_locations_2d(args): loc = layout.pos[:, :2] + elif args.dataset == "GOD": + raise NotImplementedError() + # train_loader, test_loader ='', '' + else: raise ValueError() diff --git a/speech_decoding/utils/loss.py b/meg_decoding/utils/loss.py similarity index 100% rename from speech_decoding/utils/loss.py rename to meg_decoding/utils/loss.py diff --git a/speech_decoding/utils/preproc_utils.py b/meg_decoding/utils/preproc_utils.py similarity index 100% rename from speech_decoding/utils/preproc_utils.py rename to meg_decoding/utils/preproc_utils.py diff --git a/speech_decoding/utils/reproducibility.py b/meg_decoding/utils/reproducibility.py similarity index 100% rename from speech_decoding/utils/reproducibility.py rename to meg_decoding/utils/reproducibility.py diff --git a/speech_decoding/utils/wav2vec_util.py b/meg_decoding/utils/wav2vec_util.py similarity index 100% rename from speech_decoding/utils/wav2vec_util.py rename to meg_decoding/utils/wav2vec_util.py diff --git a/train_wowandb.py b/train_wowandb.py new file mode 100644 index 0000000..f8053c1 --- /dev/null +++ b/train_wowandb.py @@ -0,0 +1,281 @@ +import os, sys, random +import numpy as np +import torch +import torch.nn as nn +from time import time +from tqdm import tqdm, trange +from termcolor import cprint +# import wandb + +from omegaconf import DictConfig, open_dict +import hydra +from hydra.utils import get_original_cwd + +from constants import device +# from speech_decoding.dataclass.brennan2018 import Brennan2018Dataset +# from speech_decoding.dataclass.gwilliams2022 import ( +# Gwilliams2022SentenceSplit, +# Gwilliams2022ShallowSplit, +# Gwilliams2022DeepSplit, +# Gwilliams2022Collator, +# ) +from speech_decoding.models import BrainEncoder, Classifier +from speech_decoding.utils.get_dataloaders import get_dataloaders, get_samplers +from speech_decoding.utils.loss import * +from speech_decoding.utils.reproducibility import seed_worker + + +@hydra.main(version_base=None, config_path="configs", config_name="config_GOD") +def run(args: DictConfig) -> None: + + # NOTE: We do need it (IMHO). + if args.reproducible: + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + torch.use_deterministic_algorithms(True) + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + g = torch.Generator() + g.manual_seed(0) + else: + g = None + seed_worker = None + + with open_dict(args): + args.root_dir = get_original_cwd() + cprint(f"Current working directory : {os.getcwd()}") + cprint(args, color="white") + + # ----------------------- + # Dataloader + # ----------------------- + # NOTE: Segmentation should always be by word onsets, not just every 3 seconds + if args.dataset == "Gwilliams2022": + + if args.split_mode == "sentence": + + train_set = Gwilliams2022SentenceSplit(args) + test_set = Gwilliams2022SentenceSplit(args, train_set.test_word_idxs_dict) + + assert train_set.num_subjects == test_set.num_subjects + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + elif args.split_mode == "shallow": + + dataset = Gwilliams2022ShallowSplit(args) + + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(dataset.Y.shape[0] * args.split_ratio) + test_size = dataset.Y.shape[0] - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + + elif args.split_mode == "deep": + + train_set = Gwilliams2022DeepSplit(args, train=True) + test_set = Gwilliams2022DeepSplit(args, train=False) + assert train_set.num_subjects == test_set.num_subjects + + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + cprint(f"Test segments: {test_size}", "cyan") + + if args.use_sampler: + # NOTE: currently not supporting reproducibility + train_loader, test_loader = get_samplers( + train_set, + test_set, + args, + test_bsz=test_size, + collate_fn=Gwilliams2022Collator(args), + ) + else: + # FIXME: maybe either get rid of reproducibility, or remove this? + if args.reproducible: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, seed_worker, g, test_bsz=test_size + ) + else: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, test_bsz=test_size + ) + + elif args.dataset == "Brennan2018": + # NOTE: takes an optional debug param force_recompute to pre-process the EEG even if it exists + dataset = Brennan2018Dataset(args) + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(len(dataset) * args.split_ratio) + test_size = len(dataset) - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + cprint( + f"Number of samples: {len(train_set)} (train), {len(test_set)} (test)", color="blue", + ) + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, g, seed_worker, test_bsz=test_size + ) + + elif args.dataset == "GOD": + train_loader, test_loader ='', '' + else: + + raise ValueError("Unknown dataset") + + if args.use_wandb: + wandb.config = {k: v for k, v in dict(args).items() if k not in ["root_dir", "wandb"]} + wandb.init( + project=args.wandb.project, + entity=args.wandb.entity, + config=wandb.config, + save_code=True, + ) + wandb.run.name = args.wandb.run_name + "_" + args.split_mode + wandb.run.save() + + # --------------------- + # Models + # --------------------- + brain_encoder = BrainEncoder(args).to(device) + + classifier = Classifier(args) + + # --------------- + # Loss + # --------------- + loss_func = CLIPLoss(args).to(device) + loss_func.train() + + # -------------------- + # Optimizer + # -------------------- + optimizer = torch.optim.Adam( + list(brain_encoder.parameters()) + list(loss_func.parameters()), lr=float(args.lr), + ) + + if args.lr_scheduler == "cosine": + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, T_max=args.epochs, eta_min=args.lr * 0.1 + ) + elif args.lr_scheduler == "multistep": + scheduler = torch.optim.lr_scheduler.MultiStepLR( + optimizer, + milestones=[int(m * args.epochs) for m in args.lr_multistep_mlstns], + gamma=args.lr_step_gamma, + ) + else: + scheduler = None + + # ====================================== + for epoch in range(args.epochs): + train_losses = [] + test_losses = [] + trainTop1accs = [] + trainTop10accs = [] + testTop1accs = [] + testTop10accs = [] + + brain_encoder.train() + for i, batch in enumerate(tqdm(train_loader)): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, chunkIDs = batch + assert ( + len(chunkIDs.unique()) == X.shape[0] + ), "Duplicate segments in batch are not allowed. Aborting." + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y = X.to(device), Y.to(device) + # print([(s.item(), chid.item()) for s, chid in zip(subject_idxs, chunkIDs)]) + Z = brain_encoder(X, subject_idxs) + + loss = loss_func(Y, Z) + + with torch.no_grad(): + trainTop1acc, trainTop10acc = classifier(Z, Y) + + train_losses.append(loss.item()) + trainTop1accs.append(trainTop1acc) + trainTop10accs.append(trainTop10acc) + + if args.dataset == "Gwilliams2022": + optimizer.zero_grad() + loss.backward() + optimizer.step() + + # Accumulate gradients for Gwilliams for the whole epoch + if args.dataset == "Brennan2018": + optimizer.zero_grad() + loss.backward() + optimizer.step() + + brain_encoder.eval() + for batch in test_loader: + + with torch.no_grad(): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, chunkIDs = batch + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y = X.to(device), Y.to(device) + + Z = brain_encoder(X, subject_idxs) # 0.96 GB + + loss = loss_func(Y, Z) + + testTop1acc, testTop10acc = classifier(Z, Y, test=True) # ( 250, 1024, 360 ) + + test_losses.append(loss.item()) + testTop1accs.append(testTop1acc) + testTop10accs.append(testTop10acc) + + print( + f"Ep {epoch}/{args.epochs} | ", + f"train l: {np.mean(train_losses):.3f} | ", + f"test l: {np.mean(test_losses):.3f} | ", + f"trainTop10acc: {np.mean(trainTop10accs):.3f} | ", + f"testTop10acc: {np.mean(testTop10accs):.3f} | ", + f"lr: {optimizer.param_groups[0]['lr']:.5f}", + f"temp: {loss_func.temp.item():.3f}", + ) + + if args.use_wandb: + performance_now = { + "epoch": epoch, + "train_loss": np.mean(train_losses), + "test_loss": np.mean(test_losses), + "trainTop1acc": np.mean(trainTop1accs), + "trainTop10acc": np.mean(trainTop10accs), + "testTop1acc": np.mean(testTop1accs), + "testTop10acc": np.mean(testTop10accs), + "lrate": optimizer.param_groups[0]["lr"], + "temp": loss_func.temp.item(), + } + wandb.log(performance_now) + + if scheduler is not None: + scheduler.step() + + torch.save(brain_encoder.state_dict(), "model_last.pt") + + +if __name__ == "__main__": + run() From 8550ef1ada4a16eb7f4ac89e7c7e2f4e15bd09a3 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Tue, 11 Apr 2023 18:40:05 +0900 Subject: [PATCH 02/71] run training. simple case --- data/GOD/ch_region.json | 192 ++++++++++++++++++++ examples/rest.py | 50 +++++ examples/view_training_curve.py | 22 +++ meg_decoding/dataclass/god.py | 96 +++++++--- meg_decoding/matlab_utils/create_montage.py | 36 ---- meg_decoding/matlab_utils/load_meg.py | 132 ++++++++++++-- meg_decoding/matlab_utils/utils.py | 56 ++++++ meg_decoding/models.py | 18 +- meg_decoding/utils/layout.py | 6 +- meg_decoding/utils/loggers.py | 30 +++ train_wowandb.py | 80 ++++++-- 11 files changed, 623 insertions(+), 95 deletions(-) create mode 100644 data/GOD/ch_region.json create mode 100644 examples/rest.py create mode 100644 examples/view_training_curve.py delete mode 100644 meg_decoding/matlab_utils/create_montage.py create mode 100644 meg_decoding/matlab_utils/utils.py create mode 100644 meg_decoding/utils/loggers.py diff --git a/data/GOD/ch_region.json b/data/GOD/ch_region.json new file mode 100644 index 0000000..b83b66c --- /dev/null +++ b/data/GOD/ch_region.json @@ -0,0 +1,192 @@ +{ + "frontal": { + "left": [ + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 39, + 44, + 46, + 47, + 48 + ], + "right": [ + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 76, + 77, + 79, + 88, + 107, + 109, + 110, + 111, + 112 + ] + }, + "temporal": { + "left": [ + 33, + 34, + 35, + 36, + 37, + 38, + 40, + 41, + 42, + 43, + 45, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 62 + ], + "right": [ + 97, + 98, + 99, + 100, + 101, + 102, + 103, + 104, + 105, + 106, + 113, + 114, + 115, + 116, + 118, + 119, + 120, + 121, + 122, + 124, + 126 + ] + }, + "parietal": { + "left": [ + 18, + 20, + 21, + 22, + 23, + 24, + 30, + 60, + 61, + 64, + 140, + 141, + 142, + 143, + 144 + ], + "right": [ + 81, + 82, + 83, + 86, + 87, + 89, + 117, + 125, + 127, + 156, + 157, + 158, + 160 + ] + }, + "central": { + "left": [ + 13, + 14, + 15, + 16, + 17, + 19, + 25, + 26, + 27, + 28, + 29, + 31, + 32, + 59, + 63, + 159 + ], + "right": [ + 75, + 78, + 80, + 84, + 85, + 90, + 91, + 92, + 93, + 94, + 95, + 96, + 108, + 123, + 128 + ] + }, + "occipital": { + "left": [ + 129, + 130, + 131, + 132, + 133, + 134, + 135, + 136, + 137, + 138, + 139 + ], + "right": [ + 145, + 146, + 147, + 148, + 149, + 150, + 151, + 152, + 153, + 154, + 155 + ] + } +} \ No newline at end of file diff --git a/examples/rest.py b/examples/rest.py new file mode 100644 index 0000000..1a26643 --- /dev/null +++ b/examples/rest.py @@ -0,0 +1,50 @@ +import sys +sys.path.append('./') +from meg_decoding.matlab_utils.load_meg import get_meg_data +import scipy.io + +def rest_data(meg_filepath, fs, duration): + data = scipy.io.loadmat(meg_filepath) + """ + dict_keys(['__header__', '__version__', '__globals__', 'F', 'Std', + 'Comment', 'ChannelFlag', 'Time', 'DataType', 'Device', 'nAvg', + 'Leff', 'Events', 'ColormapType', 'DisplayUnits', 'History']) + """ + MEG_Data = data['F'] # 203 x 391000 = ch x time sample + assert len(MEG_Data) == 203 + + + Event_Data = data['Events'][0] + for i in range(len(Event_Data)): + if Event_Data[i][0][0] == 'visual': + visual_id = i + break + assert Event_Data[visual_id][0][0] == 'visual', '{}'.format(Event_Data[0][1][0][0]) + onset_timing = Event_Data[visual_id][3][0] # trial + assert len(onset_timing) == 60 + + start_point = int(onset_timing[-1]*fs) + end_point = start_point + int(duration*fs) + rest_data = MEG_Data[:, start_point:end_point] + return rest_data.mean(axis=1), rest_data.std(axis=1) + +def run(filepath_list, fs, duration): + for filepath in filepath_list: + mean, std = rest_data(filepath, fs, duration) + print(filepath) + print('mean:{}, std:{}'.format(mean.shape, std.shape)) + + +if __name__ == '__main__': + filepath_list = [ + '/work/project/MEG_GOD/GOD_dataset/sbj01/mat/data_rest001.mat', + '/work/project/MEG_GOD/GOD_dataset/sbj01/mat/data_rest002.mat', + '/work/project/MEG_GOD/GOD_dataset/sbj02/mat/data_rest001.mat', + '/work/project/MEG_GOD/GOD_dataset/sbj02/mat/data_rest002.mat', + '/work/project/MEG_GOD/GOD_dataset/sbj03/mat/data_rest001.mat', + '/work/project/MEG_GOD/GOD_dataset/sbj03/mat/data_rest002.mat', + ] + fs = 1000 + duration = 60 #[s] + + run(filepath_list, fs, duration) \ No newline at end of file diff --git a/examples/view_training_curve.py b/examples/view_training_curve.py new file mode 100644 index 0000000..39afbb2 --- /dev/null +++ b/examples/view_training_curve.py @@ -0,0 +1,22 @@ +import numpy as np +import matplotlib.pyplot as plt +import pickle +import pandas as pd + +def get_data(pkl_file:str): + with open(pkl_file, 'rb') as f: + data = pickle.load(f) + return data + +def parse_data(data): + log_names = list(data.keys()) + for name in log_names: + logs = data[name] + columns = logs.keys() + logs = pd.DataFrame(logs) + import pdb; pdb.set_trace() + +if __name__ == '__main__': + filepath = '/home/yainoue/meg2image/results/test/2023-04-11 18:06:44.273986' + data = get_data(filepath) + parse_data(data) diff --git a/meg_decoding/dataclass/god.py b/meg_decoding/dataclass/god.py index dc55d66..7fbc219 100644 --- a/meg_decoding/dataclass/god.py +++ b/meg_decoding/dataclass/god.py @@ -1,47 +1,87 @@ -import os, sys -from re import sub -import torch -import torchaudio -import torchaudio.functional as F +import os from torch.utils.data import Sampler, Dataset import numpy as np -import pandas as pd -import glob -import json -from natsort import natsorted -import scipy.io -import mne, mne_bids +import mne from tqdm import tqdm -import ast -from typing import Union -from termcolor import cprint -from pprint import pprint -from einops import rearrange -from sklearn.preprocessing import RobustScaler, StandardScaler +from meg_decoding.matlab_utils.load_meg import get_meg_data, roi, time_window, get_baseline +import tqdm -from speech_decoding.utils.wav2vec_util import load_wav2vec_model, getW2VLastFourLayersAvg mne.set_log_level(verbose="WARNING") class GODDatasetBase(Dataset): - def __init__(self, args, preprocess_pipleine:list=[]): - pass + def __init__(self, args, split, preprocess_pipleine:list=[]): + self.args = args + self.sub_id_map = {s:i for i, s in enumerate(list(self.args.subjects.keys()))} + self.preprocess_pipeline = preprocess_pipleine + # prepare dataset + meg_epochs, sub_epochs, label_epochs, \ + image_feature_epochs = self.prepare_data(args, split=split) + + self.X = meg_epochs.astype(np.float32) # epochs x ch x time_samples + self.Y = image_feature_epochs.astype(np.float32) # epochs x dims + self.subs = sub_epochs # epochs (x 1) + self.labels = label_epochs # epochs (x 1) + + self.num_subjects = len(np.unique(self.subs)) def __len__(self): return len(self.Y) def __getitem__(self, i): # NOTE: i is id of a speech segment + x, y, s = self.X[i], self.Y[i], self.subs[i] + return x, y, s + + def prepare_data(self, args, split:str): + DATAROOT = args.data_root + processed_meg_path_pattern = os.path.join(DATAROOT, '{sub}/mat/{name}') + label_path_pattern = os.path.join(DATAROOT, '{sub}/labels/{name}') + trigger_meg_path_pattern = os.path.join(DATAROOT, '{sub}/trigger/{name}') + processed_rest_meg_path_pattern = os.path.join(DATAROOT, '{sub}/mat/{name}') - i_in_task, task = self.segment_to_task(i) + sub_list = list(args.subjects.keys()) - key_no_task = np.random.choice(list(self.X.keys())) - X = self.X[key_no_task][task] # ( 208, ~100000 ) - onset = self.meg_onsets[key_no_task][task][i_in_task] # scalar + roi_channels = roi(args) - # NOTE: Extract MEG segment. Doing this to save memory from overlapping segments - X = X[:, onset : onset + self.seq_len_samp] # ( 208, 360 ) + def epoching(meg, window): + assert len(labels) == len(window) + # n_epochs x n_chs x time_smaples + meg_epochs = np.zeros([len(window), len(meg), window[0][1]-window[0][0]]) + for i, w in enumerate(window): + window_meg = meg[:, w[0]:w[1]] # ch x time_samples + for func_ in self.preprocess_pipeline: + window_meg = func_(window_meg) + meg_epochs[i] = window_meg + return meg_epochs - subject_idx = np.where(self.valid_subjects == key_no_task.split("_")[0])[0][0] + meg_epochs = [] + sub_epochs = [] + label_epochs = [] + image_feature_epochs = [] + rest_mean, rest_std = None, None + pbar = tqdm.tqdm(sub_list) + for sub in pbar: + pbar.set_description("load subject data -- current: {}".format(sub)) + fs = args.subjects[sub]['fs'] + for meg_name, label_name, trigger_name, rest_name in zip(args.subjects[sub][split]['mat'], args.subjects[sub][split]['labels'], args.subjects[sub][split]['trigger'], args.subjects[sub][split]['rest']): + processed_meg_path = processed_meg_path_pattern.format(sub=sub, name=meg_name) + label_path = label_path_pattern.format(sub=sub, name=label_name) + trigger_path = trigger_meg_path_pattern.format(sub=sub, name=trigger_name) + processed_rest_meg_path = processed_rest_meg_path_pattern.format(sub=sub, name=rest_name) + if args.z_scoring: + rest_mean, rest_std = get_baseline(processed_rest_meg_path, fs, args.rest_duration) + MEG_Data, image_features, labels, triggers = get_meg_data(processed_meg_path, label_path, trigger_path, rest_mean=rest_mean, rest_std=rest_std, split=split) + ROI_MEG_Data = MEG_Data[roi_channels, :] # num_roi_channels x time_samples + window = time_window(args, triggers, fs) + ROI_MEG_epochs = epoching(ROI_MEG_Data, window) - return X, self.Y[i], subject_idx \ No newline at end of file + meg_epochs.append(ROI_MEG_epochs) # array [epoch x ch x time_stamp] + sub_epochs+=[self.sub_id_map[sub]] * len(ROI_MEG_epochs) # list [epoch] + label_epochs.append(labels) # array [epoch] + image_feature_epochs.append(image_features) # array [epoch x dim] + meg_epochs = np.concatenate(meg_epochs, axis=0) + label_epochs = np.concatenate(label_epochs, axis=0) + image_feature_epochs = np.concatenate(image_feature_epochs, axis=0) + print('dataset is created. size is ', meg_epochs.shape) + return meg_epochs, sub_epochs, label_epochs, image_feature_epochs diff --git a/meg_decoding/matlab_utils/create_montage.py b/meg_decoding/matlab_utils/create_montage.py deleted file mode 100644 index c14b9b0..0000000 --- a/meg_decoding/matlab_utils/create_montage.py +++ /dev/null @@ -1,36 +0,0 @@ -import scipy.io -import scipy -import numpy as np -import os -import csv - -def create_montage(filename:str, savefile:str): - data = scipy.io.loadmat(filename) - channel_infos = data['Channel'][0] - n_ch = len(channel_infos) - assert n_ch == 203, 'tell Yang-san' - montage_list = [] - for i in range(n_ch): - loc_array = channel_infos[i][4] - if len(loc_array) > 0: - assert loc_array.ndim == 2, 'MEG montage file requires 3x8 corrdinates for cube.' - montage_list.append(list(np.mean(loc_array, axis=1))) - - with open(savefile, 'w') as f: - writer = csv.writer(f) - writer.writerows(montage_list) - print('montage file is saved as ', savefile) - - - - - -if __name__ == '__main__': - DATAROOT = '/work/project/MEG_GOD/GOD_dataset/' - - filename = os.path.join(DATAROOT, 'channel_ricoh_acc1.mat') - savefile = './data/GOD/montage.csv' - - create_montage(filename, savefile) - - diff --git a/meg_decoding/matlab_utils/load_meg.py b/meg_decoding/matlab_utils/load_meg.py index 9393835..609df9c 100644 --- a/meg_decoding/matlab_utils/load_meg.py +++ b/meg_decoding/matlab_utils/load_meg.py @@ -5,9 +5,47 @@ import csv import numpy as np from hydra import initialize, compose +import json +from typing import List -def get_meg_data(args, meg_filepath:str, label_filepath:str, trigger_filepath:str): +def get_baseline(meg_filepath, fs, duration): + print('baseline: ', meg_filepath) + data = scipy.io.loadmat(meg_filepath) + MEG_Data = data['F'] # 203 x 391000 = ch x time sample + assert len(MEG_Data) == 203 + + Event_Data = data['Events'][0] + for i in range(len(Event_Data)): + if Event_Data[i][0][0] == 'visual': + visual_id = i + break + assert Event_Data[visual_id][0][0] == 'visual', '{}'.format(Event_Data[0][1][0][0]) + onset_timing = Event_Data[visual_id][3][0] # trial + assert len(onset_timing) == 60 + + + start_point = int(onset_timing[-1]*fs) + end_point = start_point + int(duration*fs) + rest_data = MEG_Data[:, start_point:end_point] + return rest_data.mean(axis=1), rest_data.std(axis=1) + + +def get_meg_data(meg_filepath:str, label_filepath:str, trigger_filepath:str, + rest_mean:np.ndarray=None, rest_std:np.ndarray=None, split='train'): + """ + Args: + meg_filepath(str): preprocessed MEG data path + label_filepath(str): label (image_features, image_idx) path + trigger_filepath(str): trigger onset path + Return: + MEG_Data(np.ndarray): processed MEG Data. size = n_ch(203) x n_time_samples + image_features(np.ndarray): image feature. size = n_images(600) x latent_dims(512) + labels(np.ndarray): image index. size = n_images + triggers(np.ndarray): stimulus onset timing [s] size = n_images + + """ + print('load ', meg_filepath) data = scipy.io.loadmat(meg_filepath) """ dict_keys(['__header__', '__version__', '__globals__', 'F', 'Std', @@ -16,10 +54,15 @@ def get_meg_data(args, meg_filepath:str, label_filepath:str, trigger_filepath:st """ MEG_Data = data['F'] # 203 x 391000 = ch x time sample assert len(MEG_Data) == 203 - if args.fs is None: - sampling_rate = MEG_Data.shape[1] / data['Time'] # 1000 Hz - else: - sampling_rate = args.fs + # REMOVE BASELINE + if rest_mean is not None: + MEG_Data -= rest_mean[:, np.newaxis] + if rest_std is not None: + MEG_Data /= rest_std[:, np.newaxis] + # if args.fs is None: + # sampling_rate = MEG_Data.shape[1] / data['Time'] # 1000 Hz + # else: + # sampling_rate = args.fs # Event_Data = data['Events'][0] # assert Event_Data[1][0][0] == 'visual', '{}'.format(Event_Data[0][1][0][0]) @@ -31,36 +74,101 @@ def get_meg_data(args, meg_filepath:str, label_filepath:str, trigger_filepath:st dict_keys(['__header__', '__version__', '__globals__', 'vec_image', 'vec_index']) """ image_features = Label_Data['vec_image'] - assert image_features.shape == (600, 512) # n_samples x image_feature_dims + if split == 'train': + assert image_features.shape == (600, 512), '{}, {}'.format(image_features.shape[0], image_features.shape[1]) # n_samples x image_feature_dims + elif split == 'test': + assert image_features.shape == (50, 512), '{}, {}'.format(image_features.shape[0], image_features.shape[1]) # n_samples x image_feature_dims + elif split == 'rest': + assert image_features.shape == (60, 512), '{}, {}'.format(image_features.shape[0], image_features.shape[1]) # n_samples x image_feature_dims labels = Label_Data['vec_index'][0] # index of image - assert len(labels) == 600 # n_samples + if split == 'train': + assert len(labels) == 600 # n_samples + elif split == 'test': + assert len(labels) == 50 # n_samples + elif split == 'rest': + assert len(labels) == 60 Trigger_Data = scipy.io.loadmat(trigger_filepath) """ dict_keys(['__header__', '__version__', '__globals__', 'trigger']) """ triggers = Trigger_Data['trigger'][0] - assert len(triggers) == 600 # stimulus onset timing + if split == 'train': + assert len(triggers) == 600 # stimulus onset timing + elif split == 'test': + assert len(triggers) == 50 + elif split == 'rest': + assert len(triggers) == 60 + + return MEG_Data, image_features, labels, triggers + +def roi(args)->list: + region:list = args.region + with open(args.ch_region_path, 'r') as f: + ch_region_info = json.load(f) + roi_channels = [] + for reg in region: + reg_subreg = reg.split('/') + assert len(reg_subreg) == 2 + r = reg_subreg[0] + s = reg_subreg[1] + roi_channels += ch_region_info[r][s] + print('ROI: ', [reg for reg in region]) + print('channel: ', roi_channels) + print('num channels: ', len(roi_channels)) + return roi_channels + + +def time_window(args, trigger:np.ndarray, fs:float, mode='')->List[tuple]: + trigger_point = np.round(trigger * fs) + start_point = np.round(args.window.start * fs) + end_point = np.round(args.window.end * fs) + + point_set = [(int(t + start_point), int(t + end_point)) for t in trigger_point] + + return point_set + + +def read_montage(args): + # montage + montage = [] + print('read montage file ', args.montage_path) + with open(args.montage_path) as f: + reader = csv.reader(f) + for row in reader: + montage.append([float(r) for r in row]) + montage = np.array(montage) + roi_channels = roi(args) + return montage[roi_channels,:] # ch x 3 + - import pdb; pdb.set_trace() if __name__ == '__main__': DATAROOT = '/work/project/MEG_GOD/GOD_dataset/' with initialize(version_base=None, config_path="../../../configs/"): - args = compose(config_name='analysis') + args = compose(config_name='test') processed_meg_path_pattern = os.path.join(DATAROOT, '{sub}/mat/{name}') label_path_pattern = os.path.join(DATAROOT, '{sub}/labels/{name}') trigger_meg_path_pattern = os.path.join(DATAROOT, '{sub}/trigger/{name}') + processed_rest_meg_path_pattern = os.path.join(DATAROOT, '{sub}/mat/{name}') sub_list = list(args.subjects.keys()) split = 'train' + roi_channels = roi(args) + for sub in sub_list: - for meg_name, label_name, trigger_name in zip(args.subjects[sub][split]['mat'], args.subjects[sub][split]['labels'], args.subjects[sub][split]['trigger']): + fs = args.subjects[sub]['fs'] + for meg_name, label_name, trigger_name, rest_name in zip(args.subjects[sub][split]['mat'], args.subjects[sub][split]['labels'], args.subjects[sub][split]['trigger'], args.subjects[sub][split]['rest']): processed_meg_path = processed_meg_path_pattern.format(sub=sub, name=meg_name) label_path = label_path_pattern.format(sub=sub, name=label_name) trigger_path = trigger_meg_path_pattern.format(sub=sub, name=trigger_name) - get_meg_data(args, processed_meg_path, label_path, trigger_path) \ No newline at end of file + processed_rest_meg_path = processed_rest_meg_path_pattern.format(sub=sub, name=rest_name) + rest_mean, rest_std = get_baseline(processed_rest_meg_path, fs, args.rest_duration) + MEG_Data, image_features, labels, triggers = get_meg_data(processed_meg_path, label_path, trigger_path, rest_mean=rest_mean, rest_std=rest_std) + ROI_MEG_Data = MEG_Data[roi_channels, :] # num_roi_channels x time_samples + window = time_window(args, triggers, fs) + diff --git a/meg_decoding/matlab_utils/utils.py b/meg_decoding/matlab_utils/utils.py new file mode 100644 index 0000000..5201401 --- /dev/null +++ b/meg_decoding/matlab_utils/utils.py @@ -0,0 +1,56 @@ +import scipy.io +import scipy +import numpy as np +import os +import csv +import json + +def create_montage(filename:str, savefile:str): + data = scipy.io.loadmat(filename) + channel_infos = data['Channel'][0] + n_ch = len(channel_infos) + assert n_ch == 203, 'tell Yang-san' + montage_list = [] + for i in range(n_ch): + loc_array = channel_infos[i][4] + if len(loc_array) > 0: + assert loc_array.ndim == 2, 'MEG montage file requires 3x8 corrdinates for cube.' + montage_list.append(list(np.mean(loc_array, axis=1))) + + with open(savefile, 'w') as f: + writer = csv.writer(f) + writer.writerows(montage_list) + print('montage file is saved as ', savefile) + +def create_ch_region_pair(filename:str, savefile): + data = scipy.io.loadmat(filename) + test_list = [] + region_ch = {} + for k in data.keys(): + if '__' in k: + continue + region_ch[k] = {} + sub_region = data[k][0].dtype.names + for i, sr in enumerate(sub_region): + # import pdb; pdb.set_trace() + region_ch[k][sr] = [int(i) for i in data[k][0][0][i][0].astype(np.int64)] + test_list += region_ch[k][sr] + print(k, sr, data[k][0][0][i][0]) + assert len(test_list) == 160 + assert len(np.unique(test_list)) == 160 + + with open(savefile, 'w') as f: + json.dump(region_ch, f, indent=4) + print('ch_region pair is saved as ', savefile) + +if __name__ == '__main__': + DATAROOT = '/work/project/MEG_GOD/GOD_dataset/' + + # filename = os.path.join(DATAROOT, 'channel_ricoh_acc1.mat') + # savefile = './data/GOD/montage.csv' + # create_montage(filename, savefile) + + filename = '/work/project/MEG_GOD/GOD_dataset/channel_index.mat' + savefile = './data/GOD/ch_region.json' + create_ch_region_pair(filename, savefile) + diff --git a/meg_decoding/models.py b/meg_decoding/models.py index a6ef3e0..0616965 100644 --- a/meg_decoding/models.py +++ b/meg_decoding/models.py @@ -1,4 +1,5 @@ import sys +from turtle import forward import numpy as np import torch import torch.nn as nn @@ -9,7 +10,7 @@ from tqdm import tqdm from constants import device -from speech_decoding.utils.layout import ch_locations_2d +from meg_decoding.utils.layout import ch_locations_2d class SpatialAttention(nn.Module): @@ -191,12 +192,27 @@ def __init__(self, args): self.conv_final1 = nn.Conv1d(in_channels=self.D2, out_channels=2 * self.D2, kernel_size=1,) self.conv_final2 = nn.Conv1d(in_channels=2 * self.D2, out_channels=self.F, kernel_size=1,) + if args.seq2seq: + self._forward = self._forward_seq_seq + else: + self._forward = self._forward_seq_static def forward(self, X, subject_idxs): + return self._forward(X, subject_idxs) + + def _forward_seq_seq(self, X, subject_idxs): + X = self.subject_block(X, subject_idxs) + X = self.conv_blocks(X) + X = F.gelu(self.conv_final1(X)) + X = F.gelu(self.conv_final2(X)) + return X + + def _forward_seq_static(self, X, subject_idxs): X = self.subject_block(X, subject_idxs) X = self.conv_blocks(X) X = F.gelu(self.conv_final1(X)) X = F.gelu(self.conv_final2(X)) + X = torch.mean(X, axis=2) return X diff --git a/meg_decoding/utils/layout.py b/meg_decoding/utils/layout.py index 2c2651a..66f398d 100644 --- a/meg_decoding/utils/layout.py +++ b/meg_decoding/utils/layout.py @@ -1,7 +1,7 @@ import mne, mne_bids import numpy as np import torch - +from meg_decoding.matlab_utils.load_meg import read_montage def ch_locations_2d(args): dataset_name, root_dir = args.dataset, args.root_dir @@ -32,8 +32,8 @@ def ch_locations_2d(args): loc = layout.pos[:, :2] elif args.dataset == "GOD": - raise NotImplementedError() - # train_loader, test_loader ='', '' + montage = read_montage(args) + loc = montage[:, :2] else: raise ValueError() diff --git a/meg_decoding/utils/loggers.py b/meg_decoding/utils/loggers.py new file mode 100644 index 0000000..dbdb5ac --- /dev/null +++ b/meg_decoding/utils/loggers.py @@ -0,0 +1,30 @@ +import csv +import pandas as pd +from datetime import datetime +import os +import pickle + + +def get_timestamp(): + return datetime.today().isoformat(' ') + + +class Pickleogger(): + def __init__(self, logdir): + if not os.path.isdir(logdir): + os.makedirs(logdir) + self.logfile = os.path.join(logdir, get_timestamp()) + print('logger file is ', self.logfile) + self.logs = {} + + def create_logger(self, name:str, keys:list): + self.logs[name] = {k:[] for k in keys} + + def log(self, data, name): + if not(name in self.logs.keys()): + self.create_logger(name, list(data.keys())) + for k, v in data.items(): + self.logs[name][k].append(v) + + with open(self.logfile, 'wb') as f: + pickle.dump(self.logs, f) diff --git a/train_wowandb.py b/train_wowandb.py index f8053c1..2358b24 100644 --- a/train_wowandb.py +++ b/train_wowandb.py @@ -19,15 +19,17 @@ # Gwilliams2022DeepSplit, # Gwilliams2022Collator, # ) -from speech_decoding.models import BrainEncoder, Classifier -from speech_decoding.utils.get_dataloaders import get_dataloaders, get_samplers -from speech_decoding.utils.loss import * -from speech_decoding.utils.reproducibility import seed_worker +from torch.utils.data import DataLoader, RandomSampler, BatchSampler +from meg_decoding.models import BrainEncoder, Classifier +from meg_decoding.utils.get_dataloaders import get_dataloaders, get_samplers +from meg_decoding.utils.loss import * +from meg_decoding.dataclass.god import GODDatasetBase +from meg_decoding.utils.loggers import Pickleogger -@hydra.main(version_base=None, config_path="configs", config_name="config_GOD") def run(args: DictConfig) -> None: + from meg_decoding.utils.reproducibility import seed_worker # NOTE: We do need it (IMHO). if args.reproducible: os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" @@ -37,12 +39,15 @@ def run(args: DictConfig) -> None: torch.manual_seed(0) g = torch.Generator() g.manual_seed(0) + seed_worker = seed_worker else: g = None seed_worker = None - with open_dict(args): - args.root_dir = get_original_cwd() + pkl_logger = Pickleogger(os.path.join(args.save_root, 'runs')) + + # with open_dict(args): + # args.root_dir = get_original_cwd() cprint(f"Current working directory : {os.getcwd()}") cprint(args, color="white") @@ -128,9 +133,33 @@ def run(args: DictConfig) -> None: ) elif args.dataset == "GOD": - train_loader, test_loader ='', '' - else: + train_dataset = GODDatasetBase(args, 'train') + val_dataset = GODDatasetBase(args, 'val') + with open_dict(args): + args.num_subjects = train_dataset.num_subjects + print('num subject is {}'.format(args.num_subjects)) + train_loader = DataLoader( + train_dataset, + batch_size=args.batch_size, + drop_last=True, + shuffle=True, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + test_loader = DataLoader( + val_dataset, + batch_size=args.batch_size, + drop_last=True, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + else: raise ValueError("Unknown dataset") if args.use_wandb: @@ -178,7 +207,9 @@ def run(args: DictConfig) -> None: scheduler = None # ====================================== - for epoch in range(args.epochs): + pbar = tqdm(range(args.epochs)) + for epoch in pbar: + pbar.set_description("training {}/{} epoch".format(epoch, args.epochs)) train_losses = [] test_losses = [] trainTop1accs = [] @@ -187,7 +218,8 @@ def run(args: DictConfig) -> None: testTop10accs = [] brain_encoder.train() - for i, batch in enumerate(tqdm(train_loader)): + pbar2 = tqdm(train_loader) + for i, batch in enumerate(pbar2): if len(batch) == 3: X, Y, subject_idxs = batch @@ -200,9 +232,7 @@ def run(args: DictConfig) -> None: raise ValueError("Unexpected number of items from dataloader.") X, Y = X.to(device), Y.to(device) - # print([(s.item(), chid.item()) for s, chid in zip(subject_idxs, chunkIDs)]) Z = brain_encoder(X, subject_idxs) - loss = loss_func(Y, Z) with torch.no_grad(): @@ -212,6 +242,7 @@ def run(args: DictConfig) -> None: trainTop1accs.append(trainTop1acc) trainTop10accs.append(trainTop10acc) + pbar.set_description("training {}/{} iters Train/Loss: {}, Train/Top1Acc: {}, Train/Top10Acc: {}".format(i, len(train_loader), loss.item(), trainTop1acc, trainTop10acc)) if args.dataset == "Gwilliams2022": optimizer.zero_grad() loss.backward() @@ -256,6 +287,17 @@ def run(args: DictConfig) -> None: f"lr: {optimizer.param_groups[0]['lr']:.5f}", f"temp: {loss_func.temp.item():.3f}", ) + pkl_logger.log({ + "epoch": epoch, + "train_loss": np.mean(train_losses), + "test_loss": np.mean(test_losses), + "trainTop1acc": np.mean(trainTop1accs), + "trainTop10acc": np.mean(trainTop10accs), + "testTop1acc": np.mean(testTop1accs), + "testTop10acc": np.mean(testTop10accs), + "lrate": optimizer.param_groups[0]["lr"], + "temp": loss_func.temp.item(), + }, 'logs') if args.use_wandb: performance_now = { @@ -274,8 +316,16 @@ def run(args: DictConfig) -> None: if scheduler is not None: scheduler.step() - torch.save(brain_encoder.state_dict(), "model_last.pt") + savedir = os.path.join(args.save_root, 'weights') + last_weight_file = os.path.join(savedir, "model_last.pt") + torch.save(brain_encoder.state_dict(), last_weight_file) + print('model is saved as ', last_weight_file) if __name__ == "__main__": - run() + from hydra import initialize, compose + with initialize(version_base=None, config_path="../configs/"): + args = compose(config_name='test') + if not os.path.exists(os.path.join(args.save_root, 'weights')): + os.makedirs(os.path.join(args.save_root, 'weights')) + run(args) From b3e7f21d17b8a557a16d522fc346c385560e40ae Mon Sep 17 00:00:00 2001 From: inoue26 Date: Thu, 13 Apr 2023 14:52:18 +0900 Subject: [PATCH 03/71] check montage --- .gitignore | 1 + examples/view_training_curve.py | 2 +- meg_decoding/utils/vis_grad.py | 0 notebooks/attention_check.ipynb | 145 ++++++++++++++++++++++++++++++++ train_wowandb.py | 2 +- 5 files changed, 148 insertions(+), 2 deletions(-) create mode 100644 meg_decoding/utils/vis_grad.py create mode 100644 notebooks/attention_check.ipynb diff --git a/.gitignore b/.gitignore index be43ad8..3339354 100644 --- a/.gitignore +++ b/.gitignore @@ -37,3 +37,4 @@ wandb/ train.sh *_debug* nohup.out +logs/* \ No newline at end of file diff --git a/examples/view_training_curve.py b/examples/view_training_curve.py index 39afbb2..894f726 100644 --- a/examples/view_training_curve.py +++ b/examples/view_training_curve.py @@ -17,6 +17,6 @@ def parse_data(data): import pdb; pdb.set_trace() if __name__ == '__main__': - filepath = '/home/yainoue/meg2image/results/test/2023-04-11 18:06:44.273986' + filepath = '/home/yainoue/meg2image/results/20230412_mini/runs/2023-04-12 22:23:57.324970' #'/home/yainoue/meg2image/results/test/2023-04-11 18:06:44.273986' data = get_data(filepath) parse_data(data) diff --git a/meg_decoding/utils/vis_grad.py b/meg_decoding/utils/vis_grad.py new file mode 100644 index 0000000..e69de29 diff --git a/notebooks/attention_check.ipynb b/notebooks/attention_check.ipynb new file mode 100644 index 0000000..821d0bd --- /dev/null +++ b/notebooks/attention_check.ipynb @@ -0,0 +1,145 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('/home/yainoue/meg2image/codes/MEG-decoding')\n", + "from meg_decoding.utils.layout import ch_locations_2d\n", + "from meg_decoding.matlab_utils.load_meg import roi\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from hydra import initialize, compose\n", + "import json\n", + "import csv\n", + "%matplotlib inline\n", + "\n", + "with initialize(version_base=None, config_path=\"../../configs/\"):\n", + " args = compose(config_name='20230413_loc')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# location確認" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "read montage file /home/yainoue/meg2image/codes/MEG-decoding/data/GOD/montage.csv\n", + "location size: (174, 3)\n", + "occipital/left\n", + "ROI: ['occipital/left']\n", + "channel: [129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139]\n", + "num channels: 11\n", + "occipital/right\n", + "ROI: ['occipital/right']\n", + "channel: [145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155]\n", + "num channels: 11\n", + "frontal/left\n", + "ROI: ['frontal/left']\n", + "channel: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 39, 44, 46, 47, 48]\n", + "num channels: 17\n", + "frontal/right\n", + "ROI: ['frontal/right']\n", + "channel: [65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 76, 77, 79, 88, 107, 109, 110, 111, 112]\n", + "num channels: 19\n", + "temporal/left\n", + "ROI: ['temporal/left']\n", + "channel: [33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 45, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 62]\n", + "num channels: 22\n", + "temporal/right\n", + "ROI: ['temporal/right']\n", + "channel: [97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 113, 114, 115, 116, 118, 119, 120, 121, 122, 124, 126]\n", + "num channels: 21\n", + "parietal/left\n", + "ROI: ['parietal/left']\n", + "channel: [18, 20, 21, 22, 23, 24, 30, 60, 61, 64, 140, 141, 142, 143, 144]\n", + "num channels: 15\n", + "parietal/right\n", + "ROI: ['parietal/right']\n", + "channel: [81, 82, 83, 86, 87, 89, 117, 125, 127, 156, 157, 158, 160]\n", + "num channels: 13\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "args.montage_path = '/home/yainoue/meg2image/codes/MEG-decoding/data/GOD/montage.csv'\n", + "args.ch_region_path = '/home/yainoue/meg2image/codes/MEG-decoding/data/GOD/ch_region.json'\n", + "plt.figure(figsize=(8,8))\n", + "\n", + "montage = []\n", + "print('read montage file ', args.montage_path)\n", + "with open(args.montage_path) as f:\n", + " reader = csv.reader(f)\n", + " for row in reader:\n", + " montage.append([float(r) for r in row])\n", + "loc = np.array(montage)\n", + " \n", + " \n", + "print('location size: ', loc.shape)\n", + "region_list = args.region\n", + "for reg in region_list:\n", + " print(reg)\n", + " args.region = [reg]\n", + " roi_channels = roi(args)\n", + " plt.scatter(loc[roi_channels,0], loc[roi_channels,1], label=reg)\n", + "plt.legend()\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/train_wowandb.py b/train_wowandb.py index 2358b24..cd38f0f 100644 --- a/train_wowandb.py +++ b/train_wowandb.py @@ -325,7 +325,7 @@ def run(args: DictConfig) -> None: if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='test') + args = compose(config_name='20230412') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) run(args) From 92484b627bc1a8e2e1b1b69e1423c8fe1990e336 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Thu, 13 Apr 2023 15:34:53 +0900 Subject: [PATCH 04/71] BUGFIX: calculate loss backward --- examples/view_training_curve.py | 2 +- meg_decoding/utils/vis_grad.py | 8 ++++++++ train_wowandb.py | 7 +++++++ 3 files changed, 16 insertions(+), 1 deletion(-) diff --git a/examples/view_training_curve.py b/examples/view_training_curve.py index 894f726..e914243 100644 --- a/examples/view_training_curve.py +++ b/examples/view_training_curve.py @@ -17,6 +17,6 @@ def parse_data(data): import pdb; pdb.set_trace() if __name__ == '__main__': - filepath = '/home/yainoue/meg2image/results/20230412_mini/runs/2023-04-12 22:23:57.324970' #'/home/yainoue/meg2image/results/test/2023-04-11 18:06:44.273986' + filepath = '/home/yainoue/meg2image/results/20230412_mini/runs/2023-04-13 15:17:53.310665' #'/home/yainoue/meg2image/results/test/2023-04-11 18:06:44.273986' data = get_data(filepath) parse_data(data) diff --git a/meg_decoding/utils/vis_grad.py b/meg_decoding/utils/vis_grad.py index e69de29..aa74905 100644 --- a/meg_decoding/utils/vis_grad.py +++ b/meg_decoding/utils/vis_grad.py @@ -0,0 +1,8 @@ +import torch + + +def get_grad(model:torch.nn.Module): + for n, m in model.named_parameters(): + print(n, m.grad.sum()) + + diff --git a/train_wowandb.py b/train_wowandb.py index cd38f0f..7e61a5f 100644 --- a/train_wowandb.py +++ b/train_wowandb.py @@ -26,6 +26,8 @@ from meg_decoding.utils.loss import * from meg_decoding.dataclass.god import GODDatasetBase from meg_decoding.utils.loggers import Pickleogger +from meg_decoding.utils.vis_grad import get_grad + def run(args: DictConfig) -> None: @@ -247,6 +249,11 @@ def run(args: DictConfig) -> None: optimizer.zero_grad() loss.backward() optimizer.step() + if args.dataset == "GOD": + optimizer.zero_grad() + loss.backward() + optimizer.step() + # get_grad(brain_encoder) # Accumulate gradients for Gwilliams for the whole epoch if args.dataset == "Brennan2018": From 6b3a989e8f10e976da621698aa879cc165a397f4 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Fri, 14 Apr 2023 19:57:59 +0900 Subject: [PATCH 05/71] ADD: seq2static model --- examples/view_training_curve.py | 50 +++++++++++++++++++++--- meg_decoding/models.py | 68 +++++++++++++++++++++++++++++++-- train_wowandb.py | 2 +- 3 files changed, 110 insertions(+), 10 deletions(-) diff --git a/examples/view_training_curve.py b/examples/view_training_curve.py index e914243..062a915 100644 --- a/examples/view_training_curve.py +++ b/examples/view_training_curve.py @@ -2,21 +2,61 @@ import matplotlib.pyplot as plt import pickle import pandas as pd +import os + +loss_key = ['train_loss', 'test_loss'] +acc_key = ['trainTop1acc', 'trainTop10acc', 'testTop1acc', 'testTop10acc'] +lr_key = ['lrate'] +temp_key = ['temp'] def get_data(pkl_file:str): with open(pkl_file, 'rb') as f: data = pickle.load(f) return data -def parse_data(data): +def visualize(logs, savefile): + fig, axes = plt.subplots(ncols=4, figsize=(32,8)) + epochs = np.arange(len(logs)) + ax=axes[0] + for k in loss_key: + ax.plot(epochs, logs[k].to_list(), label=k) + ax.set_xlabel('epoch') + ax.set_ylabel('loss') + ax.legend() + ax=axes[1] + for k in acc_key: + ax.plot(epochs, logs[k].to_list(), label=k) + ax.set_xlabel('epoch') + ax.set_ylabel('accuracy') + ax.legend() + ax=axes[2] + for k in lr_key: + ax.plot(epochs, logs[k].to_list(), label=k) + ax.set_xlabel('epoch') + ax.set_ylabel('lr') + ax.legend() + ax=axes[3] + for k in temp_key: + ax.plot(epochs, logs[k].to_list(), label=k) + ax.set_xlabel('epoch') + ax.set_ylabel('temp') + ax.legend() + plt.savefig(savefile) + print('save as ', savefile) + +def parse_data(filepath): + data = get_data(filepath) log_names = list(data.keys()) + savedir = os.path.dirname(filepath) for name in log_names: logs = data[name] columns = logs.keys() logs = pd.DataFrame(logs) - import pdb; pdb.set_trace() + savefile = os.path.join(savedir, f'{name}_learning_curve.png') + visualize(logs, savefile) + if __name__ == '__main__': - filepath = '/home/yainoue/meg2image/results/20230412_mini/runs/2023-04-13 15:17:53.310665' #'/home/yainoue/meg2image/results/test/2023-04-11 18:06:44.273986' - data = get_data(filepath) - parse_data(data) + # filepath = '/home/yainoue/meg2image/results/20230413_sbj01/runs/2023-04-13 21:52:17.333087'#'home/yainoue/meg2image/results/test/2023-04-11 18:06:44.273986' + filepath = '/home/yainoue/meg2image/results/20230412_mini/runs/2023-04-13 15:17:53.310665' + parse_data(filepath) diff --git a/meg_decoding/models.py b/meg_decoding/models.py index 0616965..23faab2 100644 --- a/meg_decoding/models.py +++ b/meg_decoding/models.py @@ -13,6 +13,16 @@ from meg_decoding.utils.layout import ch_locations_2d + +def get_model(args): + if args.model == 'brain_encoder': + return BrainEncoder(args) + elif + args.model == 'brain_endcoder_seq2static': + return BrainEncoderSeq2Static(args) + else: + raise ValueError('no model named {} is prepared'.format(args.model)) + class SpatialAttention(nn.Module): """Same as SpatialAttentionVer2, but a little more concise""" @@ -123,7 +133,7 @@ def forward(self, X, subject_idxs): class ConvBlock(nn.Module): - def __init__(self, k, D1, D2): + def __init__(self, k, D1, D2, ks=3): super(ConvBlock, self).__init__() self.k = k @@ -133,7 +143,7 @@ def __init__(self, k, D1, D2): self.conv0 = nn.Conv1d( in_channels=self.in_channels, out_channels=self.D2, - kernel_size=3, + kernel_size=ks, padding="same", dilation=2 ** ((2 * k) % 5), ) @@ -141,7 +151,7 @@ def __init__(self, k, D1, D2): self.conv1 = nn.Conv1d( in_channels=self.D2, out_channels=self.D2, - kernel_size=3, + kernel_size=ks, padding="same", dilation=2 ** ((2 * k + 1) % 5), ) @@ -149,7 +159,7 @@ def __init__(self, k, D1, D2): self.conv2 = nn.Conv1d( in_channels=self.D2, out_channels=2 * self.D2, - kernel_size=3, + kernel_size=ks, padding="same", dilation=2, # FIXME: The text doesn't say this, but the picture shows dilation=2 ) @@ -266,3 +276,53 @@ def forward(self, Z: torch.Tensor, Y: torch.Tensor, test=False) -> torch.Tensor: raise return top1accuracy, top10accuracy + + +class BrainEncoderSeq2Static(nn.Module): + def __init__(self, args): + super(BrainEncoder, self).__init__() + + self.num_subjects = args.num_subjects + self.D1 = args.D1 + self.D2 = args.D2 + self.F = args.F if not args.preprocs["last4layers"] else 1024 + self.K = args.K + self.dataset_name = args.dataset + + self.subject_block = SubjectBlock(args) + # self.subject_block = SubjectBlock_proto(args) + # cprint("USING THE OLD IMPLEMENTATION OF THE SUBJECT BLOCK", 'red', 'on_blue', attrs=['bold']) + + self.conv_blocks = nn.Sequential() + ks_list = args.ConvBlocks.ks # 200 ms = 200 samples receptive field: ks * 3 * stride + for k in range(5): + ks = ks_list[k] + self.conv_blocks.add_module(f"conv{k}", ConvBlock(k, self.D1, self.D2, ks=ks)) + if k < 4: + self.conv_blocks.add_module(f"avgpool{k}", torch.nn.AvgPool1d(3, stride=2)) + else: + self.conv_blocks.add_module(f"globalavgpool{k}", AdaptiveAvgPool1d(output_size=1) + self.conv_final1 = nn.Conv1d(in_channels=self.D2, out_channels=2 * self.D2, kernel_size=1,) + self.conv_final2 = nn.Conv1d(in_channels=2 * self.D2, out_channels=self.F, kernel_size=1,) + if args.seq2seq: + self._forward = self._forward_seq_seq + else: + self._forward = self._forward_seq_static + + def forward(self, X, subject_idxs): + return self._forward(X, subject_idxs) + + def _forward_seq_seq(self, X, subject_idxs): + X = self.subject_block(X, subject_idxs) + X = self.conv_blocks(X) + X = F.gelu(self.conv_final1(X)) + X = F.gelu(self.conv_final2(X)) + return X + + def _forward_seq_static(self, X, subject_idxs): + X = self.subject_block(X, subject_idxs) + X = self.conv_blocks(X) + X = F.gelu(self.conv_final1(X)) + X = F.gelu(self.conv_final2(X)) + X = torch.mean(X, axis=2) + return X diff --git a/train_wowandb.py b/train_wowandb.py index 7e61a5f..a7a16e0 100644 --- a/train_wowandb.py +++ b/train_wowandb.py @@ -332,7 +332,7 @@ def run(args: DictConfig) -> None: if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230412') + args = compose(config_name='20230413_sbj01') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) run(args) From c7e948bd3086f82aed6aa69b37006f8d3898b9c6 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 17 Apr 2023 12:22:57 +0900 Subject: [PATCH 06/71] ADD: seq2static model --- examples/view_training_curve.py | 4 +++- meg_decoding/models.py | 7 +++---- train_wowandb.py | 13 ++++++++++--- 3 files changed, 16 insertions(+), 8 deletions(-) diff --git a/examples/view_training_curve.py b/examples/view_training_curve.py index 062a915..8ea7001 100644 --- a/examples/view_training_curve.py +++ b/examples/view_training_curve.py @@ -58,5 +58,7 @@ def parse_data(filepath): if __name__ == '__main__': # filepath = '/home/yainoue/meg2image/results/20230413_sbj01/runs/2023-04-13 21:52:17.333087'#'home/yainoue/meg2image/results/test/2023-04-11 18:06:44.273986' - filepath = '/home/yainoue/meg2image/results/20230412_mini/runs/2023-04-13 15:17:53.310665' + filepath = '/home/yainoue/meg2image/results/20230413_sbj01_seq2stat/runs/2023-04-16 19:21:02.983480' + # filepath = '/home/yainoue/meg2image/results/20230412_mini/runs/2023-04-13 15:17:53.310665' + parse_data(filepath) diff --git a/meg_decoding/models.py b/meg_decoding/models.py index 23faab2..d3e8c7b 100644 --- a/meg_decoding/models.py +++ b/meg_decoding/models.py @@ -17,8 +17,7 @@ def get_model(args): if args.model == 'brain_encoder': return BrainEncoder(args) - elif - args.model == 'brain_endcoder_seq2static': + elif args.model == 'brain_endcoder_seq2static': return BrainEncoderSeq2Static(args) else: raise ValueError('no model named {} is prepared'.format(args.model)) @@ -280,7 +279,7 @@ def forward(self, Z: torch.Tensor, Y: torch.Tensor, test=False) -> torch.Tensor: class BrainEncoderSeq2Static(nn.Module): def __init__(self, args): - super(BrainEncoder, self).__init__() + super( BrainEncoderSeq2Static, self).__init__() self.num_subjects = args.num_subjects self.D1 = args.D1 @@ -301,7 +300,7 @@ def __init__(self, args): if k < 4: self.conv_blocks.add_module(f"avgpool{k}", torch.nn.AvgPool1d(3, stride=2)) else: - self.conv_blocks.add_module(f"globalavgpool{k}", AdaptiveAvgPool1d(output_size=1) + self.conv_blocks.add_module(f"globalavgpool{k}", torch.nn.AdaptiveAvgPool1d(output_size=1)) self.conv_final1 = nn.Conv1d(in_channels=self.D2, out_channels=2 * self.D2, kernel_size=1,) self.conv_final2 = nn.Conv1d(in_channels=2 * self.D2, out_channels=self.F, kernel_size=1,) if args.seq2seq: diff --git a/train_wowandb.py b/train_wowandb.py index a7a16e0..bdcd936 100644 --- a/train_wowandb.py +++ b/train_wowandb.py @@ -21,7 +21,7 @@ # ) from torch.utils.data import DataLoader, RandomSampler, BatchSampler -from meg_decoding.models import BrainEncoder, Classifier +from meg_decoding.models import get_model, Classifier from meg_decoding.utils.get_dataloaders import get_dataloaders, get_samplers from meg_decoding.utils.loss import * from meg_decoding.dataclass.god import GODDatasetBase @@ -178,7 +178,7 @@ def run(args: DictConfig) -> None: # --------------------- # Models # --------------------- - brain_encoder = BrainEncoder(args).to(device) + brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) classifier = Classifier(args) @@ -209,6 +209,7 @@ def run(args: DictConfig) -> None: scheduler = None # ====================================== + best_acc = 0 pbar = tqdm(range(args.epochs)) for epoch in pbar: pbar.set_description("training {}/{} epoch".format(epoch, args.epochs)) @@ -327,12 +328,18 @@ def run(args: DictConfig) -> None: last_weight_file = os.path.join(savedir, "model_last.pt") torch.save(brain_encoder.state_dict(), last_weight_file) print('model is saved as ', last_weight_file) + if best_acc < np.mean(testTop10accs): + best_weight_file = os.path.join(savedir, "model_best.pt") + torch.save(brain_encoder.state_dict(), best_weight_file) + print('best model is updated !!, ', best_weight_file) + best_acc = np.mean(testTop10accs) + if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230413_sbj01') + args = compose(config_name='20230414_sbj01_seq2stat') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) run(args) From 8227711ce97461f276b3139b93df6b9e12f247a1 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 17 Apr 2023 14:01:06 +0900 Subject: [PATCH 07/71] ADD: sampler --- meg_decoding/dataclass/god.py | 33 ++++++++++++++++++++- train_wowandb.py | 54 +++++++++++++++++++++-------------- 2 files changed, 65 insertions(+), 22 deletions(-) diff --git a/meg_decoding/dataclass/god.py b/meg_decoding/dataclass/god.py index 7fbc219..572353c 100644 --- a/meg_decoding/dataclass/god.py +++ b/meg_decoding/dataclass/god.py @@ -5,7 +5,14 @@ from tqdm import tqdm from meg_decoding.matlab_utils.load_meg import get_meg_data, roi, time_window, get_baseline import tqdm - +import torch +from typing import List +from meg_decoding.utils.preproc_utils import ( + check_preprocs, + scaleAndClamp, + scaleAndClamp_single, + baseline_correction_single, +) mne.set_log_level(verbose="WARNING") @@ -85,3 +92,27 @@ def epoching(meg, window): image_feature_epochs = np.concatenate(image_feature_epochs, axis=0) print('dataset is created. size is ', meg_epochs.shape) return meg_epochs, sub_epochs, label_epochs, image_feature_epochs + + +class GODCollator(torch.nn.Module): + """ + Runs baseline correction and robust scaling and clamping for batch + """ + + def __init__(self, args): + super(GODCollator, self).__init__() + + self.brain_resample_rate = args.preprocs["brain_resample_rate"] + self.baseline_len_samp = int(self.brain_resample_rate * args.preprocs["baseline_len_sec"]) + self.clamp = args.preprocs["clamp"] + self.clamp_lim = args.preprocs["clamp_lim"] + + def forward(self, batch: List[tuple]): + X = torch.stack([item[0] for item in batch]) # ( 64, 208, 360 ) + Y = torch.stack([item[1] for item in batch]) + subject_idx = torch.IntTensor([item[2] for item in batch]) + + X = baseline_correction_single(X, self.baseline_len_samp) + X = scaleAndClamp(X, self.clamp_lim, self.clamp) + + return X, Y, subject_idx diff --git a/train_wowandb.py b/train_wowandb.py index 2358b24..9909bef 100644 --- a/train_wowandb.py +++ b/train_wowandb.py @@ -24,7 +24,7 @@ from meg_decoding.models import BrainEncoder, Classifier from meg_decoding.utils.get_dataloaders import get_dataloaders, get_samplers from meg_decoding.utils.loss import * -from meg_decoding.dataclass.god import GODDatasetBase +from meg_decoding.dataclass.god import GODDatasetBase, GODCollator from meg_decoding.utils.loggers import Pickleogger def run(args: DictConfig) -> None: @@ -138,26 +138,38 @@ def run(args: DictConfig) -> None: with open_dict(args): args.num_subjects = train_dataset.num_subjects print('num subject is {}'.format(args.num_subjects)) - train_loader = DataLoader( - train_dataset, - batch_size=args.batch_size, - drop_last=True, - shuffle=True, - num_workers=args.num_workers, - pin_memory=True, - worker_init_fn=seed_worker, - generator=g, - ) - test_loader = DataLoader( - val_dataset, - batch_size=args.batch_size, - drop_last=True, - shuffle=False, - num_workers=args.num_workers, - pin_memory=True, - worker_init_fn=seed_worker, - generator=g, - ) + + + if args.use_sampler: + test_size = val_dataset.Y.shape[0] + train_loader, test_loader = get_samplers( + train_dataset, + val_dataset, + args, + test_bsz=test_size, + collate_fn=GODCollator(args),) + + else: + train_loader = DataLoader( + train_dataset, + batch_size=args.batch_size, + drop_last=True, + shuffle=True, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + test_loader = DataLoader( + val_dataset, + batch_size=args.batch_size, + drop_last=True, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) else: raise ValueError("Unknown dataset") From 4a4e1e570361b4e8367a00c3967dd8047a079426 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 17 Apr 2023 14:23:52 +0900 Subject: [PATCH 08/71] notebooks --- notebooks/vis_meg.ipynb | 167 ++++++++++++++++++++++++++++++++++++++++ train_wowandb.py | 2 +- 2 files changed, 168 insertions(+), 1 deletion(-) create mode 100644 notebooks/vis_meg.ipynb diff --git a/notebooks/vis_meg.ipynb b/notebooks/vis_meg.ipynb new file mode 100644 index 0000000..a87a0f9 --- /dev/null +++ b/notebooks/vis_meg.ipynb @@ -0,0 +1,167 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os, sys, random\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "from time import time\n", + "from tqdm import tqdm, trange\n", + "from termcolor import cprint\n", + "# import wandb\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from omegaconf import DictConfig, open_dict\n", + "import hydra\n", + "from hydra.utils import get_original_cwd\n", + "\n", + "from constants import device\n", + "from torch.utils.data import DataLoader, RandomSampler, BatchSampler\n", + "\n", + "from meg_decoding.models import get_model, Classifier\n", + "from meg_decoding.utils.get_dataloaders import get_dataloaders, get_samplers\n", + "from meg_decoding.utils.loss import *\n", + "from meg_decoding.dataclass.god import GODDatasetBase, GODCollator\n", + "from meg_decoding.utils.loggers import Pickleogger\n", + "from meg_decoding.utils.vis_grad import get_grad\n", + "from meg_decoding.matlab_utils.load_meg import get_meg_data, roi, time_window, get_baseline\n", + "\n", + "from hydra import initialize, compose\n", + " with initialize(version_base=None, config_path=\"../configs/\"):\n", + " args = compose(config_name='20230417_sbj01_seq2stat')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "DATAROOT = args.data_root\n", + "processed_meg_path_pattern = os.path.join(DATAROOT, '{sub}/mat/{name}')\n", + "label_path_pattern = os.path.join(DATAROOT, '{sub}/labels/{name}')\n", + "trigger_meg_path_pattern = os.path.join(DATAROOT, '{sub}/trigger/{name}')\n", + "processed_rest_meg_path_pattern = os.path.join(DATAROOT, '{sub}/mat/{name}')\n", + "\n", + "meg_name, label_name, trigger_name, rest_name = args.subjects[sub][split]['mat'][0], args.subjects[sub][split]['labels'][0], args.subjects[sub][split]['trigger'][0], args.subjects[sub][split]['rest'][0]\n", + "\n", + "\n", + "processed_meg_path = processed_meg_path_pattern.format(sub=sub, name=meg_name)\n", + "label_path = label_path_pattern.format(sub=sub, name=label_name)\n", + "trigger_path = trigger_meg_path_pattern.format(sub=sub, name=trigger_name)\n", + "processed_rest_meg_path = processed_rest_meg_path_pattern.format(sub=sub, name=rest_name)\n", + "\n", + "MEG_Data, image_features, labels, triggers = get_meg_data(meg_filepath, label_filepath, trigger_filepath,\n", + " rest_mean=None, rest_std=None, split='train')\n", + "\n", + "roi_ids = roi(args)\n", + "data = MEG_Data[roi_ods,:]\n", + "# create raw\n", + "info = mne.create_info(\n", + " ch_names=len(roi_ids),\n", + " sfreq=1000,\n", + " ch_types='meg',\n", + ")\n", + "raw = mne.io.RawArray(data, info)\n", + "\n", + "mne.viz.plot_raw_psd(raw)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "meg_raw = np.stack([df[key] for key in df.keys() if \"MEG\" in key]) # ( 224, ~396000 )\n", + "# NOTE: (kind of) confirmed that last 16 channels are REF\n", + "meg_raw = meg_raw[:num_channels] # ( 208, ~396000 )\n", + "\n", + "meg_filtered = mne.filter.filter_data(\n", + " meg_raw, sfreq=brain_orig_rate, l_freq=brain_filter_low, h_freq=brain_filter_high,\n", + ")\n", + "\n", + "# To 120 Hz\n", + "meg_resampled = mne.filter.resample(\n", + " meg_filtered, down=brain_orig_rate / brain_resample_rate,\n", + ") " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from meg_decoding.utils.reproducibility import seed_worker\n", + "# NOTE: We do need it (IMHO).\n", + "if args.reproducible:\n", + " os.environ[\"CUBLAS_WORKSPACE_CONFIG\"] = \":4096:8\"\n", + " torch.use_deterministic_algorithms(True)\n", + " random.seed(0)\n", + " np.random.seed(0)\n", + " torch.manual_seed(0)\n", + " g = torch.Generator()\n", + " g.manual_seed(0)\n", + " seed_worker = seed_worker\n", + "else:\n", + " g = None\n", + " seed_worker = None\n", + "\n", + "\n", + "train_dataset = GODDatasetBase(args, 'train')\n", + "val_dataset = GODDatasetBase(args, 'val')\n", + "with open_dict(args):\n", + " args.num_subjects = train_dataset.num_subjects\n", + " print('num subject is {}'.format(args.num_subjects))\n", + "\n", + "\n", + "if args.use_sampler:\n", + " test_size = val_dataset.Y.shape[0]\n", + " train_loader, test_loader = get_samplers(\n", + " train_dataset,\n", + " val_dataset,\n", + " args,\n", + " test_bsz=test_size,\n", + " collate_fn=GODCollator(args),)\n", + "\n", + "else:\n", + " train_loader = DataLoader(\n", + " train_dataset,\n", + " batch_size=args.batch_size,\n", + " drop_last=True,\n", + " shuffle=True,\n", + " num_workers=args.num_workers,\n", + " pin_memory=True,\n", + " worker_init_fn=seed_worker,\n", + " generator=g,\n", + " )\n", + " test_loader = DataLoader(\n", + " val_dataset,\n", + " batch_size=args.batch_size,\n", + " drop_last=True,\n", + " shuffle=False,\n", + " num_workers=args.num_workers,\n", + " pin_memory=True,\n", + " worker_init_fn=seed_worker,\n", + " generator=g,\n", + " )\n" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/train_wowandb.py b/train_wowandb.py index cc4c3f2..4180f41 100644 --- a/train_wowandb.py +++ b/train_wowandb.py @@ -351,7 +351,7 @@ def run(args: DictConfig) -> None: if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230414_sbj01_seq2stat') + args = compose(config_name='20230417_sbj01_seq2stat') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) run(args) From 3e5a95f0fb6d7263b192938ca8f11d3688321f53 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 17 Apr 2023 15:02:41 +0900 Subject: [PATCH 09/71] add: resample_fs --- meg_decoding/dataclass/god.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/meg_decoding/dataclass/god.py b/meg_decoding/dataclass/god.py index 572353c..6fd4307 100644 --- a/meg_decoding/dataclass/god.py +++ b/meg_decoding/dataclass/god.py @@ -80,7 +80,11 @@ def epoching(meg, window): rest_mean, rest_std = get_baseline(processed_rest_meg_path, fs, args.rest_duration) MEG_Data, image_features, labels, triggers = get_meg_data(processed_meg_path, label_path, trigger_path, rest_mean=rest_mean, rest_std=rest_std, split=split) ROI_MEG_Data = MEG_Data[roi_channels, :] # num_roi_channels x time_samples - window = time_window(args, triggers, fs) + if args.resample_fs: + ROI_MEG_Data = mne.filter.resample(ROI_MEG_Data, down=fs / args.resample_fs) + window = time_window(args, triggers, args.resample_fs) + else: + window = time_window(args, triggers, fs) ROI_MEG_epochs = epoching(ROI_MEG_Data, window) meg_epochs.append(ROI_MEG_epochs) # array [epoch x ch x time_stamp] From 79565c57c475ff5608042069499d3958702a58f9 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 17 Apr 2023 15:14:27 +0900 Subject: [PATCH 10/71] add: bandpass filter --- meg_decoding/dataclass/god.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/meg_decoding/dataclass/god.py b/meg_decoding/dataclass/god.py index 6fd4307..7ecaacf 100644 --- a/meg_decoding/dataclass/god.py +++ b/meg_decoding/dataclass/god.py @@ -80,8 +80,14 @@ def epoching(meg, window): rest_mean, rest_std = get_baseline(processed_rest_meg_path, fs, args.rest_duration) MEG_Data, image_features, labels, triggers = get_meg_data(processed_meg_path, label_path, trigger_path, rest_mean=rest_mean, rest_std=rest_std, split=split) ROI_MEG_Data = MEG_Data[roi_channels, :] # num_roi_channels x time_samples - if args.resample_fs: + if args.preprocess.brain_filter is not None: + brain_filter_low = args.preprocess.brain_filter[0] + brain_filter_high = args.preprocess.brain_filter[1] + ROI_MEG_Data = mne.filter.filter_data(ROI_MEG_Data, sfreq=fs, l_freq=brain_filter_low, h_freq=brain_filter_high,) + print(f'band path filter: {brain_filter_low}-{brain_filter_high}') + if args.resample_fs is not None: ROI_MEG_Data = mne.filter.resample(ROI_MEG_Data, down=fs / args.resample_fs) + print('resample {} to {} Hz'.format(fs,args.resample_fs)) window = time_window(args, triggers, args.resample_fs) else: window = time_window(args, triggers, fs) From 721b3a5a4285cf294d1325b141c9ae9c8502c1f9 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 17 Apr 2023 15:26:13 +0900 Subject: [PATCH 11/71] add: topk evaluate --- meg_decoding/evaluate.py | 113 +++++++++++++++++++++++++++++++++++++++ meg_decoding/models.py | 23 ++++++-- 2 files changed, 132 insertions(+), 4 deletions(-) create mode 100644 meg_decoding/evaluate.py diff --git a/meg_decoding/evaluate.py b/meg_decoding/evaluate.py new file mode 100644 index 0000000..4c22a01 --- /dev/null +++ b/meg_decoding/evaluate.py @@ -0,0 +1,113 @@ +import os, sys, random +import numpy as np +import torch +import torch.nn as nn +from time import time +from tqdm import tqdm, trange +from termcolor import cprint +# import wandb + +from omegaconf import DictConfig, open_dict +import hydra +from hydra.utils import get_original_cwd + +from constants import device +# from speech_decoding.dataclass.brennan2018 import Brennan2018Dataset +# from speech_decoding.dataclass.gwilliams2022 import ( +# Gwilliams2022SentenceSplit, +# Gwilliams2022ShallowSplit, +# Gwilliams2022DeepSplit, +# Gwilliams2022Collator, +# ) +from torch.utils.data import DataLoader, RandomSampler, BatchSampler + +from meg_decoding.models import get_model, Classifier +from meg_decoding.utils.get_dataloaders import get_dataloaders, get_samplers +from meg_decoding.utils.loss import * +from meg_decoding.dataclass.god import GODDatasetBase, GODCollator +from meg_decoding.utils.loggers import Pickleogger +from meg_decoding.utils.vis_grad import get_grad + + +def run(args: DictConfig) -> None: + from meg_decoding.utils.reproducibility import seed_worker + if args.reproducible: + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + torch.use_deterministic_algorithms(True) + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + g = torch.Generator() + g.manual_seed(0) + seed_worker = seed_worker + else: + g = None + seed_worker = None + + if args.dataset == "GOD": + train_dataset = GODDatasetBase(args, 'train') + val_dataset = GODDatasetBase(args, 'val') + with open_dict(args): + args.num_subjects = train_dataset.num_subjects + print('num subject is {}'.format(args.num_subjects)) + + + test_size = val_dataset.Y.shape[0] + if args.use_sampler: + train_loader, test_loader = get_samplers( + train_dataset, + val_dataset, + args, + test_bsz=test_size, + collate_fn=GODCollator(args),) + + else: + test_loader = DataLoader( + val_dataset, + batch_size=test_size, + drop_last=True, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + + else: + raise ValueError("Unknown dataset") + + brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) + + testTop1accs = [] + testTop10accs = [] + testTopKaccs = [] + classifier = Classifier(args) + classifier.eval() + brain_encoder.eval() + half_k = int(test_size/2) + for batch in test_loader: + + with torch.no_grad(): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, chunkIDs = batch + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y = X.to(device), Y.to(device) + + Z = brain_encoder(X, subject_idxs) # 0.96 GB + + testTop1acc, testTop10acc, testTopKacc = classifier(Z, Y, test=True, topk=half_k) # ( 250, 1024, 360 ) + + testTop1accs.append(testTop1acc) + testTop10accs.append(testTop10acc) + testTopKaccs.append(testTopKacc) + + print( + f"testTop1acc: {np.mean(testTop1accs):.3f} | " + f"testTop10acc: {np.mean(testTop10accs):.3f} | " + f"testTop{half_k}acc: {np.mean(testTopKaccs):.3f} | " + ) diff --git a/meg_decoding/models.py b/meg_decoding/models.py index d3e8c7b..c1c4d0f 100644 --- a/meg_decoding/models.py +++ b/meg_decoding/models.py @@ -20,8 +20,8 @@ def get_model(args): elif args.model == 'brain_endcoder_seq2static': return BrainEncoderSeq2Static(args) else: - raise ValueError('no model named {} is prepared'.format(args.model)) - + raise ValueError('no model named {} is prepared'.format(args.model)) + class SpatialAttention(nn.Module): """Same as SpatialAttentionVer2, but a little more concise""" @@ -235,7 +235,7 @@ def __init__(self, args): self.factor = 1 # self.batch_size / 241 @torch.no_grad() - def forward(self, Z: torch.Tensor, Y: torch.Tensor, test=False) -> torch.Tensor: + def forward(self, Z: torch.Tensor, Y: torch.Tensor, test=False, top_k=None) -> torch.Tensor: batch_size = Z.size(0) diags = torch.arange(batch_size).to(device) @@ -273,8 +273,23 @@ def forward(self, Z: torch.Tensor, Y: torch.Tensor, test=False) -> torch.Tensor: except: print(similarity.size()) raise + if top_k is None: + + return top1accuracy, top10accuracy + else: + try: + topkaccuracy = np.mean( + [ + label in row + for row, label in zip(torch.topk(similarity, top_k, dim=1, largest=True)[1], diags) + ] + ) + except: + print(similarity.size()) + raise + return top1accuracy, top10accuracy, topkaccuracy + - return top1accuracy, top10accuracy class BrainEncoderSeq2Static(nn.Module): From 3052b25a93c3cacd7111694fc80ff65df624acf5 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 17 Apr 2023 15:38:05 +0900 Subject: [PATCH 12/71] move: evaluate.py --- meg_decoding/evaluate.py => evaluate.py | 0 meg_decoding/dataclass/god.py | 8 ++++---- 2 files changed, 4 insertions(+), 4 deletions(-) rename meg_decoding/evaluate.py => evaluate.py (100%) diff --git a/meg_decoding/evaluate.py b/evaluate.py similarity index 100% rename from meg_decoding/evaluate.py rename to evaluate.py diff --git a/meg_decoding/dataclass/god.py b/meg_decoding/dataclass/god.py index 7ecaacf..efada06 100644 --- a/meg_decoding/dataclass/god.py +++ b/meg_decoding/dataclass/god.py @@ -85,10 +85,10 @@ def epoching(meg, window): brain_filter_high = args.preprocess.brain_filter[1] ROI_MEG_Data = mne.filter.filter_data(ROI_MEG_Data, sfreq=fs, l_freq=brain_filter_low, h_freq=brain_filter_high,) print(f'band path filter: {brain_filter_low}-{brain_filter_high}') - if args.resample_fs is not None: - ROI_MEG_Data = mne.filter.resample(ROI_MEG_Data, down=fs / args.resample_fs) - print('resample {} to {} Hz'.format(fs,args.resample_fs)) - window = time_window(args, triggers, args.resample_fs) + if args.preprocs.brain_resample_rate is not None: + ROI_MEG_Data = mne.filter.resample(ROI_MEG_Data, down=fs / args.preprocs.brain_resample_rate) + print('resample {} to {} Hz'.format(fs,args.preprocs.brain_resample_rate)) + window = time_window(args, triggers, args.preprocs.brain_resample_rate) else: window = time_window(args, triggers, fs) ROI_MEG_epochs = epoching(ROI_MEG_Data, window) From e6c885b46594fa7baa69903798c044f89bd062b9 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 17 Apr 2023 15:41:08 +0900 Subject: [PATCH 13/71] mod: load weight in evaluate.py --- evaluate.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/evaluate.py b/evaluate.py index 4c22a01..135994a 100644 --- a/evaluate.py +++ b/evaluate.py @@ -78,6 +78,11 @@ def run(args: DictConfig) -> None: brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) + weight_dir = os.path.join(args.save_root, 'weights') + last_weight_file = os.path.join(weight_dir, "model_last.pt") + brain_encoder.load_state_dict(torch.load(last_weight_file)) + print('weight is loaded from ', last_weight_file) + testTop1accs = [] testTop10accs = [] testTopKaccs = [] @@ -111,3 +116,13 @@ def run(args: DictConfig) -> None: f"testTop10acc: {np.mean(testTop10accs):.3f} | " f"testTop{half_k}acc: {np.mean(testTopKaccs):.3f} | " ) + + + +if __name__ == "__main__": + from hydra import initialize, compose + with initialize(version_base=None, config_path="../configs/"): + args = compose(config_name='20230414_sbj01_seq2stat') + if not os.path.exists(os.path.join(args.save_root, 'weights')): + os.makedirs(os.path.join(args.save_root, 'weights')) + run(args) From 185bc25601328dbffaa1fe2cfe1dbfac25470546 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 17 Apr 2023 15:54:14 +0900 Subject: [PATCH 14/71] mod --- evaluate.py | 2 +- meg_decoding/dataclass/god.py | 6 +- notebooks/vis_meg.ipynb | 248 ++++++++++++++++++++++++++++++---- 3 files changed, 228 insertions(+), 28 deletions(-) diff --git a/evaluate.py b/evaluate.py index 135994a..e1668fb 100644 --- a/evaluate.py +++ b/evaluate.py @@ -75,7 +75,7 @@ def run(args: DictConfig) -> None: else: raise ValueError("Unknown dataset") - + assert len(test_loader) == 1 brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) weight_dir = os.path.join(args.save_root, 'weights') diff --git a/meg_decoding/dataclass/god.py b/meg_decoding/dataclass/god.py index efada06..380841d 100644 --- a/meg_decoding/dataclass/god.py +++ b/meg_decoding/dataclass/god.py @@ -80,9 +80,9 @@ def epoching(meg, window): rest_mean, rest_std = get_baseline(processed_rest_meg_path, fs, args.rest_duration) MEG_Data, image_features, labels, triggers = get_meg_data(processed_meg_path, label_path, trigger_path, rest_mean=rest_mean, rest_std=rest_std, split=split) ROI_MEG_Data = MEG_Data[roi_channels, :] # num_roi_channels x time_samples - if args.preprocess.brain_filter is not None: - brain_filter_low = args.preprocess.brain_filter[0] - brain_filter_high = args.preprocess.brain_filter[1] + if args.preprocs.brain_filter is not None: + brain_filter_low = args.preprocs.brain_filter[0] + brain_filter_high = args.preprocs.brain_filter[1] ROI_MEG_Data = mne.filter.filter_data(ROI_MEG_Data, sfreq=fs, l_freq=brain_filter_low, h_freq=brain_filter_high,) print(f'band path filter: {brain_filter_low}-{brain_filter_high}') if args.preprocs.brain_resample_rate is not None: diff --git a/notebooks/vis_meg.ipynb b/notebooks/vis_meg.ipynb index a87a0f9..37b65a9 100644 --- a/notebooks/vis_meg.ipynb +++ b/notebooks/vis_meg.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -20,9 +20,9 @@ "import hydra\n", "from hydra.utils import get_original_cwd\n", "\n", - "from constants import device\n", "from torch.utils.data import DataLoader, RandomSampler, BatchSampler\n", - "\n", + "import mne\n", + "sys.path.append('../')\n", "from meg_decoding.models import get_model, Classifier\n", "from meg_decoding.utils.get_dataloaders import get_dataloaders, get_samplers\n", "from meg_decoding.utils.loss import *\n", @@ -32,15 +32,49 @@ "from meg_decoding.matlab_utils.load_meg import get_meg_data, roi, time_window, get_baseline\n", "\n", "from hydra import initialize, compose\n", - " with initialize(version_base=None, config_path=\"../configs/\"):\n", - " args = compose(config_name='20230417_sbj01_seq2stat')" + "with initialize(version_base=None, config_path=\"../../configs/\"):\n", + " args = compose(config_name='20230417_sbj01_seq2stat')\n", + " \n", + "args.montage_path = '../' + args.montage_path\n", + "args.ch_region_path = '../' + args.ch_region_path" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load /work/project/MEG_GOD/GOD_dataset/sbj01/mat/data_block001.mat\n", + "ROI: ['occipital/left', 'occipital/right']\n", + "channel: [129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155]\n", + "num channels: 22\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_63164/3869489588.py:29: RuntimeWarning: Channel locations not available. Disabling spatial colors.\n", + " mne.viz.plot_raw_psd(raw)\n" + ] + }, + { + "data": { + "image/png": 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g9UBSO9SLkI8bECCEOCOEOCiE6HNlmx+QftUx6Ve2XRchxGwhxGkhxOnS0lJ69uxJixYtGDNmDO3btyc4OJiioiLCwsIYMmQIrVq14pdffmHSpElYW1uzb98+GjVqRL9+/dDr9SQkJFBUVERGRgZZWVnk5+eTnJyMTqcjJiYGg8GgTiHVhInU/I+KiqKyspL4+Hi0Wi2pqank5uaSm5tLamoqWq2W+Ph4KisriYqKum4bkZGRGAwGYmJi0Ol0JCcnk5+fT1ZWFhkZGRQVFZGQkIBeryc6OhqTyURERMQ1bURERGAymYiOjm5wfXJ3d7/r+nQ3fk913aearD13U5/uxu+prvqUkZGBoih3VZ/uxu/pdvTJaDTedX26G7+nuuxTbWT5uG2FXYQQvwA+19n1L0VRNl855gDwvKIop6+81wCOiqIUCCE6Az8BbYFg4F1FUQZfOa4P8JKiKKNuJEfLli2VHj16sGfPHtasWcOrr75KdHQ0Go0GvV6Pr68vQgh1IaKiKBgMBjw8PIiOjiYrKwsnJ6da+EQk5iI5OZnmzZubWwyJmZF60LD55JNPGDFihNQBiRwLJISHh9OlS5c7o7CLoiiDFUUJvc7f5j85p1JRlIIrr8OBBC4b0xlAk6sObXJl2w1xcHDAysoKK6vL0S5TpkyhsrKSgoICjEYjJSUlGI1GFEXBysoKX19fAgMDsba2prq6mvT09BtcQVLfcXR0NLcIknqA1IOGTWZmptQBCSDHAkntZHqp1yEfQggvIYTlldeBQEsgUVGULEArhOhxJbvHQ8AfGuZXoygKNjY2NGrUiM8//5zCwkJGjhyJr68vLVu2pKCgAGtrazIzM2nevDlZWVkUFhZiMBhQFIVjx47VWX8lt4fq6mpziyCpB0g9aNicPHlS6oAEkGOBpHaoFwa1EGKcECId6AlsE0LUlDDsC5wTQpwFNgFzFEUpvLLvceAr4BKXPdc3XJBYQ48ePaiurua1114jOTmZ9957j7fffpuysjKaN29OaWkpvXr1YtasWRQWFjJjxgxMJhNWVlZs3LixtrotMRMmk8ncIkjqAVIPGjbFxcVSBySAHAsktcNti6GuL7Rs2VLZsWMHjzzyCP7+/jg6OuLo6EhISAjJyck0atSIf//73wQHB5Obm0tZWRm7du3ioYceIjMzExsbGzIzM83dDcktUFRUhJubm7nFkJgZqQcNm5CQEI4dOyZ1QCLHAgkHDhxgwIABd0YMdX3BaDQSGBiIEIJZs2aRlZXF+PHj8fLyorq6mpkzZ9K9e3f8/PwIDQ3F2dmZN954A09PTywsLNBqtdcUfpHceRQWFt74IMldj9SDhk1ZWRkFBQXmFkNSD5BjgaQ2aHAGtZWVFRYWFri6ulJSUoJWq+XChQv069cPgA8++IBhw4bx2WefsWrVKoKCgggMDKR9+/ZUVVUB8NVXX6nt1Ub9d8ntxdfX19wiSOoBUg8aLjXZm9zd3c0tiqQeIMcCSW3Q4AzqmsUH7u7unD59Gn9/f86ePYuzszMLFiygSZMmPP744/z000+cPHkSHx8fXnvtNbKzs3F2dsbCwoI333yT06dPU15ezuLFi83cI8lfJSkpydwiSOoBUg8aLjX3gYSEBDNLIqkPyLFAUhs0OIPaxsYGAI1Gw/PPP0+/fv1ITk5m3Lhx6jHV1dXMmjWLpk2b0rRpU9zc3BBCYDQaMZlMWFpa8sEHH/DKK6+QnZ1trq5I/iayIpYEpB40ZCoqKtBoNDJdmgSQY4GkdmhwBnVFRQUAw4YN47vvvqN79+5otVrOnDnDnj17GDVqFNu2bVOPbdu2LQDdunWjd+/eVFZWotfr2bVrF+vWrcNgMJitL5K/x9mzZ80tgqQeIPWg4VJeXo6DgwOpqanmFkVSD5BjgaQ2aHAGta2tLQCDBw8mJiaGZcuW4enpyZAhQ/j666/x8vLCysqKhQsXEhkZycSJEwGYOHEi3bp1IywsjPLycpo3b46lpSXff/+9WkpTcmfQqVMnc4sgqQdIPWi4lJaW4uLigtFoNLcoknqAHAsktcFfNqiFEA41xVbuRGrSBDo5OdGmTRsmT55Mp06dcHZ2JjMzk+joaMaPH8/cuXOZPHkyl+vGQKNGjbC1tWXkyJE0atSIpKQkhBBUVlayZs0atm7dym9TECqK8rttEvMTHh5ubhEk9QCpBw2X0tJSFEWRGZskgBwLJBAcHHzLbdzQoBZCWAghpgghtgkhcoEYIEsIES2E+J8QIuiWpbiNODs7q69HjhxJamoqLVu25N1338XNzY1XX32VlJQUHBwc0Gg06rGWlpZoNBp8fX1p0aIFer2eyspKFEVh7dq1rFu3jpCQEF577TW1+Mt7771Hx44dpVFdz6iNEqOSOx+pBw0XnU5HVVUVeXl55hZFUg+QY4GkNjK93IyHej/QAngZ8FEUpamiKN5Ab+A48J4QYuotS3KbKC8vV183bdoUvV6PpaUlUVFRvP3222i1Wl566SX1mDNnzqgLDzUaDUII+vTpQ8eOHWncuDFCCDIyMqioqCA3N5d3332XBQsW8Nlnn/Hpp5+SkJDAkSNHbns/JX9MRESEuUWQ1AOkHjRcysrKKCoqwt7e3tyiSOoBciyQ1AY3Y1APVhTlLUVRzimKotbnVBSlUFGU7xVFuQ9YX3ci1i5WVlbXvA8LC6NVq1ZERETQuHFjpk6dSnFxMVu2bGH//v38+uuv/PTTTwD07NmTkpISBg4cSGhoKA8//LC6Snzz5s2Ul5djMpk4f/48r7zyCunp6ZSVlfHKK6/c7m5K/oQOHTqYWwRJPUDqQcOlrKyMnJwcUlJSzC2KpB4gxwJJbVTAvqFBrShK9W+3CSE8RU1w8R8cU18pLS295n3Hjh05ffo0kyZN4rnnniM+Pp42bdqQkZHBqlWrKCoqwsbGhn379uHm5kaLFi3Yv38/xcXFjBo1Cj8/P3x8fHB2dqZDhw506tSJbt26odVqURQFS0tLTp06xaJFi0hPTzdTryVXExMTY24RJPUAqQcNl7KyMgwGA97e3uYWRVIPkGOBJC4u7pbbuJkY6h5CiANCiB+EEB2FEOeB80COEGL4LUtwm7G2tr7mvY2NDRqNhqNHj9KkSROio6N58sknOXz4MO+++y7z588nKyuL7Oxsli5dyrhx42jdujVBQUHs3bsXKysrmjRpQlhYGOPGjcPJyQkHBwdatGiBRqPBzs4Oo9HIwoUL+fDDD2U8dT0gICDA3CJI6gFSDxouZWVlVFdXyzoCEkCOBZLawerGh7AMeAVwAfYB9yqKclwIEQKsBXbWoXy1zvXyRk+ePJns7Gx69erFY489xqFDh1iwYAEHDx5U804vWLCA0NBQjh8/zqRJk5gyZQrTp08nNjaWEydOcPjwYUaNGkVISAju7u5s3bqVbdu28Y9//IMPP/yQoqIitm/fTrt27Zg+ffrt77hEJTMzkxYtWphbDImZkXrQcNHr9SiKIgu7SAA5Fkhqh5uJobZSFGW3oigbgWxFUY4DKIpyR86RWFr+PuOfhYUFvr6+ODs706tXL44cOcJ3331HRUUFK1as4LPPPqO6uhpvb28OHz6MlZUVQgji4+NRFIXCwkICAwOxsLBACMHhw4d5//33adGiBWFhYXTr1g1FUbh06RILFy5Eq9WaoeeSGtzd3c0tgqQeIPWg4VJWVgZAfn6+mSWR1AfkWCCpDW7GoDZd9fq3STvvuPgFk8n0p/tnzZqFlZUVbm5uGI1GrKysCAkJoU+fPjg7O3Pq1CkAioqKSE9Px8bGhvXr12NlZcV///tf8vPz2bt3L/feey+ZmZmsWbMGJycnOnXqhI2NDQkJCTz11FPs3bv3dnRXch2uzvQiabhIPWi41OSfvmopkKQBI8cCSW1wMwZ1eyGEVghRCoQJIUqvet+uNoQQQtwvhLgghDAJIbr8Zl+YEOLYlf1RQgjbK9s7X3l/SQixRNTSyKjRaOjcuTNNmzbF3d0dIQS7du3iiSeeIDw8nPLyci5cuMC6devw9PTExcUFf39/Dh8+TGVlJdu2baNRo0YEBQVhaWlJXFwcvXv3xt3dnYULF1JdXc25c+f4/vvvSU9PlwsVzYCFRYMrECq5DlIPGi41BpScLZSAHAsktcMNY6gVRbkdVRHPA+OBz6/eKISwAr4DpimKEimE8ABqMop8CswCTgDbgeHAjhtd6Gbs7k6dOlFdXc1PP/3EzJkzcXBwoE2bNlhbW2NhYcGmTZv4/vvv6d27N4cOHaJZs2bodDr0ej3vvPMO7u7u2Nvb88gjj7Bt2zZOnz6NtbU1+/fvx8vLi6ioKOLi4tixYwcdOnRg6tSpjB8/XnpLbhO/XZgqaZhIPWi41BjShYWFZpZEUh+QY4GkNrihQS2EePbP9iuKsuhWhVAU5eKVa/1211DgnKIokVeOK7hyXGPAuSaeWwixChjLTRjUN5PIPywsjI8++ggvLy/Gjx8PgK2tLe7u7jRp0oSSkhIsLCxwcHDAxsaGCxcu8Oyzz2JlZXVNLFZCQgJWVlZ06NABJycnunXrRnx8PHq9HqPRSG5uLqdPn2bPnj3Ex8fj5eXFzJkzbyif5NbQ6XR4enqaWwyJmZF60HApKSkBbu5+ILn7kWOBpH///rfcxs3Mczhd+esCzAX8rvzNATrdsgR/TjCgCCF2CSEihBAvXtnuB1wdK5F+Zdt1EULMFkKcFkKcLigoID8/n6ysLDIyMigqKiIhIQG9Xk90dDQmk4nIyEhmzpxJz549sbKyIiIiApPJRN++ffH396dly5a0b98eFxcXQkNDSU1NpaysDBsbG2JiYjAYDERGRpKTk8Ojjz6Kh4cHNjY27Nq1i7lz59KvXz9mz55NixYtaN26NS1btuTYsWMEBARw4sQJ4uPjqaysJCoqCoDw8PBr/kdGRmIwGIiJiUGn05GcnHzDPtVUgqppo6ZP0dHR6PV6EhISKCoqIiMjg6ysLPLz80lOTkan013Tp+vJExUVRWVlJfHx8Wi1WlJTU8nNzSU3N5fU1FS0Wm296pO9vf1d16e78Xuq6z7l5OTcdX26G7+nuuhTaGgonp6e2NnZ3TV9uhu/p9vVp7KysruuT3fj93Q7+nQriJvNiyyEOASMVBSl9Mp7J2Cboih9b/L8XwCf6+z6l6Iom68ccwB4XlGU01fePw88AXQFyoG9wKtACfCuoiiDrxzXB3hJUZRRN5IjNDRUOX/+/M2I/DtKS0uZO3cuHh4ebNu2jeeff56DBw+SkpLCa6+9xr333nvN8b6+vri5ufHAAw/wz3/+k1dffZWHH36YtWvXcuLECXJzc8nOzsZgMFBZWYmPjw/t27cnODiYSZMm0bNnz78lp+TPiYmJISQkxNxiSMyM1IOGS8+ePTl+/DizZ8/m888/v/EJkruauh4LUlJSaNasWZ21L6kdhBDhiqJ0ufGR1+evROI3Aqquel91ZdtNoSjKYEVRQq/zt/lPTksHDimKkq8oSjmXY6U7ARlAk6uOa3Jl2w0xGo03K/LvcHJyYsqUKeTn5zN79mw0Gg1hYWE4OzuzfPlyiouLAThz5gyffPIJ1dXVVFVVcfToUaysrHj88cfZuHEjCxcu5O2332bJkiW0adOG5cuX4+zsTFZWFjExMaxdu5ZZs2YRExMjC8HUAUFBQeYWQVIPkHrQcKlJm7dz5x1VRkFSR9T1WLB+/fo6bV9y69SGh/qvGNSrgJNCiDeEEG9weTHgN7cswZ+zC2gnhLC/skCxHxCtKEoWoL1SxVEADwF/Zpir1KRL+ruMGDECPz8/Ro0ahaWlJS1btkRRFCZPnsz8+fPJy8tjyZIlVFRU4ODgQGBgICUlJXz//fccOXKErKws3n//fVJTU+nfvz8HDx5k8uTJdOjQASEEqampFBUVYW9vz+jRo5k9e/YNU/1J/hoXLlwwtwiSP6A2BrWbRepBw6WyshKAsWPHmlcQSb2grseCffv2XbeonKT+UFpaestt3LRBrSjKAmAGUHTlb4aiKO/csgSAEGKcECId6AlsE0LsunLNImARcAo4C0QoirLtymmPA18Bl4AEbmJBIlxOi3ervPjii2zatInevXvTunVr3N3d+eijjwgKCmLixIkkJiayadMmvLy86NWrF1qtllWrVnHmzBkeeeQRNBoNx48f5+GHH8ZgMPD555/zwgsvMGjQIHXx45kzZ9DpdBw8eJBPP/30lmWW/D/t27c3twiSP+Dw4cO37VpSDxouNY6VFStWmFkSSX2grseCnJwc8vLy6vQaEvNzQ4P66vzOiqJEKIqy+Mrfmesd83dQFOVHRVGaKIqiURSlkaIow67a952iKG2vhIe8eNX201e2tVAUZZ5yk7ERFRUVtyIqAJ6enrz++usEBgbi7e3N8OHDCQgIAC4XjrG2tsbLy4vS0lJ69OiBlZUVP/zwA/feey9Hjhzh8ccfZ/bs2VRUVPDwww/z66+/cv78eXr16sX06dMZOnQoHTp0oKCggLS0NF5++WXeeadWnl0k3F4vqOSvcTvTmEk9aLhUV1/OvvrYY4+ZWRJJfaCuxwKj0UhiYmKdXkNifm7GQ71fCPGkEML/6o1CCBshxEAhxErg4boRr/axtbWt1fa8vLywtLRk7NixJCcn4+zszJNPPsk///lPGjVqRGhoKMXFxTzzzDOEh4dz8eJFbGxsiIqKonHjxvj6+vLSSy9x8eJFHBwcCAsLo0+fPjRr1oyHHnqI6upqLCwsWLJkCePHjyc5OZmzZ89y+vTpWu1HQ6Jz587mFkFyHUwmE0VFRbftelIPGi5VVZeXAy1ZssTMkkjqA3U9Ftja2hITE1On16gP1IbD8k7mZgzq4YARWCuEyBRCRAshkoB4YDLwkaIo39ShjLVKXXzhQ4cOxWAw0L17dxYtWkR0dDTh4eH079+fxo0b0759e4qKinj22WfVFHuFhYU0a9YMb29v/v3vfzNv3jy8vLwwGAy4uLgwceJE2rdvz8svv4zBYCA3N5c9e/bQv39/Pv74Y86cOXNjwSTXRXoma5fq6mo1r++toNPp1NjW24HUg4ZLTTzrvHnzzCyJpD5Q12OBvb29mqbzbmb16tUNOlb8hga1oigViqJ8oihKL6AZMAjoqChKM0VRZl0d+nEnUNseagBvb2+8vb3VnNIvv/wyDz30EG+88QZCCL755htiY2OZOnUq9vb23HvvvRw/fpxRo0aRkZGBwWBQi8CkpqZy8OBBXF1duXjxIhEREbRp0wYnJycqKyuxs7MjJiaG+Ph4cnNza70vDQHpmaxdkpKSOHTo0C23o9Vq1ZLQtwOpBw2XmpCPxYsXm1kSSX2grseCwsJCNQvY3UxVVdUtJ364k/lLBewVRalWFCVLUZTiOpKnzrmVtHl/Rv/+/enS5f/TF/r4+KiVH93c3Ojbty9VVVW4ublhb29PUFAQ33zzDb/88gt5eXn8+uuv9OvXj969e9OtWzc6derEc889x/fff0+rVq2455578PHxITY2losXL7Ju3Tp69uzJgw8+eFu9encDNUnhJbVDQkICp0+fvuUHvNLSUlJSUmpJqhsj9aDhUpM56eGH75hoRUkdUtdjQV5eXoO4T1dXV9+xBnXjxo1vuY0blh6/23BxcTHLdV999VUqKip44IEHWLp0KdnZ2URERDBy5Eh0Oh2nT5/Gz8+PJk2akJOTw549e6ioqKBfv3588803bNiwgQ0bNmBra8ulS5fU6fGkpCSsrKwYN24cGo2Ge++9F51Oh4ODw/VKuUuA4ODg624/deoUXbt2vc3S3Pmkp6eTmprK66+/zqeffvq39a420hb9Ff5IDyR3PzWOlU2bNrF8+XIzSyMxN3U9Fuh0uts+vpmDO9mgbtWq1S238Zc81HcDNYtRbjd+fn60aNECNzc3LCwsyMjIoLi4mLS0NA4fPoyrqysvvvgiu3fvZu/evUyePJmHH36Ybdu2ER8fT3h4ONOmTWPJkiX4+/tjMplwd3dHURTWr1/P5MmTefXVV0lKSuK5556T4SB/Qmpq6nW3r1y5ko0bN/7hfsn1ycvLo7CwkJSUFJKSkv52O1qt9rYOxvJ7brjUeKgHDRpkZkkk9YG6HAsURaGiooKCgoI6u0Z9oaGHfDQ4D7WFhXmfISZPnsxjjz1Gs2bNePPNN/Hz82PdunVs3bqVw4cPU11dzbZt29BoNNx3333MmzePF198kYcffpiTJ08yYsQIFi1aREREBBUVFdjY2PD+++8DYG1tzXvvvceIESNISkqiUaObLmR5V1NeXo69vb36/nqfi6IoWFpa8sMPP9CoUSP8/f1/d4zk+iQnJ5OUlIStrS1JSUkEBgb+rXby8/Nva9o8+ftouNRkWY2IiDCzJJL6QF2OBQaDAUVRGoRBnZSUdFvXwdQmt7WwixBirxBixG+2fXHLEtxmdDqdWa8/ffp0Fi1axOeff0779u3x9PRk0KBB5Ofn4+bmRlJSEitXrmThwoV8+OGHZGdn06tXLzw9PbG2tmbx4sWkp6cza9YsysvLsbOzY8qUKbi5uREVFcWKFSt47bXXOHbsmFn7WZ/43//+x44d/1/353qLQ3bu3Env3r255557SE9Pv43S3ZlkZWWh1WrZvXs3SUlJlJSU0KFDh1uKRSwpKbmtK8QbwiIhyfVRFAUhxN9++JPcXdTlWFBRUYGiKLfVWWAuambe70Rud+nxAOAlIcS/r9rW5Y8Orq+YO65YCMGAAQOueSIODAxk8eLFWFhY4OPjw//+9z/Gjh3L5s2b6devH2+99Rbz58/HwsKCuXPn8sQTT7B7925Gjx5NREQEQgjmzJlDdXU1Go2G+Ph4vvrqKzP20nzUhB/UUFpaSuvWrcnIyFC32draUlFRoRp/GRkZxMTE0Lt3b5588skGsXjkVjl69ChHjhzhyJEjZGRkoCgKPXv2vKXUUMXFxbfVoK6LjD+SOwdLS8vbmvdcUn+py7GgvLwcRVHuWM/tX+VONahrg79iUBdzOWVeIyHEViGEeVb33YVoNBp69+7NJ598gk6nIyQkhMOHDxMSEgJcjksqKytj8+bNpKSkYGFhgUajITMzk08++YTKykoyMjLU+OyKigpiYmIYMGAAp06dMnPvaoecnBy0Wi0Gg0GNf4TL6YgKCwvJz89n1apV/PLLL6xbtw64PNW2c+dOQkJCcHd3Z9++fVRWVlJdXc3atWuJjY3lwIED7N69m0ceeURd5VtZWUlaWto11//hhx94//33ef3119WpoZsszvm3qaqquulS3IcPH65zea6mpKSEPXv2MGrUKKytrXFxcaGiouKW8rwXFxdjYWFxW/shabiYO/xP0jCoMaQbQmyxEKJWahLcqfyVGGqhKIoBeFwIMR34FXCrE6nqkPp8s27WrBnvvPMOTZs25ejRo7zzzjvce++9nD59Gp1Oh5+fHwsWLCA1NZVFixbRo0cPAEaMGMGQIUMICQkhMjKSX375hdzcXFJTU3nppZd47rnnGDlypJl7dy1GoxELC4ubmjEoKipi7dq12NnZqed269aNwMBAVq1ahUajwcbGhokTJ+Lk5MThw4c5cOAAUVFR9OzZk7Zt2xIWFkZ0dDSbNm1Cq9Xi7u7OhAkTOHDgAO3bt8fFxYXKyko0Gg2zZ89myZIlODo6YmVlhZ2dHQEBAQwfPpyqqiq2bNlC8+bNOXnyJC1btmTUqFE33e/MzEzs7e1xcXGhoKCADRs2MGTIEFq2bImiKKxYsQJbW1syMzMRQmAwGOjRowdFRUWcPHmS3r174+TkxLlz59i5cycvvvgipaWlnDhxguzsbIYMGYKrqysA2dnZ+Pj4XHP98vJyoqOjr0nxWFVVhY2NzR/KnJWVhZWVFV5eXuo2IQQzZ87k1KlT2Nvbk5GRwbJly+jbt+9Nfxa/paSkhMLCQqqrq/9UntqioVf1auhYWlri5nbH3cIkdUBdjgU1YabSoL77+SsG9Wc1LxRF+UYIcQ6448pM1XevRM1iuF69ejFo0CAaN25MUFAQJSUlWFtbs2/fPvz9/Zk9ezbffvstbdu2ZeDAgWzatInHH3+ct956i4cffpiPPvqI3Nxctex5fTOoP/30U0JDQ+nfvz8GgwErq/9XxdLSUg4ePIjRaKRnz55s3ryZOXPmkJaWhk6no2PHjuzbt48DBw4wffp01YCsoXfv3uzfv59p06Zds69Nmza0adOGpKQkvL29gcv5w+GyN3vYsGG8//77hISEMG/ePCwtLdHr9SQlJdG2bVvgcsWrsrIyLl68yNNPP83OnTuJi4u76bRLu3btwsbGhqKiIsrLy3nooYc4cOAAFRUV5OXl0a5dO3Jychg8eDDe3t6kpKTw008/kZOTw3333cf69espLi6mY8eOTJ06lVWrVpGTk8PDDz/M6dOn+fTTT3F0dMTGxoaIiAhef/11tm7dytixY/nqq6/QaDQ4ODhw7tw5goKCCA0N5V//+hfvvPMO9vb2vPvuu8yaNYvjx48TGhpKYWEhx44dQwjB1KlT2bdvH926dePixYv06NGDtLQ0UlNTKS8vRwhBVlbWNQZ6dnY2hw4d4v7777/hw5NOp8NgMNw2g/q3eiNpWFhaWpKYmGhuMST1gLocC3Q6HUKIOquBUZ8QQqDVas0thtm4oUEthFgKKFdeL/nNbvOu8Psb3ElK7eHhQYcOHcjNzeWzzz6jffv26HQ68vPzKS8vZ+7cuQQGBvLss88ydOhQDhw4QGhoKAEBAezdu5eYmBgqKyuJj49n/vz5vP7669dkuzAnhw8f5tKlSxQWFvLrr7+yaNEicnNzyczM5MCBA4wZM4acnBy++eYbfHx8sLW1JTAwUL0BDhw4UG2rvLycPXv20KZNGyIjI5kwYQJ+fn6qkZ6fn4+dnR3x8fEIIbC3t8fBweEaeX7++Weys7N58sknmTlzJiNHjsTPzw97e3vatm1LdnY2bm5uaDQaBg0axPnz5xFCMHjwYFatWnVTBnV+fj5GoxFPT098fHzYsmULPj4+TJo0iZUrV9K0adNr8mArikJiYiK+vr5069aNxo0b89hjj6HX61Vv/YwZM9TjR48ercrRvHlzBgwYwPLlyxk9ejQ//PADL7zwAlVVVTg5OQHw1VdfkZaWxrPPPsu///1vWrZsyaBBg/jf//7H5MmT2bt3L/b29kyfPh0LCwt+/PFHBg0aRFRUFP7+/hw/fpzAwEA11KNXr144OjqyatUqZs6cyZ49e8jMzCQkJISTJ0/SvXv3P/18SktLMRqNahW7uiYnJwdnZ+fbcq36RkpKCs2aNTO3GGbF3t6eTp06mVsMST2gLseCkpISLC0t7yjb4+9iMpka9Bqkm/FQn77q9ZvAv//owDuBqz2h9Z2pU6diYWGBv78/lZWV2Nvbs2bNGtXAPnnyJCdOnGD+/Pn079+fX375hTlz5rBnzx5atGhBVVWVWqFp06ZNtGvXjgcffNBs/amurub48eO0atUKOzs7nJ2dCQoKorq6mtjYWHbs2EH79u157LHHsLGx4ZdffmHChAm4urpiMpl49dVXSU5Opnfv3jzyyCMsWrSII0eO0K5dO2bPns3ChQuJiYnh7NmzJCcnk5WVRWhoKEePHiU4OJiKigocHR3x9fVl0qRJhIWFUVJSwhdffMGZM2dIS0ujurqa//3vfxw5coQHH3yQ3r17Ex4eztmzZ1EUhZkzZ7Jr1y4cHR1RFAVra2uaN2/OW2+9xeOPP46Hh8cf9v/HH3/koYceQqPRAKjhEUIIpk+f/rvjCwoKWLlyJStWrLhme40xnZGRQXZ29jVlc69OTh8cHMzrr78OQMeOHQHUawM8+uij6uuPPvqI119/nXvuuQchBF27dqV9+/ZYWFiov5maqnJNmjRhxYoVnDhxgjfeeAOTyUR1dTWHDx9m5MiRODg4qOEfQ4cORVEUli9fTrdu3bh06RLOzs7XTVOl0+nUtm6WyspKKioqcHFxobCwkNTUVNq3b4+iKDecjbpRasS0tDQ8PDzqzUNobfL999/z7LPPmlsMs+Lu7s7evXvNLYakHlCXaVILCgoQQtTrcNPawtra2my1PuoDN7QuFUVZWfNaCPH01e/vRCwtLc0twk1TYxDY29uzfPlydVqqWbNmdOzYkc8//xxra2vWrFnDrl27yM7O5qeffqKgoIDOnTszYsQIEhMTef/990lOTmbJkiV0796doKCg296XVatWUVhYiMFgIDc3l9DQUHx8fAgLC6NJkybs3r2bKVOmUFFRgaWlJUuXLqV58+Zs2bKFmTNnMnPmTA4ePMgbb7zBokWLWLZsGQaDgaysLFJTU3FycmLdunU4ODiQnp6O0WjEaDQSGBjIxYsXycvLY+LEiSQkJFBZWcnMmTMpLS1Fr9fTpUsXLl68iF6vJzU1lcDAQPr168ezzz5L06ZNGTBgAL169cLe3p7FixfTvHlz2rdvz4oVK5gxYwbbtm1j3LhxxMTE0KtXrz/8DCwtLdFoNCiKQnJyMu7u7nz33Xc4ODgwbdo0LC0tqaioUFecx8XFcc8997Bs2TIqKyuZM2eOGnd+6NAhoqOjcXR0pE2bNtjZ2WE0Glm/fj2JiYnMnz//Lz08CiF46623ALjnnnsA/jTsoqSkhDlz5uDj40NVVRUmk4mqqioSExOZNGkSlZWVquFfE+rx7bff0qRJE86fP88jjzyCo6PjNW3WrA6/nkFdU8BIp9Ph6OhI3759+f7779XPbOrUqaxdu5ZOnTqxYMEC7O3t6dGjB507d2bx4sXY2dkxb948VZa0tDTy8/PVBw3gGs+/0Whk7dq1BAcHM3bs2Jv+HO8EDAYDeXl55hbD7Pj4+Ki6LmnYxMXF0a5duzppOy8vr0EY03D5nnGneqivdkz9Xf6qu7ZOtEIIcT/wBtAa6KYoyukr2x8EXrjq0DCgk6IoZ4UQnYFvADtgO/CUchNa+9up/juF38Z4WVlZ0bhxY9q3b09MTAzBwcGkpaWxbNkyPD09KSgowMfHh3/9618cPnyYQ4cOkZKSwtSpU9WY2NtFeno6Hh4ePPTQQ6SkpPDrr7/i4OCATqejpKQEJycn5s+fj7W1NY0aNcJoNKre9dzcXF5//XXc3d3x9PTk2WefpbKyEltbW4qKirCwsODixYu89957WFpaotPpqKiowN7eHqPRyI8//kh5eTk5OTl89913lJeXc88996jZUlq2bMnu3buxsrJCURSsrKxIT0/n448/xs7OjoyMDE6cOMHWrVtp3rw5x44dY/Lkyfj6+hIeHs6xY8cYNmwYsbGx2NjY/KFBnZWVpU4pnj17lvDwcBISEpg7dy779+9n/vz5dOvWjSNHjtCiRQvc3d3R6XRMnz4dGxsb9u7dy6pVq9TP5v7772fQoEGkpKTwww8/UFRUhK2tLYMHD6ZDhw4888wzzJo1i+TkZIKDgwkJCaGwsBA7Ozs1lKNfv37qw0dgYKCa2snBwYGCggLc3d1VPSkuLmbbtm24ubnRt29f0tPTKSsrIzo6murqahRFwWAwqIspv//+ex588EH1/ICAAAwGAwMHDqRjx46sWLGCfv360aZNG9Xwr1m8cz0Px549e2jbti3t2rWjuLiYLVu28PDDD+Ps7ExOTg7//ve/efvtt3Fzc6NPnz7k5OSwd+9ezpw5w/jx49FqtSxatAhbW1vatm1LdHQ0NjY2NG7cmPLycjw9PVm4cCH+/v6qHk2ZMoU9e/Zw7NgxUlJS0Ov1eHl5MWLECCoqKtBoNFy4cIG0tDRGjhyJXq9n9+7djBkzBoPBgLW1da3/lmqDioqKBp3aqobmzZvz9ddfy9LjkjozpgE1jevdaFRrtVqsrKzUWTxLS8vbFrJX29SEQt4K4q98yUKICEVRaj3oTAjRGjABnwPP1xjUvzmmHfCToigtrrw/CfwTOMFlg3qJoig7fnveb2nTpo0SHR1dm+KbjYqKClauXInRaGT//v289dZbfPDBBxw9ehQHBwc2bdrEnj17yMrKYsGCBVRXV+Ph4cHZs2fVFHF1jdFo5JNPPmHmzJnqj+6bb77h1VdfxWAwYGdnR0VFBbm5ubi5uWFvb4+FhQXl5eVqukAhBNbW1phMJvz8/PDw8CAvL48BAwbQunVr1Zi2srKitLSU6upqXFxcKC8vp7KyUi3RXllZiaWlJQ899BBff/01JpOJiooK3NzcqK6uprCwECEEQghcXFxwcXFR8yo3bdoUvV6PTqdDURQCAgKYO3cupaWltGvXjpSUFPLy8nj55Zd/9xkoisKCBQuYO3cuLi4uLF26lMcff5yvv/6a7du34+npSUhICNu2bePTTz9lxYoVODg4EB8fz9KlSzl37hwbNmygR48elJWVYTKZcHJyory8nLCwMI4ePUpAQABnz57F39+fRx99lOnTp6sPHD179qRv374sWrQId3d3xo4di6+vL0eOHGH//v24uLgwZswY0tLSKC4uxtvbGzs7O4qKinByckIIQVVVFSNGjCA+Pp6zZ89ib2+PnZ0du3fvZs2aNcDlAclkMvH222+rMexjxoy5rl4UFRVx6dIljh8/rnqOmzZtSnp6OhcuXKBNmzbqscXFxfz000/XDYupoaSkBBeXazN5mkwmLl68SNu2bVEUhczMTHx9fdm9ezf9+/fn8OHDVFRUEBsbi7W1NQ899BCurq7qja9mmvb48eMEBwfj4eHBhQsXSE1NJSUlRc0K07ZtW4xGI6dPnyYsLIzIyEhVV8vLy3F3d2fAgAFUVFTg7e1NaWkpa9euZfz48axZs4YxY8bg7+9/TThOXZKfn8+IESM4efLkbblefUQIwauvvopOp+PDDz80tzgSMxMeHl4rHsrrMWfOHL788kssLCzuWGPzjzh8+DCnTp1Sw8c6duxIhw4dfhemeKcghAhXFOVv11e5oUEthCjlsmdacNkbXJOdXACKoii1FskvhDjAHxvUb1+53r+EEI2B/YqihFzZNxnoryjKYze6RmhoqHL+/PnaErnesHXrVlq2bMnSpUspKSkhJSWFmTNn0rJlS0pKSjh//jwvv/wyGo2GgQMH8vPPP9eZLBUVFeTk5HD48GGKi4sZO3Ysly5don///mi1Wnr37k1eXh5FRUW4ubmpT7kALi4ualWpyspK2rRpg8FgoLi4GB8fHzQaDTk5OSiKQrdu3QgLC2Pbtm2cP38eX19f/Pz8iIyMxMvLC6PRiJeXl5o9Ii8vj8DAQPR6vRofq9VqsbGxUT+nxMRENBoNer0eKysrTCYTdnZ25OXlodFosLW1paysjOrqapydnWnVqhVWVlb897//Zf369Tz11FPXxDHD5QVgly5d4ujRo2rI0S+//KLGmzk4OBAdHY2XlxdpaWn079+fxMREbG1tyc7OxsXFhYEDB7Jv3z5atGjB8ePHURSF7t27k5eXR2ZmJs2bN6d169YcO3YMFxcX3NzcKC8vp6SkhNDQUNzc3HBzc2Pnzp289dZbHD9+HBsbGxwcHCguLmbcuHEYDAZiYmI4fvw4ffr0YdCgQQC8++677Nu3j3/84x/MmzePgoICBg4cSEhICMePHyc1NRUhBE5OThgMBsrKygBYvXo1w4YNo6Kigg0bNvDMM88QHh6Ov7+/mmXl5MmTWFpa0rlzZzw8PCgsLCQ8PPyaxWKrVq1i3Lhxf+pBqMlN/ney+BQXF3P69GkGDx583X2/nR1auXIlISEhdOjQgYqKCpydnXniiSd47bXXaNy4sWqQ63Q6nJycyM7OZv/+/djb25OSkoKrqyudO3dm+fLlvPPOO3zxxRfk5ubSp08fAgMDMRgMuLu74+bmpnq5S0pKiI+Pvybd4d8lJSWFHj16kJWVdctt3akIIXjooYdYtWrVXek5lNQf7r//fjZt2oSlpeVtLVx1O9i7dy9Hjx7ltddeA8DT05MhQ4awdu1aM0v214mNjSUkJOSWDOqbufsMA1wVRXFSFMVKURTnK39OtWlM3wSTgJpvyQ+4uj50+pVtN6Q26rXXRwYOHMiOHTto1KgR8+fPp7CwkK1bt7Jy5UqCg4Nxd3enZ8+eVFZWsmPHjjpdcbxx40ZOnjzJqFGjqKysRFEUVq5cyfTp02nRogXp6elUVVVhaWlJWVkZdnZ2ajy1r68vNjY2ODo6Mm3aNDXcw9fXF1dXV7RaLYGBgTz66KOUlJTw5Zdf4uLiQnBwMC1btqR58+YsWLAARVGYO3cuAQEBREdH4+3tTVBQEHl5eYwYMQIfHx8URcHNzY1p06ZRWlpKcnKyWhxnxowZODo60rZtW/Ly8ujcuTPBwcEYjUa8vb0JDAyktLSUxMREzp49y0svvYSFhQX79+//3edx/vx5HB0dSUxMpLy8nIMHD3Lp0iU1f/apU6dwcnIiIyMDGxsbDh48iMlkUuN8/fz82Lx5M1FRURw/fhxra2ssLCxISEggKSkJT09PMjMz+eqrrzCZTMTGxtKlSxdsbW1RFIVTp06xadMmLl68iIWFBaNHj2blypV8++23vPnmm6xfv57nnnuO0aNH89JLL7F7926effZZRo4cSatWrQgPD+fTTz9l586dvPbaawwcOJCcnBx27txJRUUFQggsLCyws7Ojurqa/Px8AIYMGcKCBQvYu3evGpp06tSpax7munbtytmzZ4H/D/WoCf2Ay+ExXl5e1xjT27dv5+DBgwAkJSURFxfHhg0b2Lx5s3rMqlWrSElJYc+ePdcUAnr99de5ePEiERERbNy4EYPBgKurK4MHD6a8vJzc3Fz12OPHj7NgwYJrzofLizO7d++ORqPBxcUFIQQLFixQZ31qZjlqZPbx8WHy5Mn84x//YOLEiWrYyfvvv49GoyEwMJDHHnuMuLg4fv75Z8LDw/n111/5+OOPWbVqFWVlZXz33XdERkZeI9/NcrXBaDQauXTpEmVlZernXFhYyNKlS/80DKSmGubdRFJSEk8//bS5xZDUA2qj7PQfUfO7utt+P/D7GgZGo/GOrQhZGw6GmzGopwGnhRDrhBDThRA+NzzjOgghfhFCnL/O3z9u4tzuQLmiKH/LtSyEmC2EOC2EOF1eXk5+fj5ZWVlkZGRQVFREQkICer2e6OhoTCYTERERwP//yCIiIjCZTERHR6PX60lISKCoqIiMjAyysrLIz88nOTkZnU5HTEwMBoOByMjIa9qo+R8VFaWmstNqtaSmppKbm6sWYtFqtcTHx1NZWamWxv5tG5GRkao3UafTkZycjF6v54EHHmDmzJn4+voyYsQIKisr8fLy4pNPPsHFxYUPP/yQ559/HkVRWLZsWZ30qSbMoF+/fmos83//+19MJpO6eK+yspK5c+fStGlTXn75ZTw8PHjggQfw9PRk5MiR9OzZkylTptC5c2dcXFzo378/rVq1YuzYsdx7770MHjyYPXv20LlzZ5ydnZk4cSIODg6MGDGCRo0aYW1tTZs2bSgoKCAmJoahQ4diY2NDkyZN6Nq1K3v37uXpp5+mY8eOzJkzh3379tG/f3/atGnD5MmTKS0tpVWrVsyfP5+goCCmTJmCl5cXrVu3pn///tx77714enry8MMPY2Njw9SpU0lLSyMgIIDExMTffU9arZbVq1fTt29fDh48iJWVFW3atCEhIQFHR0ecnZ3p0KGD6s0vKiqiV69eZGVl8eCDD7J161a6dOmCv78/7du3Z8yYMYwdOxYfHx+8vLxo0qQJlpaWTJw4kbi4OKZMmcJbb71FkyZNCAkJYciQIWRkZGBra0taWhpTp07FZDLRqlUrnn76aVq1aqUaxN26daNVq1ZMmjQJk8nE+PHjSUxMZPXq1VRUVGA0GunRowcDBw7E1dWVe+65h9DQUHr16kXnzp1p3rw5W7duZc6cORw+fBi9Xk+7du3o3LmzuojSw8ODyMhIoqOjqaioUMNLunTpQteuXdWHm9LSUs6dO8fgwYM5e/YsS5YsYcOGDZSVlZGdnU12dja//vorp06dUrN87Nmzh5UrV+Lt7c3atWtRFIXdu3dTWVlJeHg4zZo1IzExkerqary9vdm0aRNHjhyhqqqKXbt28dNPP7F//35ycnKIj49n5MiRarz7H40RaWlpJCUlsWHDBo4ePfqnvydHR0fCwsIwGAxcuHABuGxwN2nShA4dOjB79mzCwsIYOXIkI0eOZMiQIWqGnuHDh7Njx46/PEasWbOGNWvWcOHCBZYvX86uXbvw9/dn9erV7Nq1i+3btzNp0iS2bt3KoUOH1DZ27tzJ6tWr2bRpE1u3bmXv3r2cP3+euLg4srKyyMzMJDo6mtLSUmJiYqiurv7DMSIiIoKKiorfjXuZmZmcP3/+b497f3csnzp1Kvn5+eqYVZ/G8rvx/lTf++Ts7FxnfaqpdTBv3ry77nuqqqrC2tqa6OhodDodffr0UQt93Wl9io2N5Va56RhqIUQIcC+XPdYuwH5gJ3BEUZRacXf+UciHEOJDIE9RlLevvP/bIR/NmzdXkpOTa0Pces1jjz2Go6Mjubm5pKWl8dFHH5GZmcmbb75JYmIiJSUlZGVl/Wmat79DVFQUhw8fJi4uji5durBq1SoiIyPRarWYTCZ1weTgwYOxsrLCxcUFBwcHPDw8+P777wkJCeHxxx/n+eefZ8SIEWRmZhIXF4elpSWXLl2icePGuLi48P777/Prr7/i6urKwoUL+fe//8369etJTk4mJCQEnU7HuXPnaNWqFQ4ODuTl5VFSUsILL7xAcnIyBQUFaDQaIiMjSU9PZ/To0Tg5OTF06FD++c9/UlxcTFBQEEIIkpKSSE9PZ8mSJXz//feUlJSQnJyMra0thYWFmEwmNSRl6NChfPHFF9cs+vz66685fPgwv/76K8nJySiKgslkUsts29nZ0bJlSzVtXGZmJjY2Nmi1WtXbaWFhgaWlJRYWFuogZmtrS3l5uepB9fT0pKqqipKSEjXdXU2MeI2nXlEUbGxssLa2VhcfOjk5UVFRgaenJ3q9Xi2u4unpiYWFBaWlpTRv3hxFUYiNjSUoKIgTJ05gbW2Nl5cX2dnZWFpa0qJFC2JiYvDw8KB169YUFhZiNBpxdHRk6dKlvPnmmzz33HM0bdqU999/n7lz5xIUFMT27duZMGEC9vb2mEwmNm7cyIQJE7hw4QLFxcX06tWLqKgo9uzZw3333Ye/vz8xMTH88MMPzJgxA19fX5KSkigsLOTixYtMnTqV8vJyzp07R5cuXXj++ed59tlnSUlJwdvbWw3JqRl8U1NTSUtLQ6PR4Ofnh62tLZWVleTk5PDQQw/x6aef8sQTT7B69WomT57MoUOHKC0tJSgoiOLiYsLDw6mqqqJVq1ZUVFRw//33c+7cOdq1a3eNHmRnZ5OSknLDfNx/xrfffsukSZNuuvBNUVERW7ZsYfDgwfz000+MGTOGFStW8O677xIREUF4eDihoaG0b98euDy7lJmZqS56Hjp0qKrba9asoaysTH1Iio+PV9cZTJ48meXLl+Ps7Iyrqytjx469ZrHS4sWLadu27e/Cag4fPszOnTuZP39+rSwKulmEEDg6OvLggw/y2Wef3fgEyV1NZGSk+huobUJDQ9WH57vNS71p0yays7OZN28eer0eBwcHOnbsWKce/7riwIEDDBgw4JZCPm46y4eiKDFADPChEMIOGADcDywCbj2w7w8QQlgAE4E+V8mSJYTQCiF6cHlR4kPA0ptp73Yt/DE3U6dOJTw8nMTERJKSknjiiScYNWoUnTt3Jjk5GaPRyMSJE9U8rIqiYDQa/3ae7posD3v37lWvu2nTJgoKCqioqCAgIABra2ssLS3p1q0bjo6OuLq68vTTT/Pss8+yf/9+Hn/8cXx8fJg9ezb29vYsWbIELy8vtm/fzgsvvMDIkSOJiooiMjKSRx55hAkTJuDm5kbHjh2prq7Gz8+PqqoqysvLady4Mc7Ozri4uDBz5kx++eUXUlJS2Lx5M82aNcPHxweDwcDzzz/Piy++iLu7O0OHDiUsLEzN4Z2VlaUuhnR3d2fp0qUEBATg4eGBt7e3+qTs5OSETqcjMzOTiooKNXa2hry8PBITE0lPvxylZG9vT4sWLcjOzqa8vBxvb298fX0pLi5WF7EVFhZiY2ODi4sLNjY2zJw5kyVLlqgxtTWp6oQQ6PV6TCYTeXl5qqHu7e2NEELNQ56Xl6eGqwghMJlM5ObmUlVVpaY5cnFx4YknnmDJkiVYWlqi1WopKSlRY+Jr9KPGw1DzIKEoCk5OTmo4QmlpKXFxcVhYWGBtbU1WVhZ9+vRBCEFKSgp2dna4u7uzaNEiBg4cSNu2bUlKSrom9hjg1KlTPPjgg2RnZ7N+/XqWL1+OVqulurqa3r17ExkZyfbt23F1deWDDz7AYDDQtGlTMjIy8PDw4MSJE3z++eecPXuWQ4cOERQUxNdff42iKCiKQps2bRBCsGrVKkaPHk337t3Ztm0bFy9eRKfT0bdvX6ysrBgwYAD9+vVjxIgRLFu2jNDQUAIDA9FqtVRVVfHEE0+osfFfffUVZ8+e5fjx44SHh9OhQweCgoLYtGkTCQkJeHt706VLF0wmE7t27WLkyJF89913BAUF0bNnT/W39EdZeIKDg4mNjb2prARJSUn8/PPPzJo1C1tbW5544gkANT6/oKDgd3np77///t+1U5PeMC0tje7du9O/f38yMjLw9/enVatWJCQk8O677/LOO++g1+vRarW88cYbBAYG0qhRIwICAnBzcyMzM5ONGzeSl5fHpEmTyM3NJTY2ltmzZ3Ps2DGGDh2qXvPQoUOcPn2aZ599lqysLIqLi2ndujUmkwmDwfC7B4rKykpsbGxuKv94DVVVVaxcuVIa1BK1Em5dcDeXHK9x7sD/9/PqkL2Gxs0sSgwE3BRFCRdC9FEU5XCtCyHEOC4bxF5AMXBWUZRhV/b1B95VFKXHb87pwv+nzdsBPHkzafMCAgKUpKSkWpS+flJj4JaXl3P//fdjNBpJSUlhyZIl/Pvf/1a9lcePHycsLExdXDZx4sS/db1169YRERHB448/ztChQyksLERRFEpKSvDx8cHBwYG2bduyYMECWrVqRbt27XB1dcXCwoKhQ4eybds2EhISsLGxwc/Pj+DgYFxdXWnevDknTpwAICwsjNTUVB555BG+/PJLjh07RmhoKI6OjqSlpXH//ffTt29fli5dSqNGjXj99deJioqiRYsWODg4YDQaWbp0qeo579atG/n5+bi5ufHYY/8/ufH5559z/vx5TCYTTz75pJpx5KOPPsJgMNCqVSvS09PVoi5ffvklJSUllJSU0KNHD1avXk1gYKDa3uzZs9m5cycZGRm0bt0auJwGMSwsjPPnz6vp+mJiYmjcuDEODg706tWL9PR0GjduTGFhIcXFxVy4cIEWLVqg1+uJjY3F1dUVLy8vHB0dSUhIIDMzE3t7e9zd3SkuLlb7nJeXR3l5uZq2z97enpKSEiorK7G2tsbKygo7Oztyc3OxtbXFaDRibW2NwWDA2dmZkpISDAYDiqKoRXZ0Oh3u7u5YWFgQGhrKkCFDOHv2LOvWraNFixaUlpbi4uKiGvk1ix+dnZ1p1qwZ6enphISEMH36dLp27UpKSgrDhw8H4MMPP+Tpp59W83wvXryYxMREqqqqSEpKws3NDQ8PD86cOUNsbCy2trZ0794dDw8PYmNj8fb25sKFC/Tu3ZuLFy/SqFEjDhw4gK+vLwMHDiQ5OZmqqir69u3LiRMnsLW1JT09ndDQUM6dO0eHDh3IysrinXfeoUWLFnz00Ud4eHhQXFzM1q1b6dOnD4MHDyYmJoaAgAC6d+/Ojh07uO+++9i0aRN79+7l8ccfp3HjxiQnJ7Nx40YeeOABOnfuTGpqKgcOHKC6upqWLVty6NAhBg0aRExMDGPGjGHjxo2YTCY6dOiAn58fjRs3vsYJoNPp+OSTT6iurub555//UwfBJ598ouYuv5ohQ4aQkJDAa6+9xowZM1AUhaqqqhs6G2pygF+P6urqa9IEGgwGsrOzSUtL4+jRo8yePRtHR0dycnKwtrbmhx9+oKSkhODgYMaMGcO3335LeXk5LVq0IDo6mo4dO6LT6dSUYxYWFtjb25OcnIyFhQVlZWX07NmTyMhINb2ltbU11tbWDB8+nMaNG+Pl5YVeryc3N/eaqpA1RrdGo2Hs2LEsWbJEXSRbg9FovKZmwZkzZ/Dz8/vdcZK7g5iYGEJCQuqkbT8/PzIzM4G7z0O9cuVKFEXhoYceIj09nebNm+Pn50daWpq5RfvL1IaH+mYM6l3AGSAa6K0oyuy/e7H6QMuWLZX4+Hhzi3Fb+fbbb9mwYYNaFCQ4OJjPP/8cnU7HtGnT+Oabb3j77bfJzMxk2bJlf7n90tJSNm3axJAhQ3jttdfYsGEDFRUVWFhY4OvrS1hYGPn5+UydOpWLFy9y4sQJMjIyMJlM2Nvbq2Ec8fHx9O/fH71ez8CBA/H29qaqqoqNGzcybtw4xo4dq3qmairz2dnZ0aNHD95++22CgoKYNGkSBoOBuLi4a1Kv1VBYWMj58+cJCgrC19dXNVavNoAVRaGsrIx169ZdU01QURR0Oh1nz57F29ubjz/+WE3Fd/DgQaKjo3F2dmb37t3XTOsPGTKEAwcOYDAY6Nq1KxkZGfj4+NCtWze6dOnCmTNnEELw8MMP06xZM3VhY//+/bG3t+fee+9VvdqWlpYMHTqUxMRE9u3bx4gRIzhy5Ah5eXm0bt2aXr16sXr1auzs7HBwcOD+++9nwYIFdOvWDZ1OR0JCAnFxcYSEhKAoCh06dODUqVP06NEDvV7PqlWr8PPzU9MVtmnTRp3R6N27N9u3b8fd3R2TyYSNjQ3FxcW88MILNG3alMDAQNq2bYujoyPu7u7qd2xjY4NGo8FkMlFWVoabm5sa+jNlyhQGDhxIYWGhWkb9tdde4z//+Q8rVqxgyJAhbNiwga+//pp58+bx5ZdfkpmZidFoVIsICCEYNmwYv/76K8HBwRw8eBBHR0datWpFZmYmTZs2xd7entLSUlJSUggNDSU9PR0HBweysrKwt7end+/eHDlyhKKiIqqrq7Gzs8Pe3p7y8nKsrKwYNWoULi4u7N27FycnJywsLAgMDCQtLQ1LS0sGDBjArl27KC0tZcaMGaxZs4a33npLjfN+//33sbKywmAwMHPmTCwtLfn6668pLy8nLi6OoKAg3nnnHebMmUPTpk355JNP1GI5tra2BAcHM2jQIIQQlJSUUFpayunTp/+w6MyFCxfIy8tT4zdr9PfHH39kwYIFlJaWMnDgQD777DO+/vprtFot48ePr9OKcb+lxrt1tTf+woUL+Pn54erqisFgIDk5maCgIBRFISYmRo33r6ysJDIykq5du6qza7/88gu9evXixIkTpKWl8cgjj/Dtt9+Sm5vLnDlz+Omnn2jVqhWdOnXC0tISJycnHB0dee6555g2bRqxsbFcvHiRf/zjHyxYsIB3331XDVlZuvTyBGh5eTmzZs3C3d39d/1RFEWdhWratOlt+AQltcWfPSzeKl5eXupC7bvNoP7mm2+wtbVl3LhxJCYm0qZNG7y8vP7W4mlzc7sM6qWKojwphHgaeERRlLC/e7H6QEM0qBVFYc2aNSxbtowLFy7w6quvcuLECX766SecnZ0pKipi7ty5ZGdn8+OPP/7l9r/55hvGjx/PokWLWLx4MRUVFTRu3BgPDw+cnZ355ptv2L59O2fPnsXNzY3ExESaNWtGUlISs2fP5pVXXuGzzz4jNDSUzMxM3N3diY6Opnv37gghfuctuh4Gg+Gmw1WSk5Np3rz5X+7nb8nJyeHFF1+kQ4cOfPHFF8TFxaEoChs3buS+++5T5WrVqpU6ze7n58d9993HggULqKqqws7Oji+++AJHR0cmTJhwXS/hxx9/zLBhw2jRosU1YTl79+4lJSWF1NRUHnroIfWhwGQy8dRTT6EoCl5eXkyfPp1mzZqRn5/PU089hbOzM8XFxdjY2ODl5cWFCxfQaDRYW1uTlpZGcHAwHTp04MSJE5w6dYo5c+ag1+uZN28e/fv3Z+DAgQQFBXHs2DG+/vprbG1tiYiIID4+npdeeglvb2+Ki4uprq4mNzeXRo0aqdfz8/MjJiYGPz8/NSfrP/7xD9q3b88jjzwCwOOPP87bb7/N3r17KSws5L333sPDw0P1Nvr6+nLp0iU1Jt/a2prs7GxcXV3Jz8/HZDKpKQ5tbGwoKSnB2dlZ7XdJSQmWlpYMHjxYzcNtMpnUPOj5+fkYjUa0Wi16vZ4WLVqQl5eHhYUFr7zyCocOHSIqKko1uoODg0lKSlJjjx0dHRk+fDgHDx5Ui8GUl5ezePFitm7dysSJEzl9+jSHDx/G2dmZ3NxcAgIC0Gq1jBo1iu7du6s5wHU6HdXV1aSmprJjxw5Gjx5N165dAVi+fDkzZ878nb6kpqaybds25syZgxACrVbLqVOn1Mw6//73vzEYDGg0GhYuXKguNP30009p2rQpzZo1w9HRUa2oGh0dzalTp5g8eTKbN2+madOm5OTk0Lt3b5ydnYmLi8Pd3Z3GjRv/7reanp6Oi4vLbY2PBti9ezcGg4GCggKGDBnCe++9x0svvURERARpaWnMmTMHV1dXunXrxpYtW/juu+/o3r07Op2Obdu28cQTT7Bx40b69OmDp6cnZ8+epUOHDuTl5ZGdnU2fPn349ttvmTt3LnA5t/fHH3+MXq8nKCiIRx99VA3dURSF7OxsHB0db/vnUBf8WUjSnUpt3ROuh4uLi7o+5m40qJ2dnRkyZAixsbF07doVJycntFqtuUX7y4SHh9OlS5c6N6iHK4qy88rreYqi/HUXZj2iQ4cOSk2aroaGoih07doVCwsL7r//fpYtW0ZqaiqFhYU89NBD6HS666Z9+zPKy8vZuHEjQgj++c9/qgPt/fffT4cOHYiJieE///kPgwcPplu3bmomgBkzZhAREUHXrl1VA+12kZ+fj6enZ621l5uby5YtW5gzZw5Go5GFCxfy3HPPAZfjp5s0aUJVVRUDBw7k22+/pXHjxtfckH7++Weys7Ov8YbfKjW/65rKhzVUVlZy7tw5QkJC2LlzJx07dlRDNGpu9gUFBaxbt44+ffrQokUL4PIsxKJFi5g2bRqhoaHqYsnfsnjxYk6ePElRURHh4eEsXbqU9evXk5eXR3JyMhUVFZSUlKAoCo0bN6aqqopGjRoxa9Ys5s2bB8DkyZN5+eWXqaio4M033+TgwYOqcVlWVoajoyNlZWVYWVlhNBrVXOGKolBdXY2trS1CCCoqKqiqqsLKygpLS0vc3d0JCAjg9OnTGI1GevXqhVarxd/fnx07dqgzJq6urtjY2JCdnY2NjQ1CCLp27cqpU6dwdHTEaDTSqFEjdDodrVq14tKlS7Rs2ZLhw4fz3nvvIYSgX79+xMbGqn2t8VB5eXlRVlZGcXEx/v7+aix/eXk5Hh4eqld27dq1HDlyhOrqaoQQjBkzBk9PT1atWsW0adMwmUxs3rxZfQi5mq+++gq9Xo9Go8Hd3Z1ffvkFf39/unXrxuDBg2ndujW5ubl06NCBgQMH8q9//Qu4/CCWnZ1NbGwsaWlpODg44ODgQGpqKmPGjOGDDz7gwQcfpLCwkODgYPbt26cuPPX09OSBBx7gmWee4X//+x+5ubkIIdizZw/l5eXY2toycuRImjRpourn9dZs/JUH4xuxd+9e2rVrh7e39zWG/pV8s3h7e+Pp6akuGKuRS6/XY29vj6IofPHFF1haWjJlyhTVW/3FF1/g5uaGjY0NjRo1okePHqxcuZIJEyZQVVXF+fPniY+PR6/X06lTJ3WBa25uLk8++aRaiGnLli3qok9FUa7Jvf5btFotGo1GfeA2Go3qjKPJZKK6ulrdVxPK9Xdyst+ImJgY9uzZw5NPPlnrbZuT2r4nXI2jo6OaIeluM6hXrFiBl5cXPXv2JCoqigEDBmBra3vHxo3fjsIuHwNrFEU58ncvUp9o3769UrOoqiEydOhQnJ2d1afIH374geHDh+Pg4EBGRgbHjh37S+3t2bOHqqoqnn32WZKSkmjUqBGWlpa88sor9O7dmxdffJGioiLat2/PxYsXcXJyYvz48QwZMgQ/v5tKHV7rZGVl1XqlyJpiHKWlpbzwwgu8//77wP/fvIUQfP7558yaNet3515dma8+89ty5H/EF198gUajYdCgQWzfvp39+/cTFxdHWFgY586do6CgQI2PtbS0pFGjRkyePJmFCxcCMGLECF588UWys7N55pln1EwmNdOyNSEZVVVVeHh4YDQa0ev1arhCRUUFWVlZ6jEBAQG4urpy9uxZKioq1BSKbm5unDx5Emtra/UaNYtQHR0dsbS0pKKiQs2ramdnh9FoVCtr6nQ6fH19yc3NVWOIa4oF1Rg1o0ePJjY2lsLCQrWIT2ZmprqosUWLFkRFRdGkSROsrKzQarWkpaVhbW2NoihMmjRJjZmfNWsWlZWV6HQ6UlNT6datG/fffz92dnbXfP5fffUVFhYWPPDAA+j1ej7++GNsbGyYN28ejo6OahXRgIAAYmNjMZlMFBQU8MUXXzB69Gjat2+v5pKfM2cOzZo1w8rKSi3EVENJSQnh4eEYDAYyMjJwcnKiRYsWHDt2jNLSUuzs7NSHpLKyMr799lsef/xxysvLWb58OSaTifvuuw8/Pz+EEBw4cICDBw/ywgsvYGtry8KFC3nmmWf+sIR7dnY2P//88996EBVCqIWg/qxi5IULFygpKeGee+5Rt61bt46SkhIee+wx1q1bp1YSnTZt2u/O/+KLLzAYDMydO5ctW7ZQUFBAQEAAOTk5NGrUiMTERNq1a0dERASzZs1i8+bNpKamYjQa0el0eHp6Ymtri4uLC1lZWfTu3Rt/f3+++OILevbsSb9+/fjiiy8oLi7Gz8+PSZMm8eGHH9K6dWtGjx5NQUHBH2Zyio2NJSsr65qwoBuxfPlyhBDXfZC7k6mLe0INtra2VFdX35Ue6o8//hg/Pz+6d+/OyZMnGTt2rFqw7E7kVg3qm3EFxAELr6Sq2wCsVRTlzN+9oMS8jB07liNHjnD+/HmcnZ2xtrbm4MGD9OvXT/Wm/RXDLiUlhaioKBITE3FycqJp06bcd9996PV63njjDfLy8ggJCcHBwQEnJyf69++vTv2bi98W6qgNrKys6NGjB3v27LlmQUZKSgpweWGKr6/vdc+t74Z0DTebYrFv374UFBTQpEkT+vbty6BBg7CyskKj0XD+/HkKCwt54YUX8Pf359y5c+Tm5qqfE1w21LRaLXv27FHDM8rKyggKCiIgIABHR0cCAwOZPHkyGRkZfP7556SkpODu7k5JSQnFxcW4u7urITaFhYWUlZWpoSht27alS5cu5OfnExUVhZWVFa1atcLNzY1WrVoRFRWlehjt7e3RaDRUVlZSVVWFj48POTk5eHh4YDKZ0Gq1avWzmswp9vb2apnxTZs2oSgKtra2qkFdUz3R2tqa3Nxc7OzsSE9Pp2XLlhQVFdGpUyfi4uLw8PBg165dODg4oNPpmD9/PnPmzGH69OlYWVnx/fffq+fVUFxcTEVFBe3bt8fe3p4zZ87g4+NDfn4+TzzxBI8++ih6vR4XFxcyMjKYP38+ZWVl5OXlERYWxtatW/n2228JDQ0lLi6OnTt3Mn78eLRaLdHR0WRlZTFixAg2bNiA0WhkzJgx6uLM6OhoJkyYQLt27UhMTFQXrcLlsvR+fn6sWLGC+Ph4+vbti62tLVu2bEGv19O6dWvS09N56qmnWL58OU2bNqVz584sXLgQS0tLgoKC0Ov1+Pj4MGjQIPLz81m3bh12dnYcPXqU1q1b4+zszJ49e2jTps1NxYLb29tfky2krKzsmtkcuH72h3vvvVfNjPPAAw9w4MCBP8y64uvrS7NmzRBCMGrUKNLT069ZIDlgwADgci7ypUuXotFo1GIzNde42iu9fv161qxZw/3338+xY8dIS0ujT58+tG7dmm+//ZYVK1YwdepUfvnlF+Lj43n33Xf56quv1P5dHSd89OhRNBoNZ86coWPHjteVPz09HXd3d9U7XzMzdbeFfdTFPaGGmlm0u5Hw8HA2b95Mx44d1TCPu7WvN8MNDWpFURYDi4UQzYAHgK+vpM1by2XjOq6OZaxV7tQqPrXF8OHD2bJli2oAxsXFkZ6ejo2NDeXl5b9L+fZnpKam4ubmxtq1azEajfTp04eBAweyfft2Tp06RevWrRk8eDBHjx7lwoULLFiwgGHDhtVxD29Mzc2htnn++efZs2cPV8fonzt3DoAePXowYsSIOrlufePq1fK/XTnv5eXFW2+9xa5du5g8eTImkwmj0XjNZ6bT6SgpKeHMmTPq4lZnZ2c1RMLd3Z0JEyYghMDW1pb169fzz3/+k1atWjFmzBj8/PzYvn0727Zt4/nnn+fdd9/F1dWVsrIy4uPjVa+yi4sLP//8Mz/++CPTpk0jMjKSbdu2qXHebdq0YcaMGRQUFHDkyBHy8/OJi4ujV69eGAwG9Ho95eXlatzxhQsXsLGxIT09HU9PT0pKSjAajTg7O2MwGHBzc1PzjtvZ2ZGYmEhmZqbqFY+Pj1eL2QghcHNzIyEhAUtLS9XwO3DgAAkJCXz77bcYjUZSU1OvMagTExOxs7PD19eXffv28c477+Du7s6DDz5IdXU1H3/8MSaTSc28UhMDHxISQkREBJWVlRQWFnLw4EE1VGPDhg2UlJQwevRo9u7dy/Lly9UsHLa2toSFhXH27Fmsra158cUXSU5OxtXVlXHjxtG+fXs8PT0pKipCo9GQmZnJ+PHjqa6u5tixYzRp0oTx48fz888/07x5czXDjcFgoFu3bkRHRzN9+nQKCwvR6XSEh4fz5ZdfYjQamTt3LiUlJcTGxrJx40YyMjIYMWIEP//8M9OnT0ev13Ps2DFycnLUcKH77rtPNWibNm1Keno6KSkpZGZmsn//fl555RXVu/5HoScuLi7XvP8zD++oUaPU15aWltcY01fj7+/PU089dc396bfrKWpCT5o0aUKXLl1o1qwZFRUVaptDhgyhsLAQX19fpkyZwn/+8x8GDBjAkSNHOHv2LLa2tuTl5eHi4kLjxo2xtbVl8uTJ/PTTTxQXF6vGfUpKCk2aNKG0tJTvvvuO1q1bM2bMGDUsTKPRcOnSJTIyMujZsyd5eXlqdqGbpaKiAltb25s+vq6pq3sC3H0LEa/m0qVLxMTEUFVVpRrUN9vfkpKS3/2WzMmBAwduuY2bLuxyzUlCdAS+BsIURfnz1WL1jMDAQCUxMdHcYpgNo9HIwIEDCQwMxMHBgYiICDV1mJWVFefPn7/pFervvfcenTt3ZsiQIQQGBvL6668TEhLC2LFjCQ4ORlEUHBwcWLZsGadPn2bixIn1wquRkJCgxgbXJjVV/65e5fzoo4+yfPlyVqxYwfTp02v9mncy6enpDBw4kPj4eLy8vMjLywOgZcuWPProo3z66ackJydjb29Pz549+fLLL/H19SU7O5tDhw6RnZ3N888/jxDiT2MgFUXhlVdeYdCgQQwePBhFUTh58iQGg4FevXpdc2xBQQE2Njb8/PPPHD16lKVLl/LJJ5/Qu3dvUlNTOXLkCFlZWQQHB+Pn58d///tfhg0bRm5uLgUFBSQlJalFbS5duqSmKGzSpAkZGRm4uLiQmZmJnZ0dBoNB9fY5OzuriyFrpoiNRiM9e/aksLCQ3Nxc/P391Wn/Zs2a0bp1azQazTWhBjXp6Nzc3JgzZw5ubm40adKEw4cvZzu1sLBQPepCCPz9/cnOzlazuNSEGxgMBlq0aEF6ejpNmzaluLhYXVgLl2dk3NzcqKyspKioCKPxcm2vkJAQZsyYwXfffUfTpk05f/48Q4cOxc7ODo1Gw8mTJykrK+PRRx9lwIABjB49mi+//JLi4mKOHj3K1q1b6dWrF9XV1QQGBtKsWTMOHz6Mra0trVu3xsXFhYMHDxIWFsaQIUPw9PRk7969WFlZ0a9fPwA2b95MTEwMgYGB9OrVC19fX3Q6HaWlpezbt4+MjAxeeuklZs2aRWZmJqNGjVJzzfv4+BATE4OdnR1arZaZM2cihFCL1tx///2kp6eza9cumjZtSm5uLg8++ODvDLKaHPF5eXkUFxfj7e2Nq6urur+0tBStVlunM3U19/YdO3bQs2dPNVxHr9er4SQ14/GJEyeIjIzE3d2dhIQEnJycqKqq4uGHH2bdunXqzMdjjz2GRqPh4MGDtG7dml9++QUAb29vkpOTeeSRR/Dy8lJlMBgM7Ny5Ez8/P06dOsWsWbNYt24dcXFxPProo2g0GrRa7TVZlq5HzdqPzp07o9PpOH36NAMHDkSr1WJvb68+/NR8xyUlJfj7+9O3b99rYsn/yLNeV/cEQC3gVXP9O42atLFX628NrVu3JjMzU/3tvvzyywghMBgMN4zhf+ONN3jjjTfqRui/wW3J8qEeKIQVlyslPgAMAg5w2UO9+e9e3BwEBwcrcXF3lFO91vnnP/+JwWBQjYDTp0+r6cFOnDhBhw4dbqqd0aNHEx4eTlZWFmPHjqV79+4cOHCA9PR0li9fzscff0z79u3VBXr1Bb1e/7u409pCCIGVlZWawaJ///4cPHiQiIiIP5xWbcjs37+fgQMHqinl4LK3bsqUKXzwwQdUV1fTvXt3Pv/882sqmZWWlqqZYG6G691Ib6QHNQvZrj73t/HuP//8M6dOncLLy4ugoCBOnTrFc889p4ZbeHt7s2PHDqKioiguLubw4cO4u7urhXNq4rujo6PV6pvV1dVqzKyTkxOlpaXXFDSpeQh5+umnsbW1vSae9ZtvvsFkMnHx4kU2b96sGm41McNZWVlqEaTExESsra3p3r07aWlppKWl4ebmpsqg1WrV9IQ1OeM9PT2xsbFBp9OpMeI//vijmraxptiRm5sbjRs3Ji8vj9LSUmxtbXF0dKRPnz4cOnSIHj16oNVq6dWrF1u2bMHV1ZXc3FwuXLhAx44dadOmDYcOHWLKlCns3bsXLy8v+vbtS05ODnPnzsXZ2Zlly5Zha2tLu3btiI2NVVMZ9uvXT806U/OwsW/fPhISEpg0aZKa0WXRokW8/fbbvPXWW3Tp0oV27dpx5MgRBgwYgBCCoqIiVqxYgY+PD127dsXT05Ovv/4aR0dHZs2aRWpqKu7u7nz33XfY2trSvHlzPDw8aNKkiZq5x93dnaZNm3Lo0CHVuztjxgzVy/7kk0+qhkd8fLwaDnfu3DmaN2/+l7y+tRGGkZWVhYODA9XV1djY2KiGdVlZGRUVFfj4+HDw4EHVK5+ZmUlJSQmtW7emoqKCTz/9lFGjRrFlyxb8/PzUglLu7u5UVVXh6elJy5YtCQoKYseOHWi1WnUGJjAwkNOnT9O9e3d8fX3VnN+Koqj1A2qq5ubn5zNx4kQ2bNigpj8cNmwYW7ZsITQ0FB8fH86cOYObm5v60FxcXMzixYu57777CA0Nvabfer0eW1tboqKiCAv740Rmf+czvvr4mgetuuRmZCwqKmLNmjVqsac/Y+/evezdu5e33377d/uaNm1KVVUV27Zt44cffuCdd94BLhvhfza2VldXc99997F58+Z64WSD25c2bwgwGRjJ5aqE64DNiqKU/d2LmpOGUtjlz6ioqKB3794Aahq79PR0jEYjW7du5d57771hG5cuXeKdd97hm2++wc7OjpUrV7JlyxaEEDRr1gwvLy8GDRqkFjKpT0RHR183R3Vt8FvDKyAggOTkZAoLC69Z0CW5TE0RmavHoUaNGqlZaKysrNiwYQPjxo2r9WvXlh5UVVURExPzpzdiuDyVvm/fPiZNmkRxcTGenp5kZ2ezcuVKbGxsMBgMDBgwgJ9++olTp06h1WrVSpfW1tbk5eVRXV2NhYUFXl5e3HvvvXTu3Pmam+KKFSswmUy89957aihXWVmZWjnU0tKSCxcuEBISQnJyMuXl5VhaWqqzKjUl7mtSDzo6OlJQUICiKLi7u2MwGFQDvSYdWMeOHUlISKC0tBSAnj17cu7cOTVNnF6vx2g0UlRUhKIodO/enYKCApKTk7GxscHS0pKXX36Z1157jY4dO1JeXq6mRywoKKCqqoqKigpatGihLmx1dHREURS0Wi3BwcHExcXRuXNnjh8/rnrea6qzNm3aFJPJREVFBUeOHEEIwYYNG9i0aRM//fQTLVq0IDAwkNzcXEaMGEFAQAAWFhbExcXh7+/P6tWrmTVrFlZWVqSlpdG4ceNrFkomJyfTuHFjfv31V4xGI3v37uVf//qXWkgJUMMlcnJyWLduHf7+/mrIxsmTJ/H39yc1NZWgoCASExMJDg4mISGB+++/H29vb9Xjnp2drcaH5+bm4u3tTUZGBhs3bqSwsJBnn31WzZN+s4ZKcXExtra2NxWCkZKSwttvv81nn3123faLi4t56aWX+PDDDzGZTFRWVqprL1JSUsjIyLhmgSdcHiszMjI4ceIE48eP59tvvyU1NRUXFxfGjRtHdHQ0fn5+tG3bljNnzqDX60lNTaWyspIHHngAjUZDcXExO3fuJDAwkG7duqntfvzxxzzyyCP8+OOPWFlZ0b9/fzZs2MCUKVPw8PBAURQiIyOprKykurqa/fv3M336dJo2bcq2bdsYMWIEJpOJt956i/Hjx7N+/XrGjh1Lly5d0Gq1bNiwAYBp06ZRVFSEj4+P6qFdvHgxs2fPvkYPrq4seDXff/89Hh4e9O/fn6KiInXtBlw2wmsKTt2IQ4cOkZGRweTJk//0uJqKxjNnzrzh2pivv/4ajUbD6NGjr+kLoH6Gq1at4pdffmHx4sXAZYP9eh7tGhITE/nkk0948cUXb1gsqeZ3O2jQoD897la5XaXHXwbWAM8pilJ09Q4hRICiKHeUdfrbkrUNkZoyxMuWLaOsrIxWrVqpU71ZWVk31cbu3bvVaovDhg0jNTUVCwsLMjIymDJlCt7e3vXSmIbfx/XWJjXFQGq8BDVhDH82uDRkLC0tcXFxobi4WN1WVVWlLuwMCgpi8ODBdXLt2tIDGxubGxrTAM2aNVOL19SECPj7+/Pqq68C//8wds8991BcXMwzzzxDz5492bhxI61bt2br1q3Y2toSExODXq/n8OHD150qj4+PJz09HUtLS3Q6HVZWVuh0OvLy8rCyssLX15eOHTuSkZFBYGAg5eXllJSU0Lx5c4qKiggNDSU2NlaNedZoNAQFBRETE6Mu5AsJCSE+Pp42bdpw4sQJbGxsqK6uxsPDg6NHj9KpUydKS0tRFIWkpCQ1xVx+fj7nz58nNzcXS0tLNZXdk08+qRr/CQkJ+Pn5qV7nJ554gnfffZfExERSUlLQ6/VYWlqSm5uLjY0NERER9OrVi61bt6re0F9//ZWLFy+qhVYGDx6Ms7MzFRUVDBo0iA0bNrBnzx7WrFnD008/jZOTEydPnuTdd9/lxIkTaqrFqVOnUllZyYcffkhaWhqdOnXiwQcf5KeffiI7O5uWLVsSHh6OjY0NTz/9NAaDgSFDhqihPDUVKGvWpTRq1Ih27doRFhaGh4cHS5YsYcqUKURFRampBWv0oKysjFWrVqkzAvb29mi1Wvr378+JEye4dOkSL774Ij/88ANz586lqqqKVatWkZWVxdChQ+nevTsajUbNPw4QGRlJy5YtcXJyQlEUdu/eTWZmJjqd7qZS4Z09e5b+/fuTkpJy3bzNrq6ufPTRR6p38upFkM2aNbtuDLkQgiZNmqgpFadOnQqg5vS3trZWF4fWzPL17NnzGoPe1dWVBx544HftDho0iE2bNjFo0CA1vGb27Nn873//U1NgduzYkXPnzmFjY8OLL77IunXr6NixI0lJSaxevZrCwkImTJjAl19+yQcffMCxY8f45ptvsLe3VwuJffbZZ+pDcc34P3z4cFasWKHKoigKlZWV7N69m5KSEuzt7Rk1ahRlZWVUVlaSkJBA9+7d+eyzz+jatas67v3www/ExMSg1Wrx9PSkTZs2KIrCuXPnrpm1UxSF8+fPqw/ENaEmNZ9Teno6Bw8e5IEHHiAmJoYpU6Zw8uTJP3SgXbhwAQcHBxITE1XHUFhYmNo2XHaI2Nvbk5SUdE3u6ZoFtX9EYmIiFRUVZGRk3NCgPnr0KKdPn65zg7o2uJlFiQMBhBCv/+aJ1BJ4CAioG9HqhoqKCnOLUC+YNm0aP/74IyUlJRQWFqqlppOTk2/q/PDwcM6fP48QggULFvDdd98RHh7OBx98QGJiIkOGDKnbDtwCZ8+e/dOcr7dCcHAwZ8+epbKyEltbW8rKLk/k1JdprfpIr1692LZtm/reYDCQkJAAwIQJE+qsGEZd6sFf4Xq64erqysMPP0z//v3p3r0727dvZ9asWezcuZP4+Hh1wWNNae4a9Ho93333HZWVlZhMJpydnQkLCyMgIICysjJiY2O577771NR5Xbt2xdvbm7Vr16pe6ri4OGxtbbGzs6N9+/Y4OztTVlbGPffcQ1JSkuohdHJyIj8/Hy8vL6ysrPDy8kKr1eLq6kpsbCw6nU71QFdUVJCUlISjoyNFRUVYWlpiZ2eHTqfDYDCoecSPHz+Oi4sL2dnZatn6BQsWqLHztra2apaRJ554gtOnT5OYmMj58+dRFAVXV1cqKipo2rQpeXl5+Pv7Y2lpyYEDB7C3t8fPz481a9YAkJaWxrx589RKstbW1jg4OKDX6xFC0Lt3b/bt20f79u0xGAwcOXKEnJwcLl26hJ2dHVFRUWzatIlOnTrx/fffs2HDBoYPH46TkxPu7u7s2bOHwMBAunTpgrOzM+np6Xh7e7Nv3z40Gg333HMPLi4urF69Gm9vb0JDQ/nss8/o0KEDPXr0UGPue/TogZubG3Fxcfj5+REdHc3cuXMpKytj9erVjBo1Ss1RHRgYiI2NjRrb36NHD1avXk2PHj0oKSmhffv2rF27lq5du3L+/Hnatm3LsGHDWL9+PSUlJXz99deMGzcODw8P9u3bR48ePTCZTHz33Xf4+vpSXFzMzJkzWbp0Kb1796Znz56/092adJW/rYJ5s6SlpWFvb4+Xl9fvjOQ/+81cj9atW//OsaPRaJg3bx4uLi4UFhbi4eGBpaWlmsvbZDJx8OBBnnjiCVJTU2ncuDEajYYPPvgAKysr+vTpQ58+fa5psyYzS1paGt7e3lRWVuLs7EyTJk146qmn1ON2796Nu7s7gwYNIicnh/Xr1xMYGEiHDh24cOECS5YsYcaMGWzevJn+/fuzfPlyfH196dOnDyUlJers0urVq9Xfy4wZM0hNTWX16tXcd999ODs7s3LlSqZPn86iRYvo3LkzaWlp2NjY0LVrV15//XUmTJiAn58fW7ZswWg0smzZMhwcHHj44YdVD/rBgwcxGo3qA0JSUhJhYWG8//77PPTQQ/j6+qq5+5OTk69rUJ84cYLExETGjh17TQhIYmIijo6OxMbGqg9J6enpKIqiruEqLi7G1dWVxMTEa+Lya1AUBZPJpD4sKorCsWPH6NmzJ1qt9k8XPCqKQnFxca3PGv+VGOqrA2Gtgb5AlKIoL9WqRHVMq1atlNjYWHOLUS84fvy4OjXUunVrduzYwbRp01i1atUNzx0wYAAHDhwgODiY+fPns3btWnx8fPjvf//LmTNn+Mc//lHX4tdL/vvf//Laa6+Rm5uLl5eXmmaqIacSuhGrV69WvVJw+Ybs6OhIXl4eP//8MyNHjjSjdPWHmjLrI0aMYO/evVhYWDBhwgRWrVqlTg+//vrrLFy4EL1eT79+/fDx8WHEiBGMGTOGCxcuYGlpibOzM61bt0YIwdq1a8nNzWXSpEk8//zzzJo1i1WrVjFlyhT69u3Ljh07OHHiBB4eHmRlZeHn56cWnPH29ubkyZO4u7vTqVMn9u/fj8FgoLKyEr1er4aAdO7cmfj4eOzs7CgtLaW8vBx7e3uEEHh5eVFYWKjmF3dzc+PcuXNUVVWpYSU1RrpWq8XKyorKykqaNWtGaWmpOiNUXl6Ok5OT6sWtrKzEwsKCAQMGsGfPHvWGHBYWRmFhIYcPH+bDDz/kmWeewcvLC1tbWzw8PIiNjaV79+40a9aM3NxcioqKqKqqori4mMGDB+Po6MjatWsJCAjAw8ODe+65h4yMDEpKStSwuezsbADuu+8+Tp48SUxMDN7e3rRp0wYbGxvOnDmDu7s7QUFB3HvvvfTp0wcPDw/KyspYunQpDg4OnD9/HgsLC7p06aIWfmnWrBlt2rShbdu2FBQUkJmZ+bt0fRcvXuS9994jLS2N5cuXq+FCCxcu5JNPPgHgp59+Ij4+ns6dOzNw4EAAzp8/z3fffceMGTM4cuQIGo2GHj16sH//ftzd3enXr5+aJWT//v307NmTw4cPM3nyZGxsbFi7di0ajYYJEyZQWlrKRx99xODBg9HpdPTr14+ioiJWrlzJzJkzOXfu3O8cLjWxxVqtlhUrVqi56c1BQUEBe/fuZeLEibXSnhACCwsLTCYTDz/8MCtWrFAfCL766is0Gg0TJ06ksLAQvV5PYGAgx44d48iRI0yYMOGamYBz586xfv16pk2bRkhICNnZ2WrYybx581TP8apVq3BycqJVq1YcP34cCwsLdVF8TT7/zZs34+3tzfbt23nwwQfVjF29evXC2dmZ8+fP06VLF44fP05mZiZNmjRRw708PT3p1asX/v7+eHh40KxZM/R6PeHh4QDqDNe+ffto06YNBw4cYMaMGfz666+qXv3666907dqVRo0akZSUhNFoVMOg8vPzqaioYNy4cRw8eBBra2seeOABrKysSE5O5sSJE2pfmjZtqubODwgIIDU1FZPJxLx586iqqmL37t3k5+ejKApTpkxRw97Wr1/P7Nmz1c/3ti5K/N2JQlgAJ2/l4uZALkq8ltatW5Odnc3nn3/OpEmT6NGjxw2Lu2RlZdG3b18uXbrEihUrWL58OUVFRTz99NNYWloyceLE3+VzrU+Eh4fTuXPnOmk7Li5OLTUeEBCAEAI7O7sGn67xz6jxEtVQ4yWqrq6murq61irn/Za61IO65MiRIwwfPhydTkefPn1Yv369WpRi5syZrFixAkVRmD9/Pvfddx9dutx4iFYUhU8++YQnnniC4uJiHBwcVE/V1dUD4XLseaNGjZg/fz6dOnUiODiYXbt20apVKwwGA0VFRRw/flz1btXcND09PdUy5hqNhl69etG9e3fs7e1ZtmwZ1dXVaojUkSNH1Fzlzz77LCaTibZt25KUlKTqRU2WFE9PTzW8wt7enubNm3Px4kVMJpNqmDs4OGBra0tOTo6aQSUhIYGlS5fy0Ucf4eTkpKYOtbOzw9bWFicnJ4xGI5mZmVhYWGA0GjEajaq3vMb7XF1dTU5ODkVFRSQnJzN27Fg1llsIgU6nIzs7G1dXV1xdXRk2bBiHDh3C399fzfAydOhQ9u7di52dHSdPnqSwsBAvLy/c3d0ZMGAA58+fp3379qSkpNC1a1ciIiKIioqiXbt2eHh4YGdnR0BAAPHx8axatUo1MoYOHUrHjh157LHH+O9//8vevXsZMGAAP/zwAwMHDiQrK4v09HSaNGnCoUOH+OCDD7C2tsZoNPL6669jMBho1qwZjz/+OABvvvkmVVVV/Oc//0Gr1fLll19iY2PDlClTMJlM/Pzzz3h7e9OtWzdOnDiBs7Mzp0+fprKykjFjxvDFF1/QrVs3CgsLeeqpp9QMPStWrFBDch555BFWr17N4MGDadSo0TUzVIqi8PPPPzNs2DD1+zp79iwHDx5k/vz5N+W5rskLfXUsc12OBUIINZf95s2bGTNmjLrv0qVLfPzxx3z44Yc33d5vi/UUFxerWXRqSE9P59KlS+ri0d8uVKysrOTxxx/n1Vdfxc/PTw2FraysJDY2lm+++Ybp06cTFhbGDz/8QGpqquqFB9SHs5dffhlfX1/69etHbm4uP/74IwBPPfUUQ4YMYcCAAdjb26PX6/nss88YNmwYn332Gf/5z3+YMWMGq1at4ssvv+SRRx7ByckJS0tL1q1bR+fOnfH392fBggX06dOHyspKtFqtmoXp3nvvZd++fUyfPl3Nsz5t2jT1M1i5ciV9+vRh69attG7dGmtra0JCQli+fDlWVlY0atSICRMmsHDhQvVBYfjw4YSGhprNoPYAVimKcke5j7p06aKcPn3a3GLUG2bPns2GDRuYNWsWCxcuxM/PT407/CNWr17NY489Rnl5OXv37mXmzJkMHDhQVeyGjKIoWFhYEBkZSVhYGEIIgoKCrsmzLPk9Vw/21tbWapaUOzHN1O3gmWee4aOPPqJZs2b8/PPPhIaGYjAYGD58OHv37kWj0aihC3VFTXXI65GWlkZRURF6vZ6UlBQmTpyoTi9nZ2fTt29fSkpKOHr0KDY2NgQEBKie77Nnz/Lcc8/x448/4u/vz5EjR3juuef45ptvKCkpwdPTk4iICAICAoiMjCQ3NxdHR0e6dOmihlt5eXnh7e1NdHQ0ZWVleHp6UlZWhqWlJY0bNyY9PZ3CwkLVmMjJycFgMODq6kpxcTEGg+GaYiYAnp6elJaWUlhYqC76UxRFrTrr6elJYWEhiqJgb2+Pra2tOoWdmZmp5vXu0KEDUVFRWFpaUl5eTnBwMMnJyXh5eVFaWqqWs7e1tSU7O1utxAiXU0pmZWURFhZGdnY2Li4uFBUV0bZtWxRFoaioiNLSUpo1a0Z2djYvvfQSa9eupbi4GBsbG9zd3VUPeceOHRkyZAgBAQF8++23aLVaUlJS6N69O9bW1vT9v/buPK6qMn/g+Oe57IvsyCK4Iiju+5pbaWWaS9oyZbZMmTNWNtZMy/xmWqbJsmzfLNOazDRzqslMc8klx0QRQhEEBFFAENl3Lvf5/XHvOYO7AvcCl+f9evmSe+5yngNfuM895/t8v2PG8PLLL7No0SKklKxatYrDhw8zePBghg4dSlZWFqWlpXpLezBXUzl+/Dj33nsv1dXVuLi4sGrVKvLy8rjjjjtISUmhrKwMMC9S9PT0pKamhpkzZ561ziQlJYV169ZRV1eHh4cH3t7eemqGt7c3Bw8epLy8HDc3N0aNGkVZWRmdOnXC29ub5cuXExQURGlpKbfeeivu7u5s3bqVjIwM5s+fz6efforJZGLq1KmEhIToVxALCwv1RXaFhYUUFRWxb98+ZsyYgYuLC0ajESnlBWP+2LFj+Pj4XLAqixACPz8/CgoKWLx4MX/5y9kX9bUFq03lSqp8nDlzhrfeektvxHSuuLg4evTogaurK6tWraKsrIx58+ad97h27doRGRlJp06dKCws1Gs57969+7ySpBrtZMm0adPYuHHjJcdaVVVFeXk5q1ev5qabbsJoNJ5Vdx/MKYIODg5nvU5OTg67d+9mxowZZ52Q+emnn8jLy8PDw4Pp06frMRofH4/JZGLgwIFWX5QIgBAiAdDe3QTQGTijbZdSXn5VTgugzhSe7bnnnsPLy0tvv1t/cdjF7N27l4qKCgICAliwYAGBgYE88sgjVh5p04mNjbVa7qz2S61dYgKu6Ayh8j9aTWNrN36wZhxY2yuvvMIbb7yhl74E8xuztphTS+mwpotNpsFcTkvLhdRKG9Y/M6cJCAggJCSEoqIipkyZghCCvXv3Mnz4cCZNmsS6dev0VuT10wSOHDnC4sWL+fDDDxk3bpxeIu+2224jKyuL9PR0srOz6dChA0OHDmXLli0YDAa8vb31dvQAL7zwAs8//zwjRozQu2xq7elramqoqKjQ28SXl5dTUVFBnz59yM3NRQjBmTNnKCsrw8PDg9LSUsLDwykoKKC6uhpnZ2c6deqEs7OzXimlurqaHTt24O/vr1fXiIuL089Gl5aWcvjwYaSUSCmJiIigpKQELy8v+vTpw9dff42vry979+7Vu0eGhobqjW+0/NqjR48SHh7O+++/j6+vL9OmTeOf//wnXbp0oW/fvvTq1YsDBw6wZ88etm/fTkJCAgMGDCA/P58dO3bQu3dvYmJiSE1N5frrr6dHjx5MmjQJk8nEBx98wK+//kpwcDA+Pj5UVlaycuVKQkJCWLBgAUuXLqW6upqlS5fy0UcfUVVVRUVFBV988QVDhgzh66+/xsnJiZkzZ7Jv3z769eunT6Z/++03vWvpU089xapVq7j55ptxc3MjLi6OY8eOMWXKFLp164avr6+el3vmzBneeecdwsPDeeihhzhy5Aj9+/dnzZo1ODg4YDAYuO2223jrrbfo0aMHUVFR/Prrr3o9+Lq6OpKTkxk/fjwHDx7Ur9LMnDmTJUuW0L59eyoqKvSGQtqHrT179hAfH4+Liwu1tbXnTTq191UfHx8KCgqIj48nPj4ef39/nJyccHJywtvbWy/RCbBr1y6SkpKYOXOmPsHXFrfWt2fPnvMqphw9epSMjAwmTpyI0Wi86O9oRUUFQUFB+oebc9WvKKKVULyQmpoajh8/jqurK8XFxfr2i3UOBXNd7rKyMgIDA8+68nUhrq6unDx5krKyMv139lwXuoIZEhLC7Nmzz9s+ceLEsz5waN9Tb2/vK14/dilXcy11yuUf0vJZ6/JxaxUSEkKvXr3Q8sorKysv+5y9e/cipSQqKoqMjAwWLVp0RVUOWoorrbPdGKdPn9bPKt1www1W35890T6IWKvRgsYWcWAt2htlZWWlPqGurKzU83fPPQvWUkVGRgKcdYZu+PDhgPlv9cUWpfXs2ZOlS5fql7579+7NwYMH9ftNJhP79+/n119/Zfr06SQnJzNq1CimTJlCfn6+Xm1i7ty5rFu3jvbt29OxY0d69OhBTEyMXill/PjxepWM/Px82rVrR3FxMWPHjuXbb78lODhYr4BQXV1Nfn7+WY1k0tLS9DNoWupXcHAwp06d0s92CiHw8vLi119/1Usp+vr66nmsnp6eZGZmUlVVRd++fcnNzaVv374cOHCA9u3b06dPHz799FP8/PxISUmhurpaXxSWmpqKwWDgl19+oaioiNLSUpKTk8nJyeHIkSNs376d9u3bk5mZSW5uLh9++CH3338/hYWFbNiwgW7dulFaWqqn1+zbt4+qqioeffRRXnvtNVxcXPj+++/JzMzUa67Pnz9fr4jx+OOPc80115CTk0Pnzp1Zu3at3hDo9ddfZ8WKFSxatIhPP/2UiooKjh8/Tr9+/XBwcKBbt244Ozvz1Vdf6U3HFixYgMlkYs2aNYSGhhIREUHXrl2JiYlh9uzZehlM7UPc3XffrcfE8ePH6d27t96avnv37rzzzjuEhYWxfv16Vq5cybJly+jVqxeBgYGkpaUREhLCM888Q2pqKh07dqS8vJz3338fPz8/PQ1o/vz5APz3v//ljTfe0Kt/pKSk6L+b2geGffv2kZaWxurVq+nQoQOOjo4EBweTkpLCTTfdRJcuXUhOTubuu+9m2bJlLFiwgGXLllFcXMwTTzzBf/7zH32B6cqVK+nevTufffYZs2bNoqysjP/+97+4uLiwe/duvv/+e/7v//5Pr7RSXV3Nzz//zIQJEygvL7/khFqjlTTs0KHDeWe+a2pqqK2tpbi4WP8wqikvLz+vxF59paWlBAcH69VrCgoKLlpzPS8vj4qKClJSUs47O90QF1sE3hQN/xqc8tGUhBCzgWeBnsBQKeV+y3Yn4GNgIObJ/2dSypcs990AvIm52sjHUsrFV7IvVYf6fLW1tTz99NO8/vrr1NXVXfIyu5SS9u3bk5+fr09INm7cSHBwsI1G23jWrEMN5l/Yl156ifnz5+tNO1pqCcGWQiv1pF1GB3Me3htvvGG1fVo7DqxNy03UunCmpqbSo0cP6urqrJp73hpdrIlPYmIi3t7exMfHM2rUKL0ywLFjxwgODsbJyYkhQ4bQqVMn+vbty5AhQ3BxceEvf/kLt956K3379mXLli2MGTOGjz76iE6dOnHkyBEcHR0pKCggLS2Nuro6evXqhbe3N6mpqeTn51NdXU1ISIi+yKuiooLa2lrc3d3p0aMHhYWFFBQUUFxcjMFgwNnZWT+GqqoqPbWsfqMhDw8PfTKt1RNv164dtbW1VFdX065dO7p3735WabWamhqMRiOenp4IIQgODiYrK4vKykqcnJxo37494eHhpKamEhYWxoEDB/S870GDBlFcXMzw4cPJzs7G29ubH374AT8/P9avX88vv/zCk08+SWBgIDNmzGDVqlX4+/vj7+/P66+/zssvv0xNTQ1+fn6cOnWKmJgYlixZwt69ezlz5oye56ylFYC5XrNWUz03N5e8vDwiIiI4ffo0gYGB1NXVMXHiRL7++mtuvPFGIiMjSU5Opry8nO3bt5ORkYG/vz8RERGMHDmSuXPn8uGHH+Lp6YmDgwNJSUk88sgj/OMf/yA+Pp5XXnmF3377jcTERB566CG6du2Ku7s7O3fuxN/fn9DQUIQQZ6WrVFRUsGLFCkpLS7nzzjvp2LEjkyZNYvPmzQwaNIhvvvlGX6Pg6enJd999R01Njd7t9ZprriEoKIi4uDi+/vpr7rrrLpKTk0lOTmbMmDGsXbuWnj170qdPH7Zv387DDz/Mxx9/jMFg4MiRI3rDpOnTp/P9998zd+5camtreeONN/TJuFb3fOvWrUybNo2RI0dSXV3Nv/71L32iPW7cONLT03n77bcZNGgQERERDBs2TJ/Urlmzhttvvx2DwUBYWBhFRUV6pY+4uLizSvolJCRQWVlJt27dMBgMnDx5knfffZcnn3wSPz8/Xn31VcaPH8/48ePP+x1dvXq1XmYvICCA+++/H6PRqP8+aXX0tQ/lF0p52bhxI35+fvoHLa1G+rp16wgPDyc6OprXXnuNV155pXlyqJuSEKInYAI+BB6vN6H+HXCzlPJ2IYQ7kAiMA04AR4GJwEkgBrhDSpl4uX2pRYkXVlxcTHBwsP7H+mLy8vIICwvDZDIRHh7OnDlzeP7552040sazZqdEML+53X///fzjH/8gJCTkskXuFXODgIKCgrMm1Dt27GDMmDFW26e148DaBgwYQFxcHB9++CEPPvigXpdWVZW5cg2NgfqX6LUFne3atWPOnDmUlpbi6enJ008/zezZszl+/Dhgzgv28vJi3bp1LF26lDfffJPMzEz9Sl9UVBQrVqygrq4OFxcXHB0d6datG66urmRlZREUFMS+ffv0hjv+/v4cPnwYNzc3oqKiiI+PRwihVwzRJp3+/v6UlJRgNBrPagnt4uKCq6vrWaXRamtrcXBwwN3dHS8vLz3/98iRIxgMBr3SjKenJ25ubnpFFxcXF4QQ+oJQMNdn9/LywtPTExcXF44fP05dXR0GgwEvLy8qKyupra1l1KhRVFVVccstt7B+/XpGjhzJ559/Tp8+ffR8+OzsbKSU5OXlMWHCBM6cOYPRaCQ4OJgDBw4QHR1NeXk5gYGBxMXF6Sd46urqCAkJITMzk6ysLObNm0dsbCyPPfYYS5YsITMzkxkzZhAcHExERARPPfWUnlev5cs/8sgjnDx5ki+++EJfTBsYGMhdd93FRx99pO/32muv5bPPPmP06NF6J9K4uDiefvppJkyYwLZt2/D39+e1115j/Pjx/O1vf8PT05NFixbRpUsX3nvvPYqLi5k8eTLR0dG89957bN++nffee497772XTZs2UVtbS2lpKTExMQwePFi/QpOXl8fu3btJS0ujuLiY0aNHc/3115ORkcGWLVsoLy/X+0MsW7aMzMxMPvnkE+644w4efvhh1q9fr3fxrKuro6ioSK97v3btWoqLi/nrX/9KSkoKv//979m6dSs5OTnMnz9fj5Hi4mK92+3LL7+Mt7e33rVy9+7dmEwmcnNzcXV1JTIyktmzZ/PnP/8ZR0dHHn30Ub799lucnJzo1KkTBw8epK6uDh8fHzZs2ICrqytLly7V04u0krSjRo3CwcGBrKwsTCaTXkZ0woQJbN68GTc3Nzw8PPQW97///e8xGo28//77lJWV0atXL8rKysjJyeHw4cOsWLHCNjnU1iSlPAIXPBUvAQ9L23M3oAYoAYYCqVLKY5bnfQlMwzzhviTtB66czdvbG1dXV31CfbH8y/j4eGpra3Fzc8PHx4fJkyfbeKSNl52dbfV0guTkZD19xlp1lO3J4MGD2bx5Mw4ODvrvqLXzm20RB9Z0++23ExcXp9c619aHXConUTlbQ2NAm0yD+X3rD3/4g54WoV3qXrzYfNH03OoRWorAxx9/zPHjx5FS6g16tEmZo6MjQgg+/fRTDh48SEJCAnv27OHkyZOMHDkSBwcH/Pz8iIyMxNnZWe8GeejQIUaPHo3RaKS4uBhPT0+OHz+OwWBg4MCBpKamEhoaSl5eHpWVlbz00kt6kxo/Pz8SEhKora3FZDJRXFyMh4cHKSkp+Pv7U1xcjJOTE87Ozggh9Lb0gYGBetfL06dP4+XlRVFRkV7XvLKykrKyMkJCQjh16hT+/v5UVFRQXV1NQEAAW7ZsISAggCVLlpCTk0NMTAy9e/fm8OHD+Pj4EB0dzZ49eyguLkYIwWeffUZoaCjOzs6kp6czffp0duzYQWlpKXl5eZw+fVrPIXdxcSEjI4PKykp8fX1Zv349N954Iw8++CCpqak4Oztz6NAhYmJiiImJ4b777uODDz7glltuoXPnzsTHx+uVJh5//HGeffZZTpw4webNm3n11VcZPnw4AwcOJDExkeeee47u3bsTGRnJ1q1bycvL0/+G5eXlAeYJfkJCAp9++ilBQUFkZGTwzDPP0KtXL6Kjozl69Cjz5s0jKiqKiIgIhg4dyoIFC9ixYwcPPPAAaWlpfPXVVyQlJbFt2zZ69uxJRUUFaWlphIWF8cknn+ht3O+77z4++eQT3Nzc8PX11UtMDhs2jBdffBEnJyfi4uLo1KkT+fn5XHvttbi7u/P999+Tk5NDRUUFd911Fy+88AI1NTV4e3szaNAgvfa6liLh5OSk55drfv31V1566SWOHz/Ozz//zH333UddXR1Lly5l6tSpzJ8/n9raWvr3789NN92Eo6Mjd955J0VFRaxcuZIxY8bQo0cPhBAUFBToseDj48PUqVPp1q3beR+Ejx8/zj333MMtt9xCbm4uCxYswMnJiRMnThAWFsbWrVt59913cXZ2ZsaMGezcuZPRo0fj5+dHWlqaXvKvMVrEhPoS1mGeKOcA7sBjUsoCIUQHzGepNSeBYVfygvX/ECpn0zrWZWZmXrCjFZjLWYE5vzEsLKxV5U5rLpar1ZTqF7pXMXd5s2fPZvv27Wdtq99lzRpsEQfWpFVWyM7OBsztfuH8CZxycU0VA0KISy7SvBCDwXDeQqubbrqJUaNGUVJSop8FHzx4sH7JfeHChURHR5OUlMRzzz3H4MGDiY6OZsqUKfzzn/9k9erV9OrVi/z8fBYsWMDMmTPJz8/XJ9ZCCLKzs/XmF4sWLaJbt276AsyBAweSlpbG6dOncXNzw83NDWdnZ30BpZOTE9HR0cTExCClpLy8nJqaGqKiosjNzT3rb56fn5+eyqKdQfbw8MBkMum5s+Xl5fqCPUdHR7y9vamtreX48eMEBgZy9OhRPUXF1dWVrl27kpGRoZcz1KqPaLnrycnJREZGkpeXR1JSEtXV1Tg6OuLl5UVFRQX5+fnExcVRUFBA37599TOgI0eOJDs7mw8++ACAd999l44dOxIVFUVaWhr5+fmsXbtWL7vo7OzMNddcQ0ZGBocPH6awsJABAwaQk5PDs88+i8FgwM3NjR9//BGAcePGcejQISorK/npp59wcXGhoKCAnj17UlBQwPr161mzZg3+/v6Eh4dTVVVFnz59iIiI4M0338TX15fKykoiIyOZM2cOwcHBBAYGsmHDBgwGA2fOnCExMRFHR0eys7NZvHgxISEh/P3vf+e///0v48aN06twaVexOnfuTFxcHAsXLiQxMZGjR48SExNDRUUFI0eOJDk5mRtvvFHvsPrhhx8SHR1NYmIia9asITExUY/j2tras65s5+bm8sUXX1BZWcn111+PEILY2FjGjh3LF198QUpKCgaDgU2bNjFgwAA+//xzSkpK+Pvf/65XXzl16pRe4UPr4Pzbb79x5swZvvnmG/r06UPv3r1JT08nJSWF0tJSioqK2LhxIwkJCfj6+jJgwACCg4P58ccfGT9+PNdeey1CCI4dO6ZfNfHx8WHjxo16yc7GMDT6Fa6QEGKLEOLQBf5dqgPIUKAOCMXckXGREOL8XruX3/eDQoj9Qoj9BQUF5Ofnk5OTQ1ZWFoWFhaSlpVFZWUliYiImk4nY2FgA/RNLbGwsJpOJxMREKisrSUtLo7CwkKysLHJycsjPzycjI4OysjKSkpIwGo3Ex8ef9Rra/wkJCVRXV5OSkkJJSQmZmZnk5eWRl5dHZmYmJSUl+sKShISEC75GfHw8RqORpKQkysrKyMjIaJJjGjRoEKNGjWLnzp0XPaZffvmFhQsX4ujoyI033oi7u3uLPqYL/ZwKCgqs+nNauHCh/sFEaxVt7WNq7bE3btw4pk+fzpAhQ4iOjmbSpElWP6ZDhw7ZPPaa8pi6dOnCfffdp59dOX36NAsXLmTevHmt9phsHXvp6ekt7pi06gzOzs76MVVVVeHv70/Pnj05ePAgPXv25IknnmDs2LEEBgZiMpk4cuQIM2bMwNPTk7CwMObNm8ctt9zCiy++yMMPP8zKlSv55JNP2LRpE/fccw+TJk3io48+4k9/+hOLFy9mypQpzJ07l9jYWDZu3Mju3bsZN24c06ZNY/HixUyePJk5c+Ywd+5cZs2axcMPP8w111zDI488wnXXXcdjjz3GoEGDeOihh/D19SU6OpoxY8YwZcoUAgMDGTp0qF4Xe9asWXTs2JF7770XJycnnnjiCcLCwrjnnnv0xaipqancfffdDBs2jKlTpxIaGqqfJXVzc2PkyJH4+PjQv39/TCYTo0ePpqKigjFjxiCE4LHHHsPPz4+5c+fi6OjI8OHDCQoKonfv3gwZMgQvLy9cXV3x8fEhPDwcd3d3rr32Ws6cOcMf/vAHsrKy6NmzJ6Wlpdx4443k5eXpZz+DgoIoKCggODiYyMhIvLy8CAwM1FvPu7q6MmHCBJycnFi4cCG33347jzzyCLW1tcycORMpJd27d6e0tBRvb2+CgoLo1asXI0aM4OTJk3Tp0oUXX3yR5cuX065dOx544AHAnOZVXFxMaGgo69ev56abbmLkyJHcfvvt9O/fn7CwMAYOHMjYsWP1BipDhgzhwIEDTJo0iZqaGgICAnB1dWXAgAF6Lv31119Pp06d2L9/P8XFxXz11VfU1dVx66234uXlxfTp00lNTdXbhM+YMYOxY8fyyCOP4O/vzx133EFAQABTpkzRW9ufOnWK2tpatm3bxjPPPMMPP/zAJ598oq9NePjhh/n+++/597//ze7du/H39+e6667jo48+Ys6cOaxcuZK//e1vODk50a9fP/7xj3+wfPlyPvroIzw9Pdm7dy/btm1jz549ZGdn88MPPzB16lROnDhBnz59WLp0Ka+//jqvvfYaCQkJPPbYY2zatInvvvuOt956i+3bt/Of//yHVatWsWzZMiIiImisFpFDrRFC/MzZOdTvAnullP+y3P4E+BHz2elnpZTXW7Y/BaAtWLyUbt26Sa2tsXK2+fPn88EHHzB58uSzWkHX16lTJzIzM/H39+f777/XV+S3Jjk5OXojDGsQQuDg4MDrr7/OI488omopXwEpJSNHjuTgwYN6hQJrf9+sHQe2IIRg4MCBHDhwgCVLlvDnP/9ZL+OmXJ49xMCFXKzU2tWKj4+nXbt2+Pj4sHjxYmbPns2QIUP0ffzwww96845Nmzbh6urKsGHDWL16NatWraJjx47U1NTw5ptv8uSTT3Lo0CGcnZ1xcnKipqaGtLQ0HB0dMRgMlJSU4OrqSk1NDZ07dyY9PZ2KigoMBgO+vr787ne/Y/ny5ZSVlenNOQICAhg0aBCFhYV89dVXeldCbXxavnZNTQ2Ojo54enri7OyMo6MjJ06coLq6Wv/bo1XlcHJy0iea2u+S0WjU89m1duRJSUmA+cqulsNuMpkwGo36AtFrrrmGTZs2UVhYSMeOHSktLaVr166cPHlST51xc3Ojd+/e5OXlIaUkIyMDd3d3TCYTjo6Oej68i4sL3t7eDBw4kE2bNgHmFNbo6Gi9dF1ubi4ODg5UVVXRvn17fbFmRESE/v3Kzc3lu+++w9fXFycnJ4KDgxkwYAABAQHs3bsXHx8fIiIiWL9+PTU1NaSmpuLo6EhoaCg+Pj4MGzaMuLg4evXqxYoVK3Bzc9PTG7UGNt7e3rzxxhuEh4fz2muvsXPnTj33PyAggMLCQtLT0xk+fDiurq58+eWXfP7557z55puMHTuWgQMHUlxczJo1a3BzcyMrK4shQ4aQl5eHi4sL06dP5/PPPycoKIiqqirmzZvHhg0b6NChA2vXruVvf/sby5Ytw8vLi6NHjzJt2jSOHDlCQkIC3bt3Jy0tjYCAAMBckSQqKorTp0+zb9++1r8oUXOBCfVfgB5SynuFEB6YFx/ejjlX+ihwLZBl2f47KeXhy+0jIiJCpqamWukIWrevv/6aWbNm4efnp5f7qa+kpIQOHTpQVlZG9+7dSUpK0v94tSbaampr0fLP//jHP/Luu++qCfUVGjt2rL54Baw/obZ2HNiCEIL27duTnZ3Nn/70J9566y0Vb1fBHmLAVrZs2aKnGV2Jo0eP8sEHHzB37lzi4+NxcHBg5MiRuLi4EBwcTElJiZ6rnZubS0ZGBjfddBOxsbFs2LCB3NxcPW/a29ubzMxMDAYDBoOBYcOG8dNPP5GdnY27uzunTp0iMjKSqqoqffFbSEgIDg4OpKWl4eLigo+PD05OTvqkXZvAGo1GnnzySbZu3UpKSgpVVVX4+PjoHTLbtWuHt7c35eXlelqAluLQvn17SktLqaysxNnZGVdXV70UnVab2mQyUVlZSZ8+fUhMTMTBwQEPDw+9i2dNTY3eKAjQP3CUl5fj7OyMt7e33pY8ICCA/Px8AL2OtTaZ1WqXu7m58fDDD/PRRx8hpcTR0VFfkFpTU4OTk5P+fao/FgA3NzdKSkr0vOrKykq9uY7WcCs0NJS6ujry8vIoKirCy8tLT/UJCwvj5MmT+Pn5ERgYiK+vLzk5OYSHhzNmzBjatWvHwYMH2bRpE8XFxfqHkbKyMoqLi4mKiuLgwYO4urpSWVlJz5499Stj48eP1xs7paen8+CDD7JkyRLc3Nz0MoBJSUm0a9eO8PBwAgICiImJYcSIEaSkpDB9+nQqKyuJi4sjLy+PU6dOMWrUKI4cOUJpaSnu7u6cOHGi9U+ohRAzgLeBQKAIiJNSXi+E8ARWANGYm8mskFIusTxnMvAG5rJ5n0gpX7ySffXt21f+9ttvTX4M9kDLna6/MKy+xMRE+vTpg8lkYs6cOXz22WfNMMrGy8jIoHPnzlZ7fUdHR+rq6rj++uvZtGmTmuBcofvuu4/PPvtMX9xi7e+btePAFrSrIYWFhcyZM4dvv/1WxdtVsIcYaMlMJlODTrrUr6Kivc4bb7zBpEmT6N27Nz/99BPJycm4ubnxww8/6Av7XFxciIuL0yeLQgiSk5NZsWIF77zzDsuXLycxMZEhQ4ZQWFiIwWBgz549DBs2jC+//BKj0cjgwYM5ffo0JSUlPPjgg2zYsIH09HR98pednY2zszNJSUm4uLhQWFjI4MGDOXLkCOHh4XTt2pVvvvlGz6M2GAz6B97169frCzednJwoLCzUJ7EabZFfQECA3gSloqJC737q5uZGeHg4FRUV+mS8rKyMqqoqXFxcqKiowNHRUa9wUVRURE1NDVJKvL299brijo6O+s/HZDLh6empn5HXGq64urpSUFCAg4ODXn3GyckJo9FIeXm5np7zr3/9CzC3/9a+j9pZfg8PD9zd3fH398fT05OkpCS9wourqytCCIQQBAUF4eTkhIeHB2lpaRgMBmpqas7qjdG5c2cKCgooLy/H09MTg8FAQEAAFRUVlJaW0rFjR9LT06mtrcVgMODp6Ul5ebl+RcTBwUHvbqrVd9fKtbq6ulJaWtr6J9S2NHDgQKnl1Sln0y6RaV+f67vvvmPatGkIIfj666+ZMWOGrYfYJMrKyqy64M3Dw4OKigqioqJITk5WE5wr9N133zF79mz9bIm1v2/WjgNb0M5q5ebmcuONNxIbG6vi7SrYQwy0dTU1NRft5KfRJpBgbnDy+OOP8+STT9KhQweKiopYsmQJjo6OzJs3j5CQELZt28axY8cwmUzs2rWLoKAgnnvuOUpLS6murtYrYt12223ccMMN7Nq1i7/+9a+sWbOGuLg4JkyYQHFxMQcOHCAkJIRvvvmGzMxMhg4dCqBXajl8+DATJkxg165dSCnp0qULnp6enDx5ksOHD+Ph4YGHhwehoaGkpKTg4OBAQEAAHh4ehIWFsXPnTr2WeVBQEOnp6RgMBnx8fCgvL2fAgAEcP36cESNGcOrUKWJjY5k6dSpr165l8uTJbN68mW7duhEWFoaHhwenTp3iyJEjSClxd3fH0dGRjIwMfHx8zmqh7uLiondGfOCBB3jnnXeQUlJZWUnXrl05deoUPj4+1NTUUF1drU9ktY6itbW1eHt7U1JSgoeHB0IIjEYjbm5uFBcXYzKZ9LKOpaWlehMk7Uy+9iGgpqYGV1dXHB0dqa6upqqqCnd3d4xGIz169CAtLU0vI1pdXU1dXZ1+pt3V1ZXa2lo8PDz0KyFSSjWhvhq9e/eW2mIk5XzaG/SF4uLZZ5/lueeew9nZWS/T0xolJSXRo0cPq71+x44dOXHihP7Hp639jjXUqVOn6NixI7W1tYD1J9TWjgNbcHFxoaamhmPHjjFq1ChycnJUvF0Fe4gBpfEaGgeFhYX4+vpiNBrZvXs3RqNR7z6spZbWX+y2detWxowZw9///ncmTpxIhw4dePHFF5k1a5ZeSrBfv37k5ubSoUMHPvzwQ1555RWysrL46aefOHToEElJSfj5+ZGfn8/IkSPJz8/Hz8+PjRs3MmLECAwGA1u3biUqKopdu3YREhJCVFQUv/32G507d+b06dOUlpayb98+Bg0axPTp09mwYQMODg4EBQVx7NgxamtrefDBB9myZQs1NTXU1dXpVVpcXFyoqqoiPz+fF154gd/97ndER0frzYiuu+469u/fz4gRIzhw4AC1tbWMHDmSX375hYqKCkJCQqirq+PRRx9lzZo1lJeXk5ubi8FgwMXFBWdnZ73coYODw1l576dOncLb2xspJadPnyYkJESvn67lnRcUFBAaGkp2djaRkZGkpKQQFBTE5MmTCQ0NZfPmzaSnp+Pr64uHhwcDBw5k+fLlWtlRNaG+Gj179pRa4XnlfM7OztTW1lJQUICvr+9Z982ePZt169bpdUYvVqu6pat/tsIaxo0bx44dO/Tctbb2O9YY2gQRrD+htnYc2EKHDh3Izs7m0KFDDBkyhMrKShVvV8EeYkBpvOaMA61O8rZt25gwYcJZ76vV1dX6wtL4+Hh69OiBk5MTmzZt0nOpo6Ki8PX1ZfXq1UydOpV+/frxzjvvEBISQkhICEIInn/+ee666y7mzJlDZmYmPj4+eHl58dhjj5GdnY2joyNFRUUYjUY6d+5Mly5d+PHHHwkICGDNmjVs3LiR+++/HxcXFwYNGoTJZCIiIoJXX30VIQQxMTFs3ryZZ555hl27dvHqq69y7NgxQkJC6N69OwMGDGD16tVERkZy8uRJJk+ezNy5c7nzzjuJiorSG/kYjUZycnK4++67cXd3Z+XKlbi5uXHnnXeyf/9+KioqCA8PZ+vWrXonUCEExcXF9O7dm/79+/PDDz9gMpmYMmUKr7zyCqGhoURGRur1ybt27YrJZGLQoEFs376dqqoqUlJStBM5akJ9NTp37iwzMjKaexgtlpeXF6WlpaxcuZK5c+eedV+/fv347bff6Nq1K625Ukp8fPxZbVGb2pNPPsnLL7+s325rv2ONobVWBut/36wdB7YwceJEtmzZQkxMDMOGDcNkMql4uwr2EANK47XWOKjfhO3EiRMEBQXh7OysL+zWUjgrKyv1fOX6MjMzOXnyJH369NFbyoO5+o2Xl5deLchkMrF9+3YiIyPx8PDA19f3kifUpJQkJiaSkZHB6tWr9Rzturo6nn/+eTp06ADA448/zogRIzhz5gwbN25k5syZODo6Mnr0aMLDw/X65Nq4zpw5wz333IOPjw9vv/02S5cupV+/frRv3559+/axaNEiYmJiMBgMLFq0CC8vLyZMmICvry+7du3ipZdeIisri86dO7N7925Gjx7NBx98QHZ2Nm+//TaoCfXViYqKksnJyc09jBara9eupKenM3v2bNauXXvWfQEBAZw5c4aJEyeyefPmZhphy/fVV19x66236rfb2u9YY2j556p99pV57733+OMf/8jPP//MuHHjABVviqK0LIWFhdTU1JCfn0+vXr0a9Vra3zetQYvWHOncMpFaPvi4ceP4/PPPGT169HmNlDQ7duzQ/n42akLd+mqeNZJ29ku5MC3g9u/ff9592krkiRMn2nRMTa0pWoxeSp8+ffSvW2NZweakrey3xeVXa8eBLUybZu6LpbUdV/F2dewhBpTGU3FgXb6+vnrzmsbSqoKA+QSgdvvcmuvBwcGMHz8eIQRz5sy56GQamq67bJv76+vq6trcQ2jRtMDKzc097z5tsZh2Jqy1snZr5uDgYP3rxjZWaGu0ibQtfk/toUV3aGgo8L8JtZubW3MOp9WxhxhQGk/FQdvWVJV+2tyEWp2hvrRbbrkF4Kzaj+eKioqy1XCswtpnI+p3qXN3d7fqvuyNNqG2RbMNezgrpZ2p0a4eeXt7N+dwWh17iAGl8VQcKE2hzU2o1RnqS6vfVra++nlL7dq1s/m4mpK1z0Y4OTnpXwcFBVl1X/ZGS/m41OW5pmJPZ6UKCgoA9MU+ypWxpxhQGk7FgdIUJyPa3IRa68KmXJjBYDirQ5WmtLQU4IIrhVubhIQEm+1LdWG7OlqDhsGDG7wu5IrZMg6sraioCID+/fs36zhaG3uKAaXhVBwoTfGhqs1NqNUl0cvTzrBWV1fr27KzswH08jWtWWRkpM321bNnT5vtyx5oOcBjx461+r5sGQfWdvr0aQBGjx7dzCNpXewpBpSGU3GgrF+/vtGv0eYm1FrTCOXitJSOnJwcfVtKSgpgm0vx1paZmWmzfQ0YMMBm+7IH2iLOplgNfjm2jANry8vLA2xzZt+e2FMMKA2n4kBRKR8NoMpKXV5ISAgA9et1a21UR40a1Sxjakq2zGsePny4zfZlD7RFiVoMWpM95bdrVXnCw8ObeSStiz3FgNJwKg4ULa21Mdrc7LKsrKy5h9DiaWdVd+7cqW87evQoAOPHj2+WMTUlLd/UFlQO9dVxd3fH3d1dz6W2JlvGgbVpE+qmKv/UVthTDCgNp+JAaYpKL21uQt3aF9TZws033wzAnj179G2JiYkAjBgxolnG1JRsWenlQgs8lYvz8fGxWRUZe6r4U1xcDKi/b1fLnmJAaTgVB0pTaHMTauXytDzM+ikfJ06cAMDPz69ZxqS0DW5ubmpS2ADl5eXNPQRFUZQ2rc1NqM+tr6ycT8vDrH8ZzJ4uianmPi2Xl5eXzc4W2VMc1K/Io1w5e4oBpeFUHChNoUVMqIUQs4UQh4UQJiHE4HrbnYUQK4QQCUKIeCHEuHr3DbJsTxVCvCWu8LSWWpR4edq3sv4fGa21sT3w8fFp7iEoFxEQEGCz0pb2FAcmk6m5h9Aq2VMMKA2n4kBpCi1ldnkImAnsPGf7AwBSyj7AROA1IYQ25vct93e3/LvhSnakGrtcOSml/kZtT+UGtQVcSsvj4eFhs4V1Kg4UFQMKqDhQmkaLmFBLKY9IKZMvcFc0sM3ymDygCBgshAgBvKSUe6U5h+MzYPqV7Esry6VcmraYTvtDY0+pMh07drT6PlQecMN4eHjYrHmQLeLAFtRVt4azlxhQGkfFgdIUWvpf4njgZiGEoxCiCzAICAc6ACfrPe6kZdsFCSEeFELsF0LsLy4uJj8/n5ycHLKysigsLCQtLY3KykoSExMxmUzExsYC/yujEhsbi8lkIjExkcrKStLS0igsLCQrK4ucnBzy8/PJyMigrKyMpKQkjEYj8fHxZ72G9n9CQgLV1dWkpKRQUlJCZmYmeXl55OXlkZmZSUlJCSkpKVRXV+vtUM99jfj4eIxGI0lJSZSVlZGRkdHkxzRo0CAmTZrE7t27OXLkCK6urjz00EOt+pi0n9Phw4et/nNycHBg3rx5Njsme4m9Tp06MWTIEJsc0+7du20ee9Y4JgcHBxYuXNhsv0+tOfZiYmLs7pjs8edk7WPat2+f3R2TPf6crHlMTXFiQtjqzKMQYgsQfIG7npFSfmt5zM/A41LK/ZbbjsASYDxwHHAClmGeQC+WUl5nedw1wF+klFMuN47BgwfL/fv3N/6A7FyvXr1ITEykT58+LFu2jBEjRuDm5mZXudTW5ObmRlVVlV2d2beF7du3Exsby6JFi5p7KK2Gh4eH/nup4k1RFKVhhBAHpJQNbjdrszPUUsrrpJS9L/Dv20s8xyilfExK2V9KOQ3wAY4CWUBYvYeGWbZdlpoQXpnIyEjA3CjizTffBMwLxuxBUxRwvxw3Nzer78MehYSE0KNHD5vsyxZxYAuqhm7D2UsMKI2j4kBpCi065UMI4S6E8LB8PREwSikTpZQ5QIkQYrilusfdwEUn5vWpfMMr07dvXwAmTJig17i1lzyzQYMGWX0ftsoDtjeenp42W3FviziwBdUdseHsJQaUxlFxoNTvu9FQLWJ2KYSYIYQ4CYwANgghNlnuag/ECiGOAH8B5tR72h+Aj4FUIA3YeCX7aop+7W3BkCFDAHPpvFmzZgHmNBB7YIuzEUFBQVbfhz0KCwtj1KhRNtmXvZyVUh/eGs5eYkBpHBUHSk5OTqNfo0WUvJBS/hv49wW2ZwBRF3nOfqD31e5LXR69MtqEuqSkRE9fGDlyZHMOqcnY4mxEWFgY+/bts/p+lIazl7NS/v7+zT2EVsteYkBpHBUHSlNoEWeobUl1FLsy7du3ByApKYmSkhIARo8e3ZxDajLaKmNr6tq1K05OTlbfj9JwtogDWwgMDGzuIbRa9hIDSuOoOFCaQpubULu4uDT3EFoFrY7yiRMnyMjIAOwnh9oWqSudOnVSE+oWzl5SmEJCQpp7CK2WvcSA0jgqDpSm0OYm1PbU8c8Wqqur9cm1vUwQU1NTrb6Pbt264e7ubvX9KA1niziwhbCwsMs/SLkge4kBpXFUHChNoc1NqFWnxKtTXl6u51PbC1tMQDp27Ei7du2svh+l4exlImovV46ag73EgNI4Kg6UptDmJtTKlXNwcKC8vJxDhw4191CaVH5+vtX30alTJ7vJObdXtogDW+jQ4aJNYpXLsJcYUBpHxYHSFCfA2tyE2sPDo7mH0Go4OztTV1fHp59+2txDaVK2qNvr4eHBCy+8YPX9KA1nL/Wb1aLEhrOXGFAaR8WB0hSVXtrchFq15r1yWn3b9PT0Zh5J06qtrbX6PoQQdOrUyer7URrOFnFgC6psXsPZSwwojaPiQGkKbW5CrVw5rTmJi4uLvjDRHphMpuYegtIC2EsceHt7A+YULeXq2EsMKI2j4kBpCm1uQl1RUdHcQ2g1unfvjhACo9FoV2/WqvqGAvYTB87OzoBacN0Q9hIDSuOoOFB+/vnnRr9Gm5tQ19XVNfcQWo2JEycihMDNzc2u6ncXFBQ09xCUFsDe4kDraKpcOXuLAaVhVBwoTaHNTajVWZwrN2LECMCcX6ZdVrYHoaGhzT0EpQWwtzhQudRXz95iQGkYFQdKU2hzE2q1+ODKdezYEU9PT3x8fOzqD469LbJUGsae4sDf35/+/fs39zBaHXuKAaXhVBwoTaHNna7V8g2Vy/P29sbBwYHS0lL9bLU96NGjR3MPQWkB7CkOnnrqKVX3vAHsKQaUhlNxoDSFNneGuqqqqrmH0GoIIXBxcaGmpoZ58+Y193CaTFxcXHMPQWkB7CkOFi1axLBhw5p7GK2OPcWA0nAqDpSm0OYm1K6urs09hFbFyckJR0dHxo0b19xDaTIDBw5s7iEoLYCKA0XFgAIqDpSm0SIm1EKIJUKIJCHEb0KIfwshfOrd95QQIlUIkSyEuL7e9hss21KFEE9e6b5UY5erExwcTEhICAZDiwiVJnHgwIHmHoLSAqg4UFQMKKDiQIHIyMhGv4ZoCRNMIcQkYJuU0iiEeBlASvkXIUQ0sBoYCoQCWwDtqI8CE4GTQAxwh5Qy8XL7Gjx4sNy/f78VjsI+7dy5k02bNvHiiy8291AURVEURVGsQghxQEo5uKHPbxGnHaWUm6WURsvNvUCY5etpwJdSymopZTqQinlyPRRIlVIek1LWAF9aHntZqrHL1Rk5cqTdLdiIjY1t7iEoLYCKA0XFgAIqDpSm0SIm1Oe4D9ho+boDcKLefSct2y62/YKEEA8KIfYLIfYXFhaSn59PTk4OWVlZFBYWkpaWRmVlJYmJiZhMJv2XS7sMFBsbi8lkIjExkcrKStLS0igsLCQrK4ucnBzy8/PJyMigrKyMpKQkjEYj8fHxZ72G9n9CQgLV1dWkpKRQUlJCZmYmeXl55OXlkZmZSUlJCSkpKVRXV5OQkHDB14iPj8doNJKUlERZWRkZGRlWO6aqqiqGDBliV8cUERFhdz8ne4w9ax+T1m7Yno7JHn9O1jwmLy8vuzsme/w5WfuYPD097e6Y7PHnZM1j+vHHH2ksm6V8CCG2AMEXuOsZKeW3lsc8AwwGZkoppRDiHWCvlPJzy/3L+d9k+wYp5e8t2+cAw6SUCy43ji5dukhVc7JtS0xMJDo6urmHoTQzFQeKigEFVBwo5tbj48ePb1TKh83qUEspr7vU/UKIe4ApwLXyf7P8LCC83sPCLNu4xPZLcnJyupKHKXasS5cuzT0EpQVQcaCoGFBAxYHSNFpEyocQ4gbgz8DNUsr6Sc7fAbcLIVyEEF2A7sA+zIsQuwshugghnIHbLY+9LKPRePkHKXYtOzu7uYegtAAqDhQVAwqoOFCaRkvplPgO4AL8JIQAc5rHQ1LKw0KItUAiYAT+KKWsAxBCLAA2AQ7AJ1LKw1eyIwcHB2uMX2lF/Pz8mnsISgug4kBRMaCAigOlabSICbWUMuIS970InFezTUr5A/DD1e5LW4iktF0VFRX4+vo29zCUZqbiQFExoICKA6VptIiUD0WxJXtqUqM0nIoDRcWAAioOlKbR5qLIklKitGFqYaoCKg4UFQOKmYoDpSm0iE6JtiSEKAWSm3scSrMKAPKbexBKs1NxoKgYUEDFgWIWJaVs19Ant4gcahtLbkydQaX1E0LsVzGgqDhQVAwooOJAMRNC7G/M89tcyoeiKIqiKIqiNCU1oVYURVEURVGURmiLE+plzT0ApdmpGFBAxYGiYkAxU3GgQCPjoM0tSlQURVEURVGUptQWz1AriqIoiqIoSpNpMxNqIcQNQohkIUSqEOLJ5h6PYj1CiE+EEHlCiEP1tvkJIX4SQqRY/ve1bBdCiLcscfGbEGJg841caSpCiHAhxHYhRKIQ4rAQ4lHLdhUHbYgQwlUIsU8IEW+Jg+cs27sIIX61/LzXCCGcLdtdLLdTLfd3btYDUJqMEMJBCHFQCPG95baKgTZGCJEhhEgQQsRpFT2a8j2hTUyohRAOwLvAjUA0cIcQIrp5R6VY0UrghnO2PQlslVJ2B7ZaboM5Jrpb/j0IvG+jMSrWZQQWSSmjgeHAHy2/8yoO2pZqYIKUsh/QH7hBCDEceBl4XUoZARQC91sefz9QaNn+uuVxin14FDhS77aKgbZpvJSyf70yiU32ntAmJtTAUCBVSnlMSlkDfAlMa+YxKVYipdwJFJyzeRrwqeXrT4Hp9bZ/Js32Aj5CiBCbDFSxGilljpQy1vJ1KeY30g6oOGhTLD/PMstNJ8s/CUwA1lm2nxsHWnysA64Vqr1uqyeECANuAj623BaoGFDMmuw9oa1MqDsAJ+rdPmnZprQdQVLKHMvXp4Agy9cqNuyc5ZLtAOBXVBy0OZZL/XFAHvATkAYUSSmNlofU/1nrcWC5vxjwt+mAFWt4A/gzYLLc9kfFQFskgc1CiANCiAct25rsPaEtdkpU2jgppRRCqPI2bYAQwhP4GlgopSypf6JJxUHbIKWsA/oLIXyAfwM9mndEii0JIaYAeVLKA0KIcc08HKV5jZZSZgkh2gM/CSGS6t/Z2PeEtnKGOgsIr3c7zLJNaTtytcs1lv/zLNtVbNgpIYQT5sn0KinlestmFQdtlJSyCNgOjMB8+VY7oVT/Z63HgeV+b+CMbUeqNLFRwM1CiAzM6Z4TgDdRMdDmSCmzLP/nYf5wPZQmfE9oKxPqGKC7ZVWvM3A78F0zj0mxre+AuZav5wLf1tt+t2VF73CguN7lH6WVsuQ8LgeOSCmX1rtLxUEbIoQItJyZRgjhBkzEnE+/HZhledi5caDFxyxgm1TNGlo1KeVTUsowKWVnzO/926SUd6JioE0RQngIIdppXwOTgEM04XtCm2nsIoSYjDmPygH4REr5YvOOSLEWIcRqYBwQAOQCfwe+AdYCHYHjwK1SygLLxOsdzFVBKoB7pZT7m2HYShMSQowGdgEJ/C9v8mnMedQqDtoIIURfzAuNHDCfQForpXxeCNEV89lKP+AgcJeUsloI4Qr8C3POfQFwu5TyWPOMXmlqlpSPx6WUU1QMtC2Wn/e/LTcdgS+klC8KIfxpoveENjOhVhRFURRFURRraCspH4qiKIqiKIpiFWpCrSiKoiiKoiiNoCbUiqIoiqIoitIIakKtKIqiKIqiKI2gJtSKoiiKoiiK0ghqQq0oimIDQog6IURcvX+dm3tMTUUIMUAIsdzy9T1CiHfOuf9nIcTgSzz/SyFEd2uPU1EUxVpU63FFURTbqJRS9r/QHZaap0JKabrQ/a3A08A/GvH894E/Aw80zXAURVFsS52hVhRFaQZCiM5CiGQhxGeYO3aFCyGeEELECCF+E0I8V++xzwghjgohdgshVgshHrds18/8CiECLO2VEUI4CCGW1HuteZbt4yzPWSeESBJCrLJM5hFCDBFC7BFCxAsh9gkh2gkhdgoh+tcbx24hRL9zjqMd0FdKGX8Fx3xzvTP0yUKIdMtdu4Dr6rWCVhRFaVXUHy9FURTbcBNCxFm+TgceA7oDc6WUe4UQkyy3hwIC+E4IMQYox9wyuT/mv9mxwIHL7Ot+zK1yhwghXIBfhBCbLfcNAHoB2cAvwCghxD5gDXCblDJGCOEFVGJu334PsFAIEQm4XmDiPBjzB4L6brN0q9REAEgpv8Pc0hchxFpgh2W7SQiRCvS7gmNTFEVpcdSEWlEUxTbOSvmw5FAfl1LutWyaZPl30HLbE/MEux3wbyllheV5313BviYBfYUQsyy3vS2vVQPsk1KetLxWHNAZKAZypJQxAFLKEsv9XwH/J4R4ArgPWHmBfYUAp8/ZtkZKuaDesf5c/04hxJ8xfz/erbc5DwhFTagVRWmF1IRaURSl+ZTX+1oAL0kpP6z/ACHEwks838j/Uvdcz3mth6WUm855rXFAdb1NdVzifUBKWSGE+AmYBtwKDLrAwyrP2fclCSGuA2YDY865y9XyWoqiKK2OyqFWFEVpGTYB9wkhPAGEEB2EEO2BncB0IYSbJV95ar3nZPC/Se6sc15rvhDCyfJakUIIj0vsOxkIEUIMsTy+Xb185o+Bt4AYKWXhBZ57BEtKx+UIIToB7wKzpZTnTp4jOT91RFEUpVVQZ6gVRVFaACnlZiFET+C/lnWCZcBdUspYIcQaIB5zWkRMvae9CqwVQjwIbKi3/WPMqRyxlkWHp4Hpl9h3jRDiNuBtIYQb5jPF1wFlUsoDQogSYMVFnpskhPAWQrSTUpZe5jDvAfyBbyzHmC2lnCyECMKcAnLqMs9XFEVpkYSUsrnHoCiKolwhIcSzmCe6r9pof6HAz0CPi5X1E0I8BpRKKT9u4D4eA0qklMsbPFBFUZRmpFI+FEVRlAsSQtwN/Ao8c5ka2e9zdm721SoCPm3E8xVFUZqVOkOtKIqiKIqiKI2gzlAriqIoiqIoSiOoCbWiKIqiKIqiNIKaUCuKoiiKoihKI6gJtaIoiqIoiqI0gppQK4qiKIqiKEojqAm1oiiKoiiKojTC/wNFcJk1pWAU2gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "\n", "DATAROOT = args.data_root\n", @@ -48,7 +82,8 @@ "label_path_pattern = os.path.join(DATAROOT, '{sub}/labels/{name}')\n", "trigger_meg_path_pattern = os.path.join(DATAROOT, '{sub}/trigger/{name}')\n", "processed_rest_meg_path_pattern = os.path.join(DATAROOT, '{sub}/mat/{name}')\n", - "\n", + "sub = 'sbj01'\n", + "split= 'train' # val\n", "meg_name, label_name, trigger_name, rest_name = args.subjects[sub][split]['mat'][0], args.subjects[sub][split]['labels'][0], args.subjects[sub][split]['trigger'][0], args.subjects[sub][split]['rest'][0]\n", "\n", "\n", @@ -57,16 +92,16 @@ "trigger_path = trigger_meg_path_pattern.format(sub=sub, name=trigger_name)\n", "processed_rest_meg_path = processed_rest_meg_path_pattern.format(sub=sub, name=rest_name)\n", "\n", - "MEG_Data, image_features, labels, triggers = get_meg_data(meg_filepath, label_filepath, trigger_filepath,\n", - " rest_mean=None, rest_std=None, split='train')\n", + "MEG_Data, image_features, labels, triggers = get_meg_data(processed_meg_path, label_path, trigger_path,\n", + " rest_mean=None, rest_std=None, split=split)\n", "\n", "roi_ids = roi(args)\n", - "data = MEG_Data[roi_ods,:]\n", + "data = MEG_Data[roi_ids,:]\n", "# create raw\n", "info = mne.create_info(\n", " ch_names=len(roi_ids),\n", " sfreq=1000,\n", - " ch_types='meg',\n", + " ch_types='eeg',\n", ")\n", "raw = mne.io.RawArray(data, info)\n", "\n", @@ -76,22 +111,174 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "meg_raw = np.stack([df[key] for key in df.keys() if \"MEG\" in key]) # ( 224, ~396000 )\n", - "# NOTE: (kind of) confirmed that last 16 channels are REF\n", - "meg_raw = meg_raw[:num_channels] # ( 208, ~396000 )\n", + "window = 500\n", + "MEG_epochs = []\n", + "for tr in triggers:\n", + " t = int(tr * 1000)\n", + " tmp_data = MEG_Data[:, t:t+window] - np.mean(MEG_Data[:, t-500:t], axis=1, keepdims=True)\n", + " MEG_epochs.append(tmp_data)\n", + " \n", + "MEG_epochs = np.stack(MEG_epochs, axis=0)\n", + "MEG_epochs = np.mean(MEG_epochs, axis=0)\n", + "for i in range(10):\n", + " plt.plot(np.arange(window), MEG_epochs[i,:])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "plt.plot(np.arange(window), MEG_epochs[:160,:].mean(axis=0))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load /work/project/MEG_GOD/GOD_dataset/sbj01/mat/data_val001.mat\n", + "ROI: ['occipital/left', 'occipital/right']\n", + "channel: [129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155]\n", + "num channels: 22\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_63164/1251308816.py:23: RuntimeWarning: Channel locations not available. Disabling spatial colors.\n", + " mne.viz.plot_raw_psd(raw)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "split= 'val' # val\n", + "meg_name, label_name, trigger_name, rest_name = args.subjects[sub][split]['mat'][0], args.subjects[sub][split]['labels'][0], args.subjects[sub][split]['trigger'][0], args.subjects[sub][split]['rest'][0]\n", + "\n", "\n", - "meg_filtered = mne.filter.filter_data(\n", - " meg_raw, sfreq=brain_orig_rate, l_freq=brain_filter_low, h_freq=brain_filter_high,\n", + "processed_meg_path = processed_meg_path_pattern.format(sub=sub, name=meg_name)\n", + "label_path = label_path_pattern.format(sub=sub, name=label_name)\n", + "trigger_path = trigger_meg_path_pattern.format(sub=sub, name=trigger_name)\n", + "processed_rest_meg_path = processed_rest_meg_path_pattern.format(sub=sub, name=rest_name)\n", + "\n", + "MEG_Data, image_features, labels, triggers = get_meg_data(processed_meg_path, label_path, trigger_path,\n", + " rest_mean=None, rest_std=None, split=split)\n", + "\n", + "roi_ids = roi(args)\n", + "data = MEG_Data[roi_ids,:]\n", + "# create raw\n", + "info = mne.create_info(\n", + " ch_names=len(roi_ids),\n", + " sfreq=1000,\n", + " ch_types='eeg',\n", ")\n", + "raw = mne.io.RawArray(data, info)\n", + "\n", + "mne.viz.plot_raw_psd(raw)\n", + "plt.show()\n", + "window = 500\n", + "MEG_epochs = []\n", + "for tr in triggers:\n", + " t = int(tr * 1000)\n", + " tmp_data = MEG_Data[:, t:t+window] - np.mean(MEG_Data[:, t-500:t], axis=1, keepdims=True)\n", + " MEG_epochs.append(tmp_data)\n", + " \n", + "MEG_epochs = np.stack(MEG_epochs, axis=0)\n", + "MEG_epochs = np.mean(MEG_epochs, axis=0)\n", + "for i in range(10):\n", + " plt.plot(np.arange(window), MEG_epochs[i,:])\n", + "plt.show()\n", "\n", + "plt.plot(np.arange(window), MEG_epochs[:160,:].mean(axis=0))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(203, 76000)\n", + "(203, 9120)\n" + ] + } + ], + "source": [ "# To 120 Hz\n", + "print(MEG_Data.shape)\n", "meg_resampled = mne.filter.resample(\n", - " meg_filtered, down=brain_orig_rate / brain_resample_rate,\n", - ") " + " MEG_Data, down=1000 / 120,\n", + ")\n", + "print(meg_resampled.shape)\n" ] }, { @@ -157,10 +344,23 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "orig_nbformat": 4 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } }, "nbformat": 4, "nbformat_minor": 2 From cb7395c0c189d5ccaa7d031fa3bebe97d8d23508 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 17 Apr 2023 16:26:40 +0900 Subject: [PATCH 15/71] mod: total test sample is 50 --- evaluate.py | 67 ++++++++++++++++++++++++++++++++--- meg_decoding/dataclass/god.py | 9 +++-- 2 files changed, 69 insertions(+), 7 deletions(-) diff --git a/evaluate.py b/evaluate.py index e1668fb..8328903 100644 --- a/evaluate.py +++ b/evaluate.py @@ -29,6 +29,60 @@ from meg_decoding.utils.vis_grad import get_grad +def zero_shot_classification(Z: torch.Tensor, Y: torch.Tensor, test=False, top_k=None)-> torch.Tensor: + batch_size = Z.size(0) + diags = torch.arange(batch_size).to(device) + x = Z # .view(batch_size, -1) # 300 x 512 + y = Y # .view(batch_size, -1) # 50 x 512 + + # x_ = rearrange(x, 'b f -> 1 b f') + # y_ = rearrange(y, 'b f -> b 1 f') + # similarity = torch.nn.functional.cosine_similarity(x_, y_, dim=-1) # ( B, B ) + + # NOTE: avoid CUDA out of memory like this + similarity = torch.empty(batch_size, batch_size).to(device) + + if test: + pbar = tqdm(total=batch_size, desc="[Similarities]") + + for i in range(batch_size): + for j in range(batch_size): + similarity[i, j] = (x[i] @ y[j]) / max((x[i].norm() * y[j].norm()), 1e-8) + + if test: + pbar.update(1) + + similarity = similarity.T + + # NOTE: max similarity of speech and M/EEG representations is expected for corresponding windows + top1accuracy = (similarity.argmax(axis=1) == diags).to(torch.float).mean().item() + try: + top10accuracy = np.mean( + [ + label in row + for row, label in zip(torch.topk(similarity, 10, dim=1, largest=True)[1], diags) + ] + ) + except: + print(similarity.size()) + raise + if top_k is None: + + return top1accuracy, top10accuracy + else: + try: + topkaccuracy = np.mean( + [ + label in row + for row, label in zip(torch.topk(similarity, top_k, dim=1, largest=True)[1], diags) + ] + ) + except: + print(similarity.size()) + raise + return top1accuracy, top10accuracy, topkaccuracy + + def run(args: DictConfig) -> None: from meg_decoding.utils.reproducibility import seed_worker if args.reproducible: @@ -45,8 +99,8 @@ def run(args: DictConfig) -> None: seed_worker = None if args.dataset == "GOD": - train_dataset = GODDatasetBase(args, 'train') - val_dataset = GODDatasetBase(args, 'val') + train_dataset = GODDatasetBase(args, 'train', return_label=True) + val_dataset = GODDatasetBase(args, 'val', return_label=True) with open_dict(args): args.num_subjects = train_dataset.num_subjects print('num subject is {}'.format(args.num_subjects)) @@ -90,14 +144,17 @@ def run(args: DictConfig) -> None: classifier.eval() brain_encoder.eval() half_k = int(test_size/2) + + sorted_image_features = np.load('./data/GOD/image_features.npy') + Y = torch.Tensor(sorted_image_features) for batch in test_loader: with torch.no_grad(): if len(batch) == 3: - X, Y, subject_idxs = batch + X, _, subject_idxs = batch elif len(batch) == 4: - X, Y, subject_idxs, chunkIDs = batch + X, _, subject_idxs, label = batch else: raise ValueError("Unexpected number of items from dataloader.") @@ -105,7 +162,7 @@ def run(args: DictConfig) -> None: Z = brain_encoder(X, subject_idxs) # 0.96 GB - testTop1acc, testTop10acc, testTopKacc = classifier(Z, Y, test=True, topk=half_k) # ( 250, 1024, 360 ) + testTop1acc, testTop10acc, testTopKacc = zero_shot_classification(Z, Y, test=True, topk=half_k) # ( 250, 1024, 360 ) testTop1accs.append(testTop1acc) testTop10accs.append(testTop10acc) diff --git a/meg_decoding/dataclass/god.py b/meg_decoding/dataclass/god.py index 380841d..6de8867 100644 --- a/meg_decoding/dataclass/god.py +++ b/meg_decoding/dataclass/god.py @@ -18,7 +18,7 @@ class GODDatasetBase(Dataset): - def __init__(self, args, split, preprocess_pipleine:list=[]): + def __init__(self, args, split, preprocess_pipleine:list=[], return_label:bool=False): self.args = args self.sub_id_map = {s:i for i, s in enumerate(list(self.args.subjects.keys()))} self.preprocess_pipeline = preprocess_pipleine @@ -32,13 +32,18 @@ def __init__(self, args, split, preprocess_pipleine:list=[]): self.labels = label_epochs # epochs (x 1) self.num_subjects = len(np.unique(self.subs)) + self.return_label = return_label def __len__(self): return len(self.Y) def __getitem__(self, i): # NOTE: i is id of a speech segment x, y, s = self.X[i], self.Y[i], self.subs[i] - return x, y, s + if self.return_label: + l = self.labels[i] + return x, y, s, l + else: + return x, y, s def prepare_data(self, args, split:str): DATAROOT = args.data_root From 97f3ad28c7c7fc3224ebc8cc327070c378d34e3c Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 17 Apr 2023 16:36:30 +0900 Subject: [PATCH 16/71] mod: use label instead of features --- evaluate.py | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/evaluate.py b/evaluate.py index 8328903..27b8d4d 100644 --- a/evaluate.py +++ b/evaluate.py @@ -29,15 +29,12 @@ from meg_decoding.utils.vis_grad import get_grad -def zero_shot_classification(Z: torch.Tensor, Y: torch.Tensor, test=False, top_k=None)-> torch.Tensor: +def zero_shot_classification(Z: torch.Tensor, Y: torch.Tensor, label: torch.Tensor, test=False, top_k=None)-> torch.Tensor: batch_size = Z.size(0) - diags = torch.arange(batch_size).to(device) + label = label -1 # labelは1始まり x = Z # .view(batch_size, -1) # 300 x 512 y = Y # .view(batch_size, -1) # 50 x 512 - # x_ = rearrange(x, 'b f -> 1 b f') - # y_ = rearrange(y, 'b f -> b 1 f') - # similarity = torch.nn.functional.cosine_similarity(x_, y_, dim=-1) # ( B, B ) # NOTE: avoid CUDA out of memory like this similarity = torch.empty(batch_size, batch_size).to(device) @@ -55,12 +52,12 @@ def zero_shot_classification(Z: torch.Tensor, Y: torch.Tensor, test=False, top_k similarity = similarity.T # NOTE: max similarity of speech and M/EEG representations is expected for corresponding windows - top1accuracy = (similarity.argmax(axis=1) == diags).to(torch.float).mean().item() + top1accuracy = (similarity.argmax(axis=1) == label).to(torch.float).cpu().numpy() try: - top10accuracy = np.mean( + top10accuracy = np.array( [ label in row - for row, label in zip(torch.topk(similarity, 10, dim=1, largest=True)[1], diags) + for row, label in zip(torch.topk(similarity, 10, dim=1, largest=True)[1], label) ] ) except: @@ -71,10 +68,10 @@ def zero_shot_classification(Z: torch.Tensor, Y: torch.Tensor, test=False, top_k return top1accuracy, top10accuracy else: try: - topkaccuracy = np.mean( + topkaccuracy = np.array( [ label in row - for row, label in zip(torch.topk(similarity, top_k, dim=1, largest=True)[1], diags) + for row, label in zip(torch.topk(similarity, top_k, dim=1, largest=True)[1], label) ] ) except: @@ -112,13 +109,13 @@ def run(args: DictConfig) -> None: train_dataset, val_dataset, args, - test_bsz=test_size, + test_bsz=args.batch_size, collate_fn=GODCollator(args),) else: test_loader = DataLoader( val_dataset, - batch_size=test_size, + batch_size=args.batch_size, drop_last=True, shuffle=False, num_workers=args.num_workers, @@ -147,7 +144,7 @@ def run(args: DictConfig) -> None: sorted_image_features = np.load('./data/GOD/image_features.npy') Y = torch.Tensor(sorted_image_features) - for batch in test_loader: + for batch in tqdm(test_loader): with torch.no_grad(): @@ -168,6 +165,9 @@ def run(args: DictConfig) -> None: testTop10accs.append(testTop10acc) testTopKaccs.append(testTopKacc) + testTop1accs = np.concatenate(testTop1accs) + testTop10acc = np.concatenate(testTop10acc) + testTopKaccs = np.concatenate(testTopKaccs) print( f"testTop1acc: {np.mean(testTop1accs):.3f} | " f"testTop10acc: {np.mean(testTop10accs):.3f} | " From fcf359d5c1a367e051e53e0317d6cdd906c56f97 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 17 Apr 2023 16:45:36 +0900 Subject: [PATCH 17/71] merge --- data/GOD/image_features.npy | Bin 0 -> 102528 bytes evaluate.py | 2 +- notebooks/vis_meg.ipynb | 88 ++++++++++++++++++++++++++++++++++++ 3 files changed, 89 insertions(+), 1 deletion(-) create mode 100644 data/GOD/image_features.npy diff --git a/data/GOD/image_features.npy b/data/GOD/image_features.npy new file mode 100644 index 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z4mbPVVUes*s4wp1Sz8}>^W0fTTCanNr`0ghRf%2x0{agQ_yA$AoIcKg$6~>*?VXga n)h~dx 1 b f') # y_ = rearrange(y, 'b f -> b 1 f') # similarity = torch.nn.functional.cosine_similarity(x_, y_, dim=-1) # ( B, B ) - + import pdb; pdb.set_trace() # NOTE: avoid CUDA out of memory like this similarity = torch.empty(batch_size, batch_size).to(device) diff --git a/notebooks/vis_meg.ipynb b/notebooks/vis_meg.ipynb index 37b65a9..8f98e8d 100644 --- a/notebooks/vis_meg.ipynb +++ b/notebooks/vis_meg.ipynb @@ -258,6 +258,94 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load /work/project/MEG_GOD/GOD_dataset/sbj01/mat/data_val001.mat\n", + "load /work/project/MEG_GOD/GOD_dataset/sbj01/mat/data_val002.mat\n", + "load /work/project/MEG_GOD/GOD_dataset/sbj01/mat/data_val003.mat\n", + "load /work/project/MEG_GOD/GOD_dataset/sbj01/mat/data_val004.mat\n", + "load /work/project/MEG_GOD/GOD_dataset/sbj01/mat/data_val005.mat\n", + "load /work/project/MEG_GOD/GOD_dataset/sbj01/mat/data_val006.mat\n" + ] + } + ], + "source": [ + "split= 'val' # val\n", + "labels_list = []\n", + "for i in range(6):\n", + " meg_name, label_name, trigger_name, rest_name = args.subjects[sub][split]['mat'][i], args.subjects[sub][split]['labels'][i], args.subjects[sub][split]['trigger'][i], args.subjects[sub][split]['rest'][i]\n", + " processed_meg_path = processed_meg_path_pattern.format(sub=sub, name=meg_name)\n", + " label_path = label_path_pattern.format(sub=sub, name=label_name)\n", + " trigger_path = trigger_meg_path_pattern.format(sub=sub, name=trigger_name)\n", + " processed_rest_meg_path = processed_rest_meg_path_pattern.format(sub=sub, name=rest_name)\n", + "\n", + " MEG_Data, image_features, labels, triggers = get_meg_data(processed_meg_path, label_path, trigger_path,\n", + " rest_mean=None, rest_std=None, split=split)\n", + " labels_list.append(labels)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "300 [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n", + " 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48\n", + " 49 50]\n" + ] + } + ], + "source": [ + "labels_arr = np.concatenate(labels_list)\n", + "print(len(labels_arr), np.unique(labels_arr))\n", + "print(image_features.shape)\n", + "\n", + "sorted_image_features = np.zeros_like(image_features)\n", + "for i, l in enumerate(labels):\n", + " sorted_image_features[l-1] = image_features[i]" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(50, 512)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_image_features.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "np.save('../data/GOD/image_features.npy', sorted_image_features)" + ] + }, { "cell_type": "code", "execution_count": 36, From 9bd19a7ee7d517664861091f74c90ed32ddf94b3 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 17 Apr 2023 17:09:10 +0900 Subject: [PATCH 18/71] FIX: zeroshot-classification --- evaluate.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/evaluate.py b/evaluate.py index 89eccd4..21ed931 100644 --- a/evaluate.py +++ b/evaluate.py @@ -31,27 +31,30 @@ def zero_shot_classification(Z: torch.Tensor, Y: torch.Tensor, label: torch.Tensor, test=False, top_k=None)-> torch.Tensor: batch_size = Z.size(0) + sample_size = Y.size(0) label = label -1 # labelは1始まり x = Z # .view(batch_size, -1) # 300 x 512 y = Y # .view(batch_size, -1) # 50 x 512 - + # import pdb; pdb.set_trace() # NOTE: avoid CUDA out of memory like this - similarity = torch.empty(batch_size, batch_size).to(device) + similarity = torch.empty(batch_size, sample_size).to(device) if test: pbar = tqdm(total=batch_size, desc="[Similarities]") for i in range(batch_size): - for j in range(batch_size): + for j in range(sample_size): similarity[i, j] = (x[i] @ y[j]) / max((x[i].norm() * y[j].norm()), 1e-8) if test: pbar.update(1) - similarity = similarity.T + # similarity = similarity.T # brain x image -> image x brain # NOTE: max similarity of speech and M/EEG representations is expected for corresponding windows + # import pdb; pdb.set_trace() top1accuracy = (similarity.argmax(axis=1) == label).to(torch.float).cpu().numpy() + try: top10accuracy = np.array( [ @@ -125,7 +128,7 @@ def run(args: DictConfig) -> None: else: raise ValueError("Unknown dataset") - assert len(test_loader) == 1 + # assert len(test_loader) == 1 brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) weight_dir = os.path.join(args.save_root, 'weights') @@ -139,10 +142,10 @@ def run(args: DictConfig) -> None: classifier = Classifier(args) classifier.eval() brain_encoder.eval() - half_k = int(test_size/2) sorted_image_features = np.load('./data/GOD/image_features.npy') - Y = torch.Tensor(sorted_image_features) + Y = torch.Tensor(sorted_image_features) + half_k = int(len(sorted_image_features)/2) for batch in tqdm(test_loader): with torch.no_grad(): @@ -158,7 +161,7 @@ def run(args: DictConfig) -> None: Z = brain_encoder(X, subject_idxs) # 0.96 GB - testTop1acc, testTop10acc, testTopKacc = zero_shot_classification(Z, Y, test=True, topk=half_k) # ( 250, 1024, 360 ) + testTop1acc, testTop10acc, testTopKacc = zero_shot_classification(Z, Y, label,test=True, top_k=half_k) # ( 250, 1024, 360 ) testTop1accs.append(testTop1acc) testTop10accs.append(testTop10acc) From 3d441e96cf3df98f94d648ff838c601460fece38 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Tue, 18 Apr 2023 12:05:13 +0900 Subject: [PATCH 19/71] maege --- meg_decoding/dataclass/god.py | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/meg_decoding/dataclass/god.py b/meg_decoding/dataclass/god.py index 6de8867..465f4ed 100644 --- a/meg_decoding/dataclass/god.py +++ b/meg_decoding/dataclass/god.py @@ -127,7 +127,24 @@ def forward(self, batch: List[tuple]): Y = torch.stack([item[1] for item in batch]) subject_idx = torch.IntTensor([item[2] for item in batch]) - X = baseline_correction_single(X, self.baseline_len_samp) + X = self.baseline_correction_single(X, self.baseline_len_samp) X = scaleAndClamp(X, self.clamp_lim, self.clamp) return X, Y, subject_idx + + + @torch.no_grad() + def baseline_correction_single(self, X: torch.Tensor, baseline_len_samp): + """args: + X: ( chunks, ch, time ) + returns: + X ( chunks, ch, time ) baseline-corrected channel-wise + """ + X = X.permute(1, 0, 2).clone() # ( ch, chunks, time ) + + for chunk_id in range(X.shape[1]): + baseline = X[:, chunk_id, -baseline_len_samp:].mean(axis=1) + + X[:, chunk_id, :] -= baseline.view(-1, 1) + + return X.permute(1, 0, 2) From 05ece4daaffa0bada7dd113b1fb7e9ea232908d5 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Tue, 18 Apr 2023 12:12:42 +0900 Subject: [PATCH 20/71] ADD: another EXP --- evaluate.py | 24 +++++++++++++----------- examples/view_training_curve.py | 5 ++++- meg_decoding/dataclass/god.py | 17 +++++++++++------ out.png | Bin 0 -> 109507 bytes train_wowandb.py | 10 +++++----- 5 files changed, 33 insertions(+), 23 deletions(-) create mode 100644 out.png diff --git a/evaluate.py b/evaluate.py index 21ed931..e33dd74 100644 --- a/evaluate.py +++ b/evaluate.py @@ -112,7 +112,7 @@ def run(args: DictConfig) -> None: val_dataset, args, test_bsz=args.batch_size, - collate_fn=GODCollator(args),) + collate_fn=GODCollator(args, return_label=True),) else: test_loader = DataLoader( @@ -133,8 +133,13 @@ def run(args: DictConfig) -> None: weight_dir = os.path.join(args.save_root, 'weights') last_weight_file = os.path.join(weight_dir, "model_last.pt") - brain_encoder.load_state_dict(torch.load(last_weight_file)) - print('weight is loaded from ', last_weight_file) + best_weight_file = os.path.join(weight_dir, "model_best.pt") + if os.path.exists(best_weight_file): + brain_encoder.load_state_dict(torch.load(best_weight_file)) + print('weight is loaded from ', best_weight_file) + else: + brain_encoder.load_state_dict(torch.load(last_weight_file)) + print('weight is loaded from ', last_weight_file) testTop1accs = [] testTop10accs = [] @@ -144,20 +149,17 @@ def run(args: DictConfig) -> None: brain_encoder.eval() sorted_image_features = np.load('./data/GOD/image_features.npy') - Y = torch.Tensor(sorted_image_features) + Y = torch.Tensor(sorted_image_features).to(device) half_k = int(len(sorted_image_features)/2) for batch in tqdm(test_loader): with torch.no_grad(): - - if len(batch) == 3: - X, _, subject_idxs = batch - elif len(batch) == 4: + if len(batch) == 4: X, _, subject_idxs, label = batch else: raise ValueError("Unexpected number of items from dataloader.") - X, Y = X.to(device), Y.to(device) + X, label = X.to(device), label.to(device) Z = brain_encoder(X, subject_idxs) # 0.96 GB @@ -168,7 +170,7 @@ def run(args: DictConfig) -> None: testTopKaccs.append(testTopKacc) testTop1accs = np.concatenate(testTop1accs) - testTop10acc = np.concatenate(testTop10acc) + testTop10accs = np.concatenate(testTop10accs) testTopKaccs = np.concatenate(testTopKaccs) print( f"testTop1acc: {np.mean(testTop1accs):.3f} | " @@ -181,7 +183,7 @@ def run(args: DictConfig) -> None: if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230414_sbj01_seq2stat') + args = compose(config_name='20230417_sbj01_seq2stat') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) run(args) diff --git a/examples/view_training_curve.py b/examples/view_training_curve.py index 8ea7001..1ac5168 100644 --- a/examples/view_training_curve.py +++ b/examples/view_training_curve.py @@ -54,11 +54,14 @@ def parse_data(filepath): logs = pd.DataFrame(logs) savefile = os.path.join(savedir, f'{name}_learning_curve.png') visualize(logs, savefile) + print('==================={}==============='.format(name)) + print(logs) if __name__ == '__main__': + filepath = '/home/yainoue/meg2image/results/20230417_sbj01_seq2stat/runs/2023-04-17 18:22:35.316446' # filepath = '/home/yainoue/meg2image/results/20230413_sbj01/runs/2023-04-13 21:52:17.333087'#'home/yainoue/meg2image/results/test/2023-04-11 18:06:44.273986' - filepath = '/home/yainoue/meg2image/results/20230413_sbj01_seq2stat/runs/2023-04-16 19:21:02.983480' + # filepath = '/home/yainoue/meg2image/results/20230413_sbj01_seq2stat/runs/2023-04-16 19:21:02.983480' # filepath = '/home/yainoue/meg2image/results/20230412_mini/runs/2023-04-13 15:17:53.310665' parse_data(filepath) diff --git a/meg_decoding/dataclass/god.py b/meg_decoding/dataclass/god.py index 6de8867..2060dab 100644 --- a/meg_decoding/dataclass/god.py +++ b/meg_decoding/dataclass/god.py @@ -114,20 +114,25 @@ class GODCollator(torch.nn.Module): Runs baseline correction and robust scaling and clamping for batch """ - def __init__(self, args): + def __init__(self, args, return_label=False): super(GODCollator, self).__init__() self.brain_resample_rate = args.preprocs["brain_resample_rate"] self.baseline_len_samp = int(self.brain_resample_rate * args.preprocs["baseline_len_sec"]) self.clamp = args.preprocs["clamp"] self.clamp_lim = args.preprocs["clamp_lim"] + self.return_label = return_label def forward(self, batch: List[tuple]): - X = torch.stack([item[0] for item in batch]) # ( 64, 208, 360 ) - Y = torch.stack([item[1] for item in batch]) + X = torch.stack([torch.Tensor(item[0]) for item in batch]) # ( 64, 208, 360 ) + Y = torch.stack([torch.Tensor(item[1]) for item in batch]) + # print('debug', [item[2] for item in batch]) subject_idx = torch.IntTensor([item[2] for item in batch]) - + X = 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z)bi5OXA}&-N+g1f619$;tAeufPq5t{xK-|uXP>0t{ksXvAMHxVlG-xVXv98MdV2bV zzC~~M#x3ndX?0?!6>KnaYw3NW3R(blWL+Iz;9!;3MZ9vN1BK2nn&>1Sax!9a#h2UFJ1}0qmMCWd+H3@+fu0|C5TfRcDZJO)Fu8YeacSWF zUdMcx`;$zZa<8DH!BJcRgZ(4U|DX5`CJ{#z{{It&|64*CG_mu6vUglq_bmbN@6Ij# Kn`N4I&;JJ+v?g-^ literal 0 HcmV?d00001 diff --git a/train_wowandb.py b/train_wowandb.py index 4180f41..be8e868 100644 --- a/train_wowandb.py +++ b/train_wowandb.py @@ -143,7 +143,7 @@ def run(args: DictConfig) -> None: if args.use_sampler: - test_size = val_dataset.Y.shape[0] + test_size = 50# 重複サンプルが存在するのでval_dataset.Y.shape[0] train_loader, test_loader = get_samplers( train_dataset, val_dataset, @@ -154,7 +154,7 @@ def run(args: DictConfig) -> None: else: train_loader = DataLoader( train_dataset, - batch_size=args.batch_size, + batch_size= args.batch_size, drop_last=True, shuffle=True, num_workers=args.num_workers, @@ -164,7 +164,7 @@ def run(args: DictConfig) -> None: ) test_loader = DataLoader( val_dataset, - batch_size=args.batch_size, + batch_size=50 # args.batch_size, drop_last=True, shuffle=False, num_workers=args.num_workers, @@ -249,7 +249,7 @@ def run(args: DictConfig) -> None: X, Y = X.to(device), Y.to(device) Z = brain_encoder(X, subject_idxs) loss = loss_func(Y, Z) - + # import pdb; pdb.set_trace() with torch.no_grad(): trainTop1acc, trainTop10acc = classifier(Z, Y) @@ -343,8 +343,8 @@ def run(args: DictConfig) -> None: if best_acc < np.mean(testTop10accs): best_weight_file = os.path.join(savedir, "model_best.pt") torch.save(brain_encoder.state_dict(), best_weight_file) - print('best model is updated !!, ', best_weight_file) best_acc = np.mean(testTop10accs) + print('best model is updated !!, {}'.format(best_acc), best_weight_file) From f0a1c73178eadb42fed1b4aa9df54fc8720c5d8c Mon Sep 17 00:00:00 2001 From: inoue26 Date: Tue, 18 Apr 2023 12:14:43 +0900 Subject: [PATCH 21/71] FIX: not closed ) --- train_wowandb.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/train_wowandb.py b/train_wowandb.py index be8e868..31dadd9 100644 --- a/train_wowandb.py +++ b/train_wowandb.py @@ -164,7 +164,7 @@ def run(args: DictConfig) -> None: ) test_loader = DataLoader( val_dataset, - batch_size=50 # args.batch_size, + batch_size=50, # args.batch_size, drop_last=True, shuffle=False, num_workers=args.num_workers, From 26100e4484b20ec61e1c77218c5ceed6d53ca85d Mon Sep 17 00:00:00 2001 From: inoue26 Date: Tue, 18 Apr 2023 18:03:25 +0900 Subject: [PATCH 22/71] ADD: classification like liss --- .gitignore | 3 +- meg_decoding/utils/loss.py | 81 +++++ notebooks/check_GOD_image_feature.ipynb | 436 ++++++++++++++++++++++++ notebooks/check_cross_entropy.ipynb | 93 +++++ train_my_classifier.py | 356 +++++++++++++++++++ 5 files changed, 968 insertions(+), 1 deletion(-) create mode 100644 notebooks/check_GOD_image_feature.ipynb create mode 100644 notebooks/check_cross_entropy.ipynb create mode 100644 train_my_classifier.py diff --git a/.gitignore b/.gitignore index 3339354..48e00e3 100644 --- a/.gitignore +++ b/.gitignore @@ -37,4 +37,5 @@ wandb/ train.sh *_debug* nohup.out -logs/* \ No newline at end of file +logs/* +data/GOD/*.npy \ No newline at end of file diff --git a/meg_decoding/utils/loss.py b/meg_decoding/utils/loss.py index e7a290c..34ca351 100644 --- a/meg_decoding/utils/loss.py +++ b/meg_decoding/utils/loss.py @@ -3,6 +3,7 @@ import torch.nn as nn import torch.nn.functional as F from einops import rearrange +import numpy as np from constants import device @@ -85,3 +86,83 @@ def forward(self, x, y, fast=True, return_logits=False): return logits, loss else: return loss + + +class MyCLIPLikeClassificationLoss(nn.Module): + def __init__(self, args): + super().__init__() + self.device = device + self.compute_similarity = nn.CosineSimilarity(dim=-1) + self._criterion = nn.CrossEntropyLoss(reduction=args.reduction) + # self.targets = torch.zeros(size=(batch_size, )).long() # that's for the slow method + # self.registered_targets = False + # self.batch_size = args.batch_size + self.temp = nn.Parameter(torch.tensor([float(args.init_temperature)])) + + self.prepare_image_features() + self.same_category_length = 8 + + def prepare_image_features(self): + sorted_image_features = np.load('./data/GOD/image_features_train.npy') + self.sorted_image_features = torch.Tensor(sorted_image_features, requires_grad=False).to(torch.float) + assert len(self.sorted_image_features) == 1200 + + def calculate_smooth_labeling(self, labels:np.ndarray, smmoth_value=0.1): + + # labels: batchsize:64 + targets = torch.zeros([64, 1200],requires_grad=False).to(torch.float) + for i, l in enumerate(labels): + l_mod = l % self.same_category_length + targets[i, l_mod*self.same_category_length:(l_mod+1)*self.same_category_length] = smmoth_value + targets[i, l] = 1 + return targets + + + def forward(self, x, labels, fast=True, return_logits=False): + batch_size = x.size(0) + y = self.prepare_image_features() + assert batch_size > 1, "Batch size must be greater than 1." + # if not self.registered_targets: + # self.register_buffer('targets', torch.arange(self.batch_size, requires_grad=False).to(self.device)) + # self.registered_targets = True + targets = self.calculate_smooth_labeling(labels, smmoth_value=0.1)# torch.arange(batch_size, requires_grad=False).long().to(self.device) + + if not fast: + # less efficient way + x_ = rearrange(x, "b f t -> 1 b (f t)") + y_ = rearrange(y, "b f t -> b 1 (f t)") + logits = self.compute_similarity(x_, y_) # s + + # unnecessary steps for the less efficient way (this might be needed for fancy contrastive losses) + # positives = torch.diag(similarity_matrix, 0).view(-1,1) + # negative_mask = torch.logical_not(torch.eye(batch_size).type(torch.bool)) + # negatives = similarity_matrix[negative_mask].view(batch_size, -1) + # logits = torch.cat([positives, negatives], dim=1) + + else: + # fast way + x = x.reshape(batch_size, -1) + y = y.reshape(batch_size, -1) + + # NOTE: scale the embeddings to unit norm + x = x / x.norm(dim=-1, keepdim=True) + y = y / y.norm(dim=-1, keepdim=True) + + # get dot products + logits = torch.matmul(x, y.T) + + # scale by temperature (learned) + logits *= torch.exp(self.temp) + + # # NOTE: the old way + # # I don't know why yet, but normalization seems to be necessary to get sensible similarities (0, 1) + # logits = logits / (x.norm(dim=-1) * y.norm(dim=-1)) + # loss = self._criterion(logits, targets) + + # NOTE: as in https://arxiv.org/abs/2103.00020 + loss = self._criterion(logits, targets) # + self._criterion(logits.t(), targets.T)) / 2 + + if return_logits: + return logits, loss + else: + return loss \ No newline at end of file diff --git a/notebooks/check_GOD_image_feature.ipynb b/notebooks/check_GOD_image_feature.ipynb new file mode 100644 index 0000000..cde2973 --- /dev/null +++ b/notebooks/check_GOD_image_feature.ipynb @@ -0,0 +1,436 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import h5py\n", + "import bdpy" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "hdf_path = '../../../ImageFeatures.h5'\n", + "data_feature = bdpy.BData(hdf_path)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "y = data_feature.select('cnn8') # Image features\n", + "y_label = data_feature.select('ImageID') # Image labels\n", + "y_catlabels = data_feature.select('CatID') \n", + "# datatype = data_feature.select('DataType')" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(16622, 1)" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_catlabels.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "cat_set = np.unique(y_catlabels)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(15522,)" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cat_set.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([7.8460000e+03, 2.1265000e+04, 1.4166900e+05, ..., 1.5093049e+07,\n", + " 1.5093137e+07, 1.5093298e+07])" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cat_set" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(16622, 1000)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1250,)\n" + ] + } + ], + "source": [ + "print(y_label[~np.isnan(y_label)].shape)\n", + "dataset_ids = np.where(~np.isnan(y_label))[0]\n", + "y_catlabels = y_catlabels[dataset_ids]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1250, 1)" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_catlabels.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1518878.]\n", + " [1518878.]\n", + " [1518878.]\n", + " [1518878.]\n", + " [1518878.]\n", + " [1518878.]\n", + " [1518878.]\n", + " [1518878.]\n", + " [1639765.]\n", + " [1639765.]\n", + " [1639765.]\n", + " [1639765.]\n", + " [1639765.]\n", + " [1639765.]\n", + " [1639765.]\n", + " [1639765.]\n", + " [1645776.]\n", + " [1645776.]\n", + " [1645776.]\n", + " [1645776.]]\n", + "[[12596148.]\n", + " [12596148.]\n", + " [13111881.]\n", + " [13111881.]\n", + " [13111881.]\n", + " [13111881.]\n", + " [13111881.]\n", + " [13111881.]\n", + " [13111881.]\n", + " [13111881.]\n", + " [ 1443537.]\n", + " [ 1621127.]\n", + " [ 1677366.]\n", + " [ 1846331.]\n", + " [ 1858441.]\n", + " [ 1943899.]\n", + " [ 1976957.]\n", + " [ 2071294.]\n", + " [ 2128385.]\n", + " [ 2139199.]\n", + " [ 2190790.]\n", + " [ 2274259.]\n", + " [ 2416519.]\n", + " [ 2437136.]\n", + " [ 2437971.]\n", + " [ 2690373.]\n", + " [ 2797295.]\n", + " [ 2824058.]\n", + " [ 2882301.]\n", + " [ 2916179.]\n", + " [ 2950256.]\n", + " [ 2951358.]\n", + " [ 3064758.]\n", + " [ 3122295.]\n", + " [ 3124170.]\n", + " [ 3237416.]\n", + " [ 3272010.]\n", + " [ 3345837.]\n", + " [ 3379051.]\n", + " [ 3452741.]\n", + " [ 3455488.]\n", + " [ 3482252.]\n", + " [ 3495258.]\n", + " [ 3584254.]\n", + " [ 3626115.]\n", + " [ 3710193.]\n", + " [ 3716966.]\n", + " [ 3761084.]\n", + " [ 3767745.]\n", + " [ 3941684.]]\n", + "False\n" + ] + } + ], + "source": [ + "print(y_catlabels[:20])\n", + "print(y_catlabels[1190:1240])\n", + "print(y_catlabels[1201] in y_catlabels[:1200])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "file.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "dataSet = file['dataSet']" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['__array__', '__bool__', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__module__', '__ne__', '__new__', '__nonzero__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_cache_props', '_d', '_dcpl', '_dxpl', '_e', '_extent_type', '_fast_read_ok', '_fast_reader', '_filters', '_id', '_is_empty', '_lapl', '_lcpl', '_local', '_readonly', '_selector', 'asstr', 'astype', 'attrs', 'chunks', 'compression', 'compression_opts', 'dims', 'dtype', 'external', 'fields', 'file', 'fillvalue', 'fletcher32', 'flush', 'id', 'is_virtual', 'iter_chunks', 'len', 'make_scale', 'maxshape', 'name', 'nbytes', 'ndim', 'parent', 'read_direct', 'ref', 'refresh', 'regionref', 'resize', 'scaleoffset', 'shape', 'shuffle', 'size', 'virtual_sources', 'write_direct']\n", + "(16622, 13027)\n" + ] + } + ], + "source": [ + "print(dir(dataSet))\n", + "print(dataSet.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataSet.file" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/dataSet'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataSet.name" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "file" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DateRaw = file['createDateRaw']\n", + "DateRaw" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "()" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DateRaw.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "fa6ce55dd1c3529cbf6edc3a2a06ece370eb659233625aa57450f7bb0e28b5f4" + }, + "kernelspec": { + "display_name": "Python 3.8.9 ('basic_env')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.9" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/check_cross_entropy.ipynb b/notebooks/check_cross_entropy.ipynb new file mode 100644 index 0000000..539c70c --- /dev/null +++ b/notebooks/check_cross_entropy.ipynb @@ -0,0 +1,93 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([2.1326, 2.4327, 2.1326, 0.0141])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch\n", + "\n", + "\n", + "criterion = torch.nn.CrossEntropyLoss(weight=None,reduction='none')\n", + "\n", + "\n", + "# dummy_input = torch.rand((5, 10))\n", + "dummy_input = torch.Tensor(\n", + " [[1,2,1,1,2], [1,1,1,1,3], [1,1,2,2,1], [6,1,0,0,0]]\n", + ")\n", + "gt = torch.Tensor([0,0,0,0]).to(torch.long)\n", + "\n", + "loss = criterion(dummy_input, gt)\n", + "loss\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([1.0663, 2.4327, 2.1326, 0.0141])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gt = torch.Tensor([[0.5,0,0,0,0],\n", + " [1,0,0,0,0],\n", + " [1,0,0,0,0],\n", + " [1,0,0,0,0]]).to(torch.float)\n", + "criterion(dummy_input, gt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "1b70c5c1a21016cbfc32955d08cc77b5418ee3e266e24c666e8f7e93f3fba14d" + }, + "kernelspec": { + "display_name": "Python 3.8.9 ('basic_env')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.9" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/train_my_classifier.py b/train_my_classifier.py new file mode 100644 index 0000000..38e7a02 --- /dev/null +++ b/train_my_classifier.py @@ -0,0 +1,356 @@ +import os, sys, random +import numpy as np +import torch +import torch.nn as nn +from time import time +from tqdm import tqdm, trange +from termcolor import cprint +# import wandb + +from omegaconf import DictConfig, open_dict +import hydra +from hydra.utils import get_original_cwd + +from constants import device +# from speech_decoding.dataclass.brennan2018 import Brennan2018Dataset +# from speech_decoding.dataclass.gwilliams2022 import ( +# Gwilliams2022SentenceSplit, +# Gwilliams2022ShallowSplit, +# Gwilliams2022DeepSplit, +# Gwilliams2022Collator, +# ) +from torch.utils.data import DataLoader, RandomSampler, BatchSampler + +from meg_decoding.models import get_model, Classifier +from meg_decoding.utils.get_dataloaders import get_dataloaders, get_samplers +from meg_decoding.utils.loss import * +from meg_decoding.dataclass.god import GODDatasetBase, GODCollator +from meg_decoding.utils.loggers import Pickleogger +from meg_decoding.utils.vis_grad import get_grad + + +def run(args: DictConfig) -> None: + + from meg_decoding.utils.reproducibility import seed_worker + # NOTE: We do need it (IMHO). + if args.reproducible: + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + torch.use_deterministic_algorithms(True) + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + g = torch.Generator() + g.manual_seed(0) + seed_worker = seed_worker + else: + g = None + seed_worker = None + + pkl_logger = Pickleogger(os.path.join(args.save_root, 'runs')) + + # with open_dict(args): + # args.root_dir = get_original_cwd() + cprint(f"Current working directory : {os.getcwd()}") + cprint(args, color="white") + + # ----------------------- + # Dataloader + # ----------------------- + # NOTE: Segmentation should always be by word onsets, not just every 3 seconds + if args.dataset == "Gwilliams2022": + + if args.split_mode == "sentence": + + train_set = Gwilliams2022SentenceSplit(args) + test_set = Gwilliams2022SentenceSplit(args, train_set.test_word_idxs_dict) + + assert train_set.num_subjects == test_set.num_subjects + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + elif args.split_mode == "shallow": + + dataset = Gwilliams2022ShallowSplit(args) + + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(dataset.Y.shape[0] * args.split_ratio) + test_size = dataset.Y.shape[0] - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + + elif args.split_mode == "deep": + + train_set = Gwilliams2022DeepSplit(args, train=True) + test_set = Gwilliams2022DeepSplit(args, train=False) + assert train_set.num_subjects == test_set.num_subjects + + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + cprint(f"Test segments: {test_size}", "cyan") + + if args.use_sampler: + # NOTE: currently not supporting reproducibility + train_loader, test_loader = get_samplers( + train_set, + test_set, + args, + test_bsz=test_size, + collate_fn=Gwilliams2022Collator(args), + ) + else: + # FIXME: maybe either get rid of reproducibility, or remove this? + if args.reproducible: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, seed_worker, g, test_bsz=test_size + ) + else: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, test_bsz=test_size + ) + + elif args.dataset == "Brennan2018": + # NOTE: takes an optional debug param force_recompute to pre-process the EEG even if it exists + dataset = Brennan2018Dataset(args) + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(len(dataset) * args.split_ratio) + test_size = len(dataset) - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + cprint( + f"Number of samples: {len(train_set)} (train), {len(test_set)} (test)", color="blue", + ) + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, g, seed_worker, test_bsz=test_size + ) + + elif args.dataset == "GOD": + train_dataset = GODDatasetBase(args, 'train', return_label=True) + val_dataset = GODDatasetBase(args, 'val', return_label=True) + with open_dict(args): + args.num_subjects = train_dataset.num_subjects + print('num subject is {}'.format(args.num_subjects)) + + + if args.use_sampler: + test_size = 50# 重複サンプルが存在するのでval_dataset.Y.shape[0] + train_loader, test_loader = get_samplers( + train_dataset, + val_dataset, + args, + test_bsz=test_size, + collate_fn=GODCollator(args, return_label=True),) + + else: + train_loader = DataLoader( + train_dataset, + batch_size= args.batch_size, + drop_last=True, + shuffle=True, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + test_loader = DataLoader( + val_dataset, + batch_size=50, # args.batch_size, + drop_last=True, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + + else: + raise ValueError("Unknown dataset") + + if args.use_wandb: + wandb.config = {k: v for k, v in dict(args).items() if k not in ["root_dir", "wandb"]} + wandb.init( + project=args.wandb.project, + entity=args.wandb.entity, + config=wandb.config, + save_code=True, + ) + wandb.run.name = args.wandb.run_name + "_" + args.split_mode + wandb.run.save() + + # --------------------- + # Models + # --------------------- + brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) + + classifier = Classifier(args) + + # --------------- + # Loss + # --------------- + loss_func = MyCLIPLikeClassificationLoss(args).to(device) # CLIPLoss(args).to(device) + loss_func.train() + + # -------------------- + # Optimizer + # -------------------- + optimizer = torch.optim.Adam( + list(brain_encoder.parameters()) + list(loss_func.parameters()), lr=float(args.lr), + ) + + if args.lr_scheduler == "cosine": + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, T_max=args.epochs, eta_min=args.lr * 0.1 + ) + elif args.lr_scheduler == "multistep": + scheduler = torch.optim.lr_scheduler.MultiStepLR( + optimizer, + milestones=[int(m * args.epochs) for m in args.lr_multistep_mlstns], + gamma=args.lr_step_gamma, + ) + else: + scheduler = None + + # ====================================== + best_acc = 0 + pbar = tqdm(range(args.epochs)) + for epoch in pbar: + pbar.set_description("training {}/{} epoch".format(epoch, args.epochs)) + train_losses = [] + test_losses = [] + trainTop1accs = [] + trainTop10accs = [] + testTop1accs = [] + testTop10accs = [] + + brain_encoder.train() + pbar2 = tqdm(train_loader) + for i, batch in enumerate(pbar2): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, label = batch + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y, label = X.to(device), Y.to(device), label.to(device) + + Z = brain_encoder(X, subject_idxs) # 0.96 GB + + loss = loss_func(Z, label) + # import pdb; pdb.set_trace() + with torch.no_grad(): + trainTop1acc, trainTop10acc = classifier(Z, Y) + + train_losses.append(loss.item()) + trainTop1accs.append(trainTop1acc) + trainTop10accs.append(trainTop10acc) + + pbar.set_description("training {}/{} iters Train/Loss: {}, Train/Top1Acc: {}, Train/Top10Acc: {}".format(i, len(train_loader), loss.item(), trainTop1acc, trainTop10acc)) + if args.dataset == "Gwilliams2022": + optimizer.zero_grad() + loss.backward() + optimizer.step() + if args.dataset == "GOD": + optimizer.zero_grad() + loss.backward() + optimizer.step() + # get_grad(brain_encoder) + + # Accumulate gradients for Gwilliams for the whole epoch + if args.dataset == "Brennan2018": + optimizer.zero_grad() + loss.backward() + optimizer.step() + + brain_encoder.eval() + for batch in test_loader: + + with torch.no_grad(): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, label = batch + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y, label = X.to(device), Y.to(device), label.to(device) + + Z = brain_encoder(X, subject_idxs) # 0.96 GB + + loss = loss_func(Z, label) + + testTop1acc, testTop10acc = classifier(Z, Y, test=True) # ( 250, 1024, 360 ) + + test_losses.append(loss.item()) + testTop1accs.append(testTop1acc) + testTop10accs.append(testTop10acc) + + print( + f"Ep {epoch}/{args.epochs} | ", + f"train l: {np.mean(train_losses):.3f} | ", + f"test l: {np.mean(test_losses):.3f} | ", + f"trainTop10acc: {np.mean(trainTop10accs):.3f} | ", + f"testTop10acc: {np.mean(testTop10accs):.3f} | ", + f"lr: {optimizer.param_groups[0]['lr']:.5f}", + f"temp: {loss_func.temp.item():.3f}", + ) + pkl_logger.log({ + "epoch": epoch, + "train_loss": np.mean(train_losses), + "test_loss": np.mean(test_losses), + "trainTop1acc": np.mean(trainTop1accs), + "trainTop10acc": np.mean(trainTop10accs), + "testTop1acc": np.mean(testTop1accs), + "testTop10acc": np.mean(testTop10accs), + "lrate": optimizer.param_groups[0]["lr"], + "temp": loss_func.temp.item(), + }, 'logs') + + if args.use_wandb: + performance_now = { + "epoch": epoch, + "train_loss": np.mean(train_losses), + "test_loss": np.mean(test_losses), + "trainTop1acc": np.mean(trainTop1accs), + "trainTop10acc": np.mean(trainTop10accs), + "testTop1acc": np.mean(testTop1accs), + "testTop10acc": np.mean(testTop10accs), + "lrate": optimizer.param_groups[0]["lr"], + "temp": loss_func.temp.item(), + } + wandb.log(performance_now) + + if scheduler is not None: + scheduler.step() + + savedir = os.path.join(args.save_root, 'weights') + last_weight_file = os.path.join(savedir, "model_last.pt") + torch.save(brain_encoder.state_dict(), last_weight_file) + print('model is saved as ', last_weight_file) + if best_acc < np.mean(testTop10accs): + best_weight_file = os.path.join(savedir, "model_best.pt") + torch.save(brain_encoder.state_dict(), best_weight_file) + best_acc = np.mean(testTop10accs) + print('best model is updated !!, {}'.format(best_acc), best_weight_file) + + + +if __name__ == "__main__": + from hydra import initialize, compose + with initialize(version_base=None, config_path="../configs/"): + args = compose(config_name='20230417_sbj01_seq2stat') + if not os.path.exists(os.path.join(args.save_root, 'weights')): + os.makedirs(os.path.join(args.save_root, 'weights')) + run(args) From 5bba8d17335d42a67185e15838fa72d525fe730c Mon Sep 17 00:00:00 2001 From: inoue26 Date: Tue, 18 Apr 2023 19:00:07 +0900 Subject: [PATCH 23/71] FIX: new loss bugs --- meg_decoding/utils/loss.py | 12 +- notebooks/Untitled.ipynb | 1622 ++++++++++++++++++++++++++++++++++++ notebooks/vis_meg.ipynb | 165 +++- 3 files changed, 1785 insertions(+), 14 deletions(-) create mode 100644 notebooks/Untitled.ipynb diff --git a/meg_decoding/utils/loss.py b/meg_decoding/utils/loss.py index 34ca351..4a09651 100644 --- a/meg_decoding/utils/loss.py +++ b/meg_decoding/utils/loss.py @@ -4,7 +4,6 @@ import torch.nn.functional as F from einops import rearrange import numpy as np - from constants import device @@ -104,13 +103,14 @@ def __init__(self, args): def prepare_image_features(self): sorted_image_features = np.load('./data/GOD/image_features_train.npy') - self.sorted_image_features = torch.Tensor(sorted_image_features, requires_grad=False).to(torch.float) + self.sorted_image_features = torch.tensor(sorted_image_features, requires_grad=False).to(torch.float).to(device) assert len(self.sorted_image_features) == 1200 + self.gt_size = 1200 def calculate_smooth_labeling(self, labels:np.ndarray, smmoth_value=0.1): # labels: batchsize:64 - targets = torch.zeros([64, 1200],requires_grad=False).to(torch.float) + targets = torch.zeros([64, 1200],requires_grad=False).to(torch.float).to(device) for i, l in enumerate(labels): l_mod = l % self.same_category_length targets[i, l_mod*self.same_category_length:(l_mod+1)*self.same_category_length] = smmoth_value @@ -119,8 +119,9 @@ def calculate_smooth_labeling(self, labels:np.ndarray, smmoth_value=0.1): def forward(self, x, labels, fast=True, return_logits=False): + labels = labels-1 # labelsは1始まり batch_size = x.size(0) - y = self.prepare_image_features() + y = self.sorted_image_features assert batch_size > 1, "Batch size must be greater than 1." # if not self.registered_targets: # self.register_buffer('targets', torch.arange(self.batch_size, requires_grad=False).to(self.device)) @@ -140,9 +141,10 @@ def forward(self, x, labels, fast=True, return_logits=False): # logits = torch.cat([positives, negatives], dim=1) else: + # import pdb; pdb.set_trace() # fast way x = x.reshape(batch_size, -1) - y = y.reshape(batch_size, -1) + y = y.reshape(self.gt_size, -1) # NOTE: scale the embeddings to unit norm x = x / x.norm(dim=-1, keepdim=True) diff --git a/notebooks/Untitled.ipynb b/notebooks/Untitled.ipynb new file mode 100644 index 0000000..9b55a4d --- /dev/null +++ b/notebooks/Untitled.ipynb @@ -0,0 +1,1622 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7cfc2fa3", + "metadata": {}, + "outputs": [], + "source": [ + "import scipy.io\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "486d50f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['__header__', '__version__', '__globals__', 'vec_image', 'vec_index'])\n", + "(600, 512)\n", + "(1, 600)\n" + ] + } + ], + "source": [ + "label_filepath = '/work/project/MEG_GOD/GOD_dataset/sbj01/labels/ses_1.mat'\n", + "label_data = scipy.io.loadmat(label_filepath)\n", + "\n", + "print(label_data.keys())\n", + "print(label_data['vec_image'].shape)\n", + "print(label_data['vec_index'].shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0d5c60cf", + "metadata": {}, + "outputs": [], + "source": [ + "meg_filepath = '/work/project/MEG_GOD/GOD_dataset/sbj01/mat/data_block001.mat'\n", + "meg_data = scipy.io.loadmat(meg_filepath)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f58bdae5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['__header__', '__version__', '__globals__', 'F', 'Std', 'Comment', 'ChannelFlag', 'Time', 'DataType', 'Device', 'nAvg', 'Leff', 'Events', 'ColormapType', 'DisplayUnits', 'History'])\n" + ] + } + ], + "source": [ + "print(meg_data.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "640e6baf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 2)\n", + "8\n", + "8\n" + ] + } + ], + "source": [ + "print(meg_data['Events'].shape)\n", + "print(len(meg_data['Events'][0][0]))\n", + "print(len(meg_data['Events'][0][1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "95999637", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array(['transient_notch'], dtype='" + "" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -109,6 +118,144 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "load /work/project/MEG_GOD/GOD_dataset/sbj01/mat/data_block001.mat\n", + "load /work/project/MEG_GOD/GOD_dataset/sbj01/mat/data_block002.mat\n" + ] + } + ], + "source": [ + "split= 'train' # val\n", + "labels_list = []\n", + "features_list = []\n", + "for i in range(2):\n", + " meg_name, label_name, trigger_name, rest_name = args.subjects[sub][split]['mat'][i], args.subjects[sub][split]['labels'][i], args.subjects[sub][split]['trigger'][i], args.subjects[sub][split]['rest'][i]\n", + " processed_meg_path = processed_meg_path_pattern.format(sub=sub, name=meg_name)\n", + " label_path = label_path_pattern.format(sub=sub, name=label_name)\n", + " trigger_path = trigger_meg_path_pattern.format(sub=sub, name=trigger_name)\n", + " processed_rest_meg_path = processed_rest_meg_path_pattern.format(sub=sub, name=rest_name)\n", + "\n", + " MEG_Data, image_features, labels, triggers = get_meg_data(processed_meg_path, label_path, trigger_path,\n", + " rest_mean=None, rest_std=None, split=split)\n", + " labels_list.append(labels)\n", + " features_list.append(image_features)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1200 [ 1 2 3 ... 1198 1199 1200]\n", + "(1200, 512)\n" + ] + } + ], + "source": [ + "labels_arr = np.concatenate(labels_list)\n", + "image_features = np.concatenate(features_list, axis=0)\n", + "print(len(labels_arr), np.unique(labels_arr))\n", + "print(image_features.shape)\n", + "\n", + "sorted_image_features = np.zeros_like(image_features)\n", + "for i, l in enumerate(labels_arr):\n", + " sorted_image_features[l-1] = image_features[i]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1200,)\n", + "1200\n" + ] + }, + { + "data": { + "text/plain": [ + "array([ 1, 2, 3, ..., 1198, 1199, 1200], dtype=uint16)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(labels_arr.shape)\n", + "a = np.unique(labels_arr)\n", + "a.sort()\n", + "print(len(a))\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.16008864, -0.0097264 , 0.06219969, 0.00605699, 0.48412266,\n", + " 0.5261386 ])" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_image_features[[0,1,2,600,601,602],10]" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([], shape=(0, 512), dtype=float64)" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_image_features[1200:]" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "np.save('../data/GOD/image_features_train.npy', sorted_image_features)" + ] + }, { "cell_type": "code", "execution_count": 33, From 91cf6222886a1968d9e3176a8484ab33e5449e71 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Wed, 19 Apr 2023 15:18:21 +0900 Subject: [PATCH 24/71] FIX: createion target for val --- meg_decoding/utils/loss.py | 36 +++++++++++++++++------------------- train_my_classifier.py | 10 ++-------- 2 files changed, 19 insertions(+), 27 deletions(-) diff --git a/meg_decoding/utils/loss.py b/meg_decoding/utils/loss.py index 4a09651..e044f27 100644 --- a/meg_decoding/utils/loss.py +++ b/meg_decoding/utils/loss.py @@ -105,7 +105,12 @@ def prepare_image_features(self): sorted_image_features = np.load('./data/GOD/image_features_train.npy') self.sorted_image_features = torch.tensor(sorted_image_features, requires_grad=False).to(torch.float).to(device) assert len(self.sorted_image_features) == 1200 - self.gt_size = 1200 + self.gt_size = 1200 + + sorted_image_features_test = np.load('./data/GOD/image_features.npy') + self.sorted_image_features_test = torch.tensor(sorted_image_features_test, requires_grad=False).to(torch.float).to(device) + assert len(self.sorted_image_features_test) == 50 + self.gt_size_test = 50 def calculate_smooth_labeling(self, labels:np.ndarray, smmoth_value=0.1): @@ -118,33 +123,30 @@ def calculate_smooth_labeling(self, labels:np.ndarray, smmoth_value=0.1): return targets - def forward(self, x, labels, fast=True, return_logits=False): + def forward(self, x, labels, fast=True, return_logits=False, train=True): labels = labels-1 # labelsは1始まり batch_size = x.size(0) - y = self.sorted_image_features + assert batch_size > 1, "Batch size must be greater than 1." - # if not self.registered_targets: - # self.register_buffer('targets', torch.arange(self.batch_size, requires_grad=False).to(self.device)) - # self.registered_targets = True - targets = self.calculate_smooth_labeling(labels, smmoth_value=0.1)# torch.arange(batch_size, requires_grad=False).long().to(self.device) + if train: + targets = self.calculate_smooth_labeling(labels, smmoth_value=0.1)# torch.arange(batch_size, requires_grad=False).long().to(self.device) + y = self.sorted_image_features + gt_size = self.gt_size + else: + targets = labels.to(torch.long) # torch.arange(batch_size, requires_grad=False).long().to(self.device) + y = self.sorted_image_features_test + gt_size = self.gt_size_test if not fast: # less efficient way x_ = rearrange(x, "b f t -> 1 b (f t)") y_ = rearrange(y, "b f t -> b 1 (f t)") logits = self.compute_similarity(x_, y_) # s - # unnecessary steps for the less efficient way (this might be needed for fancy contrastive losses) - # positives = torch.diag(similarity_matrix, 0).view(-1,1) - # negative_mask = torch.logical_not(torch.eye(batch_size).type(torch.bool)) - # negatives = similarity_matrix[negative_mask].view(batch_size, -1) - # logits = torch.cat([positives, negatives], dim=1) - else: - # import pdb; pdb.set_trace() # fast way x = x.reshape(batch_size, -1) - y = y.reshape(self.gt_size, -1) + y = y.reshape(gt_size, -1) # NOTE: scale the embeddings to unit norm x = x / x.norm(dim=-1, keepdim=True) @@ -156,10 +158,6 @@ def forward(self, x, labels, fast=True, return_logits=False): # scale by temperature (learned) logits *= torch.exp(self.temp) - # # NOTE: the old way - # # I don't know why yet, but normalization seems to be necessary to get sensible similarities (0, 1) - # logits = logits / (x.norm(dim=-1) * y.norm(dim=-1)) - # loss = self._criterion(logits, targets) # NOTE: as in https://arxiv.org/abs/2103.00020 loss = self._criterion(logits, targets) # + self._criterion(logits.t(), targets.T)) / 2 diff --git a/train_my_classifier.py b/train_my_classifier.py index 38e7a02..fb1c39d 100644 --- a/train_my_classifier.py +++ b/train_my_classifier.py @@ -247,7 +247,7 @@ def run(args: DictConfig) -> None: Z = brain_encoder(X, subject_idxs) # 0.96 GB - loss = loss_func(Z, label) + loss = loss_func(Z, label, train=True) # import pdb; pdb.set_trace() with torch.no_grad(): trainTop1acc, trainTop10acc = classifier(Z, Y) @@ -267,12 +267,6 @@ def run(args: DictConfig) -> None: optimizer.step() # get_grad(brain_encoder) - # Accumulate gradients for Gwilliams for the whole epoch - if args.dataset == "Brennan2018": - optimizer.zero_grad() - loss.backward() - optimizer.step() - brain_encoder.eval() for batch in test_loader: @@ -289,7 +283,7 @@ def run(args: DictConfig) -> None: Z = brain_encoder(X, subject_idxs) # 0.96 GB - loss = loss_func(Z, label) + loss = loss_func(Z, label, train=False) testTop1acc, testTop10acc = classifier(Z, Y, test=True) # ( 250, 1024, 360 ) From 050bf2c9aa6a5f4bde40e37514a85f2b0218a9db Mon Sep 17 00:00:00 2001 From: inoue26 Date: Wed, 19 Apr 2023 15:10:43 +0900 Subject: [PATCH 25/71] ADD: matlab style acc calculation --- assets/evaluate.m | 35 ++++++++++++++ evaluate.py | 113 ++++++++++++++++++++++++++++++++++++++++++++-- 2 files changed, 145 insertions(+), 3 deletions(-) create mode 100644 assets/evaluate.m diff --git a/assets/evaluate.m b/assets/evaluate.m new file mode 100644 index 0000000..a3baf32 --- /dev/null +++ b/assets/evaluate.m @@ -0,0 +1,35 @@ +function acc_category_identification = identify_category(predicted_Y) +%% predicted_Y: trials * space dimensions +% global variables "D" for directory path and "P" for subject information. +global D P +​ +% "val_index" that specifies the indices of the validation trials: 1*300 (trials) +​ +load(fullfile(D.dp.exp_data, 'rand', P.sbj.name{P.ind.sbj}, 'val_index.mat'), 'val_index'); +​ +% space.vec: 50 (images) *512 (CLIP dimension) +space = load(fullfile(D.dp.exp_data, 'val', 'clip_image.vec.mat')); +n_space_vec = size(space.vec, 1); % number of image vectors +​ +acc_tmp = zeros(size(predicted_Y,1), 1); +% iterating over each predicted vector +for i_pred = 1:size(predicted_Y,1) + space_corr = zeros(n_space_vec, 1); + % iterating over each image vector + % calculating the correlation coefficient between the current predicted + % vector and the image vector using the "corrcoef" function + for i_space = 1:n_space_vec + R = corrcoef(predicted_Y(i_pred,:), space.vec(i_space,:)); + space_corr(i_space) = R(1,2); + end + % assigning the index of the current predicted vector to "i_image" + i_image = val_index(i_pred); + % calculating the accuracy of the current predicted vector by counting + % the number of image vectors whose correlation coefficients are less + % than the correlation coefficient of the corresponding image vector + % and dividing by the total number of image vectors minus one + acc_tmp(i_pred) = numel(find(space_corr None: weight_dir = os.path.join(args.save_root, 'weights') last_weight_file = os.path.join(weight_dir, "model_last.pt") best_weight_file = os.path.join(weight_dir, "model_best.pt") - if os.path.exists(best_weight_file): + if os.path.exists(best_weight_file): brain_encoder.load_state_dict(torch.load(best_weight_file)) print('weight is loaded from ', best_weight_file) else: @@ -179,6 +179,112 @@ def run(args: DictConfig) -> None: ) +def acc_category_identification(predicted_y, val_index): + # predicted_y: num_trials x 512 + # val_index: num_trials + image_features = np.load('./data/GOD/image_features.npy') # 50 x 512 + num_images = len(image_features) + num_trials = len(predicted_y) + acc_tmp = np.zeros((num_trials, 1)) + + for i_pred in range(num_trials): + space_corr = np.zeros((num_images, 1)) + # iterating over all images + # calculating the correlation between the predicted and the image features + for i_img in range(num_images): + R = np.corrcoef(predicted_y[i_pred], image_features[i_img]) + space_corr[i_img] = R[0,1] + + # assigning the index of the current predicred vector to image_id + image_id = val_index[i_pred] + # calculating the accuracy of the cirrent predicted vector by counting the number of images with correlation coefficiens less than that of corresponding image + # and dividing by the total number of images minus one + + acc_tmp[i_pred] = np.sum(space_corr < space_corr[image_id]) / (num_images - 1) + + return np.mean(acc_tmp) + +def run_acc_from_corr(args): + from meg_decoding.utils.reproducibility import seed_worker + if args.reproducible: + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + torch.use_deterministic_algorithms(True) + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + g = torch.Generator() + g.manual_seed(0) + seed_worker = seed_worker + else: + g = None + seed_worker = None + + if args.dataset == "GOD": + train_dataset = GODDatasetBase(args, 'train', return_label=True) + val_dataset = GODDatasetBase(args, 'val', return_label=True) + with open_dict(args): + args.num_subjects = train_dataset.num_subjects + print('num subject is {}'.format(args.num_subjects)) + + + test_size = val_dataset.Y.shape[0] + if args.use_sampler: + train_loader, test_loader = get_samplers( + train_dataset, + val_dataset, + args, + test_bsz=args.batch_size, + collate_fn=GODCollator(args, return_label=True),) + + else: + test_loader = DataLoader( + val_dataset, + batch_size=args.batch_size, + drop_last=True, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + + else: + raise ValueError("Unknown dataset") + # assert len(test_loader) == 1 + brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) + + weight_dir = os.path.join(args.save_root, 'weights') + last_weight_file = os.path.join(weight_dir, "model_last.pt") + best_weight_file = os.path.join(weight_dir, "model_best.pt") + if os.path.exists(best_weight_file): + brain_encoder.load_state_dict(torch.load(best_weight_file)) + print('weight is loaded from ', best_weight_file) + else: + brain_encoder.load_state_dict(torch.load(last_weight_file)) + print('weight is loaded from ', last_weight_file) + + pred_features = [] + labels = [] + for batch in tqdm(test_loader): + + with torch.no_grad(): + if len(batch) == 4: + X, _, subject_idxs, label = batch + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, label = X.to(device), label.to(device) + + Z = brain_encoder(X, subject_idxs) # 0.96 GB + + pred_features.append(Z) + labels.append(label) + + pred_features = np.concatenate(pred_features, axis=0) + labels = np.concatenate(labels, axis=0) + print('total predictions: {}'.format(len(labels))) + acc_from_corr = acc_category_identification(pred_features, labels) + print('acc_from_corr: {}'.format(acc_from_corr)) if __name__ == "__main__": from hydra import initialize, compose @@ -186,4 +292,5 @@ def run(args: DictConfig) -> None: args = compose(config_name='20230417_sbj01_seq2stat') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) - run(args) + # run(args) + run_acc_from_corr(args) From 69bdd78dd3f119984e175b12cf4492352f5ef096 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Wed, 19 Apr 2023 19:28:28 +0900 Subject: [PATCH 26/71] FIX: bugs --- evaluate.py | 19 +++++++++++-------- examples/view_training_curve.py | 2 +- train_my_classifier.py | 2 +- 3 files changed, 13 insertions(+), 10 deletions(-) diff --git a/evaluate.py b/evaluate.py index 3049ddf..601fbb4 100644 --- a/evaluate.py +++ b/evaluate.py @@ -181,12 +181,14 @@ def run(args: DictConfig) -> None: def acc_category_identification(predicted_y, val_index): # predicted_y: num_trials x 512 - # val_index: num_trials + # val_index: num_trialsi + val_index = val_index - 1 # ラベルは1始まり image_features = np.load('./data/GOD/image_features.npy') # 50 x 512 num_images = len(image_features) num_trials = len(predicted_y) acc_tmp = np.zeros((num_trials, 1)) + cat_wise_acc = {i:[] for i in range(len(image_features))} for i_pred in range(num_trials): space_corr = np.zeros((num_images, 1)) # iterating over all images @@ -201,8 +203,9 @@ def acc_category_identification(predicted_y, val_index): # and dividing by the total number of images minus one acc_tmp[i_pred] = np.sum(space_corr < space_corr[image_id]) / (num_images - 1) - - return np.mean(acc_tmp) + cat_wise_acc[image_id].append(acc_tmp[i_pred]) + cat_wise_acc = {i: np.mean(cat_wise_acc[i]) for i in range(len(image_features))} + return np.mean(acc_tmp), cat_wise_acc def run_acc_from_corr(args): from meg_decoding.utils.reproducibility import seed_worker @@ -233,7 +236,7 @@ def run_acc_from_corr(args): train_dataset, val_dataset, args, - test_bsz=args.batch_size, + test_bsz=test_size, #args.batch_size, collate_fn=GODCollator(args, return_label=True),) else: @@ -277,14 +280,14 @@ def run_acc_from_corr(args): Z = brain_encoder(X, subject_idxs) # 0.96 GB - pred_features.append(Z) - labels.append(label) - + pred_features.append(Z.detach().cpu().numpy()) + labels.append(label.detach().cpu().numpy()) pred_features = np.concatenate(pred_features, axis=0) labels = np.concatenate(labels, axis=0) print('total predictions: {}'.format(len(labels))) - acc_from_corr = acc_category_identification(pred_features, labels) + acc_from_corr, cat_wise_acc = acc_category_identification(pred_features, labels) print('acc_from_corr: {}'.format(acc_from_corr)) + print('category wise accuracy: ', cat_wise_acc) if __name__ == "__main__": from hydra import initialize, compose diff --git a/examples/view_training_curve.py b/examples/view_training_curve.py index 1ac5168..2a47efe 100644 --- a/examples/view_training_curve.py +++ b/examples/view_training_curve.py @@ -59,7 +59,7 @@ def parse_data(filepath): if __name__ == '__main__': - filepath = '/home/yainoue/meg2image/results/20230417_sbj01_seq2stat/runs/2023-04-17 18:22:35.316446' + filepath = '/home/yainoue/meg2image/results/20230417_sbj01_seq2stat/runs/2023-04-19 11:08:38.266128' # filepath = '/home/yainoue/meg2image/results/20230413_sbj01/runs/2023-04-13 21:52:17.333087'#'home/yainoue/meg2image/results/test/2023-04-11 18:06:44.273986' # filepath = '/home/yainoue/meg2image/results/20230413_sbj01_seq2stat/runs/2023-04-16 19:21:02.983480' # filepath = '/home/yainoue/meg2image/results/20230412_mini/runs/2023-04-13 15:17:53.310665' diff --git a/train_my_classifier.py b/train_my_classifier.py index fb1c39d..ca7ca9a 100644 --- a/train_my_classifier.py +++ b/train_my_classifier.py @@ -344,7 +344,7 @@ def run(args: DictConfig) -> None: if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230417_sbj01_seq2stat') + args = compose(config_name='20230418_sbj01_seq2stat') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) run(args) From 9822c86d11fe7fad6e5c00048d7ccd75dec3f967 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Wed, 19 Apr 2023 19:20:40 +0900 Subject: [PATCH 27/71] ADD: category --- data/GOD/category_test.csv | 51 +++++++++++ data/GOD/category_train.csv | 173 ++++++++++++++++++++++++++++++++++++ 2 files changed, 224 insertions(+) create mode 100644 data/GOD/category_test.csv create mode 100644 data/GOD/category_train.csv diff --git a/data/GOD/category_test.csv b/data/GOD/category_test.csv new file mode 100644 index 0000000..9564579 --- /dev/null +++ b/data/GOD/category_test.csv @@ -0,0 +1,51 @@ +Goldfish +owl +nan +duck +swan +nan +crab +orca +leopard +Bat +fly +butterfly +goat +camel +lama +airplane +nan +beermug +bowlingball +bulldozer +Cannon +boat +coffin +carrier +man_with_hat +hat +nan +fire_extinguisher +helmet +piano +grave +man_with_hammock +harp +iPod +knob +post +mandarin +kitchen +tower +nan +coin +shredder +Snowmobile +socks +Stained glass +nan +umbrella +videoplayer +washing_machine +buttery? + diff --git a/data/GOD/category_train.csv b/data/GOD/category_train.csv new file mode 100644 index 0000000..37af24f --- /dev/null +++ b/data/GOD/category_train.csv @@ -0,0 +1,173 @@ +ostrich +frog_1 +frog_2 +turtle +rhinoceros +snake +trilobite +scorpion +spider +centipede +horseshoe_crab +bird_1 +duck +kangaroo +cochlea +basket_clam +octopus +crested_ibis +bird_2 +penguin +dolphin +dog_1 +dog_2 +bear +grasshopper +cockroach +praying_mantis +starfish +porcupine +horse +zebra +deer +giraffe +skunk +person +gorilla +chimpanzee +elephant +raccoon +airship +babycar +bag +man_with_bat +baseball_mitt +basketball_ring +bustab +lighthouse +binocular +water_bowl +bowling +boxing +camera +tire +corn_flakes +knife +cup +cd +keyboard +tv +telephone +floppy +dumbbell +earphone +fighter +fire_engine +flashlight +horn +frisbee +frying_pan +gas_station +radio +golfball +guitar_pick +shoes +calculator +harmonica +piano +helicopter +balloon +pool +hourglass +game +submachine_gun +canoe +yacht +knife2 +laptop +lathe_machine +lawn mower +light_bulb +xylophone +Loudspeaker +candlestick +microscope +auto_bike +bike +mouse_PC +tie +obelisk +clip +Personal_Digital_Assistant +Copier +flower_pot +billiard +projector +antenna +refrigerator +gun +gun2 +roulette +saddle +bus +driver +segway +telescope +shirt +skateboard +building +soccer_ball +can +glass +boat +silver +stearing +pedal +sward +injection +teapot +telephonebox +tennis_ball +tennis +tent +astronomical_telescope +toaster +toaster2 +treadmill +tricycle +TuningFork +watch +windmill +bottle +knit_hat +tomato +fungi +watermelon +grape +sunflower +pinetree +Fern +bonsai + + + + + + + + + + + + + + + + + + + + + + + From e655f554e97446f9afb19448b380bbd0835d601e Mon Sep 17 00:00:00 2001 From: inoue26 Date: Wed, 19 Apr 2023 19:35:20 +0900 Subject: [PATCH 28/71] ADD: corr check between train image and test image --- data/GOD/category_test.csv | 3 +- data/GOD/category_train.csv | 25 +- notebooks/check_category_similarity.ipynb | 342 ++++++++++++++++++++++ 3 files changed, 344 insertions(+), 26 deletions(-) create mode 100644 notebooks/check_category_similarity.ipynb diff --git a/data/GOD/category_test.csv b/data/GOD/category_test.csv index 9564579..90a536f 100644 --- a/data/GOD/category_test.csv +++ b/data/GOD/category_test.csv @@ -47,5 +47,4 @@ nan umbrella videoplayer washing_machine -buttery? - +buttery? \ No newline at end of file diff --git a/data/GOD/category_train.csv b/data/GOD/category_train.csv index 37af24f..3bfdf12 100644 --- a/data/GOD/category_train.csv +++ b/data/GOD/category_train.csv @@ -147,27 +147,4 @@ grape sunflower pinetree Fern -bonsai - - - - - - - - - - - - - - - - - - - - - - - +bonsai \ No newline at end of file diff --git a/notebooks/check_category_similarity.ipynb b/notebooks/check_category_similarity.ipynb new file mode 100644 index 0000000..f2ad1a5 --- /dev/null +++ b/notebooks/check_category_similarity.ipynb @@ -0,0 +1,342 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "train_image_features_path = '../data/GOD/image_features_train.npy'\n", + "test_image_features_path = '../data/GOD/image_features.npy'\n", + "\n", + "train_category_path = '../data/GOD/category_train.csv'\n", + "test_category_path = '../data/GOD/category_test.csv'" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "with open(train_category_path) as f:\n", + " reader = csv.reader(f)\n", + " train_cat = []\n", + " for r in reader:\n", + " train_cat += r\n", + "with open(test_category_path) as f:\n", + " reader = csv.reader(f)\n", + " test_cat = []\n", + " for r in reader:\n", + " test_cat += r\n", + " \n", + "train_image_features = np.load(train_image_features_path)\n", + "test_image_features = np.load(test_image_features_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_cat_image_features_avg = []\n", + "for i in range(int(len(train_image_features)/8)):\n", + " start = i * 8\n", + " end = (i+1) * 8\n", + " cat_avg = np.mean(train_image_features[start:end,:], axis=0, keepdims=True)\n", + " train_cat_image_features_avg.append(cat_avg)\n", + " \n", + "train_cat_image_features_avg = np.concatenate(train_cat_image_features_avg, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "corr = np.corrcoef(train_cat_image_features_avg, test_image_features)\n", + "print(corr)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ostrich',\n", + " 'frog_1',\n", + " 'frog_2',\n", + " 'turtle',\n", + " 'rhinoceros',\n", + " 'snake',\n", + " 'trilobite',\n", + " 'scorpion',\n", + " 'spider',\n", + " 'centipede',\n", + " 'horseshoe_crab',\n", + " 'bird_1',\n", + " 'duck',\n", + " 'kangaroo',\n", + " 'cochlea',\n", + " 'basket_clam',\n", + " 'octopus',\n", + " 'crested_ibis',\n", + " 'bird_2',\n", + " 'penguin',\n", + " 'dolphin',\n", + " 'dog_1',\n", + " 'dog_2',\n", + " 'bear',\n", + " 'grasshopper',\n", + " 'cockroach',\n", + " 'praying_mantis',\n", + " 'starfish',\n", + " 'porcupine',\n", + " 'horse',\n", + " 'zebra',\n", + " 'deer',\n", + " 'giraffe',\n", + " 'skunk',\n", + " 'person',\n", + " 'gorilla',\n", + " 'chimpanzee',\n", + " 'elephant',\n", + " 'raccoon',\n", + " 'airship',\n", + " 'babycar',\n", + " 'bag',\n", + " 'man_with_bat',\n", + " 'baseball_mitt',\n", + " 'basketball_ring',\n", + " 'bustab',\n", + " 'lighthouse',\n", + " 'binocular',\n", + " 'water_bowl',\n", + " 'bowling',\n", + " 'boxing',\n", + " 'camera',\n", + " 'tire',\n", + " 'corn_flakes',\n", + " 'knife',\n", + " 'cup',\n", + " 'cd',\n", + " 'keyboard',\n", + " 'tv',\n", + " 'telephone',\n", + " 'floppy',\n", + " 'dumbbell',\n", + " 'earphone',\n", + " 'fighter',\n", + " 'fire_engine',\n", + " 'flashlight',\n", + " 'horn',\n", + " 'frisbee',\n", + " 'frying_pan',\n", + " 'gas_station',\n", + " 'radio',\n", + " 'golfball',\n", + " 'guitar_pick',\n", + " 'shoes',\n", + " 'calculator',\n", + " 'harmonica',\n", + " 'piano',\n", + " 'helicopter',\n", + " 'balloon',\n", + " 'pool',\n", + " 'hourglass',\n", + " 'game',\n", + " 'submachine_gun',\n", + " 'canoe',\n", + " 'yacht',\n", + " 'knife2',\n", + " 'laptop',\n", + " 'lathe_machine',\n", + " 'lawn mower',\n", + " 'light_bulb',\n", + " 'xylophone',\n", + " 'Loudspeaker',\n", + " 'candlestick',\n", + " 'microscope',\n", + " 'auto_bike',\n", + " 'bike',\n", + " 'mouse_PC',\n", + " 'tie',\n", + " 'obelisk',\n", + " 'clip',\n", + " 'Personal_Digital_Assistant',\n", + " 'Copier',\n", + " 'flower_pot',\n", + " 'billiard',\n", + " 'projector',\n", + " 'antenna',\n", + " 'refrigerator',\n", + " 'gun',\n", + " 'gun2',\n", + " 'roulette',\n", + " 'saddle',\n", + " 'bus',\n", + " 'driver',\n", + " 'segway',\n", + " 'telescope',\n", + " 'shirt',\n", + " 'skateboard',\n", + " 'building',\n", + " 'soccer_ball',\n", + " 'can',\n", + " 'glass',\n", + " 'boat',\n", + " 'silver',\n", + " 'stearing',\n", + " 'pedal',\n", + " 'sward',\n", + " 'injection',\n", + " 'teapot',\n", + " 'telephonebox',\n", + " 'tennis_ball',\n", + " 'tennis',\n", + " 'tent',\n", + " 'astronomical_telescope',\n", + " 'toaster',\n", + " 'toaster2',\n", + " 'treadmill',\n", + " 'tricycle',\n", + " 'TuningFork',\n", + " 'watch',\n", + " 'windmill',\n", + " 'bottle',\n", + " 'knit_hat',\n", + " 'tomato',\n", + " 'fungi',\n", + " 'watermelon',\n", + " 'grape',\n", + " 'sunflower',\n", + " 'pinetree',\n", + " 'Fern',\n", + " 'bonsai']" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_cat" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Goldfish',\n", + " 'owl',\n", + " 'nan',\n", + " 'duck',\n", + " 'swan',\n", + " 'nan',\n", + " 'crab',\n", + " 'orca',\n", + " 'leopard',\n", + " 'Bat',\n", + " 'fly',\n", + " 'butterfly',\n", + " 'goat',\n", + " 'camel',\n", + " 'lama',\n", + " 'airplane',\n", + " 'nan',\n", + " 'beermug',\n", + " 'bowlingball',\n", + " 'bulldozer',\n", + " 'Cannon',\n", + " 'boat',\n", + " 'coffin',\n", + " 'carrier',\n", + " 'man_with_hat',\n", + " 'hat',\n", + " 'nan',\n", + " 'fire_extinguisher',\n", + " 'helmet',\n", + " 'piano',\n", + " 'grave',\n", + " 'man_with_hammock',\n", + " 'harp',\n", + " 'iPod',\n", + " 'knob',\n", + " 'post',\n", + " 'mandarin',\n", + " 'kitchen',\n", + " 'tower',\n", + " 'nan',\n", + " 'coin',\n", + " 'shredder',\n", + " 'Snowmobile',\n", + " 'socks',\n", + " 'Stained glass',\n", + " 'nan',\n", + " 'umbrella',\n", + " 'videoplayer',\n", + " 'washing_machine',\n", + " 'buttery?']" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_cat" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "basic_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.9" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From e8dec42546103a7f048a939df939c9502f89cb18 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Thu, 20 Apr 2023 18:33:22 +0900 Subject: [PATCH 29/71] ADD: binary cross entropy --- evaluate.py | 25 +- examples/inference.py | 152 ++ examples/view_training_curve.py | 4 +- meg_decoding/utils/loss.py | 33 +- notebooks/check_category_similarity.ipynb | 1603 ++++++++++++++++++--- train_my_classifier.py | 5 +- 6 files changed, 1590 insertions(+), 232 deletions(-) create mode 100644 examples/inference.py diff --git a/evaluate.py b/evaluate.py index 601fbb4..5ccb907 100644 --- a/evaluate.py +++ b/evaluate.py @@ -179,16 +179,29 @@ def run(args: DictConfig) -> None: ) -def acc_category_identification(predicted_y, val_index): +def get_average_features(predicted_y, val_index): + test_labels_unique = np.unique(val_index) + test_pred_features_avg = [] + for i in range(len(test_labels_unique)): + target_ids = val_index== i + test_pred_features_avg.append(predicted_y[target_ids].mean(axis=0, keepdims=True)) + test_pred_features_avg = np.concatenate(test_pred_features_avg, axis=0) + return test_pred_features_avg, np.arange(len(test_labels_unique)) + +def acc_category_identification(predicted_y, val_index, use_average=False): # predicted_y: num_trials x 512 - # val_index: num_trialsi + # val_index: num_trials val_index = val_index - 1 # ラベルは1始まり image_features = np.load('./data/GOD/image_features.npy') # 50 x 512 + if use_average: + print('use average') + predicted_y, val_index = get_average_features(predicted_y, val_index) num_images = len(image_features) num_trials = len(predicted_y) acc_tmp = np.zeros((num_trials, 1)) cat_wise_acc = {i:[] for i in range(len(image_features))} + # import pdb; pdb.set_trace() for i_pred in range(num_trials): space_corr = np.zeros((num_images, 1)) # iterating over all images @@ -207,7 +220,7 @@ def acc_category_identification(predicted_y, val_index): cat_wise_acc = {i: np.mean(cat_wise_acc[i]) for i in range(len(image_features))} return np.mean(acc_tmp), cat_wise_acc -def run_acc_from_corr(args): +def run_acc_from_corr(args, use_average=False): from meg_decoding.utils.reproducibility import seed_worker if args.reproducible: os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" @@ -285,15 +298,15 @@ def run_acc_from_corr(args): pred_features = np.concatenate(pred_features, axis=0) labels = np.concatenate(labels, axis=0) print('total predictions: {}'.format(len(labels))) - acc_from_corr, cat_wise_acc = acc_category_identification(pred_features, labels) + acc_from_corr, cat_wise_acc = acc_category_identification(pred_features, labels, use_average=use_average) print('acc_from_corr: {}'.format(acc_from_corr)) print('category wise accuracy: ', cat_wise_acc) if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230417_sbj01_seq2stat') + args = compose(config_name='20230419_sbj01_seq2stat') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) # run(args) - run_acc_from_corr(args) + run_acc_from_corr(args, use_average=True) diff --git a/examples/inference.py b/examples/inference.py new file mode 100644 index 0000000..19fddf8 --- /dev/null +++ b/examples/inference.py @@ -0,0 +1,152 @@ +import os, sys, random +import numpy as np +import torch +import torch.nn as nn +from time import time +from tqdm import tqdm, trange +from termcolor import cprint +# import wandb + +from omegaconf import DictConfig, open_dict +import hydra +from hydra.utils import get_original_cwd +sys.path.append('./') +from constants import device +# from speech_decoding.dataclass.brennan2018 import Brennan2018Dataset +# from speech_decoding.dataclass.gwilliams2022 import ( +# Gwilliams2022SentenceSplit, +# Gwilliams2022ShallowSplit, +# Gwilliams2022DeepSplit, +# Gwilliams2022Collator, +# ) +from torch.utils.data import DataLoader, RandomSampler, BatchSampler + +from meg_decoding.models import get_model, Classifier +from meg_decoding.utils.get_dataloaders import get_dataloaders, get_samplers +from meg_decoding.utils.loss import * +from meg_decoding.dataclass.god import GODDatasetBase, GODCollator +from meg_decoding.utils.loggers import Pickleogger +from meg_decoding.utils.vis_grad import get_grad + + +def run_inference(args): + from meg_decoding.utils.reproducibility import seed_worker + if args.reproducible: + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + torch.use_deterministic_algorithms(True) + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + g = torch.Generator() + g.manual_seed(0) + seed_worker = seed_worker + else: + g = None + seed_worker = None + + if args.dataset == "GOD": + train_dataset = GODDatasetBase(args, 'train', return_label=True) + val_dataset = GODDatasetBase(args, 'val', return_label=True) + with open_dict(args): + args.num_subjects = train_dataset.num_subjects + print('num subject is {}'.format(args.num_subjects)) + + + test_size = val_dataset.Y.shape[0] + if args.use_sampler: + train_loader, test_loader = get_samplers( + train_dataset, + val_dataset, + args, + test_bsz=test_size, #args.batch_size, + collate_fn=GODCollator(args, return_label=True),) + + else: + test_loader = DataLoader( + val_dataset, + batch_size=args.batch_size, + drop_last=True, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + + else: + raise ValueError("Unknown dataset") + # assert len(test_loader) == 1 + brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) + + weight_dir = os.path.join(args.save_root, 'weights') + last_weight_file = os.path.join(weight_dir, "model_last.pt") + best_weight_file = os.path.join(weight_dir, "model_last.pt") + if os.path.exists(best_weight_file): + brain_encoder.load_state_dict(torch.load(best_weight_file)) + print('weight is loaded from ', best_weight_file) + else: + brain_encoder.load_state_dict(torch.load(last_weight_file)) + print('weight is loaded from ', last_weight_file) + + pred_features = [] + labels = [] + for batch in tqdm(test_loader): + + with torch.no_grad(): + if len(batch) == 4: + X, _, subject_idxs, label = batch + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, label = X.to(device), label.to(device) + + Z = brain_encoder(X, subject_idxs) # 0.96 GB + + pred_features.append(Z.detach().cpu().numpy()) + labels.append(label.detach().cpu().numpy()) + pred_features = np.concatenate(pred_features, axis=0) + labels = np.concatenate(labels, axis=0) + + savedir = os.path.join(args.save_root, 'inference') + if not os.path.exists(savedir): + os.makedirs(savedir) + np.save(os.path.join(savedir, 'pred_features_test.npy'), pred_features) + np.save(os.path.join(savedir, 'labels_test.npy'), labels) + + pred_features = [] + labels = [] + cnt = 0 + for batch in tqdm(train_loader): + + with torch.no_grad(): + if len(batch) == 4: + X, _, subject_idxs, label = batch + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, label = X.to(device), label.to(device) + + Z = brain_encoder(X, subject_idxs) # 0.96 GB + + pred_features.append(Z.detach().cpu().numpy()) + labels.append(label.detach().cpu().numpy()) + if cnt > 10: + break + cnt += 1 + pred_features = np.concatenate(pred_features, axis=0) + labels = np.concatenate(labels, axis=0) + + savedir = os.path.join(args.save_root, 'inference') + if not os.path.exists(savedir): + os.makedirs(savedir) + np.save(os.path.join(savedir, 'pred_features_train.npy'), pred_features) + np.save(os.path.join(savedir, 'labels_train.npy'), labels) + +if __name__ == "__main__": + from hydra import initialize, compose + with initialize(version_base=None, config_path="../../configs/"): + args = compose(config_name='20230417_sbj01_seq2stat') + if not os.path.exists(os.path.join(args.save_root, 'weights')): + os.makedirs(os.path.join(args.save_root, 'weights')) + # run(args) + run_inference(args) diff --git a/examples/view_training_curve.py b/examples/view_training_curve.py index 2a47efe..706e622 100644 --- a/examples/view_training_curve.py +++ b/examples/view_training_curve.py @@ -59,7 +59,9 @@ def parse_data(filepath): if __name__ == '__main__': - filepath = '/home/yainoue/meg2image/results/20230417_sbj01_seq2stat/runs/2023-04-19 11:08:38.266128' + # filepath = '/home/yainoue/meg2image/results/20230417_sbj01_seq2stat/runs/2023-04-19 11:08:38.266128' + # filepath = '/home/yainoue/meg2image/results/20230419_sbj01_seq2stat/runs/2023-04-20 01:39:30.648046' + filepath = '/home/yainoue/meg2image/results/20230419_sbj01_seq2stat2/runs/2023-04-20 13:06:53.825175' # filepath = '/home/yainoue/meg2image/results/20230413_sbj01/runs/2023-04-13 21:52:17.333087'#'home/yainoue/meg2image/results/test/2023-04-11 18:06:44.273986' # filepath = '/home/yainoue/meg2image/results/20230413_sbj01_seq2stat/runs/2023-04-16 19:21:02.983480' # filepath = '/home/yainoue/meg2image/results/20230412_mini/runs/2023-04-13 15:17:53.310665' diff --git a/meg_decoding/utils/loss.py b/meg_decoding/utils/loss.py index e044f27..4526fa3 100644 --- a/meg_decoding/utils/loss.py +++ b/meg_decoding/utils/loss.py @@ -92,11 +92,25 @@ def __init__(self, args): super().__init__() self.device = device self.compute_similarity = nn.CosineSimilarity(dim=-1) - self._criterion = nn.CrossEntropyLoss(reduction=args.reduction) + if args.criterion == 'crossentropy': + print('use crossentropy') + self._criterion = nn.CrossEntropyLoss(reduction=args.reduction) + self.criterion_mode = 'crossentropy' + self.smmoth_value=0.1 + elif args.criterion == 'binary_crossentropy': + print('use binary_cross_entropy') + self._criterion = nn.BCELoss(reduction=args.reduction) + self.criterion_mode = 'binary_crossentropy' + self.smmoth_value=0.5 + else: + raise ValueError() # self.targets = torch.zeros(size=(batch_size, )).long() # that's for the slow method # self.registered_targets = False # self.batch_size = args.batch_size - self.temp = nn.Parameter(torch.tensor([float(args.init_temperature)])) + if args.temp_trainable: + self.temp = nn.Parameter(torch.tensor([float(args.init_temperature)])) + else: + self.temp = torch.tensor(float(args.init_temperature), requires_grad=False) self.prepare_image_features() self.same_category_length = 8 @@ -123,20 +137,26 @@ def calculate_smooth_labeling(self, labels:np.ndarray, smmoth_value=0.1): return targets - def forward(self, x, labels, fast=True, return_logits=False, train=True): + def forward(self, x, labels, fast=True, return_logits=False, train=True, debug_Y=None): labels = labels-1 # labelsは1始まり batch_size = x.size(0) assert batch_size > 1, "Batch size must be greater than 1." if train: - targets = self.calculate_smooth_labeling(labels, smmoth_value=0.1)# torch.arange(batch_size, requires_grad=False).long().to(self.device) + # smooth_value 0.1 -> 0.5 (binaryentripy使用時) + targets = self.calculate_smooth_labeling(labels, smmoth_value=self.smmoth_value)# torch.arange(batch_size, requires_grad=False).long().to(self.device) y = self.sorted_image_features gt_size = self.gt_size + # print(debug_Y[0]==y[labels[0]]) + # import pdb; pdb.set_trace() else: targets = labels.to(torch.long) # torch.arange(batch_size, requires_grad=False).long().to(self.device) + if self.criterion_mode == 'binary_crossentropy': + targets = F.one_hot(targets, num_classes=50).to(torch.float) # binary cross entropy only can manipulate onehot y = self.sorted_image_features_test gt_size = self.gt_size_test + # import pdb; pdb.set_trace() if not fast: # less efficient way x_ = rearrange(x, "b f t -> 1 b (f t)") @@ -156,10 +176,13 @@ def forward(self, x, labels, fast=True, return_logits=False, train=True): logits = torch.matmul(x, y.T) # scale by temperature (learned) - logits *= torch.exp(self.temp) + logits = logits * torch.exp(self.temp) # NOTE: as in https://arxiv.org/abs/2103.00020 + if self.criterion_mode == 'binary_crossentropy': + # import pdb; pdb.set_trace() + logits = torch.sigmoid(logits) # torch.exp(logits) / torch.exp(logits).sum(dim=-1, keepdim=True) loss = self._criterion(logits, targets) # + self._criterion(logits.t(), targets.T)) / 2 if return_logits: diff --git a/notebooks/check_category_similarity.ipynb b/notebooks/check_category_similarity.ipynb index f2ad1a5..27fe9a4 100644 --- a/notebooks/check_category_similarity.ipynb +++ b/notebooks/check_category_similarity.ipynb @@ -8,7 +8,8 @@ "source": [ "import csv\n", "import numpy as np\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "import os" ] }, { @@ -21,12 +22,19 @@ "test_image_features_path = '../data/GOD/image_features.npy'\n", "\n", "train_category_path = '../data/GOD/category_train.csv'\n", - "test_category_path = '../data/GOD/category_test.csv'" + "test_category_path = '../data/GOD/category_test.csv'\n", + "\n", + "\n", + "target_dir = '/home/yainoue/meg2image/results/20230417_sbj01_seq2stat/inference'\n", + "test_labels_path = os.path.join(target_dir, 'labels_test.npy')\n", + "test_pred_features_path = os.path.join(target_dir, 'pred_features_test.npy')\n", + "train_labels_path = os.path.join(target_dir, 'labels_train.npy')\n", + "train_pred_features_path = os.path.join(target_dir, 'pred_features_train.npy')" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -42,12 +50,26 @@ " test_cat += r\n", " \n", "train_image_features = np.load(train_image_features_path)\n", - "test_image_features = np.load(test_image_features_path)" + "test_image_features = np.load(test_image_features_path)\n", + "\n", + "test_pred_features = np.load(test_pred_features_path)\n", + "test_labels = np.load(test_labels_path)\n", + "\n", + "train_pred_features = np.load(train_pred_features_path)\n", + "train_labels = np.load(train_labels_path)\n", + "\n", + "train_pred_cats = []\n", + "for c in train_labels:\n", + " id_ = (c-1)//8\n", + " train_pred_cats.append(train_cat[id_])\n", + "test_pred_cats = []\n", + "for c in test_labels:\n", + " test_pred_cats.append(test_cat[c-1])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -58,7 +80,1107 @@ " cat_avg = np.mean(train_image_features[start:end,:], axis=0, keepdims=True)\n", " train_cat_image_features_avg.append(cat_avg)\n", " \n", - "train_cat_image_features_avg = np.concatenate(train_cat_image_features_avg, axis=0)" + "train_cat_image_features_avg = np.concatenate(train_cat_image_features_avg, axis=0)\n", + "\n", + "\n", + "test_labels_unique = np.unique(test_labels)\n", + "test_pred_features_avg = []\n", + "for i in range(len(test_labels_unique)):\n", + " target_ids = test_labels== i+1\n", + " test_pred_features_avg.append(test_pred_features[target_ids].mean(axis=0, keepdims=True))\n", + "test_pred_features_avg = np.concatenate(test_pred_features_avg, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.06824003, 0.00055291, -0.00147425, ..., 0.194422 ,\n", + " 0.05930007, 0.11897398],\n", + " [-0.07748845, -0.06807086, 0.00291177, ..., 0.20742488,\n", + " 0.03889466, 0.10587576],\n", + " [-0.06641656, -0.01629701, -0.026875 , ..., 0.18126883,\n", + " 0.02830761, 0.13323207],\n", + " ...,\n", + " [-0.09646755, -0.06187202, 0.03544342, ..., 0.22700717,\n", + " 0.02799049, 0.10551084],\n", + " [-0.04137433, -0.06370759, -0.01147156, ..., 0.17118609,\n", + " 0.02573117, 0.04335377],\n", + " [-0.08511078, -0.06095409, -0.02380625, ..., 0.24749213,\n", + " -0.00415051, 0.04340861]], dtype=float32)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + 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5.93000650e-02, 1.18973978e-01]], dtype=float32)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_pred_features[test_labels==1].mean(axis=0, keepdims=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train Image (AVG) vs Test Image" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0.74991196 0.77040982 ... 0.48660085 0.43744312 0.46784548]\n", + " [0.74991196 1. 0.97256515 ... 0.46133996 0.47905608 0.50767602]\n", + " [0.77040982 0.97256515 1. ... 0.45814013 0.47355479 0.50608662]\n", + " ...\n", + " [0.48660085 0.46133996 0.45814013 ... 1. 0.56144309 0.5761439 ]\n", + " [0.43744312 0.47905608 0.47355479 ... 0.56144309 1. 0.61554665]\n", + " [0.46784548 0.50767602 0.50608662 ... 0.5761439 0.61554665 1. ]]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "total_image_features = np.concatenate([train_cat_image_features_avg, test_image_features], axis=0)\n", + "total_labels = train_cat + test_cat\n", + "corr = np.corrcoef(total_image_features)\n", + "print(corr)\n", + "\n", + "plt.imshow(corr)\n", + "plt.show()\n", + "plt.imshow((corr>0.8).astype(np.int8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Goldfish : ['octopus', 'starfish', 'frog_1', 'turtle', 'dolphin']\n", + "owl : ['ostrich', 'raccoon', 'frog_2', 'bear', 'giraffe']\n", + "nan : ['frog_1', 'praying_mantis', 'frog_2', 'snake', 'grasshopper']\n", + "duck : ['duck', 'bird_2', 'crested_ibis', 'penguin', 'water_bowl']\n", + "swan : ['duck', 'crested_ibis', 'bird_2', 'ostrich', 'duck']\n", + "nan : ['flower_pot', 'teapot', 'trilobite', 'cochlea', 'octopus']\n", + "crab : ['scorpion', 'horseshoe_crab', 'octopus', 'frog_2', 'spider']\n", + "orca : ['penguin', 'dolphin', 'bear', 'elephant', 'bird_2']\n", + "leopard : ['giraffe', 'bear', 'gorilla', 'elephant', 'raccoon']\n", + "Bat : ['raccoon', 'frog_1', 'frog_2', 'spider', 'porcupine']\n", + "fly : ['cockroach', 'grasshopper', 'spider', 'praying_mantis', 'centipede']\n", + "butterfly : ['grasshopper', 'cochlea', 'frog_1', 'centipede', 'spider']\n", + "goat : ['lama', 'deer', 'dog_1', 'kangaroo', 'horse']\n", + "camel : ['ostrich', 'giraffe', 'lama', 'horse', 'elephant']\n", + "lama : ['horse', 'ostrich', 'giraffe', 'kangaroo', 'goat']\n", + "airplane : ['airship', 'helicopter', 'fighter', 'balloon', 'building']\n", + "nan : ['tricycle', 'gas_station', 'knob', 'pedal', 'telephonebox']\n", + "beermug : ['cup', 'can', 'toaster', 'flower_pot', 'teapot']\n", + "bowlingball : ['golfball', 'bowling', 'frisbee', 'skateboard', 'dumbbell']\n", + "bulldozer : ['Cannon', 'gun2', 'fire_engine', 'submachine_gun', 'helicopter']\n", + "Cannon : ['obelisk', 'gun2', 'bulldozer', 'sward', 'telescope']\n", + "boat : ['canoe', 'boat', 'frisbee', 'segway', 'pool']\n", + "coffin : ['xylophone', 'piano', 'Copier', 'bowling', 'silver']\n", + "carrier : ['tricycle', 'tent', 'Cannon', 'nan', 'saddle']\n", + "man_with_hat : ['horse', 'tricycle', 'sunflower', 'saddle', 'tent']\n", + "hat : ['saddle', 'knit_hat', 'tie', 'pedal', 'flower_pot']\n", + "nan : ['Loudspeaker', 'xylophone', 'harmonica', 'person', 'horn']\n", + "fire_extinguisher : ['toaster', 'refrigerator', 'toaster2', 'shredder', 'frying_pan']\n", + "helmet : ['baseball_mitt', 'shoes', 'man_with_bat', 'soccer_ball', 'skateboard']\n", + "piano : ['piano', 'horn', 'xylophone', 'harmonica', 'radio']\n", + "grave : ['obelisk', 'candlestick', 'Stained glass', 'piano', 'bonsai']\n", + "man_with_hammock : ['pool', 'segway', 'person', 'Loudspeaker', 'canoe']\n", + "harp : ['piano', 'horn', 'xylophone', 'mandarin', 'piano']\n", + "iPod : ['floppy', 'Personal_Digital_Assistant', 'radio', 'cd', 'tv']\n", + "knob : ['light_bulb', 'telephone', 'keyboard', 'stearing', 'hourglass']\n", + "post : ['telephonebox', 'gas_station', 'telephone', 'obelisk', 'knob']\n", + "mandarin : ['piano', 'knit_hat', 'pedal', 'saddle', 'harmonica']\n", + "kitchen : ['toaster2', 'bustab', 'refrigerator', 'billiard', 'frying_pan']\n", + "tower : ['obelisk', 'building', 'Stained glass', 'balloon', 'pinetree']\n", + "nan : ['TuningFork', 'driver', 'knife', 'knife2', 'silver']\n", + "coin : ['telescope', 'sward', 'pedal', 'dumbbell', 'obelisk']\n", + "shredder : ['toaster2', 'projector', 'toaster', 'refrigerator', 'tv']\n", + "Snowmobile : ['lawn mower', 'boat', 'auto_bike', 'tricycle', 'babycar']\n", + "socks : ['tie', 'glass', 'knit_hat', 'TuningFork', 'shirt']\n", + "Stained glass : ['obelisk', 'candlestick', 'light_bulb', 'building', 'tower']\n", + "nan : ['xylophone', 'TuningFork', 'mandarin', 'pedal', 'knit_hat']\n", + "umbrella : ['knit_hat', 'glass', 'tie', 'shirt', 'toaster']\n", + "videoplayer : ['projector', 'toaster2', 'radio', 'floppy', 'Copier']\n", + "washing_machine : ['toaster2', 'bustab', 'refrigerator', 'toaster', 'projector']\n", + "buttery? : ['flashlight', 'Personal_Digital_Assistant', 'binocular', 'toaster', 'calculator']\n" + ] + } + ], + "source": [ + "ranking = np.argsort(corr, axis=1)[:,::-1]\n", + "\n", + "# test corr\n", + "for row in ranking[-50:]:\n", + " print(total_labels[row[0]], ':' ,[total_labels[r] for r in row[1:6]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test Pred (AVG), Train Image (AVG)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50 150\n", + "[[1. 0.77586603 0.86917183 ... 0.03649613 0.01915577 0.03120084]\n", + " [0.77586603 1. 0.84104548 ... 0.02249373 0.03472726 0.02643563]\n", + " [0.86917183 0.84104548 1. ... 0.05666878 0.04675196 0.04547304]\n", + " ...\n", + " [0.03649613 0.02249373 0.05666878 ... 1. 0.89741394 0.82211575]\n", + " [0.01915577 0.03472726 0.04675196 ... 0.89741394 1. 0.87863461]\n", + " [0.03120084 0.02643563 0.04547304 ... 0.82211575 0.87863461 1. ]]\n" + ] + }, + { + "data": { + "image/png": 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+Na7ckr1v2IdaiEBE3qifsQ81bzTPaJXjyi/og5XwIhoa5RiDGAOjAverK87snl+/eherPK835/ioeTie8p3+Ntoj3AAfxQPIuV6jiVqj+oRU5wk4d3/zS/UzQG8cxWgculnsF6afMxoPCWPwadLNjAVME2WeZVAKqpSSzNeSkPLY97JSZxc0exZpsqu2nVKLeQLO3fHRlbhaza/BmLSieQi+DOA4jGX1p7Ky8ipVXH58Mhg+SMhgNLHribudXGc2Ytkjys8ugZ/ESOy6khJE6QKyqewRjE4M3OgkRKkm41u8GKUmIDBjD9mbKO56mAx3fg7zn+PNCVqAQpg8kji7J0geiC6eQ/HelEr3UN045g2D8yHbx8ITGIIQgTIP4IvNw0QQMrxePyOg+a+3n+F/ffwuf3p7rxByfnj5Lj+yimVfQ2Cle16tz9n4BSEqvrT6gB8//iWWumcfGr6wfMQYDWt94NTs6WLFUg+8Wj/nPLn2AGtzAODe6rp4ACHqxC0w6RgdbzTPGKPhHzj+On9l81mWi54Tc+CO3RIMoCFWBpxC+SDGIESUT2DRdj9N3IIgp0mbX/wM2VfZpc6od1odymuOEcYheSFq8izSsUt+P+8LabVIBJQYxRgY+Vn1fYqLw0Tu8X4a2HU1DbKc8nvRDU8eAzClv0AmYFnhDKqVlVzZFD44xLXWSkIgmIDDdA5VzYZwzpEHXSZnRjkyzgDIuYwvx1NtBVETVTaofsqyGDNlS+Bm2jBNxHJfMYUkc88n3aPSUyiAtfJMrBWD5524/9mzihMgW86ZjJ9StWAJIYoRyyShBJSq+bVkbAGme/+Q7WNhBIwK/FDzPuf+iNerc7pY8Vp1zqVesgkLbpstd6tr/vT2Hv/U+oI/vrnNqUl5/GHNGA1vNM+4a6/pQs0uNNyvLlmbA1/Zvcozu+YL7UPu2mu8k9CjizWBjjFarvyCW6Hhrt0IKSlWvG7P+Sv7z3E1LjizO1o1UmlHQLPxi+INrFTPUvW8M97lc4vH/HD7Lm8NEq5EQ+IIuDJAo9USb6vZSmk0YGSAz9I/JTzIWABMk3+eK9ZWUpExohI5JYNzIIOicAFS/F1i0yoNWj0b3ONA6EKJm+cTLc7y4Wgl7MS2KeSgWDyJ9IUEGEYzA6fKihwgpDg4cQMIUdJ8QNzvJxJUDpuy4cjnHwLKIky+vpf78MMLqTgDuHTfg7jk+W9ayfMcxpsrZgyg7MR3SNhHeaY+gI43J7ZRcpzsJYwOKiTbkN9jNqK9mxl/CSVi1313VmCGl1DZKRVJwl5mKdBsPLOhUloTM6Pxe4QEHxsj8PPdG7xSnfPWcI9Wjzx1a7a+pdEjV+4ez/0Rr1XP+eOb2/yv1vLvGA0nds/7/Rnf7O5zWS1Z6oE+VJz7FSFq7tUbvt3d5tcvv8Nbw30u3Io7dkMXKy79kqXueaW+wCdc4tyveK065693b/J4PGZtOp6Mx7xeP2eMlmdujY+aPtqSbqyshA6GyM8ePsWVF8OhIkSjCG2FiqD3g6TY6gRmjbMVpri+qqwgsa1RCZ1mnCasUrOVMQN/WqNcyjqEgFq2abIZVJiliZQGq1NsmlzVlP8vrqi16DbRefshseYaAbq8l2xFJjllbsE4yuR9MVRYLIi7NJmTgYsmTsYnkY2UFVZi9GG6toTET0j/dN1RKznmnGCUY/EQodY3acmJvsw4lutVStibqqpuEnOcKnhM7KdcfBzGycBm8DJvOexYLmQ/mwBFbVC1PKtYUq+m3K+KkYh4XrStpEOTQczMxejFG1Mg7yCFfcVIGiNGJoOkiZNBZSXk/DAOxGz7WBiBEBV9qHjNXjJGyxANt8yWU7NHE+hiTa0cH4xnnJo9f3xzm99z9IQ/f1jyle5VXq6vSh3AJizootxWFyqeDmvu19fsgoA6n2qecOlX7EPNWh8IUfNkPOZedc09K67/N/v7fKp5ynpxwKhIFyr2oUEnHKBVkjH49nAHgJ/ZfIpGj9yrN1TKc2Q69qFOaS8wu0GowkrJxHdeJlFaaQuxJhN70uqjhpFcQ1DQ/DlTL62GqtQcJDe664v7OcXgM4Rbz1zb7DFoDeOhxKGxH8p+KgNMOT2o9ASAeS/uaU7dzVdTayX8MFrSZYm3wDhKFiSvomllLuf0sxUSiFEVQ1SIMNqI0XJOwLGSTAkCboYplIohQN+XzEtEjlHwlnG84XqrqpKUXeZX5KyDtYXmXGotMkcgYyLZQzmkZzn2RBrxVpQiMhm+OeEnxii1AM2Z7KfNjLmoi0cUx3EyUMM4AZ2z51mo5ErdJEp9xPaxMAJjlCq9D9wJz90Rp2bPGC370LA2B8Zo+MbhPj+8fJfrIK74nz8s+fH2iuf+iC/vX+PKyOo7RsMYLCd2z8W44rTal9qDPhyxVw2tGjAmMEZLF2uWeuDCrcSDcKsS92+8pHhOzA5Pi0+fPQ4nNHqUCkbtaPTIse14Mqz5wvIRD4dTARxTOBYWMoiyJ4BSRK2JrcXsFFRGVqr8QGIUIwECaOXUFkwovbUo5aeXnLyImFzczIcvYUSKa4vrPY9x57n0TJJhNsBzfBrDtLKG5NrmPHomEuVQhZl7mklPZuY5ZPS83HI6Rq4NUIk8lMG9RVtWT5k84tZHSACeL5TZkoqElFuf+BOx71FVC3WdDJOemI8Z0DMphaoiMRj52VrJKNjs0vfFWMxBO1VXE7CZY/vgiUHdSBVilBg1HyQ1u1pMYUBVoSootOaqkon+QjGZymlVrYkhgcpzCnYGeJWeQqAP2T4WRkBW2ppv9C/xzB3ha83WtzwcTtAqstQDj/s1P7KKPBvWnNg9X+le5bk/4rctvs3Gt+xTDUAfpFT4yXjM0gj6LtWGTcntb3xbinzyxB+jYa/FIFy5BW/Uz3ga1zyoLjHIfvtQ0yhHHy0PhxPuVRv2vuHYdvTB0oeKrW9p9Ugh8mpxRfWQWHTJ7VchwDCjf4ZMtslgXZjCA60ndzeRRsrKnumx+eVjJkS7nrmsGUzMrnf2HIyGfriJlENBm8tWVZOnkdKOqqom2vAsVi2otv6QvPg4yopvrUyofE3ZOHb9RLPN8TMpx54QfmWNfDeTr2bkmZgyDkpXZUW8kTHJzzWdByNpROW9eAizreAfmQuRyFdz97oYGKVAxeLdqUZW6DiMqBwWzQ3p/NnqlMkJvoyHEubNjUx+HtYSuz5fpPzrnCwAKSMR8/PNx/8e28fCCFTK82xc82p9AYBB2Hh9sIUIZNSUBXi/P+Pl+oov719j41t+4+Id3h1v883+fiHzaCLfPtzmfnPNk2HNWbXnXnXNxrdsQgvALb3D6MD7/pQxGlo9cr+64t1OCozeqJ9x6Ve8P5zxfDii0SPLeuCevaZRjrXpeG8443JccLfe8lJzxfv9KY12aJXytb3D9FMKT0Unq/zoUN6Ul6kyr36WglPOJfrpKN+t6xu895iRX2thGKbVznvUMMqgg+KilrRjCUGSG9v3UyyeWYVKE3Ppcr6m5CXErps8lMwq9D2lgAjkOoKk4pRJJKh5Dt97Ys/EC8hGKlFsy715QfjjLIUXc4Ij5eVjzp9H4S+ouppc/znt2VpJd+4PBVWPXTeRbDIwOcsAxGEoTMRwvRVsJHMPYDLU+Rnn4iebyUeBqBMfoVCV4+TppGxHHMZUX2GmUCsZkQISW0usK7jeTNeXuBBxGIScBZLNcQ7adhor86zMC9vHwggYFWj1KHF1dV3Sc3lCr03HK+1lyQJ8s7vPidmxMeIBvDve5hV7QaUc3x7u0uiRfagxi8Df3L7Em8vnnBhJCe5CwxgsS9MX6u+x7QrxqFUjt+sta92xC7KSHpmOpnGc2L2kFUPFXjsBJs2BnWtSyOE5ajuWemAfJDtQNq0EI8irRkLcS6xvjIBEajaRMvsvu/BpxSwrVAgSr89iUqW1MO3qaiKh5Ouw5mYcS4pTqxqlJtex0Hq1mqXVVAknyFz+XCOQmHXA5PanlfMGLwAmwktmz/ko31Uacv2DtTBMwF3e1Dws0rN7yRMxSp2B/Jxd9EyhDlP6NXsNuQ4hb6lCUGUOQxDDW9D4ysrxs0ZB5gLMGIY3sIGYiWChvOOy5YyMoWAzJcWpdCE+5VClPNM8mYshN+XaCjBoJg9AKTuNl4/YPhZGQBOLIEguB8bAG80zhlQk9HrzHEPgrr3mshLU+MTuCVHzzf4+lXK8Yi9Z6Z73xzPQcGr2fFCdUinPnVQUFKJYx0wFBjEyHlXYh59vH7HWB94db9GqkRNzoLWDuPlR08WKEDX3qwtaPXJi9+xDzR0r9QpdqFIYwOSuQkoF2YLuklH777GpPMHjrA49i1Lk1VnpEsNmF1lpLYN8Th3N383prxe3EEGHm7+T3VFhIOZ4W6XQIfa9/Jy9Ap8GnMrx7Uy/QBvwOW/NZCBerCUICR9wDqWn78cYJ5p1/IjrNGZmANLf5hM9ufZxdCm0SMfP8XXmIMzKqqWoyxSjXM5ttNQCeJ/StFEmtZ7d17yiLz/7fOyFldsu5CY9sQszAFtJqHLj+/lZzw0PzO4zk8GijMEPISXNt4+FEVAqFkEQQ8SowMYtirrQXtWFCNQFids3CSDsg+gCfHu4y0r33DW7lP6TOP/V5oKtbzg2HUNamrMHAKBVxKNolSt6Aadmx2WYiopu2S0dNXvXFDe/Up5Ts8eoQKcP4nnMMglL3XODj1li+AzUhZuI/bzWfPad6MOHqlNl91f2C0Ks0UoGTJgd9wZ5J9wchDC5iTHxDFQCBfNnuTAnM9dKBWACyA6H6ZpeXG3yZMjps+BLUZB6cVDmEMQH8UqyJ1OYePm56Rk2oqbvalUqDTO/oUxKmHCBTLAqcbeGF4mzs1U+zsKXfA83SDxKy/PKqc78/Ot03S5O959JWSkcUHOPIaTrLszMIKnYMGUxlIrlnnO1aTFQuUI0HyNzFTJx6nukCT8WRiBGxePxhBOz52lY8+n6Ced+xbvdLV6qr3lQX/DecItX6/NSpttFy5g0BwEaPfL+eEYXK35j/YxvuI53x9u815/x+eUjlqpHU8mETiv5EG3JJtyvrzj3R6zpuPQrnro1IWou3JJbdltKlu/YDad2x1N3zLeGe4SoeTic8MXFQ97u7wrdOWkVRCMvO1Yp5eckPRibWhiDMAFXefDaFwbkrKpwireVoMEwTci8KUlpxUxIck6OmT2CKKt0HkBAIvloGPdpdUnHslYchgzSlXNoGXCJyVd4/RiZBNlAaCUEngyEpZRWXr2iHxM4lybivOhojrCDGI6cHVAa6hyHT4CmUnpWYBTEAKTflVfleklI+lxl6UaFpFIlO6MSLhKdE3m1OeGH/GryvikeB5RqUh3GzNhVVoxp5goYTYwaPEQ/yDXVFQyQy6yJYfIGYcq8pFBG2SnzUbb0PIuH9svUD3wsjAAK7ldXZYKP0XJiDoypGnAXmoTqL7hfXXLuRTXoxO6n0uBQg4ZLv+IbruOHqz2XfskilSBvwoKVlhqFtRYMoFYunc+w0gMB0Rx45ETMJFcaahULVdioIOEK4rWgAkemp1KOI9MJk9G3eK0hAs4LQzCtzkL7HVHOC/AG00t6QYMuawpGP0zIN9xYoQFxmxupGIzWTkBfdm0L0OSm9OAsRg/9MJFUOqaBluPT4l1M6H88HATx7vqSsy6vM7u0Srj3Kh2jGAuPAKJ6CnWKJ5D2j87JOZomrXTp3DGCn2jMjLFkTibWoRhWlfQBQXALXEz4hYRi5Vn4ULQLBDuY2JaAhDzrIzFMmVmY31H6ee7FRedQfS9yaPn9Dkz3IEO+XG8sAqaI0XyhRiP6IF4DzDAReYdxdBOjM+iUKYrTGEn7zt/Pi9vHwggMSQ9g61sq5Yu4SKtHLtyKbXLDQ1SsjRB8ulBxMa5YmoFvH25jFiHxCwzvjre59Ev+gcVz/tvzBUe257kXCbKn7pjH4zEnNnMRalo98uXDazwd1vzW429w22zZhZovH15jYQZOzY4QFV2sGKLlm/1LXPkFXaj4NYv3kpjpMQCVcoyxwaMk7RdCWlUSWp2JMVVaTeZ1Ay7eHFyzWK8IjvZ9eW7RC/CmmuYGsBaZ8tUqA095MynvHqZ0oYLJHU37FLGQbKjyCptzz+lvqrJlVSuTQasirKnq+iZglVfKRIEt91Vi7nSvqaQ2A14qXZeAcpTVXiZ1nTgRafIs2okhmQlVbTNlYtr2ZuiSioZUDFPBVWEeVjAMgrHMnqHQQXPcriZXfr4PlBBAW5tKu0N5nsraBATP9Bzmk93aSQ4tA8A5e9ILozA/U3mXMy8yi7bk+oaPOzCokJU2i4Leq695r7+NC5qXmiuh9I5LvrT6gK/sXuVeveHpsOa02gNwv7nmb25f4oPqlFebC97rz1jogf/2fMEfefUv8Xvf/p1sFw2vNJd883CPLy0fsg81XilOzJ6f277O48MxD5ZXJQ3Zh4oHzSUf9Kf85evPE+JNN/202vOZ9gnP3DEhan766nU+f/SEK7/gO4db3Kp26B6ZUFYn99NIPhhgthqpRGiJ2oBVk0BIjkmNkhfeTEUwUh23LDn2rBs4d/v06clUi2AMUY+oGeGn8OATu00drW66kM6J2k2Mk5hFHvSFODPKJJrXOmRjYS3UZgL6chowse1U00x0ZeckIqirMkF0DgdyrYP3hfQS9wOqqVELoUfnwqg4DHINTSJLNclLGQf02Slxu5WQoHYlrFHLZUkFyoPRYsScI15di8cTgngmKwGls5iHSpoNsR/EKFqbVJnlmtRqBfu9hAYxEA9DCv3kORLkOevVMqUxE8W464R6rBvYH8QbaRrU+kjSoNlAKI1aLYlX18lzZCZAUoEbiP2EJXzYpj/yL7/MppR6TSn1F5VSX1VK/aJS6l9On/+flVLvK6V+Pv33u365Y10PLc/cuoiC7n3D+bDi6XBESHoA7+xucdtsObYd3+5uc7fecMvuODId167lzeVz7tfXbH3D55ePeKW55EF7ye99+3fypz7957ld7dAq8FvW3+TKL4ps+HvDLSoV+Mfu/xzXY0sfKn5x+wpdqHivP+NLqw/4h0++xqcXT7lbb/j04im36y3v7G/z164+zcPhhG93t/nNp+/wsxev0SjHDx+9zxeWjwiNrPiTGz8D63wgrlphjdWVVJDlUtvslqo0+avqZnGItaLU23Wlnl6tFqi6lpqBzHXv+u+mjeYJZTLZRpf4PVxtCJstse8nFeDVclLLTZoEwsEfploD72WQ5zRW08hEyEo+uSpwGG7G3cMoE8Uayb/XtRiMhDPEvpdBnia7SrUEAHq1nIBVm7QErEWt16mSTyZhzLJhxkxU3lx+nUG+wsRLvIiMdxiNPjst1N5SKNW2cn/aTM83h24xii5BJvuMQtpSVkBEqcFo5RqNkb+1baor8Mmg9LPCKSkIU6tV4lb05f4wRryCZGxU26ZrW6KWC3lvVX2Dmfmhc/lX2oZMKfUy8HKM8WeVUmvgrwP/GPB7gG2M8d/7Wz3WK7/mNP4//5tP8ZK54olfUyvPW8M9LtyKsyQIMkbLXXvNPjRFx69Sjn1oeLu/m8p3rzk2HUvVswkLnvsjvr5/idvVjj94+6v81V5CBYDLJF4SomYfaj7bPAbg3fEWX2we8sid8HZ/lzEYXm+eszYd5+6Iq0QlbrSkDgGu/IJWCW/glhXF4VaN/Hv/v/8ln/0TG0Jt0YNDH0ZiZZKwiKTT9HZ/kz2WtxhFeejQp8k1Y70V1eEXQgfvpWps3021CXn/bFxcGvi5gKfIhZN0/oVpl72RG9WGmTyUGHe5YCc6JyHJnMmYhVFcmlDDyA0BFKXK6v1d9e/5+DMNhJhwC8kaeCHavEiLzoYtk3Fm/4qxEbKQWi2n7ExmY+b0XAbZ8jvJpChrpu/kY89Dufxvxj6snYq8sprwi3MtHzOxABlHCWXys51v3ot0+zKFMtaIrkShUfvpfPl95/GSvMf//u1//6/HGH/Ti0PtVxwOxBgfAg/Tzxul1NeQzkN/25tWkVaN7KJYP49inZR+V7pnpQd2Afah4a695q3hPgB9kDj/XnXNqdlTKccQDZoqgYCGV5pLtAr81d7wY83IpT/QxYqX7BUexbk7wseWLsoD/JH2Ozz3R9wyW9pWjE3GIZap34BRgS5U9KEqmQOtAie654GVzMBS90QDunOo3pciHzV6MQAgsdyhmyZB9gDSBFR+kMHr9bTipUldQDCYCEU5AzEfiFrfGIQZIFJkoCjJimUK7yiofAxeJM1y6mkua55W8GlFTYMwhOIdSOjjJhZkTqVlwsucGTdXIQ4epZZTc5HiRncQW5Fly+HDvAAnk6leUDTOz0XAUi/GyA7F9S7PwzIVMM3CpTiO0A+o5UJES3IIM1dNnvMCcvgSR9EicI64P4jnInOlHFu+kwqcUoEV271gKWm/UqiUt2EUIz3OQrmUrchLSQaZVdNMRnlW3/Di9n3BBJRSbwK/AfhrwG8F/iWl1O8Hfgb438cYL77X9w++4p3hDrfsll/qXi6pwCfjMcskCfY3dq/zheUjvNNcuBWfap6wV02pBZD+AYkObEWJ+Kk75puHe/yW9TcTWHjgt7RP+cnDA7493ClCI1IGHHh3vEUXKn5u+wZfWn3ASguQVCvPL3Qv04WKV+tzxmi4StjBM3fE1jXcqzd8rXvA6li+M0Y7CY2alNPPq2Ky/nHZoHfpFdiM3nuIycWvKnELx7GUvkI6pnNTrlzLylVi/CT9VdadEOWYecLm1FgBwDzRMWkDKGTiuYnmWwYbTMdpdMoOmEncsmQFpoyErKQzpN45VE0B+sqEGgcBMyvBBeJ2J1hEBvmck1RmDJQuTfkzJaBZLJJeAghGmFxncpYgaQtk0K2qbyr65HAtfzckhSUfUo0B06TKdOF5CjVlJWLXFTAyzp7NxMlQ07sMccpCMMN2Sm+GZBTyO557ginLcUMwJi8WL2pPfMj2qzYCSqkj4L8E/pUY47VS6j8C/hACh/8h4A8D/+yHfG9qQ/bSklOzZ5Ni9dw+TBNp9IiPqqTpLv2SO3bDpV/RqqHUAoxBbsWoQBcr1rrj8XjMl5YP+c5wh081T+lixU8eHvDF+jFdqIpo6eW44GvdA16uLnnm1hzbA7fNlq91D+hCxS27Y6kHNJGH4yljMGx9w8KMiW0oCsdWe577o1Sm3KF8qhasbSKSGFQ/ips/pxGX7jzmpvjHPKds7bRfJojUk9cATO59ZctEAiaXNq1gKqfm0oAShVov8WnuGhRzGizMUPnUXSdNSlVXMNqJnDTP0YN8p5mx2zKoaVPqTKubYYcxqEYXIdWMEVAZ+czaSU8gpFqANJGl7VriPuTzZ5qzMtP1ZQ+pTZ7R/jBxJszsOeaJ7ZzwKvJ7mrPz0vMoW1Pd6MyEc4JjjLkse8bczIVhKRQoAi75eHkCxwTGankPua1ZqXtImo/lmJlZmd5xZlh+r7D/V2UElFIVYgD+eIzx/yvPLz6e/f2PAn/mw747b0P2yq85jU/dMUvdc6fa8KC64JE7Za+kMvCxO6HRjrU+0MVapL9DXcqBAZamL+rBIeoiOLIPNRopPnrJXvHt4Q5dqHi9OmcXa94dbvPZ5RMuxhX70HC/upJCId0X5WBDEBVk03Oi9qW7UaMdL1eXaCJbL3oFrRpBMxUQxSjVgkCW1VYq5bHnsX3wlIYTkGjF3HTjVFXQ5OJ6JjruvGy3WP6SI5+xDkMUVz8PCmNSzb9M8sKQy5MmaMnL5/xzXpVm6kTFHS7Xo8S/Loi0mWL2vBLngp8ZZbiU5s7BzESRzSo65dnk1mrp73HwkxGa7Tuv27+xGs6YkuUe3GyCpmdV9mlq6IebqbbsFZTfZ6t3Ps7MCynnnJF7iqaBRryijMGkYiwRaoHCDk0sytJFKkSwKcMyC0tiSPLrL3Z3+pDtV2wElBz1PwW+FmP892efv5zwAoB/HPiFX+5YRgVOzE5qB4LUDrRquDGxl3qQhiR0okycwEKtglQDqmngDNFSK8cYLX5281IfIKDhLtac6j2+0nxruMeJPVApzy2zxUeRQL9rN6WICYQqfNdeJ/pyxTLhFV2sWJq+tEIrWwRCmCSwX6QKZ9AuGYMbTTtv6ACIC6mYxYhzHrqJxXVV+Th5v7I6hNk1BGKcceazrFlmGM7cz+wKlwq1aKcmKVrNZMxeiDlzZ6FcMARTw42s65/3y1TY3Okou7c5zo8pzWdmgFcm2fibbvzN5i3Tyi5qP0lrIItwWCvptCpMz3JOZ86u+ygdnUr6MXP2Z8VQN8qoC9FnxiWYFVzlZxNTCHTje4mzEF/EGdK5inxbGgvKpnefw6/536mLQfpe8P+vxhP4rcDvA76ilPr59Nm/AfxepdSPpPO+A/yBv9UDZpGPTVgQUvYyr+wBIeuM0bLUfSrkqUU/QN+sBcjsvn2oOTF73htucb+65twdsQ8NjR55d7iNrzTHuiuiIo0ek/T5gesgPQaeuXXyBgS8DFGjCaWhqUeVa5TqwYZ9UjHKkz+mCsEiljF39eec9qzkk/dh9vLyWC81A7MJlim6hb6qp5XNzPL78wFeVkVZOWPvRAZrTliZb7OKtXKN4QWe/osTYl6VaO20QubjlO/MsI75cTJTMIdB2XPK96NFex+YBFAzMp7FVXLokkOfElI5yevP07dzynSpDCRhHsmTSLqIxUXP+wV98/1ktuK8eCmv3PkY+U/zWoMcgswLgz7sfSRyGHODNyN75VCo0Mt/EGShGONfhg+tbflbaj0231zUpT14iJpaOXah4cotuXZtaUO+1ANXfsEr9UUBDc/divf9Kce2K9WAY7BFH+Dntq9TqSB/iy1XfsHluOCzyyd8a7jHUg/8uuZd3h/OeLe7RWj0jdZijR4xRDa+5chIu7SH45mUKw9NyQ4s9VDqCzKeAQgeYLWExbPOQ6GW6jA15NJX6XJT+g5aKxhBJ3nhuO8K+BdhSs1Z6ehLU1M0BYFcz59Rf3lnCYgKswGTGWXZVc9mR8+09wYBH4v2X1VNrbzT+UtMn4+bB3M+rlIQ9QRoZcNRynRT+q8fpsmfqNCqqmS1holzYEIBTuOQVlOdJMP1zBhm7cJc76Bs4SHgg+T7VTKgMQphK2MVUEIl1TaU1ulKT6SeLCw6qzUo0y23G8+9GnwQQlR+3glniEnbgKQfGPPzSM9hrioNoJpGCEZHq2L0or5JMpunS/+uEBoN6NIe/MovGaLl/f6MMZrSGcgQebV+zq0gDUXuVdelW3D+L1cD3q+vWOmBLx9e4/HhmH/s/s+x1h1drDAEvtY94GJccWIPXDgRDfkXb/0Uf27/Oo/HE77WPWAMokI0MhUpvT+csdQDJ2bP2/1d7lfXvDfc4sTu+WzziL9w/UMlrLhjN/gmSvFQXsVsEs/QoLtBBEczQgwptw44prAhNwjJXIGEJcgETwSXwvJLQGFeCZpaBmc6jhom8ozKghOzNFuYN9/IMmLOC8Jfq5uDKwNgzoGpCwZRJlF2zaEM8th1AmxlfCG5yrJiBbm+rkMdHcnkG6RJSPRBJlNdyUjI/RJgIhfN9Q/9kG7NFn3EMoFTGlTCBiQ9mkVD56FG1gxI6UVaSVUqW08xfaI3gy7of0yS8hnkzbJlUVMMXpYGJ2kLCi1cjH/pZmxyKtjf0BBQi1lb+HEs4KBMEjNhQt5La/ucTv4epcS/Ysbg93Nb6wNr3bE2B45Mx7E+cK++5szuyz65L8BadwDcs9ciAqJHzpKO4FIPVMpx7o/YhJanw5oHyyv+h/MfKsd5d7zFy9Uld6otSz1wZndsXcOf27/O7169D0gXojfbZxgVMCpw5YQg5KNm43MacseJ2XOSrvF/vP4id6otmsiVW/BWdw/TK/ARNTjU6IlVCgtSZaEa/U3AKru6WSRjFhYUjkBqU67qaqISQ9H6m1clFp3C4l2YGxmAcvzkduqjFfp4zVQim7bKThV8M+bijY5G2kzMxhAl22D0DVkyqrpoDGZXvQCaucAqGydjhPWWQ5vUhCRX/+VuQnlfYpDvZoN443kmRmZi1AlTryphVBEVJXkac28gE3nyeebHn/+sZ40/8nGzR5ePZUwBYPPzl4Yh2cv6bpc9i7jMSV1KqQlEzUzGvMWpbXpMz03ZWUu0D9k+Fp7APjSlH+Gp2XGq9zx1x2x9w5l2nJgDH4xnAGmSiwdw7lYs9VAUgAE8mjUda33gtx5/g0u/pA8V7463+JH2OyXOv19dccsIuy80msfjCf/J5Qn/8tk3+SNXb3DPXpew5H51xVO35r7peFBdYFQoIOEmtLxWndMqxxv1U8ZoC67wp6sfQ28PRRAyplVZBVm11ejEzZ8DZyEWl7PkiLNr3TSwE6MTnZsUfOvqhppMAesyizC+UDqcOuvkSrtw6NBHq6myD1DKlSKnUoG2T9oBxqRmm6l5Rka6MxKdMw3BTHJbQSr4cpffkgVIBUiMopEXfZrMwRN2+6QwPDUqjZmMU9SHIyFJb8d+SJTbqdVZTLJeseuJ1aQaXMqZc6FUiITdTiZmAudCKrCK+wOqqvAXV5jcRaqyU+UhKc065+dnevdmKwVETTM9N++T+lN6NtmAJxpwfr+C1Qzl3lVdEXf7FIpNhUFRdYTr6xQairxcBNSQVK774QeTHfh+brV2rLSUDJ+aHfsozMCl7rnySx4Op7zRPAMojUm+2d+n1SNXbsG73S1u11s+3z4SQRC/4pE75baRHoJf273MP3H7Z3juj/i57Rsc20PiH2jW5sCD6oKvdQ84MQf+yNUb/Iun7/KfXd/j8XjCuVtxv7rmrt3QKgEOc9PUfWj4kfY7vO+k6jGgee6PeDIeswktKijioiYY0f5X+14mjtbEtkZv0iDPtNq8imSwJ8SJwJNX+LqWnxPaLZV8FUWpSOtS0y86+U3BCFRmDAaPzvr5RmNSgYxaqInGmgd0zt1rg1q3s5BApZVMF9CvcAyy4EWaRCQqsp4PxBinstzkZSibBD+dmyr95hWI6V6jSU1EU0VhKW6KsxApy4SrJM+VgcRhRK2WhItLVGUnJp9zws+fYRQKZAIuF6A15vatKb2pVGIPpnuakYck/BEvRa2PShlwwShS1iZLjatUU4AWpaZSD5IrDZeLKY1cV+i2EUMwYzbqRTtxBubEJMTDiyHA1YfPv4+FERiC5c3qKe+7Myp8kRnLEmAexcYvWJsDr1tpDPKp5qloDtbPCjlorQ9chiVP3ZqlHtgFyR682l7wyJ1wy2z50uoDbpstS91TK891aPl69zJjMBzVHffsNf/Z9T1+3/oR/+m15czuynVqJZ2Q96Hh2bgmWEk5+tQ3oQsV9+w1p2YnqcUIqncondJJWVcwSoggwE8P6Clfn2nCaZvXxH9oKjFTfY0pyrdxn6rd8t99mNiKWoG2xTvAJOFQkElVGwjTQJVN4uLirObrgWQU9JTjnqcgjSlFQHmyx91+yp2XrEY6S6bwZiKMVuI55KabwygrnLWlzVpp4a00DP0UH+ea+tm55fMevEevj+Sc4yiu8jjCTG1YsgES5pRnkSah0qmPAmHiFujJzddNMiz7QzGYBbdIzypnSlRVJTwhEjdbqR1YpJDqBQXn0g8hZ5kOnRgoKHTmPC6USkVkmcb9cfcEKuX5ic0P86X2A94Z73KsD3jERZc036EU7vyV/eekM9DiwMYveBrXvFE/Yxdq3h1vTSnFqPny4TUeNJc8Ho4JKNp2ZKV7vtY94MzuCg8g8xEu3IpaOR6PJ/yn15Z/ev0t/uz+Hl/vXubtwx3u1hu+uPiAu/aa+9UlG7/g5/Zv8l53xmeXT3jkTtj7hkqLWInyTEU0uRY8r6K5wGZe4AOF/JHzvXEcy8vPZatK+ZsNM3L8Ps8RZ57BLDVWOAZpFSoKOfMUnotTCJEnaZ60GVwzhrjZTlr2MYJLrb9mcl0qo+c6iuHph6mu/0aeXU334FwB/IocVxJD0YkLH/tZ2W8KleJhJ4YvG41cZtukxh578bpUNra51DlRj+cZA0kdaqKngJmo5EV4T5x3bs6exwy8i+M4eXCjA4+IvWTKuKFkMDI5KEbBJnTmR8wxGSgkMdXURDeWd1KAwb6fqlUzaJqasMTdhxQkzbaPhRHoYsXFuGRXN8LGs5S8f6NHvtPfog+We6trrsYFa9NhEiPvQXXJZVIaatXI3jdcuCWV8izMwAf9Kb9m9T7v92elC1EXKirlCz7Q6JExPYpdaDhP1Yt/dn+P37Z4KMVMQKVd2efheFooxcdWGqRsxpa16RiDlRRhfo85rdU2kgYEosnutCrGQMXU2y67/mnQUlnY7m5mDGACkho7hRXGFKS+gFRzBt4MCCzHytqDmedfpQ63jYQhghWo0oYshlSckpl52pRBDRQgLOauyIhBEGZbyoFnPYI5zRUhDOWmIgIipmMl2XW5flWMkUpgZZHrzhM7o/L52EerdG0GtWyl/h7k+nIBTlWBlRV48pQSNpM6J6nViUw+UqiRawBMLZN0sZAQ4sjOmoEKICsdlNWk2ZAlzoyWhSK1E1f5PWVNiRlDNMYo+gGHQ+k+VAqo8jtIxrWIzf4ypcQfCyMwBqn287NkRYgaj5J0W7Ut7cHP7I4n4zFdqDgxOwyB94czjkzHiTlwy265ZbdoFTk1O/7y9ed5pbrAIK58rTy3rHwPKESgnAW4X11xv5IB8vXuZdb6wBfrCz5wF1z6JbXybMKCSnkejSd8qnnKQ3UivRP8Eb928V4RNJW0UEoTemkWSiINCeM6ThM7ZwhCJObWJZnuqvUUq+ctxkm0E1KoMAMYtUkpunkX4lCOmbkGBbFOBkhl0tGLtOC84mZvImkfxt1eOubM41ZtJwwilRkX49b3MpCDF6ovTBWORWQzhS59L6u6ihORyM4qJpWaNAdyVoHk9eRqxRDl+2nFB+TefBD6dk4hvhhmIdgGVWLdhTBNuvKe5H6zwQLEYObwIU/OogGQ+BqZo5AzO/MW9fMK0DCTCMueWtZsnBHNSm+CuSEOqVFppn9/j+1jYQS0CiX2PjJdERJNbRYZo+F2vQVktX+9fp4yCkIkej4c0TSO1g501IW0E6IiRM23+nvcr64IUfML3ctJrVgGTSYCybl7nro1d+0GgLcPdwD4wF3wWvWcW2bLt4Z7PBvXXLuWlZWy4jMrsmYv11elgKhVTua41UlZCJkEPlnoyiRcYEbc0XpS4ClFRQn8S+AUUFbfIr3VplU5y4G3rayOnQyYDBSqBBaS1IWE+qsh5Z5VPUvnGV1WunJ9TS39EXU1EYAWixSDqrTSU9J90rizmcgwXnQSVSUiGznuLvFqNjYJSC16+yUUUVNKMV9XlJy6ym50qsBTRksXnuT5KCBiC7FHWTt71lPO/yban+5Fm7Kqq7pOyHwy9IX1qye8og8pvPAl06G0XE/Zwuz6YfLg8r2mzxQQx5khyGBg20zeC0hT07zi57GjZ2NojuO8sH0sjIBRIhSam3+gYElPF6syoRvlCFFTaVdqBnyiBjdJ+7/VI3vXFPJQ1gjYJO3CZeL8a2LBAVo1cmQ07w9n+Ki5bzpaNaJV4G69odKOS7/kltlKtqGSPgjBKBrtSstyQ0Sn1GGb6wciEED5lAMfXMnhx+T+36DM5pW/EH/SIJl7AUqVmFYGe5z2me+bxTKsqBCVgqEX5adfPC+kgU+ZEOXYoZ5WsxcYaKUnQD5HSCFGJq7kDMaHbMWlDi9cV4jTyptLg1OjzRsrfDqGGEE9rc4Zb8gr6ov1E/M6A2b3m8k9MANIzXSPN3oJ+CmtO9MHLCSf2fG/iymZnn3MbNH8nEw619xDzFTj2fmLmAuADlPYlw1AfrYJW/qo7WNhBKzybH3LWktZ7m2zxaPYj00p4Nn6JvX40yXPv/ELGuVY1qm4KGq0ityxG4wKDKlIZm06rvyCpe55tT7n4XjKidpz116nsEN4/xvf8qC6KHn+Ly4+AERP4FvDPcbK8rn6CWt94AN3xlO3xiN9Ez/TPObcH1Erx8pKxgCFtBb3CZzrBlEYNgY9ZDGO6eWV2C8h+bmsuLiUSbwz55ZLfA03hTC6jqKgSycr8AiF1x9iQfpj8Kkdlp/y92lTQGm2MUbRFIDEVOtFLLTrJRzIRS+l7t1PIB5MjD6QkMDlFT0N6IyYe08udsr58MwTyKFPaVmWjYxTE1MuF/fkNGl2nbNAqxEPSVzoIGh/ptXmCQ0T3pGfb/Zkcrs3P3tnUCZ3kTPLz7PvJ1JWNuw5vs+FTMGXfP68viDm8CrXYOgM2ia8oUpt1Yx4KnFeh5HuNXM0fuCiIr/arVIeHzUr3fPUHeON4qk75jyV7I5hogZnzYFWjTwOJ/TRcs9e04WqrPyndkerRr7ZvwSI/FelfGk6MgbDhVux0j2awMPxTNh/qZnIJrRFxWgXGjZhwbNxnQzVgR+qhTC0Cw3vDbe4GJd0dU2lPI/Hk9LJSAXSQIplICgnEmCMbsoMvKgDqKeVrnDt52BeBo/y7+MECkXN5FK+WO02k6FWL65KShdpc7kGDf6FlTvX3GfkPhOLTAawZgZkLqAxCtYQh1HCmgJ2hcm1LpTaAD6RW3Lcn41dptTmDkMqYQPjDNvIyrtKCQqfJ1mqTFR5YmQlnlEVo3LjWZHOVcqJ07/5XEwG4EXJdUIQHcFE6FK5/0B+3oVT4IsxKeFirkWACa8IUVZ6L/ectQxVJgI19Uy1eYaFNDkd+dGhQD713/EtP9+lloKdEDUGoewGxO0+SQj8GA19tFTK0ySy0JVbcu5XbL1gBE/dMe+Ot7nyC06rPW/t7pZW4ld+ydY37H1dvIxGj7zd3+XKL3nujopXoFXg4XjK1rdcu5a9r/nAnfH18YQvVFeiM+BbLscFv7B7hRA1R6aji5Z3utuY7rtzs8LCCzdXmhuIv1j+Uu3mXMkpA9OqMv+9pB3VjHuejpdZaIkKrEo13gwEm8WaheabXei5qs0wTESU/HvePxUP5f/KsaHw95XKLb/TteWqv9mmEhZRBEzmK9jsuiflHZEdnzfcLDjCvGdgduGTYSyVmplrkI5ZmraUKkU3MQtzb4bsaYRZb4d8ffl95tDEzDyeF1fpmFb85C2UvpL57yFdS6ZMz4uI0tiYG8uJjqync+axU330ev+x8AQU8KnmCV0Usk2rRp6649JKvFKe+9UVa92VFTiLg9yrNpKWi4b71QWnZs+3hnsYpCXYZ9onPOxOODGH0mhkYUYa7Uo58H5ouF9dc2L2Rcw0WCEodaHi0XjCyvY0WjgEu9Dwjf4lfv/x2/yp7cjvWP8iv9i/wmfqJ/zN/mWeDMc02hHqWayYV/x2xvWfT+g82VLOPM5WDtXU3yVPRWbB5S0j3HOMIeewQ0KOlRxDaTsNlKz1p7So4GS58LpOZB1XJnvpfYgRFWJtxM3Oq81c1MKnhhmVldi9dPJJhBsQ45TDgJypSJkCVdc3pcsSsFkmVdbRq+viVRRKbtlXQ+5DVlVJCclNnYBs5i60k+JQTIy/fJyE8Kt6ISSsRTsdIxvXkNqPlSYgsRCYdE5Nhljk2/OYl2ef8aMZQAgTjyF5eqVHRFUJCWkm8y5eoJso5okQVgzJ2E+Zow/ZPhZGQPr/7XnkTjnWBzoqHlQXfLp5zHN3xFf2r+Gj5o3mGSvVFw5BpR173/DecCZNQ/Uo3kPUoAK/ZvEez9wxt2rJPNyyW565I9am4+XqkpUe8Chu2S3vDbeEmRhafqT9Dkvd83P7N7lld3yqeVo6D3k07w232PiWP7Ud+X3rR/zRq9do1cg3+pdY6oEj00s2oiLF93qa8FmgYrVIHsHspedqr/zymbmdKQbE3eTeFx7BnAwyjPLSQxTCSE4d5YGWST0+QCqgL5r6JRxQaSX1pTcCy0US/IxCsc1udjYSatYPMK3iCtKADSXtV3LwmOmeQ4SQ3PSUigv7fSkUiodD+p4pZJ5y3Sk7Umof8oR8IeWXY3q9XBCGEWbhTxyGqZMPlK7LKmEIqm3l2vJqnPUds3GeaSlkUC/ud6imIez36OUypU3TNeVVWgsfI6f5VAoHSuemOEvVZrwnG+coz0yl61WVhB1xSL0Ncg1FNhgfsX0sjMAYDef+iK1vqROy/vXuZa5dW/5+v7oShSHVU1nHz2w+RaNHjm3H5bhg5xpO7J5OH3g4nHCUlH5C1Oy8iIlufcvWSbGSJiaREsPWt6Ua8LXqnPfdGT4q3uvOOLYHHqoTzuyeSju2vuViXHI5Lvgd61/kj169xv/29H3+3fPPcGIOGALPxxUBhXJQKv+GJIdVVxJLOy+xbFbbAVnxE9GnKAhjJ08AErFlErwsTTBhWglyFSLIgHmRSELO8yOIc4iovGLlmoMQwcRJxiplLNSilQmey4KHcWrRxeT6C41ZoVYrocPma0pEpHLfNqf/SPRa2U81DerQldhaZc8k5+NH0e9TM7BT7idM2EGVJ6afUoVtM1NDYiIt5U5JwacJLunaeOgkRGkbuCaFcUp0DyGFb0yGVVkUorWQvQZ9lHoG5PNmgzHjFqjlcuJGJE+msBhzgVF+/zGCVYVLoqpqysRUdfFocprye6UH4WNiBAA+GM6okrt9x25Ymp59qHFB46Lhb+xe4x84/jrvjHe58otiAPpguVtvWZtOOhiFmi8uHlIpxzN3zE9fvc5vPn2HVjm0CtyrN7zb3WLrG5ZGFIqWeuCzzSP+x+svlv36UPHZ5ZMSkozRYIh8pnlMV9f8wu4VfrF/hVaN/Lvnn+FfO/sG/9bTX08fLEem5+XqklBHAQGNAlWjdgfoB2EOek+oF+hMGXa+oMVFzGOupTen11o9xZ4gLz4LfugZKy9Tf5MXEh1gZuh3xibmikDZoJhUKjxnHBYVG1WIMLlhSpynwoyW1W1/mGS6My8g8/NzPYBWN7sTzY1LBjjtjH2Xv7NoC9gp9FhB4kVoI5bnlVl/EaYYOzMrs4eQGrAoa4hjFnGtxOgtl7L6juO0mttpZY3ZMBUjNAMXA+U40krN3XxXxkx5/MzfSMIiQKkpKKXC2ZAlsDQXIIlxrSfvKBvuGAsm8L1ERT4WwGClPLfslteqc37D4h0q5VjrjlfrC45sT60db7bP+SubzxKiwhC5V2+4di3XbpEKjjz37RUPqksejqeceykQ+dL6EX/x6RdEaUiNRRUYxMNo9MgYDX/h+oc4q/a8UT/lleqCB9VFARMfDiepMWngPPU0/MLyEZ+rH7EPDSfmwL/19NfzB+/8FHeqLSf2wJVfokcR11C7DtWn9GBTlwaleruXl98PN8MBpYpwhsR0SYY7x/dzoDBp/8dhlK5E87RhKgOOXZcMTPIActw/kwHL3XcLeOhnTVK1vlGeW4CvvEpBkfHOf5PimYnJJy5qOn7WM0jXGv3URqyw7LKGQMY1ErCZrzcj4KptxdMIHr1cSkiQPQM1mxTJvY85VVgKc1KzzlRuTIwTzdZa6SCUuxF7L3H9Zjt1N8oZhDBdU8EGirqSvnG8uNlKN6bDodRBiNS7iKtE56ZnMCcYpSKxXDBWMgTOy3HzojDXK8haCt+DNfix8ATGaHhQXbAPDd8a7tHqkVaPjF6q+D7VPMVHzXLR88Ptu/zs4VNUyvOF5SO2vuX9/pSjtqNNk/bM7kozkqvDq/z47be4Zbc8sBesjqU5aavG0sFIlIkP+KgZo+W5P+KevWbvG9am49cu3uO5P0pEoIGHo9CUv9q/wlIPGAJ9sPyRi1/H7zn+Ob463uHReAqRAiKVvLJSEvcpJZmCQzch1TbX72fpLnFD80umqicgLVt6KJkBQpyKdgpCrqZVlxmwOGtYmSsY88qOTitdlvNWOQZOufeuE3c81a6zP0j1WzZAZbIGouu4IaIZU0FLidORUCBGwmYr4UBVFwMmRUxJTyCDfRm9Dz5pCSTPiThJfKeYO2YeQiIGqdyXAKZrtelZDKNcT84o5PqD7U56AGYsI6deYVr155hMvvZc77/dTT0MjRGgdHRT1+JDN/UyGJi8vnkK0fvEybA3PIc4N5K5+CqFOvHF9/ER26/aCCil3gE2yONzMcbfpJS6BfxJ4E1EbPT3fK8GJD5qPhjPZl2CpU35M3dUWH0Px1NOzIG3hntc+QVHpuPhcEqrM9I/0IWKfWh4pbrg2reMseE7h1v88NH7pTUYUNqYy89Ncfmv3IJLv+TJeMyp2Qk7MViGxExsExFItARqngzHHJme5+OKIyMrzFfHO/xQ9YxTvZfsQMYAEhVWxYQc5xLPajYoQVbK3F025+FrTUyy2zG5ljnOU1lPQKmEwpvJHc9trjOtF01EqtoKb90Y8EnnTqmJHJT/ln9PVGWAwlvXapo084rGuWahC6l1mJoyEfnY2YVWWqi/aU4Utzy73drOMhM5BSgiIlkApLAsYaoizBhJTlvmgqKMdQCqNqVicK4ShKEY5oyrF3ahNrN0Xc7qxMkDqatUl6BT+7T6JnU3Z1gySSl5OdH71GDVT8+vXE+FijZVJc4yRUWt2cze87TFHELOvccXtu9XOPAPxRh/ZNbn7F8H/nyM8XPAn0+//7KbVoEmufZ9yuEv9YBHT5V/sebM7qYuxqFGJ9pxF2tJ+6mRlR5Y6p5blTQtza3BKuVZ665oFchDEJYhUCTOcpuxvGUqsE+plkf9SUldnlV7Xm+ey+fjKR/4JS+ZPX45o5fmLXe6macHXyjz/NBGEX6WZ57Ta+cuOszy1H4aIDnPn+XFMwdhvmKQEPGQV0c7GYAC+uliIFRTT7X7cHOA58mYufD5fkHCk2xQ9M0BW64/U15nAh6FmJPBtJxtSPcv+fJZmJMnSN4KuOmLUbjRtKPsp29mUzJDMxcY5Tg771cwhpnnBSWMKZ5DwQzijXx/eWbBT+pQ+Tnk688Act4330/GRHI4klKzuVuS3Ft6Zzls+5DtV9yQtBxAPIHfFGN8Nvvs68A/GGN8mBqX/mSM8QsfdYz2ldfiK//qvyqpTB1FsXlQKA/RiFetPQQjv6sIBNDzAjlDauAA0USIoIJC90h3YBPlux6Uz7l0iiy4byKmV4QqooKSv/l0PC37SoWIfMd0ilBHQgXKQagjepTvhTqKAagiqtNUl/IyTaeIFkIVsTspUMnnV3kO1+AXEeXA7hXVDsYVjOuYGIhgDwrt5BmESu7dDHK9bgH2AOORXHfUEe0VPl2f6eRcbnnzPqst2H0UOr6ZnrdKzzlU4BtFqOV300dcq7CHiB7TubJfGeV3lZ6ZiuBrhXZyX/k9APhWESo5pgoR04FbKaptyqd7GNYK00dCLfsD6AH0KOd2S4Xy8nxCpdBjxPQQrOwf9fQuTRdBpbR5epe+UrIcRso1ovLzk2frlgpzSN81KQNbSwbIjBFfybs1XcQ3MnZRSN2IStc1ZM9HVmi/ANPLfSgv1xttAhR1Hp9yXyrItYwrhRnkXblWYcaIW8j5Qi376DwWltM9Kge/8B/8a9/fhqSzLQI/oZSKwH+cOgvdnzUgeQTcf/FL8zZkrT3ms//5hdx4ksnKRTeZTZdVepWPUoufwU4Nqn/BSia0PSOwsbLExqA7SbVErRM4l+L02pZyX709EBe1HHNWsRatlpB6nq7Jbr1S0g3Yp/RSKhv9m/+7W/w//5H/F4/GU/ah4S9dfJbWjHx+9YSfuXiDs2bPygwcfEVAiUfSbHm1vuDKL3g2HvHXnrzBb7/3Np9ePOXCraiU51uHO2zGFhc1KztwUh0IUXE5LvjM8hnf2N3ji0ePuBinVt5n1R4fNW/t77AdG764fswYDTvfsLYdf+H9z7PratxoiFERvEpS9nKv2gRWi543Ty559/qEy6sVpyc79l1Dt2kwraOqZGaHoFAqpuNovNMcHR/Y9xXea4wJuNFIEeKyp60cvTN4rzlsG9rVwOVlK+Nh0Jy+fM1uqNA6sGxGjA4chorDYBn2NSdnO/Z9RYyKZTuwOdSMuxrdeI7XB2rr2fc1lfHshuqF+imF1hFjAloHtvsGPxh07akqj/eKGDR1MzKOhhg0tvJ4p1ksBvrRMo6Gth3ROtANlqZ2HPoKYwLea7SOnB7t2RzaUvwIcOdox9Wh5TDKNPRe0zYjLn1HhrI8x2GwxKB47e4FF/sFzy9WtKuBYbAcHx0wOnL/aINWkeeHJd1oebDcY3TA6kDvLL/wH3z4BP5+GIEfjzG+r5S6B/w5pdTfnP8xxhhVHkk3Py9tyE4WL8ccmKjRyyTP+xkjKTYvdfnKO0JbYXYDYVERtcJkI5Bdzqzqa6UNeDRGdP57D0aJ1PfgxLoa2QelUMGjfBBNQO1lglsztRP3EbxcCzDFxjZdY+eKhiBAdal5NJ7y+4/f5z134Ov7l7gcF7xeP+PdhagR5ZZpG9/igiZExTvdbRrt+NziMb/U3uNzi8d8df8gCaWMpVBKExmDdGf6dct3eXe8xYPqMqUpO766eZmtaziyPZfjkpXt6XzFZpSsSh8sF8OC9VHH9tDgnSZGhR81MShwGlUFqsUoE0VHjqqeQ18TesPoDSEoGBU0MvFDkIkv71hwijhqXCoY8p2FxqO0VOQNg8V7jVIR7zXRa5zTEJTgCEBTObqhYhwNe5hNEIhBMTgxKN5pfJRJS9onRCX35HWZ/ItmZN/VaX2RD4fBoLUmBCX3DljrGboGU3mGPmsWxhRtaYbRMg6W4BShVvRdQ1U7RmfwyaiFUYMCv5Tzh1wJCLgg9611ZFGPPD8/oq4dLn23aUZqG3DZmI6a3VAzegOKYhgGZzlqe07rA523GBVpK0dAMbiK2+0O9xEVnPB9wARijO+nf58Afxr4UeBxCgNI/z755Y6j/HfZiflJBFRzQr1VkUKP1VmYInsB2XsoFXpiWPTg5BjZw8jnjVG8ChfK5Fa52UMGgnzapzDuUjyd+ejDSCkSeiEfuw8N77kDAXipuaLSnjFaqhQH7EN9g4uw80JmGqPhyi3Trcl5G+1E6Vh7QlQM3uBSFeRzf8TeN9JqHZFXc1FIUVrJfy4aBm9xaSC6qLE6pIkiAzuC+LoJ+5CwWP4+OMMQjHgHSiZ5DLJv8ArnNMGLIZHJJN8lqOIV5LgnBnVjwsWoMsAv5/YSqhDgMFS40ZRHn/f3XjxCoVQElA5yTZkiG8UI+Nk5vFcyiRADELxOnot4MDGdN18fCmLQqaOcKt+Ta5nuJT+7kH4mP0cl+81X95CeZ95CMmTkZxKne3QhGaZ0vNGLQVU6lmcH0I+WzltCVIxB042W0Ru0iumzm7jTfPtVeQJKqRWgY4yb9PP/HPi/Av8N8M8A/3b697/+ngdKnHeVWmGrQGFt0aYV2MkqjVbovUgp6/1MkSVQUGyVW0ZBkbBSh4mUo7yR/ZMApx7dbMU3RRWYYRRiT6boDg7VDTOALhSDoHaHQpjJEmKmU/yli8/y9f1LvNRc8c+d/gz//Pk/yV/mcxx8xbNhxWW/4LQ5cK/Z8Kw/4nJYcFRJpuGQEPw+VFyOorFYBc+nFk/56f5NHu6Oaa14Qd/Y3KXzFbeaPT909JCffP55rPIcVT2XwwJqeNytee/qBIBH7ZrN0HJ/ec2XL16RFbwzqDoQ+8SKM1EmeFSs0ur5ZL+WlTu5uq636IMmRIsLaeX3Oj3fFMJ1mv6QhFd1JDhFHAx4RagCvpEJUtUOtTOEhUON6buDYrttCZ3BLh21lWfTDRUxaNTeMK4NL59dMwbN4+cn+IOslKE39JXFLoIYjJiuV4vXoVVk3FXoxmNsCg1HjerEw0nyq7iDRdlA9JrgLb51xbsIXgyWd0YwVFcVIymTXhEGw6GvOVr0dEB3qFE6cHVoGQaLtZ7tRkIF5yTkUIBzmmGwhFETnYYIm11L8IYYlDzLUdN3FbTwZL8uzyZE8cwWVjzH3v3geAL3gT+dUFYL/IkY43+vlPpp4E8ppf454NvA7/llj2RkRYlWSzye0E/lgrjrppp+VkoadyiVxDkSBbVK7LZkMGJdFTQ+tpV8RytUn+q4C9vK3jBCheyR0nqyTCrBJoyWcuDKSjFQxgD6AZp6EgtxnmihNdL6fOdr/vnzf5I//bk/wz/zzu/g08tnXLuFMCKD5nJc8PiwZj9W1NpTG8ejbs1Ft+Ct7i57V7MdpUvzW9u7bMZG4tkUFgzBUmuPi5qfuniT281OsIaoeHN1DsDeVRy3PZXxtMZRtzsa7amMZ73quE7xrrOBEFRZuUAGVl07KuO5d7Ll/eGUW0d7zoFDr2nOuuLKSpwdaGtBbje7ltP1ge2hwY2Gph3Z78R1XraDxLzOYExAnQ1UladbenAajhynx3vUSVp1o6zkSkUWy579bcXZes+jyzVKwUu3r9j1NYMzhKBZNAOLesSagDWebqhoKsf1vpUQxwaMCZwuDxxGy0ZFwlKzXPYs65Ft18ASls3A5fWSdjGUz185ueJ6aLjcLlk0siCdLDqebo4YR8OyHTj0MkFvrXecb1ZoHThadSgV+eJtcZB/7oNXuXW2Y9fVDH1FGDW372w4anp2Q003SsjknBi7zlmeXx4RgqI9GlgvJZzL8f+GBuc1gzdYbdF25DMnz/ifPmLq/aqMQIzxW8Cv/5DPnwO//W/5QDO1HYDYWDEEqdJOOVWq8VSMEtNn2eXKTqq9mW7Z1KBmqQPvibaRSe4DsbKSx52jNBqi0qhgJuAwhxjOF4EPjBHjkvPMw5j6CDRJSnwsVNxQRT6/esLr9TPGaPnLfI5/5p3fwZ/41F/kX3n4m3itPeel5oqXq0ve7u/igsGuPHeTlNorzSU/GT7P6805jXbsnKTkDr7Cas+66qmNY111fOHoMQHFy9UlX96JyvK73RlDsFyOC46rjs8cPUOryKPdMcfHHWMwHHzFZ9dP+fbFGREKMKh0TO6+PIIxWtp65KQ+8Pb+FkrBddfIqmclrh1Hi3cabQLjYAuQ5gdD147i3o6S3hFw0LIHmtqhdaTvK8Jo0MsBvEKvRsJewpf9rkUbT9uOCXswjOlYPuhy7AjsDjWut9jaQwPdaNkdmgICdoOs1jm+B3i+WbFse4yJjAfDPrbEqOg6qXg87OvyTEavGUfDo82arq/wzqB1oO9q+tESgsT/m10roZyKjF6MHFAwjPN+yXZoaOuRzb5h6CpOT3fsu5rL6yUb22BMKMCg7w37owqXwFYijIMlLhTHy47b7Y4QFW6pGb2hsY4QFUs78MHu5COn38eCMUiMqN4RGysGwclEpbKTOq/WKD9OuVObJmsIEysvCXIqP06rdXbR3YwimyW/QSZrbaU3YCWEk9jWRQosGoVSlYQKSoki0JiooKOTakDnJTOQS4GTgbI7xc9cvMG7izMqFTj4ik8vn/GvPPxN/OGXfoo/evUa3+lvszcNr9bnvN+d8p3tGef9CqsCn1s85nNHT6iU5+vX9zlr9vTesnc1592SLrl4V+2CI9PzUnPFL+xe5X59zdv7O7y/lxc/BsN3/BmNdezHin1f8+XnDxi8oTKep9URfS/HqmpHt68BMQTGBowNKBWprBie47bn6nrFsh7xQbMPS1btgDWCwkcoHsFieWBwljfPLjg/LHnYn+CdoW4clZbJX1uPNZ6dkjh32YxwZ093qDFHjpfXG66bgc2hlXg4TeCTo56rzSJNqkjdjJy1B/aLGr3q6MeK2npuLfY805HNvuXWesf95YZffPgyMSqqyrOoRyrjMSrivMEvFEfLnjdPz/nFhy8LeJcwjYwLKBU5Wx54lib83fWOD0bDZ+4859sXZyJjp+T5jaNhWY3s+5plM7CqB0Zv+OLJY67GBb90eZdbqz3ffnwbHzTeGaracetoT2U8gzdsuoZd0sL8tXcf8pX4gO2upWlH3jg553m3Kt7fZmyxOtAYh1WBhRlZrS4/cvp9PIxAiLI6+zhVl2U3Pjfs6IdJvjqHAm1C+X1msk3knJjUbgiBuGoF+BsdcdkImJTj+VQLHmuLGqWyT2/2qbY9eRikfoIZcMwAYPpXjU6KgQ77cr0gOduzZs9nl+L2PRtWXLsFr7Xn/NGr1/j9x2/zE4crHtgLPnBnrMxQXtxZsy9sRo9Y81o7yQbYgY1pGLzB6MDtdsdvPnqb16rnPKgu+WubT0ufBePKv522WB04jBIiHDcdfQKSFnYkeENwit5XEn968QKCiTgbC6D3cH/M080RYdRsDg3doQav2B4aYoQxGZMYBRDsd4LCf1ufMYyW6BXRa3qvUEY8CJdW0nGwjJuai6jw16mDj1O825zSHypM5dP+co7+UBF6w8n6wH7fMIyay6MFm+2C4BXayEp/GCr6weKc4enFmqv9Au+1sHR7S3eoxSDUjqG3hIPlajR8tasZO5sAPrn/UVlq6xkHy/l+Qd9V+MHwdLPC9ZZvPb9Nd6gJXhE7w2gjpvFcpH3H0XDFghgUX7EP6J3l+tBy7gx+bzkYSXX2XcWzsBLwMijJQhws/Wj5ypMHXJ6vYBQP6KuPX+Jo0fO8X9H5isNY4YLmKoonMgTDfqz5qO3jYQRiQG925NbapRBFqUnYEWC7nyqvtMLsJASIXT81fJzl/3NFnc5c/UOH3tkpdMjyVrk9uPfEfZfafvWFWUam+ubJn4t7UlUXzkk14DBKLUDueRdgZQZC1OxDzWUvGMBLzRXf6W/zE4crfrx9zM/0t/h69zJ9sCztgNVy/V2o+Nb2Dm+2z7Has3ON4AKJyGp0wKhIY6RbU+6r4KLBqkDnLVpJWk+rwBAsPuEIVgd6L5mH4/owZQG8lpRfUGCjhElewM8QFL1Pbr5XJWwgwDia5PomuxkElVYmgpN0muTcFSq5xWHUQuwCIiZlGtKYCNO/BfFPYBdAcBptA3jFrqvxg1zTphNjFL1KsI8qnokMJY3RgTAYAfuCSqlRMHbKSESvcaMVoLAOYhgT0i6gZHLRUzrVOUN0kt6MAdnfRslQOHnmIShCMBjrQUWuu1ayBioy9FYIcDpCDIydJVh5z97JvaEl9AkqyvtBju0TzbzWns5X+KgwOlCryJAWyN7/gLID39ctC2/k2Dun8uafaT2l7UKUmu4PE0vIwhIwtWbKFVVQ8IUb6qzlXOk/hLRUWnvPMwIvagLOKLi5Ai/TZQ++KiIop80BFzQvV5fsTcMDe8HP9Lf4QvWc69Dy7e72dAtE6aHQ7NDJpctpP4DeCac9pw+NChzrjsuw5NTuuXRLjmvBt20SXW3NSGsl5z54Q++suJvBysBE0HuvJF2FAmUCOnkCde04qTue1SvGymJMwFqP11BVvhBbQFK4Sqe4W0XaemT0BtdVKB3RJpbUnLE+2WBFsIamGdnrGqoA0VBVrsT/xkho4rSWnLuGynpcO+KdYd327PcNWFFittbTVo6xEuNlKydGsHFoE3AJkAyJqHOIELRFmUDTDhx8U+5BpwyCMQFtopCJnCEYeTaDFoJQrD1j8oRI91pZT1W7NDzlnR01PZ2T1d1WnqEWIlUICtt4bArDZNgpohfDv6hHdlVLVBpdBZpG3qvVHqsCjfEYHdAq0ljHrWaPn/ETXtw+HkYgUog5N3LxMPGuy2RPnoCRLrAqzNo+QwkJSi32iySJ0vrLT97Ai+fM7cLy5J4St9NxSoFMLqX1N42F90LBRRXJ83vNhstxwdv9XV6tz0WvsHuZ69Dymeopv9S8zHv7U1rTEVB4NK8vLlJFZMCaERc1CzPSuYpDSkf13vKN/j7XtuWZO+as2vF8XElNhatZ2gEXNS7lrysrwq5aRckoBE3bjuy2CbxqKC6w1gFj5F+ApR04Wx44HMS9rCpPnxB2paJ02kUAK2UETxh6S1MJSKVsEIANmUxNO7JqhsICvBws1gTU0om3YSJGyWS2NlAZj9cKaz0haA5eJfTfM3rDUd3zzEppotKBxnqW1chYm3Qvch9VymRklqMxTiaOjpBwkLYecc4QvEYbMXJV7VjUIzFKJgCEtFNbh208q3Zg19WCH3hF1cjzMDqwXvZY4wu4eNbs2egWHzSrRY8bDMtmpEtGQquINXJ9ewV9UBy1PZUOaBsI6RkuUxbGqMjSDhxcxRDEG2ysw0VdvMcP2z4eRkCRVtg8gaaijRsTPNexp7bQKu0bc3UeUJRmYwAnUtZKa6iTN5E4AUVIUyvBB7K34Zx00U317UWvfy7/Tb5e2V819c3+drNNE3FBc4gVz/ojHh/WuGB4vztlZQb6YPl2d5tfal7mjt3wW87e5q39XXa+5tm45pu7u1y7loUZaLTjkNy9gLh8AEMwPOpPePtwl7Xt2LlGaMVBc3AVVvlCGskrdSYRZc+iMl5cV69TWCC04YgmqCA2EVhXHYexEvcUIRjhJYZtFwPW+sn1jkoEfwdDP1p80GJT83eVuNGDMYBhcFYQb5cKRhIZKCQikZBAAxboE44QBsO2a6jS5Lrq28KuU0aIQiEqcZFNIATN6HXiDajCAdMa8VQyaScqQtDCiSBRobWEFZtEiDokyrD3uqQtczpPqYgy4BIluB8lyzEnLu3Ghk3fsOvqkk71mVgVFC6FMtP5JcvggyY4jbHiwYxeSF9XQ8sQLLtRiuqqapDwxf1d0IasBJIzeecsaBFDaviQXfwMHGbPIFXMxXnFmNUTa9DmfUXNJeZ039yld16eRL4Ga0XRJ+vIJ7HPufJuaUqRNOUmwHCq/vI13Gm2krYJhsthwX6ssCvPd7ZnNMaxtJJffm9/ym85e5sfW32Tten4+c1rXLuWx/s1e1fzu+5+pVzu4/6Y1ozYRib30g70oeK9/SmvLy941K1ZVz2NEYN1UolnMYbclUmxqnpBvVXkrD7wHR0xlayuhbXH1J9A60BTORZmLAPZaiEMZcagUlMDEp3IRCEkMk02PjoQ1JwpCD6IlzKP+6PXRQXJ6Mktrq3DqEifeAA4Ie1UacXsRys4gJPqrJgYdDEqud4oYYdUTQuJyJgpzJCLEnwkMytj+V8ykukeB2eFBZmMqzaBYbAJzIvUlRciT/5u0IwzXKP3VtiQzjAaCV98yjZkQyAGJRsqeV+jl3dUN6MAhuk6swFwQbOoRlozsnc1KMpY+LDtY2IE4hSnm1RANOtue0OoM6/Wc1GNJLl0Y0uTteT383HiOPs915kHcCTBTsEdlNdiTEIgZm6qMaUxiLjLutSJS02/EfELK3r4fhF5tb7gne42tXYcVT21Fh5ATgNmELA1HW/t77I2Hf+z5TfQKvCLu1dojON2kzoox4pX63P6YGm0yKBVaZU/H1bYVJpndeCV9hKAQ1VxVkmbNElbyTnvtluGYFmYkVebC/4GD1i0I4dDTTQQlLjG2ogLbVTEB8X5sOSoGbg2C7QOVNZz0FKjX+iy5Lg3SIRUyfdjypk3taNP3P2q8iyboeTueyNpvT1I3b+meC/ZlfdRAONxNGAii2bAaPF0auvRJqBswCacIsfDefIpBXXtsDqwO9RY66mtK+Qrb2Ky57p4NsYGUBGbQh/nDG0lE2sYrIQKlewr35Hv0grzr6lcusbpfhrjsMZjrS7MQWt8uo+x1BYAWKvwHtZNz/l+Ue4j36MLU3g3aEnP7l2dxoOnGz/mykLAtMK+uOVUXS7WyUIRqUDoRa7+jS3zAYrmvJ5lF/RE+83Hy1rxeUv7KZg0+bKCT+rlR66NV0kSTGeOu5SZih6iK+BgnSxyTgOCuOYBxc7X/PzmNbQK/IPLX+Lr+5d4vl/x2uqSn9p9mj5YXq5FcLUP8uqCdizMwMr2aBU5rfY86Y84sXuejWusDux9XYRXQhQm2cKI+7m2Etdu9q2AfE5cTUZNMBFVBZyOuNGgTeDx/piL/QLfGw51XcKB6BVDX5VVklSPEIMiOkHUvRPabvAG3xkYNYdku8dUVRicZneoYdDEQaMGxb6rcYPBWVNWvXE0uEGOMTgrqb1UgOVGQ/Qa76NU+flI31u8nQqEukNNVTv8aNg7g2vk/bjBgJN7HwYrWQQT8AnoHHrJ4Y+joa8Mw2BLWlSpiEuEKe80XZxCpn1f0/VVgY2UimzrkX6sGFMRlTGBfqykBsNModucCLUdaslOHCyHVDLvvcY3I31jcVGeRxcpHsEPtHbg+7qFIK2ZZ3z86W9paOXWUm1qjAlCKJop9k7egb4hIiE8hEFW7CwCmbd5BmDeiso5aJsbmQNpnjHLJJCaZmaWodWFiGT3imfjEZ9bPObKLTk0NY+6Na80l3xu8Zh9aFJ78y0eXRqd/uLuFb6+f4n/y/3/kf+w3nDHbvjq/gEA7/enuGi4Gls6L30V7y+u+dH123iki9OdasOT4ZjzYcXGNYS0wq3MwBAMvbe8s71N5y1P7cCDxTWLZuDQ17TLQXL/VhBwY71kAUzgqO35wsljXNT0veVo0ROj4nlvaZcDq1ZCmxzX9qNkEPa7lpdOr/FB8/6TU/EgjsUYVpWjrRyrZmBwBjcaTo46rpKL7XrDnZMt+0QFbqpR0H4vBJz3zCkhKI5WHaM3nC0PQrE14gm8dLJhVQ083BwXLY+jpuf5billuMdShh2B47ZnUzVsty1147h7vOXptWhVjoMtpcUhKJpm5LjtuQxS93B7LWXB62WPSxhBCKqUE9852nGhFxgdWVQydk/bA0ZFWAkI+OT6iLvrLee7JT5ommqkqRw+aKny9JqTpuOsPfAtr2lq+dvd9Za7iy1fWj/i6bDmSXMkbFLt2Iwtd5otZ/XhI6fex8MIRIq8VRxHSjfcJI9dZLUQkESNSdAxKfHGcaRIdGfppayWE0WMUh361Hm3nrINSbZbZb36zBXoejnWOE4S2tlbiEnzP/eRA+l2m5WC20ltp9rBX3vyBr/U3iufXXQLfjJ8ns8diZLxt7Z3uNXseH1xwTd3d3m8X9MYx/P9iv+w3vB/uP2L/JtP/j7e259ideB6EDrr9dDQJxfv0W7N0+6Is/rA0/6I15cXfO3qPqM3HEZxsw9DVcCzfrRcVgupRBwtH6xO2B0axt4KwcZLejDbvtoK8n59aHlnd7vEl5t9i9Yy2bSWcMF5IyW2iUtQNU5WfaBzltBZWLgbMbTVgXtHWx5eH+N6I9oCTkpwGTXbrik8gxAkNVhZz/PdEt8bgvUYLb0Jl9VA351KqtNrLvYLxsbgg+LQ1+VZeK/pdzUhKFZJF8AFzegMoTOMKbwwJrC/bomDZrnqiEk5xZgg2GUCGV0Qw5gB2P2mwabMwK2jPaM31Nazbnoh8hxaltXAmDwbrSKHTcNz6+m6CmMks+ODluyHlXLmJ9sjzpaHktp849YFB1fxUnvNe4czDr7iXrtFE/nm5g5aRbauYfexJwtB0kpPkyjTcGerqkqtsW4Qgowhy3Ln1lMF+y5toJJXkeP8pNQqB02ufxGqhNLqO8f5mY5cOui4CW/IPeCyZPYoir+qTi7rCn77vbf53OIxWkX6UPFWd5fXm3Ppv4jizfY5WgVOzV5anbma282O11aX3LEb/s0nfx//zv2f5w/G30AfLA8WV5zZPV/f3ufxQQzGy4tr7jfXnNgDn1895seWb/Ff6d/Io+6YzlXUxtGakc5XPD0cMXrD6+sLAqpoGFzvW3yWI49KynKD1BIMCbfQWkDIb1+fEYNmudqz7xqpzEvx+jimlJoOhGjKYz3fLQXlrwK5bBeSQEfQnB+SAEoi4YS9FUjIq1Kia23iJUSFSgi8tiJ24rxoCVz1rXAesgvdShYlRkVdSRrwuO15dLlmse6lUi/V7/cJ2ZcKwMDgpT7BNg61iHSHGlv5om8QUtGQHw2HVNVojefQ1+gqEINmGDRPLta8dEs8oatOqL8ZlG2tY/AC2ObahMyZWC0GKutTrYLoFhgd2Q2C/nf7mnfiLY6XHV+5eMCb63O0inxndybvK+Ehdaoz+ajtY2IEZvn1BNSVlT8zyrKbHwKMTKlD56bU3TyPn/rTqTYJN1ZiZOI4TvLLMYBHWIJmAvmK4lCMsBUmY26kmZtDAHKNwzilGqsqST9LodK4jnx68bS48pfjgr2T+Pzr1/cTO1CEQioVWJiB33X3K9y1G35q92m+un/Ae/tT/mD8Dfzb9/86f+6w4Bv9Szwej1nZgZeX1+KaB8u1W/DO/jafO3rCnzr/UTSRV9rLolVgdWDnGqGVuoqVHdhlDkEwnKwOnKf0oG59KoqRvLgxAiZaE7hV73lkjoVmawKLZqA7b/G15NytDYS0Chsr7jNKBmQEbOOwldBulYrUleTdBycxr649i2akr2tMHfCbCpOISlpFjtoeBWy6psTsoxeQrklkJWMkdteZrBQVTeUYvWZ3aDj0deEcxFihdeRo2U3CVDaIEUnAYN9JS/aq8rjRpJp/8XqUEjIQiErS6E1J9zUpbHFOp4kuaxKASVyAq0PLdt/QNI56IaFOzrKMyTjoVCthG8dR0xfq92rdMQzTFM4pwu0g3uhpc+D+6gKjopSTf8T28TACaaUuYJ3WkFafvNqrcdbHrZrlPY0RFz+76QgoF1OTiKKqC7LK53OpaqJPZKAQUtuulKFIx52LX07y3YgRybUCKinH5iYQlUIFiiRYo8U13I4NO9dw1uypU2VgiBqb+iMCdLEqwJ/VImf+5w4LfvtizxCfsvEtfTBsx4bWjhynFKBWkQ+6UxrtaPTI1su57jRbtmNTSovHJEbioohP3G23OG8E4Iqkmn+h9EYk5bZe7xicZetqdkON7wyDk/y/6jV+IQh3TOlF77IHFyEISDUMlrpJ/HyvAAlHtIr4lMILaWWmF/BQjapMpEUzihqRN+K2e1348+ump9Kep7tVCUV8EEASKAIb2Y22VhD1/lDhK41aSBXk2AlV2Kec/Dgawqix7Ui/q7HtKKUtTtM7k4RUxGB4r6lrJ0BmSlVmkHBfV6yaQQquUlji25SCDFKVOOxr9inzUNdiDG6EQaNm2zdSJj0YxsoSgmJzaNgcGuyp1IZcHaSAyOiAi6eFVfpR28fDCMBNNV2Y4QIhNa5UIiqSlYOyiz46IQu9mCKEtGrPMgI3vA0/reDZCFgDu720pMrnmXsXmYRUIF4tvQFfaPxZlF4jRRLMqEAVPCHTfr1lTBM5T8iDlwH7an3Oy/UV7/enXA8tDxZXfKN/iSE+5be1z3jkTlmYW1DDaXWg0Y53D2d0TlKAISoeNJdsfcPri3OejytuVzvOlju+ae7xUJ3w+uKcva/pg+Wl+pq/Mn6Kuh0Ze5GsYtTEFPl4FbnaL4gRrodFIsLIJBn6SuS5k6xYRDICxcImhDtLYeWS3JCKlMaUow9ByWiMSjIFThHrIAbGa8ZhQuCBIgwCEpdfd61MjijofxwMqko8gUQCautRagWiYcivNK3ImeCTrzsX8YSgi2egqyDZDRuTkpKUSsfkGWQQFCV/l1oBLaSrqNj3tWQvkoG86lsxolrKqk0tPI0wuy9JPQbGQRGU8CRKsdYo6chsfHontSFVYjdmBaXGuL8LsgN5ouXinDBrwphX/X4oabgy4bMxgBmQSBIomcH/mQtQBEJybC+xY4QkKZaOm8IRpWYeQAolCkcBKHLPPgh+AXLM1JbKHhTfOtwhRE2lRRHore1dDr4qdN5M51yYER8Vj/tj+mAZo8FFyfee2T2Px2M2vuWRO+W3Lb7Jk/GYL1+/wk4FgtHsnQywRnuO7YEP+lOObVf6MQK835/x8HDCwVVcjEv6YDm2HY+GY1bNwMWwLGkpTETZgK6CcAV0YHTiQcSoIMevOhBNLHUCWkeUjUVmTPYVko2thD/fHWqK9BZ5Yqd9VZR6/Eby8tFOuX1rA7WdakVcrekrWdlr6wr1WOtISGXJSsWJA6ADxvoycSFJjMVEOKo82gZhG+aaDOtTCbHgHNFkCrEuEzBPeFm4QKEKWBrScSojWIKInah0T/K8msZx2JuEe8j9SSn2VGcAYrAGZ5OUmhhSFxXGijBMiEoo1gmfMamcGCgG4cO2j4cRgGnVnTMBEy33xj7ei2GYI/YzsA6tiHmVJ3kU/YAyrUx85xLImI2GfD8zE0sHYJhaY2UugHPSeSdfT24FlfvVp8/yNWsHm1HKOXeu5qf7N9mMDVZ7zrslGyOxW+8snatS2a+EBX2wXI0t10PD17f3WdmBPhgW5hZPxmN+3eI7/OjyLb4xvMTPb16XgiBvuTALNq7hXrPhTrXlb1y/itWep8MarULCAETJyKjIziehkqHibL1nV9dChU01+lXtaBIpxtSBo6rniTrC1o7aitLQ5bYWrv0iAWWJ5Zbjcd8ZFssUy18vqNsRlwb3ctmzbAZ2vZTz1suRunbsnEabiPdKZLnS5Bi9pjIBo4XQo1KRjU904Np6hsoTWpkcbT1idCwU66ZxoupbizjJ7tBQ19MYMyYQGy8TK2c9vKbbNixOeqmW1BGlPOtWwLYYRJcgBEXdONqEcQA0rdROHDWSCQhB+ACN9RzVQpIanKFuRtxoWNQj3jr6MfXdaAZq49lXlaQDV1se+mOGSrITdS3nKw2dEpZhErswpDArMyo/bPuYGAF1E4zLEzT3ik+ufJzrBeSVOkZpu5XbdOeVzPvUJju1pQrN1DuvTmnEtJLn5o4olRh/ltJVJ8TU9tmWxg7lqlVmFDJ5M/PwIVDEPgdveLgTQG1dSfVY1gNQwMFVIg/VSM9DgM5X9KPl8WHNy8trtmMDNXz5+hV+dPkWP9ZeA/BX/WfwiQQ0BMvVICmjSnmGkOL/YDitD6zMwF7X7F1NnXoyLozIXK9qSZV5rQl68qSy4GVlpNgoK/QUryGXFUNydydR0BgltDAp7o+9QS3G4gHYJOYRcxigYhmwWgfJBCQkfXSGURm0YlLXSdvgBCCsjC+fmwTYuRnVFyTTkD2KEGSSuDRBYxY/nRF9QiI8FTHU9ExKxDMj9YDUN4jCkKKpXaFq52sECNEXabnMMgTxGLTSdMN0PK1iMnqeOonMpllTOBwx1Ue4VFsAodQZNMYVCvSHbb9iI6CU+gLSaixvnwb+T8Ap8M8DT9Pn/0aM8b/73kdLEzsX5by4ZcBwVlsgK/702bxN1bwrjXRtnT2AzCLMxkarZABmJcpG35zMuWutSZ7IrFpQJW9AvpszGhqUJlSwsoPEYwZaKwh3Zg1mPYCM/srlRSrlCdqRh25jpBKstSOn1YGdCnxjeImAposVn1s94eH+uFxuiIqlHtj4lnXVoxHNgYWWa2nNyIPFVaEcL/WATqmnQ1+VWFartArO6Kt7V5efsxIuKfrK/P8YhAOQ3d4sr+WcBitU4uAVMWq6oUr2VryHobeMlSf2BpeGRjdaxsSJMOlZqURBjl4MjEvFPjqFE2GQXH9V6WJkKhPoBjFS2ySNFoIufAuJGrVoERgB64yJ4hXZWGTLS7GpN0VlOOMT42A5mJBowyGpDKd0o59CgX6sGCpXDGpWYhq9KcrE4yjArU+y4iEodq7GZYHT9NwyB8SaNKa0Kp6AUI/NZLA/ZPsVG4EY49eBH5GHpwzwPiI5/r8B/h8xxn/vb+uAiQegrJHJndJ4Kot5ZM0/bScBUJBcsk2c/9xyKbnkqmnEi1gu0ndN+k/Lvyamya2n89WV9HUfk5RZI3X7KjMHIcmLpRirshIOVLV4GjlVCEQDJ9WhtFKTy42sq46rdsHtdlditt5bhqQapFVkYQbuL655tFvz8uJa4veqo9GOYDQ/v3mdv+o/w+dWT/j1y+/w9GTN5bjgVr3jvb1kCJ4Oa2rtOK2ELeaCLhmCV5oL+lDho+Z+dYVSkavdolS9Ba/ABkiD21ZTXYIMQo2qHSEYuAGuSWFOlvNWOkI9FROZepIFj04Avz6X91aObpuoyE4RkWrCjLJXtaOaYQJV5RlH8RR8FjjNSHgQCjLIyiwVfNmLERc9b+NgUwyfxlSW/U5FVCCpzSmWT4BjKmLK0ysbiWEwye13hVsAk+Sa1oFcOJWLo7K8Wz9aSU2agLUhkZF00Wvcj1WhVysTJTsRBAw8rntc1ChVUelAa0d6b1mkfz9q+36FA78deCvG+O0PRel/2U3c/ZySk0cfhAswE/hUMKUQlZrUhYeB0rY59yFpaqTJY5AJrPVkUECMDYhxaGqZ1KnTbqkxIE3+EKbagdyjL4cLeb+6mo6XjJYZZFX+dct3ee6P+MbmLkOwfOHoMUem5zcfvU2lPEYFvtHf51F/Qh8qzocVK9vzo+u3edodcb+55totCCjePZyxd7WsQlGL3NfJmv/byz/J26MM+j/Gj/Odwy2e9yvRMxhbOm9x0XB+WLLra66GBb2T9Nzr6/PCgY9REQdB7r3RBCsAYRb0WNpB2IW7CpviYNUZ1MIXMFCpyDhISiur/8QowF43WIIXjoCqZXIMg6xyLmUMADHQXqG8DPAxPecxpSVLftxLbUHGMEKVwLkqFBGQkJD0vrcYE0VZeSPkpBgUQUvKT6koeLJTRKvQdaDbNOhKeooVJSVSjQHiucSgqJvIeKhoj3rhEjjNbtMK5NSbpE0gFYdYbnh+EQEA+4PBNRIejIMtIiTjaHC9JTrxDGrr2Hm5Pj8adONY1iOvH13wvF8WkVEpTnPcX2x4fFh/5Oz76EDhb2/7p4D/9+z3f0kp9WWl1B9TSp192BeUUv+CUupnlFI/M4TD/A/579Nkz3Je3ouXkOPyGym/OHkLkIDGVPCT+tPHF+P2nEFIpJ9SXhzEABVsIMuKzUOE+XGSlqF8d0orKi8EoXfHWzwb13S+wqpAQPFSc8Vr1XPW+sBL5or79opDqPnO/oyLYcHB13g0Z/WBE3vgfFhyPbYCIEaR+dqPFZ0TNeG3R83nK8XzsOTgE6PMWzZDw97V7MamxIU+xfg+ldluxyY9jlTmaqKMjFwE5BWuE6rto92xVPNlRl5QRBuxybX1boqtMyagrFTfAcRRE0ddzuf9hDHk4qBSjagjMZXXukRF7lPXnxiUrPSVGCc/asZDxaIeZaLO1qKcdguZz69zp6SEbXiFH01q6oHIgqkkSRYQFaRUv6+1CJy86F7PdRJETUbJfabUok3PrGgFjKb0Blgm8RIGXboPCSkp9WOoPLpK2hFORFdQ4DdVuQ4XNBvX4KLBR8V2qHm8XXPZiZT9Vd9+9yRM2/ejNXkN/KPA/zF99B8Bfwjxo/4Q8IeBf/bF791oQ1bfS/mYadWfcvHJGCSPQMVYCDpRKxSmeAFoLa65UsREDMpcgFhX0pQkn0epSTDEmhlRaWZ85unAZAiUSg0uc39ESN6BEJwKk9F73AI+s3zGg+qSc3XErWaPiyIv9gu7V3lQXQJwGZY8c8esbcfrywsATqs9K93ztD/i8ytRHf6gO0U6N3suzIIhEYpu1TsA/mq/4Mfbjp+od1yOC15ZXjFGzRCEEJTZZ5XxvHZ0IdqHruKsPnC8ykU76f0oJD2oRYhDKWEH3llsue4a+rbi9HjPYagYD+Ki5uxAjrVzPtyNVtJVlWdoq9Ln0GhJGdZWQDJjAn0UpH1spVVZOIgq0diMGCPSWr4RtV6jItebhdQ21J4QIrmkt3fipi/rkUU1FjJSjpVt5YkR6jp5Myqyage2h4bOqZLOHICmHadVeRBvqW5H2nqUmn9nqK3DrXspu5658jKcI6t6YJ+Ectta5NeX1choJExp25FBRU6ODvJMR5FVq5JiktYBtXDcW2/l99qjFxKiLNuBddOzMCNWhUIFbxOWdL/dlJL1D9u+H+HAPwL8bIzxMUD+VwaD+qPAn/lbOUgu2gFKKk/YgrP85jgIDTgEYtfL5B6dZAHqupT5SjOQviDCcRdQrklCogcxIl7SiMrkUmExBNEHUR/eJ+8kpSAz/Th2naQGM0g5jNK8xDn5e+47ECP2AN/Y3aMPFo/mh44e8lMXb/Ll3Wvcr6/5a5tP46Lh1O45q3bsXMOjTsp/n/RH3Kk2vL684MeWb/Gnzn9U8ICoOLYHNq7hKnWaeW9/yh/jxzn4mp+od/w793+ef/rtfwgQIC/XCOxdzflBKtTeV6f0ziZFmoVw31Ne2jOl3pSOrBYd67Zn10tGwSYBjBKDJ+entk6Yh4lJ50dTCoj0kch5A2gbSuwegsbHkJRwPIetNNHIbndWGsodf0ZnqKzHaCfYxCDMxVfuXArBx0slotJC2PFBVHdiVKXxSG1doRR3XcVq2RcDCYigCZ7jZcfzwdJ1Ih/unaZqxOMJWT1oli0IXqNrV/o3iBaDxO0uaM5Wh4JNqJTDH4PwOxb1KPwJSIBkKICmnEB6RGaZeVNJDwbnTJE5syrQR1sMQC5VH6MuWhMftn0/jMDvZRYKKKVennUk/seBX/hljxAlRkdrqCtp6JFj7SzmoYTUE31ALduy8sdhkLLiuioCIrmXvKpn9GJrZKIm4C+ThtDT8clEJGNQq+UUemgzeQtJ+qx4LTGHC5OXoRoxOOMRfPHoEUemI0TNTz7/PLebHQ+aS97e3yGgsCpw6ZY8H1dsRkHzX2kvObF7ngzHfO3qPv+V/o2C8OuRB80lH/Sn3Gs2vNRes9QiO/adwy20ilyOC/7pt/8h/sSn/iJ/4L2/Hxc1d5odmsivXb3HM7fmpy7e5EvHjzj4ChcN/+jZz/EHv/JPSOONwaI0+EFL+22r2HnNft9grKfzVhDrzrKx0tCDpLa7OzTidqcJleWviEjWwWt8ZzBt7vYr5CBrVWriKcU7/WAJOysufeodKO67/Dt6IdL0fSUAYNC8//wEgNP1QQREuxrTOg5DRTfa0q9gt2/Y7NpUFiwLwOFQgxL9gNzz0HvNxWY5hSgJFJyn8/pReiSGdI1KR3a7Fls5MQIpjah0YD9UpYoxH78ynsNQsd03Rccxk51Anpm1oRhFbQUvuzq0jHvBQQDhbZhQUoFi6BWbRSN9B+zI4XtIjH0/ehH+TuAPzD7+vyulfgQJB9554W8fveUQIHcWClHYd3UtEzAp+qjklsdU5afqWjwB5yb3XAkIGIcRfXoiXoOSVGCEWaGQFx3CLB9WSeYhDmPBFlRVCT+g6yeegVbSxyAxA4GJyGQMse8FlNRwMS756uZlaf6pPAdf8W53xvv7E+kH4C3HdVfSOTlb8Gxccz6sGL3hUXfMK63QgLe+4dh23Km2VMqz8S1Ph3XSnLe8srwC4A+89/fzhx/8Rf7k5k2eOQGF3uru8Ww44rjq2LgWTWRlev7L57+J2ytZNfZjVVJ9WocEiURq44tn9fJ6w6oZOGmki9E74+3StssHnbKssneXOvLcPt7RjZaudhwvOy63S2KE06MDrXXsBpkg17uW28c7nkWp2d9tWk6XsoIaHTiqBirjue5brkzLQQdevXVZOAIgnoNSomB0spierVaRzomk1xfuPGEztDzdrWgqR5O+e75f0HUVde05anuMDlzuFkXay2hhLRod+NTxOc+6FQ+vj4sAaO8NR/UgKH56XleHlntHW/xKl8+Pm45Xlld8Z3uWnpli2fbcWUqDmcY4rodG0sdIMVJlPJ8+eca75owYFfePN1x3LaeLA4exYjcK4croQGUiJ/WheDer6gcUDsQYd8DtFz77fX/bB1LclBcbnXgGJMOQV/py1UlObK4TOEfsnUuGgJK2u1EkVM6rwCg5l01hh/dTOjCpDilGmfDWEummrIDSqeAoGZhsPBL/IKaJsHUNGtH/D1GVWD4X/YC47dlaH6pKesq5hsNY0TmRLd85qQUYo+FvXL/KEEwRjwikngBR3H4XNX9y8ya/++gt/uTmi6z1gTEafvHqZUJUfPHoEXtf83rznL8+vsnVoZU6+H0jWvpOoxtfVqXM+KtTR5zL7RKjonxvW7F1mq5x5CYdQAEIw2Cmhh9OcxUXDJ0les15UNS1LwVC/a5mWzuGi5ZxaWFTcb5YCptuIey53VhzsVvQ9xV+U7E9qdn1tagHW89u1xLG1DcAaCsJHbKW4Tga/ubT+wB0XSXyYrWIm/S9YBwh6OKW911F0470ThODpmlFbfiteFsYf6Nln7gL19sFl5Vn6CwnJ3tRVPKa5/tVqZRUwK6v2Y81F/uFtE67WtA3rpB9jtqefdIfzLiDsZ5KB667hv2m4f2EuRyGiqO2L2PJB41PY8pqYXk+71YfOf2+X9mBX91W8u+jrLTeE7OEt5kZAhJe0A9Tya/3CblPBiHr/mW+QIyTwchCIKnu/wbLL0YpRpoxEqPzxGGQz3K8P283FlIIkPAAYSbGYgh0QouPbF+6A9+q94zBMAZTDINNdN511XFSdZxVewyhoMd1ai5yp9nyfFxxOS6xWog+mshpdeDI9pzUHUOwBBR3mh3P3Jo/ufki/+DylzAq8mwUlZzeWx71Jzzuj/lOf5v7zTU+KvqUdrONwyxc6gcwTWqjxbX0aYL0zpa/KxuKpoCg8Sa5whGcSmDhlGKTL0WMiSVGDym0yBTf2BvwWXtAXMvMtJQP5PMqsS6lTkAae6CmMuJ+tDIZQyYzCWC3qEchBqUCoH1fSyWlu1lbIPck31sueylnLokp6XLUj5Z9V1PXToaVooQxcXb9PhkjpSKtHTE6SL9DG3CjKbLwVocbYF55tlFwjjjqJDKqyvmPamk2OzrBZTon3sFFt8SoHyww+H3YZoj8HKXPW87dx5kEeXa/i8wXN+oHCqo/R/et/f9T9+dB127peRf2W2s9857e8RvPOX36dLd6lFpqS5bUchRANkjGRtgWYKewC0PF5RQkFQI4tgkhQFWK2CQhDlUJfwQbF9gU2JSDI4Ml5EEeIrlH9aDu091n/uZ33OMzrSF/3Ot59vtJ3a0uSyKHp+rU+b532O/+9rvXeu5139f1uyD0o+YghBtMQa1lfju4Dq0bG5XD46gQCH18/MFPAAyMwVFwFCsMlwUO0x3XfSW7dHQhz9OGt90hjU7QMRjExhwAj6IPJoJEhYJTmJ5EezZ9znEqk4CzTtDlw/GhcQm1TcUW7PWoNpzpms+3L/C+9BnXZcXb20My7XhPec7GFSxMzdN+TqI9KrPUfv+6izSYuFA0g2B6WKQC60gg8+Mgx5iAipBSMYEq/CgQIo7M9hvBsFCHBtjgTiTzMQ5cjYwChczVtXFkicOlFp/LqNNF2a/zERYaR4DA6H3IEicIsyhS6swQUDIYkAJdZ+idbFbDBjBIpNPM0rRRCBb2z3/4f5rubcAhKIrUMgSsZIkdYZ+DKMkF0fgniUxgktSNFKfOGfk9xucxbIQmWoRV6iP6TQxDWnvamDg1sBNyY0UvoNxYfX6z691RCSikAoD9qxs1/SFGiAF7ye5g2BmUhD7sF/MYoyvn99D18hjD3f4GNGT4+nCz0hjhoTJeHP0C42/9hrw4+MgkvFG5xJ8fnEf3Chc0k0S87qu+4FE9Z2LaUczR+YQkavgHIGTj5Y02MXL+bVzK1goP4DDdsvOZ0IiymlKLIcgG8SH4IJTZj00ecN5Pebs74VOb9/KZ5mVeTC/4+OIhWnn6+KY476d8V/FEmm3sp7KiA0jGOX4TdQKJ9nIncoptK2ajQUz0K53X8kJI82+QIttW5v3D5wZNgFJB7oI6yOcjwnx4XOc0TZuybnJpTMZuPDoIfqwzo90YkFjz2M/orZTYzkeVX2zitZ0smmFy0PQJwevx3++9oouioL6Rqsc5TdvtNQtNLNnH6UWEhnonXMChIdk7gZIOCsCmSwUa2kfhVHzag7y67lJpfMLY6xit08MxyxlpsBKnE8NGGjem1iW0cZrw7ncRhkBoGhHnxNAR/LDQIzcAopRX7b0BRu8FQAMgNPr9Qxwxqkj5wcZy3oe952D0/UcTUSa36v3Zfr/RyEYixw4xL+0XvPQy4t+VJmDBeUwDr+1OaFxK5xIeLBfM49lt10twpAuKIhl4iCp21r1Qgb28uc7qKU2U+37D3OJxvaBKOiamG+PFBzyXVoHLuuLczjjvpnx5eReAt7eHfHzxkLvZNf3c8NruFICLdsLU7NFTIk6xNH5QyO1FLiEoWpuQGUeayyZmlUHHzvYQm7WXxircDd+/QDfjEUKByRzTqhnBoVJcDZutMAswEmHW6TAuojxxo8Bm6N6LWUjGl0nq6KO92SiJACuyntvVhtevjui7hCLvaVpRPQ4oc4WU3T5RaONukIn2d2KtPYOteRgRDuj1xAgxaV41AgdxGqWEMzDLW+EJeDVWSYNmowt72bLWnjJ3zy1okT2LFyM3djz7KxXIsmgj9nr0J3Txdem9xodUEoh+M7wDv6HXjbvHCOS4aQ2+eeYfvPtaje6/MDTjBn+/kjO7GicCdj/CSxIR+nh3YxHrscRXZoCDJEN9GysQv686QJyFejAe3ZjBRmQZRqOCkISGNCCQXf3Jdj4CJrQKNF06auIH6+dgDRZktqG2Kb0zPFbCA7Bes9MZWaQRbdtsBEo4r/kHVy8LcSiqCzPt+KXlffq54bdOXuMf8D58UKxtzqevXxq/z/vojotzex1L5CFrcNXlTLOOSSkR5c+i7x3iYoh3LRPBo8GoESWWpo4+8fL/KIudRU7foFHQMQpMpaIdcCYwiXbgAXFWpnKW7jKBmlQxxmyYAGgdJPQ0bkaLomGWNnxgdsbT3XSUHA9+/kR7eYwo4Q1ejSM7YQAklNWAIWH8+JA7AHLUmBcN2zrnuNyRR3//daT8HBdbrusiSnrlNRo2fxeFVVniSGNsWmOTsXcwhr1E1aWOMWTaePK0JzWe1kv1YG4oLzPjRofkb4qB6Df0Umqc3assJgUNI7oR6rEXEo3z/6F2HaYK6obpaNgQQOb2WkuXH/bn+5CMUwBViKwy9BZdFIQQRhZhgOf/7Pd3fYafZbSMDZUewaO2Cnxo9pSVFb7bk2JGYSzzecMXLu4xz5txwbtoOZ6kLafFhtL0vLk55joteWl2xSTmCb5UXnLVVyOv8IXqmvv5FctOEo9fnF7xUB3w4fkT1rbgQ9MnPGkXvKc8p/cJr+1O+Qe8j3/r9LM8dS1/o3yFN9pTvnF5IuVsG8tZq/EuNsWswrtAXnSEoLjclWxr0QjYiLoiQO01TRQYiQ9fybGiNQIUjXHkbSsR5fU655HVZJljiArvGxG7hDqRpJ1Oc10XoxFHqZ5Nm0nWQGdwrdz5thHJrUCixjuDD8LxX7ee67rkspmw3JZ4pyVkJUjXvSGNOHNL08h0wMY7fZ+KcceM1mMd+8A6EobTkTvYREnza2cnOKuZThp2u5w0dby5PGJbizx7OGKZqB9wTtPVKbZPmEyaUdcgVYPCYkb9wrrLaftURFJNwjK6FKdVK2NE7emNxjqpIlPtKJOe1W+mbPg35Bo2qRtjv+eMSDcDRICRNAzyf/N8a2OoJsZcQvnL/md8K5NTbEwOSsKb0JKxQgGGvAFgf6QY/n/juSivxqAQGzTrriArtvTe7O/0jtHIM1iKhyZO42RBeKKFNOgRCWaUpM0YPK1PR7SUD5rWJtQxk2DnMmqXsnH7N4EPiqeuZaY0Lj7moFMffh068dHIJa+Lj81B+Sfum2ZKBUw+JBr7+BLKkULH5GAb04GdS/C9OAtDdBjqCPzwet8cJKi424bRoUg8jw+sQKM9SaKwiR8X8zggUvvvGZpwMtCR5zEqCq1+jhw0Nie1bGRDReCCHHEknCWqHmMF4RI3ei50PB4MIS42OgyN+dVQj/FnwXOv5VC27yXHsek6QFjSniWlULBuvCWt1yhrpPJysgkYnYxI8/9xpBIPHf80EWHQ0AS8aeBGxDtkqSy6YQO4EUO2Dwzd38mBuDiTvfnIDOlF8Y4+mH6M3k8IBsDo4E0IQY4RwxhQG2SGNfQtop15xI/B1uVcdSWJ9tyuVuRaBEMDDmo4F2bajROC0vTMkoazpKOLPPwq6Wj6ZESCDUQgydfTI6xzZ0VjYINhYlpeyi8AWJia837KRSuBJH+jfAUXNP/8/B3+je2L4wgvWI11ShpzSVyEPvL1IvSj6dKxpLbW4FYppJ4Q95ngFK1KCVZLWb5JaRIvhp1Ezr2ul5Fbp1KJ9x4SfFuDyh260QSrMLVmWxUEpyinrchr+4R6l0v3/zolOY49GiNqQ7tLxLno5E49mHZS4+n7BNsme/tzK30gpT11m9JvM1Rj8IVDmSBQ1NZgSisbQHRT9l0yBpw6G8/exuNaw87n+CYZEeJtk7JSe+Sad1qapUFAq/IEwNloGQ5KmIFtIs3BoHCdWIcHMZNqNSEJ+F5Douj7wCTvsM4Io+EGTWmadr+5BqLfmCvsU4i93zfuBn/+QBUaNoYb8l7aTnwCNyjAKk3xbSd3srqWPkA6IbQdeMcQciIbSVz0UQDk6wZTluIRGMp+Y6Aso5YgNghVIroA2G8Kg4jIaTm2OIn5mk3lbP6Fq/ukxvH+2Rln6ZQy6ZlnNZ1Pxp7BYVbzQn4FwL1yxaPJgiHQ9LTYcCdb8aQTgEhpeirdcTtd8tLskk2fc5jVLLuSf+rwc/zli+/nM/3L3M5XPO3nfFfxhKlp+fT1S7zRnrJzGf/G9kX+zL1P8eE3PiTn8iwitSLeCxUgCKorSRzvnV/yjesTtr4gjW46VzhM5kkzOzYEYX83swirL4RIGQ4KlcfxX26ZVi1tL0GeXWlF9lvKxMEBZdmNXfe2lyqoqlp6a6hbWTTDHX1StjLV2ClM7kZUOZqR7yf/RrnDuiQ6/XQMJU29VACF9EC0kfHk0OzMcjuGuOg4uVBaMF/W6rEq0mVs9npIcheFSMmYnRBi/yaLQqahIZpldj+FSYSeZYynz4Q6fFA2Qn3OU3QZPRDaUxUth0VN7804RpymcoTME8uC5luuPjV21///eC2yW+GHD38KNa1GTX+o6zHqe0R/RdDH6DOI/MBxsgDyqqfZfloAspHMptB2MsqLH6Ntoxeh3J/5m2YvK9Zm3xBMkufUiMPXhLpGTSd7qXK/H2k+/Oc/SPbbz0c6rPeK2UR+iW3M5AtBFkhR9KTGjXLb9a6gzMW7b4xnMalll+8TJnknabaRQKOi7h1gPmmoI2b7eLJjWRejg24YEw2YMB8bT9YavvTJ/5Qf+tzvHzHd1mrKoh9n60PjbNelfOTkKZ9/cp+jyY6DoubVJ7eEm5faaO6RMWJmHHlieXwt9uNJ3nFU7ihMzzurQ7ZNxqToxGHXp9yabHi6mTHNW55cz8dSfl41bJqcD58+5ZOHr/Fmc8LffvB+QEQyk6KjTHvyRKYMvddcbSrmVcOdyZrayvRkXjQ8vFxgjOejt5/w2tUxs7zj2WrK4TSabdyeW7CtM9LUce9gxSxteLRZsG3Fpj2YdroB4ZVYDoqad64PZAFGx+V5LQItoz2n5YZVV0hASmxCinFLmAh3D1f4oFjWBVkir92QJbjrpW/xnsUVz3YzVk3OrGjZNDmJcdhIJMqjpbt3hkUlkuzLbYXWns//rv/jZ0II3/8r19+7pBJAZu2rtSzc4Szug9y9rZWFrYbmoIM+uvySRBqFAw8wBOh3I5tQTSf45Ro9jPqaVkaBWkaNKpOy3+9q9HQiny9jqe+daA2GZmQElAbXQdehqgo1ick5wRNau49Tc45kF9g2mbDnncY3hpXf8+683avJthtprpnUURYSO1VHBZszgcvYtMuKnquu4nC2GxfP9aYaUeGDHXjg6/kgiT4qcuwhykrjBjIozn7oc7+fn/n4n+M/vPwB/upbH6MOGW0nFuBp0XK5nvC+03MC8MbqSDrkiSDQksRR5f34fFa7Qu6shee6Lui6hA/de4oPiq+8fo/D0zWLsqG1ktyTJ5bMON65PuBkumXd5jiruXe65HJbMc9anNd85ew2b1wfU6Y9aeJo+4Qy79i18hhvPDnhxVuX0XobOKm2JNqx6qZ01nC+mZBH6/LOZnzw6Iy314fcP1zyZDWTAJOYCARiMzZx8/zcm+/hlfc8Y9PkuKBYNzm3pptx4znIa6Zpy1VRclpuebSZ8+b1EYnxrHcF7z254KIRL0gRNSKpdmyaXKTNec8qIs+yxLFtMnwOdyYrqqRj0+cssob3VWf8Xfs+XFA8u5yT5T1Hkx3nmwltk0UDmFQoK1VQR8rxt7veHZvAcO4fnINaE/obvL6ICXuualF6XMiEqCfQHqWihyBWPypuEgMyTBW5/HkUDgUg7AVEg1MwM2BD7Afsn9sgISZN4zjQ7fsHSstmoQ3KO1QQS+lApFGZzLBtL8m2rUvHBpkxfmxQ1bVsHEXVjWWiwC8Z6T/bLKPtE+o2HYMuhl++1uIG3PUpu12kCUfl2rDwhyTdYDU6c7ii4z+8/AH+6OEv8sXlPc7rKdvI/1OIB/6lyRXPtlMmWcdRVXO7XLOz4n0/OFhxu1xz3ZVcaFFIzrKWnc7Q8x3TtOUk2/IVf59tnfNdx2ckWrr2AtcM7HTKIpOA0OzQ8f75GRf5hCrpqG0qCyNIJdN7yeib5S2XyMeS1HJ/sqQrDW9cH7Pt5a49zboYlCJ26UXZiLvO9LyyOOco23GxrTDac1CIWWnd5WTGUfcpR8WWt8IJqRZTkY9S5ONiS+MStuRM05aH2wUvzq55/+SMzhser+YUiaVOHEf5jse7uYxxlTAfj/Itmy7naldye77m6Xo6jh3z1DLJemapcAJ2NmPZFXwt3OJuteJuteLvXU+ZFB3fffiIv1u/QhOPL/J+kRtmmQnU9d1PGx7K9oEwPJTdMJbhYagG+hj2MTbm2Ov55S8ypx8WpzGospCegtL7ScSgOhzGiGlC6LobjxfL/zRhnATchKDeIByFqDZUKoi+IHjQBm+kC+wiYSa0euxmN7uMYDX0CqcM5PuFHoww5Jo6E1pNkGiwoWMN4jl3Wu919Z0kBsVXQHJRnMb1egzGBOH4KRjHaTbSdZou5a++9TG+uLzHH733t7j2FT93/RE+d/YCTy+lB/GFi3ts6pzDosajeHMl9mVjAus2Z9Nle6GKM3LssULsuWgmPN3NJO1YB16/PiYExTRvmaQdD5cLdk3GW+qQDx6d8ZXz23xteYtlXXBY1XKscZo6jgeHa7hznq2nOCthLo82c1ZbCSNpbISAxHP4elNygeQiXrcljU2Y5dKT6GxCZ80YdZ6kjqPpjm9cnoBXvHl+NLoejfY82ByM/v7OG87WUyE+xyNC7wzzvGHd5Jw3kzEgpU/31OGBMfjW2SH9JuP2e5+yyGq+enaby21FY+9gtNCLQ1CcTLcY5bmsK/plzi63fP7iBVabEt/tDUreSfx6Er/3Zn7Br7zeHZvAIP8VQf84+w8DSWj4MqPxjUcXiRwTcPv0H371TjeAPoDnBEIh3JAbg2wo8XGUsqNacBwxxgbiKLLWsZEYjxXKh301cEOUpF2k6g4I69illnGPdOBl1COfGxa8Vx56LaBPJZ1rZ4VbR68hEnB9pP742M0nKDxCBHJdNMtYjU8HrYWiiR5+b/f8P2c1SmvqkHFeT7l0U36geIdHk0O+cHFPfP4Bdp2QiG3QTNKOJ6sZ87IZDSw+qNGG7L2iRhKR20bETXWfQmPos4Q1ReTvW3ots34Xkdyl6emdIdGepkvZJiJkGmTBaWYZcg0GZ2BTZ7hOc9lUItftDF20/wIxLUkWUtemtHlPHRjzBNs2lalFdBn6TiYB1stjq9rQV3I86m4sYBf5Bm2fUG8zyqznspnQe03XChz2IQt2fTbO/3W0ZlsvsWJNk+KdAaspkz4e4QSfb7QnMY7OSmN0F/MI2j5B9UrozKXIuIMVcRZabighNkPbJh11Dt/sendsAsOl42IxBowXyS88d94f5/5RJSi5g3pv4Q0+qgLVGCaiIv5LmUCwft93iNfNbMNwI1tQsgzz/Tk/QlBxesSWDdRilWfysb4bbcY+uo0Z+PgmjGYUuasz8uxQIbY8omHESNDliEqMvL1gGak/8jIESDzOSLVhsijdjXJWsQPL3Xc4NqSpwzv5t6hEfk5Z9LRdwrZL+ZvLD/HELriXXvGxoyecX81EPowQfmdpS2YskxiM4Z0eARiJkTftABzVCtpEFIs1MkocgkqNQc73UaI74MlKI1BOzZ6skxqHL/oxbch7IfLqKKsdHI9d7AcMx6g8tWNZr1SI2QayOIbsgc6KIrKMWYHOKcjkNV/kDessxx50ghuLd/7jasuAMR8k1WmsuHZ9yiTrmE1r7pfXvJEdRY+/vC5J3PxAYCVZ5qi3Bj3team6onYpRdHT94N+RAxIJpMU48Ym9NbATCYpWgXS3NIFJRxE4wmJjBnLbKhSv/Wye5dsAvGuPNiCh/N+H8DHkttaVBuRYm0s2wdhznCnJhKFEvZqvq6HNN1HioeA4Gj0vhJQaj81uEEQlo1lLzySj8W+BUTASEw7GuzLw9cEj/Jyt5JMPBisr8Ej1J0I9FTRBRai90+bvUtMKcYeQiCRaWTqR+adcxqcUIFDEHGM0kH077EHMABDZaPxY2kYQOblsaQ2ESLyubMX+MLFPT529IQXiiu+76V3ePX8FlXWM8/XaOV5sp0zyToSJXdPk/hRvisobUeRWJzXbJOMPLEUzkKcgQ/iqLpPMcpzMt2KecZrzrsJtk/Y9hlJIo+Tmb2t+aTaMcsanmznEdelxqlIme6PZkVqx6BScRrKxjTIjzMjen8An/XCLxylx54itZyWG1qX8KhLeM/xFU/Ws/g6yes2aPuH55fFxd05Q5laHtYHYwBIFX9Glfak0QqeGI9SPdlCqEArm3PdVTIpUoGjyS72SbTwJ2KGxHJXUs4aiqwnM46q6GJbTcaDAcgTxyST6cu7XzYc4iInLqRoHhq8/Lqq9hFg8ez+XNiIu7GAbzoNiYs3UolGU5Ix4OxeWNQpWdCpgEFUruOT2rMMGYnDe29D6LrRqDSg0JVS8jVdj44q5bSUIMybJg6TeGwipFudyLnaR8+3MR4bm4VZ4ui0wCtDULjY/BlGQU1sAKlY2nuvmZQNW7cPDAk3jhrEUtPbQXIdP2bNGPv99HKOs5rzqxnf99I7/GNHXwXgqq24W654fX3Mk6sZ945WknXYGFTRUySW3kvqrtGeNLoabW9YtdE/0ct8fYgeu1hPaMuE08mGTDuWbcGbyyNcazhfTZ47yw5aCqM9h1nN26tD1rsC5xSv3Lpgk+RiiY4CLEk38mKyinivJBGTUZX2tENZr6JJySZjWEiRWqZpRxMpP95rFnnNO1cHdCHhTE+Z5u3oTkxukHuMCpyvJ5zMtnzx2V3aNpGFH0VhIA6/RO0Jwnfma5ZNwZfO7rKtc+aThjS1zNKW43xH7VJWfUHn94rJRdlIqlN8X2WpHY9kJlZkuZFJRPKu5wkMa+PG3TQ4v9fvD9y++DlV5PsFn8bRXZTyquF8H0t+pWVRqrQQSXBdRwoRctzwQf6eqb3WQJuYaGz2/QKIYqEbDZY03fMNQCYDvR2Vjz5lDOIIKjDJe5oupQ+Dky72P+KdR7AJApi0vSEx+9l+oj2z2ZblrhyVYDbO+ZNUQBrWpijlmRUtu510t8tKHILea5omfe444J0oAU3iqPKOy/WEIhvyF6UX8er5LQD+xTt/l//owT/G15en8Z/umGYtTzczVGNo65Rtno65f9Zp1k1OkfXC69sJFdgs5G5o1D7RZ9emPLAL7s1XXG9KZlULHrpdhko8/VQ2ltQ4ZnnHrs/47LP7MgTaZOSTjirpeHFyzZcu77BtszFZyKjAJI3HjLyjbjOysmEbm5iD1uL+YsmyLdi1KSFIk3DXZixTIS7dPl7yy8/ujL965xWbNh+DQlq3T/lZNzneK+5MVpyvJ7je0MbR4KrLcV74gqn2McBE8fBqwaToRjdlnlgmacd1W/JoMx9NRFkiFULbJbzvzjm/9OweG78HsA6ZBXnRcVjI2HLVFdhvQw34jjYBpdR/Avwu4FkI4WPxY0dIDNnLCEvwnw0hXCk5tP/fgN8J7IB/IYTw2V/jJ6AqMdkonUrycJ6JEQf2uv9hsZWlHAkGNd9wp07M/k49ZA0Oo8cBDDpMFvz+MUedQZLArkbNCimVrUWVpUwB8gx8JpuI8/tqpCr3xxGlQTsZERqNyxWTshVYhTUjeabI+r1r0KsxEFMhZ1gXY7ymRcuqlpl7YjydTWQfskYUejfGPgPmuox3WJO4sVNstMcCed6PZ/W86ETXH+OtjQq87/SclyZXfOHiHrsoOKqynqu24j968I/xf3nvX+Jff/P3kWlLlXb8xK0v8eXJfX5mVfGeW5e8PL2k9QmrvqC2KWXSkyhHbhyfPH2dqWn5z772A7R9wu9736f4pdWLfOXiFvOi5TDfse1zPnb3MZ2XqcLt2YZFVvPeyQWfvXyRdZvjY7NslndMs5b7syVaeR6sD/jlJ3f4x1/5KuftlMtWxpS3yzUg3fvBlfcjt1/ny8u7WK+5W63oveHVy1vkieWDp88A2PY5qXFsupzfcvw2//07H+T+YsntakXjUq7aisN8x6PNgtQ4Pn70kF+6vM+P3X6VL63v8ebyiEebBYfTHXWW8gOnb/P65kQ287ShMJZbxZq3txImUxjLG1dHnEy3LE3BxXrCR9/zmBeLK95pJE26c5JQ9fHFQ94+OOIXH72HRVVzu1qPI12j/Ri2uulyapsySbvfECvxnwP+I+DP3/jYHwd+LoTw7yul/nj8+/8WQZB/IP73g0gOwQ9+20cfnIAxX1DdUAMSvHD8nBNb8FDqB//cnVuZ2Ky7mWI8sAaTZG/wIToCnchYgb0haewx3MgnGNDkA5gkfnwQHIW6QZWDaH5vbArW4jN4eXHNNG3pvOHZbkZqHIusZpa2PN7NaV3CImtGvFhpei67iqe7OR9cPOXN7TFV0nGU7djYjFVXYoNmmrYjRjzRgid7sp1zUkpZ3cTYqcyIiwykmmhtwqrL9wYWFXjv/JKvXtwiAM+2Uza13MnS1DHP19wtV3x9ecq//ubv4y+876/wR976Cd66OOKn1XfLy2E8V7uSSTpFE1i1Ba0z7PqUzDgeX815fXLCRTOh2eSoWeAvvi7CtXnRcrdakRvLui94uFlwXO6odzlPguLN+pjl7ZJUS1Otc4aXp0tOsi1v7w75xsUJSgVOplucV7y+OeF8N6HtJa+gMD2HWT066Y6qml88e5kmchG+enWLedZyUIr47K3lIU2Xcjrb8j2HD3ltc8Lff/Ze6jpDzQOfe/ICSgXmRcskEdTZts34qrlN3af8/YtX2PWi4ei15ByEoHhze8z5bsJBUVMYS+flfWKD4bop+ZHbr/NoM6dMerYm42i25cuXd7mYTDjOt2Tasu0zFlnDm/Uxn396nzLrSSNDcJq2TJM9XyLXlpNcKFMvFxc4FH/9Wyy/72gTCCH8vFLq5V/x4Z8E/pH45/8U+FvIJvCTwJ8P0kn7BaXUwa/AkH/zyztULtUAzon8Ni4qpdS4EUiZr/fqwa4XqW+WMgJHhv+cgzTugGkiTUJrBWd+M/Y8TaNTMYzagiH3QHwMDnScIAycwuAZQKiDrJkIRxmqDN3DO6vFiJp2TnNrseGN3RHzouVsPaXvDefZhMNKiLHOK6Z5x9VOFntuLG+tDnmkF2OHOwTFMzUd7+rW6VFevGpyEuNp+oS7szWdM899X2YkEvtyV2KiPuAb1yd85OQpb6yOmGRSRtqgJcxUeV5fC0s205Y/8tZP8Bfe+zf5Q/pH8UHLhlQ1lKlgrLQKVGnHQS6JyAdZzaaVqPSdzZge7DidbkdW3q1qzSxtuGgnvDS54uv2lO89eMCqLZjnDU/TmfD5nQR8VGmP9ZoHuwOqpOP9x+fYoNl0Ioo6LTajbbZMZQM4yTd4FEf5jlVf0NqEW5MN6y5nmrWj5/4o35Fqx2VdkRvLeTflvZMLll3JruwojOXefIXRnvfPzmJzU8xap/mGedbQuJSTcsO9yZKNzbldSCWilUcjvMgBCVe7lHnaUC06vry8S5FYXp5eyiQlQmd9kBg5HxSTtOMo3dJ6yU/Ythnfe/oQG+Po3rYpB3mNVp6n/YynzYzC9Hx9cysusv/+my69X09P4PaNhf0EuB3/fB9458bXPYgf+zabQGxg9X2c5/s9bsywDwIZ3IOJgb4j+Dib73uBfCRJbNI5KdtBRnxNszcN3ZhChCFRqOtkcRsjf44Nx7De7HsRsSIITbvXHkwq1GSy9xs4T+itNAjzDNMGrpcTfGtk3m88D7sDlILlaoLvZb7bp4n42yMTb2VKXGvGkMoQba6uMULb0YEks3sIptP0W3GbtUWKMgHXiMfgelONOK8QnXqTsmVb5xEYkrD1BZ9vM7T2HFUiBCqTnsxYnmznPLmakaaOKu146+KIP6R/lD//np/nd776O7lqSq6XE+y0IY/TgGGWbZ1mmRWstgVttEevryqc08xKwXlfNBOu2oqLrYSiZInjYXPA5bZimrW01rDtM1ZNLkguk7PO8jHMM9GeSdqxanLWm5Kn0xnXtWjze6f5uj/lQXJAG4lGj6/mHM+3bPtMRm0RVf7kes7TYip9hj5h16U8Xs945fCSq13J6tmUr1nD7cWavs94Wx9RJD3PdjNaK3Fvy7pgGickifY8W0+Znra8sTrisKhZtgVNn2B0oEp75nnOusvZtDm7VlKc3KHidrni77z9PtLEcVglIyzlpNzwmYuX4qao2a4KPpvcZ5L1rJuc1arkgT4cx6Vp6iJv8dtTBH9DGoMhhKBuYmm/g0sp9UeAPwJQ6Ok4egvRIRgG8Q3I4h9HdGq094o7MH1Otjuc+VVZStmeGALZvoOfi7Q3WCtNw6ExSNQLpMk4VBW/wGARjh9LE8IQehL9BaooIofQ76cWxmALxcFiG5s9wqA7mu5YNTlV1rOOYI4BbwVSsmvtqbOMadmy3hVUkx2p8WO+vSjgZDpgvaQDD3Lkg/lOlHFJziJvpIlkReSybeXoMC/a50ZGaeJYlLKIb5dr3lwd8WQ1Y5LLeOne0Ypp1vITt77ET6vvxgfN73z1d/JffuC/5l99+GP83NmCKu84zHfPEXKPMmHoP84lfOV2sebr04551fCRw6dsnZBwAU4nW/EQaMvTeoZSgfPdhFcOL3mhuubB7oDapiNH0QXNabHhsq2YZw02CMX3pNjw4uSKZYSuTJOWWdpI0ErsC/y2W6/xheV9Uu04yEUmzIFEwE/TdgS97GxGZiz/6P2v8/D4gHfWB8wyQbxrFZiYjjwRv8FHDx5zXk15ZXLO69sTVn3Bew8vebRd0HQpL5885PVwzDTtOCx2ZNpymm247Ce8yRFl2qNV4J3tIcu2YF41/Mjt18dgmeF498L0WrQH+TFvatENTLOWxiZMZ6KVD/F9dDrZ3jgKOr7yLdbir2cTeDqU+Uqpu8Cz+PGHwIs3vu6F+LHnrueyCJPTMIpsnCMYs1f7jcivAFkqd+LB0dfH0n6YKLQt1LXoApoWlSaQDDQhLUeBpoXo4R4qgdERGLyMCIejhDxRwnYnVmKQ7wfpCbTtCEMVIlIQybMDektSB3axUxy8wrYJl+yVbk2doWLA5ZCI45wmTdxIn9Has2tyyrwTaWubom5gvATguc+9r7t0hGf23rCsi3gUEDimVYZnUco7OAhDUBwc1gIhsfL987KRObry9MHwdDPjy5P7ANiguWpK/tWHP8a/dfev8+knL1Ikls4naNWTacfTesaqE7TWpsuZz2taL8eSedZSu5SzesquTwVXFslJmXaQdJRZz8dPHvHm5oity9jajF0vkduDqeeimaBU4NluxrIu6PoEHzTvbA/pveF2uWaeNlx3JY+2C5a1HBO+ur7NRxePeWt3xKbPybRl0+boIrDuirGDL5gvUTt++fFdXji+JlGOTS9egdYn47x/a3Mu24qjrKLzhsL0nNdTjoot1mt6b2JvKOHF8ooPlY/5pe2LXLQTtl2kHHnFUbHl0ldsmpzPX73AS9MrStOz6aXJd2/xlNqlTJKO9a6AquGl2RWrthhDVwaRlg2axiVxw//WCUTfsZU49gT+PzemA38auLjRGDwKIfwxpdQ/CfwryHTgB4E/E0L4rd/usRfpafjho3/meXDoIO7RShZZUcgdNgJBpWcQvzbmEaobTTm8lxHezc5/TCcG9hMCfQMt5rwcHYpibyHWRj4WkWah6/fQEW0I6zVqMd9DTXb1eJw4+z0f4fx/0kMf9f+1xldOVIJ+kA0jcuQ45xepXxCoxyTe4duE0GpUq+V4ZML+6xWgA6oxhCRA5uVW4JUEVm7iLz8ivAcVou8MJncCBCkcWSXORQnciF/j9u5H1Rj0kQRuTquG6+WE4BWLxY4/8eH/lj/z+o9xtS1HJt+wwQyBpmXeiZfg63PswqEKR+g0qnCcHK+ZZB2Pr+aiaYgb5vRgR1NnpJlltyqgMZAETGXHN3uSOupVAY0m2RiS924EBLJO0bOeybSh7xPanRyTBn8GCkKvmR7txJNwVVId7dA6UO/y+OuPasXc0l0U3HvlnMdPDwhekZa9aDuGXAEVIt1IMZvX1E2KjXkC3/3iI67bkrcfH2FST1F2dF1Cmjp2mzzmG0Jfp+jUE6yWeHgTmB9vR+/Ets44PdiwazOuL6ao2pCe1OS5IOlX64os75kUYjVvG5F5DzSnt/7Qn/yHtxIrpf4i0gQ8UUo9AP5t4N8H/kul1L8EvAX8s/HL/xqyAXwDGRH+4e/kZ8QfFO0DN0p/pWRUOAiDkmQEggxS3eBa2QCGEeIg+hlgIXm+nwjcxIfjRrT5sOCB2Ag0YDuZPFTlXjY89BRUVDOmKXS9SJethbIYuQNBgyks5BHPFRLywwat9wm4kj7rxqQbHzl8wSlxEWoRB7nM4EoN0XtgohFpRGqV4qIbbBjWGqqqZWN1lBnDTdzW6GdOPSYTMVKV9xwcrFi3+ehQNIlHFT1tnfKeW3I+LlOLnTZUeUeRWP7M6z/G77j7VR40h3zl6vYoxyVnbEi+NL+icwlfWOekmePW4Zpdl9LZZHQReq9YTBus06TzHalxnE63HOY73i4OWW2LkcpjTKDMO7LEcWu+4Z2nh7jK8Uc+/Pf5macf4XJRopXM20PRsklzmjalKjomeRdNPoqTaodSgQc6MC1aiUSLY9o8tWyanGnR8uQ6RwHf8/JDGpdwtp1wUu1GyvOL8yt++ckdPnLnCZ1PeLyesVGCGnu0mfPy4pKrRcmsaDkoalZtwceOHtO6hK9e38J5zbNmQZZbnNH4TFFVLXfnK6ZpK9VjkCTjT956g09XL/HWwxMmZcud2ZqrpqQou1GxCIwEYzMV8dNb32LZfafTgT/wLT71Y9/kawPwL38nj7v/Jvbd9qEzPyj0kGPAkE2o0pSgNIIFU4OwPj7OABe94fob+gVZJtXBr5L4qvFnKiNNSTXIjG9mDCTiugtaEWyQCPQhG3G4lH4OXxYSRkqvJNOKJqDvk6ivZ+QKKiXa+WGK0LUpk6IbRSJp6ui6ZEzDLcouugD1yLl31oxvhG0tfYcmF335sDkkEe9Ve0nGDYUYcsS62nG7XLPphIScRClwkVi2ecrL00sm6VRm/4mk3nY+4bXzYx40h/zQ/DVOszWfuXxpTELOjWXZFsySljzf8kVzj7zoOSm3rJOcdZtTJJZFVrOaFkzzNv5aBL/lg2KRNhyVO9HLI7bmIrEcF1sebhYoFZjMGnkdgphwskQ0JkflTsanek6bd+RGGpwDk3+WNRznW9ZdTpX2mBjtPUk7Xphc8+r1LZEiJ4Ey7bFBSvtJ1rPIa7a9/JzDrGZSDqM6WZR60sjcPrEcZjtmRcs8b7hdrJmmLbOkwYWSVHu+7/ghf6cTcnIT06anRctBXjNL5BjhvGaSSADt3WrFxUHFS4tr7lVLnD+JXEEf8xJFaj0ATWdpy2e+xfJ7dygG4xW8F9fgoPG/aSAaosAG8U/UFAB71kCk46obkmElf5CzvR3Q4TegpUqkvmFIERqu0Y+ATBuGDfamnRieO4aMiLHB+zB8y+AijCWys3ofeaVi2ZlZksSN4snhp1hn6HtDkoj/ILAHU/ooGVXxMVw0KllnZFPwkWUXgZnyWomktlH7WO2Bm7/rU667clTSJVGH3ntJyml9giaMHedEe7TqyVPLV65uc5qt+a7yCemJ44vLe/igSWLzbesyJklL8NIg3VpZPNlQBQTNJOs4LTc8282kvPb7VKah4SjxXTJmW6QN52bKxbaij32eT12/B2C03iYTxyxpuYja/j7afLedzPL1NLDsS3zYM/2Hn7W2MnacJB3pREJeLrZzeme4NdtIfmTs9l91MnK97ipSJZDWIWBmmrUjAHajcvpCkyjP42bOs3qG9Zqty6jyTvoQXSonurgJ2iDQ0uHPT9s5m/jcbNC0LonZAqCCwiCnxDLp6aMeYdAlfLPr3bMJDIt8sBIPIp1B6TcsrptioOFjsG8egmgL4p06Qt6eW/RjJDk8VxEEKwt6n3MQF7NObmgDblzDZuUcY6DqYFxycjaXSO1o6x2afcaPi/zmvmPt878o5wVE6Z3GD0YgLwBQ57Q09aK6UHh4UlH01sQ3UHyaXqPUYISKU45I+g1OjXeL1a7gQlcjeTeJWQSDF2DVF6zagirtxjFgpsXmmiWOz1y+RHri+OHJN6h0x3k/5bqveH/1jM8sX5IFlsrdaZhYDInBy64Y7/oP/WIsf+9MVsyShm2SSQiHE0ty5wxtkXBUbCWgs5douc4nvDI957KuRD4cNFp5jPK0XsQ900zGhy4oXiyv+PLyrkxctEwHMm3Z2Yy3V4coFTjOtyymzWghTqMAq3VJvPtafETi+6A4a6ejY9Bo/9wirfuUJjb2ho0w0Z4vnN2VbAXlcUFYAEVq6eJUQCYiavzzts9ompRHK2E9iAw6iVOjaDRTeXxri1HrW13ffoD4P9i1N/zs+QByh0fpvfV3kObebGYO5fzN/wZpcESSjVbiQVY8yIkHUVEMJBkeb8SNIxwCFRuMKk3la6N1ee9tMKInGC5jZJKhuWHiAbzcdf2wgOOZ3nsJ6BhgmiEafUJQMuOPR4ThTj8QiOT5DYx/PWLBrdXRZBJfp6DGTDsdVWwDFnwoPdyQexfvlkNI51BeWqepbUrrzGiCAcZxHoBH8cXlPX6pfok/sPgcPzB9ndNszaWd0LmEy05gHkpJRPimzceQjQG8edGKXmBZF3Q2YZbsjwfqxhFBIUj2Kumpsn40WAF8pHokIiDj8SjBr8cKqLdmfG0S7fn45G2yG/LredLynuqS2+Wa3smEIDfSiJxlcjYf7LlGecro4gPGyPTa7n/etstuAEQCPsQEapeys9n4nL3XzPJ9EpRzwknwKDItZKLjyGesnYSrivFL3Ja903Sd6B58YEwkcnGMPJivvtn17tgE4oJUaYpSauy+h+FIMJTlY+6ANPFUmsoivIkW1+oGdkzO/6Ft99HjxsjmMMzzg0iNwxBAOvxcY2JvIDb5uj6KjYZoNFEyhihhDrv6edmxtQLqHVxeAXBKDDqxWYZXeCuCIaXCmFp7M2k2OMmcu3kNSkGlpNtteyPoaa+wVuP6/dcPMNGB3j6MopyTsjxE/mFmBJ01ywTrPcRsFYllUTZkidz9tAp0Xo4LR5ko7IYKRkpWzXk/5Rea+/xPiocskpqTdD2Wo4M/okp7kngUGBZMqh33SoFtNtG7MEsbbJCE3cE9Z7QfLcNH2XZ8a+i4GO8k1xzlO+ZFQ6YtjUvZ9dmony9Mz6JoJLgzSsed1yy7Eq08LxaXnGQbylR6Fb9l+iaHRc3dakVqxB0JcpQZeIEeRao987yRMtwZUh2PIF6P6VBGh3FRJkq+b9tJRZBqiV0fwk8XZcNpvuF+ec3L1QVH+TamFTuKRN5HQzU1+Eq813RWmrqdNZi4QU+yfYLSr7zeHZsAal+eD+dr5/Z35Jsdfa0E7jmo+qwVxV5v94/RtqL8u1n2W/e8EhFulO/iPSBNR5gIzo024gE0qpIbx4KIHlPGoCaTvZch3PAvxDGbuAUZX+0QR2YAasjMi/FcckcjBl940twKSyDEr1X7jeW50j7xDCz9AW4hAZsSWDkcS25WGt5LLsDgWuusEd17nDdrJYvDB9m8EuXIjJMg1EzK4UHcpFSItlXHdV/x+e17+PPL7+NfPvwiT1rJTsyj/r/I5O6axu/zQZJyvmv+jNIIqERrkUNPjZTnmRHybhUdiNZrjvMtj+pFjEjfx3T9jdVHuGyrkU5UmL13YlE2+/N5k/PLu3tYr8lin0GrwJWd8LhZyPy/l6/JE8vj3ZxZLozB02LDcb6lSOTfkmmpFg4yaRYO5bfzmlR7bNwEM+OYZzGCPt9RJkJJXpQNH108JgBdl5BllkXWkGjH2hY8bhds+pzS9OTaybElc5TxtQxhn3HovRoBL0b7MbX5W13vjp5ACPu7rr6RKQD7UJLejrLhsN3Fo0FcBDcTiKL4Rw3qQS+PPTbtRnGR2WsFbo4ObRz7KT0uqoEuNDIKeivNQBWEQbDeQJ5LL0FpeZ7Ip52VLHm8QjWa9bbAdWbcubGaoGLIRWdEH5B6glXstgWuia+DgjFxRiMf7+MGk4lmQCVCD3KNif0ILY9pJR58TPHpEmxrsCbAJsUCj6/ndF2Cnu+wVjL+WpOwTTJsb3BOk0cz0KbNWW0LHucNmy4fewjLtuAgr3l/9YxLO0Erz7/99JP8qTuf5se/+pNyrm0NT58tsMebkT9YZD2dM3y+e4EXptdcbUuMEbRYHYNYB/my93oUV2l1ymm5YdtkdNdyHOuODLfSNZ+3L3C5rei9Fuz2rpTIsM5g7nrOVlO8Vzg0q7bgfDklPfQSDKMcWnkuNhXGeD52AO9cH3BQ1ZytpygV+FJ/V874bUaR9XgUT67nwhcIEkn2YLkYG73XXcmzyzkmkernCfsJiPWa89WEv9l8QKzmTUK3yXgdRjdmbVPOthOmecfL8wveXh2yPa8Ix4zCM+/U2HNxkVG5jHkR3+56d2wCMDYBQ4hz+xvpw3LGj05BE0QsFIEjKkkIN9KIudFYBERjEIKYh5Qew0yf70GoETEWhtTi2BBUOgaJpKkoEdtO5MZDM9B5qQiS6DtwDiKww2WK6bwez/9tnXIwq2mKnpcPr3hLH9L1idwZU0sbI7eNEmPPnYMVgHDj491vGBOWVRv18Wb05SeJ7Px6GqjblOO58AeGWf2gKhxENkniaJIh597zoXtPmaYtF81E7o5aRoGrtuBqV/LJ09d5fXLCrXxN60WdN5/X/M32A7w0v2KWtGxdxmeWL9G5hC/6e1RJx49/9Sf52Q//Vf705fv4vHmR2aLme04esbY5O5txu1iTaMc3Vqd03vA7Xn6Vz1+8wEFe89rmlJenFxwVO9ZlTu8Ms7LhsKilIjE9Lx5ec5b1rNYVnzh8h09fv8S9yZJ7k+U4UnuQHfBwvWB+3PDRg8ckynNvsuRZM+OjR4/5nHuBo2LLui/4xuoUHxQfvf2EziV8fXOL1arkfUfn3K7W48K9U675+uoUozw/fPwGf59X+Ik7X+IXrl9hlRc8Wc/40PEzLpoJr0wvqE9lQd+vxP78/uoZz7o579SH2Jnm6xen/MiLb/D25pA3nh1zb77iQ4unWC9Rdt979IDapXz/9A12NuN6XXI4qfnAwRlXs4qzejJOTjZdHulGvVi6teer32LtvTuOA8N2qdS+ETc04HxcwDcpQlFNuM8ZvIH1AhHvDCX5YBwaGn/DHX2oNLyMH0NdE6yTkd+wKfiw/74xFs2PEefjcWPAkykl3gMtcEFtBWDpnPj2CUSBUMJlXdH1kirTOyHfSENOzovO6nGUNXSGu07K+ySVUaILsQcQicHeS0ffByn9m14ajV0n4Z1DtLfskSoGoCgJwszFc36SbUco6LItRiJQljimRjaIN7fHPK4XALReBD9dJPDcytfcK5fMs5qTYsNhVjNLG/705fv4Xx9+DZ162i5hkrSc5PKzHtdz1n2BVoF/9PhVnjUzblVr7lXyOHezpZTdqR3FMLVN6eLiqK08B1SgD4YXqmsu24qzZipdeRSdlwVxXRfYIDmQj3dzPjZ7RK4lBajzCSf5hvuT60g5KrlsKl6ZnpOXPeuuoLEpy1Z8CZddxa5Px2NZ6xK+trvDIq3RsYF42VZ4FMfZZnzeALm2PGkXPKwPJFzEZmSJNEY7LwGuwNjHWNuc1zYnPKoXPO4POc63olAENIHapjR9wnVdcNWUcawbMWg3HuebLr93RwLR7fDDxz8lenxrn48ci0cDpdS4sEfrbpKIg/CGOzA4tw8X6S2qEgCJmlTydV2/b+AN12BXThLCZiOGoLZDFbk8l4EyNFxDvLl18rnBdKT0DTRaz9kf+DjXP1bjGnmDKh1IMoftzKgKDDEPQBkvJxAtZbttUpTxkmmXepLckuWWepdRVh27TU5oDSSi+PNOEXqNKeTN4xpDdVDvwyiiFDc4JQpCIzl2OioPP/TiE77y+j2RMydeJLqpF31DrzCLnizvaTY504Md66uKYip9Dfv6FHenHWW5Ser2b7ob7kedOf7rT/4/+eNv/F6+9ug2rjV7jLqHdNLT16kccXodnTCBdNLhesMr986ZpQ1ffnyX/ryEiSVYxQ986A0+9ep7yWcts6plU+fjJEXHqYF3mizvqTc5JvUcLracPZujU4/vNWnZ451g14YMwdCI+zOZ9BRlx+bZhOpkxwsHS157eiIu0EHBmcrvIMkc3TpD5w6TOrwzuMZgCsd8thv9HyZx48SmaVLcMpPW2FT6F741lItGjm7x/YPVJJM+lvzQXhfjkRWv5PcFo5xc544st2Oj+bV/7n//Lk4gGhptN8JER+9AJPeGYbLuvGjnB9txG0vwoVIYcOCD/2cQ/QzpwtaikiI2G/0+VUhHkdEwXA9723AIATVkHbbxY/Hsf5OArBJD2MYxjzEEI8ANYj6dt4q86Pc6c6fj4g/jpuCVIs8s3onunlKkwEnq6FpZ0PUuIyt6VNmPHX8/zJNj6KcpHPOqYRnE+KRT2YQGi2kbH0trWeiF6Tk8XY8W4z5LMMk+y6/MesFczwKn0y3OaeZVwzxr+dpFSZo58qIfG4+JCgL1yHqePlswW9S0XcIff+P38sde+u/4U/w4b14cSWMwTjBePLzm4XKBc5rmokSVFp0EFlNJK5ok0jQ8nO1YR5+D7RKWbcnRrRWzvBNYSGrJSjcSgTf90H33XMYQ0VcOLgC4XpekmWUeI8G7Lhm1Gy6zY/Ots4bqZMesbLFBs5hJbNkkjgu3XUpqxOG4SwXaeme25sl6RpfJke/WVBgGXd5zXG1pbMqul8g3teg4mO/YxhShyfGWSd6hp2FszlqnuTNb83glISb5LakuhopyGCuHKEwry45p0Y4j2G91vUs2gW/ysSFL4CYuDEb7cBg0/4OjUIm5RsWFPboIfSzTlZK+wpBbqtW4UQwNwoCPY8kbcuM8lzShZDAnRYcjogpTg2cgzWJjM1Yu2qAcYwx28IrQGXZbgX9kuaWN/ABtxi2O4BVtk+EaQzq341m+76R091bmw1aHMWRiHBlmLooVA84prjcVXfMrfsUK+k4qguA0rpdw0HdWhyzKhu86PuP162PWCMBzgIIaFfh97/sUf/H178cHxaxs+cihONpUIV6Ak3LL1ma0NqGxyThjt8cbvufkEZOk5a+9+jH+FD/O77vzWX42+whfenqXMu9YlA2dN7x8dMlVU3IJLCY1J9WW+9U1nzt7gTeujuS44yVzb141pNqz6WUW/+hiwY+893WWXcGyK+MkY8ekz3lndcgwJPvY7cdctyVH5Y570xWdNzxYLki0597JJYn2XNQVIQiA9XtOHvG3X/sA906uOSjqmGwk0JK6Tzkoaz529JjPnr3AR4+f8Hg35+l6xmUtqUZl3vHywSWXzQSAg7KW55bXrLqCRdGwbnPWdS54OafZ7XJeWCx5eXrJxmY8q2c08SjxI/fe4Ekz4/Nvv8hiLgajq11JG3MZ9toDSUrKEnczxfxXXe+enoAxcibvBnbgHuApd1+/n/0PXXy4sTjtSB4KnfQARhS483IUGFKEByYhRE/CDTWg97EHYRjDT0Ge24Al914gJINQadAp3KxkfGxMqn3LAyfzX++FaDuUz96riLCWLwsBKYfZg0pHXINT46Y5NPyC1aP8eDAIhVgCBicla/Cy6LUO5EU/lrF4IRFvm4yLbcVFM2FgHGRZtMm2GVfbkl9aiUN8AHpsXcbTekboNLsuZd3vGYAhKFpnRGBkDWubY4PBdZo3L4742cuP8H3zd/jBe29htCjazjeTUQw0qxraPuGirvBB89HjJ2Qxn3GQS+s4g6876bugAus+Z9mVrJqCB8sFD7cHJNpzd7aiTHsmeTeGgi7bgmVXjOfmLHE0NuXpZiaLJ0qNH2wPUCpwua1YtQXbThSUAlFRXNflKIQarjy17FpZtHWb0fmEVZOPuQWtTWT0qR3nm4kExsbo90nZQpy2PKlnnDdT1l3Ous1oXULtUtZdIUOzCEVxXkdb+fOj4CEYZfg3frPr3VEJIOd8GdPdEPpEgjAq7EVA/obXH6JKRI2+AWAcMQo4RIvDD0b7r9CLwl7jH2SDUVrHxp7a/xf2x4Px/6O6MJqdkgEkovfVhQ+4QlFWbSwxFT71VEXHDsbzYIiz/7zo4xRBzEK1ksiwrpM4rCy1dH1Cb6QCqKqWxDgZKcU7gEOPmwwqcDCtuYy+gcGYNK0aZnnHIysbQqdSktyO9tPrumSat2Mibt2nXKwnhMAIBb1VrYUI1EhjTBUiHV63+egFGDao3os6cmczXu8KhuSgLz29y8R0fO/sHXJj+fSzF1EqcNWUHJc7zneyIVytK86qKVUixiiXqRG3vn8LKA4nNesm56qtWDUF2yYTZ6aVZKSDTMr3s92Ep5spRu8VfSEoyQRIezZtzqbOSVOLC4qjqubZZkoaI8NXjYBgDqqaVLtonTac1VMOi5oH2wMA7k2XvNFLh/+tq0MRB0V4TBudk9YbGpfQdGmkTQfuzVdk2vJGHDM+281Gr0aWOA7zHU+bGY/Xs7F3MUTcGiOYemulSZynNoJr9Uio/mbXu2MTGO5sUd5LCAQHEjSqUEUstX3YMwcGf/+g7hu4gTD2AGRjcXv60IAu0/GocaMiGD0KN+/sgzEpSfYmpRD2hqEYa4a1YmuOR5JhtOlTKGJ5hwGXy+48JOMOSjuTOLG3GjG3VFFIVKSiRnNewjcl908ENkOJrhW0xstGoyVExAVPkigJ7cjcSCEiPh/YJ+46a5hWLZNMKDmpcUzSjl6bGIDhacuEXZuOUNBZ2nDVioV2mnW44zVpVM8No8zhz4PQ6Hax5nE9j5WPiKW+cH6P3Fh+dP4qmsDnLgRaclpseLyeMS1aEc94w6PrE5o+IU/ceMzY9RnTrOVRP2eatVxuhADc2ITGJEyLlpNSFIXP6hm9l7visBirtMdk7Wiy0SrI3T1XY6DIYb6jjQq8F4+vuYwj10y7mCzs0WkY48PqyC0cgkXmacPxRAJZd302+g7GCHRvmJby+tdtxmmxofeinXBecxBHoY1L6HzCIm1I9BClJ4SpSdpyMt2y61OKYXrSp2SJHf/+G0Eb/k2/Rv8/xPO7dIxxnqDc6PEfY8VgbMqFUVMwZAHq/RhwuFNDhIaG/fgwXtJjiDbk3t7YBGJ1EAYHXnQbDlXB2LyMz3k4qph41u8Z1XeDoquzJiLEXUwdklgs2alFjtt0kvQ7yTtuTTcyTrSiA5dZv2LbRhqNixuOl8cfeHS9NWy7DGs13qdj32Dg2jun8Vo4B22fsItveq0CD5eL8XmeTLecTjY8sAsO8x25sVy0Ey62FaeTrRCBMvmZQ1LOUGIPUuDOGXETZnV0gAcWZUPdp3z62YtoAj9x+EucdxM2fc5ryxNx0gXFrBDcl4lwks7KG7owolgcAKOXdYXtBcmdGce8bJimHUXSjynDu1ZMSIuikcTmPuXuZMW6K6g7WUCDHDlPJPhj0+fcqtacraZ8ePGET3UvEYKitqlEowWRC1dJx5vLI25NNtQ25eFSFIevXp4yj0nGnTOclBvZ5OKI1QdFmfa8d37B1a7kjdXxSECa5B33q2teKc951s34yuoOW5dxkm0ps56+N8zyDh809yZLHu/mo7ZjMDjpqOT8dt6Bd80mIBAO8Q6g9Z7qCyIDDgFVDItaAklVnjEa5YfWx03asFLPdz18GLMCZeHHMWSWClJcBYIW2fCQNAzscwdvnPWBOFmw47FBJQl+vUFFArnyYUSGKSW+/RHuqSQ+PEQd/67NRp0ASPxYZyXeGqRYMipATNcdzEUqHhuslbLbGJEfOysd9zLvx7tAH48bYjiSMjrN5Tndmmx45/qAnU7ZNdnIJuijYejefMW2z1n3BS9NriRaK7FCur2a471iNS2YZKI3GKqI98/O+Hz3At9YnZIoTzoRcU/nBdWtlFQA592Ef+v+T/NvvvVPC99vWdKXog0YegAHVT2689Z9Qe/MGHAyjVFbX7m4wzRvmabdyNez3lClPdOYfjwvBJ3WOcOT7Zxtm0n6kpIguKNyR+cMb10fcjQR085LR1c8a2ejZz9P5HkNd/3OJSyKhlVbsG5ytArcmm54sp6Race6l9fmvJ6y7gsO8x0XzYRlXXA82fHpxy/ygeMzHm/n7JqMu4crblVrtjbni+v7oh3QnonpeH1zzK7NmFXynN+6PhyDaAavgI7ZDIP3onXfeqm/OzaBeJ5+DtAxBHzEM7oi3ml92C/KPCM0beQCSvUQnASZjtTfyCVUk0q4ADFmbNRH+ABtN2LFgvMjj0A0CwPmPL5UNy3OSSK6hDQZicMq3X+daaDe5AQnM2+1NajDDt+LBbhfZyLvTQzLLpGVHhRdk+CtCIxsawQVlrlxMoAKZJUETPRdQrPJpGEItEFRb3JQCOV3m4lkOVKKtZGxYD9MDVpDV1qepjNOplsWWc1b6nAsV63XLNuC603Jx+4+5uFmwdftKVnixsZWCEIEmuYtp+WGRdpw0VbcK1fk2vLCVBb9P3r8Kv/BW/8ED5cLXj66pDzouWpkhLnpc/7EW7+H/+SVv8w//eU/yL17lyzyhlemFyTacbtY8+XLOyPCbHDvfeL2Az795EWeXchR4wP3n/HS9IpvrE4kag2Z4/iguNhVwk7Qbqwgvuf4Ea8ub/HoasFiUnN3Icedx/WCNHH0zvD9B2/zF1/7LQxZg51NMEqIwo/Xc5QK3Juu2HQZt6sN06xl1RacbScsyobWJRzkNW9dH+KD4v5iSaZdTBz2PLxacDLbjuf/PO95fCWb//ceP+BWuuZZP+Pt7SHLvuAgqylOej7/+ks0i4QXD6556/KQeluQZBZjJHh110gzUStGWMs3u94dm8DNs/hI5pGAD5VnEQXeyxxe6cgN1GBF3UeW7n0Cyo7egWGEOFCElapG+S/ey+eHkd50CjCSg1UqYIeRUTjwC4MH140sAZVlsW8RicNpJhMFrbETRTHpsHHx+tKK5r3qqPKeq3iHzvNeIsfi0SFLHNs6G+fjXZdQ5jKr7nupFrJMzu996nC5lDvOaTEhaVHlHc+3bDI7gjkH9LjWclYnKFTuJO0ob8eEnw8enVGantL0nHcT3lweMauEbnNc7vjegwc8bA54Ws8g6USIFI8lz3YzHvoFzmserA/IjONqW/I7Xn6Vv3f1/tG/cNUIyOO43HFabHhteULnDP/0l/8gf/u7/xI/9Lnfj88UP//wFe7NV6y7nN5pjqe7UUsP8Pb2UDaFozWbJidPLF+8uEtnDfOipSikGrisK2ycl2+6nABM044vXd5lmrVksf/y1vqQ5U42ptvzNR9ePOUvv/Vxdpucx2rOrJCm6a1SchQGvHiiHW2fcN2WIy4tSxw/fveX+dmnHwLgaLKL2YWaTZ9TJR2TLGWWt1zXBat1xb/xfT/D4+6A//db382uT/ny9V0+716gj+DTSSL5B482C5QZDFGKSdGRx36P0QEzkSPUIm++bT8AvoNN4FtEkP1p4HcDHfAa8IdDCNcRRvoV4NX47b8QQvijv9bPGGdjwzx/UAvGHkFo272PABiSgofGXehECajyTDaEKBtWRlgCoe9RLmYR3NxoRpVfHCGaGwjxGCISQCoR75/rDaDVCDxVeS7PYaAl9/L80k3g+roYoaKqVzSVk7v2yQ63ysDDTmeoykIc6e0AOs0yGkH8LqHNMmgNyipC7tna4bURgxCRHNQXTlKJtwnnQdFdxbNJNoBJY0pyLZWHbjS29DzRAWc12aHjK+e3JSHYajEktQa89DfqXc6qLbjcVqO33rYJ6XzHzaTeZZfGrrc0Mz9/8QK3qjX0IgS6BGZVw/luwuP1jKZL2S1L7t275Ic+9/v51Cf+S37bF37vmK7kgubsasbST0gzS98bylL0BecXM3nt6oR6sWJd5+wuK6504PK45KBsJLegkY3jST2nKHqufMXJbMumE/5gmfWygTYprjOca8/PXX0XzinUk4K10+wymQ5cbiqmZcvlciIGURXYbAsS47mVtRjlefjsFv9V/31s6wx3qHlyNUPrwHzSsDXS1Kz7VKLflXT0/5M3PslyW9JsMpSC3TyjzHrWu5wnbcLR4Zb3HlzISHCbcpZNObqz42o5wbYCjw1O4VuDKR1nZkrwGvNtTETfSSXw5/jVEWQ/C/yJEIJVSv2fgD+BpA8BvBZC+N7v4HH3VwiEzRY1U/tSPxKEGSTAeIKFsNtBVcnRoO/38A+lZHFHNmCoa9FVK01oW9ThgSzqzVbu3rBPFM4z2USqBL/dRc+A2lOK23ZMLRIdQ5w8NC1+t0PVjUwMBsDpMO4E6UlYdePPGj3paeps/+9PvQSPOFnMygRCF1VeSr7PZB7XGEJ0Aw5lvQUC4j5EiypQXhCpMPoqIbRGpMKpPLaAQ2SMGKxsDFp77p0uef/8jK8tb4lFmMC2zzhfTeh2GbdnG54ExTxvmGYt57sJHz95xC/2ydgkG1x0JsJUh0bnQV5zkm/lWFRaFpOatk9Gll4Iir7s5c6VKX7bF34vP/Ox/4Lf/dWfYtvnXGyrUW6dpoJi00q4f9W0ZbfOBZqqPLOypZ2k5HkvYSqxgTog1rKsZ1FJOE2eWE6LDWerqUxh0p7ZtKZuI/UncUxLy0VecnAksV4u6i0mWYebySzeaM+kasmM4/2zM5Z9yRvZMbdna56pKfemS9ZNjo139KG30Uc5s/cK5RX3pktmecsbncSrHVQ181wyIYbn27mEDx494/zZHBMnEIeLLRdXU4zx5FWPLQf8OHjv90Gz3+T6NTeBbxZBFkL4mRt//QXgp36tx/m2l1Jylh6nAkHO7MM8PsaGE9y4gAGZ08d04qAUSiejxkDluTxm/DMxymwMHBksysMdPpKBVJFHT0In5/1IKFJGj0akASaijBGjU1mMcNSBRaCMRjmgGyACoDoFU4vfJZipRdk4AQlGpNCDFEHL19o22oWdwq1TVK9QrSYkogh0IQqHAiinCAZ8vMPjFNt1AesU5RCFoApgAs4EeV46YGo9ShsutxUXuTSrhu58EjXuKvEsspo362OepjNaa3jl8JI3N0c0dTZGi2kVIhJMQj+mRjIGXtucsrEZJAGdBE6qLRd1xdW6IoCU2anllekFP//wFRLt+d1f/Sn+7Hf9Bf7s1W/lF/XL7KKktqkzktTxnhMBbTy8Wowo8YO85hOH7/D58gVWbTFGfskRS1iNHzp9ytcvxCn4yVtv8Or69ghp+e7DR9y/c82nrt/D1y5O0SrwA7ff5m81GaeTLctWJgmHcROxTqY4x8WWs/WUO5MV31if8tbVIUXec7ad0DsjycBxcgOMLs0itRgd2MUm6TcuT9huCvwu4f5LF3zg4AxNoEo6nu1mOK/5wOwZ590UnTqs1TzYHHC9qvCNwSlovBo3zMGI9Js9HfgXkXTi4XqvUupzwAr434UQ/s6v+QghRNZ/HPkFJ2fxoVwfI8T3M30Zeclt8jkt1BBfPvQYIpp8/JwxkMqYTwXhAao0HaPQx4lEmokBSUfPvhGG3ThiHCzG0fQ0YtF0ECahUnQzxcHdFXlqqbuUzabgYL6TNNzZmnfyA+nQpxYTZ+qD2m7XZJwsNmyafJwMDLZhgGkpneHhbjqoxvLU0sekooOq5rKsRshECELqnWTipgPYVgVl2Y0+gCrpOKxqtrF8HObM/VRLLt/tktxIOOYL1TVbl/Eom3OY71ikDR7FLJEknM4nbJAx2MvTC+5mS35h8jKLacP96pq75Yqzajp2vn1QJHESUSUd2z7nz179Vv7AwaeYmYafdh8bI86LxFIlopV46eiK66pk22a8Up2TRhxXoj3H+ZaTbMNqVvDOVhpzL1TXQhEikGvL7WLNepFzu1qTa8vD9gCtAndnawAmpmVSyLRhljV0PiFRjsJIH+Ew3/E984csu5L3Ti6oXYZWkvxU25QytXzi8J1RQ3G/WpIby0Gy42k7p3aphLU0BaeTDfVkx9l6ykFRcytfU+ieRM/pfMJBtuND5WP+bvcBssLywtE1L08vMdqznu8Rd0OVpFUYU4m//C2W369rE1BK/ZuABf7z+KHHwEshhAul1G8B/opS6qMhhNU3+d59DJmaPBfkCURsV4g2XSsLPjb0ACnLy0JK/xtpQQxiIC/a/rH55zyhbiIRKJWNpOuj/dePbsAxcjx+7xhyYu04sdinFhtCXctG0O2zDFVUK5o2sI1nY9sbCfFYBHbbglXe0dbpOOpLEjeq6QBsZ9i1mYBGI6rcWgGCKAVNLIe7PsFZTW8Mtk/oY/y495EtF5uBPnIIOy18vabO5DjgxJew0ZK6U9uUZV0IEce4kZ+3azM+e/kiqZaO+arJebA7kGSgVcHbxSFHpSy8bSKLoLHpmGd4VOx4ksxxvaG1hs9FnX2VdDy6Phk3v9vFmnWX44LmYlvxi/plZqbht01e5ReK9/J0NZONzmvJMGwkwWcQVH1jewrAVVux6TJZhKbnIK35SneHed7wjfUpu16st19I7o/HmKum4qoRK29uJF4sUY4vL++yqXOeGmkeDxixIgalPtrIzL/uU657sTBf1NUYhb7aFXx1fZuLuiLVnqvoa+i9YesyvnZxyr35iutQCNnJmVE9+aq6zTyrY1x7wjUVv7y7xxurY7JUfv6yF97DoKIcmBTDyDDVQjP6Vtc/9CaglPoXkIbhj8WsAUIILdDGP39GKfUa8F3Ap3/l9z8XQ5aeyju/LPbsgEjxVcbICG6IFtfx7l3k+6ZfIpivYew3RpYPJiBtpLIIxf6uHQIYv28yZkITUlkmDcmuR+eRUuyjAGjIQRw2meFn3sCLD1mEKkvxmcxr+96gtCep5K6qjWNdF5jUjSO/4U4tT9tjEyMCISd3cR1n/rDHaA0c/jSLqjAvcBCBjso5tSy7IXBnZAsk2pPE2LNy2uKc5sOnT/nK2W22TTbSh3whY0gbN4R1K02xLLFC/YkuOBrDaluMzycZ6MfxbvTkasa6FHPMK/fOmSQdb1wd8aXzu/JYfYL3moOq5suXd+idNAFtZ9g1GT/tPsYvFO/lu6bPuFcu+aXL+5xvJqybnK5L+NDtZ5zXEwGVNpMxaNRoT92nvLY6YZLKPP+d6wOqvGOetfROjEOdTcjTnk2bjZhzgKZLmVWSHXA824oicbpjmrVs++xGqKnmYdwIvrE64Xwj8WiTmCFgreHxds5hUbPrMx5v58yzlpUuxvn9GxdHcV3sUWBGBc7rCY828/E5LWj4zMVLY+zaxbaSdKJdPvaDBl+IC4pJ3rHrM3b9jWP0r7j+oTYBpdSPA38M+J+GEHY3Pn4KXIYQnFLqFeADwOvfwSPKcSDP5G7vovNvMABpJbP7+HfJ/dubg1Sa7Mt1p/eLfFj8IIIg7/cjRnnCsclnENyYJgQ/jgdHnkFiZDwZq4vRcDTIlW+CTuNEQyUJrlBUec8u/qgskQZYEX0CgzHIGMkWTIwfoRnilOtHnNa0aMW1GEdPvdP0SkCSaeKiEEiJkswKLXiainqusaIcC8hGIItUpKuDRfiTh6/xxvXxiMwS+8Vebz6L0JHOifBmY3JMlM2SBGEpxudnnR6PNklU+vXx7jabxoxD42TDyRR54uis0HqvtiXH0x1LP0EZ0UFc7UqermbcK5f8I/OvUumOv958SB63lZL7pNziUVzWFXVMNpoWLdO8IQTFs91Mmmq9oM4nkxW1TcdsgTRuXG2fjLRlZ7XQidPAJGu5RB5bwZhNMAiZQEaO5zuZmhSpZZ61oxMR4CCrBQVmBUFunY5mJKnSqqKLv1v5Hafxdzcg5PNUxsLLGL0+mKxAvCeDKnQwkh2UzR6N/uvpCXyLCLI/AeTAz0aBzzAK/FHg31VK9Uib64+GEC5/rZ8RfxAMdB4dFX8M/P+BNxgNP0MzLy7g0N94jMgfwNnn1YKDCzD4/ZFh+B5EUqy0jrwCgYaGtgOjRjnzKFEON1WDg5lIjZbmm8xDo/0YFgKMi0FSiYbJRsBpRYLs/gNfvkjFDOSCILYH7j3Im1bfOD60fUSW590ockmN0Gud12gjb6o8cdEoE0v2PsGowJvNCWXsWnc2IYkUn5Nqh9GeXS8z8ZenS5HhZnvHoKksqDAmA9V9On4OpMM+K6Uc/fLjuxzOdngvwasDeXiYZeepSF7TzJKmlqYW4k6eWn7p8j6V7vho9ZDdvYzPnr/Its54uFzwibvv8Hi3kKpHSeJTZw2ZEVFTbQXTnUVDjQ1m9DVMlHg1hg68mH0Cs8WWO5M1l03F4/WMo+mOs+WUXZNx/0jO9Y2VkJMiSnVDUBxPduOdu0z7Mdx0Z6X5N5zV5f2gsc5wMt+OUutdn8rXRy/AbLYeoa6ZtszylrpLWRQNPlckyrPtxcKdJXZ0rd6q1pJR2BXfdul9J9OBbxZB9v/6Fl/7l4G//Gs95q/+Ri8z/MEENFB7Y+ef0oyjP5wnaBUnA91o9WXQ9PcWskHH7wm7Dj2pZJxoLcSosfHvShOMH6sPiRwXQu/AERwZA0PYCFFKPHARe3tjQqDBSEai7iQlWF4bwWjLPNiMi9dbjdVyt2+tnJcV0PfS3EsTh3KadZPLXSluKCbSeIfHGdBjfQzDCAFhA25F+CLGInHLGe1F9aY99S6nqlr+9oP3y+Yx5BmoyMLPGg6zms8+u88s7zjJtjzYHcRR4P4Ob8w+GahzhgRZANaLgOmwqKltSn9eso6LWsfXJTeWwvSs+4IyEyHQ0CdJUmkC9l5zvpnw15sPsbuX8TsWX+JZM2Pd5LiguGwnPFnNuDXb4IOijch06cIz9hyGzIBVW1D3CZNozFq3+3LZRPPPLG95obqmcQkPzw740Mmz8fW8mVQ0qBBrm0o1F9HhDVIpTGLq8rqXqmOQM2sVmGQ9vXdMs5ZlW0R8uBkriEXecLda0bpEUO/eCKE4tTxdT3lhsZQqrxeZeWIcaUyNWneFRLJpF+kX3/x6dygGkbGdKvYpKcHxHA14FAsNIh0bJwhaSyd/0PTHJqD0A2Joad+jqmpcyAOebNAkjOV/msqIUJv9OX9Ajyk5u4fBdpzo/SSiigj0iBcLbQdaoftAHRt5zilBjVctfW9YTFvaOkUnUi4PjAHvdSTmSNl9EUU5A2DUOY3NIifQyuQijWOg4KUhZJ2mbVOWpqBtUwgKl9ox9KTLDF1nSBIxEPXWjKDTYZGouBCebOe8vToU6WnW8vbuULry2nNabMZ48DKOyRZpQ1skNPGse5xv0eqUg6ymSjremojQZ141491QmmFShg+NyLLs0CrwnpMLqqTjqq1YN0I2/uz5izxrZvxr9/46f6z+KZ6uZnztySkExeyolSwEJz2Vadpyr1yy7gpsiK68vObV81vUbcr9+YrOGRqbcFAIG3Cd5IInz2q+dHVXkOK55aKZkCWOLlKOEi3HMq1gmrY8WB9I8GhbUfcJs7yjdQbrNfOs5u3lAdO84/7kms4nTEzHPBWYig+KdZeLxdgZ6jbj1nzDPGvY2oxV5BvO84YPzp7ygekzfubtD7Hpco4KOZEPQbBDYxDguhXX42E+ntp/1fUu2QTk2sNB9d40lCSiABw0+l1MD7J2VPPRd89NFtAe+iDfU8ZSaOAWWkuIJiJBjUfprw+CE/NBjhGdCJHUEF4yVAJDVJr3shnVNWrQJhSFiIfSBLxG99DtspGhp3aG3bFsJst1iW8HkQ/UTo14cEyAXvPAHOBaIxtFZ0bQSJuK+AdEYdj3UWiUBlbrUr7WQ609bi0bq89lfOm9hJ261mATD9cpdWvgoKHMO2Z5TMCNDTLrNetdQbPJuD9b8o2LE95/fE6iPZetbFBJ6sgSx8PNgnMz5ajYUiU9R5nkApyWG0ojd9xgFbZLxrsVyOay6XLWTc4nbj/g7a1Qjsq05yjfYlTgoplIwk6bsK0z1k3OH6t/iv/su/4CP/nFP8z2egGNxt7VEta5m1BFgMhVW7FsCpo+wVpDM09JE+nBfHD2lM9fvcDZxYzOGl5aXDPLWi7riuu65P5syfumZ3z9wS3cQpqXzmu5K8dKKARF49KxGQrwnsUVX3hwn/fevmDTZTzdSYbhxaaic4ZJ1nERx8KD+nKzy3nP6RWJcazOJyxT8V2URiqq3hsybTnvpry2PGH7cEZzlJHftmMjNY+ekmaXEaxGpw5lAufl5Fuuu3fPJjCc5YeYMGMEFRY/NzTz1PCMo4AHpQQQkib7qiFKg5XJ9s1FpFkXhoriRq6gGJWEOeB3OxEIDdiwJD6vaA/GKlQvG1CwViqH4CHP8at1bFoGwGErxeJwS2fF7dfPDIcz0Y931rCY1WybjDQRjt2myaVjn3d0VkaDPnFMynZ8s4nyzY/HgGEUtIsQjeFO5b3mhaNrNouMNAZQbBopR6u8G0vO5Hgf37VrUy6RO8oQSDqkFeWTDq1iiR0ktXeeNTzbzahXBbfmG5QKXGwrVo0kIr/JEa1N2DYZLx5eU9uU3/LBN1l3BZs+G0NDH/Vilimynk8/eZE8tZxfzKimLQ+vFrx0dMW2y/jQ7WejTdcFxdPVjJ/84h/m733vf8H3/eIfom1SPrp4zGcvX2SSdaOFODOOWxM5JlzWkkWwKBqs1/zdp69gneHgYItW8PbyQN4rKlC3GU/NlMum4vh4w7rN+F9+4G/xyfIN/vU3f5/ASqIluo1wEOs16zbnui4pip5lU2CdxhQ1J9MtdZ+OFODTYsMkkX7E42sZny6bgixxzE+2LMqGB5sD2tgLanuJbFt3BZ0zhImlqlr5ntxiEj9mDOSlRLNNi5Yy7Wnsu91FOPgEBnLQcJ538c2eJjLjJ5be1oKB0Lu9iEgpRAwfy/LBLtzHruEQMBKRYaGL+K+BTdAK1mwwBIn0eBsXesEIFRmaj4MmYcCi+yBVxw06snKBXZvGXqfn7uGKJ9ezsYu72+W4zmALieRK439GSxjJdNKMZ/8ilYbQqimEsBM3EsGTG5w1dHVKnznun1zz8GJB76JdF0GCdZ0R+oz2bOscFw1LJnHcWmxGqMjZekpTZ5jEMZ80vHJLSvIH6wNOpqK1XzU5NkhmII3mnaeHTGYNfZ+w6w1bk4/Dle465yzr6WzCOw+OObq1GoVPh5NagCB1xTTreHYxpzxaQ4DdOid4xXVV4rzmvJ5wUm75xN13uGwnfO3JKdvrBd/3i3+IL/3Qf86/c/YR/tuHH2G1K/BecTAVlp/zkty7bnPOr2aUVTtGm3/t4nScxLjI5BtzHlVgnrW8fXkopJ6859/71D9JkjnyXFBlw4YL8L6jc752cSquytgIbfuE9bXc6f+JF77K2/URv3x+m6ZP2PUpy125/73EUXCRWA6LmmUr1YuEw3pOJ1vKpOfJdsaz8zmHxxupQiJa3jmF90n04+0lxz7so86+2fXuYAxqJdp9rfdn8eGuPjQBh/Tf9oYlcggpNfvPh97uswPbG8eEIax0SBoaQktHoZGU8/LnAK3Yj4ePBWvliBA5A+Pm0UjkmV+tGLMIul6syV4aX7YXB1/vh7O8NPhcLzmELir8hoy64Y3VO4GMuCjHTbXc5Z3XMiJ0kuAz5g3GoNLhzp4aoRfZ2EvwTnQHg/49eFEYeydsgCcXCx5fzaOBRuOd8Ps2Xc5xvmO1LVhFe+p6U3K+mdB0KclGyMmDmlEh/0Z52aXiWq0rmiYVLHje0dQZfS88gLcvD7leSfkdvFQsoU6EnejUyB1IjTS4Hu8WvHV1KBqLRtM2Kf/O2Uf4kydfHH0CAJerisutlN+7Po2gFKEwNTbhwfqA3S6n6wwHpVQGzS6jrVOcU6SJE9lvjAXbrgu8jZvuAH+JDL/HyzlvLY/IIzFqWe/ZCqHT1G3G2/URl2313LguNY6+Tsdj1WpX8Cz6GDqbsK1ztrUkPr1xdsQ7qwW7TnpJLx9cRtCM6DqI/3bbJfS9oY2j4V2fjcrPb3a9O3IHktPww4vfM8p7g/PS6BvyBaMeYNwQBshonomLcFePZfhw5w5NI3fvrkPNZlK2D5kGcc4vm4SXz8WpRNhuZYHnufQabqYND1CRmGcYum4vHJpUwi2oBLuFtTz+n30Y/nH5RYWgqHc5d46XBOCwqLluStZNzqxox+7wQIfdthmHVU2VdizbYjSbdPEXmyVOSvV4dvex8z0cE1wseZ+tp+PXggh5tArj1KLtEialLEylAvcnSzZ9zmVTjQk2ifZsuowfOH2b1zcnnBYbntYzTooNPmg+8/BF/sUP/3180Hzq+j10/vkCs3OGTxy+Qx8Mf+vRBzDa8+Gjp6x7YQIe5juqpOMrF3c4KnfkiYz0jPIc5DWvVOd8Y3vKRTOhtQkBkTPPUsF/f3TxmL/9+P34oPhL3/1n+VNPf4w3t0dYr5mmIoVedSXn9YRJ2nFY7PDxHH+ab8iN5c3NEYWRf+vOyvh0mrSs+oJEeX758W2+5/4jvnv+CIAvru5xnG95uDsg05ZPHLzDTz/8KD965zUuuwnvbA/YdPnYI/jEyTt8Y33K7WLN7XzF03bO987eYWG2/L3VBzhrpvzykzvcP1pKlRQUs6zlfrVkkrRcdhMe7+bcLtf8+PEX+ZvXH+ZTj17ileMLXqqueFTPuWqrmAfpJR4uKMpEEogyY/nLP/Ifv5tzB8Lz8FCtwIqJSPn4+eAjZDTe3Yfsv6jOI87v1YD58h7KQgYjEfs1hprELIKhpzCMG/GDZyHu1Gm25xoOG9OQQzBUIM7FjSBIQ7DvYuqxRveBdZ0R4t3Z1VKeb+uMXSl30xBgt8s5Txw+EmK18WP+X9scxJgwAUWEiBHrIj1oKEVDgK3OSVOH7Q068gm224LtjYZVkoohqKnl7mp3CbZPKI5FgtqVhkebOZuYPwBSFm/bjPN2yvluwqotuK4LXpxc8c72kK5N+JmnHxlHX69Mz/lI9Yg7yTV/Y/URbqVrPn39Ei9U12zqnCy1IxZ81RSSFWAck6zjpekVX7y4y7rOmZUtnzh8hzRGoTc2EbGO2oeY3q1WfPbyRVa7Auc0f+rpj/G/OP1b/MfqR3l1eRvrBXK67mWU+Gg153a1GpmDP3nrG/z02XfzdDNlkvX84OmbfKB8yqu7O/zC2csUieV33/0CZ7U01n7+7P1M0o5tn7HuClZdTpFYvra9RQiK1zYn4pmIG0DvRQdw2VVcNyVXTcnTYkamLX/v+n1Yr7loJtR9Spn3bLuMTZPTNinZ6VVcDoGDtGaXpaz6gk9v3su6z2nqjDevDmltwpP1jLpJhRCtRTWaJQ6XK2zQ0N+4mf2K692xCYA09mBv4tF7kc7Nkl0CP2UUh1IoncYswl8xItRxkeaxx6DUvlyXH7T/+uEaRpFDcy9OEUJ8LoN2QA1R6tE+PPYlhn5G34HSmBb6bQY37L2dNdg2QU8ayQxwShJ/MHI8sBqVeILT2BhMQlB0rREnYGfwKuALed7DJEClXjILrEbpgG1kkfsYWuq8AquFVmw8oRvSnxVup7jKK7QOvHF9zGpbYDuDjpTiNLL+L9tqVKgZFVj2Jb03+HXK5aIkS6TZd1lXfDW7zVG+47Kt+Lx9gXuTJV9b3RJmQenGDWDbZDQmGZmA31idSDryZUU7Sfl8+QJH+Y6rmDLc2QRrNW3isc5wsZtI2e2lz/Lm9oj/WP0ov/PgC9zJXubT1y+xsxmJ8iy3Jc5p3led8/ZaKoW/+uzjPN1NqdsM6wyfvXyRp5OZ5EFGc9anly+zaXKWaSnPQZtx4+qskdm+T+is4U6x5jPnL7BtM8GIO0PbJrydHY3pwKu2iGDSnq3Nxt7NbptTVh1dK4Gxp6VUKeftFK38uLn4oHm8naPj7+VsO6FpU7wztI1GxyOYj4Tjm2Ksb3a9OzaBASumojNQM4I5Riy4NnstwI1IsOD9848VwZ/KCGxUmoP++UU//jw9bjYhJPuvkQeOc38xD4Whh2CMCBi1uRFIqkZ58VAtAPgEdD5sYOBbOb8nmaPtU7QJ+ECMJ/coo0ANuv4w6vttH3sIXo2LfawOdMBbiRMDxkASU1jyzGL7RDYDJ0apYVPxwUcDkcbkklZ0Um3Z9hmdNXRx4ypSS2LcyK7PU4Fx9k5LmGi55vXZybiXhiBn+NYm9F56HZfbinuTJS7Ipn5cbYU4tCvHDvbABPRWMS9arrRgtlZtMR5HjJav7WJDs7OGKpcpwMG05nIl5+1Xl7e5k73MJ6o36YPh58/eP3bHg1c8aA65N1my6gvWfY6NABWtA7s+5VktmC/rDGVq6byhrjPyA+EKuqBH6fAkE5Vlpi13Zmuu+5JUe4qouxjozgDH5W5UcBrt6bxh02WjrNgkTijS0VHpgyJRAmttXcI8bXhles5lN5HjXJQHhyAGs9ZLFTn4R4YMil83Weh/kGsoyQfNfwhjhx/YI79BFm2SPOcWDEM+wUD+HehBzkslYIxsMPFuPmLLYcSaqfg1Yy9BaVSejJXJIBgKyY07/yBnviEVDmE/knSFYj6rx19omyaUeQe5jOGGCLEkETmv8/s0obZPuLNYizuskI8NHn8VJboAafp8b7eKqOq6S1mUjYwy45tgcJVp7WUT0p62Tckyy52JJAOL+0/UhFnimOVCyTEqcLtcS1xZVvN1f8o0aZmnDZOpQC+Oyh3JxOFjCGim5Xzaey3pwJNr3jFinz7Idmwr2SxOyi1FsodeFEXP5XHJUVWTG8txvqW2KXWfMs3FezCc91ddMY4Bs8wxTVusN3z6+iUJJ80u+cHjN/nFi5e5vVhL+KtLRbvvNbfL9cgcOCp3TFLBqFmvuTXboAnMkpa86JmmbQxTSaKLsAfkrj5PWjZGzuEn5YZ1X2AzTW4svTfcn1yPvYbCiIrPBkGNmcLTe8O6zXlpfsVZPR2Pb7m26LTmmZvhUUxNi081i9jInBUtx6Vg0TdlFjH0clya5zKhKMxepvzNrnfHJjCM9mLDL3i/L+GHrv94l97jw9Xg6BuOC79SA6BVTCOSxwlDb0GZ/WYz6AqixRhrhSKkZSQ48gSeSyxyYG9UFiHKi6OUeMSiadH4Aziv0IXguJo+4ajcUXeiVCxu6Of7yKDrLML/z80oeQXxHmSJHUeHg8TVByn/hkZeE+/cwx1p4AykiYuxV1FsFCcPtU1ZddOR2huCIovI8N4bJqnMsxdZw0m+4UFywCxtuOwq+j4hFNKAmyUtWnlql0rSrhb2fa4tKfK4mz6j7EqOi+14l+qcGTcPgINyz8Y7yURs9NrqRJ6XtiRKca9cioovUnpmZUOVdOxsxs5m/PzZ+/nB4zf5LZM3qUzH17a36HxCqh1vrw8lXCQ2JU8nG+6WK0n36QuM8hwXkpq8tjmTvGPT57RWNPzzrJEjRleOHITOJxxmAhvpvKVMwp6UnNY83c3JE8thtsMHTWk6yohO77xMMLK4ERdZT+cTrvsqPp5h3RW8rY74yPQxryfH4/vmbrnkaT1/Tqw0sBQAMm3x4TcXKvLrv4awUHi+QTh82onWX6hCVu78bbs3/7gblmCthbLjnHT9h3M97NOFh40lUoqD0uPoMDStKACTRFBm08nzzcG2lXGh97JpwL5KGUjELmoFHPuILEQ6LISbnHMdaDtRsPVpQp+ZcYoA0HWGx+v5WB3k0Ttuo8jHaD/e5W2UGvs4TgxByWjQi1NtsPg6rymynlnacF2XceOQ49eQbdBZE51shrpPcUGNk4Qu6taBMQ7ryXZOu0vZpDlP9JyLiBofRlMAl7uSB9mByGPja/FwveDubMVBtuNZPWPd5eNGeFmL4Ghobq1mBQdpzSRtRZxkRXCz7XPWbc6tyUZkx16z6krWfU6iPI1N+MWLl8m15bfPvsTOZXzq8j3yb4kCnLfXhxIiYhxn7ZTrpmTTZhIj1mfcqtY83EgOw6PVnMR4JmlHY9Oxetn2ElM+TxuuupLGpcJViFbjziY8yha0LqF10mcoTI/OZbN8sD7ABcW6LnigD8bNb93lI1B1YB5s+pwn3ZynmylNJ9kHj+sFj9dzWmsi5FSqvnWXs8iaMXvhW13vjk1gCAtNzA1xzw3lYJLsNQHOj4GkY5k/3MmHa5guNC368ECkvREPDozHjpE74N2eMJSme4JRETuq1hJI9hVGdDo+RyIeBEODeAhAE1lysSQPmmTg5u+KMYGo702MoRLrrVIBl+jR1ivwCD3eIYazHkil4bymbUXkkmg/GpWG+Co9JBN5z+1qwwdmZ1xGzX9qfCTzNpxvJrS9HDm6NonyYUXdZpR5Nza1jvJdNOfIPFyZQNOmtHHM2HphDqposmnblIfrBWUq8eap9nTAdcSN914AKtO042In5/qmSTEmsK0N72wP+Up3hzEhKE5bbNBj827d5nTWcM4EFxTLaPS5vVjz2u6E1n8v/+zBp/ja5hYP1gf0TtP2KbOywUXHZW3TUcrc9gk7r6n7VFiCcWyrVGDV5Wz6bMwu6J3h6W7OYbEbWQHOaxIljcHBNdhGP0PvNZmRfMLaSjW4aXKqvGPbZRJMo8KoErVe01r5fczzhi9f32XXiBirswnXTUndpTGLUI8irSKRKcxlU/2P4Tig9gDRwdk3EHyMkQpgEOfYThyAIJp+5yONWO1VfGk6Lviw2cjjte3oQRiPHUN+gLrRiwA5Qvg2AkLiuf+mhDkeF+it6BCKXH6GtZJzEMVGphGyEEgVYNtkJPAezbacXc1k4aYWEyOzh0hFCSmN5/io8LPWjE3BPJd/Q9PJnV/yBkVtmKeW7S4X33ocO3bW0FvD61dHPN1NWW5LksTRR3LRrsnIM3EfyuhSjQGZSQwCVSpwVNWs+oLHV/MRfhG8oio6ciMVRBdDQQA5u3aG+XHDdV1Qb3Iu4xEpMY6z3WSMBnu4XIyNruFKEj+eb9+5PqDrJXq8zHoOihprDZd1xfnVDK1FVfdoNY9iKHFVdj7hU5fv4WubW/zf3/NX+Oe+8ge5XFWEaNqqsn5k/lln2NY5WWa5NdtwtSu52pVsNgVHB9s4DYiuRO3lSBcUnTe8en6L+4sl103JxXpCFQGqV11JmfQstyXzqmGWtay7nHnacJDteHN1zKJsuNxUJIljvctJEk+Z9SRZN6YirZqc66bkpdkVZ5sJu21BYnbcmy5FumzNaBxS0ZLex4boPHu35w4Q0LNp7LCXct5Pkr03oO8ZYshUlkqgKB6VFjKi29U3xnRyZldGi9pvQJIlBpJOPAVDBdALXmyQFw8RZ2pSRfVhj6qK6D8QV2JoWtlEIvxUV+XzegUnhg1UBnFfKnMBeOi4QJsu5Xa1ZrkrRx05iLfcOSX7mVdMo298XrTUrXgDXDCEqPjzXkU4iWcWJcYiJLKstwLEFG2BljgyZ+i7ZHQkSrhJgo4y2CxxLMqGCyq6KHdOYkBGVjb8yO3X+cWzl2mtZBr8tluv8dX1bT5/XTHJO6pUqoVpFsYGmA8ac9fz0YPH2IXhZ9cCA/nY7cesuoKnm+kYDTYvmjEY5Ek9J8t6PnT6lBeqa76xPh0758Pmc5DXNHO5ew/Br4fFjtvVivdV5zxoDqldOuoMHqwP+Oe+8gf5uY/9JX78K7+Hxibcny5ZtiVn24lkLc5WfODwjNxYXluekBjPD919iy9d3iWPVumBTVAkPVudMS9qvvvwEX+/ey+fPHqdn3v2QdJU8iZ3bYa1hnuTJbUVzkJmHLer9dhv+fG7v8xfe/RRvBert/eaepfxT733i5ykG768uUftUppK8gg/MX+bs3rK233Cts14tFngg/SWQlDSH4/R9blxzPOGRH1r5Pi7QzFoTsIPH/+UUIGHJuCQHRApwaFp9rqBoZyPlcNQNYTe3hDvRB9AJs0+NZ2KQtBaWeTWyt+jNDk4L4s4os71bCrcQ2LFEBkHYXA0KoUq8uhvGPwEfv/1SvH093+U7W/fjHc25yQ0ZPizcxrfGZLcjix9pSDLBKYhtuMkqpz9aCkOXjGdNKOysG3kbuS9GsVAAB+985ivnt0mBIRNqANFRJTVdYbSgb5NSDLL977wcJynn9eizOuixbhKRcRye7rmfDfh1mTDts8ok56PLh7zX33xE5wcr5nlLdsuo3caowOLoqG1CWerKS8dXdE60cu/cnDBdVuybItxQQ88w95J/2a5K1lUNZsm59Zsw65PmaYdk7TFBsOqLVjWxdjo9EGNi3qw3d6bLLFB82Q7j2W7Zr0tePHkmp/7yH/Dn758H//dk4/SWokNt9ZQ5kL3GY5UxxNRF75zdshsWnN3tpZJj0s4LrasOuECHhU7ihhK2rmEeVbztatbGO253pbcO1jxwuSas2ZKojzvm57xuFmw6gvevBS0mI6jRYCmT8bOvybQebFFf8/8IX/77AOUSc+D5UJoQ9pzua3GcJkBL1YVHZNc8HKp9vyd3/EffFPF4LvDO3AzfmwIHxmuOMeXu3om2PAkAW32GQBpTCCK04Ix3Rj2o8YBIzaEhoJ8z8AWHNSDxux7AbEXoZJEJM1ptj8O/Eqs2KAheO7fJf+TM5rcHYcwjlGNl3hRCFoT575yjk4ziSVvNhk6xmg//5J9k8077H+W94p1JMpUeU9Zynm9aVPaTkwpg75fKXjt6lgSdPNtJP/KGDMzbjzLWq/JjJNwz14cc2/tjgi9FvONTUY8l3WaTZdxtSvxXnFvsqRIes6ezXn9+nh8yqmRRQzSrAsIpstHmbMPEr3d9AlXTcl5PeWyrli3GXWbjmfmw1zcmY1L6b0811VfsOqKkdTU9ikh4r3+9OX7+N8cfn0kNSuEgGS09FFsnNKsW/m3OqtJjchxXTyjb/qcdZePGoSrpqJxKVsro7pdl46TC+s1j7YLrpqSy6binfoQj2LVitmp7w3bdTEqJ4ER/DpUEJMYwmK9Fsn5phRnYVDj790Y0X94Jx6U1iZM0+5/BNHkiItvgH2oJCGE2Ll3HmLO36D2C0NF4MXiG5omugOTPe5rHOm5sToYQkbDrpa7em8FZFqV0UUo3gM9qeIRJMgmcUNnQJZCr6R6GCqJwV8wKAmzuLiigUhr+aX02xQTgaJp6uhauatba0hTJyHMTkliTW+Yz+tY8mtCSGnrFKVlI9nW+dgIAtn9tQlkmaNphIN3tp3QNCn1NhtDR4ZKwVmDSRyuzXCJZpZ3vL0+5JXFeewlDNDQyK134tP/6tUtphHSeZDXbPqc6dGOk2rHLGvQ08CL5RUfn7xNHxJ+eXcPh+ZZM+NHT77B1x/d4npdcm+6ok2sMPaylruTFU+2c77n+BFfurzLyWxLnlg+eesNcm35QnKfB8sFa5uP2PQX50s+MHvG3336Cl+7OGW3y/no0RN+8tY3+KvPPs66z7ldrjnKd7y9PmRWNmjtuT9d8t89+Sh/7fHH+Asf/M/5k49+gvPNhJPplu8/fptb2Yo36lO+dHkXgH/mhc/y590P8tL8ikebBb3PRvjJgHE7zHdcNieyqWU1T+sZ89gTuDXf8P75OV9fnmJU4Ch6F3xQVGnH/UO5+y+bgjyxrJqCeidjyVdm5xynW1a25LKr+Nz1i3z34SPe3B7zKAjh6e5kRWcTVtuCJLEUWT/2nnyATZ99G67QP3wM2f8B+J8DZ/HL/mQI4a/Fz/0J4F8CHPC/CiH89V/rZwDo6SSyBaNeQBvIjXj7B3ZfIRHiwxleKTXerW88UhwBeskfHJqNA7Bk4BCAoMblSY9mpOHFUmW5JxPfyCIcOYNdPzoMh+zB0PWQ7KsKl6r9HV8HdO5GV12ZSQqRs3r82GABTRLHLsIlJ2U3xoS7KAzyUSGWaD+yBb0WpaD3iknVisItJvV4PeQOqHEC0ZCO4zqC4tlqyv3DJUfZbgSLjEq2WMH03jDP2nGkJs00G1OXA8f5lmVf8uXlXb6+uQUIr2/VFnz06DFPujm+11LlRO1BCEqEMl3Bts14dXlrhIycFhteXd/mdiEleBfvuBMl1KHGJXz+6gVsTHPW2pMbK16A3RTrBB0+TeU5O2eosp5lW9JamWD8yUc/wb9652f5d/vfhQ+KN7bHfPriJRLtuTNZcVZP+emnH+Picjo26DqboIAXp1fUNhVPg0tZNzl3Jit++/FX+CtPvnfEjzU2YZJIPLlYhXs0gbvFkrNuylcvb3NvuuLSl6OasCi72FSsqF3KZTth0+VMs5bS9JzmG5LUcjLd8onFO2z6nCYmQYnKU35HVXzORv36KoE/x6+OIQP4v4YQ/oObH1BKfQT4/cBHgXvAf6+U+q4QwrfuSsRrBHsQ/QM2nu/TZL8Qg6DAhyNDMGafIAxjpaByCQ4hxoYNjUCVQLBuH3U2Hg1iGR/HfMFa0SUM5OPh+RA1CSHI8xr6EcP0YmAYDk9Ji5W26+SsbxLPQVVzsZ6MMVQhiJXXGBvtDkIctrmUxm30ik+rBlXuo85hnyqTxCQaF3l/Pgi9KDeOLJNm0YAJhz1UxHuNiiDUw+mOJ6sZF1vxzzqpZgAAZIJJREFUB7RtKtVJ1lNkPfcXS169vMVBWWO95sn1HA5g0+bUVyUPtMylb9J3nddkieV8OeVz7gWs06Rlz3zS8GC5kOcSlY91lzItWh5dLchSS9OmnK2mMu9e5DFYpR/FV+tWZvBnFzMODrbkac/t+Zo3N0c83UQvQMxrZMLIarBRa2+9SH/PNxP+3f538Qdu/wN+bvkRfvHJS7S9zN+l455w3k84PNxwXRd0XUIe+zrvbA7p4lHpwVpQ5mf1lP/m6cc53034vtMHfPbZi2zqnC9e3WPd5CTG81SJLFk8B3tc++3ZmmebKds654Xja3pneHN5FF9LmXT4oPjy8i6PVnNeOb3g6WbKL1y9l2cbyUSwzrCK48SjcsdBXo9CrG91/UPFkH2b6yeB/yLmD7yhlPoG8FuB/++3/S6FnPEHbQDsZbk+WoWThKD884YfHwh4WXw3g0vaTu7qEVYaBrFPK2ctoQbtjUrDYFUlBr/doiYTwO/TiQegaAh7J2FsFqp06BuYPXNg6A0EafZorcczbt0nVEUrqPDMYhJFkfdjcvCAjAaYF63MiPtEJpLRBzAwBX3kE+ioLVAKJoXcJbtOfrVFatm1grU2RvR4JnoAQlDUbTpWIhKJJRBSrT1l3lFGGMiyLUYa71G+42kxJTcWXQR2R7KAq7QXHp+O5W0naUXpoeeo2NL5hK+u79D0kZwbyccDSEOpwGJSk2gZjw3hHberNVdNxSaOKYfncVDU48jOecXZZsL7j86ZZD02TmOOyp0kHbVTaiuKvHnR8mw1JUkcJzE+7eeWH+Gjk4e424rPnb1AZuS5uaAokEbd8WTHMzuNvxvBefeuZJZ33KrWPNnOyeIkokp7rrqKPLFMD1ruT65ZNQXTvOVutWJnM0rT03nD/aMl2y4jBMXpRFR+Q59hcWNi0lg5Srw0ueThcsFlXTHLu6h43HLVSCJyqiWReNAx+KBH5uM3u349PYF/RSn1h5BgkX8thHAF3EeyCYfrQfzYr335KN8dlE3B3Rj5xc1huBsPUebDwo/ZhWOZP1iJh3wBayHJn99Ahq9zEKIWQCUm6g3iRuQ8JGH/M+PPGn/u8HU+JhFrxRjsZzTaCjfOe3ELhl6zVkLmtc7QtZIbWAfQOoziIa0CtjOsUyEMD3f+vklAgU5EWBSe6wlID3RTS3/C9obLXUnbpvRtQtfJqFAbURoOCb39NkOlXqKtEzsuLOf0GKNtnWbXpnzw9BlvLQ9JtRvhmuuuQGtZxINScJq2zBMh9WoVRinuC9U1v2zv0nUJ904uaWzKppU48TKNJfJixVvrQwlOSXu++/ARubZcNZLoM7gYh9fppcU1by8PaPuEZpeRnHh+8PRNPnv5Irs+ZZJKFuJ1U4623ruzFetcNsbvP36bN7bH/OKTl3C3Fd83fZs7+Ypfun6B83qCdYYPHz/hc09eYJq1XCUlSaQV+djwS7RnknT0XjM3jsL0XPuSJ9t5DMUN5NqN39d5+T1vbcaylY1y5Qtqm45xcmfXU24dbMTboC1XpozeA0+uLbemG15/esLxgYSkbvqM9a4YNRR9zDRI4gh6EEF9s+sfdhP4fwD/HgKm+feA/zOSSfgdX8/FkOmpzNeLYg8btWF8Z6vBrjusMKsiEzDKhgce4eA4HIJLhixDHfbd/BvxZuMVfQvBIj9ruAap8E3aUfx6pcz+ucYGI1lUG0bEmbLgOjOGZarG4CtNXxtcqSQ81IPXidiJ49c5E8BqNpsC3xhQMkUIETTqek3IHSFuLnglgNIk/P/a+/NgW7b7vg/7rO7V4x7PdOf75geCeCQIgYQ5SRTJ0DYpUqYtUxLFP6ySkzJVJeUvp2zHlYoS27JTlZJVlUosVRQztF2mHKZc1BAykiyaCUVKAAESAB8egPfwhos7nXvmPfXcvVb++K3ufS74HsCQJt8lcFfVrXvOPufs3b13r1//hu9A2SqsCxplGdAUgVCIHf7AaCUgGtdkVKU7PqQ73zpFm6YRFSLPEY6slfelrAPOi3S4m6/qiCKPyIJ22BCh13IQbrgdn3PRjghUx5urA7Qntuk9/iCrQzZFRBc5vkBcMAlKlnlCVQZMxgU3ry14UAnkuEfRtUY4E2sdMXEgmF5RKW9DXk6OOBpNOC4m1EazbmI2TlI8KyJe3jlxCEqPK+GKT51JCfDpk1tci1Z87+gNOuvxC6cfRvuGb5084EuLA1Jdo538W9nqIUsDsT1rHXqwt2kbRbVQhFXILCyEN1GFdHYqx+/L5CHyOzaFmLlcuHNva59It4x0ReS1JH7DaT1iUSU8KObunBWrPOaBmnG2HNGWAW3oU5QBXesThMIk1f5Xr8Z/T0HAWnvUf62U+jvA/8t9+wC4felXb7nH3u05fqcNWU8KAuEH9LLj+GDa7Sy+v/N7l6LbJegu2smEt62M9dzUAdgqFA0n4An4qJcZNzJytG33GNtQKYX1cNMHT/6u6wSB6HnbrKNnMppO7trhVg7cGEWaVuQ2ZpxWLBuR5VK+wXfIOGvcZvUtYdTSeLIJsYrOswOL0NdiYWZ8gYlarUDhXGg7h0kQvrm9RCH2fKEgt1oeM3GHH3cDv2BdR9SVFp2CcDu7rltN1kQcTDIivyWvA+pOpM6skXS8Jxnlbch5M8JTlsNyhqfk3I7y6VDqiFefmLFq3xBpCSKHhfQKulpktz+5eFbupH7vzKTQvpCmtGc4L9JhXKrDFk9ZXs+vDWw6CRhmkFQPQwH89KKg7xQHQ6Mz9Ds+u7hFZz1eio94dveC03zEo2omlmTOQQq2OgtKic172enBB6IxPk2jIappWx+l+0mC8Pt7PETgdaSBNHpv7S443YyGslGHvZuwz9ivQEk/oyd8nRcpOuhIIkFqRlGL72zbrVV0WuzIfWeh3r9/77Z+rzZk1621h+7bfwP4nPv6HwA/p5T6z5HG4MvAb/zuntRt6F4H0PO3OoNBiEoDJxSqt2QeZR9P0+FxjIHTAhiswXqiT08H7jMHkLu752H6CYE12I7htYa+yiB6YgdlIRVdUjX2+5LCCosw6KRDbxQl0gizVvHc/JzPlyFto4nimtghxXr9v7rWHEw39NZfrdMUrFwa3/cFmsbhC0LBGgRhyzQtuVinjGPxOBCHNcEOeC6oKGUl4DjhkKwICUPZCDrowE0E4qBlHNTkVUjgd3x45wGn9ZjD9YS8DUmDhlNHcLo1WrBuI+6udjjajEkC2ZRnm5RXrj5iWSXY0qdzzcpxVNM5BN0kqPjyYodAd1ydrjl1nfU3zg64PlkTucZm13pMZhmTqGIWFiyKZIA0J3HLWFd8/OQ5kRfvfK5MNuzFmfjxOXrwW8v9AbfxufPrXButRNknaDgtRvzC6Yd5dveC/+2z/5D/5O6P8SsPXmZxNOHueGe404e65fZkQVaHFHVAbxpyJVmzqhOCoCWrQsZJNUi71a3m2nTNrdGCs2rEh2cPOKqmfOrRbf79D/5j/qNXf5Tdcc5CCZJ0XUWcBSMa63GYzzhaj7k62bAfbThJxnRGcXu65JnROW8GB2LFrlsCr6Oz3jARmEfibvxP32Pr/V5tyL5fKfURpBy4A/y07A/7mlLq54HPI27Ff+V3MxkQ723HFwAJAE09zPFpakzeDiAdWzmUn/G3E4AwlI17uTcQBtjlCm9nDk3jHIa3vYWhJ+Cch23XSQOyrqVRWZYSQJzM2IA5MBZbtVsj1KKUUWTvf6A1IIGj6xR1GQ3AoU0ZUZYBrx1epyk1tvEoOkGr9bW/1h2m9jlZjfF9M7jxNI109H3tZv2dh+9bwFKuIzBQA2e1kIl8R0rqx5T99CAKWppAFG7rSuP5xgFlxMBzdyz26bOo5CDZUHaaZRCzqSPe2uzz/OiMF3bOCf2W0rHcNmXE64sr7pqxzJOSWViQNZFMSDohy+AJlqFuBcG3mxbsRDmbJmJ3lNN0Pt88O+KXLz6Ads7LAFp1TNKSpvW5NlpzK13wuYvr3JwsOfLHTMOKk2zEqomJtYh/JEGLh2QDV9I1RRNwkSdo37A3yllX0j85KcZUrYBu2s5H+4bTfMR/cvfH+Dsv/D/56Xd+gqyIxN03KVHAdx7cYdUmpM66zVOWa7M1d1Z7JLrhuZ0LskbKgMNsSt6GXJls5PVK0Qv47eXN4XX/xpf+ZXzf8PL8hIsk5bxMKVvNohIi1EG84Xq6ZFUn3M12eLSeUFYB6zTiC8trHK/Hg7xY4HcyRtYdV8drNk30mIXdV67/SW3I3O//deCvf63nfWwpBM7bK/t2vRioS/uVg0O6el45pJ/yekxARy9R3msICmGoRiUJNstRkzH06sPKPgYWwveFZqy1TCLSRPgHcSwb3/EWLvcRVBSJdXlZSbZQlgI62mTOGNXg12CNhx90gt8vNKQMxqJYhQqNU/6RkZUFSSFdxz5fxeiopSpDTCPSY70TkZhmumZi0OH5lihuhqbfIkuGzKEPBNZa6lYL/8BaUTvyLDf2L2g6n904483zfcoiZB1GVJ0m8mVTffveXf758fMsayHV/MDNL0kKH00HffuRrtmLpGT49vEdPp/f4Fvm8KXNFT66f4/DsxlJ2PDh/Yfcz+Ycb2QDXklFsOQ75nf577/8bXSdYpy0fOzqXUZ+xWvL68KzCKxsEHdcL45POC9T7p7v0NSaZ2YL/vT13+ZTy+eojc9EV6zbiAebGaOw5iJP+K7rX+b15RWUsvzZW7/FLx59C6fNiBj45r1HfOvkAY+qGb/y4GV++p2f4L9/6Rf5cfOn+cD0mDubPcpO89pSgESN8Qg8wzgQbMOVdE3ehpwVKbFuub+e4ynLfpzxIJuJi3OIiI10PrURpePO0cNfO7smfZLO45Xrh3x0fo8dnbHpYt4u9tmoiJ2w4Pp0xVuPDjjPhLfRuUmRtdK36ToFMYMd+ldDDD4Z3IHwiv2e/T+3Bf2IER698ajtZ/b9WfabsZf8GkZ/vauQ9A+U5w1kJLTvTEl7PkLo+AmdkIT6VTdb0NIgGWYGqXFgQBb2fQjRLWi2wqPIZjv8sy9R/8CSuhK6qGk89vfXnF2M0UEnmUAnoiU6kpIBK3gCAeBAl2nCWbWVi7IyETCNh20VSlt01IroaOuRTKpBpDQdVUOwsFbRlBrlW5K0dihEJ89tFR++fZ9Pf+lZiUJGoQof5jXa9RWu7i3ZlBFFEZIkNavjMd/5ylt8+v4t6rNYXJO0JRjVzMYlSll24oJIt9xbzFmtksEQw1OWLIulrg5bpw8Iz+xe8HA1Jd9EqEcxJjLENzJGcc2miNibZIyCmsP1hGwjhhtVHrC3t2GVxSKxVWkO5pvhWKN46w8Q6o6T8wk3DxY8Op/StR67Oxln52N2djYDGGkcV5R1wOJoQjirePHKKb/0Tb/ES7/yl2R0mkiDsGd2esoOxh+esmw2Mb5vuLa74v6jHQDSSeXgJv4ACkuiejBgKc4SJlc35HmEqX3muxuem59zmE1ZbNLB4r5nj2bLhJ29NeeHM/S4oa18fCdl15UydUrnBWnUSLniGV778f/4CVYb7gxmvXlM+pumxjjDES+KhBwEg1rQIAtuOsxqI1393mo8TWSDgxidxpEQiLJMsgXfg6oeuAciKCLaBd3FEn9vV8Z+dXNJzMSNCV1gUmGAzQvJTlbgpeljSsS2M/iFfYzUYTuPxSqVhlyP1lMWz5GD+rYIyqI8wQOko5KyCAmCjioL8QIjF+KsGjZxv8l1fxE4kIynLFXrYY2g9PT4cTqp1p3zQDA83Mx44dljAq/jzukuTaoJwpZn9y6YRQWfP77GzdkSNbXEfssbrc+99ZxbewvKmSDoegPSuvOZhDIPP8ynzNOCF3dPWdcxb7x5nXQ/58b+gvMspes8bu8t+ObZI46rCUpZDtWUdecx390MhhtH/pjzTco5KbvjnA/uH3NWjuhmHusqJIoasnXMH3vuHgDLICGaC1pw00Q8XE3JqpDducCRJ+OCwDc8M70gCRoWRczeKGccbhWS7o5lHPqB6TEv/cpf4gvf/3/jT3z2z9M4sZPdvQVFIwIi86jg3mqG71n2r51xnglGYGd3w7OzC7RnuLvaYRaV7MQ5J8UYYxWZH7KfZhSzgEWeMB0X5GVIXobcWezysWt3+d7nv0RuIj6xeoFlHTMOKl71brBap3zgxcMBfXlvMWeWlOzGOUvnUhR4hpsTYTC+9h7b78kIAp63NfXsG3zKk43lPz4BEPZe7Gi/PiCEHzEndUKfvr81CRmlW+3CKBKEYX8HB7f5PZSWKYLvzEsIA1TXoUYzyURCwV/bunY6Ag0qiVHC23SvydbG3LdCJXaAF9+3mE4PcFBjFI3Sg4pMELZiJNJ5aG2oKxnRWasFEdj46LihrTTW9wbGoR3eLkdHrjVBJM/lewZrPNK0oqyCx8UnjZhooGQKkVUidT2OK+bjYgANPVpPuHcxB+BquuLTj25xY7ri6mzNJKzQquOdB7f58HMPaK3HWTYFBFyzKBMmUcXJeszVdE2iG0YHubyGM/9clRHnecIn62doXcNtElfkYURrPJZVzCSUm8HOWCTZTpZjLrKEUHfM04L/5cv/H/7jT/4opvX41ulDfvXkpQFd12sCamfdVrcCJb4+WbOpIx5uZBpR15rjdjzgAMTnwWealNzZ7OF5hj/x2T/PL3zLz/L5ZsbfevgDgoFoZgLsiSAvI77v2Tf57OlNYR6ezQmCjncu9rg+XQ0j1dZ6TIJqkP86LKacrMdkpynjg0wwI43POKo5Kqb8fP6xwfVpJxUw1TwtWBxPeLiaEuqWR4spxijqRnO8HAsKVcsN43Qz+qNAILJu9q62Xfi+a/9u3oE9tfeyQlBvTd5rBl5i9Q3EJM/JhnPpNYyM+74SN6A8DxtFEnhgoCcPTEPYmqNYR3oqykuOSR3Wl5GddWCgLm5JHZADGCirvi8IuXUnx+f7ZkiTAaJgmzIbbfH8LV1YqhYn0qF7KLKIl4a6I4qbwWm4cg7JnmeG3qjvMOaioiuklli3QyPJ9wx1jxFwppu+Z2ga0Q/cNGIVVnZbdeEevx54ZlDE6TfkzZnQe3vprX7k2Uum1a0m0u2Q5RR14HQBDeOwQgF5KZlZ78b0Pck76LCjT65GQU3t+XTWGzQBR0HNqo7wFBRtQOS3TgQ0lNd0UOAeBwCCSVBA2WmSpKbpPD7fzPiOaDOMH/vGZWu3HJBeBUopycb691W7oNwDhjxlGemKB/lsO+Yc0J8yguwlyeWz3uIkms6nv5h7+Hk/+TFGSkxjFHHYETsE5XutJyQIIHfu6JJfmuP196n4Y1z9KHRNQJfe9nZl/XIdfFQ/YpS7/2A+0geWnpik/S3U15UVg0YBONmwXpvwUkQNtBMX8bc8h/61fQ+rJDXvu/7WisVWr9jTOG0Az7dbCTKkbpT6T2roXmVYhEEUSnko1Q2/b62irX2aShPGzVBqiECpdO6lkhEiURS0Litwx2AVB9MN6zKiavRw19hLMzxlOfFkHHVRpUzjipcmJ9z1dgd0YJA0nGQjRmHDlclm0CEE6Wp/rrkuWH1leevRAdNJPqjzzNOC0BNpr8ihDq8ka8434oOwkxZoJcIYWSMIuJu7S7eBamrj87+6828SRQ2d7nh1dWMIMAqYRiXTsBR0YhMOoiQVmsp5B/RXzjQuhY7sAleoW77z4A6vLa+jfQmqfQbwU1c/waey5/m18kURXlGGg8mGk1JMTHzPoMcZI11TtAGpowEHfsckKEm1eA6clGOqzmH9ry95fn4+mJo0nQCGYi3isWlQy2s5FGA4q9hJJaOyVg3MQd9v0VocqWLd0rhJ0XutJyMIXLo99x1/G2hhEA4Zgd6i/0C69z3hCIaN9xin3wqT0DpdwsuIv2Gzek66vGcMNq2jBjsyklLYx/OHQWhU9QjB4eC3mAOUhwkVSVJTO8x/13ncnC15tJ6wkxac5wl1rQkCUfQpGhGljIOWKvCFigqYqKbtfKpWhEa17pjEclesO38A+oA4+4pkecDz03PeslvufuWOI76E7usbdQBXxjJTv7+ZDxcWyjKOKjZVxE6UM9J6YMKN/JrKaHzfsp/mzKKCvA2pOo2vzEC3NRauJWvO65TOoR6ToBlm2j1Yp79b5q3M13tefey3xH5L5oRLe6st7WS7jVWMopqyDtiLMta18PK1Z8RaTBli3Qi335Ovp2HJxrny3B5fcG+z4wxBBAoc+B23JwtWrYzosiJid28xZACfyp7nj6Vf5mQ+5o3lFXaiHIPixfEp46AaWH+tL5oAN0ZLyk7wBLOwZBYUvBif8FZ5wMePnuO5nQsRezWa1noDD2M/3hB5HctGmtf7UcbIr2TkOgqYRiU30wWvmevCcxj4Hz5p0AxqT09+EACZ6V/S+QO2abqzLFe+t4XqXkYN9nfgtocOs4XuhuHjBiWulLBsf8fWjdTy/UjSCZWie9+BDlsj//c+iIEDBjl7Mtu7IffwZmNRLQPVF4Tqu6ojyirg1IiRZtd4w5ivVwwGqVEX5pLJp7LS0b/0GOAag57wEGAoG9rG57QcDQo5vZdBH6eqKqDT3SBMWYdy1yk7MevsjKL0e7FR4Q883MyoOx9PGY7zybCJjRGF4KwJKRxIp78L1Z0g/760OiBvAqxRjMKGogkGVd/AmW2Mw4rD9RTfM5wvR3QTN7d35Kqy1YOCEDCYss6TUkoDq3iQz1nVIjo6CsUXYFnLRq47n6IOyDwJJmt3ty3awCkPJQMUWClLVgsYqjEebSvqy0Uzw1OWXytf5GQ+5tn4nMhreWN1harTvLq4AYiIahI0LMqEVRlxGEwpnZBp1WnWYcS6ibmfzcmqkPutqAQ92kwGifjcExmz3pvRWMUsKPlCdm3QW1RK5OIXDm5c6+1nDDLCrF1m817riQkCg/X3JVLQY+srR5nOYWgY5anef6BjMCIJ3LSg76Bdzixgixi0BrjkX3Dp5wPK8NIxiV6A2/AOcDSMN/vmpmfwG5kOmFbgwHSKxSalc2OinldgfGH99eO6utY0lXaAIEXXiCWY6eR5+v+tlWNTMOD/+4BiOsXhakrt5MmAQY8QJEhYo+haT946V2dmyN2xV7rVvqEzauCnZ1VIa0WxJtKCCOxfWz4Wi+9MR+RjUwJfVYZxUA8NzHkiaL+69fECS+BS3F6hWLljGEU1O1EuYh5ONFWEWowjgCqKJhC0ZesPPgeeq6n7u3vWhMM5TeOC3H0f+J2TBfeZRPVw3I3rR/SsPM8dVx+AtGd4Y3mFyGu5HZ+zaBLeWoovgnHCrvO4ECMXtTUEUSBKSc6XoeoERXjvbO5KELkGe2r1QA1370dlfKaBlC1Nrelixap0uhZqiwrd9id8d5k+6ZLjMNB2xRrLjfGGzMAIgq9P2XtNP9iShTwF3lZ63LrNb6vadf/dqV4WIOlNTrW/FQvt5c1692NfpgbK8+SYekMTjEwaLmcA/WRCSWnSBYo4bjChNLk6p2HneYaDScbJeiQXbtgS6na4SPuovTcR84te979XDjZGDbDhPisII2lEha6p1nQ+47giHzIAgQEHrmHYe/cZKw5EkW6ZRwXjoKI2/jC/Fhixoep8vm33AV/0r3IQbVjXEa/MD8naiDfVPrenF+yEBRd1MnDXexcig+K7994B4O2jfbI64Ft2DzmPU06KMYluSHVN3WlujFeDE5LviQHIh6dCP3ngOvmxy0DGQUXZBVSOJnu4nPLR+b3BZCT0Wqa6Qnsd53XKUT6lNr4o82ykTNqJ8kH7/0q6ZqRrLioBI/Wc/3FQcbScMI8KiKQJqJVhJ8p5Y3WFRZPwY3uf5Zf9DzH2K46qKas4ds7KVhSCxmfczXcIPXFUMtZjN5TPtzLCY9hUEXtJTuR3LIuY66MVz4wuaI0YmrbWYzfM+ejoDnkb8ubZPjenK2K/4aQYk0UC7fZd4EgCcSTuRUY/+x5774kJAijvMdvwAQkIAtvtH/PVFr8PsuEu1fgyp0c6+X0w6YVA+r+JgscbfCDlhtLO0owhY+jT/6HvMGQqxjURve0Y8zLF2Cqslk58VUaCWXAvWZUhD53Sr209ai9AR0JG8XxDEMjXyyKmqgTdl6SVgwmLiGmPDQAJBE0RYDtFO6kEfejJ3bW/OwSBoesU2u+YxuXgUNT6hrb1mMcSAB5kM07WY4osJIjaISBZq/js+U2KJmAaliyLmNN0zHklmc3nH11jlFSus85wB1XK8mgx5Z/zghhvdIrAN/zWyS2MVezEYtN2Z7nLLC7Z1GJqusliRqmMF5d1QtFIIOw9C4tWNm5vz/bi7imLMOEXH7xCL7F+bbJm40fURnz8duKc10+v8M/r53lhfsZFmXJe7rN2mv+PsulAN26dRt+1mYwSte64t5qRlwKDPphsMIjg6FvLfX7Z/xB/Ye8T/PU7P4qximfGF9xtdgbB1i+tDgj8jtNizHE+kdKnmLKpI07WI6xVPLN7wUWZDNbkR7nwM3qPhevpih2d83MPv5NDNxq8v5yRhg3nm5S61oOQbdt6zMYlJJA3gl58r/VkBIFerMOqremIcwFCeVtMfhjIFCFNpLbvLcl79J6PMPua7QkrrbH+FloMbA1OLhuStuZxpiHIz5JEoMGD70G7PWZwx6oZzFJh6Av4paWs9TDuq1vxB6wazYv7Z7x9tkfr5MVGcU3ZiBx42/q0jWaSVpKGOtZZGLbkWYy1EDrkmHT3IXZAIN83eGFLlsVUnc9qkxA6DEJTS38iKyJRH1bQOaTZvcWcizjh9mRB7Wp6kMyiX/+zq6/zz89eoOwCxlHNC6NTdsOUN8xVPnTtEeOgYlGnGCsCmuOwYh4WdMbjR659jjfyazxaTDBW8creIwDuZ3OKWlyEVlXM1XTDokoGtt210YrnR2csmpQ3V/uc5ulQpjw3Ox8adW+cHRDpju+79hZvbfa5Fos5qLFqyFAOsyk3Z0u+Z/dtPrO6JT0N9xonxVgER9x7rZTlSiJcgCupeEL6nuX7nn0TEPz/i+NTXl3cEDKUX/HX7/wo//XLf5f/8OGPcGe9yyJPmMQV6zziarriMJ8R64YryZp5UPAd43c4bHb4R+pD3B5f8JsPbzMfFYOa1Lfv3+NKuOZhNee8TjmvUt5S+/zUjU/w8clLfOr4NqHf8c07R3y2u0ETtMMYUbI8KJqAWLfMnD3au60nQ23Yra3TUK/McynF7kd0Tva7T8tt02x9APqOP7JZbV0PwCHbG5T2qycDOX8BOoNtu+Fvh01f19L46+3HPAdpBsk8OiNBasAMODETrekiNcyf+zT/ZD3GGI8vX+xQFiFNJZs+K0PqWvoAQdDKLL+TznfhbK7rWguaUMkUYJxUxEmNrwVM1MtpWUAHLeOwFokwR9mN4pr9nTUvXTkljFqStMJzZYXvGQ6SjJdGJ1xN14zCesgCfGXJq5DPrW+QO3yA7xnezvZ5VE6YTAtqoxnrmgM3Q+/7BYf5lM4qPr54gc4qqnVEXoUc5lMOcwEWjUMx+lyXEbWROv3KaMN3HNyl7AKKTkZpp5sRTeczS0p24oKLKuWsHHF/PZfxaxFxXo+ojeY3T29xd71D6ezSyi5gGlYsyoRfPv6mgdAU+w0/tPcFRkEt+AZfgl9jfFbONCRvQzabmElU8dnTm3zq6DanxZijauIuA8VRNcVYxX/48Ef427f/R56bnDNPCxEhneTcihcAlK30Ah6VU35j/SKfuHiOstU8yOZDk3R3lBMFLb95epvfXt2k6rRrKsq++K3sucG1aTfJeSk9ZhaLQanvid9lFLRML4GyWvuk4wScDZkdZMG97d261+7rv3YyYLYopHEXheIU3G/w0EOpCMpycC9WUQ/7bbZZA7jg0nsZ+APASAXu98sKNXav6TQG6DpUEg+lhrJW+gd9+VA3At7RWrwIq2Bo1qFkAmAtlCaQBl/r0VglKbyyGCNTgK6VRl2+jvCCvpkn6bQOhIVXNVqwA0Z4AzgcQNv4tI0YXNZOjci618h9Q+R3gl9oPUyp8ZKWcVTzcCM1c2c8cRIOa+rO53QtPPc7y12qRnNjtBT0WxMT+w1FGXC4nnBRJigYWHWLUtL4stGsophNE+GFwlY8Wk+IgpYbY5n5P1hK131VxdIPUIZlk/Dlix2hIzvdgNjRk/tJxLOzCxZFwiyREuVeNmdTR2RVSBy0rJuY2kijsjOe6DsGLTevLtiJco6KCX/v0Uc4zUekgSgCrcqIptEEQctzOxecFSm+L9r+vVio7xnOqxGLUpqbqzjmmfEFd9a7/OV7P8j//Zl/xnd95icESt35fG51Y5hsXFQpe3HGnWyXqtOcXEzAKnZm2eAkfLScUNbBY+AtX1kCZfji8irLKqauNG+e7Iuc+WJK5STYAZk8GQ9G4lwU6ksYnK9YT0YQALmbRtEg5jFYfCWJ3Fl7+7CmlSmCGxsO0uD2Uj8hioQ/gEv9nZWZ8h09uG1d6u94A1E0kI0Gu7O8EC5D3ciosO22WobOKg3lYfNMgoLBWZxf6hEoBq/43lMgjWvWmVzotvRFvsx1/JUPodMfKF0DUEct1niDJmDf/YUt+Mf3W7J1jO2kAen5Bs91omczoefGQcumiIbsYjySO0ffrd9PNtxZ7HK4mgq7rdJMxgVJ0LI/ybg2WkmH3jNs2ojj9Zjnd845Lca0ZcBGgTcqB7Sf5xB+47Di/nLGo/WEJGzwg45rk7V4BxQR7zR7aIdVuDLecJKNCHXHg+MrvBPuEUeN0G2NxyiqBzutEphENb99/yZxLDeMvVHOpo6GTn7VaNrQI9ESNLQypHFFqDveuLhC7tydtGf4Ywf3uahTHmVTRlENThUoa0Ji3XJtdyVkqLO58MrGGZs6IgnEDq1oA+42OyzyhDvs8l2f+Ql+/dt+nu979SdcK0kNICStzDAZ2NQh03HBap2yl2ac5CM2ZcTOOB9ATJ6yzCKZCNzNdvCVAMhu7i8Edl2kzupeDcrPaVpxc7bkZrrkKJx8VUPSJ6Yc6GnEl2fxvetvTw/G86UZ511i69UNtq5F+MM5Ddu8kH/rjWzWSi6cnvr7mL+g+3mvZWDXwvnG94cSo3ciwuEUzCbD5LkAlqJIfk8peR1jZSLR1KhOAEKmEaRf13oUVTCwztBWcnfPEkQtWCXSUr3Ut29EKhwZ9cjdW+7yQoSUGrAsQrDCV+jho/0FVDorsrwKhmbioohZbRKKPJLnq31OC7EDm8QVz+2e8x3P3OUHb36JZyYXXOQJn3t03ck+WK7Ga145eMSiStiNM5Te6hFoR1tdV5GYheQjFPDBvWMmYYXpfB6tJ3RGhEhuzpa8vHOC5xkerSeMo5p/7darTKcFzx+coZ1RaN8AXFaxuBtbRdX5PH/1bFBPPsvSQU9PhD+6gQZdtmJeYq1ywquiKdCPBH/r+DYPNzM6q8iqkGWWEAdSS6+riPuPdjjPE4KgIwxaRroe/BeWVUzRBKzKmEksfZwkaPi+V3+Cv//Kf8Pz8/NB3dg6+HTj5NBBVKOTtOLexZwfvfka33PzHfIqHPwc+x5LD3e2VhHojgdnM6xV3JosBjBaEIjvgLGKk2zM5y+ucpKPOM1H77n3nowgoBA+vvMR2LoFO5hv3QzjOYwrGXpcv3MI7tl92+aeSH/ZPHfNRQkOgGQdfTYB8lzeJc2CXoUYtvRgs9UmVL0LEkgwCPSgLGTrWo7JnZfnCZFIKTtoBKIEP+BHnXMguoSBsDj5cPm3O86lpnPpe7/qSpPlAkHWgegNKm1EOqwVKfFlEUsQcoAi3986HweB8AqUcgEIWOcxF3nCg+WML5xe5deOXuDNxT5VpamKQLwNXYB6Z7XLsog5zid86+2HjOKaWLeMw4pePdhTVpBvQSuMP+vRlT610+UrqpAvX+zwMJsxjSuuTdZ4yvI/HH2QrAg53oypHUBolcfkZci6iESc1I3CNnVI23msFymbE7nQ286nqjRls+Uz1K0WCG0nr7/IEo6WUtOXrWZTRNIT0K2k3bqjM4rDbDogKns2oEX4B6sqZlVGg5R73focL8fkjQiolo3mL9/5cf73t/8hN9OlAHrcVGbThKyqmKrRXJ+sCfwO7Rv+6aMP8uv3XyAvQ3GKQrgQnrJsqlBGxm1AVoa0ZcAH9k4ou4C9UU4YtuymBc/Nz9kb5QPQq2r0MGZ+t/XklANBuJXlsjIJUARD2g9sCUCeJx4CRrr39HdqGLr2tgbP0Y17boA3Sh8XDnXZRE9cUp6H59yEVBBgtR78DAc9wx6X4MqPnkLclxEDa9FYTKCYj3O6VBB3RRWyO8loOoF0XuSJgFrcXbQXrYycTPj+WH732u6KuvPJQ6n5Ar9jHNWD9BhIo1D7hlFY0xqPvA64Mt5w5u4APQ5hElVSjy53CfyOlauzfc/w/P4Zu1HOaTkidzj7yBcvwqrz+djBXe5ke3jKsBMXPLf/gMb4fGl5wHOzc3bCHGM9WocT6KyiNT6LOuGF8Rl74YZ3jvaIw4bn5ucCkTUe40CMOdZNLLN4oNvxuOF8BT+6c48vrq9y6BiKqRMvmYYFR/kUPy4EOVeFfHT/Hud1yt1Q9PpvjhbMg4KHofQcEt1wY7TkfjanNR4vTU8Z6YpXL25wc7Qg8rrHOul5G7IfZ2zKCO0Zrk9XGKtIdc2N0ZLDQILEC+MzvrQ64Gq64la8GHoAmybiP3/0L/NTB/+CogtYNTHXE3mO/WjDsWsuBn7HRZkwDiv8qQiOHowynhlfkPg1p8GYVRRzEG94Jjnn9c1VPmtusmkivmlyxDvZHkUS4HuGspOMoe8v9LS5z7/H1ntygoA1DCYgffp/WUG4aQchj0EYpBf6vCTwMRB5jBFTEGtERbjfxP3r9OYm/tbvwILzGnQeBZ4a1Ixsr0vYBxGlB48B2xkU7RaI5MoZr7asi3hI0cdJxfl6hO8bcmckaowiCFsmqXgMGNfhLauACy8ZVHGNhVEkWn9R0Do4qNzl2tZNBqwiDwN2RgVFFdKN5HcsDOPHvApZFDGZkyY3nUdVaZ6fn3FWjjjMp6zKeHCzScNmuEO+vdnnNB/hYVlWMW/bPcZBxd3DXS5mMg6rWk3vitv7Jx6fTykO5E40HRdcGW84L0esnBlH3oTUnc8orPnyYofdUc6jiwnrMhqYemdFyk5cMA+Fn7BuIu4u5zSdz/4441+99UXuFru8uT5gUSZ0VrGX5ORtyFE+peo0VeezzBKKNuDF2SkPsxlfWh5IACojVmV8iUYscNsrkw0PshnWwt3VzkDg6az4HpYOzns33yHwOw7zGYf5bNsDAD6xeJaiC/j+3df57c1tvrC8yiSoOConAy5gUwj+oEcX9j6E97L5gBMAkSn/5PmzLCspn+8t5izKRDAlpdjcgdwLJ2nFPCmoWj3gLN5tfc1yQCn1M0qpY6XU5y499v9QSn3G/bujlPqMe/w5pVRx6Wd/+2s9/7Au8wYuAXDslvO6xQ70ozn1FYdvpS8wgIysEXqv2aIIt/h+t74Cntz3F/C93/GzAXbsgoF1I0ullGQybfs7nlfYewJpLRs9wHbTqMbztyNL7Qgv/XzXuo3UW4L1Rpk9JLWsA6omcEo1QmceWIiOsZc3omjUC4/0wKGemirHp/B9y6qOB3x+7YhKdSvpaOgJtLbqNPO4EG1BhyisjcYPpJcwjUpnJrJ1zFGAr7uBWVg1WjD7LtiB3AVvjJeDBkBnhOHYB4DWSXjlTchZORqMNMaRGJcWTcDdYpfzKuVqvJZzNkJXro0ezErq1meaioPxSTlm04RDt1/7hnFUETjyje9Zrk2lPClbGePOIpEB6zX8+/6DsYKM3NQRvhKeQ1aHj5GRVk3Mb29ucxCueWUuGr3ToOQgyQj9jivTzQA5Llst2BB3/pOwJPJbqlZTdgE30yW+EmhwGtUkQcMsKRmlFWlcMUlLRkk1nHek24GM9W7r92RDZq398/3XSqm/ASwv/f5b1tqP/C6ed7vkSh1MQOSJL23wfjNe9gUcaMCO5NP/3mVkoBzsV7yW6Q/8cWJSj/Xp63/PgY+0foy0NGQbl5enRGmoqrYIRnC9gD6+KcoiZDwqHbmlZkkyiIb2G7jn1iu1VerpV+6stYxRAzYcBJWIFlHT2Dn3WDcdUO5U++DjeZZIdwPcWERMOpaVCHQGytIE/rAxei+7znhMgpLY8fB9z7IT5zybnvPO+S7zuOBqvKaJvQEX3xqPaVjiKcvNVC6R+yc7NHHNLC6lzGg1iW44iDesm5ibsyWd8ZiOyuG1b6bLAexTtnqAw94cLYaN8vnTq7TG4yPz+xzFE1bO9jz2G1emyOadhBWh3+EhiMp5VBDrhiM14Xq6khGpnRL63WAlTghndsROnNNab3gvZmFJ1Wk8xIfxOJ9wJRGNwYsqRSsjWATdcj1Z8YXlVby54Xsmb3I73uWiGbFqJTPZiXPWVcgoqIe7vKcs87DgSiwMzKrTjHXF88kpWSsZ3TSsuDFasqxjTpUImPYNx0iL0Kr2vrrW79fMBKy1vwqcv9vPlHTA/hzwd7/W83zN9RXy3/3dW/UCIT1Ovy8FekJQTyDaHpQECU+yBhVF23r9shSP729x/64EUEpJD0C51+9tzS+Thfog4vvbBqbytjDiS41Fq9RwSCIftrULa7qt6QcwkGO2pyHHuSziQbL6MhMs0h1R0FwSF7FDNtDf8aeRbMBQy5zcXsoWZNwo4hPWKnw3Vku0sP9GUc3UWWC1jgcf+2IkWnWaNGgIvZYPxI+oa82qism6EGM96ZwHFeOgYqIr95EaEr9GBx27SU7iTDkDh9A7LGbsRDm7kfwsdN6Byl3Uod8xDYVe3Hf0a6MdXbYZRDiOqimh15LohnFYEXodxkow6G3Vtep4cXzClXQtPAUkBZfAJfP4pvM5q0bDpvJ9y0kxZhJUpEFNqhtmQcEsLBwN2hOEZCDgnL04GwhMfbYwCSruZrvca3b5nvRL7AcbKqPZuAwncP4LvvushA7dURtNbTSToOJKvOa4mZA4UFPVabQyXFRi05bVIctK9Aha45FqyQBa8wcHFvoTwJG19kuXHnteKfVpYAX8b6y1/+xrPotSMpO/bCailNTh/fLcpnObG+sEPkFEPj0fEo1yvoMDaShQj4GChmkCYI2VMNi7GysF4RYtKK8rgKShadk0WxejXs7sMsoxiYfpQJfA/jgb7ubLIuaDe8ecVykfnB3xqr4x+NPtRDlZs1X33YQyf05dZxigi0VuSynL2DUA66AVUAgySup56IHfcTNdkjchsW7orMcmCIfOfc8vN67RaIHA69h1klftpZRXK0PgGa7EawCKLmAaRRyEGz6b3SYIOr5l95CJLjksp2RtSN7KXN4g6fZL6TGPKlHQKduAeVQwD/PHLs6zUmSwUjd+s1YReh1znQuCz4tpu+1xjfyaM2U5iDei4gt8ZHKPX1+8iLHi89daj8Sv8SIzcAgCr3OmKPK+Xo8FsJT4IvSx9kVe/MOzB/z28iZNJ8QvYxV7kQitZG3Ii/EJ6yYmb0N2w4zDYsp3jN/hN+yLAxAo8lv81rIfbTgqJ0yDkotmxCfyl/i3Zq/yifghD/If5KXJCR8/em4ofcajjINkw0HoRtakQ1/gY+N3+NXlB0jDhlg3fGD0SDIWhN3Y255rT97/VNeM9e+vHPhq6y/weBZwCDxjrT1TSn078PeUUq9Ya1df+YeP25CNHEhIRnA9fPex1cOFQVB6ffMQHHoPofR2BmtFJMQqT2b5vTpRP0Z0hqI0W4PSnnVIK2AkQQheCgj2UqnRNy17qLCc0LYP4I7Lr2Tj93fw3iR0U0csG7HHbjtRsll7MesqoqgDtN9RNcJAa4xHrFuWhaSIVSPQ4t5Bp5fl6mm4je/RGDHEuLvZcZj3SJqNrtkHkNeBiJI0mtBNBzZlJHWtZ9hU0UAhBmhan7vZDq0VKuu6jjhvRpxVI/KNBK/OJhwXE/d2KSo0Brkgj+upbDxPehWrOpZeg5MlM1axLGI34QgomoCm84iDlqNqStYJuCarQ5rOYxQ2TAOp70e6ZpmLGefMl6CbtSGbOqQzHonfUHQBRRswD11AaWJWVUwa1Jz4Y2oj8t/LKiFvAtIAjqqpEIAcqzLz5Toa6YqTcsxb5QH3szlVJ1OOTR1x2OzwsJgOQKANQog6rqQJqJVh1Sac1BM+ET/k28JT9mOZEmRlOJiIlo6qXRufutOsm4isFsj2i/EJyyYhq0KSoOGLm+ucFCM510DKwd4dqg58Yr/l9+VK/F5LKaWBPwN8e/+YcyOu3Ne/qZR6C/gAYlr62HrchuyKpanBj7cGJLCF4/Y1ede6Ln4otbrr4A+KQj1JqJONqJSbKHi+49ybrYJQv3orcuWB9sSLQHnDRu6Vid0xb5uMfUCKIgEgaS2/W5RDqeE1lsJtWkH3dXz64S3isOGNxQGrQpCDVSOIuKwMnXKQcMVx+I6689nkkYiWeoYoaofmnVIWFYj2UeqyAmMVm1zsvC2wySO0UzSurbxWLy8GQlH2lDzHRZ7I3doZWSglrsTGiM/fokxIZzWbKuIOu2S1sA2/uLhC4JlBsQgQdx4twepesSNNrzIQhltccriaUtYB40Q8C/ZGOQ8uZkyiitOLCWEkNlpFF/DG2YH7OMTMtTEdtfE5z1J5j2qfrIj59dXLnJUjhw3w8WNDY8Q63AJ3Vnv88PXP83NvfgfGKK7PO754fpVVFnNzd+m0B0Xl6FOPbtNZxTwpsVaxn8rd/kE+o+o0Hz96jqwKuTZdU5mAk/WIf6Q+JJbpFxOmY7Fxvz6RDKrpfE6LEYWTOnuQ/yD78Yb/4vY/4bs/+W9TrGN2RgVN61NUwdDM6xGT2pO0/369w+unV9icpUyTcjB9CR1c23OBvrOKZRXThTWnxR8MWOiHgC9aa+/3DyilDpRzClFKvYDYkL39tZ/KCmBHKVSaCsQ3CC71Avwtyi8IB49ANUrk9zrnC+BMS4ZAYg3eKJXAMACQ3B2/7weEwSBThpM2U3HkSgrxKgAGHwPc36o0QSVO9ThNoXbeCI48BOBo3CRhQ6A7ylxks4pKmkVt67PZyLiu7TzisBGtPAcU6vnsxirxmtMdpvMp8oheTNRaRVXJ+O90MXZ6gtLxN1axWQjVt641tVMcrmpNsYnIVjFVEVA5iWsFXJuuKctAXicTGuu16VrEQfyW7736NosqIa+CoQEp0ose37JzyMvzE9ZVyLKQ1F17hsUmcc65Ed0qpGt91pUYdqax1Pkvzk45XE7Zn2QsXPZkjKD7jooJN6YrmSzkEZO4YhZJFqCU5XAxFR3/idyhiyZgWcRirGp8yk5cflZFjLGKX3r4CsDwHt8Yr0iihqwOOcnGHC0naM/w73/wH0sT1YovQNEG3L3Y4cFyxlmWcmW0IfA77p3NeW1xDWsVt8cX0tC0itVaGI8n2UimEYVgDXbinDRoeGlyQm003/3Jf5tXv/PnmMzzQdlIayOloq54eXpCrBtWpQT283rE1cma3asrJmHFN+8cEet2EDTtRUgUApxKdMMru71r4O9cX9N85LINGXAE/DVr7X+plPpZ4OPW2r996Xf/TeA/AkR1Q373H37VFwBm/r797tm/Lt/02gF1w2Ap1nf7wwBb1SI5nmWo0WjrD2BEK3BQKMLV/86bQAWBQIszh/X3nOuQp1CTiWMLWmxZ4k0n2PVGXj8KpQzoJxN1IwKorgdg29a5G1WDulH/80c/9QrZD2Q0lQYjmyWZVJR5iOdbulzLuxQaPDffVb4VG7LOQwcdxTpCeZYwaajzED/sHpsODGPByofaI5jWGCPS4pNxwXqT0Db+gArsVYjXG7njd62H51tuHVywcnP59ekIWg9v3DAal8SOt1C3PlEgyj3nWcr+OGNVRlycjVEKJvOc1LnhTKKKwOt4ZXbIrzx4mc6IVv/p/Tn7txbklcCZfeeFGOiOF3dPOc4nHJ2LfLavxWgzDlqMFQh0FAjZqZ8KnCzGdI0v5h+OMZlEDau1TF52pzm+Z1jmCWkkJibGKMJQ+BZ7uxs6ozgYZQOkt+4k/a8aLTTvqCavpTxbHEm5s3t9SRLIxqtbH0+JMOu9C2ED+p4dvte+YRxXrIoYaxVR0BD4UrtnZUixjpnMc37rY/8t3/Xpn+R8MSKKG7TuGEU1gSf2cMbC9cmao82EVRbzTdeOef3RFUZJxWqTQN8U7uSamE4L0qh2JabhN//Uf/au5iNPhgNRcGC/e/fPOvMRd3E7so5IeamtS3DTCM+gnwhYO1CLhz5BGAw4fjVKZRMnMXaTy6bvx36uNJBsok/z62HCoPpJQz+WVEoagz2Mufc3sEZ0B3qnIudQfPhnX8L80IVEZ6PEb7D1mU5yAYOUvTqsIY0aOiMz8r4ncDDZcJbJ3SQOWof/l5Fe4kA8jbtg206wCLOxoN2KOuDGdMWj9YTWmWU0nT+4DFeNwHGbRnQK5klJVgf4nmUWlyS64Zn0glUb8bmT6wMoJ9ENz43P6aziXrZD7Dd89u4twqhlPpY7nHYS6FkTSomxSfne2+9wVqV89u4toligxEpZbkxXHMQb3lntUXUymvyLz36cn3nne7gxXvLm+T63ZtLgFDZcxyiUQNP3N5allFWrPOb6fCWW504o5LnZOaHXcX8zJ6tDqtYnddj60O+ItJRQqzLiYJTRWo/j9RhjFDujgpfnJ7x2do3z5YjpuKBxmocv7p5SG82jzUR4CEnORSmktcAzTKOSk3zEj958jX/66IOMw4pVJZnIKKgHF+aVGwe2Tor8n3zbz/KJaod/73N/BhBSVA8emkYlWskk4t5qRlZE7ExypmFFaz1WpRiSjoItDbwvzapO8z/+4N98gh2I+mXMY2hArNP5HcA+ZqjhBaDjNm9Tb+HFxoor56As7GGrClVWj6MSYWAEWi79PshGThwyEbYBo0clWiOipkDvXDQoIfXBCMAwbE7BFQmZKC9DpzMo+gPGKEqHFbCOcda2HudZSlkGw4ixNycB6HSLpwSO3L9Gr9PfND6eZ6VZZ7xhJNm2HknUkQYNpYMo98yzZREP+nazUNR+ii5gUadkhUiWL/2YzA8JvY6ryYplFXNuUmzr0fkeZR2ID96lMWRda5pSc3ezQ+0mAY3TUxwlFaHX0hifZSHcgChqOKznLDMxLsk2McUop3KNrqaTxmLd+s48VDKbWLeslQiHbkqxV/c9w0kxHkoq7Xes8whjPNJI9ANWZYzvmQFB2TkBWICFSrhIUskeap+8DIWcpSzrOqa13hbqfSkg745yQKTe75U7Yv4xleZt7QhHfiMTl14tumo0VRnwiWqHPx4viYOW5SZh6W0by5HfUqHJmpCqCaiLgDLqpecEdZh7AZkOBw1G7XdD2fZe68kIAhbZZHEknfy+i183YC418vpOfNtiPR8V4KB1/iXzkV5SzEGCy1Ke0/dR1iENjREegtuwKghEtgwe8xjs1Y4HgRGHEAQGC/XB1LRfl7MHj8E9GKRG29tfs1ilBGFLVQY0pR7sw3qWX6gbjC9Zge+L2KiCS9JRPlUTUNby/No3NLXGtJ5LseUYI79l7KiydedTqGBApPVBIwydfLdvyEqBJH/x5Cp17RPHkm1MHUX4bD1id5JRtAH/7O6LTNNSehC1yKsbK8KnZREKoy1siaKGehPyzvGepOuVz2gvI88jVuuUd6wAnACu76w4vJjy97/8rZSbkHfqfUyuOVmPh40Qagli1orYyup0xHQ/YycuuDVfsmkEjt1W/oAG7J/fUxatDUUum6RsNEUeESe12Km1mpPFmLb20U734LwUq7T57oa8DB2D07Kqo4EcVdTSg+guBdSj5YSdcc5vHD5L0/hc5MmQ5YGgF8ejjLLVFFUgfhBxw7/3uT9DHLT81Rd/hZ9/9DHeOdvF9wU38HA1ZScVGHC+jrh17YJFERN4hlUeSxlpFFke4WvDwXRDGtSDGMl7rScjCICDAzuJr15rsOfl9xyCnvDjbMgw7i7cW467Dv9wd9baWZU9vqwxW6Zfr1rUqwU5fMEwHegZin0QMXYbJLRGKW/oNwybvzc4cclFFMlF27Ye46hirSN2xzmnZoTRogkg6i+ubHCROwpE1WaU1INkVNd5W2Uzq2gasS0LwhZfG4JAWHBFFbCqI/JavPJa4w0jys70/oVQVZpOG0ZRjolgFDbScbeKphGl3CCQFPOVZw957VzceAPd8b1X3+YzF7d4+yIhTSvGsRCB4qCl7TxmiaDq3gZuTGVKfJcdRlHNrdmSZSUchV4r4EoqXfS82WY/N585Yx4XXJSJHIvfDZnAlemGZdCKoEgljcC9NCM8uOAg2QwNstroQV48CRv+tedf5f979NLwur0wK8CV+WaA266riLLVvHJdmmp3FruMXQbRU4FzT1SBr49WHOUTvn3/Hr95eluk3mFoXh6MJHj2vYzQ7zhINgOkd1XGVI6hudwk/Pyjj/FDB1/gwXSHVxc3BjHVSVjx8uyEV9V1aidM22deg92dsx8TlKQvOA//vVGDT0YQ6DOVfiNe7lMMm7HnFVyS9+oBPHXjygO9TdcBmhrrRa634IhCTsvQXuYQGLPtOVS13Pn753CYAtUfl7m8ya0jKLUOPHRJ4QgjwCVlCfV2E2Z1OFh/XUb7aQfY6UUhQNiEnfEItIysep15z7OkkUxASqdHB9L0CxwBZggsrU+L72TLOnoPAs+T5mJVBSjVkQQN10YrJkFF2V4bsAe7o5xJULGoEm7HF5yNRqLbl2oir+WZ8QWneyOuT1fMo8IxGwUfcBBt0M5d6IOzI4xVvPXoAG9seW58ziN/wnE+YR4X3EwXZG3ER/bu89riOvk0ZJ5KTX4lWvO6usppMaJ0lOCZcxa6NV5wfzOnbEQ78cP7D+W981u06oi8lkWTUrTCwNRhzX6wYS/JKdqAFyanXNTpIHR6NVkz0hWt8TkLRiyqhI/O7/GLD17hY9fuclQIGCrSYgzSOZ7GM6ML8jbkSrjm5ng5BKBJLMHxmfEF97L5AAXWXsdBuBlKpKvJmndWu4IJ8WLeOdvlwXSHPzF5nW8f3eEL5Q3uFruM/JoXkhOWTczbF3vMkpIb4yWh37GpIrFRi6uBd2GsaA/0fZp3W0+GnoBl4OtbY7ZcgLbd4vb7OrszMpcfvnc6gT0IqFcH7oz8yy8JLA66gmYLAe66AT9grYwXLzdLrXMstk2vU3hJb8AZk1rnnjzgGfoyw8jdunV3XtN4W+JN52OMonNpOWxx/uBqdeORhNIw9JQrC9wmDh2zr4/wTeMP5CDRmpPAZYwaPA17j8C+WSTiShIMenps4jsLLcdvn4XFgMi7V+6wF2XEvjS17ha7Ir2dFgNEeKRrQr9lGpTsRxt8R99tjS8041Lu/Js2HBx55mHBC8kpAFeC9XDHnkYlHpbYa5iGhdCpq1AMRVPJLBJfaM49JHqkKyZByaqJWbcxzSVtvZ7M89rmhkCFsewF2aArGHgSNCKvZR7kTMOCeVSwozMWm5TvnX6J1opgia8MkSeNRcm05BgeVnMmuiLW7SBkuhMXJL5kHKmuuRKvBwRf3Ykd2m6YP+a94PuGVxc3yE3En0zu8QPjLzALCiKvoTdO65uFV6INO5HoB/SaDv1n3FukeV8lCDwZmQBWNlRf1ytvqLcBt6nYdvCbVpyFeh1Ch/0flu87t2BfSD1wqVlnxXrMqkuZgB1+R3oSPduwG9J+eeGe2mwGeXN5/HLm0oEvGAdlBCVonNGodWIhda1Zl6L4azvZpLliSNG1VrSNNHq07mhcBmCMR1PLBZAH0smvW03j5Mv70SBWoTwrmUTr4/nSfKyMpjW9XqGP8ixd7dGEWoQwmkhkr5wdWqgFaVZ0gQhsFDPxEXC1dtlpNk00oCJ7fQBjFbtRjo8ZkHqV0azbCNzd6biYsK5l7l12muN6Qm18jpsJjfFY5xGRbkl1jfam1EYPdzahBWtWdTzAm3vX4fN65PD7hqrTHHeTrRKRMw4puoDauNq+TTivRnRGsakjLvxEcPlKaMEH8YZNF+N5htwIohJkcy0bUfsBBl+A8zqldGPGWVSK8IiJOQ3GtNYjb0POa7Ebg5R1I+/Bm+t9iiYYoMzWnecXyhtc00ti1fBifMJn1rfp8AQQ1Yp70v18zrKOKWr5nEKtKd1YMPA7l2086UKjXOpcPkYkuqTn3/cEAr397X7E15OMXPo9LN8TAlFVYZ2zjjzu3pDLhCX3tQqDLa7AZQEDP8B4Upa4gNS7HcsfKteIrFBWuATWQ1x+GuH6i2qQT1f5ZF2MKTR4FttBZWTjSiORoTSoqwDTitagaTyMQjavEVNSYxTWeBJgWg+VtHSNh6elfvb1thbsKcVab+8KyjUe69ZnFpYs63iYGPihxaBYOR+8uvPJGmG67SeCaS/agKwIpSnmAkDncOv3yzmbJuIkG/GR3ftc1Am0noNKiwBpqIUg84XVNbRnuJvtkAYNj5yt2nE+oTZ62MCRy2Rq4w8KvFWjORhlLNYJh/mUPAwGnoBBsa7jIW1flRFlqhkHkrWc1ykb1y8oWyERndYjWuNxtB5zPV3ydrFP0/h8YvUCqzJCKUsa1O7SdNmX8wU4r1IaR0LqXEq+qUJWkbyHqyqm6oQM1FqPrBYl6YerqTRhI6ENP1yJgMrdYpe/136UF+MT/uTodY6aKV9cXx2OY1VGAjNuNGUR0gY+tWOJGtsN5qtfbT0ZQcBTQrwJt5x8q3tD0q2Sr1LiRqySRPACDsBjw0D+zprtiM7N8NVkLCPCNBagj1KCM2jbgeUHiEIRYLN88EVUUSh3+6raZgN9PyBwCsRlORCIBl9ExyloRorbBxcD3n2dxVzfWZGP5e7Rw4V9T8AkjZuTT6KKTR0yi0qON2N8b2sKGuqWutUcjDaEnlhX507RtxfB6MUsXpidDj5/PWGop5muXXf7PE+YJyWzsODF9IQ37BX2xxm506v3sNRG9PpSXTMLS3aDjN88e4Zb4wU3ZkdsmnBQ++lJLrHfUBvNLCwZRzVFJ+esRw3XHIzW98TFZxaUZF3IyK9ZNjEjXbO7kw0z/HmYsyBlRkngd4ReS2t8plFJ6MnvJLphMpKaftXEbOqIF8anjP2Ku2qXTRMxjUpRBzaaj87fobMen17cZhxKQ3M3zvGUYVGJFuHVyYZVnbBREVHUsqxjdtItVmA/ypgFJZXx2Q1zdnTOW2ofgEBJQJtGJWlQcxBvyFwJNNbVQMYKvZaLKmWelAMOoEKzkxZMwoqRX+Nj+Mz6NkfNlL+8+y/4T5sf4jic4HuWUSg2besmJg6EbWkdbXonLtBKjFv78uDd1pMRBHqOvhsNDn0ApRx6UDa37Rt2PZGnZ/+t5A19zJEYZDPnhYB3jB2MSW1ZDiUHOPx/I81FW9d4sage27bZyoWBNBCt3sKT88J5Ddjhb7kkf+7XgsXv1X+MI4a0nce3HBzy6vENjBKd+MBJYjedx3meUNYBO3HBTloIBt91xDdFhOcZDjvRuW87wQGEuiUJm6HLvSxi7vk7A9S0t+T2lHjYV03AkoSyDKhbTZVofq19kevpCt/Vj2UrLDiRALd82+wBd4o9KiPyVTeTBUUXkFch33PlHSKvHfT3iy6Qi08ZnpueidFGtMNrj65zuJryvTfeoegCjkqB6O6HGW9v9piHBbHf8vz8jLrTvDw55oPJIZ/Pb/CbZ8+wrET/P9EN3zQ54rQes65jHmUTms7nh/de5VOb5zFW4LUm8PjQ+JBH9ZTXFtd5ZnLBR6d3+X8fvUJrPL515yGJ3/Da8jrPjMRc9EEhDbz9aMPdbIcdJzc2Dqph0qI9w8iv+EJ2jWlQ8tHRHX7u4XfyUzc+wW9lz/HF5dVBYKRoA55Jzvnk+bPcTJc8n5xy3Ez42PgdXoxPuF/vcF6P+MzJTW5OloIDaDUvz054ITmhQ9Hh8cX1Vf7T5of4mzf+Gf8uf5xPHD3LKKj51tlDvrC+hkcyBPueWao9Cd7RV9EUeDKCgMKVAd5WhaOX9+4JQpe7+Z430IVt6Wp+Z/whxCFJ723lWIBVBWmCatSWIdh38dUlp2NHVR5Qg9aIv4FxtGWHXRgYh/1dX6utT6JDEOL76NxydjEScRFPjDvPFmO61uNVe4PF+QiMIgtiPG3EO6BvMxSatx0IqK+FTS2gI+VZaudFYDqFaT2yToFCdP2DjiYX3H++jrCNhwrMoF/gaYOpfTAKVXlsogB1YOmskpS2SMVNufVZ5nJhVbXm7nyXzxzdFHmzzuOdaI+RrlmcjflU+gzX0xWbNiJrhL0X60bkx1Y75G3IXpQJpsoqHpUT1nXM4XqCtYokbMjKkPigEdVf4/FNu8ec1mN+rX6Zd1Z7A5OycPDhl8fHvLXcp+58jk+neNrwK4tvZt1EHGZTilocn+4EuxyuJ+RlxMlmNPgfLsqEO9keB9GGh6spD5YzroylzDkvUk6SMY/WE65PV2TLhFe9G8xdJiDAH829xZzOSK1/uJry8clLAn6qYso6INAdWRny+ka8AjZ1xLqNGPk1v7r8AMsm4fXTK1ydrFllMcZC1QTk64hX1fXBkvzMybEdhxP+Xf44/+ebn+DF1z9EZxSpvsYXTq6SbyKUL0HeGofTCKSHM4mr995+TwRsWO/b74p/FG/qaKi9W1Df9Qen/mMGYo+tm61MeXUpEMAWPRiEgib0/YEkZKt62KQ9E9Cbz0SNuE/9o2hgAw46B/3koVc0vjSFUFG4VTjqOQSe4vDPf5DqB1fUtX5MQMR2Cj8wtBtHXNJWlIK1IYwalIIiDxmPS6mf85DRpKRptHM59vD0tvtvjRIuwjpAT2uSVJB2z10748H5jKbWA/mo9zusV5HwFqzCGzdc219yfD4VOvIyQjUKJi3JpGRvnPPi7JRPPnyGJBS0YbaK2d9fs85jquMURi3juSDlyjKQHoiyhGFHdpoSTCu61sfkmumVDZmzU1OeiKHoQHQWz04nKN9gskA4FUFHGLeEQUvdaCZpySwuOVqPsVaRPZhgRy07exuem5/zxukVyiIUJp1VxElNHLRkpQCY2kZs4ZKoZr2RtF8HLS8cnHFepJwtBDKsg45RUpEVEabzmE5yVuuUNgvAQjirmI5KNs7SLU5qEW9x+pF1pbm5v+DB2Yy2DIhG0kPwfbOVmAsbsipkc5aye3XFzemKN44OqIuAW9cuqB0TsjMejWOMjmKZMpwvRrzxA/8l3/Xpnxx4FT0wrW18wqjlmR1RRz4rUnxl+fV/5f/4BMOGlchz9SQdpTUWGccp3xuYg7hNqMJwqxFgrSD/HFDHdgbVU4Iv6xJqLXd1cKQii9IOkGTMtrbv7cfTBFuWwwRCBYGUDVUlWIIeGWjkmAYX5N4/0Yo1+eUAYBqPeFzT1JooFgCRaRVeIFLgphMKsQ6kM9gZj2d3L7hjd2XK4OjIrRUCTG9s0rYeXeOj0nbgKACDZbWvO3zfAh1hKIpEy15erBFK8aaMCKOGUVyTRy11pfF8I3JlVvHZ4xvM0oLAM3zk4AG/pW+KyWVaYvYVo6TimdmC1no8XE2pW4HtJmGD3YOdUYEFHt4XFeDZNBtcmEPdMokE717ONLOk5CQc4zuz1Fu7C1H6ySRDMZHi1mzJpo4od0PSVARI3jzf54W9M+5c7GxJVHFFGgjysW412s/JnFgrY/FJ3B9nHG3GTKKavfmGVR6TRDW3p0vWacR5lnJ+OOMDLx4ODbudtGAalQLgihU3pyvuL2fin7h3xpsn+6zKiHFa8YHb99k0EfcWc1JnoFJ1mlg3JEHDNCmZhBWvP7rC7jSjjAIWjmw0c72Cog1YlRGjsGYU1HRG8V2f/kn+ybf9LD/95T9N2QUcZ2N8z3B9tCL0Oh5mIuISeGZoZL7bemKCAFG0Fe7o9f+Cxw9PBYHU9b4vNXw/Pehrdt93G1+hXKquRqls5N7nsOcd6F6Iefv6Ko6wqtyiCZ2/QJ8xDK/RMwudToFYqjsxlH66YTzaRDEdF9Sus12VAZO0xCaKZ2fnfP7oGp32iKKGNGxonD9Ajww8mGwo2oBpKjiEdRFJ1153g8R4UYtisedYgmlcM0tKjtWYeVJQ1IGMHX2DDZxb8KW7EVqhHC5gd5TzrTsP+czZLZpki2yzVrExHlfTNWPX1R6FIt/1zOSC326uc22y5ka6HJBtWRMO0lyd8Xh5foKH5eH9XepGGl+B0/obBRXGenx5scPt+UKadNdypkHJ/c2c58bnLJt4KAdk7m3ZjXOiqy1Lp45sjMcz6QVVqznJxOl3L8m5niw5LGYsyoQb4yUPncW5FzZcH6346OweH794nlSLWtMDNSPyO54ZnfOF5TVGUc1m3LgGqXNYUpab6cJtTrFjS8OGb9454qX0mNZ4nBcptyaLQRZ8USYkgUiea2X4wOgRX9xc5/XFFW6PFhxvxoPDUuAZOqu4MV5yJdpwP59Tdz47Uc63zh6S6mu8dbHHT3/5T/Of3f77/LUHP8ZKx0yjkrILOC3GQ6OxF4J5r/VkBIGeyNP3AL5y7m4sli2Kryf8WCNy47auZdP3z9XTfK3FLldCOR7gvD1HYEs86u3IbVlhViu8JJag1JuQdh2WUPABztxUAkXrJMkfLz1AZMdUJyIY47gSokkMmyJimpaclSPGiXzgvW1038FtjUcXNRwkG67FK169uAFIEAjD1nn9ySw5cFp8aSiipJNInlP7Qj/tX9tzunXGeFTGY+wkzpvGksYVbedzuhnxa8ULrDYJtlMEUUsayx2kacWlaBxUHOZT1qVAaldVLGIXZUJn9oexVy/Z3QusXkwEtYdVdJ0asPQgEmw3Rkta4/Hl8x1Gcc3FcsTOLGOxSvE90eHP8ogoboSd6OLyycWEMGrFnr3WPCymPFpPKKuAMBSF3qNiyuFaegQ9JbdqJDjWrWAdjjdjDkaZ6P0tR0RRy5vBAcfrMZ3xaCufUVDzaDGlF459zVxnkctU6aQYc75J+Wx3gzvxLg8XU0Ld8fbFHnujnHeybU9j3USclaIxcOLEPt5e77HaJOykBW3ns8rld0O/ozUey1oC3bqJ+cJaegDGKMou4K89+DH+F1d/lV+Mv43KaF5OjgF4qzzgUTnlINgM05l3W09GEFDuburu4kIQQmC4l0RCbYdwAXqTUkcCUr1oSA/wATAeSjutQd+DQLDctmkGYZD+9ZTnDYFD9YYlnoftiUme7/ADRl7vkvipCqKhbLFe5I5LAT4mhOuTFdOgpDY+j7Kp0E7jjL0o46waEXoiJukry7qJBhRdFWm+efKI+8UOL0zOuKgT9Nw4XX+HGLMK7cM0FFhq1oYOLiumIf3deDfJqTpNb1bSN9ZU6zOKanbigi+f71CVIaWy0oBsFbUV1eMwkAZTVgeMdc3dNmC1ShhPSprWZ71JiJOatvOFxNmKyEnvt2A6xUkxEhccLWCmaihvDFkdcqikH1FkMVHQ0lY+ZxdjTOmznopQinJMy8rJooe6o61FS6DrxP/wokopnChKZRSbJEQpS9UKorJqxZex6YlAWTwwKi/KhFUW05aByLBXMYVjcfpRx93FfPCC7NmSVSkWaVkkrtJN0PJoNRnMQbvOGcsmAVUZsFYWT03YVFLOdsYj9EXSHatYlfEAEhOMgciKFXVAWYjJqkdCvolIxxXH2ZiVjvmH0Uf4k9Mv8mZ1jc9lNwiUoXHj2shrCZ58xKBLv3tDUMfQ69Ny65iCv4MM2duUPfZUPQDIff9uk5HB3+ASKMlNJVSvI/Ceh6oGxuEwDnQuxkoJi07+t/SybmW33bi9EGRvl112gRhl6pqyC8gaoeq21uOknlC4oFAbMZDorCgDd1bRukZfaz3OqpTWSte6snogIYFwyXuVoqYTTf3AM1hfOakuEQtpaukDWMD6Cl+bAVJcN5okEkbaPCq47+24c7aEUUPoxEuVq/F7hKO1oAOBOF+mL/fL88S3L3SBUDumpB910vP4io9CuBjtwMaL0sax57T4G/otYdhRlWLM2pt5BH4nxi5qK+/ueRbtoL1tJ5boUSgoyyAQ2G8Ydo4l7jFPSk5cVuV54txUawlmgd8ROm/IXoSl66TU200LfE8av9pJgPmeU3cOGvneEY0iLaQkY5QIzDh4eGcUbeAT+sJuFJCXjH6nUUljfd6srnE1WOKNDG/k12gdeai1/ldlEj4hQcAO9mDKmMeFRfrleQPNV7XtMPOnM5fovJeoxL3oZ9NCYFC+GcaCtscZ9LThS/wDO5ifqi1PANyYcktl7mt/W9db1mEvgQ5gDV7N0JltjPDt10S0qce6iYdNHTnTzawRrf7aE+OP42jMlVhm1Zta4Lm9Z13becP8P2/E4LLreQpWcV6kAzZgXUaDiEfd+gI/9gWTUNbBIHHdOxQrEBq0L+airUu147BxQcng684FPOlYy1tk8AFrRdC0dnh+a93m1B1028es8WhbR5ZyJZHvywaxnSJKG0qzzXqso1ortjVzjxC1Fkwr83vPM3Lszu0YHq8yhWMmrj5x2BAHLatWE7hGZlE6uzdPSq2yDuhKzW6cc7wcO19HF/SUBBPfvU7T+WjHRVBKNvpunFG6dNz3pE9SenJj6Iw3aAKaTgRHMh2S5RFGG/y4cu+dpnZycrIdxIL++mhF2QW8nBzzuewG3sjw0eQOcz/nX6xepLGeyKN9lXLgySEQ2UvpymVV3844Vp/dforeJfnwXoa8/7v+efr/PW+7Oa3Zpv/96rv88JhV2ZANXFYTvgxjHlyO7BapeHkpD9U5lp8VWG7vHtQDenqml+9gsL2braD8YBJUXGpfDg5FIBeT75nhrh7pdtCXi/12IB31S2p0CQAgIhRtJ+xC3zOuUy9mJII9cBsVGWtFsXTvI69l3cTDBe4pmdv3hpfKvT3GeC7lhq71Bsy9dFLt8LH3DkJVK3fYpvHxPYOpnL1a7Q9gJYFVO/VjIy6/ZS7irL5v8bRhU0cDIatzNuz9+9bTk33POtyGBMGepi3vgzeQujrrYNCdQL6XdYzpPDoXyOpL7tG9EIwxAuCqSsEINJ3PeTlywh485iHRl2eNE0zpH/dc1uA5YNKmFtPTrvNonAKRNR5h1BJ63fDeBkoygLfrKzwXnPJSekygjJQDTz5YSG3NPPqNNlCIt5sUawdobi8PrrQPcTxwB3oRkN5KTHnBIAqiomirDQCiWuw7kFIUSr1f14OYyNAbcPqFgBxLtNUsUL1QqZtq9NmL8j3aFG6kOQZF4C7EuvOJtLj4LG1M6NJVrQxBUBP7zeDeE3otb673hWkXFcNm7Wm+PWgl1o0IenYJsd8KjDSJmDnfvtJZWledpnE1qEiVyxhvHNSQwkpJM6osQqwnIiaR7tCOCbepo0E/Pwi6QY5rsUrRQUfozDY8JXh2oUgbykK8Bn3P4EUdSVIPOolR0BJqObZJVJOXInnuJ4727EtgVK4EmiclV9I161qO1bbitjx3YhvGKkLdDSPCaVSyF2Ws62gQ41TIFKVuHcXaF0OU0O9EPTkUe29fGcleYggCkQL3tZCxkrAZHJJA3KJm4xJjYRpXdMbj5mzJSSYuxdOoZJKKNVikW3Y852lovOGYptNiuOQPppuBqAXS6DVWPgeAIBQcwMNsxjQqeas8oLEerfH5F6sXOUkn/Nj4VVKvpjIBp834PbffkxEEQFLsfjPBQPMFf3vXrRtsGKCqWjbmpX6AUlpqcLNt8gk0uHbAomigKitEWATTyYTBWpQxqEAPfALbih2Z7RGMvYCpkzDfQpe94Xd7rULbjziVpMh1Kyl3ohu0SwP7MdcQFNydPG+lv5AEDesmHvTlro4uaO0cEPx/n0q2xnNjOUlPe+x+//z9XbBPt40VkZHA6wZPvdaKt0Hhi3ah9sSPLwkbRmEtCkVBRdGKfHjsSx2b6Eb6HUYN04e+9vQ9wziS+nWpu6H2DaN2mFg03Va41FOWSVCyroQzceKLeKkOuiETGEW1ew/lvVxUCV7QMXY+iF4sgbGLtn4J/erJUU2riXwR8NS+mKemQcM8Eqqy9gxR2A4+iVfHa86KVHoGrtSIQ6nTRbVHGn+JbiCBogkYBTWMxD5t6ezQAOaJ2+QIfdqgqANBF7adTxrVTuWpGxSB+kwmcDJhPRegJ1IpZdGq41E5HT73xnp8cXOd1Kv5S9O3+HSt+a3i+ffcek9GEOjdfHriT68YFMhGu2wn/piVWD+Td1oAg6CI0/oTkRD3mO9EQC6NCful3B1cIYhCbyxjG5tVWwlxrV0vwgpACKCpBMvQowd7w9Pe49BICr8XZy5V9/B0M/D2e0GJ3Ug87so2ACV47966etNGQ9Psss+c9mQCoJRcgFcTMc+8Gq9prDcYgI4DISZp1aEDaTi25vLPRfTy3kJUchUMenfAYPu1qmNGQc1z8Rlf2lwZLmxrRSfBH5sBtz7AnK2iabUIo7rXM05MNXT1bf88kd+6xqYw86zxMK7vMwpqAq8jb8KBQhx6j2cKxgoVeNf5BabusdgXu7RYN5yX4lEwjUrOipTdJGfTSNCtO9E78JzAyyismUcFmyYi8AzrIubmZMnpZkQcNkNaXjsXZq068iYldlnNqow4CidDCehhqZyhiFiL+aS6ltItlOB2fzljJxYA06AI5ARBauNToQcugLGKsyIVrwfrcxBsiDx5DzuriLyWygR8utb8S5FlbR695/Z7MoJAX6/jbzf4ZZkuGOTGBsw/jvYLgzJxTy6ynXMr6nEHl4VJLqP73PP21uSEoTzey49dJiS17daCDGTzW7XVOOjVh0DGlE2LasU4ovXkQ61aPVhij0YLd1FLPdj/deQcfxvjsxMWZE0oyj61zKMbIzVk4ObHPe/8qJiwdDN7raTROAqMWHspseDqXX+tVYNh57KKmVEOtXaveOTrzg1MJGtpcfXxpRmNGF0GKNcpnwQVtfEpGskYelVg+V3jXtsFDnDS3K6edhnN2OEcekBU3+ichiW5a5wCGBQ7Uc5pImpDWR0S65bQb6GJhmmMBCRv6L9MwwqtOrd5pK/gK4OxnnND2galK9F6W8Z4Yi4qjVLf9WPkTmzd38S6ZRYWtNYn1OGgINz3gC7rBbTGZ6xrjPU4LUa8MDnl0WZC1emh+Rf43SVBEH94HyOvYxJXeI7SPA1Kii6QsaDxRS/B05w2Y36reJ61ecQPp084d0ApdQJkwOn7fSx/AGufr8/zgq/fc/t6Pa9nrbUHX/ngExEEAJRSn3o3csMf9fX1el7w9XtuX6/n9V7ryRgRPl1P19P1vq2nQeDperq+wdeTFAT+r+/3AfwBra/X84Kv33P7ej2vd11PTE/g6Xq6nq73Zz1JmcDT9XQ9Xe/Det+DgFLqh5VSryul3lRK/Qfv9/H8fpdS6o5S6lWl1GeUUp9yj+0qpf4HpdSX3P877/dxfq2llPoZpdSxUupzlx571/NQsv5P7jP8baXUR9+/I//a6z3O7X+nlHrgPrfPKKX+1KWf/a/dub2ulPpX35+j/oNb72sQUEr5wP8F+BHgQ8BfUEp96P08pv+J1g9Yaz9yacz0HwC/bK19Gfhl9/2Tvn4W+OGveOy9zuNHgJfdv38H+Ft/SMf4e10/y+88N4C/6T63j1hrfwnAXY8/Cbzi/ua/cNft1816vzOBfwl401r7trW2Bv474Mff52P6g1g/DvxX7uv/CvjX379D+d0ta+2vAudf8fB7ncePA/+1lfVxYK6Uuv6HcqC/h/Ue5/Ze68eB/85aW1lr3wHeRK7br5v1fgeBm8C9S9/fd4/9UV4W+CdKqd9USv077rGr1tpD9/Uj4Or7c2i/7/Ve5/H18jn+VVfO/Mylku3r5dzec73fQeDrcf1xa+1HkRT5ryilvu/yD62MY/7Ij2S+Xs7j0vpbwIvAR4BD4G+8r0fzh7je7yDwALh96ftb7rE/ssta+8D9fwz8ApI6HvXpsfv/+P07wt/Xeq/z+CP/OVprj6y1nbXWAH+Hbcr/R/7cvtZ6v4PAJ4GXlVLPK6VCpAHzD97nY/o9L6XUSCk16b8G/hXgc8g5/UX3a38R+PvvzxH+vtd7ncc/AP4tNyX4LmB5qWz4I7G+oofxbyCfG8i5/aRSKlJKPY80P3/jD/v4/iDX+0oltta2Sqm/CvxjhCv5M9ba197PY/p9rqvALzhpMg38nLX2HymlPgn8vFLqfw58Gfhz7+Mx/q6WUurvAt8P7Cul7gN/Dfg/8O7n8UvAn0KaZjnwl/7QD/j/j/Ue5/b9SqmPICXOHeCnAay1rymlfh74PNACf8Va+95aXX8E11PE4NP1dH2Dr/e7HHi6nq6n631eT4PA0/V0fYOvp0Hg6Xq6vsHX0yDwdD1d3+DraRB4up6ub/D1NAg8XU/XN/h6GgSerqfrG3w9DQJP19P1Db7+f0KuQ6cCm4P4AAAAAElFTkSuQmCC\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pred_image_features = np.concatenate([test_pred_features_avg, train_cat_image_features_avg], axis=0)\n", + "pred_labels = test_cat + train_cat# + test_cat\n", + "pred_corr = np.corrcoef(pred_image_features)\n", + "print(len(test_cat), len(train_cat))\n", + "print(pred_corr)\n", + "\n", + "plt.imshow(pred_corr)\n", + "plt.show()\n", + "plt.imshow((pred_corr>0.8).astype(np.int8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Goldfish : ['snake', 'frog_2', 'trilobite', 'ostrich', 'frog_1']\n", + "owl : ['rhinoceros', 'frog_1', 'snake', 'scorpion', 'ostrich']\n", + "nan : ['trilobite', 'ostrich', 'frog_2', 'frog_1', 'snake']\n", + "duck : ['ostrich', 'trilobite', 'rhinoceros', 'snake', 'frog_2']\n", + "swan : ['frog_2', 'frog_1', 'snake', 'ostrich', 'scorpion']\n", + "nan : ['trilobite', 'ostrich', 'rhinoceros', 'frog_2', 'frog_1']\n", + "crab : ['turtle', 'frog_2', 'trilobite', 'frog_1', 'scorpion']\n", + "orca : ['frog_1', 'scorpion', 'frog_2', 'rhinoceros', 'turtle']\n", + "leopard : ['rhinoceros', 'trilobite', 'ostrich', 'frog_1', 'frog_2']\n", + "Bat : ['frog_1', 'frog_2', 'scorpion', 'rhinoceros', 'ostrich']\n", + "fly : ['rhinoceros', 'frog_1', 'scorpion', 'ostrich', 'trilobite']\n", + "butterfly : ['frog_2', 'frog_1', 'rhinoceros', 'snake', 'trilobite']\n", + "goat : ['ostrich', 'frog_2', 'turtle', 'scorpion', 'frog_1']\n", + "camel : ['frog_2', 'scorpion', 'frog_1', 'trilobite', 'rhinoceros']\n", + "lama : ['frog_1', 'frog_2', 'snake', 'scorpion', 'rhinoceros']\n", + "airplane : ['ostrich', 'snake', 'scorpion', 'turtle', 'frog_2']\n", + "nan : ['trilobite', 'frog_2', 'snake', 'frog_1', 'rhinoceros']\n", + "beermug : ['rhinoceros', 'frog_1', 'trilobite', 'frog_2', 'ostrich']\n", + "bowlingball : ['frog_2', 'frog_1', 'snake', 'ostrich', 'trilobite']\n", + "bulldozer : ['frog_1', 'frog_2', 'snake', 'scorpion', 'ostrich']\n", + "Cannon : ['frog_1', 'frog_2', 'rhinoceros', 'snake', 'trilobite']\n", + "boat : ['frog_1', 'frog_2', 'turtle', 'trilobite', 'snake']\n", + "coffin : ['frog_1', 'scorpion', 'frog_2', 'turtle', 'ostrich']\n", + "carrier : ['frog_1', 'frog_2', 'snake', 'ostrich', 'rhinoceros']\n", + "man_with_hat : ['turtle', 'frog_1', 'trilobite', 'snake', 'ostrich']\n", + "hat : ['scorpion', 'frog_1', 'frog_2', 'rhinoceros', 'ostrich']\n", + "nan : ['frog_1', 'frog_2', 'turtle', 'snake', 'trilobite']\n", + "fire_extinguisher : ['trilobite', 'frog_2', 'ostrich', 'frog_1', 'scorpion']\n", + "helmet : ['frog_1', 'frog_2', 'snake', 'ostrich', 'scorpion']\n", + "piano : ['trilobite', 'frog_2', 'frog_1', 'scorpion', 'rhinoceros']\n", + "grave : ['frog_1', 'frog_2', 'ostrich', 'scorpion', 'snake']\n", + "man_with_hammock : ['ostrich', 'frog_1', 'turtle', 'snake', 'trilobite']\n", + "harp : ['snake', 'turtle', 'trilobite', 'ostrich', 'rhinoceros']\n", + "iPod : ['frog_1', 'trilobite', 'frog_2', 'snake', 'ostrich']\n", + "knob : ['snake', 'trilobite', 'turtle', 'rhinoceros', 'frog_1']\n", + "post : ['ostrich', 'scorpion', 'trilobite', 'frog_2', 'snake']\n", + "mandarin : ['scorpion', 'trilobite', 'turtle', 'frog_1', 'ostrich']\n", + "kitchen : ['frog_1', 'frog_2', 'turtle', 'trilobite', 'scorpion']\n", + "tower : ['frog_1', 'frog_2', 'trilobite', 'turtle', 'snake']\n", + "nan : ['trilobite', 'frog_2', 'frog_1', 'ostrich', 'scorpion']\n", + "coin : ['snake', 'scorpion', 'frog_2', 'ostrich', 'frog_1']\n", + "shredder : ['trilobite', 'frog_2', 'ostrich', 'snake', 'scorpion']\n", + "Snowmobile : ['snake', 'ostrich', 'rhinoceros', 'frog_1', 'scorpion']\n", + "socks : ['trilobite', 'frog_2', 'scorpion', 'frog_1', 'rhinoceros']\n", + "Stained glass : ['frog_1', 'rhinoceros', 'snake', 'trilobite', 'frog_2']\n", + "nan : ['frog_1', 'frog_2', 'snake', 'scorpion', 'ostrich']\n", + "umbrella : ['rhinoceros', 'ostrich', 'snake', 'trilobite', 'frog_2']\n", + "videoplayer : ['rhinoceros', 'frog_1', 'trilobite', 'frog_2', 'scorpion']\n", + "washing_machine : ['frog_2', 'frog_1', 'rhinoceros', 'ostrich', 'turtle']\n", + "buttery? : ['frog_1', 'frog_2', 'turtle', 'snake', 'scorpion']\n" + ] + } + ], + "source": [ + "test_start_id = 0\n", + "test_end_id = len(test_cat)\n", + "\n", + "pred_corr[:test_end_id, :test_end_id] = 0\n", + "pred_corr[test_end_id:, test_end_id:] = 0\n", + "pred_corr\n", + "plt.imshow(pred_corr)\n", + "plt.show()\n", + "\n", + "pred_ranking = np.argsort(pred_corr, axis=1)[:,::-1]\n", + "\n", + "# test corr\n", + "for i, row in enumerate(pred_ranking[test_start_id:test_end_id]):\n", + " print(pred_labels[i+test_start_id], ':' ,[pred_labels[r] for r in row[:5]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train Pred, Test Pred, Train Image (AVG), Test Image" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "768 300 150 50\n", + "[[ 1. 0.57596924 0.53442588 ... -0.01486964 -0.02870013\n", + " 0.01908956]\n", + " [ 0.57596924 1. 0.25942643 ... 0.02026624 -0.02124821\n", + " 0.0072545 ]\n", + " [ 0.53442588 0.25942643 1. ... -0.01264118 -0.02551319\n", + " 0.04785919]\n", + " ...\n", + " [-0.01486964 0.02026624 -0.01264118 ... 1. 0.56144309\n", + " 0.5761439 ]\n", + " [-0.02870013 -0.02124821 -0.02551319 ... 0.56144309 1.\n", + " 0.61554665]\n", + " [ 0.01908956 0.0072545 0.04785919 ... 0.5761439 0.61554665\n", + " 1. ]]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pred_image_features = np.concatenate([train_pred_features, test_pred_features, train_cat_image_features_avg, test_image_features], axis=0)\n", + "pred_labels = train_pred_cats + test_pred_cats + train_cat + test_cat\n", + "pred_corr = np.corrcoef(pred_image_features)\n", + "print(len(train_pred_cats), len(test_pred_cats), len(train_cat), len(test_cat))\n", + "print(pred_corr)\n", + "\n", + "plt.imshow(pred_corr)\n", + "plt.show()\n", + "plt.imshow((pred_corr>0.8).astype(np.int8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(np.arange(test_image_features.shape[1]), test_image_features[test_labels[0],:], 'r-')\n", + "plt.plot(np.arange(test_pred_features.shape[1]), test_pred_features[0,:], 'b-')\n", + "plt.show()\n", + "\n", + "plt.plot(np.arange(train_image_features.shape[1]), train_image_features[train_labels[0]//8,:], 'r-')\n", + "plt.plot(np.arange(train_pred_features.shape[1]), train_pred_features[0,:], 'b-')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "goat : ['scorpion', 'trilobite', 'bulldozer', 'turtle', 'camel']\n", + "grave : ['rhinoceros', 'snake', 'frog_2', 'frog_1', 'trilobite']\n", + "post : ['rhinoceros', 'scorpion', 'ostrich', 'golfball', 'turtle']\n", + "videoplayer : ['trilobite', 'turtle', 'rhinoceros', 'frog_1', 'Goldfish']\n", + "knob : ['ostrich', 'camel', 'scorpion', 'snake', 'man_with_hammock']\n", + "nan : ['trilobite', 'snake', 'ostrich', 'frog_2', 'turtle']\n", + "socks : ['frog_1', 'frog_2', 'crab', 'rhinoceros', 'nan']\n", + "socks : ['frog_2', 'scorpion', 'nan', 'frog_1', 'snake']\n", + "carrier : ['turtle', 'snake', 'frog_1', 'frog_2', 'crab']\n", + "leopard : ['ostrich', 'trilobite', 'coin', 'frog_2', 'frog_1']\n", + "hat : ['scorpion', 'frog_1', 'frog_2', 'ostrich', 'crab']\n", + "owl : ['nan', 'snake', 'frog_2', 'horn', 'rhinoceros']\n", + "carrier : ['frog_1', 'buttery?', 'ostrich', 'coin', 'owl']\n", + "crab : ['scorpion', 'crab', 'turtle', 'trilobite', 'camel']\n", + "fire_extinguisher : ['trilobite', 'frog_2', 'frog_1', 'turtle', 'ostrich']\n", + "washing_machine : ['man_with_hat', 'turtle', 'frog_1', 'frog_2', 'scorpion']\n", + "nan : ['scorpion', 'camel', 'ostrich', 'trilobite', 'crab']\n", + "coin : ['snake', 'frog_1', 'kitchen', 'lama', 'nan']\n", + "Snowmobile : ['frog_1', 'snake', 'nan', 'turtle', 'frog_2']\n", + "goat : ['frog_1', 'frog_2', 'nan', 'scorpion', 'owl']\n", + "fly : ['scorpion', 'frog_1', 'frog_2', 'coffin', 'bulldozer']\n", + "iPod : ['ostrich', 'snake', 'trilobite', 'lama', 'nan']\n", + "socks : ['frog_2', 'trilobite', 'ostrich', 'frog_1', 'basketball_ring']\n", + "knob : ['turtle', 'frog_1', 'rhinoceros', 'trilobite', 'Fern']\n", + "iPod : ['frog_1', 'frog_2', 'submachine_gun', 'man_with_hammock', 'canoe']\n", + "owl : ['scorpion', 'crab', 'coin', 'rhinoceros', 'trilobite']\n", + "beermug : ['frog_1', 'frog_2', 'man_with_hammock', 'rhinoceros', 'trilobite']\n", + "umbrella : ['rhinoceros', 'trilobite', 'crab', 'ostrich', 'swan']\n", + "crab : ['frog_1', 'frog_2', 'Goldfish', 'deer', 'rhinoceros']\n", + "harp : ['snake', 'bulldozer', 'camel', 'crab', 'trilobite']\n", + "nan : ['kitchen', 'snake', 'rhinoceros', 'trilobite', 'turtle']\n", + "grave : ['ostrich', 'frog_1', 'snake', 'man_with_hat', 'frog_2']\n", + "lama : ['frog_1', 'snake', 'knob', 'nan', 'man_with_hat']\n", + "nan : ['trilobite', 'turtle', 'rhinoceros', 'duck', 'Goldfish']\n", + "lama : ['rhinoceros', 'ostrich', 'frog_1', 'snake', 'trilobite']\n", + "shredder : ['crab', 'camel', 'trilobite', 'scorpion', 'bulldozer']\n", + "tower : ['turtle', 'coin', 'frog_2', 'frog_1', 'trilobite']\n", + "Snowmobile : ['trilobite', 'coin', 'rhinoceros', 'snake', 'nan']\n", + "buttery? : ['ostrich', 'coin', 'scorpion', 'nan', 'frog_2']\n", + "Stained glass : ['frog_1', 'rhinoceros', 'turtle', 'coin', 'ostrich']\n", + "carrier : ['snake', 'rhinoceros', 'nan', 'frog_1', 'nan']\n", + "duck : ['rhinoceros', 'trilobite', 'turtle', 'frog_2', 'frog_1']\n", + "swan : ['scorpion', 'nan', 'frog_1', 'snake', 'frog_2']\n", + "swan : ['rhinoceros', 'frog_2', 'frog_1', 'nan', 'man_with_hammock']\n", + "fly : ['rhinoceros', 'nan', 'ostrich', 'turtle', 'snake']\n", + "kitchen : ['trilobite', 'frog_1', 'rhinoceros', 'turtle', 'scorpion']\n", + "umbrella : ['ostrich', 'nan', 'frog_1', 'corn_flakes', 'snake']\n", + "swan : ['frog_2', 'trilobite', 'turtle', 'camel', 'ostrich']\n", + "tower : ['frog_2', 'frog_1', 'turtle', 'swan', 'nan']\n", + "nan : ['frog_2', 'frog_1', 'nan', 'snake', 'ostrich']\n", + "airplane : ['ostrich', 'snake', 'camel', 'scorpion', 'trilobite']\n", + "leopard : ['ostrich', 'frog_1', 'frog_2', 'camel', 'scorpion']\n", + "orca : ['scorpion', 'rhinoceros', 'turtle', 'bulldozer', 'ostrich']\n", + "bulldozer : ['frog_2', 'snake', 'frog_1', 'coin', 'rhinoceros']\n", + "owl : ['coffin', 'snake', 'ostrich', 'frog_2', 'frog_1']\n", + "fly : ['scorpion', 'frog_1', 'snake', 'frog_2', 'trilobite']\n", + "Goldfish : ['snake', 'nan', 'rhinoceros', 'frog_1', 'turtle']\n", + "iPod : ['coin', 'frog_1', 'frog_2', 'trilobite', 'snake']\n", + "piano : ['trilobite', 'coin', 'ostrich', 'bulldozer', 'rhinoceros']\n", + "nan : ['frog_2', 'ostrich', 'frog_1', 'snake', 'scorpion']\n", + "airplane : ['scorpion', 'turtle', 'crab', 'helmet', 'coin']\n", + "buttery? : ['trilobite', 'turtle', 'rhinoceros', 'man_with_hammock', 'frog_2']\n", + "videoplayer : ['snake', 'scorpion', 'ostrich', 'frog_1', 'rhinoceros']\n", + "hat : ['frog_1', 'frog_2', 'scorpion', 'nan', 'turtle']\n", + "harp : ['scorpion', 'ostrich', 'nan', 'snake', 'trilobite']\n", + "orca : ['frog_1', 'frog_2', 'nan', 'penguin', 'trilobite']\n", + "bulldozer : ['scorpion', 'turtle', 'nan', 'bag', 'frog_1']\n", + "fire_extinguisher : ['trilobite', 'frog_1', 'scorpion', 'rhinoceros', 'turtle']\n", + "butterfly : ['trilobite', 'snake', 'frog_1', 'nan', 'bus']\n", + "leopard : ['trilobite', 'rhinoceros', 'frog_1', 'snake', 'nan']\n", + "shredder : ['scorpion', 'trilobite', 'windmill', 'snake', 'post']\n", + "bowlingball : ['man_with_hat', 'snake', 'frog_2', 'frog_1', 'nan']\n", + "butterfly : ['frog_1', 'rhinoceros', 'scorpion', 'nan', 'frog_2']\n", + "Bat : ['frog_1', 'golfball', 'turtle', 'frog_2', 'earphone']\n", + "Goldfish : ['snake', 'frog_2', 'turtle', 'injection', 'starfish']\n", + "bowlingball : ['frog_1', 'snake', 'nan', 'frog_2', 'scorpion']\n", + "socks : ['trilobite', 'turtle', 'camel', 'ostrich', 'goat']\n", + "carrier : ['turtle', 'snake', 'frog_2', 'frog_1', 'injection']\n", + "iPod : ['frog_2', 'nan', 'turtle', 'ostrich', 'frog_1']\n", + "kitchen : ['frog_2', 'frog_1', 'rhinoceros', 'turtle', 'trilobite']\n", + "lama : ['turtle', 'trilobite', 'nan', 'frog_1', 'bulldozer']\n", + "nan : ['frog_1', 'frog_2', 'trilobite', 'nan', 'Loudspeaker']\n", + "harp : ['rhinoceros', 'crab', 'frog_2', 'frog_1', 'turtle']\n", + "helmet : ['frog_1', 'frog_2', 'snake', 'nan', 'man_with_hat']\n", + "helmet : ['swan', 'turtle', 'frog_2', 'crab', 'trilobite']\n", + "nan : ['trilobite', 'turtle', 'frog_2', 'crab', 'fungi']\n", + "umbrella : ['turtle', 'crab', 'ostrich', 'trilobite', 'camel']\n", + "goat : ['frog_1', 'lama', 'Goldfish', 'scorpion', 'rhinoceros']\n", + "harp : ['frog_2', 'frog_1', 'turtle', 'snake', 'nan']\n", + "grave : ['ostrich', 'scorpion', 'swan', 'snake', 'knob']\n", + "nan : ['coin', 'turtle', 'scorpion', 'trilobite', 'helmet']\n", + "fire_extinguisher : ['ostrich', 'tent', 'scorpion', 'snake', 'nan']\n", + "umbrella : ['snake', 'rhinoceros', 'nan', 'frog_2', 'trilobite']\n", + "bowlingball : ['frog_2', 'snake', 'frog_1', 'turtle', 'owl']\n", + "coin : ['frog_2', 'scorpion', 'frog_1', 'crab', 'nan']\n", + "coffin : ['frog_1', 'turtle', 'frog_2', 'scorpion', 'crab']\n", + "owl : ['frog_1', 'rhinoceros', 'frog_2', 'duck', 'nan']\n", + "helmet : ['ostrich', 'turtle', 'coin', 'frog_2', 'crab']\n", + "mandarin : ['frog_1', 'frog_2', 'rhinoceros', 'scorpion', 'turtle']\n", + "Snowmobile : ['trilobite', 'snake', 'nan', 'man_with_hat', 'camel']\n", + "nan : ['trilobite', 'ostrich', 'socks', 'rhinoceros', 'teapot']\n", + "owl : ['turtle', 'rhinoceros', 'Goldfish', 'scorpion', 'kangaroo']\n", + "nan : ['scorpion', 'rhinoceros', 'frog_1', 'frog_2', 'nan']\n", + "beermug : ['ostrich', 'trilobite', 'scorpion', 'turtle', 'camel']\n", + "crab : ['scorpion', 'turtle', 'nan', 'frog_2', 'snake']\n", + "harp : ['trilobite', 'snake', 'Goldfish', 'bowlingball', 'frog_1']\n", + "hat : ['rhinoceros', 'ostrich', 'scorpion', 'crab', 'turtle']\n", + "butterfly : ['rhinoceros', 'frog_2', 'frog_1', 'turtle', 'snake']\n", + "nan : ['ostrich', 'trilobite', 'crab', 'camel', 'scorpion']\n", + "nan : ['trilobite', 'rhinoceros', 'ostrich', 'scorpion', 'crab']\n", + "fire_extinguisher : ['coin', 'rhinoceros', 'frog_2', 'turtle', 'snake']\n", + "fly : ['ostrich', 'trilobite', 'rhinoceros', 'lama', 'nan']\n", + "boat : ['coin', 'frog_1', 'frog_2', 'scorpion', 'nan']\n", + "Stained glass : ['frog_2', 'frog_1', 'snake', 'coin', 'rhinoceros']\n", + "duck : ['ostrich', 'scorpion', 'turtle', 'rhinoceros', 'tent']\n", + "nan : ['ostrich', 'scorpion', 'buttery?', 'frog_2', 'frog_1']\n", + "washing_machine : ['frog_1', 'snake', 'scorpion', 'turtle', 'frog_2']\n", + "knob : ['frog_1', 'rhinoceros', 'trilobite', 'snake', 'coin']\n", + "piano : ['frog_2', 'trilobite', 'nan', 'snake', 'coin']\n", + "Goldfish : ['snake', 'ostrich', 'nan', 'fighter', 'frog_2']\n", + "iPod : ['coin', 'trilobite', 'frog_2', 'snake', 'rhinoceros']\n", + "buttery? : ['turtle', 'snake', 'frog_1', 'coffin', 'binocular']\n", + "lama : ['crab', 'scorpion', 'ostrich', 'frog_1', 'frog_2']\n", + "coffin : ['scorpion', 'turtle', 'swan', 'orca', 'Goldfish']\n", + "mandarin : ['scorpion', 'frog_2', 'frog_1', 'nan', 'buttery?']\n", + "bulldozer : ['scorpion', 'frog_1', 'man_with_hat', 'rhinoceros', 'shredder']\n", + "socks : ['crab', 'scorpion', 'ostrich', 'horseshoe_crab', 'snake']\n", + "owl : ['rhinoceros', 'frog_1', 'ostrich', 'turtle', 'trilobite']\n", + "butterfly : ['snake', 'scorpion', 'trilobite', 'frog_2', 'frog_1']\n", + "nan : ['trilobite', 'turtle', 'frog_2', 'ostrich', 'man_with_hammock']\n", + "Cannon : ['rhinoceros', 'boat', 'turtle', 'man_with_hammock', 'crab']\n", + "butterfly : ['frog_2', 'frog_1', 'trilobite', 'rhinoceros', 'nan']\n", + "nan : ['ostrich', 'camel', 'kitchen', 'trilobite', 'snake']\n", + "man_with_hat : ['rhinoceros', 'turtle', 'scorpion', 'frog_1', 'nan']\n", + "leopard : ['scorpion', 'swan', 'turtle', 'snake', 'frog_1']\n", + "post : ['ostrich', 'turtle', 'frog_2', 'nan', 'snake']\n", + "coin : ['ostrich', 'scorpion', 'turtle', 'swan', 'trilobite']\n", + "nan : ['frog_1', 'ostrich', 'frog_2', 'trilobite', 'snake']\n", + "leopard : ['rhinoceros', 'trilobite', 'turtle', 'snake', 'goat']\n", + "orca : ['frog_1', 'rhinoceros', 'snake', 'frog_2', 'dolphin']\n", + "Snowmobile : ['scorpion', 'ostrich', 'rhinoceros', 'snake', 'nan']\n", + "coffin : ['frog_1', 'scorpion', 'owl', 'nan', 'crab']\n", + "hat : ['bulldozer', 'frog_2', 'scorpion', 'frog_1', 'nan']\n", + "fire_extinguisher : ['frog_2', 'scorpion', 'crab', 'bulldozer', 'trilobite']\n", + "shredder : ['snake', 'frog_2', 'frog_1', 'nan', 'shredder']\n", + "crab : ['frog_1', 'turtle', 'trilobite', 'frog_2', 'crab']\n", + "airplane : ['rhinoceros', 'snake', 'trilobite', 'scorpion', 'ostrich']\n", + "mandarin : ['trilobite', 'ostrich', 'rhinoceros', 'nan', 'coin']\n", + "post : ['scorpion', 'crab', 'rhinoceros', 'frog_2', 'frog_1']\n", + "shredder : ['turtle', 'rhinoceros', 'bulldozer', 'scorpion', 'frog_2']\n", + "nan : ['turtle', 'frog_1', 'rhinoceros', 'frog_2', 'snake']\n", + "mandarin : ['scorpion', 'trilobite', 'frog_1', 'man_with_hat', 'crab']\n", + "washing_machine : ['frog_1', 'swan', 'frog_2', 'rhinoceros', 'turtle']\n", + "man_with_hammock : ['nan', 'trilobite', 'ostrich', 'snake', 'rhinoceros']\n", + "Goldfish : ['trilobite', 'frog_2', 'turtle', 'coin', 'ostrich']\n", + "man_with_hat : ['snake', 'ostrich', 'turtle', 'trilobite', 'Goldfish']\n", + "coffin : ['frog_1', 'scorpion', 'frog_2', 'nan', 'bulldozer']\n", + "helmet : ['rhinoceros', 'swan', 'trilobite', 'scorpion', 'frog_1']\n", + "Goldfish : ['trilobite', 'frog_2', 'man_with_hammock', 'babycar', 'coin']\n", + "Snowmobile : ['rhinoceros', 'ostrich', 'crab', 'coin', 'snake']\n", + "nan : ['frog_2', 'nan', 'trilobite', 'turtle', 'frog_1']\n", + "duck : ['trilobite', 'kitchen', 'rhinoceros', 'turtle', 'centipede']\n", + "man_with_hammock : ['ostrich', 'trilobite', 'snake', 'frog_2', 'rhinoceros']\n", + "goat : ['ostrich', 'camel', 'frog_2', 'swan', 'turtle']\n", + "videoplayer : ['trilobite', 'turtle', 'rhinoceros', 'frog_2', 'coin']\n", + "Cannon : ['frog_2', 'frog_1', 'owl', 'rhinoceros', 'trilobite']\n", + "orca : ['frog_1', 'frog_2', 'man_with_hat', 'turtle', 'knob']\n", + "tower : ['frog_2', 'frog_1', 'turtle', 'nan', 'snake']\n", + "coin : ['turtle', 'scorpion', 'camel', 'snake', 'ostrich']\n", + "kitchen : ['ostrich', 'trilobite', 'scorpion', 'frog_2', 'buttery?']\n", + "knob : ['nan', 'ostrich', 'trilobite', 'scorpion', 'shirt']\n", + "helmet : ['trilobite', 'frog_1', 'nan', 'frog_2', 'camel']\n", + "videoplayer : ['rhinoceros', 'snake', 'trilobite', 'frog_1', 'frog_2']\n", + "camel : ['frog_2', 'frog_1', 'scorpion', 'nan', 'turtle']\n", + "Bat : ['frog_2', 'nan', 'frog_1', 'owl', 'bus']\n", + "mandarin : ['ostrich', 'scorpion', 'antenna', 'trilobite', 'rhinoceros']\n", + "coffin : ['turtle', 'frog_2', 'snake', 'ostrich', 'frog_1']\n", + "washing_machine : ['rhinoceros', 'ostrich', 'swan', 'snake', 'trilobite']\n", + "nan : ['frog_1', 'turtle', 'frog_2', 'nan', 'snake']\n", + "nan : ['frog_1', 'snake', 'scorpion', 'golfball', 'frog_2']\n", + "airplane : ['rhinoceros', 'trilobite', 'ostrich', 'frog_2', 'scorpion']\n", + "Stained glass : ['frog_1', 'coin', 'rhinoceros', 'scorpion', 'turtle']\n", + "man_with_hammock : ['lama', 'snake', 'ostrich', 'rhinoceros', 'turtle']\n", + "Stained glass : ['ostrich', 'coin', 'trilobite', 'rhinoceros', 'frog_1']\n", + "crab : ['ostrich', 'trilobite', 'turtle', 'rhinoceros', 'nan']\n", + "post : ['trilobite', 'rhinoceros', 'ostrich', 'snake', 'coin']\n", + "bowlingball : ['scorpion', 'trilobite', 'frog_1', 'ostrich', 'frog_2']\n", + "man_with_hammock : ['frog_1', 'turtle', 'hourglass', 'scorpion', 'nan']\n", + "Bat : ['frog_2', 'crab', 'nan', 'coin', 'frog_1']\n", + "piano : ['man_with_hammock', 'trilobite', 'rhinoceros', 'frog_1', 'ostrich']\n", + "iPod : ['trilobite', 'rhinoceros', 'coin', 'ostrich', 'frog_2']\n", + "crab : ['ostrich', 'rhinoceros', 'frog_1', 'frog_2', 'bowlingball']\n", + "tower : ['frog_1', 'trilobite', 'snake', 'ostrich', 'frog_2']\n", + "camel : ['scorpion', 'nan', 'swan', 'turtle', 'auto_bike']\n", + "lama : ['snake', 'frog_1', 'nan', 'trilobite', 'horn']\n", + "camel : ['scorpion', 'frog_2', 'crab', 'frog_1', 'nan']\n", + "Cannon : ['frog_1', 'frog_2', 'nan', 'snake', 'crab']\n", + "swan : ['frog_1', 'ostrich', 'rhinoceros', 'camel', 'frog_2']\n", + "goat : ['trilobite', 'turtle', 'rhinoceros', 'owl', 'giraffe']\n", + "leopard : ['rhinoceros', 'trilobite', 'snake', 'frog_1', 'frog_2']\n", + "kitchen : ['frog_1', 'frog_2', 'turtle', 'snake', 'crab']\n", + "beermug : ['scorpion', 'ostrich', 'snake', 'frog_2', 'frog_1']\n", + "tower : ['trilobite', 'ostrich', 'crab', 'frog_2', 'scorpion']\n", + "shredder : ['ostrich', 'lama', 'owl', 'camel', 'swan']\n", + "nan : ['frog_1', 'snake', 'scorpion', 'ostrich', 'frog_2']\n", + "knob : ['turtle', 'snake', 'frog_2', 'nan', 'camel']\n", + "helmet : ['snake', 'scorpion', 'kitchen', 'crab', 'nan']\n", + "butterfly : ['coin', 'frog_2', 'ostrich', 'owl', 'frog_1']\n", + "nan : ['ostrich', 'coin', 'frog_2', 'crab', 'snake']\n", + "airplane : ['ostrich', 'nan', 'snake', 'camel', 'turtle']\n", + "socks : ['trilobite', 'scorpion', 'rhinoceros', 'frog_1', 'ostrich']\n", + "bowlingball : ['frog_1', 'frog_2', 'rhinoceros', 'snake', 'buttery?']\n", + "nan : ['rhinoceros', 'scorpion', 'trilobite', 'frog_2', 'glass']\n", + "nan : ['frog_2', 'frog_1', 'nan', 'scorpion', 'owl']\n", + "bulldozer : ['frog_2', 'ostrich', 'coin', 'frog_1', 'trilobite']\n", + "nan : ['scorpion', 'frog_2', 'frog_1', 'crab', 'swan']\n", + "buttery? : ['snake', 'man_with_hat', 'rhinoceros', 'scorpion', 'nan']\n", + "carrier : ['scorpion', 'ostrich', 'trilobite', 'man_with_hat', 'tower']\n", + "buttery? : ['frog_2', 'snake', 'ostrich', 'frog_1', 'turtle']\n", + "duck : ['ostrich', 'rhinoceros', 'snake', 'elephant', 'swan']\n", + "umbrella : ['frog_1', 'frog_2', 'ostrich', 'snake', 'nan']\n", + "buttery? : ['frog_1', 'frog_2', 'scorpion', 'trilobite', 'man_with_hat']\n", + "boat : ['coin', 'nan', 'snake', 'scorpion', 'frog_1']\n", + "lama : ['frog_1', 'frog_2', 'nan', 'scorpion', 'owl']\n", + "nan : ['rhinoceros', 'frog_1', 'nan', 'nan', 'frog_2']\n", + "kitchen : ['frog_1', 'snake', 'trilobite', 'nan', 'videoplayer']\n", + "Bat : ['scorpion', 'frog_2', 'frog_1', 'trilobite', 'coin']\n", + "duck : ['ostrich', 'owl', 'frog_2', 'frog_1', 'coin']\n", + "coin : ['snake', 'buttery?', 'scorpion', 'frog_1', 'nan']\n", + "fly : ['turtle', 'scorpion', 'ostrich', 'swan', 'nan']\n", + "bulldozer : ['snake', 'ostrich', 'scorpion', 'nan', 'crab']\n", + "man_with_hat : ['trilobite', 'turtle', 'ostrich', 'bulldozer', 'rhinoceros']\n", + "Bat : ['rhinoceros', 'frog_1', 'scorpion', 'crab', 'turtle']\n", + "bulldozer : ['nan', 'frog_1', 'turtle', 'snake', 'man_with_hat']\n", + "nan : ['trilobite', 'frog_1', 'frog_2', 'rhinoceros', 'snake']\n", + "kitchen : ['frog_1', 'rhinoceros', 'knob', 'scorpion', 'frog_2']\n", + "boat : ['trilobite', 'rhinoceros', 'frog_1', 'frog_2', 'microscope']\n", + "Stained glass : ['rhinoceros', 'frog_1', 'nan', 'scorpion', 'frog_2']\n", + "fly : ['rhinoceros', 'frog_1', 'trilobite', 'frog_2', 'buttery?']\n", + "man_with_hammock : ['turtle', 'scorpion', 'camel', 'ostrich', 'crab']\n", + "coin : ['man_with_hat', 'turtle', 'crab', 'snake', 'scorpion']\n", + "washing_machine : ['ostrich', 'trilobite', 'snake', 'frog_2', 'turtle']\n", + "Cannon : ['trilobite', 'frog_1', 'ostrich', 'snake', 'rhinoceros']\n", + "coffin : ['scorpion', 'frog_1', 'crab', 'frog_2', 'rhinoceros']\n", + "man_with_hat : ['frog_1', 'frog_2', 'snake', 'scorpion', 'nan']\n", + "swan : ['nan', 'frog_2', 'frog_1', 'scorpion', 'snake']\n", + "hat : ['scorpion', 'frog_2', 'crab', 'frog_1', 'snake']\n", + "washing_machine : ['frog_2', 'frog_1', 'rhinoceros', 'nan', 'turtle']\n", + "Bat : ['frog_1', 'ostrich', 'rhinoceros', 'frog_2', 'snake']\n", + "carrier : ['frog_1', 'nan', 'frog_2', 'kitchen', 'owl']\n", + "man_with_hat : ['frog_2', 'snake', 'frog_1', 'nan', 'owl']\n", + "grave : ['frog_1', 'rhinoceros', 'frog_2', 'bulldozer', 'turtle']\n", + "boat : ['frog_1', 'ostrich', 'frog_2', 'owl', 'swan']\n", + "tower : ['rhinoceros', 'snake', 'ostrich', 'swan', 'turtle']\n", + "nan : ['snake', 'trilobite', 'ostrich', 'nan', 'frog_2']\n", + "camel : ['frog_1', 'scorpion', 'frog_2', 'ostrich', 'refrigerator']\n", + "mandarin : ['turtle', 'trilobite', 'ostrich', 'crab', 'camel']\n", + "man_with_hammock : ['turtle', 'swan', 'ostrich', 'frog_1', 'frog_2']\n", + "piano : ['trilobite', 'scorpion', 'snake', 'frog_2', 'nan']\n", + "post : ['ostrich', 'crab', 'camel', 'trilobite', 'frog_2']\n", + "nan : ['frog_2', 'trilobite', 'frog_1', 'turtle', 'coin']\n", + "goat : ['ostrich', 'snake', 'crab', 'owl', 'nan']\n", + "Cannon : ['snake', 'ostrich', 'crab', 'camel', 'turtle']\n", + "Snowmobile : ['ostrich', 'scorpion', 'rhinoceros', 'chimpanzee', 'coin']\n", + "duck : ['trilobite', 'snake', 'ostrich', 'frog_2', 'frog_1']\n", + "nan : ['trilobite', 'snake', 'nan', 'camel', 'frog_2']\n", + "nan : ['owl', 'nan', 'rhinoceros', 'turtle', 'airship']\n", + "boat : ['snake', 'turtle', 'nan', 'man_with_hat', 'frog_2']\n", + "fire_extinguisher : ['ostrich', 'frog_1', 'coin', 'scorpion', 'Snowmobile']\n", + "piano : ['frog_2', 'frog_1', 'scorpion', 'rhinoceros', 'nan']\n", + "videoplayer : ['frog_1', 'scorpion', 'frog_2', 'rhinoceros', 'keyboard']\n", + "beermug : ['frog_1', 'turtle', 'rhinoceros', 'scorpion', 'frog_2']\n", + "piano : ['rhinoceros', 'frog_2', 'trilobite', 'frog_1', 'coin']\n", + "harp : ['turtle', 'snake', 'trilobite', 'man_with_hammock', 'ostrich']\n", + "umbrella : ['ostrich', 'rhinoceros', 'snake', 'frog_2', 'injection']\n", + "videoplayer : ['rhinoceros', 'snake', 'scorpion', 'frog_1', 'trilobite']\n", + "man_with_hat : ['turtle', 'frog_1', 'Goldfish', 'ostrich', 'trilobite']\n", + "hat : ['ostrich', 'turtle', 'trilobite', 'helmet', 'rhinoceros']\n", + "orca : ['rhinoceros', 'frog_2', 'scorpion', 'nan', 'ostrich']\n", + "orca : ['scorpion', 'turtle', 'trilobite', 'camel', 'nan']\n", + "grave : ['scorpion', 'frog_1', 'fly', 'ostrich', 'frog_2']\n", + "airplane : ['scorpion', 'frog_1', 'nan', 'coin', 'trilobite']\n", + "camel : ['nan', 'frog_2', 'rhinoceros', 'nan', 'trilobite']\n", + "Cannon : ['frog_1', 'snake', 'rhinoceros', 'scorpion', 'kitchen']\n", + "Stained glass : ['snake', 'trilobite', 'rhinoceros', 'frog_1', 'kitchen']\n", + "grave : ['frog_1', 'frog_2', 'scorpion', 'owl', 'nan']\n", + "nan : ['trilobite', 'rhinoceros', 'scorpion', 'ostrich', 'frog_2']\n", + "bowlingball : ['frog_2', 'ostrich', 'trilobite', 'turtle', 'snake']\n", + "knob : ['turtle', 'trilobite', 'swan', 'snake', 'scorpion']\n", + "nan : ['trilobite', 'frog_1', 'snake', 'owl', 'buttery?']\n", + "nan : ['scorpion', 'trilobite', 'snake', 'frog_2', 'rhinoceros']\n", + "Goldfish : ['frog_2', 'ostrich', 'camel', 'crab', 'nan']\n", + "camel : ['frog_1', 'scorpion', 'Goldfish', 'frog_2', 'buttery?']\n", + "shredder : ['trilobite', 'rhinoceros', 'frog_2', 'owl', 'frog_1']\n", + "nan : ['frog_1', 'bulldozer', 'trilobite', 'rhinoceros', 'coin']\n", + "beermug : ['ostrich', 'rhinoceros', 'coin', 'bulldozer', 'crab']\n", + "post : ['snake', 'frog_2', 'scorpion', 'trilobite', 'swan']\n", + "swan : ['snake', 'scorpion', 'trilobite', 'swan', 'ostrich']\n", + "beermug : ['trilobite', 'frog_2', 'coin', 'rhinoceros', 'turtle']\n", + "boat : ['coin', 'turtle', 'frog_2', 'nan', 'trilobite']\n" + ] + } + ], + "source": [ + "test_start_id = len(train_pred_cats)\n", + "test_end_id = test_start_id + len(test_pred_cats)\n", + "\n", + "pred_corr[:test_end_id, :test_end_id] = 0\n", + "plt.imshow(pred_corr)\n", + "plt.show()\n", + "\n", + "pred_ranking = np.argsort(pred_corr, axis=1)[:,::-1]\n", + "\n", + "# test corr\n", + "for i, row in enumerate(pred_ranking[test_start_id:test_end_id]):\n", + " print(pred_labels[i+test_start_id], ':' ,[pred_labels[r] for r in row[:5]])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "horn : ['kitchen', 'turtle', 'crab', 'ostrich', 'trilobite']\n", + "kangaroo : ['frog_2', 'trilobite', 'nan', 'fighter', 'kangaroo']\n", + "grasshopper : ['snake', 'frog_1', 'duck', 'frog_2', 'turtle']\n", + "tent : ['frog_2', 'scorpion', 'nan', 'tent', 'frog_1']\n", + "bear : ['frog_2', 'duck', 'frog_1', 'owl', 'bear']\n", + "refrigerator : ['ostrich', 'shredder', 'scorpion', 'snake', 'duck']\n", + "treadmill : ['rhinoceros', 'trilobite', 'crab', 'treadmill', 'socks']\n", + "horseshoe_crab : ['scorpion', 'ostrich', 'trilobite', 'frog_1', 'horseshoe_crab']\n", + "Loudspeaker : ['turtle', 'camel', 'snake', 'frog_2', 'tie']\n", + "bustab : ['frog_1', 'frog_2', 'nan', 'man_with_hat', 'owl']\n", + "flashlight : ['scorpion', 'turtle', 'rhinoceros', 'frog_1', 'yacht']\n", + "keyboard : ['frog_2', 'frog_1', 'keyboard', 'man_with_hammock', 'nan']\n", + "game : ['rhinoceros', 'coin', 'frog_1', 'game', 'frog_2']\n", + "hourglass : ['hourglass', 'turtle', 'snake', 'camel', 'ostrich']\n", + "dog_1 : ['scorpion', 'nan', 'frog_1', 'frog_2', 'tricycle']\n", + "dumbbell : ['snake', 'frog_2', 'frog_1', 'nan', 'buttery?']\n", + "crested_ibis : ['snake', 'crested_ibis', 'nan', 'swan', 'ostrich']\n", + "kangaroo : ['rhinoceros', 'frog_1', 'frog_2', 'trilobite', 'nan']\n", + "teapot : ['snake', 'turtle', 'nan', 'auto_bike', 'nan']\n", + "game : ['trilobite', 'ostrich', 'frog_1', 'snake', 'scorpion']\n", + "Copier : ['rhinoceros', 'ostrich', 'crab', 'swan', 'snake']\n", + "balloon : ['trilobite', 'ostrich', 'rhinoceros', 'snake', 'coin']\n", + "roulette : ['frog_2', 'frog_1', 'snake', 'roulette', 'ostrich']\n", + "clip : ['turtle', 'helmet', 'camel', 'crab', 'clip']\n", + "submachine_gun : ['trilobite', 'rhinoceros', 'crab', 'frog_1', 'scorpion']\n", + "tricycle : ['trilobite', 'camel', 'tricycle', 'lawn mower', 'nan']\n", + "tent : ['tent', 'turtle', 'ostrich', 'scorpion', 'camel']\n", + "driver : ['turtle', 'camel', 'frog_2', 'snake', 'goat']\n", + "giraffe : ['ostrich', 'lama', 'giraffe', 'nan', 'trilobite']\n", + "stearing : ['frog_2', 'nan', 'owl', 'frog_1', 'rhinoceros']\n", + "flashlight : ['turtle', 'flashlight', 'trilobite', 'ostrich', 'frog_2']\n", + "light_bulb : ['frog_1', 'nan', 'owl', 'frog_2', 'snake']\n", + "saddle : ['frog_1', 'turtle', 'scorpion', 'frog_2', 'crab']\n", + "Copier : ['rhinoceros', 'crab', 'ostrich', 'snake', 'shredder']\n", + "knife : ['turtle', 'knife', 'trilobite', 'rhinoceros', 'glass']\n", + "Fern : ['scorpion', 'frog_1', 'trilobite', 'crab', 'Fern']\n", + "knit_hat : ['frog_2', 'turtle', 'frog_1', 'owl', 'nan']\n", + "billiard : ['frog_2', 'snake', 'owl', 'frog_1', 'roulette']\n", + "canoe : ['turtle', 'frog_1', 'canoe', 'snake', 'swan']\n", + "deer : ['ostrich', 'trilobite', 'rhinoceros', 'frog_1', 'coin']\n", + "penguin : ['frog_2', 'frog_1', 'nan', 'owl', 'rhinoceros']\n", + "camera : ['trilobite', 'frog_1', 'rhinoceros', 'nan', 'coin']\n", + "skateboard : ['rhinoceros', 'crab', 'snake', 'ostrich', 'trilobite']\n", + "bike : ['bike', 'turtle', 'frog_1', 'trilobite', 'nan']\n", + "bus : ['bus', 'frog_2', 'nan', 'ostrich', 'frog_1']\n", + "centipede : ['ostrich', 'trilobite', 'rhinoceros', 'clip', 'snake']\n", + "centipede : ['frog_2', 'frog_1', 'turtle', 'snake', 'owl']\n", + "ostrich : ['ostrich', 'scorpion', 'rhinoceros', 'bowlingball', 'frisbee']\n", + "gun : ['frog_1', 'rhinoceros', 'scorpion', 'frog_2', 'nan']\n", + "horn : ['turtle', 'swan', 'ostrich', 'nan', 'horn']\n" + ] + } + ], + "source": [ + "# train corr\n", + "train_start_id = 0\n", + "for i, row in enumerate(pred_ranking[train_start_id:train_start_id+50]):\n", + " print(pred_labels[i+train_start_id], ':' ,[pred_labels[r] for r in row[:5]])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def calc_similarity(x, y):\n", + " batch_size = len(x)\n", + " gt_size = len(y)\n", + " \n", + " similarity = torch.empty(batch_size, gt_size).to('cuda')\n", + " for i in range(batch_size):\n", + " for j in range(gt_size):\n", + " similarity[i, j] = (x[i] @ y[j]) / max((x[i].norm() * y[j].norm()), 1e-8)\n", + " return similarity.cpu().numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TRAIN: Acc Top1/10 0.5924479365348816 0.94140625\n", + "TEST: Similarity Acc 0.9981326053889612\n" + ] + } + ], + "source": [ + "import torch\n", + "import sys\n", + "sys.path.append('../')\n", + "from meg_decoding.models import get_model, Classifier\n", + "\n", + "classifier = Classifier(None)\n", + "Z = torch.Tensor(train_pred_features).to('cuda')\n", + "\n", + "Y = []\n", + "for l in train_labels:\n", + " Y.append(train_image_features[l-1])\n", + "Y = np.stack(Y, axis=0)\n", + "Y = torch.Tensor(Y).to('cuda')\n", + "\n", + "trainTop1acc, trainTop10acc = classifier(Z, Y, test=False)\n", + "print('TRAIN: Acc Top1/10', trainTop1acc, trainTop10acc)\n", + "\n", + "similarity = calc_similarity(Z, Y)\n", + "acc_tmp = np.zeros(len(similarity))\n", + "for i in range(len(similarity)):\n", + " acc_tmp[i] = np.sum(similarity[i,:] < similarity[i,i]) / (len(similarity)-1)\n", + "print('TEST: Similarity Acc', np.mean(acc_tmp))" ] }, { @@ -67,246 +1189,292 @@ "metadata": {}, "outputs": [], "source": [ - "corr = np.corrcoef(train_cat_image_features_avg, test_image_features)\n", - "print(corr)" + "np.sum(similarity[i,:] < similarity[i,i])" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TEST(AVG): Acc Top1/10/K 0.03999999910593033 0.32 0.66\n", + "TEST(AVG): Similarity Acc 0.5604081632653062\n" + ] + } + ], + "source": [ + "Z = torch.Tensor(test_pred_features_avg).to('cuda')\n", + "\n", + "Y = []\n", + "for l in np.arange(1,51):# test_labels:\n", + " Y.append(test_image_features[l-1])\n", + "Y = np.stack(Y, axis=0)\n", + "Y = torch.Tensor(Y).to('cuda')\n", + "\n", + "testTop1acc, testTop10acc, testTopKacc = classifier(Z, Y, test=False, top_k=25)\n", + "print('TEST(AVG): Acc Top1/10/K', testTop1acc, testTop10acc, testTopKacc)\n", + "\n", + "\n", + "similarity = calc_similarity(Z, Y)\n", + "acc_tmp = np.zeros(len(similarity))\n", + "for i in range(len(similarity)):\n", + " acc_tmp[i] = np.sum(similarity[i,:] < similarity[i,i]) / (len(similarity)-1)\n", + "print('TEST(AVG): Similarity Acc', np.mean(acc_tmp))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TEST: Acc Top1/10/K 0.03999999910593033 0.12 0.5\n", + "TEST: Similarity Acc 0.5281632653061226\n", + "TEST: Acc Top1/10/K 0.019999999552965164 0.22 0.58\n", + "TEST: Similarity Acc 0.5485714285714286\n", + "TEST: Acc Top1/10/K 0.05999999865889549 0.14 0.42\n", + "TEST: Similarity Acc 0.46693877551020413\n", + "TEST: Acc Top1/10/K 0.019999999552965164 0.3 0.6\n", + "TEST: Similarity Acc 0.58\n", + "TEST: Acc Top1/10/K 0.0 0.24 0.66\n", + "TEST: Similarity Acc 0.5714285714285714\n", + "TEST: Acc Top1/10/K 0.03999999910593033 0.26 0.68\n", + "TEST: Similarity Acc 0.5893877551020408\n", + "{'testTop1acc': '0.030', 'testTop10acc': '0.213', 'testTopKacc': '0.573', 'test Similarityacc': '0.547'}\n" + ] + } + ], + "source": [ + "mean_Acc = {'testTop1acc': [], 'testTop10acc':[], 'testTopKacc':[],'test Similarityacc':[]}\n", + "for sess in range(6):\n", + " Z = []\n", + " Y = []\n", + " for l in range(1, 51): # test_labels:\n", + " target_id = np.where(test_labels==l)[0][sess]\n", + " Z.append(test_pred_features[target_id])\n", + " Y.append(test_image_features[l-1])\n", + " Y = np.stack(Y, axis=0)\n", + " Y = torch.Tensor(Y).to('cuda')\n", + " Z = np.stack(Z, axis=0)\n", + " Z = torch.Tensor(Z).to('cuda')\n", + "\n", + " testTop1acc, testTop10acc, testTopKacc = classifier(Z, Y, test=False, top_k=25)\n", + " print('TEST: Acc Top1/10/K', testTop1acc, testTop10acc, testTopKacc)\n", + "\n", + "\n", + " similarity = calc_similarity(Z, Y)\n", + " acc_tmp = np.zeros(len(similarity))\n", + " for i in range(len(similarity)):\n", + " acc_tmp[i] = np.sum(similarity[i,:] < similarity[i,i]) / (len(similarity)-1)\n", + " print('TEST: Similarity Acc', np.mean(acc_tmp))\n", + " mean_Acc['testTop1acc'].append(testTop1acc)\n", + " mean_Acc['testTop10acc'].append(testTop10acc)\n", + " mean_Acc['testTopKacc'].append(testTopKacc)\n", + " mean_Acc['test Similarityacc'].append(np.mean(acc_tmp))\n", + "\n", + "print({k:'{:.3f}'.format(np.mean(v)) for k, v in mean_Acc.items()})" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['ostrich',\n", - " 'frog_1',\n", - " 'frog_2',\n", - " 'turtle',\n", - " 'rhinoceros',\n", - " 'snake',\n", - " 'trilobite',\n", - " 'scorpion',\n", - " 'spider',\n", - " 'centipede',\n", - " 'horseshoe_crab',\n", - " 'bird_1',\n", - " 'duck',\n", - " 'kangaroo',\n", - " 'cochlea',\n", - " 'basket_clam',\n", - " 'octopus',\n", - " 'crested_ibis',\n", - " 'bird_2',\n", - " 'penguin',\n", - " 'dolphin',\n", - " 'dog_1',\n", - " 'dog_2',\n", - " 'bear',\n", - " 'grasshopper',\n", - " 'cockroach',\n", - " 'praying_mantis',\n", - " 'starfish',\n", - " 'porcupine',\n", - " 'horse',\n", - " 'zebra',\n", - " 'deer',\n", - " 'giraffe',\n", - " 'skunk',\n", - " 'person',\n", - " 'gorilla',\n", - " 'chimpanzee',\n", - " 'elephant',\n", - " 'raccoon',\n", - " 'airship',\n", - " 'babycar',\n", - " 'bag',\n", - " 'man_with_bat',\n", - " 'baseball_mitt',\n", - " 'basketball_ring',\n", - " 'bustab',\n", - " 'lighthouse',\n", - " 'binocular',\n", - " 'water_bowl',\n", - " 'bowling',\n", - " 'boxing',\n", - " 'camera',\n", - " 'tire',\n", - " 'corn_flakes',\n", - " 'knife',\n", - " 'cup',\n", - " 'cd',\n", - " 'keyboard',\n", - " 'tv',\n", - " 'telephone',\n", - " 'floppy',\n", - " 'dumbbell',\n", - " 'earphone',\n", - " 'fighter',\n", - " 'fire_engine',\n", - " 'flashlight',\n", - " 'horn',\n", - " 'frisbee',\n", - " 'frying_pan',\n", - " 'gas_station',\n", - " 'radio',\n", - " 'golfball',\n", - " 'guitar_pick',\n", - " 'shoes',\n", - " 'calculator',\n", - " 'harmonica',\n", - " 'piano',\n", - " 'helicopter',\n", - " 'balloon',\n", - " 'pool',\n", - " 'hourglass',\n", - " 'game',\n", - " 'submachine_gun',\n", - " 'canoe',\n", - " 'yacht',\n", - " 'knife2',\n", - " 'laptop',\n", - " 'lathe_machine',\n", - " 'lawn mower',\n", - " 'light_bulb',\n", - " 'xylophone',\n", - " 'Loudspeaker',\n", - " 'candlestick',\n", - " 'microscope',\n", - " 'auto_bike',\n", - " 'bike',\n", - " 'mouse_PC',\n", - " 'tie',\n", - " 'obelisk',\n", - " 'clip',\n", - " 'Personal_Digital_Assistant',\n", - " 'Copier',\n", - " 'flower_pot',\n", - " 'billiard',\n", - " 'projector',\n", - " 'antenna',\n", - " 'refrigerator',\n", - " 'gun',\n", - " 'gun2',\n", - " 'roulette',\n", - " 'saddle',\n", - " 'bus',\n", - " 'driver',\n", - " 'segway',\n", - " 'telescope',\n", - " 'shirt',\n", - " 'skateboard',\n", - " 'building',\n", - " 'soccer_ball',\n", - " 'can',\n", - " 'glass',\n", - " 'boat',\n", - " 'silver',\n", - " 'stearing',\n", - " 'pedal',\n", - " 'sward',\n", - " 'injection',\n", - " 'teapot',\n", - " 'telephonebox',\n", - " 'tennis_ball',\n", - " 'tennis',\n", - " 'tent',\n", - " 'astronomical_telescope',\n", - " 'toaster',\n", - " 'toaster2',\n", - " 'treadmill',\n", - " 'tricycle',\n", - " 'TuningFork',\n", - " 'watch',\n", - " 'windmill',\n", - " 'bottle',\n", - " 'knit_hat',\n", - " 'tomato',\n", - " 'fungi',\n", - " 'watermelon',\n", - " 'grape',\n", - " 'sunflower',\n", - " 'pinetree',\n", - " 'Fern',\n", - " 'bonsai']" + "array([[-0.02829436, 0.021185 , 0.03548387, ..., -0.03496352,\n", + " -0.0449915 , -0.03700984],\n", + " [ 0.00924444, 0.0474132 , 0.048658 , ..., -0.00230785,\n", + " 0.02518838, 0.04114052],\n", + " [ 0.03573305, 0.10414703, 0.09177056, ..., 0.03593158,\n", + " 0.04384382, 0.10292213],\n", + " ...,\n", + " [-0.00134944, 0.01704587, 0.03670729, ..., -0.0302972 ,\n", + " -0.0075427 , -0.0084782 ],\n", + " [ 0.03754435, 0.05194615, 0.06902866, ..., 0.00701771,\n", + " 0.00141289, -0.0053163 ],\n", + " [ 0.01080816, 0.04355977, 0.03786531, ..., 0.00411169,\n", + " -0.00474188, 0.03192064]], dtype=float32)" ] }, - "execution_count": 14, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "train_cat" + "similarity" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "similarity = calc_similarity(Z, Y)\n", + "plt.imshow(similarity)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.04108303 0.04088428 0.03548387 ... -0.05713566 -0.06061254\n", + " -0.06656156]\n", + " [ 0.05157361 0.048658 0.0474132 ... -0.02635343 -0.02831518\n", + " -0.0399184 ]\n", + " [ 0.10414703 0.10292213 0.09177056 ... -0.00605685 -0.01238619\n", + " -0.01508587]\n", + " ...\n", + " [ 0.04187033 0.03670729 0.03446947 ... -0.04422709 -0.05808438\n", + " -0.05826079]\n", + " [ 0.06902866 0.05455588 0.05391106 ... -0.01362184 -0.0174164\n", + " -0.02178394]\n", + " [ 0.04902737 0.04355977 0.03786531 ... -0.03734122 -0.04704913\n", + " -0.06549982]]\n" + ] + } + ], + "source": [ + "print(np.sort(similarity, axis=1)[:,::-1])\n", + "sorted_similarity = np.argsort(similarity, axis=1)[:, ::-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "horn : ['treadmill', 'hourglass', 'grasshopper', 'penguin', 'tent'] [1085, 644, 198, 155, 1052]\n", + "kangaroo : ['treadmill', 'grasshopper', 'kangaroo', 'horn', 'hourglass'] [1085, 198, 107, 532, 644]\n", + "grasshopper : ['kangaroo', 'horn', 'grasshopper', 'penguin', 'game'] [107, 532, 198, 155, 655]\n", + "tent : ['kangaroo', 'Fern', 'deer', 'bustab', 'kangaroo'] [109, 1192, 255, 362, 107]\n", + "bear : ['bear', 'camera', 'grasshopper', 'treadmill', 'game'] [187, 415, 198, 1085, 655]\n", + "refrigerator : ['grasshopper', 'bear', 'treadmill', 'game', 'tent'] [198, 187, 1085, 655, 1052]\n", + "treadmill : ['dog_1', 'teapot', 'penguin', 'centipede', 'hourglass'] [176, 1022, 155, 75, 644]\n", + "horseshoe_crab : ['hourglass', 'grasshopper', 'dog_1', 'clip', 'driver'] [644, 198, 176, 796, 900]\n", + "Loudspeaker : ['refrigerator', 'dog_1', 'Copier', 'grasshopper', 'game'] [849, 176, 813, 198, 655]\n", + "bustab : ['grasshopper', 'hourglass', 'refrigerator', 'horn', 'knife'] [198, 644, 849, 532, 436]\n", + "flashlight : ['horn', 'penguin', 'game', 'roulette', 'treadmill'] [532, 155, 655, 875, 1085]\n", + "keyboard : ['penguin', 'kangaroo', 'grasshopper', 'tent', 'submachine_gun'] [155, 107, 198, 1051, 663]\n", + "game : ['treadmill', 'hourglass', 'tent', 'grasshopper', 'dog_1'] [1085, 644, 1052, 198, 176]\n", + "hourglass : ['horn', 'horn', 'grasshopper', 'bear', 'teapot'] [532, 532, 198, 187, 1022]\n", + "dog_1 : ['grasshopper', 'kangaroo', 'horn', 'roulette', 'treadmill'] [198, 107, 532, 875, 1085]\n", + "dumbbell : ['penguin', 'grasshopper', 'game', 'treadmill', 'hourglass'] [155, 198, 653, 1085, 644]\n", + "crested_ibis : ['treadmill', 'grasshopper', 'game', 'light_bulb', 'refrigerator'] [1085, 198, 655, 714, 849]\n", + "kangaroo : ['penguin', 'grasshopper', 'bike', 'kangaroo', 'refrigerator'] [155, 198, 764, 107, 849]\n", + "teapot : ['refrigerator', 'dog_1', 'camera', 'hourglass', 'grasshopper'] [849, 176, 415, 644, 198]\n", + "game : ['grasshopper', 'knife', 'submachine_gun', 'dog_1', 'roulette'] [198, 436, 663, 176, 875]\n", + "Copier : ['billiard', 'grasshopper', 'knife', 'horn', 'bustab'] [827, 198, 436, 532, 362]\n", + "balloon : ['penguin', 'grasshopper', 'tent', 'game', 'dog_1'] [155, 198, 1052, 653, 176]\n", + "roulette : ['treadmill', 'billiard', 'knife', 'game', 'grasshopper'] [1085, 827, 436, 655, 198]\n", + "clip : ['grasshopper', 'billiard', 'kangaroo', 'tent', 'penguin'] [198, 827, 107, 1051, 155]\n", + "submachine_gun : ['horn', 'grasshopper', 'kangaroo', 'tent', 'bear'] [532, 198, 107, 1051, 187]\n", + "tricycle : ['giraffe', 'tent', 'tent', 'submachine_gun', 'balloon'] [257, 1051, 1052, 663, 631]\n", + "tent : ['grasshopper', 'kangaroo', 'gun', 'horn', 'camera'] [198, 107, 858, 532, 415]\n", + "driver : ['penguin', 'skateboard', 'light_bulb', 'treadmill', 'game'] [155, 933, 714, 1085, 653]\n", + "giraffe : ['billiard', 'treadmill', 'grasshopper', 'giraffe', 'horn'] [827, 1085, 198, 257, 532]\n", + "stearing : ['bike', 'penguin', 'knit_hat', 'skateboard', 'kangaroo'] [764, 155, 1131, 933, 107]\n", + "flashlight : ['kangaroo', 'grasshopper', 'treadmill', 'dumbbell', 'penguin'] [107, 198, 1085, 494, 155]\n", + "light_bulb : ['bear', 'grasshopper', 'horn', 'Fern', 'penguin'] [187, 198, 532, 1192, 155]\n", + "saddle : ['light_bulb', 'hourglass', 'tent', 'submachine_gun', 'dog_1'] [714, 644, 1052, 663, 176]\n", + "Copier : ['penguin', 'light_bulb', 'kangaroo', 'bike', 'treadmill'] [155, 714, 107, 764, 1085]\n", + "knife : ['bear', 'horn', 'treadmill', 'billiard', 'teapot'] [187, 532, 1085, 827, 1022]\n", + "Fern : ['bear', 'camera', 'grasshopper', 'refrigerator', 'Fern'] [187, 415, 198, 849, 1192]\n", + "knit_hat : ['treadmill', 'hourglass', 'bear', 'game', 'light_bulb'] [1085, 644, 187, 655, 714]\n", + "billiard : ['knife', 'treadmill', 'horn', 'grasshopper', 'bear'] [436, 1085, 532, 198, 187]\n", + "canoe : ['bear', 'horn', 'dog_1', 'roulette', 'penguin'] [187, 532, 176, 875, 155]\n", + "deer : ['game', 'penguin', 'bus', 'bike', 'kangaroo'] [655, 155, 891, 764, 109]\n", + "penguin : ['submachine_gun', 'treadmill', 'knife', 'bear', 'grasshopper'] [663, 1085, 436, 187, 198]\n", + "camera : ['refrigerator', 'bike', 'kangaroo', 'game', 'bear'] [849, 764, 107, 653, 187]\n", + "skateboard : ['penguin', 'treadmill', 'grasshopper', 'bear', 'giraffe'] [155, 1085, 198, 187, 257]\n", + "bike : ['bear', 'submachine_gun', 'treadmill', 'dog_1', 'light_bulb'] [187, 663, 1085, 176, 714]\n", + "bus : ['billiard', 'balloon', 'canoe', 'ostrich', 'bus'] [827, 631, 668, 8, 891]\n", + "centipede : ['hourglass', 'grasshopper', 'penguin', 'dog_1', 'game'] [644, 198, 155, 176, 653]\n", + "centipede : ['refrigerator', 'dog_1', 'bear', 'grasshopper', 'hourglass'] [849, 176, 187, 198, 644]\n", + "ostrich : ['treadmill', 'grasshopper', 'game', 'tent', 'knife'] [1085, 198, 655, 1052, 436]\n", + "gun : ['grasshopper', 'bus', 'canoe', 'kangaroo', 'roulette'] [198, 891, 668, 107, 875]\n", + "horn : ['submachine_gun', 'kangaroo', 'grasshopper', 'dumbbell', 'treadmill'] [663, 107, 198, 494, 1085]\n" + ] + } + ], + "source": [ + "for i, sim in enumerate(sorted_similarity[:50,:]):\n", + " print(train_pred_cats[i], ': ', [train_pred_cats[s] for s in sim[:5]], [train_labels[s] for s in sim[:5]])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['Goldfish',\n", - " 'owl',\n", - " 'nan',\n", - " 'duck',\n", - " 'swan',\n", - " 'nan',\n", - " 'crab',\n", - " 'orca',\n", - " 'leopard',\n", - " 'Bat',\n", - " 'fly',\n", - " 'butterfly',\n", - " 'goat',\n", - " 'camel',\n", - " 'lama',\n", - " 'airplane',\n", - " 'nan',\n", - " 'beermug',\n", - " 'bowlingball',\n", - " 'bulldozer',\n", - " 'Cannon',\n", - " 'boat',\n", - " 'coffin',\n", - " 'carrier',\n", - " 'man_with_hat',\n", - " 'hat',\n", - " 'nan',\n", - " 'fire_extinguisher',\n", - " 'helmet',\n", - " 'piano',\n", - " 'grave',\n", - " 'man_with_hammock',\n", - " 'harp',\n", - " 'iPod',\n", - " 'knob',\n", - " 'post',\n", - " 'mandarin',\n", - " 'kitchen',\n", - " 'tower',\n", - " 'nan',\n", - " 'coin',\n", - " 'shredder',\n", - " 'Snowmobile',\n", - " 'socks',\n", - " 'Stained glass',\n", - " 'nan',\n", - " 'umbrella',\n", - " 'videoplayer',\n", - " 'washing_machine',\n", - " 'buttery?']" + "" ] }, - "execution_count": 15, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "test_cat" + "similarity = calc_similarity(Z, torch.Tensor(train_image_features).cuda())\n", + "plt.imshow(similarity)" ] }, { @@ -319,7 +1487,7 @@ ], "metadata": { "kernelspec": { - "display_name": "basic_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -333,9 +1501,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.9" - }, - "orig_nbformat": 4 + "version": "3.8.5" + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/train_my_classifier.py b/train_my_classifier.py index ca7ca9a..056290e 100644 --- a/train_my_classifier.py +++ b/train_my_classifier.py @@ -247,7 +247,7 @@ def run(args: DictConfig) -> None: Z = brain_encoder(X, subject_idxs) # 0.96 GB - loss = loss_func(Z, label, train=True) + loss = loss_func(Z, label, train=True, debug_Y=None) # import pdb; pdb.set_trace() with torch.no_grad(): trainTop1acc, trainTop10acc = classifier(Z, Y) @@ -266,6 +266,7 @@ def run(args: DictConfig) -> None: loss.backward() optimizer.step() # get_grad(brain_encoder) + # break brain_encoder.eval() for batch in test_loader: @@ -344,7 +345,7 @@ def run(args: DictConfig) -> None: if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230418_sbj01_seq2stat') + args = compose(config_name='20230419_sbj01_seq2stat') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) run(args) From 674d53512d954431a61da68b77bdfe50e0e1b59e Mon Sep 17 00:00:00 2001 From: inoue26 Date: Thu, 20 Apr 2023 18:41:54 +0900 Subject: [PATCH 30/71] ADD: pairwise accuracy via similarity --- evaluate.py | 47 +++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 43 insertions(+), 4 deletions(-) diff --git a/evaluate.py b/evaluate.py index 5ccb907..be1c842 100644 --- a/evaluate.py +++ b/evaluate.py @@ -188,7 +188,7 @@ def get_average_features(predicted_y, val_index): test_pred_features_avg = np.concatenate(test_pred_features_avg, axis=0) return test_pred_features_avg, np.arange(len(test_labels_unique)) -def acc_category_identification(predicted_y, val_index, use_average=False): +def acc_via_correlation(predicted_y, val_index, use_average=False): # predicted_y: num_trials x 512 # val_index: num_trials val_index = val_index - 1 # ラベルは1始まり @@ -220,6 +220,46 @@ def acc_category_identification(predicted_y, val_index, use_average=False): cat_wise_acc = {i: np.mean(cat_wise_acc[i]) for i in range(len(image_features))} return np.mean(acc_tmp), cat_wise_acc +def acc_via_similarity(predicted_y, val_index, use_average=False): + # predicted_y: num_trials x 512 + # val_index: num_trials + val_index = val_index - 1 # ラベルは1始まり + image_features = np.load('./data/GOD/image_features.npy') # 50 x 512 + if use_average: + print('use average') + predicted_y, val_index = get_average_features(predicted_y, val_index) + num_images = len(image_features) + num_trials = len(predicted_y) + acc_tmp = np.zeros((num_trials, 1)) + + cat_wise_acc = {i:[] for i in range(len(image_features))} + # NOTE: avoid CUDA out of memory like this + similarity = np.empty(num_trials, num_images) + x = predicted_y + y = image_features + for i in range(num_trials): + for j in range(num_images): + similarity[i, j] = (x[i] @ y[j]) / max((x[i].norm() * y[j].norm()), 1e-8) + + image_id = val_index[i] + acc_tmp[i] = np.sum(similarity[i] < similarity[i, image_id]) / (num_images - 1) + cat_wise_acc[image_id].append(acc_tmp[i]) + + cat_wise_acc = {i: np.mean(cat_wise_acc[i]) for i in range(len(image_features))} + return np.mean(acc_tmp), cat_wise_acc + + + + + +def pairwise_category_identification(predicted_y, val_index, use_average=False): + corr_acc, corr_cat_wise_acc = acc_via_correlation(predicted_y, val_index, use_average=use_average) + print('corr_acc: {}'.format(corr_acc)) + print('ccorr_cat_wise_acc: ', corr_cat_wise_acc) + similarity_acc, similarity_cat_wise_acc = acc_via_similarity(predicted_y, val_index, use_average=use_average) + print('similarity_acc: {}'.format(similarity_acc)) + print('similarity_cat_wise_acc: ', similarity_cat_wise_acc) + def run_acc_from_corr(args, use_average=False): from meg_decoding.utils.reproducibility import seed_worker if args.reproducible: @@ -298,9 +338,8 @@ def run_acc_from_corr(args, use_average=False): pred_features = np.concatenate(pred_features, axis=0) labels = np.concatenate(labels, axis=0) print('total predictions: {}'.format(len(labels))) - acc_from_corr, cat_wise_acc = acc_category_identification(pred_features, labels, use_average=use_average) - print('acc_from_corr: {}'.format(acc_from_corr)) - print('category wise accuracy: ', cat_wise_acc) + pairwise_category_identification(pred_features, labels, use_average=use_average) + if __name__ == "__main__": from hydra import initialize, compose From 64725a41469282d71ee71698b9104952c935def7 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Thu, 20 Apr 2023 20:15:48 +0900 Subject: [PATCH 31/71] FIX: modulation of evaluation code --- evaluate.py | 8 ++++---- meg_decoding/dataclass/god.py | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/evaluate.py b/evaluate.py index be1c842..afd0ac5 100644 --- a/evaluate.py +++ b/evaluate.py @@ -234,12 +234,12 @@ def acc_via_similarity(predicted_y, val_index, use_average=False): cat_wise_acc = {i:[] for i in range(len(image_features))} # NOTE: avoid CUDA out of memory like this - similarity = np.empty(num_trials, num_images) + similarity = np.empty([num_trials, num_images]) x = predicted_y y = image_features for i in range(num_trials): for j in range(num_images): - similarity[i, j] = (x[i] @ y[j]) / max((x[i].norm() * y[j].norm()), 1e-8) + similarity[i, j] = (x[i] @ y[j]) / max(np.linalg.norm(x[i], ord=2) * np.linalg.norm(y[j], ord=2), 1e-8) image_id = val_index[i] acc_tmp[i] = np.sum(similarity[i] < similarity[i, image_id]) / (num_images - 1) @@ -260,7 +260,7 @@ def pairwise_category_identification(predicted_y, val_index, use_average=False): print('similarity_acc: {}'.format(similarity_acc)) print('similarity_cat_wise_acc: ', similarity_cat_wise_acc) -def run_acc_from_corr(args, use_average=False): +def run_pairwise_acc(args, use_average=False): from meg_decoding.utils.reproducibility import seed_worker if args.reproducible: os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" @@ -348,4 +348,4 @@ def run_acc_from_corr(args, use_average=False): if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) # run(args) - run_acc_from_corr(args, use_average=True) + run_pairwise_acc(args, use_average=False) diff --git a/meg_decoding/dataclass/god.py b/meg_decoding/dataclass/god.py index 2ab904c..48bc0bf 100644 --- a/meg_decoding/dataclass/god.py +++ b/meg_decoding/dataclass/god.py @@ -128,8 +128,8 @@ def forward(self, batch: List[tuple]): Y = torch.stack([torch.Tensor(item[1]) for item in batch]) # print('debug', [item[2] for item in batch]) subject_idx = torch.IntTensor([item[2] for item in batch]) - - X = baseline_correction_single(X, self.baseline_len_samp) + if self.baseline_len_samp > 0: + X = baseline_correction_single(X, self.baseline_len_samp) X = scaleAndClamp(X, self.clamp_lim, self.clamp) if self.return_label: label = torch.IntTensor([item[3] for item in batch]) From c5fd7de039d36be7c4ffb372e8b88b8aebb72bcb Mon Sep 17 00:00:00 2001 From: inoue26 Date: Thu, 20 Apr 2023 21:19:09 +0900 Subject: [PATCH 32/71] ADD: regression loss and similarity loss --- meg_decoding/models.py | 16 ++ meg_decoding/utils/loss.py | 24 ++- train_regression.py | 350 +++++++++++++++++++++++++++++++++++++ 3 files changed, 384 insertions(+), 6 deletions(-) create mode 100644 train_regression.py diff --git a/meg_decoding/models.py b/meg_decoding/models.py index c1c4d0f..aac70e1 100644 --- a/meg_decoding/models.py +++ b/meg_decoding/models.py @@ -19,6 +19,8 @@ def get_model(args): return BrainEncoder(args) elif args.model == 'brain_endcoder_seq2static': return BrainEncoderSeq2Static(args) + elif args.model == 'linear': + return LinearEncoder(args) else: raise ValueError('no model named {} is prepared'.format(args.model)) @@ -180,6 +182,20 @@ def forward(self, X): return X # ( B, 320, 256 ) +class LinearEncoder(nn.Module): + def __init__(self, args, input_size=20): + super(LinearEncoder, self).__init__() + self.linear = nn.Linear(in_features=input_size, out_features=512, bias=False) + self.scp = args.scp + + + def forward(self, X): + if self.scp: + X = X.mean(dim=-1) # X: batch x ch x time + return self.linear(X) + + + class BrainEncoder(nn.Module): def __init__(self, args): super(BrainEncoder, self).__init__() diff --git a/meg_decoding/utils/loss.py b/meg_decoding/utils/loss.py index 4526fa3..0985ac6 100644 --- a/meg_decoding/utils/loss.py +++ b/meg_decoding/utils/loss.py @@ -102,6 +102,10 @@ def __init__(self, args): self._criterion = nn.BCELoss(reduction=args.reduction) self.criterion_mode = 'binary_crossentropy' self.smmoth_value=0.5 + elif args.criterion == 'similarity_crossentropy': + print('use similarity_crossentropy') + self._criterion = nn.CrossEntropyLoss(reduction=args.reduction) + self.criterion_mode = 'similarity_crossentropy' else: raise ValueError() # self.targets = torch.zeros(size=(batch_size, )).long() # that's for the slow method @@ -126,21 +130,29 @@ def prepare_image_features(self): assert len(self.sorted_image_features_test) == 50 self.gt_size_test = 50 - def calculate_smooth_labeling(self, labels:np.ndarray, smmoth_value=0.1): + if self.criterion_mode == 'similarity_crossentropy': + self.similarity_matrix = self.compute_similarity(self.sorted_image_features, self.sorted_image_features) + self.similarity_matrix_test = self.compute_similarity(self.sorted_image_features_test, self.sorted_image_features_test) + def calculate_smooth_labeling(self, labels:np.ndarray, smmoth_value=0.1): # labels: batchsize:64 targets = torch.zeros([64, 1200],requires_grad=False).to(torch.float).to(device) - for i, l in enumerate(labels): - l_mod = l % self.same_category_length - targets[i, l_mod*self.same_category_length:(l_mod+1)*self.same_category_length] = smmoth_value - targets[i, l] = 1 + + if self.criterion_mode == 'crossentropy' and self.criterion_mode=='binary_crossentropy': + for i, l in enumerate(labels): + l_mod = l % self.same_category_length + targets[i, l_mod*self.same_category_length:(l_mod+1)*self.same_category_length] = smmoth_value + targets[i, l] = 1 + elif self.criterion_mode == 'similarity_crossentropy': + for i, l in enumerate(labels): + targets[i] = self.similarity_matrix[l] return targets def forward(self, x, labels, fast=True, return_logits=False, train=True, debug_Y=None): labels = labels-1 # labelsは1始まり batch_size = x.size(0) - + assert batch_size > 1, "Batch size must be greater than 1." if train: diff --git a/train_regression.py b/train_regression.py new file mode 100644 index 0000000..a834d12 --- /dev/null +++ b/train_regression.py @@ -0,0 +1,350 @@ +import os, sys, random +import numpy as np +import torch +import torch.nn as nn +from time import time +from tqdm import tqdm, trange +from termcolor import cprint +# import wandb + +from omegaconf import DictConfig, open_dict +import hydra +from hydra.utils import get_original_cwd + +from constants import device +# from speech_decoding.dataclass.brennan2018 import Brennan2018Dataset +# from speech_decoding.dataclass.gwilliams2022 import ( +# Gwilliams2022SentenceSplit, +# Gwilliams2022ShallowSplit, +# Gwilliams2022DeepSplit, +# Gwilliams2022Collator, +# ) +from torch.utils.data import DataLoader, RandomSampler, BatchSampler + +from meg_decoding.models import get_model, Classifier +from meg_decoding.utils.get_dataloaders import get_dataloaders, get_samplers +from meg_decoding.utils.loss import * +from meg_decoding.dataclass.god import GODDatasetBase, GODCollator +from meg_decoding.utils.loggers import Pickleogger +from meg_decoding.utils.vis_grad import get_grad + + +def run(args: DictConfig) -> None: + + from meg_decoding.utils.reproducibility import seed_worker + # NOTE: We do need it (IMHO). + if args.reproducible: + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + torch.use_deterministic_algorithms(True) + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + g = torch.Generator() + g.manual_seed(0) + seed_worker = seed_worker + else: + g = None + seed_worker = None + + pkl_logger = Pickleogger(os.path.join(args.save_root, 'runs')) + + # with open_dict(args): + # args.root_dir = get_original_cwd() + cprint(f"Current working directory : {os.getcwd()}") + cprint(args, color="white") + + # ----------------------- + # Dataloader + # ----------------------- + # NOTE: Segmentation should always be by word onsets, not just every 3 seconds + if args.dataset == "Gwilliams2022": + + if args.split_mode == "sentence": + + train_set = Gwilliams2022SentenceSplit(args) + test_set = Gwilliams2022SentenceSplit(args, train_set.test_word_idxs_dict) + + assert train_set.num_subjects == test_set.num_subjects + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + elif args.split_mode == "shallow": + + dataset = Gwilliams2022ShallowSplit(args) + + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(dataset.Y.shape[0] * args.split_ratio) + test_size = dataset.Y.shape[0] - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + + elif args.split_mode == "deep": + + train_set = Gwilliams2022DeepSplit(args, train=True) + test_set = Gwilliams2022DeepSplit(args, train=False) + assert train_set.num_subjects == test_set.num_subjects + + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + cprint(f"Test segments: {test_size}", "cyan") + + if args.use_sampler: + # NOTE: currently not supporting reproducibility + train_loader, test_loader = get_samplers( + train_set, + test_set, + args, + test_bsz=test_size, + collate_fn=Gwilliams2022Collator(args), + ) + else: + # FIXME: maybe either get rid of reproducibility, or remove this? + if args.reproducible: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, seed_worker, g, test_bsz=test_size + ) + else: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, test_bsz=test_size + ) + + elif args.dataset == "Brennan2018": + # NOTE: takes an optional debug param force_recompute to pre-process the EEG even if it exists + dataset = Brennan2018Dataset(args) + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(len(dataset) * args.split_ratio) + test_size = len(dataset) - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + cprint( + f"Number of samples: {len(train_set)} (train), {len(test_set)} (test)", color="blue", + ) + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, g, seed_worker, test_bsz=test_size + ) + + elif args.dataset == "GOD": + train_dataset = GODDatasetBase(args, 'train', return_label=True) + val_dataset = GODDatasetBase(args, 'val', return_label=True) + with open_dict(args): + args.num_subjects = train_dataset.num_subjects + print('num subject is {}'.format(args.num_subjects)) + + + if args.use_sampler: + test_size = 50# 重複サンプルが存在するのでval_dataset.Y.shape[0] + train_loader, test_loader = get_samplers( + train_dataset, + val_dataset, + args, + test_bsz=test_size, + collate_fn=GODCollator(args, return_label=True),) + + else: + train_loader = DataLoader( + train_dataset, + batch_size= args.batch_size, + drop_last=True, + shuffle=True, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + test_loader = DataLoader( + val_dataset, + batch_size=50, # args.batch_size, + drop_last=True, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + + else: + raise ValueError("Unknown dataset") + + if args.use_wandb: + wandb.config = {k: v for k, v in dict(args).items() if k not in ["root_dir", "wandb"]} + wandb.init( + project=args.wandb.project, + entity=args.wandb.entity, + config=wandb.config, + save_code=True, + ) + wandb.run.name = args.wandb.run_name + "_" + args.split_mode + wandb.run.save() + + # --------------------- + # Models + # --------------------- + brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) + + classifier = Classifier(args) + + # --------------- + # Loss + # --------------- + loss_func = torch.nn.MSELoss(reduction="mean") + # -------------------- + # Optimizer + # -------------------- + optimizer = torch.optim.Adam( + brain_encoder.parameters(), lr=float(args.lr), + ) + + if args.lr_scheduler == "cosine": + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, T_max=args.epochs, eta_min=args.lr * 0.1 + ) + elif args.lr_scheduler == "multistep": + scheduler = torch.optim.lr_scheduler.MultiStepLR( + optimizer, + milestones=[int(m * args.epochs) for m in args.lr_multistep_mlstns], + gamma=args.lr_step_gamma, + ) + else: + scheduler = None + + # ====================================== + best_acc = 0 + pbar = tqdm(range(args.epochs)) + for epoch in pbar: + pbar.set_description("training {}/{} epoch".format(epoch, args.epochs)) + train_losses = [] + test_losses = [] + trainTop1accs = [] + trainTop10accs = [] + testTop1accs = [] + testTop10accs = [] + + brain_encoder.train() + pbar2 = tqdm(train_loader) + for i, batch in enumerate(pbar2): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, label = batch + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y, label = X.to(device), Y.to(device), label.to(device) + + Z = brain_encoder(X, subject_idxs) # 0.96 GB + + # L2 regularization + l2 = torch.tensor(0., requires_grad=True) + for w in brain_encoder.parameters(): + l2 = l2 + torch.norm(w)**2 + l2 = args.l2_weight * l2 + loss = loss_func(Z, Y) + # import pdb; pdb.set_trace() + with torch.no_grad(): + trainTop1acc, trainTop10acc = classifier(Z, Y) + + train_losses.append(loss.item()) + trainTop1accs.append(trainTop1acc) + trainTop10accs.append(trainTop10acc) + + pbar.set_description("training {}/{} iters Train/Loss: {}, Train/L2Loss: {}, Train/Top1Acc: {}, Train/Top10Acc: {}".format(i, len(train_loader), loss.item(), l2.item(), trainTop1acc, trainTop10acc)) + optimizer.zero_grad() + total_loss = loss + l2 + total_loss.backward() + optimizer.step() + # get_grad(brain_encoder) + # break + + brain_encoder.eval() + for batch in test_loader: + + with torch.no_grad(): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, label = batch + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y, label = X.to(device), Y.to(device), label.to(device) + + Z = brain_encoder(X, subject_idxs) # 0.96 GB + + loss = loss_func(Z, Y) + + testTop1acc, testTop10acc = classifier(Z, Y, test=True) # ( 250, 1024, 360 ) + + test_losses.append(loss.item()) + testTop1accs.append(testTop1acc) + testTop10accs.append(testTop10acc) + + print( + f"Ep {epoch}/{args.epochs} | ", + f"train l: {np.mean(train_losses):.3f} | ", + f"test l: {np.mean(test_losses):.3f} | ", + f"trainTop10acc: {np.mean(trainTop10accs):.3f} | ", + f"testTop10acc: {np.mean(testTop10accs):.3f} | ", + f"lr: {optimizer.param_groups[0]['lr']:.5f}", + f"temp: {0}", + ) + pkl_logger.log({ + "epoch": epoch, + "train_loss": np.mean(train_losses), + "test_loss": np.mean(test_losses), + "trainTop1acc": np.mean(trainTop1accs), + "trainTop10acc": np.mean(trainTop10accs), + "testTop1acc": np.mean(testTop1accs), + "testTop10acc": np.mean(testTop10accs), + "lrate": optimizer.param_groups[0]["lr"], + "temp":0, + }, 'logs') + + if args.use_wandb: + performance_now = { + "epoch": epoch, + "train_loss": np.mean(train_losses), + "test_loss": np.mean(test_losses), + "trainTop1acc": np.mean(trainTop1accs), + "trainTop10acc": np.mean(trainTop10accs), + "testTop1acc": np.mean(testTop1accs), + "testTop10acc": np.mean(testTop10accs), + "lrate": optimizer.param_groups[0]["lr"], + "temp": 0, + } + wandb.log(performance_now) + + if scheduler is not None: + scheduler.step() + + savedir = os.path.join(args.save_root, 'weights') + last_weight_file = os.path.join(savedir, "model_last.pt") + torch.save(brain_encoder.state_dict(), last_weight_file) + print('model is saved as ', last_weight_file) + if best_acc < np.mean(testTop10accs): + best_weight_file = os.path.join(savedir, "model_best.pt") + torch.save(brain_encoder.state_dict(), best_weight_file) + best_acc = np.mean(testTop10accs) + print('best model is updated !!, {}'.format(best_acc), best_weight_file) + + + +if __name__ == "__main__": + from hydra import initialize, compose + with initialize(version_base=None, config_path="../configs/"): + args = compose(config_name='20230419_sbj01_seq2stat') + if not os.path.exists(os.path.join(args.save_root, 'weights')): + os.makedirs(os.path.join(args.save_root, 'weights')) + run(args) From 3a0d28adc0b2a2847241cdc695b7ed2eae01db43 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Thu, 20 Apr 2023 21:49:08 +0900 Subject: [PATCH 33/71] MOD: LinearEmcoder input args --- meg_decoding/models.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/meg_decoding/models.py b/meg_decoding/models.py index aac70e1..ae79a08 100644 --- a/meg_decoding/models.py +++ b/meg_decoding/models.py @@ -183,8 +183,9 @@ def forward(self, X): class LinearEncoder(nn.Module): - def __init__(self, args, input_size=20): + def __init__(self, args): super(LinearEncoder, self).__init__() + input_size = args.cahnnel_size self.linear = nn.Linear(in_features=input_size, out_features=512, bias=False) self.scp = args.scp From f0f8bc03f69cdf4ac70d50d9624a875f5b13fb2b Mon Sep 17 00:00:00 2001 From: inoue26 Date: Thu, 20 Apr 2023 21:58:15 +0900 Subject: [PATCH 34/71] MOD --- evaluate.py | 2 +- train_regression.py | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/evaluate.py b/evaluate.py index afd0ac5..5f48d10 100644 --- a/evaluate.py +++ b/evaluate.py @@ -311,7 +311,7 @@ def run_pairwise_acc(args, use_average=False): weight_dir = os.path.join(args.save_root, 'weights') last_weight_file = os.path.join(weight_dir, "model_last.pt") - best_weight_file = os.path.join(weight_dir, "model_best.pt") + best_weight_file = os.path.join(weight_dir, "model_last.pt") if os.path.exists(best_weight_file): brain_encoder.load_state_dict(torch.load(best_weight_file)) print('weight is loaded from ', best_weight_file) diff --git a/train_regression.py b/train_regression.py index a834d12..ec6fde3 100644 --- a/train_regression.py +++ b/train_regression.py @@ -190,6 +190,7 @@ def run(args: DictConfig) -> None: # --------------------- # Models # --------------------- + args.channel_size = train_dataset.X.shape[1] brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) classifier = Classifier(args) @@ -344,7 +345,7 @@ def run(args: DictConfig) -> None: if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230419_sbj01_seq2stat') + args = compose(config_name='20230420_sbj01_linear') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) run(args) From 96ffa8828859d1e2aec94f36220e5339444e269f Mon Sep 17 00:00:00 2001 From: inoue26 Date: Fri, 21 Apr 2023 10:17:54 +0900 Subject: [PATCH 35/71] FIX: roi ch diff between matlab and python --- evaluate.py | 6 +- examples/kamitani_regression.py | 380 ++++++++++++++++++++++++++ examples/view_training_curve.py | 4 +- meg_decoding/kamitani_lab/ml.py | 37 +++ meg_decoding/kamitani_lab/preproc.py | 54 ++++ meg_decoding/kamitani_lab/slir.py | 277 +++++++++++++++++++ meg_decoding/kamitani_lab/stats.py | 111 ++++++++ meg_decoding/matlab_utils/load_meg.py | 3 +- meg_decoding/models.py | 19 +- meg_decoding/utils/loss.py | 25 +- train_my_classifier.py | 22 +- 11 files changed, 919 insertions(+), 19 deletions(-) create mode 100644 examples/kamitani_regression.py create mode 100644 meg_decoding/kamitani_lab/ml.py create mode 100644 meg_decoding/kamitani_lab/preproc.py create mode 100644 meg_decoding/kamitani_lab/slir.py create mode 100644 meg_decoding/kamitani_lab/stats.py diff --git a/evaluate.py b/evaluate.py index 5f48d10..c1d6439 100644 --- a/evaluate.py +++ b/evaluate.py @@ -307,7 +307,11 @@ def run_pairwise_acc(args, use_average=False): else: raise ValueError("Unknown dataset") # assert len(test_loader) == 1 + if hasattr(args, 'channel_size'): + args.channel_size = train_dataset.X.shape[1] + brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) + weight_dir = os.path.join(args.save_root, 'weights') last_weight_file = os.path.join(weight_dir, "model_last.pt") @@ -344,7 +348,7 @@ def run_pairwise_acc(args, use_average=False): if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230419_sbj01_seq2stat') + args = compose(config_name='20230420_sbj01_linear') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) # run(args) diff --git a/examples/kamitani_regression.py b/examples/kamitani_regression.py new file mode 100644 index 0000000..f499d5f --- /dev/null +++ b/examples/kamitani_regression.py @@ -0,0 +1,380 @@ +'''Generic Object Decoding: Feature prediction +Analysis summary +---------------- +- Learning method: Sparse linear regression +- Preprocessing: Normalization and voxel selection +- Data: GenericDecoding_demo +- Results format: Pandas dataframe +''' + + +from __future__ import print_function + +import os +import sys +import pickle +from itertools import product +from time import time + +import numpy as np +import pandas as pd +from scipy import stats + +from meg_decoding.kamitani_lab.slir import SparseLinearRegression +from sklearn.linear_model import LinearRegression # For quick demo + +from meg_decoding.kamitani_lab.ml import add_bias +from meg_decoding.kamitani_lab.preproc import select_top +from meg_decoding.kamitani_lab.stats import corrcoef + +from meg_decoding.matlab_utils.load_meg import get_meg_data, roi, time_window, get_baseline +import tqdm +import hydra + + +def prepare_dataset(args, split): + DATAROOT = args.data_root + processed_meg_path_pattern = os.path.join(DATAROOT, '{sub}/mat/{name}') + label_path_pattern = os.path.join(DATAROOT, '{sub}/labels/{name}') + trigger_meg_path_pattern = os.path.join(DATAROOT, '{sub}/trigger/{name}') + processed_rest_meg_path_pattern = os.path.join(DATAROOT, '{sub}/mat/{name}') + + sub_list = list(args.subjects.keys()) + sub_id_map = {s:i for i, s in enumerate(sub_list)} + + roi_channels = roi(args) + + def epoching(meg, window, preprocess_pipeline=[]): + assert len(labels) == len(window) + # n_epochs x n_chs x time_smaples + meg_epochs = np.zeros([len(window), len(meg), window[0][1]-window[0][0]]) + for i, w in enumerate(window): + window_meg = meg[:, w[0]:w[1]] # ch x time_samples + for func_ in preprocess_pipeline: + window_meg = func_(window_meg) + meg_epochs[i] = window_meg + return meg_epochs + + meg_epochs = [] + sub_epochs = [] + label_epochs = [] + image_feature_epochs = [] + rest_mean, rest_std = None, None + pbar = tqdm.tqdm(sub_list) + for sub in pbar: + pbar.set_description("load subject data -- current: {}".format(sub)) + fs = args.subjects[sub]['fs'] + for meg_name, label_name, trigger_name, rest_name in zip(args.subjects[sub][split]['mat'], args.subjects[sub][split]['labels'], args.subjects[sub][split]['trigger'], args.subjects[sub][split]['rest']): + processed_meg_path = processed_meg_path_pattern.format(sub=sub, name=meg_name) + label_path = label_path_pattern.format(sub=sub, name=label_name) + trigger_path = trigger_meg_path_pattern.format(sub=sub, name=trigger_name) + processed_rest_meg_path = processed_rest_meg_path_pattern.format(sub=sub, name=rest_name) + if args.z_scoring: + print('z_scoring start') + rest_mean, rest_std = get_baseline(processed_rest_meg_path, fs, args.rest_duration) + MEG_Data, image_features, labels, triggers = get_meg_data(processed_meg_path, label_path, trigger_path, rest_mean=rest_mean, rest_std=rest_std, split=split) + ROI_MEG_Data = MEG_Data[roi_channels, :] # num_roi_channels x time_samples + # if args.preprocs.brain_filter is not None: + # brain_filter_low = args.preprocs.brain_filter[0] + # brain_filter_high = args.preprocs.brain_filter[1] + # ROI_MEG_Data = mne.filter.filter_data(ROI_MEG_Data, sfreq=fs, l_freq=brain_filter_low, h_freq=brain_filter_high,) + # print(f'band path filter: {brain_filter_low}-{brain_filter_high}') + # if args.preprocs.brain_resample_rate is not None: + # ROI_MEG_Data = mne.filter.resample(ROI_MEG_Data, down=fs / args.preprocs.brain_resample_rate) + # print('resample {} to {} Hz'.format(fs,args.preprocs.brain_resample_rate)) + # window = time_window(args, triggers, args.preprocs.brain_resample_rate) + # else: + # window = time_window(args, triggers, fs) + window = time_window(args, triggers, fs) + ROI_MEG_epochs = epoching(ROI_MEG_Data, window) + + meg_epochs.append(ROI_MEG_epochs) # array [epoch x ch x time_stamp] + sub_epochs+=[sub_id_map[sub]] * len(ROI_MEG_epochs) # list [epoch] + label_epochs.append(labels) # array [epoch] + image_feature_epochs.append(image_features) # array [epoch x dim] + meg_epochs = np.concatenate(meg_epochs, axis=0) + label_epochs = np.concatenate(label_epochs, axis=0) + image_feature_epochs = np.concatenate(image_feature_epochs, axis=0) + print('dataset is created. size is ', meg_epochs.shape) + return meg_epochs, sub_epochs, label_epochs, image_feature_epochs + +@hydra.main(version_base=None, config_path="../../configs", config_name="20230420_sbj01_linear.yaml") +def main_meg(args): + ## prepare dataset + train_X, train_subs, train_label, train_Y = prepare_dataset(args, split='train') + test_X, test_subs, test_label, test_Y = prepare_dataset(args, split='test') + + ## preprocess + pass + ## feature prediction + pred_y, true_y = feature_prediction(train_X, train_Y, + test_X, test_Y, + n_voxel=20, + n_iter=200) + + +# Main ################################################################# + +def main(): + # Settings --------------------------------------------------------- + + # Data settings + subjects = config.subjects + rois = config.rois + num_voxel = config.num_voxel + + image_feature = config.image_feature_file + features = config.features + + n_iter = 200 + + results_dir = config.results_dir + + # Misc settings + analysis_basename = os.path.basename(__file__) + + # Load data -------------------------------------------------------- + print('----------------------------------------') + print('Loading data') + + data_all = {} + for sbj in subjects: + if len(subjects[sbj]) == 1: + data_all[sbj] = bdpy.BData(subjects[sbj][0]) + else: + # Concatenate data + suc_cols = ['Run', 'Block'] + data_all[sbj] = concat_dataset([bdpy.BData(f) for f in subjects[sbj]], + successive=suc_cols) + + data_feature = bdpy.BData(image_feature) + + # Add any additional processing to data here + + # Initialize directories ------------------------------------------- + makedir_ifnot(results_dir) + makedir_ifnot('tmp') + + # Analysis loop ---------------------------------------------------- + print('----------------------------------------') + print('Analysis loop') + + for sbj, roi, feat in product(subjects, rois, features): + print('--------------------') + print('Subject: %s' % sbj) + print('ROI: %s' % roi) + print('Num voxels: %d' % num_voxel[roi]) + print('Feature: %s' % feat) + + # Distributed computation + analysis_id = analysis_basename + '-' + sbj + '-' + roi + '-' + feat + results_file = os.path.join(results_dir, analysis_id + '.pkl') + + if os.path.exists(results_file): + print('%s is already done. Skipped.' % analysis_id) + continue + + dist = DistComp(lockdir='tmp', comp_id=analysis_id) + if dist.islocked(): + print('%s is already running. Skipped.' % analysis_id) + continue + + dist.lock() + + # Prepare data + print('Preparing data') + dat = data_all[sbj] + + x = dat.select(rois[roi]) # Brain data + datatype = dat.select('DataType') # Data type + labels = dat.select('stimulus_id') # Image labels in brain data + + y = data_feature.select(feat) # Image features + y_label = data_feature.select('ImageID') # Image labels + + # For quick demo, reduce the number of units from 1000 to 100 + y = y[:, :100] + + y_sorted = get_refdata(y, y_label, labels) # Image features corresponding to brain data + + # Get training and test dataset + i_train = (datatype == 1).flatten() # Index for training + i_test_pt = (datatype == 2).flatten() # Index for perception test + i_test_im = (datatype == 3).flatten() # Index for imagery test + i_test = i_test_pt + i_test_im + + x_train = x[i_train, :] + x_test = x[i_test, :] + + y_train = y_sorted[i_train, :] + y_test = y_sorted[i_test, :] + + # Feature prediction + pred_y, true_y = feature_prediction(x_train, y_train, + x_test, y_test, + n_voxel=num_voxel[roi], + n_iter=n_iter) + + # Separate results for perception and imagery tests + i_pt = i_test_pt[i_test] # Index for perception test within test + i_im = i_test_im[i_test] # Index for imagery test within test + + pred_y_pt = pred_y[i_pt, :] + pred_y_im = pred_y[i_im, :] + + true_y_pt = true_y[i_pt, :] + true_y_im = true_y[i_im, :] + + # Get averaged predicted feature + test_label_pt = labels[i_test_pt, :].flatten() + test_label_im = labels[i_test_im, :].flatten() + + pred_y_pt_av, true_y_pt_av, test_label_set_pt \ + = get_averaged_feature(pred_y_pt, true_y_pt, test_label_pt) + pred_y_im_av, true_y_im_av, test_label_set_im \ + = get_averaged_feature(pred_y_im, true_y_im, test_label_im) + + # Get category averaged features + catlabels_pt = np.vstack([int(n) for n in test_label_pt]) # Category labels (perception test) + catlabels_im = np.vstack([int(n) for n in test_label_im]) # Category labels (imagery test) + catlabels_set_pt = np.unique(catlabels_pt) # Category label set (perception test) + catlabels_set_im = np.unique(catlabels_im) # Category label set (imagery test) + + y_catlabels = data_feature.select('CatID') # Category labels in image features + ind_catave = (data_feature.select('FeatureType') == 3).flatten() + + y_catave_pt = get_refdata(y[ind_catave, :], y_catlabels[ind_catave, :], catlabels_set_pt) + y_catave_im = get_refdata(y[ind_catave, :], y_catlabels[ind_catave, :], catlabels_set_im) + + # Prepare result dataframe + results = pd.DataFrame({'subject' : [sbj, sbj], + 'roi' : [roi, roi], + 'feature' : [feat, feat], + 'test_type' : ['perception', 'imagery'], + 'true_feature': [true_y_pt, true_y_im], + 'predicted_feature': [pred_y_pt, pred_y_im], + 'test_label' : [test_label_pt, test_label_im], + 'test_label_set' : [test_label_set_pt, test_label_set_im], + 'true_feature_averaged' : [true_y_pt_av, true_y_im_av], + 'predicted_feature_averaged' : [pred_y_pt_av, pred_y_im_av], + 'category_label_set' : [catlabels_set_pt, catlabels_set_im], + 'category_feature_averaged' : [y_catave_pt, y_catave_im]}) + + # Save results + makedir_ifnot(os.path.dirname(results_file)) + with open(results_file, 'wb') as f: + pickle.dump(results, f) + + print('Saved %s' % results_file) + + dist.unlock() + + +# Functions ############################################################ + +def feature_prediction(x_train, y_train, x_test, y_test, n_voxel=500, n_iter=200): + '''Run feature prediction + Parameters + ---------- + x_train, y_train : array_like [shape = (n_sample, n_voxel)] + Brain data and image features for training + x_test, y_test : array_like [shape = (n_sample, n_unit)] + Brain data and image features for test + n_voxel : int + The number of voxels + n_iter : int + The number of iterations + Returns + ------- + predicted_label : array_like [shape = (n_sample, n_unit)] + Predicted features + ture_label : array_like [shape = (n_sample, n_unit)] + True features in test data + ''' + + n_unit = y_train.shape[1] + + # Normalize brian data (x) + norm_mean_x = np.mean(x_train, axis=0) + norm_scale_x = np.std(x_train, axis=0, ddof=1) + + x_train = (x_train - norm_mean_x) / norm_scale_x + x_test = (x_test - norm_mean_x) / norm_scale_x + + # Feature prediction for each unit + print('Running feature prediction') + + y_true_list = [] + y_pred_list = [] + + for i in range(n_unit): + + print('Unit %03d' % (i + 1)) + start_time = time() + + # Get unit features + y_train_unit = y_train[:, i] + y_test_unit = y_test[:, i] + + # Normalize image features for training (y_train_unit) + norm_mean_y = np.mean(y_train_unit, axis=0) + std_y = np.std(y_train_unit, axis=0, ddof=1) + norm_scale_y = 1 if std_y == 0 else std_y + + y_train_unit = (y_train_unit - norm_mean_y) / norm_scale_y + + # Voxel selection + corr = corrcoef(y_train_unit, x_train, var='col') + + x_train_unit, voxel_index = select_top(x_train, np.abs(corr), n_voxel, axis=1, verbose=False) + x_test_unit = x_test[:, voxel_index] + + # Add bias terms + x_train_unit = add_bias(x_train_unit, axis=1) + x_test_unit = add_bias(x_test_unit, axis=1) + + # Setup regression + # For quick demo, use linaer regression + model = LinearRegression() + #model = SparseLinearRegression(n_iter=n_iter, prune_mode=1) + + # Training and test + try: + model.fit(x_train_unit, y_train_unit) # Training + y_pred = model.predict(x_test_unit) # Test + except: + # When SLiR failed, returns zero-filled array as predicted features + y_pred = np.zeros(y_test_unit.shape) + + # Denormalize predicted features + y_pred = y_pred * norm_scale_y + norm_mean_y + + y_true_list.append(y_test_unit) + y_pred_list.append(y_pred) + + print('Time: %.3f sec' % (time() - start_time)) + + # Create numpy arrays for return values + y_predicted = np.vstack(y_pred_list).T + y_true = np.vstack(y_true_list).T + + return y_predicted, y_true + + +def get_averaged_feature(pred_y, true_y, labels): + '''Return category-averaged features''' + + labels_set = np.unique(labels) + + pred_y_av = np.array([np.mean(pred_y[labels == c, :], axis=0) for c in labels_set]) + true_y_av = np.array([np.mean(true_y[labels == c, :], axis=0) for c in labels_set]) + + return pred_y_av, true_y_av, labels_set + + +# Run as a scirpt ###################################################### + +if __name__ == '__main__': + # To avoid any use of global variables, + # do nothing except calling main() here + main() \ No newline at end of file diff --git a/examples/view_training_curve.py b/examples/view_training_curve.py index 706e622..3c5eda5 100644 --- a/examples/view_training_curve.py +++ b/examples/view_training_curve.py @@ -61,7 +61,9 @@ def parse_data(filepath): if __name__ == '__main__': # filepath = '/home/yainoue/meg2image/results/20230417_sbj01_seq2stat/runs/2023-04-19 11:08:38.266128' # filepath = '/home/yainoue/meg2image/results/20230419_sbj01_seq2stat/runs/2023-04-20 01:39:30.648046' - filepath = '/home/yainoue/meg2image/results/20230419_sbj01_seq2stat2/runs/2023-04-20 13:06:53.825175' + # filepath = '/home/yainoue/meg2image/results/20230419_sbj01_seq2stat2/runs/2023-04-20 13:06:53.825175' + # filepath = '/home/yainoue/meg2image/results/20230420ß_sbj01_linear/runs/2023-04-20 22:04:01.525056' + filepath = '/home/yainoue/meg2image/results/20230420_sbj01_seq2stat2/runs/2023-04-21 02:04:47.889601' # filepath = '/home/yainoue/meg2image/results/20230413_sbj01/runs/2023-04-13 21:52:17.333087'#'home/yainoue/meg2image/results/test/2023-04-11 18:06:44.273986' # filepath = '/home/yainoue/meg2image/results/20230413_sbj01_seq2stat/runs/2023-04-16 19:21:02.983480' # filepath = '/home/yainoue/meg2image/results/20230412_mini/runs/2023-04-13 15:17:53.310665' diff --git a/meg_decoding/kamitani_lab/ml.py b/meg_decoding/kamitani_lab/ml.py new file mode 100644 index 0000000..3d42dc9 --- /dev/null +++ b/meg_decoding/kamitani_lab/ml.py @@ -0,0 +1,37 @@ +""" +This file is a part of BdPy +https://github.com/KamitaniLab/bdpy/blob/main/bdpy/ml/regress.py +""" + + +__all__ = ['add_bias'] + + +import numpy as np + + +def add_bias(x, axis=0): + """ + Add bias terms to x + Parameters + ---------- + x : array_like + Data matrix + axis : 0 or 1, optional + Axis in which bias terms are added (default: 0) + Returns + ------- + y : array_like + Data matrix with bias terms + """ + + if axis == 0: + vlen = x.shape[1] + y = np.concatenate((x, np.array([np.ones(vlen)])), axis=0) + elif axis == 1: + vlen = x.shape[0] + y = np.concatenate((x, np.array([np.ones(vlen)]).T), axis=1) + else: + raise ValueError('axis should be either 0 or 1') + + return y \ No newline at end of file diff --git a/meg_decoding/kamitani_lab/preproc.py b/meg_decoding/kamitani_lab/preproc.py new file mode 100644 index 0000000..a6752f7 --- /dev/null +++ b/meg_decoding/kamitani_lab/preproc.py @@ -0,0 +1,54 @@ +""" +select_top +This file is a part of BdPy. +""" + + +__all__ = ['select_top'] + + +import numpy as np + + +def select_top(data, value, num, axis=0, verbose=True): + """ + Select top `num` features of `value` from `data` + Parameters + ---------- + data : array + Data matrix + value : array_like + Vector of values + num : int + Number of selected features + Returns + ------- + selected_data : array + Selected data matrix + selected_index : array + Index of selected data + """ + + + + num_elem = data.shape[axis] + + value = np.array([-np.inf if np.isnan(a) else a for a in value]) + sorted_index = np.argsort(value)[::-1] + + rank = np.zeros(num_elem, dtype=np.int) + rank[sorted_index] = np.array(range(0, num_elem)) + + selected_index_bool = rank < num + + if axis == 0: + selected_data = data[selected_index_bool, :] + selected_index = np.array(range(0, num_elem), dtype=np.int)[selected_index_bool] + elif axis == 1: + selected_data = data[:, selected_index_bool] + selected_index = np.array(range(0, num_elem), dtype=np.int)[selected_index_bool] + else: + raise ValueError('Invalid axis') + + + return selected_data, selected_index \ No newline at end of file diff --git a/meg_decoding/kamitani_lab/slir.py b/meg_decoding/kamitani_lab/slir.py new file mode 100644 index 0000000..3464dac --- /dev/null +++ b/meg_decoding/kamitani_lab/slir.py @@ -0,0 +1,277 @@ +# coding:utf-8 +""" +SLiR (Sparse Linear Regression) +https://github.com/KamitaniLab/slir/blob/master/slir/slir.py +""" + + +import numpy as np +from sklearn.base import BaseEstimator, RegressorMixin + + +class SparseLinearRegression(BaseEstimator, RegressorMixin): + """ + Sparse Linear Regression (SLiR) + Parameters + ---------- + n_iter: int, optional + Maximum number of iterations (default: 200) + minval: float, optional + Threshold for minimum value + prune_mode: int, optional + Dimension reduction method (default: 1) + 0: do not reduce dimension + 1: reduce dimension based on A and lambda (slow but accurate) + 2: reduce dimension based on weights (fast but inaccurate) + prune_threshold: float, optional + Threshold for dimension reduction + converge_min_iter: int, optional + Num of iteration to initiate convergence test + converge_threshold: float, optional + Threshold of convergence test + verbose: boolean, optional, default False + Verbose mode when fitting the model. + verbose_skip: int, optional + Interval of verbose outputs during iteration + Attributes + ---------- + coef_: array, shape = (n_features,) + Coefficients of the regression model (mean of distribution). + alpha_ : float + Estimated precision of the noise. + lambda_ : array, shape = (n_features,) + Estimated precision of the weights. + Examples + -------- + >>> import slir + >>> model = slir.SparseLinearRegression() + >>> model.fit(x, y) + >>> predict = model.predict(x_test) + Notes + ----- + `compute_score` is omitted to reduce computation time. + """ + + def __init__(self, n_iter=200, minval=1.0e-15, + prune_mode=1, prune_threshold=1.0e-10, + converge_min_iter=100, converge_threshold=1.0e-10, + verbose=False, verbose_skip=10): + self.n_iter = n_iter + self.prune_mode = prune_mode + self.minval = minval + self.prune_threshold = prune_threshold + self.converge_min_iter = converge_min_iter + self.converge_threshold = converge_threshold + self.verbose = verbose + self.verbose_skip = verbose_skip + + if verbose: + print("SLiR (Sparse Linear Regression)") + + + def fit(self, X, y): + """ + Fit the ARD Regression model according to the given training data + (in the same wa as logistic.py in sklearn??). + Parameters + ---------- + X: array-like, shape = (n_samples, n_features) + y: array-like, shape = (n_samples) + Returns + ------- + self : returns an instance of self. + """ + #################### + # Check values + #################### + # Reshape 1d array label to 2d + if y.ndim == 1: + y = y.reshape((len(y), 1)) + + # Check size + sample_num = X.shape[0] + dim_num = X.shape[1] + label_type_num = y.shape[1] + if X.shape[0] != y.shape[0]: + raise ValueError('The number of samples are unmatched between x (%d) and y (%d).' % (X.shape[0], y.shape[0])) + + dim_num_org = dim_num + + # Transpose + X = X.transpose() + Y = y.transpose() + del y + + if self.verbose: + print("InputDim:%d/OutputLabels:%d/TrainNum:%d" % (dim_num, label_type_num, sample_num)) + + #################### + # Initialization + #################### + X_var = np.mean(X ** 2, axis=1) + Y_var = np.mean(Y ** 2, axis=1) + + # initialize alpha prior, weight, and noise variance + alpha_0 = 1.0 / np.mean(X_var) + SY0 = np.mean(Y_var) + if alpha_0 < self.minval: + A = self.minval * np.ones((1, dim_num)) + else: + A = alpha_0 * np.ones((1, dim_num)) + W = np.zeros((label_type_num, dim_num)) + SY = SY0 + + # prepare covariance of data and label + YX = np.dot(Y, X.transpose()) + YY = np.sum(Y ** 2, axis=1) + sumYY = np.sum(YY) # scalar + + # prepare covariance of X + if sample_num < dim_num: + XX = None + else: + XX = np.dot(X, X.transpose()) + + # Not pruned index list + self.valid_index_list = np.arange(dim_num) + + # prepare temporary variables + A_old = A[:, :] + dim_num_old = dim_num + + #################### + # Iterative procedure of ARDRegression + #################### + for i in range(self.n_iter): + if sample_num < dim_num: + XA = X.transpose() * A[0] + CC = np.dot(XA, X) + np.eye(sample_num) + XC = np.dot(X, np.linalg.pinv(CC)) + + # Update weight + W = YX * A[0] + W = W - np.dot(np.dot(W, XC), XA) + + # Update gain + G_A = A[0] * np.sum(X * XC, axis=1) + else: + # In the middle of iteration, this branch may be runned by the dimension reduction + if XX is None: + XX = np.dot(X, X.transpose()) + + # Update weight + SW = XX + np.diag(1.0 / A[0]) + inv_SW = np.linalg.pinv(SW) + W = np.dot(YX, inv_SW) + + # Update gain + G_A = np.diag(np.dot(XX, inv_SW)).transpose() + + # The sum of weight variance + WW = np.sum(W ** 2, axis=0) + + # Update noise variance + SY = (sumYY - np.sum(W * YX)) / (label_type_num * sample_num) + + # If noise variance is too small + if SY / SY0 < self.minval: + dY = Y - np.dot(W, X) + dYY = np.sum(dY ** 2, axis=1) + SY = (np.sum(dYY) + np.sum(WW / A)) / \ + (label_type_num * sample_num) + SY = np.max([SY, self.minval]) + print("*") + + # Check Gain value + G_A = np.maximum(G_A, self.minval) + + # Update alpha + try: + A = np.sqrt(A * (WW / SY) / (G_A * label_type_num)) + except: + print("Update error @ alpha") + + # Pruning dimensions + if self.prune_mode > 0: + # Prune the dimensions with the estimated precision of the + # weight over threshold + if self.prune_mode == 1: + A_all = A[0] / np.max(A) + # Prune the dimensions with the weight variance over threshold + elif self.prune_mode == 2: + A_all = WW / np.max(WW) + + # Prune + activate_index = A_all > self.prune_threshold + if np.sum(activate_index) < dim_num: + # Update dimension size + dim_num_old = dim_num + dim_num = np.sum(activate_index) + # Reduce the dimensions + A = A[:, activate_index] + W = W[:, activate_index] + X = X[activate_index, :] + YX = YX[:, activate_index] + if XX is not None: # sparce case: sample_num < dim_num + XX = XX[np.ix_(activate_index, activate_index)] + + # Update valid index list + self.valid_index_list = self.valid_index_list[activate_index] + + if len(self.valid_index_list) == 0: # Error + raise RuntimeError('All dimensions are pruned.') + + if self.verbose and (i + 1) % self.verbose_skip == 0: + err = SY / SY0 + print("Iter:%d, DimNum:%d, Error:%f" % (i + 1, dim_num, err)) + + # Check for convergence + if (i > self.converge_min_iter) & (dim_num == dim_num_old): + # Stop the iteration based on a max value of A diff + Adif = np.max(np.abs(A - A_old)) + if Adif < self.converge_threshold: + if self.verbose: + print("End. -- Iter:%d, DimNum:%d, Error:%f, AlphaConverged:%f" % (i, dim_num, err, Adif)) + break + + # Store A as A_old + A_old = A[:, :] + + # Not pruning dimensions + else: + A = np.maximum(A, self.minval) + + self.__A = A # alpha + self.__W = W # weight + + # Copy sklearn's coef_, lambda_ and alpha_ + # Set 0 the pruned dimensions + self.coef_ = np.zeros(dim_num_org) + self.lambda_ = np.zeros(dim_num_org) + # The coefficient of regression model + self.coef_[self.valid_index_list] = self.__W + # The estimated precisions of weights(coefficient) + self.lambda_[self.valid_index_list] = self.__A + self.alpha_ = SY # The estimated precision of noise + + return self + + def predict(self, X): + """ + Predict using the linear model + Parameters + ---------- + X: array-like, shape = (n_samples, n_features) + Returns + ------- + C : array, shape = (n_samples,) + Returns predicted values. + """ + # Transpose and reduce X + X = X.transpose()[self.valid_index_list, :] + + # Predict by inner-producting and adding average of label + C = np.dot(self.__W, X) + C = C.transpose().flatten() + + return C \ No newline at end of file diff --git a/meg_decoding/kamitani_lab/stats.py b/meg_decoding/kamitani_lab/stats.py new file mode 100644 index 0000000..0379628 --- /dev/null +++ b/meg_decoding/kamitani_lab/stats.py @@ -0,0 +1,111 @@ +""" +Functions dealing with correlation +This file is a part of BdPy. +""" + +__all__ = ['corrcoef', 'corrmat'] + + +import numpy as np +from numpy.matlib import repmat + + +def corrcoef(x, y, var='row'): + """ + Returns correlation coefficients between `x` and `y` + Parameters + ---------- + x, y : array_like + Matrix or vector + var : str, 'row' or 'col' + Specifying whether rows (default) or columns represent variables + Returns + ------- + r + Correlation coefficients + """ + + # Convert vectors to arrays + if x.ndim == 1: + x = np.array([x]) + + if y.ndim == 1: + y = np.array([y]) + + # Normalize x and y to row-var format + if var == 'row': + # 'rowvar=1' in np.corrcoef + + # Vertical vector --> horizontal + if x.shape[1] == 1: + x = x.T + if y.shape[1] == 1: + y = y.T + elif var == 'col': + # 'rowvar=0' in np.corrcoef + + # Horizontal vector --> vertical + if x.shape[0] == 1: + x = x.T + if y.shape[0] == 1: + y = y.T + + # Convert to rowvar=1 + x = x.T + y = y.T + else: + raise ValueError('Unknown var parameter specified') + + # Match size of x and y + if x.shape[0] == 1 and y.shape[0] != 1: + x = repmat(x, y.shape[0], 1) + + elif x.shape[0] != 1 and y.shape[0] == 1: + y = repmat(y, x.shape[0], 1) + + # Check size of normalized x and y + if x.shape != y.shape: + raise TypeError('Input matrixes size mismatch') + + # Get num variables + nvar = x.shape[0] + + # Get correlation + rmat = np.corrcoef(x, y, rowvar=1) + r = np.diag(rmat[:nvar, nvar:]) + + return r + + +def corrmat(x, y, var='row'): + """ + Returns correlation matrix between `x` and `y` + Parameters + ---------- + x, y : array_like + Matrix or vector + var : str, 'row' or 'col' + Specifying whether rows (default) or columns represent variables + Returns + ------- + rmat + Correlation matrix + """ + + # Fix x and y to represent variables in each row + if var == 'row': + pass + elif var == 'col': + x = x.T + y = y.T + else: + raise ValueError('Unknown var parameter specified') + + nobs = x.shape[1] + + # Subtract mean(a, axis=1) from a + submean = lambda a: a - np.matrix(np.mean(a, axis=1)).T + + cmat = (np.dot(submean(x), submean(y).T) / (nobs - 1)) / np.dot(np.matrix(np.std(x, axis=1, ddof=1)).T, np.matrix(np.std(y, axis=1, ddof=1))) + + return np.array(cmat) \ No newline at end of file diff --git a/meg_decoding/matlab_utils/load_meg.py b/meg_decoding/matlab_utils/load_meg.py index 609df9c..e2a6df4 100644 --- a/meg_decoding/matlab_utils/load_meg.py +++ b/meg_decoding/matlab_utils/load_meg.py @@ -113,8 +113,9 @@ def roi(args)->list: r = reg_subreg[0] s = reg_subreg[1] roi_channels += ch_region_info[r][s] + roi_channels = [r-1 for r in roi_channels] print('ROI: ', [reg for reg in region]) - print('channel: ', roi_channels) + print('channel (-1 is done because matlab starts from 1): ', roi_channels) print('num channels: ', len(roi_channels)) return roi_channels diff --git a/meg_decoding/models.py b/meg_decoding/models.py index ae79a08..2d5925f 100644 --- a/meg_decoding/models.py +++ b/meg_decoding/models.py @@ -185,14 +185,15 @@ def forward(self, X): class LinearEncoder(nn.Module): def __init__(self, args): super(LinearEncoder, self).__init__() - input_size = args.cahnnel_size - self.linear = nn.Linear(in_features=input_size, out_features=512, bias=False) + input_size = args.channel_size + self.linear = nn.Linear(in_features=input_size, out_features=512, bias=True) self.scp = args.scp - def forward(self, X): + def forward(self, X, subject_idxs): if self.scp: X = X.mean(dim=-1) # X: batch x ch x time + # import pdb; pdb.set_trace() return self.linear(X) @@ -250,6 +251,14 @@ def __init__(self, args): # NOTE: Do we need to adjust the accuracies for the dataset size? self.factor = 1 # self.batch_size / 241 + self.normalize_image_features = args.normalize_image_features + + def normalize_per_unit(self, tensor): + print('normalize image_feature along unit dim') + # array: n_samples x n_units(512) + tensor = tensor - torch.mean(tensor, 0, keepdim=True) + tensor = tensor / torch.std(tensor, 0, keepdim=True) + return tensor @torch.no_grad() def forward(self, Z: torch.Tensor, Y: torch.Tensor, test=False, top_k=None) -> torch.Tensor: @@ -259,6 +268,10 @@ def forward(self, Z: torch.Tensor, Y: torch.Tensor, test=False, top_k=None) -> t x = Z.view(batch_size, -1) y = Y.view(batch_size, -1) + + if self.normalize_image_features: + y = self.normalize_per_unit(y) + # x_ = rearrange(x, 'b f -> 1 b f') # y_ = rearrange(y, 'b f -> b 1 f') # similarity = torch.nn.functional.cosine_similarity(x_, y_, dim=-1) # ( B, B ) diff --git a/meg_decoding/utils/loss.py b/meg_decoding/utils/loss.py index 0985ac6..f6eba88 100644 --- a/meg_decoding/utils/loss.py +++ b/meg_decoding/utils/loss.py @@ -106,6 +106,7 @@ def __init__(self, args): print('use similarity_crossentropy') self._criterion = nn.CrossEntropyLoss(reduction=args.reduction) self.criterion_mode = 'similarity_crossentropy' + self.smmoth_value = None else: raise ValueError() # self.targets = torch.zeros(size=(batch_size, )).long() # that's for the slow method @@ -115,10 +116,11 @@ def __init__(self, args): self.temp = nn.Parameter(torch.tensor([float(args.init_temperature)])) else: self.temp = torch.tensor(float(args.init_temperature), requires_grad=False) - + self.normalize_image_features = args.normalize_image_features self.prepare_image_features() self.same_category_length = 8 + def prepare_image_features(self): sorted_image_features = np.load('./data/GOD/image_features_train.npy') self.sorted_image_features = torch.tensor(sorted_image_features, requires_grad=False).to(torch.float).to(device) @@ -130,10 +132,21 @@ def prepare_image_features(self): assert len(self.sorted_image_features_test) == 50 self.gt_size_test = 50 + if self.normalize_image_features: + self.sorted_image_features = self.normalize_per_unit(self.sorted_image_features) + self.sorted_image_features_test = self.normalize_per_unit(self.sorted_image_features_test) + if self.criterion_mode == 'similarity_crossentropy': self.similarity_matrix = self.compute_similarity(self.sorted_image_features, self.sorted_image_features) self.similarity_matrix_test = self.compute_similarity(self.sorted_image_features_test, self.sorted_image_features_test) + def normalize_per_unit(self, tensor): + print('normalize image_feature along unit dim') + # array: n_samples x n_units(512) + tensor = tensor - torch.mean(tensor, 0, keepdim=True) + tensor = tensor / torch.std(tensor, 0, keepdim=True) + return tensor + def calculate_smooth_labeling(self, labels:np.ndarray, smmoth_value=0.1): # labels: batchsize:64 targets = torch.zeros([64, 1200],requires_grad=False).to(torch.float).to(device) @@ -195,7 +208,15 @@ def forward(self, x, labels, fast=True, return_logits=False, train=True, debug_Y if self.criterion_mode == 'binary_crossentropy': # import pdb; pdb.set_trace() logits = torch.sigmoid(logits) # torch.exp(logits) / torch.exp(logits).sum(dim=-1, keepdim=True) - loss = self._criterion(logits, targets) # + self._criterion(logits.t(), targets.T)) / 2 + if self.criterion_mode == 'similarity_crossentropy': + if train: + positive_targets = torch.nn.Softmax(1)(targets *torch.exp(self.temp)) + # negative_targets = torch.nn.Softmax(1)(-targets) + loss = self._criterion(logits, positive_targets) + else: + loss = self._criterion(logits, targets) + else: + loss = self._criterion(logits, targets) # + self._criterion(logits.t(), targets.T)) / 2 if return_logits: return logits, loss diff --git a/train_my_classifier.py b/train_my_classifier.py index 056290e..5ca978e 100644 --- a/train_my_classifier.py +++ b/train_my_classifier.py @@ -244,13 +244,13 @@ def run(args: DictConfig) -> None: raise ValueError("Unexpected number of items from dataloader.") X, Y, label = X.to(device), Y.to(device), label.to(device) + with torch.cuda.amp.autocast(enabled=True): + Z = brain_encoder(X, subject_idxs) # 0.96 GB - Z = brain_encoder(X, subject_idxs) # 0.96 GB - - loss = loss_func(Z, label, train=True, debug_Y=None) - # import pdb; pdb.set_trace() - with torch.no_grad(): - trainTop1acc, trainTop10acc = classifier(Z, Y) + loss = loss_func(Z, label, train=True, debug_Y=None) + # import pdb; pdb.set_trace() + with torch.no_grad(): + trainTop1acc, trainTop10acc = classifier(Z, Y) train_losses.append(loss.item()) trainTop1accs.append(trainTop1acc) @@ -281,12 +281,12 @@ def run(args: DictConfig) -> None: raise ValueError("Unexpected number of items from dataloader.") X, Y, label = X.to(device), Y.to(device), label.to(device) + with torch.cuda.amp.autocast(enabled=True): + Z = brain_encoder(X, subject_idxs) # 0.96 GB - Z = brain_encoder(X, subject_idxs) # 0.96 GB - - loss = loss_func(Z, label, train=False) + loss = loss_func(Z, label, train=False) - testTop1acc, testTop10acc = classifier(Z, Y, test=True) # ( 250, 1024, 360 ) + testTop1acc, testTop10acc = classifier(Z, Y, test=True) # ( 250, 1024, 360 ) test_losses.append(loss.item()) testTop1accs.append(testTop1acc) @@ -345,7 +345,7 @@ def run(args: DictConfig) -> None: if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230419_sbj01_seq2stat') + args = compose(config_name='20230420_sbj01_seq2stat') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) run(args) From 20b832a605b316180ed9a3c52a9ad9648c4dd79b Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 24 Apr 2023 14:46:35 +0900 Subject: [PATCH 36/71] ADD: kamitani lab regression --- examples/calc_correcoeff.py | 56 +++ examples/kamitani_regression.py | 592 ++++++++++++++++++-------- examples/view_training_curve.py | 3 +- meg_decoding/dataclass/god.py | 10 + meg_decoding/matlab_utils/load_meg.py | 2 +- train_regression.py | 2 +- 6 files changed, 495 insertions(+), 170 deletions(-) create mode 100644 examples/calc_correcoeff.py diff --git a/examples/calc_correcoeff.py b/examples/calc_correcoeff.py new file mode 100644 index 0000000..12dafdf --- /dev/null +++ b/examples/calc_correcoeff.py @@ -0,0 +1,56 @@ +import pickle +import numpy as np +import matplotlib.pyplot as plt + +filename = '/home/yainoue/meg2image/results/20230421_sbj01_kamitani_regression/sbj01-0.25_0.45-0.531/ridge_regression.pkl' + +with open(filename, 'rb') as f: + data = pickle.load(f) + +def get_averaged_feature(pred_y, true_y, labels): + '''Return category-averaged features''' + + labels_set = np.unique(labels) + + pred_y_av = np.array([np.mean(pred_y[labels == c, :], axis=0) for c in labels_set]) + true_y_av = np.array([np.mean(true_y[labels == c, :], axis=0) for c in labels_set]) + + return pred_y_av, true_y_av, labels_set + + +preds = data['pred_y'] +gts = data['true_y'] +labels = data['test_label'] + +preds, gts, labels = get_averaged_feature(preds, gts, labels) +print(gts.shape, preds.shape) +corr_list = [] +for pred, gt in zip(preds, gts): + print(pred.shape, gt.shape) + src = np.stack([pred, gt], axis=0) + R = np.corrcoef(src) + print(R[0,1]) + corr_list.append(R[0,1]) +print(len(corr_list)) +print(np.mean(corr_list)) + +def clip_sigma(data): + std = data.std(-1) + mean = data.mean(-1) + data[data > mean + 3*std] = mean + 3*std + data[data < mean - 3*std] = mean - 3*std + return data + +ch=0 +gts[ch] = clip_sigma(gts[ch]) +preds[ch] = clip_sigma(preds[ch]) +fig, axes = plt.subplots(ncols=2, figsize=(12,6)) +axes[0].plot(np.arange(len(preds[ch])), gts[ch], label='gt') +axes[0].plot(np.arange(len(preds[ch])), preds[ch], label='pred') +axes[0].legend() +axes[0].set_title('corr coef: {:.3f}'.format(np.corrcoef(preds[ch], gts[ch])[0,1])) +axes[1].scatter(gts[ch], preds[ch]) +axes[1].set_xlabel('gt') +axes[1].set_ylabel('pred') +plt.savefig('/home/yainoue/meg2image/results/20230421_sbj01_kamitani_regression/calc.png') +import pdb; pdb.set_trace() \ No newline at end of file diff --git a/examples/kamitani_regression.py b/examples/kamitani_regression.py index f499d5f..d96a4e5 100644 --- a/examples/kamitani_regression.py +++ b/examples/kamitani_regression.py @@ -12,12 +12,12 @@ import os import sys +sys.path.append('.') import pickle from itertools import product from time import time - +import itertools import numpy as np -import pandas as pd from scipy import stats from meg_decoding.kamitani_lab.slir import SparseLinearRegression @@ -25,14 +25,17 @@ from meg_decoding.kamitani_lab.ml import add_bias from meg_decoding.kamitani_lab.preproc import select_top -from meg_decoding.kamitani_lab.stats import corrcoef +from meg_decoding.kamitani_lab.stats import corrcoef, corrmat from meg_decoding.matlab_utils.load_meg import get_meg_data, roi, time_window, get_baseline import tqdm +import random import hydra +import matplotlib.pyplot as plt +import pandas as pd -def prepare_dataset(args, split): +def prepare_dataset(args, split, manual_ch=None, onsets:dict=None): DATAROOT = args.data_root processed_meg_path_pattern = os.path.join(DATAROOT, '{sub}/mat/{name}') label_path_pattern = os.path.join(DATAROOT, '{sub}/labels/{name}') @@ -42,7 +45,7 @@ def prepare_dataset(args, split): sub_list = list(args.subjects.keys()) sub_id_map = {s:i for i, s in enumerate(sub_list)} - roi_channels = roi(args) + roi_channels = roi(args) if manual_ch is None else manual_ch # if onsets is specified. this setting is ignored. def epoching(meg, window, preprocess_pipeline=[]): assert len(labels) == len(window) @@ -73,20 +76,37 @@ def epoching(meg, window, preprocess_pipeline=[]): print('z_scoring start') rest_mean, rest_std = get_baseline(processed_rest_meg_path, fs, args.rest_duration) MEG_Data, image_features, labels, triggers = get_meg_data(processed_meg_path, label_path, trigger_path, rest_mean=rest_mean, rest_std=rest_std, split=split) - ROI_MEG_Data = MEG_Data[roi_channels, :] # num_roi_channels x time_samples - # if args.preprocs.brain_filter is not None: - # brain_filter_low = args.preprocs.brain_filter[0] - # brain_filter_high = args.preprocs.brain_filter[1] - # ROI_MEG_Data = mne.filter.filter_data(ROI_MEG_Data, sfreq=fs, l_freq=brain_filter_low, h_freq=brain_filter_high,) - # print(f'band path filter: {brain_filter_low}-{brain_filter_high}') - # if args.preprocs.brain_resample_rate is not None: - # ROI_MEG_Data = mne.filter.resample(ROI_MEG_Data, down=fs / args.preprocs.brain_resample_rate) - # print('resample {} to {} Hz'.format(fs,args.preprocs.brain_resample_rate)) - # window = time_window(args, triggers, args.preprocs.brain_resample_rate) - # else: - # window = time_window(args, triggers, fs) - window = time_window(args, triggers, fs) - ROI_MEG_epochs = epoching(ROI_MEG_Data, window) + if onsets is None: + ROI_MEG_Data = MEG_Data[roi_channels, :] # num_roi_channels x time_samples + # if args.preprocs.brain_filter is not None: + # brain_filter_low = args.preprocs.brain_filter[0] + # brain_filter_high = args.preprocs.brain_filter[1] + # ROI_MEG_Data = mne.filter.filter_data(ROI_MEG_Data, sfreq=fs, l_freq=brain_filter_low, h_freq=brain_filter_high,) + # print(f'band path filter: {brain_filter_low}-{brain_filter_high}') + # if args.preprocs.brain_resample_rate is not None: + # ROI_MEG_Data = mne.filter.resample(ROI_MEG_Data, down=fs / args.preprocs.brain_resample_rate) + # print('resample {} to {} Hz'.format(fs,args.preprocs.brain_resample_rate)) + # window = time_window(args, triggers, args.preprocs.brain_resample_rate) + # else: + # window = time_window(args, triggers, fs) + window = time_window(args, triggers, fs) + # import pdb; pdb.set_trace() + ROI_MEG_epochs = epoching(ROI_MEG_Data, window) + else: + ROI_MEG_epochs = [] + for r, o in onsets.items(): + args.region = r if isinstance(r, list) else [r] + roi_channels = roi(args) + ROI_MEG_Data = MEG_Data[roi_channels, :] + duration = args.window.end - args.window.start + args.window.start = o + args.window.end = o + duration + window = time_window(args, triggers, fs) + single_ROI_MEG_epoch = epoching(ROI_MEG_Data, window) # n_epochs x n_chs x time_smaples + # print('DEBUG: ', window[-1], ROI_MEG_Data.shape) + ROI_MEG_epochs.append(single_ROI_MEG_epoch) + ROI_MEG_epochs = np.concatenate(ROI_MEG_epochs, axis=1) + # import pdb; pdb.set_trace() meg_epochs.append(ROI_MEG_epochs) # array [epoch x ch x time_stamp] sub_epochs+=[sub_id_map[sub]] * len(ROI_MEG_epochs) # list [epoch] @@ -98,176 +118,408 @@ def epoching(meg, window, preprocess_pipeline=[]): print('dataset is created. size is ', meg_epochs.shape) return meg_epochs, sub_epochs, label_epochs, image_feature_epochs -@hydra.main(version_base=None, config_path="../../configs", config_name="20230420_sbj01_linear.yaml") -def main_meg(args): - ## prepare dataset - train_X, train_subs, train_label, train_Y = prepare_dataset(args, split='train') - test_X, test_subs, test_label, test_Y = prepare_dataset(args, split='test') - - ## preprocess - pass - ## feature prediction - pred_y, true_y = feature_prediction(train_X, train_Y, - test_X, test_Y, - n_voxel=20, - n_iter=200) - - -# Main ################################################################# - -def main(): - # Settings --------------------------------------------------------- - - # Data settings - subjects = config.subjects - rois = config.rois - num_voxel = config.num_voxel - - image_feature = config.image_feature_file - features = config.features - - n_iter = 200 - - results_dir = config.results_dir - - # Misc settings - analysis_basename = os.path.basename(__file__) - # Load data -------------------------------------------------------- - print('----------------------------------------') - print('Loading data') +def get_average_features(predicted_y, val_index): + test_labels_unique = np.unique(val_index) + test_pred_features_avg = [] + for i in range(len(test_labels_unique)): + target_ids = val_index== i + test_pred_features_avg.append(predicted_y[target_ids].mean(axis=0, keepdims=True)) + test_pred_features_avg = np.concatenate(test_pred_features_avg, axis=0) + return test_pred_features_avg, np.arange(len(test_labels_unique)) + +# def metric(predicted_y, val_index, use_average=False): +# # predicted_y: num_trials x 512 +# # val_index: num_trials +# val_index = val_index - 1 # ラベルは1始まり +# image_features = np.load('./data/GOD/image_features.npy') # 50 x 512 +# if use_average: +# print('use average') +# predicted_y, val_index = get_average_features(predicted_y, val_index) +# num_images = len(image_features) +# num_trials = len(predicted_y) +# acc_tmp = np.zeros((num_trials, 1)) + +# cat_wise_acc = {i:[] for i in range(len(image_features))} +# # import pdb; pdb.set_trace() +# for i_pred in range(num_trials): +# space_corr = np.zeros((num_images, 1)) +# # iterating over all images +# # calculating the correlation between the predicted and the image features +# for i_img in range(num_images): +# R = np.corrcoef(predicted_y[i_pred], image_features[i_img]) +# space_corr[i_img] = R[0,1] + +# # assigning the index of the current predicred vector to image_id +# image_id = val_index[i_pred] +# # calculating the accuracy of the cirrent predicted vector by counting the number of images with correlation coefficiens less than that of corresponding image +# # and dividing by the total number of images minus one + +# acc_tmp[i_pred] = np.sum(space_corr < space_corr[image_id]) / (num_images - 1) +# cat_wise_acc[image_id].append(acc_tmp[i_pred]) +# cat_wise_acc = {i: np.mean(cat_wise_acc[i]) for i in range(len(image_features))} +# return np.mean(acc_tmp), cat_wise_acc + +def metric_kamitani(results:dict, label:list): + pred_percept = [results['predicted_feature_averaged_percept']] # n_preds x n_units + cat_feature_percept = [results['category_feature_averaged_percept']] # n_sample x n_units + + pwident_cr_pt = [] # Prop correct in pair-wise identification (perception) + # cnt = 0 + for fpt, pred_pt in zip(cat_feature_percept, pred_percept): + # ind_cat_other = list(range(len(cat_feature_percept))).remove(cnt) + # feat_other = cat_feature_percept[ind_cat_other,:] + + # n_unit = fpt.shape[1] + # feat_other = feat_other[:, :n_unit] + + feat_candidate_pt = fpt # np.vstack([fpt, feat_other]) + + simmat_pt = corrmat(pred_pt, feat_candidate_pt) + + cr_pt = get_pwident_correctrate(simmat_pt) + + pwident_cr_pt.append(np.mean(cr_pt)) + # cnt += 1 + assert len(pwident_cr_pt) == 1 + return np.mean(cr_pt), {k:v for k, v in zip(label, cr_pt)} - data_all = {} - for sbj in subjects: - if len(subjects[sbj]) == 1: - data_all[sbj] = bdpy.BData(subjects[sbj][0]) - else: - # Concatenate data - suc_cols = ['Run', 'Block'] - data_all[sbj] = concat_dataset([bdpy.BData(f) for f in subjects[sbj]], - successive=suc_cols) - data_feature = bdpy.BData(image_feature) - - # Add any additional processing to data here - - # Initialize directories ------------------------------------------- - makedir_ifnot(results_dir) - makedir_ifnot('tmp') +# Functions ############################################################ - # Analysis loop ---------------------------------------------------- - print('----------------------------------------') - print('Analysis loop') +def get_pwident_correctrate(simmat): + ''' + Returns correct rate in pairwise identification + Parameters + ---------- + simmat : numpy array [num_prediction * num_category] + Similarity matrix + Returns + ------- + correct_rate : correct rate of pair-wise identification + ''' - for sbj, roi, feat in product(subjects, rois, features): - print('--------------------') - print('Subject: %s' % sbj) - print('ROI: %s' % roi) - print('Num voxels: %d' % num_voxel[roi]) - print('Feature: %s' % feat) + num_pred = simmat.shape[0] + labels = range(num_pred) - # Distributed computation - analysis_id = analysis_basename + '-' + sbj + '-' + roi + '-' + feat - results_file = os.path.join(results_dir, analysis_id + '.pkl') + correct_rate = [] + for i in range(num_pred): + pred_feat = simmat[i, :] + correct_feat = pred_feat[labels[i]] + pred_num = len(pred_feat) - 1 + correct_rate.append((pred_num - np.sum(pred_feat > correct_feat)) / float(pred_num)) - if os.path.exists(results_file): - print('%s is already done. Skipped.' % analysis_id) - continue + return correct_rate - dist = DistComp(lockdir='tmp', comp_id=analysis_id) - if dist.islocked(): - print('%s is already running. Skipped.' % analysis_id) - continue - dist.lock() +def run_meg_fit_and_evaluate(args, ch_ratios=1, manual_ch:list=None, onsets:dict=None): + prefix='sbj01' + random.seed(0) + ## prepare dataset + train_X, train_subs, train_label, train_Y = prepare_dataset(args, split='train', manual_ch=manual_ch, onsets=onsets) + test_X, test_subs, test_label, test_Y = prepare_dataset(args, split='val', manual_ch=manual_ch, onsets=onsets) + # import pdb; pdb.set_trace() - # Prepare data - print('Preparing data') - dat = data_all[sbj] + ## preprocess + train_X = np.mean(train_X, axis=-1) # get SCP + test_X = np.mean(test_X, axis=-1) - x = dat.select(rois[roi]) # Brain data - datatype = dat.select('DataType') # Data type - labels = dat.select('stimulus_id') # Image labels in brain data + ## feature prediction + n_voxel = int(ch_ratios*(train_X.shape[1])) + print('n_voxel: ', n_voxel) + pred_y, true_y = feature_prediction(train_X, train_Y, + test_X, test_Y, + n_voxel=n_voxel, + n_iter=200) + + ## evaluate + pred_y_avg, true_y_avg, test_label_set = get_averaged_feature(pred_y, true_y, test_label) + results = {} + results['predicted_feature_averaged_percept'] = pred_y_avg + results['category_feature_averaged_percept'] = true_y_avg + acc, cat_wise_acc = metric_kamitani(results, test_label_set)# metric(pred_y_avg, test_label_set, use_average=False) + print('ACC from binary corr', acc) + print({k: '{:.3f}'.format(v) for k, v in cat_wise_acc.items()}) + + ## save + savefile = os.path.join(args.save_root, '{}-{}_{}-{:.3f}'.format(prefix, args.window.start, args.window.end, acc), 'ridge_regression.pkl') + if not os.path.exists(os.path.dirname(savefile)): + os.makedirs(os.path.dirname(savefile)) + # with open(savefile, 'wb') as f: + # pickle.dump({'pred_y':pred_y, 'true_y': true_y, 'test_label': test_label}, f) + print('results is saved as ', savefile) + # import pdb; pdb.set_trace() + return acc + + + +@hydra.main(version_base=None, config_path="../../configs", config_name="20230421_sbj01_kamitani_regression.yaml") +def main_meg_repetiton_roi(args): + onsets = [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4] + duration = 0.2 + + roi_names = ['occipital', 'parietal', 'frontal', 'temporal', 'central'] + for roi_name in roi_names: + acc_list = [] + args.region = [roi_name+'/right', roi_name + '/left'] + for start_ in onsets: + args.window.start = start_ + args.window.end = start_ + duration + acc = run_meg_fit_and_evaluate(args) + acc_list.append(acc) + plt.plot(onsets, acc_list, label=roi_name) + + plt.xlabel('onset [s]') + plt.ylabel('Acc') + plt.legend() + plt.title('sbj01 - 200 ms window') + savefile = os.path.join(args.save_root, f'ridge_regression_{duration}s.png') + plt.savefig(savefile) + print('figure is saved as ', savefile) + +@hydra.main(version_base=None, config_path="../../configs", config_name="20230421_sbj01_kamitani_regression.yaml") +def main_meg_repetiton_N(args): + ch_ratios = [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1] + plt.figure(figsize=(12,6)) + roi_names = ['occipital', 'parietal', 'frontal', 'temporal', 'central'] + roi_name_pairs = [] + results = {} + for n in range(1, len(roi_names)+1): + roi_name_pairs += list(itertools.combinations(roi_names,n)) + for roi_name_pair in roi_name_pairs: + print('============================\n{}\n=================================='.format(roi_name_pair)) + acc_list = [] + region = [] + for r in roi_name_pair: + region += [r+'/right', r+'/left'] + + args.region = region + for ch_ratio in ch_ratios: + acc = run_meg_fit_and_evaluate(args, ch_ratio) + acc_list.append(acc) + label = '-'.join(roi_name_pair) + plt.plot(ch_ratios, acc_list, label=label) + results[label] = acc_list + pkl_file = os.path.join(args.save_root, f'ridge_regression_ch_ratio.pkl') + with open(pkl_file, 'wb' ) as f: + pickle.dump(results, f) + + plt.xlabel('ch_ratio') + plt.ylabel('Acc') + plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0,) + plt.title('sbj01 - 200 ms window') + savefile = os.path.join(args.save_root, f'ridge_regression_ch_ratio.png') + plt.savefig(savefile, bbox_inches="tight") + print('figure is saved as ', savefile) + print(results) + +@hydra.main(version_base=None, config_path="../../configs", config_name="20230421_sbj01_kamitani_regression.yaml") +def main_meg_repetiton_onsets_per_ch(args): + savefile = os.path.join(args.save_root, f'ridge_regression_var_onsets_per_ch.csv') + onsets = [0.2, 0.25, 0.3]# , 0.35] + plt.figure(figsize=(12,6)) + roi_names = ['occipital', 'parietal', 'frontal', 'temporal', 'central'] + + ## initialize + results = {'acc':[]} + for r in roi_names: + results[r] = [] + + cands_onsets = list(itertools.product(onsets, repeat=len(roi_names))) + print(cands_onsets) + for onset_list in cands_onsets: + # onset_dict = {k:v for k, v in zip(roi_names, onsets)} + onset_dict = {} + for r, o in zip(roi_names, onset_list): + onset_dict[r+'/right'] = o + onset_dict[r+'/left'] = o + print('===============================================================') + print(onset_dict) + print('===============================================================') + + acc = run_meg_fit_and_evaluate(args, ch_ratios=1, onsets=onset_dict) + results['acc'].append(acc) + for k, v in zip(roi_names, onset_list): + results[k].append(v) + + df = pd.DataFrame(results) + + df.to_csv(savefile) + print('results is saved as ', savefile) + + + + +@hydra.main(version_base=None, config_path="../../configs", config_name="20230421_sbj01_kamitani_regression.yaml") +def main_meg_run_manual_ch(args): + acc_list = [] + manual_ch_list = [ [136, 137, 139, 151, 152, 154], + [136, 137, 139, 151, 152, 154, 135, 153], + [136, 137, 139, 151, 152, 154, 135, 153, 134, 149], + [136, 137, 139, 151, 152, 154, 135, 153, 134, 149, 133, 138, 150, 155], + None] + for manual_ch in manual_ch_list: + if manual_ch is not None: + manual_ch = [c - 1 for c in manual_ch] # matlab -> python + acc = run_meg_fit_and_evaluate(args, manual_ch=manual_ch) + acc_list.append(acc) + print(acc_list) + +# # Main ################################################################# + +# def main(): +# # Settings --------------------------------------------------------- + +# # Data settings +# subjects = config.subjects +# rois = config.rois +# num_voxel = config.num_voxel + +# image_feature = config.image_feature_file +# features = config.features + +# n_iter = 200 + +# results_dir = config.results_dir + +# # Misc settings +# analysis_basename = os.path.basename(__file__) + +# # Load data -------------------------------------------------------- +# print('----------------------------------------') +# print('Loading data') + +# data_all = {} +# for sbj in subjects: +# if len(subjects[sbj]) == 1: +# data_all[sbj] = bdpy.BData(subjects[sbj][0]) +# else: +# # Concatenate data +# suc_cols = ['Run', 'Block'] +# data_all[sbj] = concat_dataset([bdpy.BData(f) for f in subjects[sbj]], +# successive=suc_cols) + +# data_feature = bdpy.BData(image_feature) + +# # Add any additional processing to data here + +# # Initialize directories ------------------------------------------- +# makedir_ifnot(results_dir) +# makedir_ifnot('tmp') + +# # Analysis loop ---------------------------------------------------- +# print('----------------------------------------') +# print('Analysis loop') + +# for sbj, roi, feat in product(subjects, rois, features): +# print('--------------------') +# print('Subject: %s' % sbj) +# print('ROI: %s' % roi) +# print('Num voxels: %d' % num_voxel[roi]) +# print('Feature: %s' % feat) + +# # Distributed computation +# analysis_id = analysis_basename + '-' + sbj + '-' + roi + '-' + feat +# results_file = os.path.join(results_dir, analysis_id + '.pkl') + +# if os.path.exists(results_file): +# print('%s is already done. Skipped.' % analysis_id) +# continue + +# dist = DistComp(lockdir='tmp', comp_id=analysis_id) +# if dist.islocked(): +# print('%s is already running. Skipped.' % analysis_id) +# continue + +# dist.lock() + +# # Prepare data +# print('Preparing data') +# dat = data_all[sbj] - y = data_feature.select(feat) # Image features - y_label = data_feature.select('ImageID') # Image labels +# x = dat.select(rois[roi]) # Brain data +# datatype = dat.select('DataType') # Data type +# labels = dat.select('stimulus_id') # Image labels in brain data - # For quick demo, reduce the number of units from 1000 to 100 - y = y[:, :100] +# y = data_feature.select(feat) # Image features +# y_label = data_feature.select('ImageID') # Image labels - y_sorted = get_refdata(y, y_label, labels) # Image features corresponding to brain data +# # For quick demo, reduce the number of units from 1000 to 100 +# y = y[:, :100] - # Get training and test dataset - i_train = (datatype == 1).flatten() # Index for training - i_test_pt = (datatype == 2).flatten() # Index for perception test - i_test_im = (datatype == 3).flatten() # Index for imagery test - i_test = i_test_pt + i_test_im +# y_sorted = get_refdata(y, y_label, labels) # Image features corresponding to brain data - x_train = x[i_train, :] - x_test = x[i_test, :] +# # Get training and test dataset +# i_train = (datatype == 1).flatten() # Index for training +# i_test_pt = (datatype == 2).flatten() # Index for perception test +# i_test_im = (datatype == 3).flatten() # Index for imagery test +# i_test = i_test_pt + i_test_im + +# x_train = x[i_train, :] +# x_test = x[i_test, :] - y_train = y_sorted[i_train, :] - y_test = y_sorted[i_test, :] +# y_train = y_sorted[i_train, :] +# y_test = y_sorted[i_test, :] - # Feature prediction - pred_y, true_y = feature_prediction(x_train, y_train, - x_test, y_test, - n_voxel=num_voxel[roi], - n_iter=n_iter) +# # Feature prediction +# pred_y, true_y = feature_prediction(x_train, y_train, +# x_test, y_test, +# n_voxel=num_voxel[roi], +# n_iter=n_iter) - # Separate results for perception and imagery tests - i_pt = i_test_pt[i_test] # Index for perception test within test - i_im = i_test_im[i_test] # Index for imagery test within test +# # Separate results for perception and imagery tests +# i_pt = i_test_pt[i_test] # Index for perception test within test +# i_im = i_test_im[i_test] # Index for imagery test within test - pred_y_pt = pred_y[i_pt, :] - pred_y_im = pred_y[i_im, :] +# pred_y_pt = pred_y[i_pt, :] +# pred_y_im = pred_y[i_im, :] - true_y_pt = true_y[i_pt, :] - true_y_im = true_y[i_im, :] +# true_y_pt = true_y[i_pt, :] +# true_y_im = true_y[i_im, :] - # Get averaged predicted feature - test_label_pt = labels[i_test_pt, :].flatten() - test_label_im = labels[i_test_im, :].flatten() +# # Get averaged predicted feature +# test_label_pt = labels[i_test_pt, :].flatten() +# test_label_im = labels[i_test_im, :].flatten() - pred_y_pt_av, true_y_pt_av, test_label_set_pt \ - = get_averaged_feature(pred_y_pt, true_y_pt, test_label_pt) - pred_y_im_av, true_y_im_av, test_label_set_im \ - = get_averaged_feature(pred_y_im, true_y_im, test_label_im) - - # Get category averaged features - catlabels_pt = np.vstack([int(n) for n in test_label_pt]) # Category labels (perception test) - catlabels_im = np.vstack([int(n) for n in test_label_im]) # Category labels (imagery test) - catlabels_set_pt = np.unique(catlabels_pt) # Category label set (perception test) - catlabels_set_im = np.unique(catlabels_im) # Category label set (imagery test) +# pred_y_pt_av, true_y_pt_av, test_label_set_pt \ +# = get_averaged_feature(pred_y_pt, true_y_pt, test_label_pt) +# pred_y_im_av, true_y_im_av, test_label_set_im \ +# = get_averaged_feature(pred_y_im, true_y_im, test_label_im) + +# # Get category averaged features +# catlabels_pt = np.vstack([int(n) for n in test_label_pt]) # Category labels (perception test) +# catlabels_im = np.vstack([int(n) for n in test_label_im]) # Category labels (imagery test) +# catlabels_set_pt = np.unique(catlabels_pt) # Category label set (perception test) +# catlabels_set_im = np.unique(catlabels_im) # Category label set (imagery test) - y_catlabels = data_feature.select('CatID') # Category labels in image features - ind_catave = (data_feature.select('FeatureType') == 3).flatten() +# y_catlabels = data_feature.select('CatID') # Category labels in image features +# ind_catave = (data_feature.select('FeatureType') == 3).flatten() - y_catave_pt = get_refdata(y[ind_catave, :], y_catlabels[ind_catave, :], catlabels_set_pt) - y_catave_im = get_refdata(y[ind_catave, :], y_catlabels[ind_catave, :], catlabels_set_im) +# y_catave_pt = get_refdata(y[ind_catave, :], y_catlabels[ind_catave, :], catlabels_set_pt) +# y_catave_im = get_refdata(y[ind_catave, :], y_catlabels[ind_catave, :], catlabels_set_im) - # Prepare result dataframe - results = pd.DataFrame({'subject' : [sbj, sbj], - 'roi' : [roi, roi], - 'feature' : [feat, feat], - 'test_type' : ['perception', 'imagery'], - 'true_feature': [true_y_pt, true_y_im], - 'predicted_feature': [pred_y_pt, pred_y_im], - 'test_label' : [test_label_pt, test_label_im], - 'test_label_set' : [test_label_set_pt, test_label_set_im], - 'true_feature_averaged' : [true_y_pt_av, true_y_im_av], - 'predicted_feature_averaged' : [pred_y_pt_av, pred_y_im_av], - 'category_label_set' : [catlabels_set_pt, catlabels_set_im], - 'category_feature_averaged' : [y_catave_pt, y_catave_im]}) +# # Prepare result dataframe +# results = pd.DataFrame({'subject' : [sbj, sbj], +# 'roi' : [roi, roi], +# 'feature' : [feat, feat], +# 'test_type' : ['perception', 'imagery'], +# 'true_feature': [true_y_pt, true_y_im], +# 'predicted_feature': [pred_y_pt, pred_y_im], +# 'test_label' : [test_label_pt, test_label_im], +# 'test_label_set' : [test_label_set_pt, test_label_set_im], +# 'true_feature_averaged' : [true_y_pt_av, true_y_im_av], +# 'predicted_feature_averaged' : [pred_y_pt_av, pred_y_im_av], +# 'category_label_set' : [catlabels_set_pt, catlabels_set_im], +# 'category_feature_averaged' : [y_catave_pt, y_catave_im]}) - # Save results - makedir_ifnot(os.path.dirname(results_file)) - with open(results_file, 'wb') as f: - pickle.dump(results, f) +# # Save results +# makedir_ifnot(os.path.dirname(results_file)) +# with open(results_file, 'wb') as f: +# pickle.dump(results, f) - print('Saved %s' % results_file) +# print('Saved %s' % results_file) - dist.unlock() +# dist.unlock() # Functions ############################################################ @@ -307,9 +559,10 @@ def feature_prediction(x_train, y_train, x_test, y_test, n_voxel=500, n_iter=200 y_true_list = [] y_pred_list = [] - for i in range(n_unit): + pbar = tqdm.tqdm(range(n_unit)) + + for i in pbar: - print('Unit %03d' % (i + 1)) start_time = time() # Get unit features @@ -322,7 +575,7 @@ def feature_prediction(x_train, y_train, x_test, y_test, n_voxel=500, n_iter=200 norm_scale_y = 1 if std_y == 0 else std_y y_train_unit = (y_train_unit - norm_mean_y) / norm_scale_y - + # import pdb; pdb.set_trace() # Voxel selection corr = corrcoef(y_train_unit, x_train, var='col') @@ -335,8 +588,8 @@ def feature_prediction(x_train, y_train, x_test, y_test, n_voxel=500, n_iter=200 # Setup regression # For quick demo, use linaer regression - model = LinearRegression() - #model = SparseLinearRegression(n_iter=n_iter, prune_mode=1) + # model = LinearRegression() + model = SparseLinearRegression(n_iter=n_iter, prune_mode=1) # Training and test try: @@ -352,7 +605,7 @@ def feature_prediction(x_train, y_train, x_test, y_test, n_voxel=500, n_iter=200 y_true_list.append(y_test_unit) y_pred_list.append(y_pred) - print('Time: %.3f sec' % (time() - start_time)) + pbar.set_description('Unit %03d' % (i + 1) + 'Time: %.3f sec' % (time() - start_time)) # Create numpy arrays for return values y_predicted = np.vstack(y_pred_list).T @@ -377,4 +630,9 @@ def get_averaged_feature(pred_y, true_y, labels): if __name__ == '__main__': # To avoid any use of global variables, # do nothing except calling main() here - main() \ No newline at end of file + # main() + # main_meg_repetiton_roi() + # main_meg_run_manual_ch() + # main_meg_repetiton_N() + main_meg_repetiton_onsets_per_ch() + # main_meg() \ No newline at end of file diff --git a/examples/view_training_curve.py b/examples/view_training_curve.py index 3c5eda5..c475419 100644 --- a/examples/view_training_curve.py +++ b/examples/view_training_curve.py @@ -63,7 +63,8 @@ def parse_data(filepath): # filepath = '/home/yainoue/meg2image/results/20230419_sbj01_seq2stat/runs/2023-04-20 01:39:30.648046' # filepath = '/home/yainoue/meg2image/results/20230419_sbj01_seq2stat2/runs/2023-04-20 13:06:53.825175' # filepath = '/home/yainoue/meg2image/results/20230420ß_sbj01_linear/runs/2023-04-20 22:04:01.525056' - filepath = '/home/yainoue/meg2image/results/20230420_sbj01_seq2stat2/runs/2023-04-21 02:04:47.889601' + # filepath = '/home/yainoue/meg2image/results/20230420_sbj01_seq2stat2/runs/2023-04-21 02:04:47.889601' + filepath = '/home/yainoue/meg2image/results/20230423_sbj010203_seq2stats_regression/runs/2023-04-24 00:01:31.293371' # filepath = '/home/yainoue/meg2image/results/20230413_sbj01/runs/2023-04-13 21:52:17.333087'#'home/yainoue/meg2image/results/test/2023-04-11 18:06:44.273986' # filepath = '/home/yainoue/meg2image/results/20230413_sbj01_seq2stat/runs/2023-04-16 19:21:02.983480' # filepath = '/home/yainoue/meg2image/results/20230412_mini/runs/2023-04-13 15:17:53.310665' diff --git a/meg_decoding/dataclass/god.py b/meg_decoding/dataclass/god.py index 48bc0bf..706ff5b 100644 --- a/meg_decoding/dataclass/god.py +++ b/meg_decoding/dataclass/god.py @@ -17,6 +17,13 @@ mne.set_log_level(verbose="WARNING") +def normalize_per_unit(tensor): + print('normalize image_feature along unit dim') + # array: n_samples x n_units(512) + tensor = tensor - torch.mean(tensor, 0, keepdim=True) + tensor = tensor / torch.std(tensor, 0, keepdim=True) + return tensor + class GODDatasetBase(Dataset): def __init__(self, args, split, preprocess_pipleine:list=[], return_label:bool=False): self.args = args @@ -28,6 +35,9 @@ def __init__(self, args, split, preprocess_pipleine:list=[], return_label:bool=F self.X = meg_epochs.astype(np.float32) # epochs x ch x time_samples self.Y = image_feature_epochs.astype(np.float32) # epochs x dims + if args.normalize_image_features: + self.Y = normalize_per_unit(self.Y) + self.subs = sub_epochs # epochs (x 1) self.labels = label_epochs # epochs (x 1) diff --git a/meg_decoding/matlab_utils/load_meg.py b/meg_decoding/matlab_utils/load_meg.py index e2a6df4..6747afb 100644 --- a/meg_decoding/matlab_utils/load_meg.py +++ b/meg_decoding/matlab_utils/load_meg.py @@ -109,7 +109,7 @@ def roi(args)->list: roi_channels = [] for reg in region: reg_subreg = reg.split('/') - assert len(reg_subreg) == 2 + assert len(reg_subreg) == 2, 'got {}'.format(reg_subreg) r = reg_subreg[0] s = reg_subreg[1] roi_channels += ch_region_info[r][s] diff --git a/train_regression.py b/train_regression.py index ec6fde3..c296ca2 100644 --- a/train_regression.py +++ b/train_regression.py @@ -345,7 +345,7 @@ def run(args: DictConfig) -> None: if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230420_sbj01_linear') + args = compose(config_name='20230423_sbj01_seq2stat_regression') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) run(args) From 9054f63f72e50f350f7448d67db7fba46af43113 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Thu, 27 Apr 2023 12:24:17 +0900 Subject: [PATCH 37/71] ADD: various loss --- evaluate.py | 2 +- examples/inference.py | 2 +- examples/view_training_curve.py | 7 +- meg_decoding/dataclass/god.py | 4 +- meg_decoding/models.py | 10 +- notebooks/check_category_similarity.ipynb | 1659 +++++++++++---------- notebooks/image_featuers_check.ipynb | 47 + train_wowandb.py | 3 +- train_wowandb_cv.py | 368 +++++ 9 files changed, 1290 insertions(+), 812 deletions(-) create mode 100644 notebooks/image_featuers_check.ipynb create mode 100644 train_wowandb_cv.py diff --git a/evaluate.py b/evaluate.py index c1d6439..f5649f2 100644 --- a/evaluate.py +++ b/evaluate.py @@ -348,7 +348,7 @@ def run_pairwise_acc(args, use_average=False): if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230420_sbj01_linear') + args = compose(config_name='20230424_sbj01_seq2stat') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) # run(args) diff --git a/examples/inference.py b/examples/inference.py index 19fddf8..c159d0f 100644 --- a/examples/inference.py +++ b/examples/inference.py @@ -145,7 +145,7 @@ def run_inference(args): if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../../configs/"): - args = compose(config_name='20230417_sbj01_seq2stat') + args = compose(config_name='20230424_sbj01_seq2stat') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) # run(args) diff --git a/examples/view_training_curve.py b/examples/view_training_curve.py index c475419..ce5b932 100644 --- a/examples/view_training_curve.py +++ b/examples/view_training_curve.py @@ -64,7 +64,12 @@ def parse_data(filepath): # filepath = '/home/yainoue/meg2image/results/20230419_sbj01_seq2stat2/runs/2023-04-20 13:06:53.825175' # filepath = '/home/yainoue/meg2image/results/20230420ß_sbj01_linear/runs/2023-04-20 22:04:01.525056' # filepath = '/home/yainoue/meg2image/results/20230420_sbj01_seq2stat2/runs/2023-04-21 02:04:47.889601' - filepath = '/home/yainoue/meg2image/results/20230423_sbj010203_seq2stats_regression/runs/2023-04-24 00:01:31.293371' + # filepath = '/home/yainoue/meg2image/results/20230423_sbj010203_seq2stats_regression/runs/2023-04-24 00:01:31.293371' + # filepath = '/home/yainoue/meg2image/results/20230424_sbj01_seq2stat/runs/2023-04-25 00:34:28.269896' + # filepath = '/home/yainoue/meg2image/results/20230425_sbj01_seq2stat_cv/runs/2023-04-25 21:00:55.483213' + # filepath = '/home/yainoue/meg2image/results/20230425_sbj01_seq2stat_cv_norm_wo_dilation/runs/2023-04-26 11:27:01.295948' + # filepath = '/home/yainoue/meg2image/results/20230426_all_seq2stat_cv_norm_wo_dilation/runs/2023-04-26 16:05:36.849896' + filepath = '/home/yainoue/meg2image/results/20230426_all_seq2stat_cv_norm_wo_dilation/runs/2023-04-26 16:19:27.018492' # filepath = '/home/yainoue/meg2image/results/20230413_sbj01/runs/2023-04-13 21:52:17.333087'#'home/yainoue/meg2image/results/test/2023-04-11 18:06:44.273986' # filepath = '/home/yainoue/meg2image/results/20230413_sbj01_seq2stat/runs/2023-04-16 19:21:02.983480' # filepath = '/home/yainoue/meg2image/results/20230412_mini/runs/2023-04-13 15:17:53.310665' diff --git a/meg_decoding/dataclass/god.py b/meg_decoding/dataclass/god.py index 706ff5b..17aa2c2 100644 --- a/meg_decoding/dataclass/god.py +++ b/meg_decoding/dataclass/god.py @@ -20,8 +20,8 @@ def normalize_per_unit(tensor): print('normalize image_feature along unit dim') # array: n_samples x n_units(512) - tensor = tensor - torch.mean(tensor, 0, keepdim=True) - tensor = tensor / torch.std(tensor, 0, keepdim=True) + tensor = tensor - np.mean(tensor, axis=0, keepdims=True) + tensor = tensor / np.std(tensor, axis=0, keepdims=True) return tensor class GODDatasetBase(Dataset): diff --git a/meg_decoding/models.py b/meg_decoding/models.py index 2d5925f..41d9402 100644 --- a/meg_decoding/models.py +++ b/meg_decoding/models.py @@ -146,7 +146,7 @@ def __init__(self, k, D1, D2, ks=3): out_channels=self.D2, kernel_size=ks, padding="same", - dilation=2 ** ((2 * k) % 5), + # dilation= 2 ** ((2 * k) % 5), ) self.batchnorm0 = nn.BatchNorm1d(num_features=self.D2) self.conv1 = nn.Conv1d( @@ -154,7 +154,7 @@ def __init__(self, k, D1, D2, ks=3): out_channels=self.D2, kernel_size=ks, padding="same", - dilation=2 ** ((2 * k + 1) % 5), + # dilation=2 ** ((2 * k + 1) % 5), ) self.batchnorm1 = nn.BatchNorm1d(num_features=self.D2) self.conv2 = nn.Conv1d( @@ -162,7 +162,7 @@ def __init__(self, k, D1, D2, ks=3): out_channels=2 * self.D2, kernel_size=ks, padding="same", - dilation=2, # FIXME: The text doesn't say this, but the picture shows dilation=2 + # dilation=2, # FIXME: The text doesn't say this, but the picture shows dilation=2 ) def forward(self, X): @@ -270,8 +270,8 @@ def forward(self, Z: torch.Tensor, Y: torch.Tensor, test=False, top_k=None) -> t if self.normalize_image_features: - y = self.normalize_per_unit(y) - + # y = self.normalize_per_unit(y) + pass # x_ = rearrange(x, 'b f -> 1 b f') # y_ = rearrange(y, 'b f -> b 1 f') # similarity = torch.nn.functional.cosine_similarity(x_, y_, dim=-1) # ( B, B ) diff --git a/notebooks/check_category_similarity.ipynb b/notebooks/check_category_similarity.ipynb index 27fe9a4..90d2305 100644 --- a/notebooks/check_category_similarity.ipynb +++ b/notebooks/check_category_similarity.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -25,7 +25,7 @@ "test_category_path = '../data/GOD/category_test.csv'\n", "\n", "\n", - "target_dir = '/home/yainoue/meg2image/results/20230417_sbj01_seq2stat/inference'\n", + "target_dir = '/home/yainoue/meg2image/results/20230424_sbj01_seq2stat/inference_last'\n", "test_labels_path = os.path.join(target_dir, 'labels_test.npy')\n", "test_pred_features_path = os.path.join(target_dir, 'pred_features_test.npy')\n", "train_labels_path = os.path.join(target_dir, 'labels_train.npy')\n", @@ -34,7 +34,20 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "def normalize_unit(data):\n", + " assert len(data.shape)==2\n", + " data -= np.mean(data, axis=0, keepdims=True)\n", + " data /= np.std(data, axis=0, keepdims=True)\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -52,6 +65,10 @@ "train_image_features = np.load(train_image_features_path)\n", "test_image_features = np.load(test_image_features_path)\n", "\n", + "#### preprocess :: Normalize unit\n", + "train_image_features = normalize_unit(train_image_features)\n", + "test_image_features = normalize_unit(test_image_features)\n", + "\n", "test_pred_features = np.load(test_pred_features_path)\n", "test_labels = np.load(test_labels_path)\n", "\n", @@ -69,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -93,28 +110,28 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 46, "metadata": {}, "outputs": [ { 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-7.57615268e-02, 1.93852242e-02,\n", + " 2.41133213e-01, -5.33845834e-02, 5.79571687e-02,\n", + " 1.23147227e-01, 1.67241171e-01, -1.13679416e-01,\n", + " 2.41125271e-01, 1.14017732e-01]], dtype=float32)" ] }, - "execution_count": 6, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -322,25 +339,31 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[1. 0.74991196 0.77040982 ... 0.48660085 0.43744312 0.46784548]\n", - " [0.74991196 1. 0.97256515 ... 0.46133996 0.47905608 0.50767602]\n", - " [0.77040982 0.97256515 1. ... 0.45814013 0.47355479 0.50608662]\n", + "[[ 1. 0.14448165 0.19268592 ... -0.13486348 -0.21210947\n", + " -0.1698305 ]\n", + " [ 0.14448165 1. 0.91250556 ... -0.11322869 -0.08779042\n", + " -0.08709269]\n", + " [ 0.19268592 0.91250556 1. ... -0.12676909 -0.09600682\n", + " -0.08757214]\n", " ...\n", - " [0.48660085 0.46133996 0.45814013 ... 1. 0.56144309 0.5761439 ]\n", - " [0.43744312 0.47905608 0.47355479 ... 0.56144309 1. 0.61554665]\n", - " [0.46784548 0.50767602 0.50608662 ... 0.5761439 0.61554665 1. ]]\n" + " [-0.13486348 -0.11322869 -0.12676909 ... 1. 0.19233846\n", + " 0.11884173]\n", + " [-0.21210947 -0.08779042 -0.09600682 ... 0.19233846 1.\n", + " 0.19470739]\n", + " [-0.1698305 -0.08709269 -0.08757214 ... 0.11884173 0.19470739\n", + " 1. ]]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -377,63 +400,63 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Goldfish : ['octopus', 'starfish', 'frog_1', 'turtle', 'dolphin']\n", - "owl : ['ostrich', 'raccoon', 'frog_2', 'bear', 'giraffe']\n", - "nan : ['frog_1', 'praying_mantis', 'frog_2', 'snake', 'grasshopper']\n", - "duck : ['duck', 'bird_2', 'crested_ibis', 'penguin', 'water_bowl']\n", - "swan : ['duck', 'crested_ibis', 'bird_2', 'ostrich', 'duck']\n", - "nan : ['flower_pot', 'teapot', 'trilobite', 'cochlea', 'octopus']\n", - "crab : ['scorpion', 'horseshoe_crab', 'octopus', 'frog_2', 'spider']\n", + "Goldfish : ['octopus', 'turtle', 'starfish', 'dolphin', 'frog_1']\n", + "owl : ['leopard', 'raccoon', 'ostrich', 'frog_2', 'bird_2']\n", + "nan : ['frog_1', 'snake', 'frog_2', 'praying_mantis', 'grasshopper']\n", + "duck : ['duck', 'bird_2', 'crested_ibis', 'penguin', 'bird_1']\n", + "swan : ['duck', 'crested_ibis', 'duck', 'bird_2', 'ostrich']\n", + "nan : ['flower_pot', 'cochlea', 'trilobite', 'teapot', 'basket_clam']\n", + "crab : ['scorpion', 'horseshoe_crab', 'octopus', 'spider', 'cockroach']\n", "orca : ['penguin', 'dolphin', 'bear', 'elephant', 'bird_2']\n", - "leopard : ['giraffe', 'bear', 'gorilla', 'elephant', 'raccoon']\n", - "Bat : ['raccoon', 'frog_1', 'frog_2', 'spider', 'porcupine']\n", + "leopard : ['bear', 'giraffe', 'gorilla', 'chimpanzee', 'elephant']\n", + "Bat : ['bird_1', 'raccoon', 'spider', 'frog_1', 'gorilla']\n", "fly : ['cockroach', 'grasshopper', 'spider', 'praying_mantis', 'centipede']\n", - "butterfly : ['grasshopper', 'cochlea', 'frog_1', 'centipede', 'spider']\n", - "goat : ['lama', 'deer', 'dog_1', 'kangaroo', 'horse']\n", - "camel : ['ostrich', 'giraffe', 'lama', 'horse', 'elephant']\n", - "lama : ['horse', 'ostrich', 'giraffe', 'kangaroo', 'goat']\n", - "airplane : ['airship', 'helicopter', 'fighter', 'balloon', 'building']\n", - "nan : ['tricycle', 'gas_station', 'knob', 'pedal', 'telephonebox']\n", - "beermug : ['cup', 'can', 'toaster', 'flower_pot', 'teapot']\n", - "bowlingball : ['golfball', 'bowling', 'frisbee', 'skateboard', 'dumbbell']\n", - "bulldozer : ['Cannon', 'gun2', 'fire_engine', 'submachine_gun', 'helicopter']\n", - "Cannon : ['obelisk', 'gun2', 'bulldozer', 'sward', 'telescope']\n", - "boat : ['canoe', 'boat', 'frisbee', 'segway', 'pool']\n", - "coffin : ['xylophone', 'piano', 'Copier', 'bowling', 'silver']\n", - "carrier : ['tricycle', 'tent', 'Cannon', 'nan', 'saddle']\n", - "man_with_hat : ['horse', 'tricycle', 'sunflower', 'saddle', 'tent']\n", - "hat : ['saddle', 'knit_hat', 'tie', 'pedal', 'flower_pot']\n", - "nan : ['Loudspeaker', 'xylophone', 'harmonica', 'person', 'horn']\n", - "fire_extinguisher : ['toaster', 'refrigerator', 'toaster2', 'shredder', 'frying_pan']\n", - "helmet : ['baseball_mitt', 'shoes', 'man_with_bat', 'soccer_ball', 'skateboard']\n", - "piano : ['piano', 'horn', 'xylophone', 'harmonica', 'radio']\n", - "grave : ['obelisk', 'candlestick', 'Stained glass', 'piano', 'bonsai']\n", - "man_with_hammock : ['pool', 'segway', 'person', 'Loudspeaker', 'canoe']\n", - "harp : ['piano', 'horn', 'xylophone', 'mandarin', 'piano']\n", - "iPod : ['floppy', 'Personal_Digital_Assistant', 'radio', 'cd', 'tv']\n", - "knob : ['light_bulb', 'telephone', 'keyboard', 'stearing', 'hourglass']\n", - "post : ['telephonebox', 'gas_station', 'telephone', 'obelisk', 'knob']\n", - "mandarin : ['piano', 'knit_hat', 'pedal', 'saddle', 'harmonica']\n", - "kitchen : ['toaster2', 'bustab', 'refrigerator', 'billiard', 'frying_pan']\n", - "tower : ['obelisk', 'building', 'Stained glass', 'balloon', 'pinetree']\n", - "nan : ['TuningFork', 'driver', 'knife', 'knife2', 'silver']\n", - "coin : ['telescope', 'sward', 'pedal', 'dumbbell', 'obelisk']\n", - "shredder : ['toaster2', 'projector', 'toaster', 'refrigerator', 'tv']\n", + "butterfly : ['grasshopper', 'fly', 'spider', 'centipede', 'praying_mantis']\n", + "goat : ['lama', 'deer', 'kangaroo', 'dog_1', 'horse']\n", + "camel : ['ostrich', 'lama', 'giraffe', 'elephant', 'zebra']\n", + "lama : ['giraffe', 'goat', 'horse', 'ostrich', 'kangaroo']\n", + "airplane : ['fighter', 'airship', 'helicopter', 'balloon', 'boat']\n", + "nan : ['tricycle', 'Cannon', 'carrier', 'gas_station', 'telephonebox']\n", + "beermug : ['cup', 'flower_pot', 'can', 'flashlight', 'teapot']\n", + "bowlingball : ['bowling', 'golfball', 'frisbee', 'billiard', 'skateboard']\n", + "bulldozer : ['Cannon', 'gun2', 'submachine_gun', 'fire_engine', 'helicopter']\n", + "Cannon : ['bulldozer', 'gun2', 'obelisk', 'telescope', 'nan']\n", + "boat : ['canoe', 'boat', 'segway', 'pool', 'frisbee']\n", + "coffin : ['piano', 'xylophone', 'Copier', 'bustab', 'refrigerator']\n", + "carrier : ['tent', 'nan', 'Cannon', 'antenna', 'hat']\n", + "man_with_hat : ['horse', 'shirt', 'tent', 'saddle', 'knit_hat']\n", + "hat : ['tie', 'knit_hat', 'socks', 'saddle', 'teapot']\n", + "nan : ['guitar_pick', 'horn', 'mandarin', 'harmonica', 'xylophone']\n", + "fire_extinguisher : ['fire_engine', 'toaster', 'washing_machine', 'flashlight', 'refrigerator']\n", + "helmet : ['baseball_mitt', 'shoes', 'bag', 'boxing', 'watch']\n", + "piano : ['piano', 'horn', 'xylophone', 'harp', 'mandarin']\n", + "grave : ['obelisk', 'candlestick', 'coffin', 'Stained glass', 'bonsai']\n", + "man_with_hammock : ['pool', 'segway', 'Loudspeaker', 'socks', 'boat']\n", + "harp : ['piano', 'horn', 'xylophone', 'piano', 'mandarin']\n", + "iPod : ['floppy', 'Personal_Digital_Assistant', 'radio', 'cd', 'earphone']\n", + "knob : ['telephone', 'light_bulb', 'stearing', 'dumbbell', 'keyboard']\n", + "post : ['telephonebox', 'gas_station', 'telephone', 'lighthouse', 'coin']\n", + "mandarin : ['piano', 'guitar_pick', 'nan', 'harp', 'knit_hat']\n", + "kitchen : ['toaster2', 'refrigerator', 'frying_pan', 'bustab', 'building']\n", + "tower : ['obelisk', 'building', 'Stained glass', 'lighthouse', 'balloon']\n", + "nan : ['TuningFork', 'driver', 'silver', 'knife', 'knife2']\n", + "coin : ['dumbbell', 'cd', 'telescope', 'pedal', 'sward']\n", + "shredder : ['refrigerator', 'projector', 'toaster', 'Copier', 'toaster2']\n", "Snowmobile : ['lawn mower', 'boat', 'auto_bike', 'tricycle', 'babycar']\n", - "socks : ['tie', 'glass', 'knit_hat', 'TuningFork', 'shirt']\n", - "Stained glass : ['obelisk', 'candlestick', 'light_bulb', 'building', 'tower']\n", - "nan : ['xylophone', 'TuningFork', 'mandarin', 'pedal', 'knit_hat']\n", - "umbrella : ['knit_hat', 'glass', 'tie', 'shirt', 'toaster']\n", - "videoplayer : ['projector', 'toaster2', 'radio', 'floppy', 'Copier']\n", - "washing_machine : ['toaster2', 'bustab', 'refrigerator', 'toaster', 'projector']\n", - "buttery? : ['flashlight', 'Personal_Digital_Assistant', 'binocular', 'toaster', 'calculator']\n" + "socks : ['tie', 'shoes', 'hat', 'knit_hat', 'glass']\n", + "Stained glass : ['tower', 'building', 'candlestick', 'obelisk', 'bowling']\n", + "nan : ['xylophone', 'mandarin', 'guitar_pick', 'horn', 'piano']\n", + "umbrella : ['knit_hat', 'flashlight', 'bag', 'binocular', 'shirt']\n", + "videoplayer : ['projector', 'toaster2', 'Copier', 'radio', 'calculator']\n", + "washing_machine : ['bustab', 'toaster2', 'projector', 'toaster', 'Copier']\n", + "buttery? : ['Personal_Digital_Assistant', 'flashlight', 'calculator', 'binocular', 'toaster']\n" ] } ], @@ -454,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -462,18 +485,24 @@ "output_type": "stream", "text": [ "50 150\n", - "[[1. 0.77586603 0.86917183 ... 0.03649613 0.01915577 0.03120084]\n", - " [0.77586603 1. 0.84104548 ... 0.02249373 0.03472726 0.02643563]\n", - " [0.86917183 0.84104548 1. ... 0.05666878 0.04675196 0.04547304]\n", + "[[ 1. 0.71083848 0.6981841 ... 0.07079657 0.07715051\n", + " -0.04361469]\n", + " [ 0.71083848 1. 0.65821863 ... 0.04573421 0.03526444\n", + " -0.0544816 ]\n", + " [ 0.6981841 0.65821863 1. ... 0.01845445 0.03246007\n", + " -0.02771519]\n", " ...\n", - " [0.03649613 0.02249373 0.05666878 ... 1. 0.89741394 0.82211575]\n", - " [0.01915577 0.03472726 0.04675196 ... 0.89741394 1. 0.87863461]\n", - " [0.03120084 0.02643563 0.04547304 ... 0.82211575 0.87863461 1. ]]\n" + " [ 0.07079657 0.04573421 0.01845445 ... 1. 0.59777301\n", + " 0.29577247]\n", + " [ 0.07715051 0.03526444 0.03246007 ... 0.59777301 1.\n", + " 0.44624586]\n", + " [-0.04361469 -0.0544816 -0.02771519 ... 0.29577247 0.44624586\n", + " 1. ]]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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j9n59ra5GRRgNgTDhLC7hT5O+y1Vv5uEcM8TqclQE0RAII849+/n8ZwMp/dlZzt090upyVITQEAgz9o3baDU/kcLxLko/6MvRp63pwagih4ZAGGq9dAudLvuarMFLOJ9SbnU5Ksw1OQRE5GpP67Hqn9Mi8hMR+Z2IFHpNj7yGAiHg/MZO9Pn3w7iKW1L2gI41UE3X5BAwxuz1tB5LAYYC5bgfOQ7wSvVnxphVAahTXeKyP39G7/u30zlTODWxgsrbh2OLi9PnEqhGC9ThwM3AQWPM4QB9n/JRwoJMkhfE0ubnBZy/eQC3/Gqz1SWpMBOoEJgGvO31/gkR2Ski6XV1JBaRmSKyVUS2VlEZoDKiU6v1uzj752Sm/XkVWyf1tLocFWYC0Zq8BXAHsMQzaTbQG0gBjgEv17acMWaOMWaYMWZYLPocfn+cviOFG1/8jBXjBzPrk4XY4uOtLkmFkUDsCdwGbDPGFAEYY4qMMU5jjAv4B+62ZCqI2izOJHNQLI4jBTzR71ZWH/gMbHary1JhIhAhMB2vQ4HqHoQeU3C3JVPNxFVezvjkobx/JBN7+wSry1FhwO9ehMA4YJnX5D+JyC4R2QmMBX7qzzpUE7icTOl/C89t24D9qt5WV6NCnL9tyM4BHS6Z9j2/KlIB4fy6jOcnTGXAooNse3Iwts3brS5JhSi9YzCCOfe5A+DMs2cov0vHGqjaaQhEONvm7cT+swMFtzv5+kF9nLmqSUMgCsQv20Lnj2M5NcRQ8ogGgbqYhkCUaP+Gp69BikvHGqiL6IProkhSegb2B1I5Ob4SI6OIqXDRZskWq8tSFtMQiDIJb2ZiZBRbXprNpgp4YUmK1SUpi+nhQBSKqXCxqQK2ne9BTI8rrC5HWUxDIAq1WbKFF3qlsO72gfxl40JsrVtbXZKykIZAFHPkf8VT/cexct9mHWsQxTQEopzr3Dkmdh/OyiNZ2Nu1s7ocZQENAQUuJ5P7jeXFHeuw99HnEUQbDQEFgPP0aX45bhppy/ZgvpNidTmqGWkIqG84D+Sx6bFUKp4ro3yKjjWIFhoC6iLyaS7M7kThZIeONYgSGgKqhlbvZtFpQwsdaxAlNARUrb4ZazDI8PX3NAgimYaAqlNSegadtgonb77A+Tv1UZGRSkNA1av9vzJIftcOj53UqwYRyqcQ8PQPOCEin3tNSxKRdSKy3/M70TNdRGSWiBzw9B7QPtphrtW7WcQ9n8ANszP1PoII5OuewDxgwiXTngHWG2P6Aus978H9CPK+np+ZuPsQqDAnn+by2V39eHHdQr2zMML4FALGmE1AySWTJwPzPa/nA3d6TX/DuGUC7S95DLkKU84Defxy0Dje2/NvHWsQQfw5J9DFGHPM8/o40MXz+nLgiNd8BZ5pKgI4T59mUvcRrDqSja1tW6vLUQEQkBODxhgDmMYso70Iw5jLyaRrbuQvuz4kpueVVlej/ORPCBRV7+Z7fp/wTC8EunvNl+yZdhHtRRjeXGfO8PTYaYxZ8Tlm1CCry1F+8CcE3gdmeF7PAN7zmv6g5ypBKlDmddigIogj7zDrfjAa5+9LqfgPvY8gXPl6ifBtIAO4WkQKROT7wB+BcSKyH7jF8x5gFXAIOIC7IemPAl61ChmSsQPHX7tSMLVKn2Icpnx60KgxZnodH91cy7wGeNyfolR4iVuRRaeEVE6MMDhbjCIpPcPqklQj6B2DKiAS3sykQ66NkoGG0hk61iCcaAiogElKz6DDduHkjVX6PIIwoiGgAipxfgbJK+w4Zp7Cdf1gq8tRPtAQUAEXv3wLbV5sx8j/3Yq9by+ry1EN0BBQQWHbvJ1t9/TlubVLsLdPsLocVQ8NARU0zv2HeH7ITSzf/ZGONQhhGgIqqJxfl3FH91TWFuRgi4+3uhxVCw0BFXwuJ7f1SeNvez4k5sruDc+vmpWGgGoWrvJynhw9jYmrc2HEAKvLUV40BFSzcRwp4P2HxmB/qZiKSTrWIFRoCKjmlbWLcy8nUzi9irL7daxBKNAQUM0ubmUWHVe15ESq9jUIBRoCyhIJCzLpsN1GyQAda2A1DQFlmaT0DDrkCidHOzh3t441sIqGgLJU4vwMLl9r4/yMUpxj9On0VtAQUJZrvXQLiS+3YdDLudj79bW6nKijIaBCgn3jNvbc14tnPliCPTHR6nKiioaAChnOvQf449CxLN21VscaNKMGQ6COFmR/FpEvPW3GlotIe8/0HiJyXkRyPT9/D2LtKgI5S0uZcsUo91iDuDiry4kKvuwJzKNmC7J1wHXGmIHAPuBZr88OGmNSPD8/DEyZKqq4nNzWK5XX9n1ETLL2rQm2BkOgthZkxpgPjTEOz9tM3L0FlAoYV0UFj6Xdy13rcpCh/a0uJ6IF4pzAI8Bqr/c9RWS7iHwsItcH4PtVlHIUFLLkgZuJf+UElbcNt7qciOVXCIjIrwAHsMAz6RhwhTFmMPAU8JaI1NrCVtuQKV+YnN2U/HcPjs6o1LEGQdLkEBCRh4BJwP2eXgMYYyqNMcWe1znAQeCq2pbXNmTKVy1XZ5O0Ip4To3SsQTA0KQREZALwNHCHMabca3onEbF7XvcC+uLuRqSUXxIWZNJhm2eswUMaBIHkyyXC2lqQ/Q1oC6y75FLgDcBOEckFlgI/NMaU1Pa9SjVWUnoGHXYIJ0c5OHuPjjUIlAbbkNXRguz1OuZ9B3jH36KUqkvivAxiz43k7P2niSseSsyGHKtLCnt6x6AKO22WbKHjrHiufmk39v5XW11O2NMQUGEpZkMO+Q9dyZPvvYu9Y4dmXbe9QxIS41Mv37CgIaDClnP3Xmaljubt3JXN+o9y6qefU3Fr5LRY0xBQYc15qph7e1zPmq+2Ii2b51Lz29dcRstV2c2yruagIaDCnnE4mNBzJHP3ryemW1erywk7GgIqIpjKSv7fyLu5b2M2tpRrrS4nrGgIqIjhOHacN+8dT8L/HefCBB1r4CsNARVRXLlfUPT73hx7pIIz03SsgS80BFTEabEmm6RlrTl2g4uSh/UW44ZoCKiI1HZhJh2z7ZQMNBoEDdAQUBEraW4GSTuFUyOdemhQDw0BFdGS5mZw2QYbp6eeoeqWoVaXE5I0BFTEa7M4k86vxtHzD3uxDbzG6nJCjoaAigqxH+VwdGYyj76zEnuXzlaXE1I0BFTUcO38kjmj03hj63IktoXV5YQMDQEVVZxFJ3ig11jWHM7SIPDQEFBRx1RdYMKVI3jz0L+J6drF6nIspyGgopKpusCDw6bwg82fYhvUz+pyLNXUNmS/E5FCr3ZjE70+e1ZEDojIXhEZH6zClfKXs+gEr313El1fK6Dq1mFWl2OZprYhA3jFq93YKgARuRaYBvT3LPNq9dOHlQpFrp1fcvi5qyl69Dxn7o3OG4qa1IasHpOBhZ7+A3nAAWCEH/UpFXSxH24lYXFbjo2NzrEG/pwTeMLTlThdRKobyl8OHPGap8AzTamQ1nZRJh23ROdYg6aGwGygN5CCu/XYy439Am1DpkLNN2MNhkfXWIMmhYAxpsgY4zTGuIB/8O0ufyHQ3WvWZM+02r5D25CpkJM0N4Num2yU3HUuah5M0tQ2ZN283k4Bqq8cvA9ME5GWItITdxuyLP9KVKp5tV2YSbf0OLr85mBUPKqswec0e9qQjQE6ikgB8FtgjIikAAbIBx4FMMbsFpHFwBe4uxU/boxxBqVypYKoxZpsyo5fy8OLP2D+Dak4jhdZXVLQiKehsKXaSZIZKTdbXYZSNcR07cLc7GU82OcmTGV4n7v6yCzNMcbUuCFC7xhUqh6O40U82Ocm1uRtiaiuQ940BJRqgKmsZMIVw1iUv7nZW541Bw0BpXxgHA6mp0ziycxPIq4JqoaACmv2DklM//Jos6zLeaqYWZPvpMe8wzhuipxHlWkIqLDmKjvNvKcmN9v6nLv3svcX/Tn1ZDlnp0bGDUUaAiqsGYej2ZuDxmzIoc1bCRy9yUXpQ+F/i7GGgFJN0GZxJp0y7RQPCv+xBhoCSjVR4jz3WIPiYS5O3xe+hwYaAkr5IWluBl0+FYrvKKfytvAca6AhoJSf2r2VyWXzW5L0bD4ytL/V5TSahoBSAdBydTblP+3MPW+uJyY5vB6hoSGgVICYnN0sGzeU2Z8twhYXZ3U5PtMQUCqAHAWFPHrVLaw+lBk2Yw00BJQKMFdFBeOTh7I0/xPsHZKsLqdBGgJKBYPLydRBE3k6ayP2fn2trqZeGgJKBYmzuIQ/TfouV72Zh3PMEKvLqZOGgFJB5Nyzn89/NpDSn53l3N0jrS6nVhoCSgWZfeM2Ws1PpHC8i9IZoXeLcVPbkC3yakGWLyK5nuk9ROS812d/D2LtSoWN1ku30OmTGIpTDMXfD60g8OUaxjzgb8Ab1ROMMfdWvxaRl4Eyr/kPGmNSAlSfUhEjcX4Gxj6K4sEuYh5IJeHNTKtLAvxsQyYiAkwF3g5wXUpFpKT0DDpnCqcmVlB5e2iMNfD3nMD1QJExZr/XtJ4isl1EPhaR6/38fqUiTsKCTJIXxNLm5wXI8AFWl+N3CEzn4r2AY8AVxpjBwFPAWyLSrrYFtQ2ZimYtP8jG8fOO3Dl/AzHdky2tpckhICIxwF3Aouppnm7ExZ7XOcBB4Kraltc2ZCramexdrBg/mFmfLMQWH29ZHf7sCdwCfGmMKaieICKdRMTued0LdxuyQ/6VqFTkchwp4Il+t7L6wGdgs1tSgy+XCN8GMoCrRaRARL7v+WgaNU8I3gDs9FwyXAr80BhT60lFpZSbq7yc8clDef9IJvb2Cc2+fm1DplSIsLdP4LltG3h+wlSc+w4G/Pu1DZlSIc75dRnPT5jKgEUHcV0/uNnWqyGgVAhx7jvIticHc+bZM5Tf1TxjDTQElAoxts3bif1nBwpud/L1g8G/xVhDQKkQFL9sC50/juXUEEPJI8ENAg0BpUJU+zc8fQ1SXJQ9ELy+BhoCSoWwpPQMOmcJJ8dXUvEfI4KyDg0BpUJcwpuZJC+OJebHxzGjBgX8+zUElAoDcSuysP8mkYn//JiYHlcE9Ls1BJQKE5Kxg3W3D+QvGxdia906YN+rIaBUGHHkf8VT/cexct/mgI010BBQKsy4zp1jYvfhrDyShb1drSP1G0VDQKlw5HIyud9YXtyxDnufnn59lYaAUmHKefo0vxw3jbRlezDfSWny92gIKBXGnAfy2PRYKhXPlVE+pWljDTQElApz8mkuzO5E4WRHk8YaaAgoFQFavZtFpw0tmjTWQENAqQjxzViDQYavv+d7EGgIKBVBktIz6LRVOHnzBc7f6dtYAw0BpSJM+39lkPyuHR476dNVAw0BpSJQq3eziHs+gRtmZzZ4H4GGgFIRSj7N5bO7+vHiuoX13lmoIaBUBHMeyOOXg8ax6stNdc4TEo8cF5GTwDnglNW1BEFHInO7IHK3LVK360pjTKdLJ4ZECACIyNbanoke7iJ1uyByty1St6suejigVJTTEFAqyoVSCMyxuoAgidTtgsjdtkjdrlqFzDkBpZQ1QmlPQCllActDQEQmiMheETkgIs9YXY+/RCRfRHaJSK6IbPVMSxKRdSKy3/M70eo6GyIi6SJyQkQ+95pW63aI2yzPn+FOERliXeUNq2PbficihZ4/t1wRmej12bOebdsrIuOtqTp4LA0BEbED/wfcBlwLTBeRa62sKUDGGmNSvC4zPQOsN8b0BdZ73oe6ecCES6bVtR23AX09PzOB2c1UY1PNo+a2Abzi+XNLMcasAvD8fZwG9Pcs86rn723EsHpPYARwwBhzyBhzAVgITLa4pmCYDMz3vJ4P3GldKb4xxmwCSi6ZXNd2TAbeMG6ZQHsR6dYshTZBHdtWl8nAQmNMpTEmDziA++9txLA6BC4Hjni9L/BMC2cG+FBEckRkpmdaF2PMMc/r40AXa0rzW13bESl/jk94DmfSvQ7ZImXb6mR1CESi0caYIbh3kR8XkRu8PzTuyzFhf0kmUrbDy2ygN5ACHANetrSaZmR1CBQC3b3eJ3umhS1jTKHn9wlgOe5dx6Lq3WPP7xPWVeiXurYj7P8cjTFFxhinMcYF/INvd/nDftsaYnUIZAN9RaSniLTAfQLmfYtrajIRaS0ibatfA7cCn+Pephme2WYA71lTod/q2o73gQc9VwlSgTKvw4awcMk5jCm4/9zAvW3TRKSliPTEffIzq7nrC6YYK1dujHGIyBPAWsAOpBtjdltZk5+6AMtFBNz/bd8yxqwRkWxgsYh8HzgMTLWwRp+IyNvAGKCjiBQAvwX+SO3bsQqYiPukWTnwcLMX3Ah1bNsYEUnBfYiTDzwKYIzZLSKLgS8AB/C4McZpQdlBo3cMKhXlrD4cUEpZTENAqSinIaBUlNMQUCrKaQgoFeU0BJSKchoCSkU5DQGlotz/B3WuT47VQpv9AAAAAElFTkSuQmCC\n", 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" ] @@ -511,12 +540,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -530,56 +559,56 @@ "name": "stdout", "output_type": "stream", "text": [ - "Goldfish : ['snake', 'frog_2', 'trilobite', 'ostrich', 'frog_1']\n", - "owl : ['rhinoceros', 'frog_1', 'snake', 'scorpion', 'ostrich']\n", - "nan : ['trilobite', 'ostrich', 'frog_2', 'frog_1', 'snake']\n", - "duck : ['ostrich', 'trilobite', 'rhinoceros', 'snake', 'frog_2']\n", - "swan : ['frog_2', 'frog_1', 'snake', 'ostrich', 'scorpion']\n", - "nan : ['trilobite', 'ostrich', 'rhinoceros', 'frog_2', 'frog_1']\n", - "crab : ['turtle', 'frog_2', 'trilobite', 'frog_1', 'scorpion']\n", - "orca : ['frog_1', 'scorpion', 'frog_2', 'rhinoceros', 'turtle']\n", - "leopard : ['rhinoceros', 'trilobite', 'ostrich', 'frog_1', 'frog_2']\n", - "Bat : ['frog_1', 'frog_2', 'scorpion', 'rhinoceros', 'ostrich']\n", - "fly : ['rhinoceros', 'frog_1', 'scorpion', 'ostrich', 'trilobite']\n", - "butterfly : ['frog_2', 'frog_1', 'rhinoceros', 'snake', 'trilobite']\n", - "goat : ['ostrich', 'frog_2', 'turtle', 'scorpion', 'frog_1']\n", - "camel : ['frog_2', 'scorpion', 'frog_1', 'trilobite', 'rhinoceros']\n", - "lama : ['frog_1', 'frog_2', 'snake', 'scorpion', 'rhinoceros']\n", - "airplane : ['ostrich', 'snake', 'scorpion', 'turtle', 'frog_2']\n", - "nan : ['trilobite', 'frog_2', 'snake', 'frog_1', 'rhinoceros']\n", - "beermug : ['rhinoceros', 'frog_1', 'trilobite', 'frog_2', 'ostrich']\n", - "bowlingball : ['frog_2', 'frog_1', 'snake', 'ostrich', 'trilobite']\n", - "bulldozer : ['frog_1', 'frog_2', 'snake', 'scorpion', 'ostrich']\n", - "Cannon : ['frog_1', 'frog_2', 'rhinoceros', 'snake', 'trilobite']\n", - "boat : ['frog_1', 'frog_2', 'turtle', 'trilobite', 'snake']\n", - "coffin : ['frog_1', 'scorpion', 'frog_2', 'turtle', 'ostrich']\n", - "carrier : ['frog_1', 'frog_2', 'snake', 'ostrich', 'rhinoceros']\n", - "man_with_hat : ['turtle', 'frog_1', 'trilobite', 'snake', 'ostrich']\n", - "hat : ['scorpion', 'frog_1', 'frog_2', 'rhinoceros', 'ostrich']\n", - "nan : ['frog_1', 'frog_2', 'turtle', 'snake', 'trilobite']\n", - "fire_extinguisher : ['trilobite', 'frog_2', 'ostrich', 'frog_1', 'scorpion']\n", - "helmet : ['frog_1', 'frog_2', 'snake', 'ostrich', 'scorpion']\n", - "piano : ['trilobite', 'frog_2', 'frog_1', 'scorpion', 'rhinoceros']\n", - "grave : ['frog_1', 'frog_2', 'ostrich', 'scorpion', 'snake']\n", - "man_with_hammock : ['ostrich', 'frog_1', 'turtle', 'snake', 'trilobite']\n", - "harp : ['snake', 'turtle', 'trilobite', 'ostrich', 'rhinoceros']\n", - "iPod : ['frog_1', 'trilobite', 'frog_2', 'snake', 'ostrich']\n", - "knob : ['snake', 'trilobite', 'turtle', 'rhinoceros', 'frog_1']\n", - "post : ['ostrich', 'scorpion', 'trilobite', 'frog_2', 'snake']\n", - "mandarin : ['scorpion', 'trilobite', 'turtle', 'frog_1', 'ostrich']\n", - "kitchen : ['frog_1', 'frog_2', 'turtle', 'trilobite', 'scorpion']\n", - "tower : ['frog_1', 'frog_2', 'trilobite', 'turtle', 'snake']\n", - "nan : ['trilobite', 'frog_2', 'frog_1', 'ostrich', 'scorpion']\n", - "coin : ['snake', 'scorpion', 'frog_2', 'ostrich', 'frog_1']\n", - "shredder : ['trilobite', 'frog_2', 'ostrich', 'snake', 'scorpion']\n", - "Snowmobile : ['snake', 'ostrich', 'rhinoceros', 'frog_1', 'scorpion']\n", - "socks : ['trilobite', 'frog_2', 'scorpion', 'frog_1', 'rhinoceros']\n", - "Stained glass : ['frog_1', 'rhinoceros', 'snake', 'trilobite', 'frog_2']\n", - "nan : ['frog_1', 'frog_2', 'snake', 'scorpion', 'ostrich']\n", - "umbrella : ['rhinoceros', 'ostrich', 'snake', 'trilobite', 'frog_2']\n", - "videoplayer : ['rhinoceros', 'frog_1', 'trilobite', 'frog_2', 'scorpion']\n", - "washing_machine : ['frog_2', 'frog_1', 'rhinoceros', 'ostrich', 'turtle']\n", - "buttery? : ['frog_1', 'frog_2', 'turtle', 'snake', 'scorpion']\n" + "Goldfish : ['praying_mantis', 'watermelon', 'chimpanzee', 'sunflower', 'glass']\n", + "owl : ['light_bulb', 'glass', 'sunflower', 'praying_mantis', 'horseshoe_crab']\n", + "nan : ['watermelon', 'chimpanzee', 'horse', 'bear', 'tricycle']\n", + "duck : ['tricycle', 'sunflower', 'harmonica', 'praying_mantis', 'airship']\n", + "swan : ['frog_2', 'frog_1', 'praying_mantis', 'chimpanzee', 'Loudspeaker']\n", + "nan : ['xylophone', 'bear', 'glass', 'hourglass', 'frisbee']\n", + "crab : ['sunflower', 'building', 'hourglass', 'antenna', 'light_bulb']\n", + "orca : ['snake', 'chimpanzee', 'light_bulb', 'frog_2', 'sunflower']\n", + "leopard : ['hourglass', 'bear', 'light_bulb', 'chimpanzee', 'balloon']\n", + "Bat : ['light_bulb', 'bowling', 'raccoon', 'basket_clam', 'tent']\n", + "fly : ['sunflower', 'ostrich', 'light_bulb', 'watermelon', 'chimpanzee']\n", + "butterfly : ['light_bulb', 'bowling', 'deer', 'rhinoceros', 'ostrich']\n", + "goat : ['hourglass', 'sunflower', 'light_bulb', 'pinetree', 'cup']\n", + "camel : ['watermelon', 'light_bulb', 'sunflower', 'sward', 'bowling']\n", + "lama : ['sunflower', 'light_bulb', 'tent', 'building', 'pinetree']\n", + "airplane : ['chimpanzee', 'praying_mantis', 'tricycle', 'telescope', 'pedal']\n", + "nan : ['building', 'pedal', 'tent', 'pinetree', 'tv']\n", + "beermug : ['hourglass', 'chimpanzee', 'bear', 'segway', 'sunflower']\n", + "bowlingball : ['sunflower', 'light_bulb', 'camera', 'glass', 'cochlea']\n", + "bulldozer : ['toaster', 'pedal', 'watermelon', 'harmonica', 'light_bulb']\n", + "Cannon : ['sunflower', 'light_bulb', 'frisbee', 'tricycle', 'tennis_ball']\n", + "boat : ['pedal', 'sunflower', 'cup', 'tricycle', 'tent']\n", + "coffin : ['light_bulb', 'sunflower', 'projector', 'tent', 'frisbee']\n", + "carrier : ['tent', 'pedal', 'pinetree', 'antenna', 'sunflower']\n", + "man_with_hat : ['praying_mantis', 'grasshopper', 'porcupine', 'sunflower', 'frog_2']\n", + "hat : ['pinetree', 'tv', 'Fern', 'tricycle', 'antenna']\n", + "nan : ['bear', 'frog_2', 'watermelon', 'frog_1', 'kangaroo']\n", + "fire_extinguisher : ['harmonica', 'xylophone', 'Loudspeaker', 'basketball_ring', 'antenna']\n", + "helmet : ['light_bulb', 'praying_mantis', 'bowling', 'hourglass', 'cochlea']\n", + "piano : ['giraffe', 'balloon', 'sunflower', 'praying_mantis', 'watermelon']\n", + "grave : ['chimpanzee', 'tricycle', 'light_bulb', 'sunflower', 'tomato']\n", + "man_with_hammock : ['bear', 'raccoon', 'porcupine', 'hourglass', 'horse']\n", + "harp : ['hourglass', 'tricycle', 'praying_mantis', 'light_bulb', 'pinetree']\n", + "iPod : ['light_bulb', 'TuningFork', 'hourglass', 'frisbee', 'sunflower']\n", + "knob : ['praying_mantis', 'watermelon', 'chimpanzee', 'snake', 'sunflower']\n", + "post : ['clip', 'chimpanzee', 'keyboard', 'praying_mantis', 'hourglass']\n", + "mandarin : ['frog_1', 'frog_2', 'praying_mantis', 'snake', 'hourglass']\n", + "kitchen : ['tennis_ball', 'sunflower', 'cochlea', 'harmonica', 'hourglass']\n", + "tower : ['Fern', 'light_bulb', 'sunflower', 'frog_2', 'penguin']\n", + "nan : ['tomato', 'bowling', 'chimpanzee', 'harmonica', 'clip']\n", + "coin : ['light_bulb', 'sunflower', 'Fern', 'clip', 'lawn mower']\n", + "shredder : ['sunflower', 'light_bulb', 'watermelon', 'building', 'glass']\n", + "Snowmobile : ['Fern', 'light_bulb', 'tricycle', 'frisbee', 'sunflower']\n", + "socks : ['praying_mantis', 'segway', 'chimpanzee', 'sward', 'Loudspeaker']\n", + "Stained glass : ['frisbee', 'building', 'sunflower', 'harmonica', 'praying_mantis']\n", + "nan : ['praying_mantis', 'pedal', 'saddle', 'chimpanzee', 'tricycle']\n", + "umbrella : ['light_bulb', 'hourglass', 'chimpanzee', 'watermelon', 'bowling']\n", + "videoplayer : ['praying_mantis', 'sunflower', 'tricycle', 'clip', 'giraffe']\n", + "washing_machine : ['hourglass', 'light_bulb', 'tennis_ball', 'telephone', 'sunflower']\n", + "buttery? : ['harmonica', 'horseshoe_crab', 'frog_2', 'praying_mantis', 'watermelon']\n" ] } ], @@ -609,32 +638,32 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "768 300 150 50\n", - "[[ 1. 0.57596924 0.53442588 ... -0.01486964 -0.02870013\n", - " 0.01908956]\n", - " [ 0.57596924 1. 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\n", 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\n", 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\n", 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\n", 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" ] @@ -672,12 +701,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 55, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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XDHffzQg96MbOOUYkRE4pnQZgXRRxeYHbyLNB5G0QFLkXKQc1qagWls6UyHMx2CkiyKzHX/0K2GEH12iTiAOLF7OFrtzqKptEHqVphcPNj9kL4ltGKuUss1G/GStylY1cFWEQIk9HNQfNtCru99+3fr/zjnPBszBphxns9MqzGM/XX7NB1wJEzmzkhJDzCCFfEUK+WrNmTVpxZMNGbs4LgWEsT6W8X7GDKnLRfNJZFXkAMk0iztzFrrwS+OortmnEL79IgaR8R0nkfoOd6ZhWVGvCh4GHIo/MtEKpt/ugKnL5PqjI1u9tpJhs5KoBbxnZXrYhIuSMyCmlkymloyilo3r37p1WHLEEy26UXiuWaUXwWvGy0Qa1kYtEzhPJ12BnEPgRuSpuvjCSh+9wSmxiGzYA557L3MYA4Lvv2KbAMoEuWeKc2CMjUxu5isjd6k++x5kSuUqRF6JpRdW2b7/dO/1iInK3Ga0iolq24YcfoonHBYmsxh4xTEXeJkxD5kiTyE1FTkusA16K0IsIRKgGClVEXiyDnapyrl7NvmtqXKNNQiB5PpuwtpbFd8wxbOBQdo385htrh3c35NK0IiPTTRGSSddXf9tzpYMCa9d51q8D2SZy7ublBp9+SEHY6Em2iXzWLFbHO+7I/qv6hrjGjBuiUuTbbZdVz5fidD/sUIyyp1nhpo2cCqaVqBV51KYVN+TaRs4Vec+e7llSEXlbG5vyrqqPoMiUyFW+00Ft5JmqtKCDna+9wXaECrSDNbBsSQqP0bOt+MISeRC7Mp9B5wbxGqPubcNFvD1ESWqiOYnjlFOAnXZS50u+LpVib1kTJjjf9NK515QCTzzh3Lw6i4jK/fB5ANMBbEMIWUoIOSeKeGU4bORBCPGZZzwVFI+iXVTkQYj8zjuZr7SbjfzBB63fXu6H6Qx2ul0TlMgXLQIuvVRtMxU7vziRRxW3hyK3ea1wiA17xoz8ELnXG1XQfASt5zVr1CtoephWbFw742v2Q95VygWHHU5wLh5DLWrsfuSqfPstpCWEsYUMQ+TGImE2vZUNIudtwasv+ZlW9tyTbTrBd+GSw4TB228D55wD/OlP4a9NE1F5rZxMKe1PKS2hlA6klD4eRbwyHETuN3izYAFw2mnAr3/tGqfjPqVSdiX68MPO8xwHHODeID//HKiosCcSlSKnFBMnAm9AMj/wdOrqGLlOm6aO59RTgfvvZyv6ffEF2xeTk6zY+UV1ospnuoqcg6flR6CDBjmPBd16LlPTyqJFzLYvImjnPvZY5tUza5bz+iBEXtWV/aivD5TcSuN2pBBLT5HL5Tfqqh2COcWPyMX+4EXkUYK3U7lMPXpYv73Km0xas4HnzAm+IqYb+HaRfpPwIkRxmVZKWHZNG7kfkfPO7lGhjvskK/KLLrKfF9NpbHRX5Bs3stdiMZ9REXkqhT/9CTgWbzjzDjCCXrfOvg4FwBRCR4f9jeD889m+mN99Zx2jlL3JiFPdVeTFB/08BjttHVcmX674/TrL0qXOY0E7WBgiV5VxyBDgrLPsx4IS+eLF7HvkSPvxVEppxli9WuLayi7sR0AipymuoImayL/9lnkO8TzIkJdhMMKEInIxXqNd2IicKhaOe/997zr1U+9uily8936mFR72D38ALrxQyLAiX5dd5p0ft6UYtI2cIbSNnN+8piY2TV1hMvFV5ID3oKqbrXzjRqBbN3siESpyJWTTi0xiEycCU6bYpybzNbJ53RDCloQ87TT7lluqfHJXNA9StZlW5DVXOEGno3qCkmk2BjuDpu21k5NUn6vrSjFoEPDWW9YxU5G7rfYngUfZgYSayA8+GNhtN3tgEXL/SEeR+xE5kdZk/+EHlq///Ae/YAsQUMzALt75ksHfmOQyuS0AxyEq8vJy67joz65qE3yGrhtUq5wC4dYrConiInJDkTc1G/fWjxB5Y1m4kC0c9bjT4uOryAG7kgwysg8wldC9uz0f/AZPnmyFe+gh9fUq+E1gSCaB4cNZWQG1Ul682O7GqCLy55+3X1NSoiYvTuQexOZpWuHlEBt42F1t/JBPG7mXd5F0D5etq0Bbm33F32RFOEXOYRJ5kIltIlwEjI3IE5ajW329Iho/G7msyDkJ19fjDRwDAHgap3vnSwYXBJnYyEUir6pyhgkDl3XnX3whZXtGRIniInJDkR97x97s7SfsBAfFKLLcd6fO7oP2JunJKdpIZTvjP//JfodQ5FdjImIwjj36qDPfbvAb7GxvB+bOtWzjKiJZtsw+sCkTOWA3qRACdOlivWa3C27UfIajBwF6ErmYb46gcwySSWYDHTLEOxx/MDz4IPCb3zi9VvJF5FIcdY2MLG0enzHj3gQ1rRhF60CC/QlLbLKAUZlWjHxv3AhUVyv2DPEj8hSxxWPe++Zmcy5HGSTiVq2eKaazaBH7lvtgGNOK8IBCZaUzTBi4mFZ+dUa5bRWCKFFcRG4o8kW1VViyBOGJXL4pzzyD5CLL/joN++LAOw7FrU9sZg+nIPJfsAX+XHchqOidIsJDkU/C1aDpVL2faUX2zlERiWxv5n7BXPUQYnmjAIzE49ZmHuecw0z/qRQ8TStKrxU3TxORyPv0UYeR8H1tHzyJM4Hjj/cOyOvq4ouBZ58tWNNKfSMjEuVkWhfTCqVsGOT7763/gEG8Ks8YEUFs5CpFbpSdv6Q+9phHvAY52nZSdCPylhZ3IlcpcvGh8957wN/+lrZp5c2ft8eANbPQwicFikSejiJ3M61kEUVJ5IBxb4PayN3CnHYaOm6eYP79GWwLqZ+WltmCrVnUZM1MNuI8Gm/iOvwZy2CQvkCuLU0pdDS2uJtWwqCjw6EezTECGfJOPCrTyrJl/jZyFZEbaT/zjBA8gI3cpsjdiFzsqHyA2AePz9sb566fhFVNXUFA8Sn2UgeUH3oygaczFyFTRa4g2fomhSJ/6232w4XIGxrYctxjx7L/VLaRZ8O0Ytxrzr+OW+pnI0+HyFWKXMzrlCnABRc422FAIr/o019jBe2HlejHjodV5G5vAprI1eBT9AGjwWeqyGEnmo1gNslSYg/X59CdLX4x0qlDteN6AMCHH6KiKoaD8L77YKeBQGPYRxwBXH65Le22dhelF0SRL1qkNq2Iakwm8ljMLAOPsqUF4W3kbtPqRfbycGUU0dKeQAdK8NaC7QEA9+FSdUC5k8keDn5eKyoY7eiKK9gcElco6v9rjMRHdSMd7bWuiQ0i2oic+xMKYzRNTdbt4VFwnqMi8fopchXJhzCt8KCOccigppVUipmMuLnNy7SiUuQqcpePSUT+E7bCmfi71dSNjLUkWfkI75FhbeRyPXvtBJUlFBWRi4o80GCn/KqoqNgOYZUCk8hjTsI3o+f+vkbVNcF4elPKFoM64AAAwMcY61TkUudpRwmWoz/+h9HOvHMsWsQGa4XrW9sCDgiqiHzBArt5gRM57wR1daxyeWOsqrKZVniUzXWtVicMaloJQuQBp6O3drD8rW2tsqXnzIjLyoX8hi5ZYp0LqqCMurjnHjaHxBWK+t8FX2P/+ZMdbZErcptphdedUIa99gL69mW/efOWHadMG3kWFbmrI4kfkSeFH9XVwIknsv8tLeYKpCWQ+q2KyFXH3OYpGOmdiSfxFM7EZ5/Zy9LcYdQ9N62IRO6hyJuaDF0g84pW5N6Il1g3xqHIVU8/mcgVN0VF5GUxDzchicgbIdx0eQaerMgVRL495mBv/E953ryWl4OrodaA/qhuDYmvPplMWkTOVR+XeyNGsO/GRtYwjbhMRb5aGDcIYFqhAJ5bMBrtquV9RFbgLOWD1iSLt7a5i3dAuQ74SG0qBbzyit1HPKjfb6amFUW+VIpcNQtSnFvEwzoW3kzXRh7E/VBS5J7x+hE5oDSt2NIzzjmgOuZF5KkUYkjZs2i026YkS9ck8jLBtOpxr/v0oejSBWzJXY7XXrOlmSsUGZFbr+kOG3kqhQ8/tM9rCaLIxVf/BjC/XZUiF9MBFEROqXOXdVmRSze2HSWogzD7TNVoOjqsXpNKIQWC818Y6whWixoQUDwBgZjcvET4BClRkfOw3B575JHse/58myLnfaNlrWAcVRA5f03l9fsmjsap82/CrVBIWF6+f/3Lf29RA62LmdmhtrnKO6CXIp8+3X5OqP+WFrh34kwHO3n6BmpRg1UNbBawbZtUTmguDxQX3g1lI69FDa7HLUgiFmxCkEqRt7cDY8aw6e1BTSuyohZMK+beAG4FVV1vxAEA/8NoLEd/q/4/+wz4/HPEDU+xZBKsDxjhU4ZLpG1PAqm8KjQ2GvGLK0Ief7x6/EVANuYFFRmRe9vIDzjAvk5OWEW+Fuy1nt9wJcz1J9hNtCnyuXPtdm9O5E8/zUbVFYrcL382RZ5KYSGG4PU5WzuC8YHav+IC66Dfoj3JpOW1Is+65EvNArbBTlORrzPCJxLKBiubVvjbznwMc+aDd8px4+xqyAO804cmctGu7zKF/eWX2eoK332buSJ/FqdgGvYFevbETOxsi+N7bIeZ2Bm9UYvnvh4OwM5PDkITQCnQfvcD5m8h+xaRB1DkF+EhTMD1eB8HhVLkrSvWWcd+/pm5vJ5zTvDBTrlttrSgGexhZipjDtVEGg9Fvjf+hx3xnXV/R48Gfv97i8g7KBMMX35pu9xM1+ZmE+Beu6377lL/QXYyDIviJnKhwldtVHRoucZ8FHkt2Ihma7v/K7HStNLWZu983LTy3XdsVN2DyFMg7opcIPJUmFsWhMhlRc7RpQvzcf/gA/VgZ53RkXr0CGRa6QJmH7fVFwdnr1jMk8hnYmdMBHNc5vW8tpmNUXyEsViAITgTf8dKCOYZL0Xu0gH57Eo+m91ZqGQwT8VYDL/BsxiDaUB5OXbFTOtcKoXt8b39GOxN1mxLlDIXWCHR9nag7eHJUKEdJYH9yOvBxEaHyuSlIHK+F0Dbw486woGQ4IpcJuKWFvON2PEAU1W2SpELYmQdXzhMADettDerb17rPgexH6q9BLzgN+lMQtCl9MOguNYjtxE5BXbdFQDQgjL0e/oO5wUhFTkn8pa24ERuDnYa52xqQnRjMs7vBMvIKXaQdpSgzE2RC6aVFpQ7w8AyZdgG/QIS+f6YimGt8zEZ51vnSkqA//s/9lulyH2InOeDE3kpWBmURN7Whh8xDIPbCMo8poCPxnS0oQx/wF2CImd1vBa9sCUWmGGf5CYmSu1MItrIXVYH5M+2DtVOVEa4QJ1RjL9cum8unby1dgMAJgBsRN69O1vsDMz/c+RI4AnjLafZmOmcjmnFdZBYCCO202Q7a/lt6y3TWksLWKuUidxQ5B3tFODtwU2RNzeHI3IXu7mtxJIw4oq8eYN6DKx12xFA7bbhFbncjqQxLcDuobZxY7gl5oOguBR5qWQjN8BfyRxI07TS0u6xQpuXjZxSG5Hf9swgy6UJAGpr8R0s24/YQdpQqm40kmlFJsLZ2N5UqUBIIjds5B9hfzyK8+znREIVFDlvs8uWAavQh3keeLzp8HriZVUReV1zGbbBj7jgt8RTkfM416LGrOfVjc74bHVOqd3Zmas2gcjvwWWIIYlkSzswfjwS9cyNs73N3bTCF7jzhKjUAhJ523LLhdRhs332WfPc3LnAHDDXS0rZOk5eE4KuxQS8iuMc6fP2wknUBoUi72gzXGBbBBv/eqO/yERuQJz30OGhyLn5LZBpxcVGbnuzkPLCibypIYnrcCvuwyU2gm2lpUzAiDyRjiLneRPSt7k5Z0GRFy2Ri6+gjhvPp22HHOxcAzY9PIwitxGTpJivfZhNFjI9NbgboQGxg7SiTN1oOjrQ2kJBCHD38l87iHBPfIY/YaLZCWzwW+rVS22IRK5Q5Kc9shf6YZWrIuf1yr85KamIfGMrq4f33wdQVoa52BYfYQz6YQVW8EkaALqCsWctepn3vK7F+RB3ELnYc0T7pUHkV+AeUMSw4YflwMsvI/HefwAAHR5EHqgzCh08VSbl06Xu25JCG+dE7rLWiGhmm/PqPE9FfhuuxQl41brYOMfj2GC8BViRWyRkI/IOpiaPmmU50K9eG3deN2iQmYa4x26ynaIZ5fj1e+dgETa3rhO8VmRFfss/t8YL+JU9fypDc1OTnQtcFHnd2iT+jOtwGe7D0q0PNM+30lL2OibwRkNzAtfhVjSjHFdhEl7GCQCklx154l1rKy7H3ejy1YfmIbEONZFLNnJelw5zA197wbjZ61GN3+MBNLcIatW4E+ITvMkgGVGRO7oyn11pEJSXIrfiNUwsfFlTA0EV+YY2Vr6Jy890EGGjQeDc1ikr8iuvBE7ES854weydTS0uTUBW5B9/DPz8s9OjzoXIOUHwby8iF/dgXd3UBcMxF/vjI6xCP3wtDBCqiFwFG5GnUnZFriByjroEM60lmtjaJq4zaCVF7qrOhcpqKetuj8LFbNOakh7ugNqMAPub6KqVKdAOVrZ2lLDZjpMmuWQMDkXO24+VQeue2EwrbUlzmIHDJHJRkScSVl8R6jGZpHgHh+Kfi0fjYjwgFKbZ7Mcikbe3Aze+tCNOxgv2RFVELni+iGXk4Dby2jVWflYRSyi0pkocivz+WWPxZ1yHR3Eu7sRVGI+XkQKx7R9OiVOR34vL0ZiyTKs2N2dN5FZ2KSUmmbrZjfmT9SbchIfwezwzfw/rnILIOeqbS/ERxgBQzNw0XACbDXK2EblsIzdgErk089JB5C6KnL/OpkDUNmZYM01lIr/rLuAVnIhlcLr1HTdhF1Q9/YjtGAWwEINBE4KnQjzOvD2GDnUSeffuGStyLjjXrAGWb7C/WYjlEYncy6PD07QiLpoVi9ke1HWtjBgT7cwk5WVaETtjt27qYGLemxL2QG5zAdpSVns0y+hC5KKKXol+dkVuy4cdc+cCo64+EHXobrZBhyIXB1YlRS5jVa2CyONxtSLvACrA6pf3i5Xoi5nrhph9R+xDc+Y40wPgSuS2dpFK2R5IXJGLe2fXpqyZxK2pEoci5xPPVqC/eewBXIyhQ4WsEKnPK+6XWIfV1coSZYTiInL5Dca44UoiP/1082ZzkoulnDNBVTuWfPzLIOyPjzAb2zsJI5WyDXB6DnYaMJWTpLgdRK5qnMmkaepJ0Zg9PQG8jPYErAb5sfFgEjfPffPzfvIV+AYjsQUW4nc3CFPlhYp3EHmp+k2C1+tduBKfY3ezXlQmIP4G1NoK7HzmCNs50XbLifxRnBtckVOqNjEZXivcnAYAdW2sbrlymzErjg65fRhL+jpU+HPP4dVXmQce93AUO3hTiV3xuhO51CaAQERehx5oN8K3x+z9QXZzveEGYMYvPfEuDjGVeL3cftyIvN2Z77+91BMP40JQEsMX8415EW6KvINatmqjLe+KGdj1x+eVppUZM9j3QAgzcAFXG7nDtCKME/EHa+1a6wFbm7TmcagU+YZWFt8SDDKPib8BoIVKbVHIWxIxJBEz3ywfftj00YgURUXkMonwG67s1P/4hznxxTwvvmoZv5VuVwbqUO309ZaIPJRpRZIzDiJXddiODtMdMogil/NaWkrNPHyB3TCzzGM5AACrDNe9R55kef7sM+DNuv3M8w5Pq3iJpyIHgJtxo3mvVA9drzEJkfirwJT1uzgUizDE9RpOxAAMh2vrgbYUm2FnzMSyph4AIaanEmAROb8vL75Rjom4xh55ebnaRn7qqfjLnY144glrZWKRkDbEe9iCu/kSt1J7m5iBXTCrSeF7D4WKNtCRsLdBsc5bUWo+d+NImu1mQ0wyrYQg8unfVuEiPIzJ68djj5sPx39wqF2RS0TOhQ3vF8uNhefWGc4GYr3xuWt9IKz/AwQ3rQgPcdPTaZ1A5CmJyCVFvqSRnecDy2K+ORxtWiDyVpThBtyCg/ABAJuOihRFReSyIm9DKd7CEfgAB6ovMG6IOTDWKlQ4b2QeewjGkbQ1KkrhIPJ1EJSrnyL3I3LZy8R4OHDFmqQxVyK/BTeySyR3Mv663YwK7IEvsOvGj5XXs2udHkCjRwPH/HgnrsIkUDiJvDFV4Uvkq9DXrMeUor5bUu6tW1Tknq5yAhyKXOiYj+C3+AY74+9LDgQIsd2/unZWt+I9vAG32vdGNbxP6hast6WZAkFTE0uXW9BaidXePqnf0Rb+h4Vqc2CbROSjMAMjO9QO7a5EHrfHLd7T1ehjI/L1xsziemJ/0CCZNN9GbO1U4s/u3a26ntm8HQDgJwy1DZDbiHzuPDM/bt5mYv3Xz2Rr4zsEVxDTSjKpJPK1dcLckQ4FkRvt+ZNPgNeW7g7ArsIdRC4rckGQbUA3/IItzf8lT0527uEaAYqeyI/CW7gWt6kvaG/HKvTB64bb1bo6a80QvM2WCPVS5C0od9oHJSK3LWPrp8h9TCtN66XXRb46WwfLYwruRM7hReR+aEOpa7g7cRUj1aSdtBuSlaGIXMQs7IRGVHqaSRrQ1XVQu09ZnfIaL0XO0ypFu5PIO7oo07HtjWq4R9Zef58tTAvKsbKW3aeGZRuALl3Q+ssy8/xvvzwbAHAangYA7HfxSGXe24S6EO2yKrgReXvMXp/iPf0Su5m3ayO6YINhWpHjevHVBErQgX0xDb/DX8zjDa32+1jT0yJp22xeNyJH3OwPbmZC25vMax84ysACZaDI64S5I8lq63wyYTOt8N18EmjHWuHNTc53c6uL+yGA/liJF3Cy+b/kfx/bjfQRoaiJ3IsAAABtbbgRN5t/1y3eiHl/fAIXnLAa9cefCYARublbj4RGVDnVSCplNqq+WGkROZCRjXwGdkXVIXvjlVfY/9mzgbfeZB2BK/KwRP46jkFHB7HnwQONqLI1UtkleD16oG2l3WVhQ7LKLNeaNWx/5yRiNte4lehn90RAAi0ox0jMQg3WunZoALgRtyAGihQIWlGGfrA20u5VpnYXcShy4UFjLpVK2gBCzLkDgDuR22AocvE6gNXvyrXsfjbMWQQ0NqJ1dZ3j8mo4j5nlwRrb/2mwTFon4Z+O8ErfbwAdcXfTyqM416yOn2CN2NUTu2nlvY8Y2X2CfW3HG1rsb089qq26bqSsja1Bb6ZsuUNBu5rI3dqk2Fa4DZ+HfRansNVCOZGLpCC7H7oReb1VhmVt1hhJayphM600NQHlsVbckrjVlj853y2t0puix9Z0JWjPymhnURO5m+fCRlThakzEewuGWovFg5lBDn7oaPzt1T54DqcAYA2Lj6LLaEQVhmCRlZ5B5Lwhbo0fsQr90ManRFOqJIGmrdgkINm+KBL552AeNa+/zv7vuCNw1AmGXZkaZgnqbiNX4Ti8bv4OQuQb0cUWTh4j3BUzsAb2HXwakpXm5JPf/57t7/wODrUp8iQStg7WjArUJxgRtqIci0V/YhfUohdaUI6esB4kPcvUG1U4JoUoFHmC2hU5QQp1lKnSIEQu2tYBZrJobmVl5qpV9VDvDvW2bR/VnIAdMNt2rEFQyS/hJCwVRQPCK/IKNOEnDHUQeQ+ss8WVAkFNd7W44T7/ABBDEl2rrKf9/FZ2H1egv32wU/RaQdxhI5dhU+RGvvg1v8GzbLVQgyxT8RLcgJtZ3agGO5uaMB9D8Qu2sJwj2qy2+dZaa0OSNkmRNzYCVfEW1JTaBUMTKrHjjsDovj+z+IShretxCz76xb09ayJHcCL/Ky7AJFyNe74fh5+xFY7CmxiN/6EWvbC8lRHIK4ZjfwcSrkQud1aZyIdhPgDhFdjN/fBKZr9ulfIrEjlX0m0/LbKFEU0PScR9ifxbjMBknMtWsxMgk5NqOdm1o4+yxc83HDqyhq0SuFaqDwBo6LDeNvgAYAcSjrEHMf1mVKAhXm3+XyWujSKgu6Bel2MAWlGGGlivpW5EbrsHkmmFt5nmZCkQi2EdeiKBdgwuXWmqbM83PW5akepiAbYwfze0uBO5myIvb29wrsMtQR4LcreRq4l8EJagFr3MF0O+gNl2mIt62g2NqMQ7OAQn43lM+qs0+Gmgoc1qw6VoQ1W5RdJzW1gdrEB/IB5HazKBSy8FfllotUVRkbeh1NpeTYDNRm4ocgfpG4p8fmwb3Iob8BqOQ22yGgdiqhXG8FjaGvOxFX7xfYNvTTJFPr9xAFatYkReGW9VEvmAAcBtO7G3JK7IOxDHBFyP/d++0jWNBDo0kctE7qYyF2EwAKAjyRrrNpiHGqzFF9gdSWr3C00ijkpY0nOb6pXm76UYaIu3vaFFSeRLMdDTRs5taPI5m/3dINa2z+yLKC3BIJMEKWKYiV3QHXUYIqwrIuN8THa4+Yl1RaGYAAJgl+kP4xpYS3L+zAQHtq1Y5AjLsaG9Aq0oxd13293kZSJfLSj5JlSiIdFDeY7j13jedl+WY4BDkdeUqWdWtKIM61GNsfgQP6+txlX/2BFH4l+oRY1Zl40dZaZppSfWoX/ZWizHANyNy7EQQ2wmHBtcFDl/q0gkgA2GIucPjaeEXeHdiLysrYF1cgMlcNqAZ8DutxZUkfMyD8IS1KEHmpvZmyEn8m3xAzakumAXzMQ4vIMX5VmUAhparbjL0IqqcivPG1NMBKxEPzSRKhy6aDLuvx+4/QGr7YmKvAMlGIlvHGmoFXml3R/eeDjzPlqP7rbJYwBr5xtrLbnsReQJtDOf8ZISbL34fYwYwd5Iq2It6FVub2eNqEJlJVCeZEKCe12tRw9HvDJK0O4+8SADRLJoFiFkHID7AcQBPEYpvd3nkrQgE7nS5Q4WMczbuBlaUY6t8DO2xC94C0cBAPpW1KO+2Vr1TVTk21WvxLw6Zo65XXI9a/v3B8DatWgC80AYgoUABHupmyJvURO5SK68LLK742JsblOzczEc9+Ni7I4vMBqfwQ0NsWqIY36iPbUF5a51J4LPXtvGg8gbOipwIf6CJ66xP5RkIhcH7vwUeWkpxfNtp9hs3UpFXuJO5G/hSHyMsfjT+73w4pzhAIbjE+xjPsDWtXfF/HUdWIk+BpGvw6sNB+BDsB2eDsT7WKkabBSIvBrrzfXklxsTrgYPBhrW2xX5AFh7qboq8pY6myIvQ6vpF87xIC62/XdV5DHruiRi5gzKzcFmFvOVItajJ0rQhi2wAC20HD9iG2V8IhrarDYcQwqVP88GsIctzHr0wL/rRuPjZubxUbdBrcgBYB62tV1bDmYeeRAXoRp1NsFhEx+GIl8aY94k18O55147SvDld/Y3QTf0wHq0JuNY2cZMbatWAY3Tv0VVrAw1Xew273XoicpKoGIt21yluS2O5ejvej9ElFSUWKuyRYiMFTkhJA7gYQCHARgO4GRCyPBM41VBJnKb658ATgxLWtn3QCzFqbAWHNqhaqFZ6TKRb1vtosRgTOJYudJsiAOx1J4PN0XeQjAN+5oq+VZcB8A+mMXjaEMp8J//oMpY9vUz7Okwi4zGdBuhqdBQZleM4uDcRnQJpB74igLDypa4hqlrrcB0aau6RlT5E7ngt/wGjrWFrVJYj7gi71Zmdaqepe5EzpfNXVJvpfMDtsV/cBgA4IEV47H1XefhTRyNffAJ+pXbbdflsPv0m2axsjJQMCLvBmuXJF6+zTe3yE7cg3K/bZkftPhGIaIMLTYil9NXQfRwGYYfzd8dMeuhOhs7YA52AMAUOQCsXm0NzvVCravdXoVL55xr/k4hhsoWe3kqEm2oQ7Vtok1bG0GlMQegHSWeg9u9UIvFGIyL8SBOxz+wRBg/Ed/cUq2GIqcDHXFwtKIMPy2ySHMl+qMvVirD9sQ6rN9Yihl1lqtg49J1qIo1o6ar/e2oFr2YIm9nbeyetadjMyy39Wc3lHTxcdBIE1GYVnYH8BOl9BdKaRuAFwAcE0G8DnAi56+dTwuvrCLkV/UBWI5usOxc25fMQwO6GbOu4uYSqwCwdVf1jQaAtsUrbaYVTuTXYCIW0sFIJSneFH2ODVxzYynGYBp+YyxByknmH0L+OdG+h0Pw7mH3mJ3rz7jWMZOsH1aiF6SNliU0lNgfcjKRL5MGzlTg21kOKV3uGqa2ucqhdBrQ1UHky4UlAppQiQbirl74QoGTcS5uwM3ohTWmIi8XtuGTbZccbSg1H9TTl1p1d7uwSiQHRQzH41X0r7D7hcsbAF+MB9AD6/BO/Z54ESehGZU2UuZEPmiQZX4Qifw/k2bha4w0Z6fKKEOrzbTCXShVxHM7/ogqYh8fmIzz8E+cBABY3tYbi7A5vsEIfCustjlInh0JRmBhiFxECjEkJLv+8I5vUY/uqO2oBmCt5DwObCGy9eihVMZlxoNLfHvh4HUgzsKtb4jhHDyGW5uvcM1fC8qxTtI722GuMuyheAf//rwH3li8i3lsGTZDZawZNd3sZWxDGSorgS4p9iD/pGU3AMy10w8lXT0G0jNAFES+GWBrIUuNY5GDE3k/48a+i0Nt5/fBfwE4iXwzMH/ejzAGF+w+A4MTrLE0oCs6kLDtCNSjZCPu2edVqNC2iF3HiZznYyX6Y3zH87hr+Sls02UXfGG8gnIiFyG+XZyC59CMCuyEWWhDGd7G4bawfbDat/M1lNrd40QifweHOh4OKnAi7xVTq0gAWNNY6bAXb0QXxwYY4hTwS3Ef6mPVcEM8zhTjuXgMN+MmDMByLMNmaEcpyuNWpxraRf3QbUUZ6rYa5TjO8yCqVwDYAbPRv5J1Sm4blx90k3E+6tAD4z69Hr82XAFFLxP+oBo4ENjYXgYCik+xNwA2KFjRuwtGYpZNNIgoQ6tNkXNTGDffieiNNegStw/Q98UqnISX0APr8MzawzAEi7AzvsHp+IcZRiTJQdXsgdIT62xvFmFQijYQyUd1OL5HCnHMaxqECtJsjuvthG9BkMJa1DgU+Wj8z3wDURH5YMNz7Avsbh5b01COJ3AOWj08jL7FTvjrF3a7+R74XBn2bDyBZJLgibl7msd+wjBUkWaUVsTRVaqjyko4xJSf3z8AlHTz9x5LBzkb7CSEnEcI+YoQ8tWaNWv8L1CAE/m2+EF5/nLcA8DuXRFHB3pjDVBRgTGYhkeOfBvdY6wRz8IILMEgm0oqJ624bMRUqNC8aA0WYxCaUAmClE21/YhheGndgRiFL/Eo/s+zHCoiXy8Q+QAsRwO6Yhz+g2qsx9ewVEIpWlGGNsQcSyHZIQ4mAnYi/y3+6hgYElFKGNksWcKWUuGDjgfjXdyEGzF0qJX2kvqu2Cj5M/8Rd5irMqowE7tiepuTaDl6SFafAVhueoWIG2NvWelB5An1yv1b4meHnXoAlmOHnsuRQDsewkUAgM+xp+JqO87AU7gdfwTAOnG3ijabQ8KzOJXlGa1m43Uj8nLJtMIXZRsM5/jEBnTDqg77w5OLkUQ3d7PFCMzC4ZiCrz5uRJ8u7J7WYG3airwfVoJQJ5EDwD+W7o/usQbT3bs/VqAH1mMtajDbMPVwfIJ9zHpREfm+hkC7HPeax579xXupCQA4CB9g4YYaU+0DTNQ9c8jTuAT32cJuYTgPJGkcmxFrIlcVaQLicYcps6ICKEs22Qh+XoAxhkIm8mWATd4NNI7ZQCmdTCkdRSkd1bt3b/l0IPDp4Y51FwzIr8MAW2gpBmotcpBMohthlT8WH2MetrV1lgrS4rro/36zHsRgLMYEXI9KNNnmUG5Ad3zVNBzbxn60ddbx453xqIhcRBtK0YESdEc99seH0jmnjW2uNGAEwDaYuDkW2XySAeDvOBtxOGdkAkCvRB0ARuQ9ewIklUQKBO/gUNyIWzBMIPK5K/1t7aXGfekmEMb7zXu5BUdfyRvRRuRxK88DK9TjBK0oQ11HF3RDPc4b+QUAYBvj4d8Xqxz25zhS2KPPAtShGsfjVVSgCTcIE8nc0B8rMN5YIngl+qNHVRv6rv7OPM+9L8rQajZeNxdDWZGLacgQTQwAMKrnL6Zyb2x1H0irRh2m4EjsOqIDVaUsLTdF/vUTX+MW1UbZAkQiPwTv4Fd4AUPxk3m+IVVlzo3pg9WowVo8gt86BlVjoGbfVRH5aEy3/e+Getzyw0meeRPB1+gB2IPx1MGf4D5yOeb9QDFt/xsxHXuiGxrQrQt7GB5B3rauJU1AImES+aAytul3askyYNEiJhIN/IRhqBA8rVQo6V0dON9hEAWRfwlgGCFkC0JIKYBfA3gzgngd4Iq80qWyVCvrmR0hHmfLbCaT6E7sdsrNsRi9ejCCKH//X8Ajj8jROOCmOIfEltiI/KmnnGHciHxbzMXv8JA5kt8VDTjQWGzHC9tinuPY6csmAgCmYn/s6eLdknRxWuKv7akUI3KkUiCA+eAqS1gPup9X+4/UbwXmxygqv2UdzpUXOVREzteKF00rJcT+IPoCu2Hb+HzMwkjM3zgA1ajDw4e+ie9//4hpBumLVfivalCqpARVxsO5CVW4GTc5guwK+5on/bDSNlDetbQVQ+863/zP7fQikTsWYQNQXdqIBJI2GzmHalC7H1Zii3KL8L48+E9syQEATR5Ebj7AUikMqmbk3RPrlOabkVvUowescYMEceatH1YiRtnxg/A+XsDJtnGDRlplTpbpg9XohVpQg3Iex9m2uFSK/Emcgctwj8OE8TJOVJavD1aZv6uFvItzIzbHYuZXWFaGrbch2Lfvj9jTMLds2MgI5uDUu+Ys2yo02hT57wex5Rp+fvQDYM0aG5EDrK2XKgQlR6Kv+k0xU2RM5JTSDgAXAXgHwFwAL1JK3VYRzghuRD5p2GO4HX/EGDgXhDIHN+Jx9kkmHa+Sm2Mx+vViDZIPMk2rOQ5vvAEH/o1xnnnsl6g1XfvOOIO9gskQO3+9oJR/wHa2t4Mu2IiDwBZ8qDEas6iiuygGzrpJZdsDn9sU6B0nfYkOxPEkzsCbhjvmztW/2K75sWUwuhrWEk7kIkoNIi8V1PEg2DfNELGN8aBRvcIfjimOYzKR9xMG/PqXrsXwgfXoj+WOPSl3w1cYkGCdecqqUahGPRIkie16rsJO+BYAm76/n9FOKkrarU03AixLxwerOXpina1uZy+vMR9agLVRiUjkMik1/bgU809mE8a4It+8q0VCnEBOwj9Rixp8sOOluBB/wbzRZ2FvfMKWl1CsdfMhxmIjqvAlRmH529/gv9gHce6Pmkxiu94s3g4kUIN1OHb7+eiKDfgY+2E69gQ6Omxr1sSIVddHgO1OvSV+MW3kfEKbbLbic7H6YpVZlsPwNo4R16/ZckuTyMUH1xl4GvfgCoySHqBbxdXusOIM4ZPwovlbtKNvjsVscTq+cYqwClxZKSvL4ZhiPtwq1y4G4nHzvp22+cc4K/YUrsWfAcAxeL0d5qKSqCcYAgDpk541wg+R2MgppW9TSremlG5FKf1zFHGqQAhTBl2wEX8T9pg8rt90/BF3oC9Wm8Q4Cl8CAPbDNBYoZi3kU0ntI/5VaMTBe7PK5694+1Z8haOPBnYosavd/TAN8zEUn4KZBmRbW3Vio2mv3H9/dTlEE1A3NGAvfGr+P17YjqsCzdgaP2IzLEUFmjEde+IHwYyyBIOwatKTtrgfkHyNK9BsW6Zg3F4NiCOFM/A0jsJb+BBj8e7YiRgoeHHt2OUX7GVYPtREzso3oAtTdfvgv9hbKIMMTuTym8hFeBBTcCRuxg2240ceqb7+MtyDo6un4bu732ODtQKR8wlSU1v3MY8RQs2ZneNi7wFgYwX/wlFYsfUYNE24Fy8Znh5eRN4La1CJRpyLR/HcCa9gKvbHrbgOBPaH8u9Hf+kw+yXQzlSdsRtRL6zFBmFMoaJrAr16s3O8XYwcYMVxONhr/rl4FDVYhwMwFXGkUFKRwMcYwzxjhDV8Zj70Pyw6/LcYi49RhSaMwgz037EX9hHvTyqFoT2ZcuaD7K/89n3Uohf2w3+ZQk0msQvY5LQuaMAH+9yECbgWJ+BlvIwTcRNuxDWYaD7I+NuE+GDbpeRb83cfrDYn7xyM9+wE+M47pnmHf4uCpQpN2BFWXF3L2vDQFneZ/6/CJDyDU1GGNkyrHIerMAl34CocLTwsehv3pQfWsy16+N6wgk/zF2+uwr/PeAGVaDZNREnEgXnzzAdMt7JWPJE6E9sYA+bfw+5pvR3mYqvYQriho6dz8lskoJTm/LPrrrvSdPHydtfRRRhEKUA/2+W39IgjKG077GhjXiX7JEFoB2L0nxhPkyDseL9+lFZUUPqHP9DGoTvRPTCd/gUX0D3xP7oe3Wn7vJ/ptC1Ot+IZPJhSSunNPe6lAKVn4gl6MN6xpcM/UzGW7o8P6KMDbqAdfQfQdsTpFBxGUymW59dfp/S5+G9oDdZQgNLl6EcBSi/FPWYc47v9m96AmygF6BQcRivQSGdhR0oBegnupfviY3u6JSXs+69/pRSgV+BOejqepJ9jNxpHOx1WtZTehqspBeiluMe8bPnLnzrLcMopdN06Sheedh1dgMF09YG/pvcYWbvrLkrp8OG28NNeWkkBSmed/zCd+fdvKAXopMSfbFFehrvpTbiB3o6r6BQcRgFK/4ZzKUDpOLxNG/c8wBbnon1Opn/5C6X19caNFs6lALoUA9j/nXai9OWX2e/jjqPf3/kWnYttzLD/qLmE9sJqds9iT1F61VWUXnklTZZV0GuqH6bfYXsWdtttKZ040Urnwgsd9XJ0v88oQGkjKuhGVLLj4jVG3gBKr8LtlF53HaUA3RI/mUEuwb2Ujh9P6bff2q67PnEbfQ3HULp6NaU33kgpQH/A1nQ8/kmnnPUSnYZ96C24znmvNtuMfR97rHXsyCOt3++9R+kpp9ivWbbM/n/FCtpyyyR6Ph6hC7E5O/bUU/Ywb71FKUBX9mFtkJ5wgrLtbxixD70Md9MmlFMK0A7E6EV4gH5w2J20vu8wevHFLGgKoOfhrxSgdB2qKTXq7ZxzKKVLltAFGEyvxCTagRgdgw/pGzjKlk4zyugJeIkClDZ360PprrvS1ehFz+7xCm1AlRW2Z0/HvTkBL9El2IxOwz7s3M47U7rFFqydnXGGvZ7uuINSgC5HPzoGH5rX3ItLaHespx3H2uvhNDxl3n+A0n/jUPpG+UmsSeEhuhCb0/PxCD0Lj7Oqf/mTtLmPdQt8RRWcmhWi9vtkQuR0v/2sijz4YHbsKPtNV34224zSLl0ovewySrfaynl+4UJKR4yw/hs3OrnVMLoavfzjByjdfntKBw60/ouoqKAr0JeuGbyr+tpvvqH0D3+wNUL+uwMx9kAyOhcFKC1nHYdOnuyIKwlC6Yknmv9bUEpXog/9GVtQOnOmM+3TT2d5vOgikyTa2yn96Scj79tuaw//ww/se8IEdv6UU2hqy63od3ufT9eh2uzUqs8y9KetKKF0zBj7uSOPtNeXWx3vsgulr7xi5pM+/7z9/BZbUArQ+uPOoI3lPSm98kp2z7t0oXToUCvcsGGU3nab9f+SS6zff/oTq7cDD6er0Ntex/fea/1+7z12r8rK2f264grzXCMq6GfYnbYefSIrz+zZ6vKsXUvpnXda/99+2/5f/lRUsO+TTrKOjRtn/Z46ldIzz7Rfs2KF/f/Spezeicf4w5F/Xn+dfW9uEP1xx6nzI/YZ8XPeeZT27UtTCxbSjq/ZQ6wZZXQ5+plhWlspTSYppbW1gfpXGxJ0CTajtKaGPdABRspiuN69bf/XlvRl7U0M06cPpbvvzu7L//2fdXzVKkrvv9+Z9lVX0VaUMDExfrztXDPK6DL0pxSg69GdHU8k6AIMtkQkWB9ejIGUzpmTPvdRSt2IvKjWWgHAqoWDvw479h8TwKcJCqYVcRElE4mEPR7jVTgWJ+jtM/nGRCrlPv02FkM/rDI9QhwYOBDob/mhih4xcaSY540YN/8tbSAMMC8Ac1YNgDK0oS9WY0ssUC/Yw18veflLS5FIAFttJZRLBF8di9d/PA6SSmKHrovQA3Wo8JiVOAAr2MCcbMood/cHduSVl5lS5703znUrbUFlrMW63yUl9rDGio22eDnOPBMgBGWtG9AHa2DboFHMp9G2SEc7u1/CMniVaMYe+AKlJUZ7dWujiQTQxRg4LysDDjvMuz3zzUfEMOLyyLGYfeNsuWwAa2vXXWc/Jk+n5XZ3HpeLJ5dyE09CWD5WrQLZYgjiE5gHUDla0Z+Pd5x9NkpLjWIEvPcl6MBALGN1xt1h5LJJddezfZU5EGxi9Wqgl+G+WSZ4gSUS1gwmEZMmoRTt2AzLHf27HK0YYDhUVPMxoI4ODMEim4tw/IjDMOi5O4DhWZn0XuREzivVq+HzGxOPs3BJ9eCQORgqp6MgSlesXOlJ5J55jcX812BQEblbfKpRVsCbyHlZZSKQ9+TkmxnzcMYgsu3e+EEuq5xft3pPJOxELocT65kQFiYskcfjrIOrSFMkHU4CvH4WKQbh+LVu5YnHLSLnTtdepM8h3hOxPfO8q/LgBZlMZSKX2wCPU9WXYjF7mu++az/fsyfw+OPuafshkbDqSm5HQcoKADU1zrTLy937jZh2OqiuBk4+2TdYuig+IhfBK1V+KovgN4or8mRSrcg50XNQHyWlwvr17oNmqg79ySf2817lAOyNSCZfGW6dQ7Xymqzu5TLIaowTOQ+XSCg3YPaE3CHk/HJyU10nErkMsZ4JAe65B/jwQ+cbl0zk4rmgRC53+rfecuZHfttRlYfPq/Brc12tQVLbDjlie47HnQ/iIG04HmdkcwBbNMy8n8L8C1wtLHHA25GXIueQ24983/zavYySEvXGEoC6rN27O4+pFHlZWfaIPMsoPiIPa1oRR6f9iFxsFLzxyUTpp9D9FLl4/Xbb2eMNQ+R+ityNyONx4JlnnMfEuGQiCGBaQUdHOEXuZ1pRrZzF0wpK5Pz1+8cfM1Pk4jmFacUTfkQejzu3VXcLK6Yn7kIjE7msyIMQZSzGhMg1xoqfp7JZqTbTysSJwM7GjGBOjqoHOH8b4pDDuJlpVFDVsajI5bKp+idX3yJkIuf9TyZyuT+nS+Rh3uzTQPERudgIgphWZEXuZiMPqsj9OoWfIhfjk3+nQ+RhFTlgdVIOP9OK3PH41kGyaSUM0iVyP0XOz8lkEpVpRSRJlT1Vhp9JLR63SEWVFxEiyYhELv5OV5HzMHLaoiIHrLg5kQcxrWRC5Fts4TzmZVpRISiRA9kj8iyjMHMVFG6NT4SoyL1s5ImE3X7spsj9OkUYRZ4vIneLNyiRy6aVKGzkQU0roiJPpdzvD7ePi+nJ5OJH5Bs22OMU8xmPByNyPxMYP3733f6mFZFkRNOK+Fs12BmGyOWw/B7zuuL/uWmlRTGw7afIw7STIUPYBrYivAY7VX27Z0/nMdlGzvMr39OwRB6LhXtQRYTiU+Rer9MqcCIPYiMfYC21mhdFLjaSM890xpEtIpdf//1s5LJpJQobufyfE/nBB1uv8zycqMjl9uA2uFhS4jSdpWMj5+2pe3d/e6p4rR+ZXn45cMUV3mH7CJNJRPL+/ntn3kUEMa2IbzIi5MFOWZE3N8MBWZHzej7oIPv/IBDNjxxeilzVt1WKnI83+Clyue78iDzswG1E6BxEHlSRx+PsRqsaUjxuf3JHrchVykzMt6zIVYpUReRukFWZF8KaVlReK5nayOV65qaVyy8HNhOWlE0kgK23Zr8Pty/vC8BOnPJ4SljTisqLhNd79+4sz36q3M9GroJb2G2FxdFEIpfTi9K0Irsf8v98icogipxjtLFiYdB2su++wE03OY+XlFj5cWzk28YGtx94wDqmInJ+3+SHXqZELsfHoW3kHuCVE9RrJRbz7gCiaSVdRR6VaUVlIxbJj6fj1inCeALwuGSXM46gppUwkOtJLgcvP0+LIx4Hhg1jG4RedJEzXl6/fqaVVMp+XkXkqnM8fq5Ic0nkhxxi/Rbt4nJ66bgf+plW3BS522CnKk3ZTOOHm29W16/YduR02tuBsWPZnnscKiLn7cuPyHla2xgrNvr1KzcizzKKj8jdBj/cINo043H3DhCLRWMjT9e0InutuI3Wy78pBaZMAe67zx42DJHzsEGJXOW1EsRG/v771oQIuZ7ka/kbiUzkvNw1NZaLoQg3G3k6ilyMk5t3eNk5kfl5rvj5kXtdI+Ooo4AvvmBK1U2QZMtGLhN5167ebV1V3rBEzvNy+OHAMcdYx73eTFVvUUGI3M1GztvFRx8Bb76pTSuRYfJky9YWRJHLNnLVqyCH+BR3mxCUK0WuahBuRH744cCOOzrT22knBAJPl9sX5Q7qNyGI28j9iPzAA60y+yny7bdn39XV9jrzu87NtJKOH7l47qOPgPnzLY8d7vXAO/5+Lvs1RqXIeX52281uI1alJ6vCIA8RniYvH4fbhKAuXbyJPApFzuOYMgW49VbruGo+BQePW0xfZabkRC4Pdrop8n792IPUry7dBumzjOIj8q5drd0aeKV6PSVlG7mbIgeAceOA445jv1UNQvVfRlSKPAyRA+oHzKxZ3nmV48rEtKIKFyRNDpmQ//AHNsHmqKPsx/0epEEVuZfXSixmV2axGPPSGDqUmTd+/3vgL39h53g4NyUWdLBThKqMYvxe7VhlIw8C3pfqpaWGZfLlD5BMiDyojdytrwQZK5LfsGTINnJefvkhOEjaElEuF7+uqgq45BLgscfU+ckyio/IAeeNCarIYzFvIieELSIO5EaRy3GL16o8IryUSJCJEW6QFblMBCNH2v9zTwWZyFWuX27wW/87HgeOOMJpPgni/gWovVbCmFZEk4l4XUkJG0jjHiRutlY53qgUOY8zzGBnkHT4bz6QzCEPdgYlclX743EFJXL54cohpsvDyHmR37BkuN03Md+PPw68+qr7eTHusjJm3tx8c+QDxUnkckP36twi2fiZVsS43BR5pkTu1aHDKHIe1kuRB4VMxHKneOMNewf/z3/Yg4Y3Wp4vv7oVIaexxx7h8srhNvNW1UaCErmsyL3q1k+R+/mRq6BqI2EUedABN9Wg4Xbb2cclZNNKvhS5eA9UilyeVOVH5LxcXnV19tnOXU7cxmR4PtJ5G4oAxUnkslr26miiIgpD5G6KPF33wyCDXmI5VJ1EjFt+5XV75QsCHq+bjby6Ghgzxn7slFOY3RCw8u1Vt+JglZhGnz7A8uVqV0KOdGzkYRW5XH+yacUNbm5s8rWZKnKZyMMMdrpB5Y0D2MvuZlqpqkpfkQdFENMKL4PX7Nggb+xB+4sbH/A03MqYZffD4p7ZyeGlyMWK9rORi3FFbSMPosz8Gp+XaSUTyIpcVZ9uE0XE692IfPlyy0efE6/4wBSW71UiCtNKZSXQ0GD991LkgLtpRYY8aCYjKtOKGH8s5j4eka4id8u/7LUSRJHzPLrFFRQqIpdNR7x+ZVOk20NAhjzYybH77urwBarIi5vIwypyPxs54K/I/RCVacWPyP38yNOxkbuZVgBvE5MfkauI2i//bggz2Cmiqiq4aQUIrsi5y6pbB84Gkfu196CEKbYnN3942bTC39q8FLlq6QQgO0Tu1o7kN3E3qB56ra3u98tPkYctY0ToHKaVMIo8XRu510JNgLVBZ5hlbN3S5vn1Op8uEXqlyzupqj7l/KhedVXTtWW4LZUb5Bq3vKnCqkwl4jFKgxO5FxGMGMG+lyxRn0/HjzyI1wqHvL48b+dh03FbbkBW5FwIVVR4E3lYRa6qnzCK3O3aiorwppXSUvd25qfIwzywI0RxEzlHGBt5WNNKUOJx84+Wz8diwFVXeedV/i3HIaaTDUUexLQSRpGrEGYVObdlDVTwMq3IZRC9bORzQU0ro0axb3lhJ450FLnq3rkR+YQJ9nB+ClSVN8CdyN28VkpLC4/It9nGWs4hLJEHhZxPvo2WX53rKfoKyOQVRJGna1rxI2g5HT+FQAgwaZL/mjFBvWPSVeTiVvXpmFZUXgFBiJznN6wHA0cmNnIvIk/XtMJnql54oXd+MlVqYRR50LTcPEFEyKaVAw9k3zU14U0rXvbjKIh87lxg6VL78fJy777E4wpKtDwfNTXA11+zWbZyPvKA4iRyjjA2cv7K6TdpRVbkHH7E4zeYGdZrJSyRy/ni6Uyc6PQYAYB//csZl5dpJQiRh1lvJVumFTfilG3kQDREzhdw+sMf1OfTUeQqcA8hOa6gppWyMvveo0CwtyLZa+WBB4CffvIn8qgVudiPVUQuzzfg15aX2+N5+202S5ejspI9nF56yT1vqnzGYmx+BU8/z+uUF/dgJ4dXJQYd9JDjkonRbzKDn/JyO//aa9aehtlQ5FdfzV7733jDPQxPy8390OsaMS9hkC3TirholojKSue1nMi//pqRkxyew4+Egzycw7xay/f0nnuAc891xgmoiVxcjpmjpQWYOdO+G1EYIhfXWuHmhCgVuaqOVcfkwVy3fiASuUi2hx3mDPf+++75kuE2IUjVLuXZxVlEcSrydAY7gXBEHlaR+ylut/PHHmtN986EyL0myviRSBAbuYyw9SrnJV3TUNSmlaFD7cqKQ7SRZ/LaHIVp5bLL7Gt4eBF5LMaWE1D5mcvlCFIut7VWxHMysqHIefpBBzvFQVkeJsjWfH6QhYK4BIiMHA58dg4ilyvxiCOs39y22NISrGLdCEYkkGuvdV7n12GDvGKH8RPn53k+d92Vvd6rFm/yK7e81kpYRR6G6GQbeRBk07TiNrYRRpHLOPhg63dUphURfkQuh5HPuf1XQVbkqnMykkn/AVu/vAH2MvD0Ewm1+6EMPl4jKvIgOzr5QSZyPkisEiQ5NLdk1LoIIeMJIXMIISlCyKioMuWLLbdk3zvswL7lCvvNb6zffEXDJUvCKXIO1eDchAnui+nky0YeiwF33mlNm09HkXvZyGWoPGjCIIwiz6bXCg8nk0wmRP7gg05CFfPzySfh4pMhDsC7kVMQU0WQcsleKyJ4n9htN6B3b+t4MqmO22tHJT9F3rcva9sPPRRMkauIPIqVCeV88nbjtutYjpCpTJgN4HgA0yLIS3AcfDBbl5lvLMArbOutmR3wV7+ywnLSX7QoPSLnCOp+GNZGLiIMkcuKnCPsVnhiXF42ci+Xz3QabLqNXL4/8gCeG5FXVHgrcnlNDb4VmBhnUIieI6pr5UXIwkI0FaSzExRHELt9ECKX4+3RI32vlSlTLMIV6660lPXhY45xH+wUsdturH6uv96KJxumFf5wUhG5mP9Cdj+klM6llM6LKjOhsNtuanvrzjvbK41vRNHQEM604nbczUskm14rbgMpqnyoZqT6NSJeNt6BgpBDujbyoHlyCyuntfPObMF/Oax8r8vKvIlc9AoB7Nv+hSXyWMzbo8pPFPi9pYimgjAmqmyZVuJxe5533FEddxAij8WscG75CzKzs2dPtgnIvvta9vIoTSscKkXetSt7Yy8W00oYEELOI4R8RQj5as2aNdFG7qZOAXuH5OG8JgH4+dPyNJ56CthrL+t8UEWeDpEvWwb88IM9rN9MU1VYv3Rfe42ZZ/hbjFccbkQ+YQJ7W/IDvz6KwU7uRSHG67doFmAncnnRJdEMEPZB5afIM+3goiIPQ+RyOYIQOb/GT5EHIfIga7MQ4u/bHcS0IoKv5hiFIpfLpVLk69cDCxYUlmmFEPI+IWS24qNwTnYHpXQypXQUpXRUb9GeFgW87K2EMNKdPt2qWJHcJ02yrznsZyPnOOAA4NNP7ekAmZlWVMuKAuy1n+8ZKKcn43e/Y998yQC/NAGrXgYNYgOmQdSym/vh1lsDAwf6X19dzcjzwQf9w4pQkaDKf1gug7yMLeA92CkiE9NKNrwZRCLPZHlcr3zwc1z08IlAInifSCTsfe/ii73XWhE3keZQKXK3eQmq9ci9wBV5t27+Yf0QRJFzX/4ceq34SgNK6UG5yEhG8LuZp5/Ovh95hH337AmsWMF+H3usfa1tPxu5n4IkBHjiCWdjjXqw002R77WX++QgN6SjEN0Uuco7RIXSUiDom5mYf74FnNt5rhzlB+/gwd5E7oV0iDzIZLWgcbnlJ6ypICyRp1KsHufNUz+cVYr8u+/YoKSbIv/uO2savVu6nMjdluoNq8gPOwy44ALgppv8w/ohjI1czFsh28gLBkE9IHjj6tHDeUyOyy0NN4iK4qyzgNGj1WlHNdh5/PHsW1TebgiqyL0QdLBT5R2STp5UePZZZ70C9k7CN0cW1dd777H/cprt7cHyEbYT+plWwmDOHHX8QHhTgbwJMc/bqac6w4rCY+ut1Q8N1WCn11hRSQnzNBP7nyq9u+5ib2yqiU1AeCIvLWUiTh7QTgcF6rWSkbGOEHIcgAcB9AYwhRDyDaX00EhyFgZeNnJVONG0onoFFxF0bRC3GYVy2lEp8v32C25fDmojDwM398Ogr/vpDHb6TcYCgA0b2Dff5V48r1Lk4njJL7+EW/jLDX6mlaA46iinSU2MM6wil93vCHF/KwnyIFIpcje/+VjMOy5RCB1zDLMzuyGIH3m2kK4izzIyqgVK6WsAXosoL+lDpcgvusi5s7yKyIO+bgYl8mwMdmaKoBOC0o0zHdNKGKXqR/rieb55RFAiV3k4ZQrRtJKOIg8qSOTJQOnArZ0Faa9eRK4abPZCkPQ4wiryKCETeYEo8s5hWlEp8gcfBM47zx4uiGnFrSGFsZGrELVpJQyyocjd8hpUkUdJcH5E7qYS/WzkbssN+yEqRe7XlsQypgOvBeQyJXK5Xv2IPMyDTyTyXO/Io5qfAGgijwRBbeR8FNzPR/iQQ4AXXrAfy7QhBukY2drKLRs2cjdF7mcjT2fnJT/TispGHkSRu81A5HBbbtgPfn7kAPDMM8CXX4aPG7DylA6Rf/yx9Tvo8s9uUPWJdBW5n2lShEjeWR5EdCDMFH1N5CERlMi5/dSPyN95x5odGnb97GJU5OmYVtzcD4OaVtKdEOR33su0ItdpUK+VsAgy2HnqqdamFDIOOggYO5Y9SFTgZeQDutttpw5HCDB+vP3YfvuxwUTAe8p6EOHBbfQrVvgrcj/lHEaRiw+FXBO5nD+v9WM0kYdE0MHO+nr2LZpWgjaEXCjyWIzNMPzrXwtPkXvFKV5fURGsTtNRukEUOX/FDWojzzaRp1O3VVXAhx+q/a0BS5DwZQS+/17tlplKAS++qM4fkDmRn3AC+25oyNy0kq6NPIe+2gDcTSsq5ND9sHOtR+5HDnV17Dud6ddBt1bLRJEDln/72rXB8hUE2fZakRtsEO+EMPC7RpVeECIP6n4YFlH6kavAiVx0sZwxw75Rhhd4W/ZyXwzSXgcPBqZOZW6CfJ3zTE0rYWzkhOTPtMJRIIq8cxB5WEWeDpH7bSwRhSIXUUymFTl+r/QyWWg/zP6kolthrhW56GqXjfhVLpZlZcH3n+T1lakiB6x5DEHcD4PkKYwi7949f4qcfwfZ1CYH6BymlaCKXGVa8WsIYdfPdosviB+5KrzcGOTFnTLJk5xWunHyJRceeyxYekD2Bjv32MN5PtdELuYpV4o8DLivfBBFns5kKNV1Kq8OEWEUOfe26dYt/4qcQ55sBWgiD42gFZaJInd7WMhLkmZqWuFwUzbz54c3u2Tb/bCmhnmAnHMO+x81OYYZ7Jw6FVi1yn5ernsePptEnk1Fvvvu7JuvPR8WfBGpKBQ5h7xZucqM5YUw6fEZmrfdln+vFYDtAfrNN86wkyfnIkcAOguRB1Xkxx3HvsVX0kwHO7/8ki3KE7VphYeTVVOXLvYHURBke0KQ/D+fg52VlUCfPuq8yQOQ8oSgKBGFH7kbJk1iO8bzTVPCgrtoZmojFyG7lcr16rZuCkcYRV5VxdI79dT8m1YAYMwY9Vo0I0YAjz7qDJ8FdA4iD2ojf/xxptbEzhW0Ibi5H8pbT0WlyCsqgFtuyXw3Ga88cWTqRy4j6s7Ft+7bZRf1+aCDoTK5uu0tGQXcTCv9+2ced0mJu0dLEGRDke+8M/vmoiCsIg9jI1ddlyvkOr2A6ByDnUEVeWmpu1rzQy6m6Mu4/vrgYb2QDRu51zVRr7UyfjwjnzDbmqnOq1RyLk0ry5ZFsyZ2pgijyIPepylT2MqGfMA1XSIPez9yrchznV5AFGauwiKoIlchLJG7Iaj7YT6e6NnwWonaxdAPXgtEhVXkYvhcKvIBAzKfVh8FLriAfYubRMsI+wbZo4d94++wg53p9o9CV+SZeGmFwKalyFUI67WSqSLPxxM914o81whL5GJ9FONgZ6YYPTr4ks/pEqXKQ8gLmSryHBFm2iagLKMAW1kayESR+92QsO6HbihkRR61jdwL48axb5W7VrooZCIvpAdeGGRaL2FdI9Ml8kJX5DlC5yDyTJ7KUdvI3eDmTpgLFJIinzSJ7Yaejj+8GwqRyLPpR54LZKp0w97fTAc7c0WwBToo2zmI3G/CiBeimqLP4bf5QSEq8nQm8KRLgIlE+v7PbghaPu6tIW7WsCmaVoKA59trqVsvhN2NJ90+XAjuh17YZx/2feKJ2cmPgSJtZRJyQeRBp+gXI5GnA796u/fe6NN0Q1CvlZkz2bc4+zNKIliyhK15IsZb7Io8XSJXbecWJL2wfbjQTSvbbcfKdGh2N07rHETevTuw777Ac8+FvzYqG3lQIi9E00oQeO3ZqcKll2aeZlAEVeR8RqSXd0UmGDjQ8nXvLKaVdIk8XVt3uopcD3Z2AsTjwLRpbLfssAh6QzLxjBGvD7pCXZTIhyLPJYIS+bXXsglh4i7u2rSiRqZEHhbpEnmuCbVA72dh5qoQEVSRu4G/zk+bFk1+wiAbjb2QlGZQIk8k2ISwXEwI2tQVOcDGQvi+oscc4x2W11fY9PI12Flg0EQeFEE7pJuiOOQQ9p3J1Op84uyz7f8LSZmEHcwV/3utJ50Jil2Rp0usIn7+GVizBmhqAl55xTtsuiaSfJlWCgxF2soiwEUXhQvPO/zFF6vP+93gigpgwQLg6afDpVsoGDGCdZZCJKigg52q/2G9K4Ki2Ac7+TZ0XFGng0SCfSoq/OshU0WeK2gbeYHhwQfDPcVLSlj4229Xnw9i4xsyxNqeqxCw997pX1tIBJWJIs8WkRe7aeX++4Hp04Fhw3KTXrG5HxYYNl0iDwp5nWU3ZOICmS9MnWptUhAWhaTIMyFyeRG1qFCIby5hUFYG7Lln7tLjy8AG3eWIQytyAJ1lrZVcIOwC+8WA0lL/3c3dUEhKUyvy4sfTTwNvvqneRNoLxTKzM8vISC4QQu4khPxACPmWEPIaIaQ6onwVHoIq8kLGJZdEF1chKc2wRC6Sq7aRFwZ69ADOOCP8dXqwE0DmppX3AOxAKd0JwI8Arsk8SwWKoMRVqIqcUuC++6KLr5iJXJtWOg+0HzmADImcUvoupZTPcPkMgGK/oyJHWBv5poJCUpqZeK2EnUoeFNq0khsU+hT9HCHKx8vZAP4dYXzFhWIc7EwHQR9suUQmilzP7NQIgwK1kfsOdhJC3gegWpPyWkrpG0aYawF0AHjWI57zAJwHAJtHvfpdIWBTIXKOQlKamRB5tqAVeW6xic/s9CVySulBXucJIWcCOBLAgZS6sxildDKAyQAwatSo4mE7eXdwDYZCUpqFSOR6sDO32MQXzcrI/ZAQMg7AVQDGUEqboslSkWNTUeSFROR+kDtdLshVm1Y6JwqMwDkybWUPAegK4D1CyDeEkL9GkKfiRIHe4KyhmJWmNq1opIvOqMgppUOjykjBozNOCMoExaw0c2laKbAO3+mgZ3YC0FP0/RGUmPVgZ/EgF/brWIx9CqzDdzrkur8VqIApzFwVIzY1Ii/EBi3uxekFfo+yuYAZIcX9sNNQo0AfzHqtlahQoDc4ayg0Iv/sM2CrrYKF5UsSn3VW9vLDFblG50KB9nNN5EGhbeR2BFGb06alvyhXWIgbKvuha1dg7Vq212u2oBV550SB2sg1kfshrI28syPMzM59981uXjJBz57ZjT8W00SeC+gp+gC0jTx6aEWuAWjTSmeFVuSdHFdfzbZyO/fcfOckN9Ak5Q1tWumc0ERe5PC7cb16+W8w25mgScobWpF3ThQYgXPolqaRHjRJeUMr8s6JAm33hZkrjcJHgTbogoEe7Oyc0Iq8SLGpDF4GxY03sm9N5N7QppXOiQK1keuW5oeSEvZdYDcub7jpJvZwK4b6mD0bmDIlP2lr00rnRIG2ez3Y6YfnnwceegjYddd850QjLLbfPvyu7FGhpCR3k6E2ZfBNak45JTfpFagi10Tuh8GDgTvvzHcuNIoNf/wjsHJlvnPR+dG3L9DSkruHZoEROIcmcg2NbGD4cPbRyD7KynKXVoEqcm0j19DQ0AgLTeQaGhoaGlFCE7mGhoZGkUMTuYaGhkZQFOi8Ek3kGhoaGmGhbeQaGhoaRQqtyDU0NDQ6CbQi19DQ0ChSaEWuoaGhUeTgRK4VuYaGhoZGlNBErqGhoVHkyIjICSG3EkK+JYR8Qwh5lxAyIKqMaWhoaBQsOplp5U5K6U6U0pEA3gJwQ+ZZ0tDQ0ChQdMbBTkrpBuFvFYDCLKWGhoZGlCgwRZ7xMraEkD8DOB1APYD9PcKdB+A8ANicLwavoaGhUUwoVkVOCHmfEDJb8TkGACil11JKBwF4FsBFbvFQSidTSkdRSkf17t07uhJoaGho5AoF6n7oq8gppQcFjOtZAG8DuDGjHGloaGgUOgqMyDP1Whkm/D0GwA+ZZUdDQ0OjgME31C4vz28+JGRqI7+dELINgBSARQAuyDxLGhoaGgWK3XYDrr8eOP/8fOfEhoyInFJ6QlQZ0dDQ0Ch4EALccku+c+GAntmpoaGhUeTQRK6hoaFR5NBErqGhoVHk0ESuoaGhUeTQRK6hoaFR5NBErqGhoVHk0ESuoaGhUeTQRK6hoaFR5CA0D6t5EULWgM0ETQe9ANRGmJ1Cx6ZU3k2prMCmVd5NqaxA9so7mFLqWHUwL0SeCQghX1FKR+U7H7nCplTeTamswKZV3k2prEDuy6tNKxoaGhpFDk3kGhoaGkWOYiTyyfnOQI6xKZV3UyorsGmVd1MqK5Dj8hadjVxDQ0NDw45iVOQaGhoaGgI0kWtoaGgUOYqKyAkh4wgh8wghPxFCrs53fjIFIeQJQshqQshs4VhPQsh7hJD5xncP4zghhDxglP1bQsgu+ct5eiCEDCKEfEgI+Z4QMocQcolxvNOVmRBSTgj5ghAyyyjrzcbxLQghnxtl+ichpNQ4Xmb8/8k4PySvBUgDhJA4IeRrQshbxv/OXNaFhJDvCCHfEEK+Mo7lrR0XDZETQuIAHgZwGIDhAE4mhAzPb64yxpMAxknHrgbwAaV0GIAPjP8AK/cw43MegEdylMco0QHgCkrpcAB7AvidcQ87Y5lbARxAKR0BYCSAcYSQPQFMAnAvpXQogPUAzjHCnwNgvXH8XiNcseESAHOF/525rACwP6V0pOAvnr92TCktig+A0QDeEf5fA+CafOcrgnINATBb+D8PQH/jd38A84zffwNwsipcsX4AvAHg4M5eZgCVAGYC2ANstl/COG62aQDvABht/E4Y4Ui+8x6ijAPByOsAAG8BIJ21rEa+FwLoJR3LWzsuGkUOYDMAS4T/S41jnQ19KaUrjN8rAfQ1fneq8huv0zsD+BydtMyGqeEbAKsBvAfgZwB1lNIOI4hYHrOsxvl6ADU5zXBmuA/AVWAbsQMs7521rABAAbxLCJlBCDnPOJa3dpzR5ssa2QWllBJCOp1/KCGkC4BXAFxKKd1ACDHPdaYyU0qTAEYSQqoBvAZg2/zmKDsghBwJYDWldAYhZGyes5Mr7EMpXUYI6QPgPULID+LJXLfjYlLkywAMEv4PNI51NqwihPQHAON7tXG8U5SfEFICRuLPUkpfNQ536jJTSusAfAhmXqgmhHABJZbHLKtxvjuAtbnNadrYG8DRhJCFAF4AM6/cj85ZVgAApXSZ8b0a7CG9O/LYjouJyL8EMMwYCS8F8GsAb+Y5T9nAmwDOMH6fAWZH5sdPN0bA9wRQL7zGFQUIk96PA5hLKb1HONXpykwI6W0ocRBCKsDGAuaCEfqJRjC5rLwOTgQwlRoG1UIHpfQaSulASukQsH45lVJ6KjphWQGAEFJFCOnKfwM4BMBs5LMd53vQIOQAw+EAfgSzNV6b7/xEUJ7nAawA0A5mNzsHzFb4AYD5AN4H0NMIS8C8dn4G8B2AUfnOfxrl3QfMtvgtgG+Mz+GdscwAdgLwtVHW2QBuMI5vCeALAD8BeAlAmXG83Pj/k3F+y3yXIc1yjwXwVmcuq1GuWcZnDueifLZjPUVfQ0NDo8hRTKYVDQ0NDQ0FNJFraGhoFDk0kWtoaGgUOTSRa2hoaBQ5NJFraGhoFDk0kWtoaGgUOTSRa2hoaBQ5/h+u4ybg1UUt0wAAAABJRU5ErkJggg==\n", 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" ] @@ -712,12 +741,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 56, "metadata": {}, "outputs": [ { "data": { - "image/png": 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Y/jSXa5R9X3eOcLQ9tavXqpP4+8DfBl6e978G+KqqZqsNHgPuy9v3AY+m8dFJRJ7M9b/kGxSRh4GHAS6Md1xj9zo6zimyPqA4YBknAIs9Q6q4nBPWvvpS/3sbC8sSNpM4khyApoSvq8LfHY+r5iRE5LuBJ1T1t6+2jTWo6iOq+qCqPngwXLyeTXd0nF+YEtEIxZoLd8n45Xw7fOBbWPQZLjtYpay0aFiml7jB8SS+FfgeEXkTcAG4A/gZ4C4RGTM3cT/weK7/OPAq4DERGYE7gT+9hut3dNy6yI5WYpN1dsmAbeKaUtFHprJJL7IcL1nHmxB3qsjgbC40smMvcSM5CVV9h6rer6qvBt4C/Lqq/gDwG8D35WoPAe/P2x/I++Tjv656xhRCHR0vNpi4YUrGYVgUip6rCKHmMNoyX88va+Z2i1IzSN22V3aeghthJ/HfAu8Vkb8D/A7wzlz+TuAXReQS8GUSYenoeElCByEejinCvUWnsmXKwekeqqAyy6Zojq6dy1P8iLGun8PZFatLyMutcyJQY0jRuk/BdSESqvrPgX+etz8HfNNKncvAX70e1+vouNUhs5aVjSVYrdZu3d6suoUqQh2aLsWoaJy/xrBYXBqy30aJoH0KusVlR8ceUEWmKq7hgLdtWkmuU0yvLTJV5gQkEwYz7S5RqJzHqA5SE4szohOJjo49oATCneKS9Ts6gmFOWhZ5ClIkquJ/ITmYbdtuWMyyozPtjhGJLKsbmg2vzhAIt0em6ujYJ/wMtCXKEpIu7i5pBmnC2fmVj1gvia5FnvKWmP7/BHROoqNjD1AR4hgYtgEVXQycfDq+g01VH1woutERANtUXWJSZO9QBknMyTAsUa9CXu044+JiJxIdHXuAqBKmWIsba191CxrjtncCzrjlz0JEsiJTCUucCu/kBem8eLqDVycSHR37gOkJbFJbnEqzgjxOHGjcwwGYp2Xbl6umbOU+iE0xojp7tOxOJDo69gANAR1S4BnmNJlLlnHHDbSTHihiSZWmb414ALoZIKZVjRKwxnQeIfTIVB0d5xUSY4onMS+2ETKzxILw3IShDTIjjTl3Wy8El+5PEfP4tJUUiWciEn11o6NjX5hisn70AWVa4rAWm9K2fUxMf15ZRo31qkf2+hTn2CV9CbSj45zD+2H45cw2SvZxS5Uh7BKH4JY5W+9SXx/Qngu0o+N8QkMgXhjrJU1bAvXeoGfVP7gys7a0XKAAZEtLCZribQ9hlxAdg85JdHScJ2Tz6Z2Atbbt6lVlPojNWrDc4ALlhqb9U9A5iY6OPUBMNxDjknuj5Qx8OP2TYj9Y3Rgrr05RRWcQH0fCYMl5zoBOJDo69om4QgCKMlEbs2utrCvT+brLGTjuohhhrdlhWPkp6OJGR8eeIEYXjADIihhgBk8+dF1c4Qrs13AM6pWiLUHoZtkdHecXaq7bgyTTaTtgIfBbncMYatNt4wZ83dK4Fv0EgySRw5SYZodhbup9daOj45xDBIYcF6IEwc02DH6yQ73q4XQPagFnrK5TXmoICMmHw64nwwlm3yvoRKKjYw8QdeJBtrpUafQT3mnLnLNsUtu2EYgVRzCxetqExzP9RF8C7ei4RdDGlGgVmS3h8HAE4lj7iciuvqPVa5yAzkl0dOwJErX23WhXOGw1AgBneq25zI57ouBdv4OADLWnaalneTtuTi7Qjo6Oq4AGWYLDeNhKhy1vwqJo9KHxYQmXf+KFdLeuj7F5Cjon0dGxD2RjqhKFak2MgEVUKPVcNOw5O2+teYGKJBsMibvihbveWRy8OpHo6NgHcgKdkquzdeoy+wbVZZnSJrSFn4uawtK1nqNDWIhCcfBq7C0kBcI9S36sTiQ6OvaAYichgg4sCXlicsJa9dNoHb9Ed5WUeV+HtCoSD8bsjm7LqFrEDA0BGdeSENfoOomOjj1AWo6hEAJfaXHaavUWmie5bUOOduUC5hb7CQtx2ayYyBm4iLZLLxgicpeIvE9Efl9EPi0i3yIi94jIh0Xks/n/7lxXRORnReSSiHxcRF53Ldfu6LjVIaZTsGxec8605YPQTHOKYjUv+8xp38pkOyXnru20BLGxc/LqiUTX7pxtJay9U3CtnMTPAP+Xqv47wF8APg28HfiIqr4G+EjeB/gu4DX59zDwc9d47Y6OWxuawucXYyjV45WMPpBtjLWvhuknjDOo8nS46/k2XgCumkiIyJ3AXyQnBFbVI1X9KvBm4N252ruB783bbwZ+QRN+E7hLRO692ut3dNzq0JCS9moIdYSqIaTsWrD82/LlOMBmXJZCx2F3adSyk+c2iziyGZf61t4NThj8APBF4B+JyF8Afhv4UeCVqvr5XOcLwCvz9n3Ao+78x3LZ510ZIvIwidPgwnjHNXSvo+N8Q6IuSXu91yfUnIUIoMfn5rD6XrdRdB6bwqnIdlqOmbHWDQ6EOwKvA35OVb8ReJZFtMh9UQVeEG+jqo+o6oOq+uDBcPEautfRcb6hY0A3QwqrL4IOw8IFDCFxDLY9DsvPl1m99piVh3QdgqvvlaVn4CSuhUg8Bjymqh/N++8jEY0/MTEi/z+Rjz8OvMqdf38u6+h4yUF3vvgr1pfm7dmubrTbnsNwk1+dG3lZIfEizRmsLeEaiISqfgF4VET+XC56A/Ap4APAQ7nsIeD9efsDwA/mVY7XA086saSj46UFIwheRMC5hw9DmuieQISkvzAdhm3rkPQMOibCUGww5sVuQlRT7AkHNV3GKbhWY6r/EvglETkAPgf8cLp9fkVE3gb8MfD9ue4HgTcBl4Dnct2OjpckZIolxqUthQqh6AuMWFRTWDXtl/gS5jaeTawn6ozjQ0i5PUwncXmudB0y6JnC110TkVDV3wUeXDn0hpW6CvzItVyvo+PFBFGKoZNxDYUwrNkvtFGpbNnTm2GvLHNqCMlmAjLhmJe63cGro+McI8YlN6dxCbn8rJCotf+F3z6KyGbMzmBZbzHNyByzB2rPBdrRcW5REgYfjEkkMDNqM5aC7MDl1IatExgrCtDGv0MtrmVJ7jOg01xS/p2Fk+i+Gx0de4CoZnFjyedZgsP4WBHVSU24udZrNLfThtcvVp1WBrvBak5AJxIdHXtCWaIMLI5cPo9naz7tlkR3ljwNbtuC61ZLoSHU7udnQBc3Ojr2ieLazaLEdOU72xklN+haXEvvCUoKPlOC6ppNhXEsN2EJtKOj4yohU1yWKM1M2py5/FKmX+rM5VVEKasbVriP2eUGLc5iEQiJgzlDQNxOJDo69gUvMli8Sa8zsGOz+/qXuLgrAWfaYzmGJn411ZZNrX6PTNXRcX6hQsrglTNsqaRIVVhWcdMfwEIkyqRudBHeJ2NlFaQKhuvP6blAOzrOOVSL4hKoJ3503MRxkaysjXbf1fVm3ctSqazqOtbQOYmOjn2hVRoG0NiYYq8oJIs9hS/P+ypS0gGq99WoVk0iSF/d6Og439Acss7iSURNvhewZOVquQuze/BlDcTVFdXkLm56DW8rcRR3RZJj0IlER8c+IJIsLjdDMXgqHIKGWq9gYsJaBO2mzXbS6xjyISFlJnZOXeMAFojmBHQi0dGxD5jFpaX5izFN5CrGpekWdCkv+Taaurb82RKKyG6av+IgFrvvRkfHeUUKf59XNxSQIdstkHJj+MlbFI+2XQeqKY5a2WLTTLBT3Amn5xgzJ+E9TG9wjMuOjo5rwUnWjieYW5fJ7hWZral21kmoBAiKWnIeO37GILjQiURHx/mBFw1gXXywcofivGUiy4q+Ql6A+3mLTiQ6OvYAycpKfN4Nv4rhzaWN4/Cem0F29RRW5ohMWTHxbWq6LnBTkvN0dHRcBVLsSpLOwNswGIIsPzsmrqwkD3ZEY0V8sJiXiKS8G17B2V7zGHQi0dGxZ1QOWB5rmbygtro8zkLT/+fzZZ6P51ZOQBc3Ojr2AIkRic4py2BLlcWlu1Feel8LLyoYEbF4E+Ya3uo0pnnXH+QUdCLR0bEnaBDElihD43hlooXpGmAJXHucG7nfzw5gOoZk1QkLFxFjIiZD6ESio+PcohIJnN9FbHQN3mvTKwda1/Kim2isNX3szHJuWG//GHQi0dGxB5TEOCLJWnoGBkHDkFY9GjsIoJrovqzK7hVA5nx8yMpMUZQUyUqHYfHvOKO40RWXHR17QjW5LQ6lj0npuABpJrO3exBNy6jiAs+09QlppUO8efYZ0TmJjo49ofhszE7c8H4WQeoViSZkncxzrbOwNjOBEBV0I4t/iFeKqibLzRsddEZE/paIfFJEPiEi7xGRCyLygIh8VEQuicgv5xSAiMhh3r+Uj7/6Wq7d0XGrI4kcIeXwzH4YFjE72TS4nJ9jk1k8LJnCdcg/nyd0WLKV+3Z99vGyfwqumkiIyH3AfwU8qKp/HhiAtwA/Cfy0qn4D8BXgbfmUtwFfyeU/net1dLxkITlzl1ldSvmfkzhhOTxjTCsUPh6E8+D055S8nz7mZaTOuzHHFL/COIpTcK06iRG4KCIjcBvweeDbgffl4+8Gvjdvvznvk4+/QcQLZR0dLx2YnYS4hL5Y1Ow5JnuGOe7+tlN9zLZj9u6033ZCtlMKbBNTPbFz22udgqsmEqr6OPD3gH9DIg5PAr8NfFVVzWL8MeC+vH0f8Gg+d8r1v+Zqr9/RcUtjJ9L1igWk6RHagDNrdWy7PXbc9XYC6x6PaxE37iZxBw8AXwfcDrzxattz7T4sIh8TkY8dzc9fa3MdHecXFqTW7BbsZxN6HPISaVhP+deaX5u1pZUNwQXBXXE7P4M+Aq5tdeMvAX+oql9M15VfBb4VuEtExswt3A88nus/DrwKeCyLJ3cCf9o2qqqPAI8A3Hnha89mXN7RcYtBRYhjYBgDqlqbX5sOwYLKGJpkwLbts3mV7XwN3Qwp70YEkSYfKNxYxSVJzHi9iNyWdQtvAD4F/AbwfbnOQ8D78/YH8j75+K+rnoHX6eh4kUJsOXLOuonZ6ShgWaq0aeIVjW7bu5rLnP05dFGCljgVubzoMETO5OR11ZyEqn5URN4H/EtgAn6HxAH8H8B7ReTv5LJ35lPeCfyiiFwCvkxaCenoeElix9jJ0MahxMWJiEoJXeedtDx8jAmROpCNOYitEZ0TcE3GVKr648CPN8WfA75ppe5l4K9ey/U6Ol40UEW2Edlmg6jWjVsEmWY0SBIXRJA5ooMTOY6DSAkqI8OwrGZkvw6ZY2oXeiDcjo5zCxH0cEDjmCb0ZqxD1wE6asUp6Ki7Dlwe2hyHxQu08vuYF12Edgevjo5bBhZBSo4TJdaWMXHeo01kKvUrIEZArJ0SVr+7ind0nG+0jlveLmJN31BN8Gxx6bkKty2qqMqS+q+yp+jiRkfHuYfMuigWSySqrHj0hMIHi2ltIyriEJM9hSk4gywBZ8qKiXP06kFnOjrON6rkPEPWEVhyHti1mDyD/qC1rdDNkInLkIhRALaaCEQIy3VPQI8n0dGxB6ikXKCrlpMe5r0Ju0SjOS9lBavb8HoOPWOQmRadSHR07AvilIvFfJqdiQ6LW3kJTCMuVL4Rghh3zpcssuiwrHYYF1GiY52CLm50dOwJ4ty1q6zisOgQ5ojYcqWPRuUjYjvuQKZFt6EuG7mUZDxZ5zFPS7unoBOJjo49YNXiMpAMp0olWbKH235Vf0XxuKbYNNri7TCKMvTGx5Po6Oi4Sng7hkrE8MufJlJ4Gwd3jjZ5NnbQOok5r1D1UbNPQOckOjr2gN0w9025s4OAYtVQcQ7FEcwIR+MdKlZdUkpBsfMtGpbXhZyATiQ6OvYAMXuIKdZ2ErDYMaSd9dyfhiYQbjm/iBMg0ckwFpnKOJPtxGno4kZHxx5QVhaGJfCM+qAxBnPoWiMO1obpFaLTOdi/JSRuA9n4OqegcxIdHXuA2IpDDn7LHBGb8F656GNQxpgtsKThNlgsKMERjRkhe4FOczo+x+RdOgTOyiN0ItHRsSeogIS8UQyr1MWOoOYgIscbXg1OsWlzP9tQiJWbEtSsLYPsJixeQScSHR17gA4pma9uBnRoIkcVW4lc2Sb9MBTLSZ/ez4euqxCSWbZGkDEUXYQl/NEx1ImKj0EnEh0d+0AElCXv57zoB6pIUjEuPhtZuWnxL0u9PNHFO4lByv/p2l8MtRQGSccmb5ixjk4kOjr2hBLjMnuClrgQXtfgRY/WVbw9DpUiU1QTffEh7OaYomCpdi/Qjo7zjDqwzDKpSwj91qDK0FpMNoRDRVJU7CauRKkXhJRsj8SB9KAzHR3nE5UX6LAYPTEv/hiVFWbmCLyvRhXJyhy9QhIzCIDYsqplCCPpNXJo/RSM5nRjqm4n0dGxRxRCENn98uM4jiwytMckxqQENWJRgtik9vz5QJrxdv4ZolJB5yQ6OvYGmV1ejKybKMrKmKJaE/ISZqt3cEpIr5A0WwvJ9XRjEbCyCBJJegkTa86QC7QTiY6OfaGk9PMh6Ewv4bbNfqKFldt5Q1N3zUPU6ya670ZHx/lFZXHZxpw0q0szjPIJe8ARhbC78gGLAZbIsgQaNRGPac6cikDQMwXC7TqJjo59odEx7Cxnwrrlpbd38CsXdszS+M1xcUc30eIMnEOLzkl0dOwBKRZECisnMwv7P8juZHacwW78hyZ4TJt93ALZqPMW1bAQjjPg1Foi8i4ReUJEPuHK7hGRD4vIZ/P/3blcRORnReSSiHxcRF7nznko1/+siDx0pt51dLwEUGJVBjd5gyw/2CUQ0XEP4upWZWFp38XHXC58Ni/Qs5CSfwy8sSl7O/ARVX0N8JG8D/BdwGvy72Hg51J/5R5SztBvJuUJ/XEjLB0dL1nYpDZ37kAVrQpYVy56LqCJYFWVWVYvt+y5k5D4ethJqOq/IGUB93gz8O68/W7ge135L2jCbwJ3ici9wHcCH1bVL6vqV4APs0t4OjpeMhCvf8hLoGJKTK9M9F/76JSalh3cm1v7+m1AXdglCms6kBVcreLylar6+bz9BeCVefs+4FFX77Fcdlz5DkTkYRH5mIh87Gh+/iq719FxC8DbO9hk9yHpTKTwyk0LGrMjWjS6DM9ZmG2Elft2ztLNa7tLUFWlLPReO1T1EVV9UFUfPBguXq9mOzrOJ9pJ7Sd3pY84Jmitr98qIr0r+RDqHB/HBc5dwdUSiT/JYgT5/4lc/jjwKlfv/lx2XHlHx0sSxRzb2T7sJOpZ00/4bVN4tgSmIjRhJ+5EUnIeExZvBVdLJD4A2ArFQ8D7XfkP5lWO1wNPZrHkQ8B3iMjdWWH5Hbmso+OlC68P8DYT/hedrqG1qci/4oMR4249yM5etSl3qXs9vEBF5D3AtwGvEJHHSKsUPwH8ioi8Dfhj4Ptz9Q8CbwIuAc8BP5zuR78sIv8j8Fu53v+gqq0ytKPjJYMq61ZrcVlVXBEN1pYuW9dyq2PRuL1LefEIlfW2GpxKJFT1rcccesNKXQV+5Jh23gW869QedXS8BFCiZUvOiTEkt+6d1Y2dEx030E5yz2U4fw6NUufceAH6COgWlx0de4MWK0uKIZXOpLiTPkaEQ+FAXGJgz5VU8S8DWfcQUQRidie3WS+SVjlOQScSHR17gKgStvHU5DzSihkubH5FPvISqNUXSCsaIymJsHcOm+ZlifUMDl6dSHR07AEqQhwDwxjQGGHMU9G8Q9dWNXyg23GoJ761myNqp21Bh6GcUzKOvwAbCeheoB0d+4fI8rV3EarsWLVtNhOqdWauY/QMprSUOdf31plnNMvunERHxx4gqoSpFjdEZTG3LhXdRG6WK4tpt+MoioISUmhLy+kRsyhi7buMXqfhfBMJETg8AFhV4KhISTRSRfHJKMobi/sHhQJXGZ0btAqhEuo8l+8Yp4iLMXgG+Lpt277Mkq1UiVuaL4fm/Ak7bea+aQhVLMOdNlfudzlw+r3ZGMtUX+PUsXAaeJ9Upko640yU2+dfjp/wTFb76q5jZT7hDSQ23WJC7gSkbc5ty9q+HLf9zDfcyZ++diRs7V4ASf/jc0rYklY97NHnYxpSmQZAl/Mgl0kqRyFuYD4UJILMcPBMamy4osRN6uewVfjMzlBVONdEQkNgvj0TiSHUA22Kne0AYyjymo6hyF4qpP24PNSwnas61fXyQ9HBPfxZl4no/u2YedqlF0uXKGOqaJDlIefjQIpbOCsqlDwJElN9yH1QRcdQ2tRB6j7n+42HI7KdS5sAcQzpKxVjHjcgT+J4YazHz9+v5WvIfUkxFkN9v3ZfMfcvj6vMMV3HXbOc555Huf/tvNSzcbDxtuV+NyYEqSf0HMtzJcv3yzsR0cNFa29tpujUVO9D+Rq7Z6JjSBMwt6dZWVhiTrpxsL7tZPd2z1qDELaRmMcrTJE/fe3ID/3Ah/jS9uVMMfDUdJE7xud5/PJd/NGT9/Dc0QYB5pivLco8Bw43EyKKiDLNA1GFMURmFQ7HGRFlO6V7v/eOp7jj4DLPTQc8fXTIo0/ckySapzfIbSmbuB4N8B5OxLkmEqJKuDJlU9L8tbGvhGUx2s7oNqUu02FABklhyUPSDOsclq+c1d8M6d8TiWH5klQa5VlTirQppn9j23IfylfcJpq1MbsvUMjLWt55Z9Z0jt1LJOWFNK7IUrNBuk4IVZ+N0wiQiIRIIgQBhm0obKyOIV1rnlOKt7LOHpevpfXDjs2KBEcg2/vNRFY3Q/08sjejHoz1OOXxF7euX863cfDjbYyFHxNPHLNsbc9VRRjGZXxlO6NxUQQSl3ssnJs9B0j9y++N5NR4VfBYO8+41uytWYhrqxdoxYU8BoM9zykStvCl7ct5+XCZy7LhStwQxN4/ZciEIGaKGUSJucyQaLIUzaIdCyHd30FI2blGmcu2ZE5DgqLxbMrLc00kgEWZ0wb3jABJtpJ8FzZxkgWbfaGoH5ozWa1YRIUleaouA6+Kxlj+iaTYgE7JJM35lQwIKQ+CuH6HHAqdAAOFA9ASdzDfS4mejLEXy7LXHNN1LYahqrtvZ+Lr07u5e/dZnawfhbhqfoFUkTnJtaXvuc3KZJjlHphjIQoE40wyJzGzqMpdn6s5lcelsPQxv9XDch0V1wdVJATUngfk0G1Lo/4aXmb374HOuZ8q6X5Le+na4vYXZaCJW1q/O/66QjlffXo+YIqBy7LhzvE5npouEFU4CBNBtEz0MvFFk0lF0GRHFZQYFQ2JcAzAwTAzxZAT9ChBIqNEGODJI/cOmlgiLGUn4PwTiegfniwPqrJXD0kRE3K2ZPcFAP9V1/QS+zqZhUSGXDe1U6hsjAgWoFTcRMxfpDLpmoQqTiEkG4HJyb0zhbiptQ2FIFi76Uu+vMRF2SSSUrXBEordxgISdbR09pv8ZTM34tHljcystkBFXHf8CIahOqdMkiL+LGJXfW6ecMami1Bkiajua+0MejRzPTZ2Fl1pJn3tIRG+yvcgvR/lnZhn2LhXO8Z0D47LS8NkOqvEqZlptDTE1T4AS/tGBNM9KWHpj713eczNkpK8zClZDJQIT00XuRI3PDVd4O7NczwzHXJx2HIwzDx/tEFVyo9MNOaY9oMkEcOORxWO5iHVBaY58Oz2kCDKURwTsXBKDo2SguEWGfB4nH8iAZiCzqzT7AW0Y+V/yFF4omNPYSEugewB5+rYpLMMSTuZmUPWB2TuYnKKNn/twEJYVJd2V5RhOgiiw3Jdu5fcB1M2et1IuY79QliuYZxP/vqXOkNYYiiK7MYggEI0vM6gUjq2rLSDRVMCEEJJc+//k49AKCy73adMuZ6NAXn8/SSz64iUcPHqx6xVjrp7qwh286/+muXeczYr/94YUffcgr0zubyMgXsf7f4xAuE+WOnZCwjcMT6fxAgVnpkOuWO8zJW4yc0kDkFyu5LLEieRjo0hMpHEC1FhCLEQjHFQDseJgzBzFEc2Q9JXSIjMua2k6LzVOQn/VTJWHHbXkh1KCHEca1m+tCxfMHeNxMbXOg/Posvs2vHHrH6+WlUebeLqQoB8H+2r5ZM6e3HF30uZXA3RMLHGcQDVktgcFxPfEgS1mexuhaCK3Fxdg0VsK5yTFrm2XVHYWWGwMTb5Pjb1mvveEQdiRMNQ6pgotOPBaByTu8/6Gos4RR73alXEws6765oOIhXIMgaWmVu1/jhA5SdRuEA/lnPiJB6/fBcAB2Hi4rDlStzwdYdf5dLwCmK8mCY/EFVQUcclDGyGbdlPXUvH5xiyUjPw7DYp/a9MIxFBrW+Dpo05cxOn4HwTCVgG28uq7Uto9cyTzpfZJLJ9g7Huvu5x2yJpUohTeLmJyBAWYxh/zIU8X/06z7GynBN/fkVwHDts9cv9Djv9LUTQ6pkZrmb53bh7m1Cm7G2IQOluaIiQU9KWPmcdSVJgZtm7iIS6XEd1qaehEJuqvwb3PE30Ym2MIhURrsYz99muUSnrtBGr3K/yzDRiU7JgNc/e7mcNcSHU4sZ4fE75oyfvyboD5WBIlOTS8AoOhpnbD4+SqDBHogqbYSaqEEQZh5kgym2HRxxNIxc326yzUC5PIxfGiaN54NmjA56+clhEl2FYdEUSlHAwowfzer8dbg0ikb/GVe7CqAurbJjdxDddg3EdXkPuvwZQhxGDIlsDOyx2db7vXysatITGlsh826Xf7r58gpYxVPdf2jrOACZptOq+rxDVypa/JbbWH1uxaAniyhgUfcY018eLaCS7RkLVJF+4mWop0crz85GZRafUfCDKu7H28XDXrbwsrX9z817ZedZ2wz2sjq/tr1yzPDPXr7CF5442DFlJ+fzRJq1mxIvcfnjE197+FM9sDxnCyBwDh+PEdh4YwtLXqMLBMDOGSEAZQmQMkcNhYgwjV6aR57cbttuBaRo4OEjLnjJGNpv0rEI45l1yON9EQlkGt5VV18xJTVdQAoy6rxTUbLfXPfgX0z942/bX9S+/6tKOESVrr5GrK3bfwxO+48KT+etCIgRrExJS+cGmPn/t5TVdxpqo4TNDeXnec0BuXKSdmC2xKsQ47E7QNsWdb8MIhD8epO6r6uKLYBzMNKNj85zacS9EKa6U1fddsnf7+15DUYI3x1fqZ7UEtnqhmlckQiSI8sz2kHsOn+O56YBnpwM2YSagRIRBYtFlzBo4HNKKyCi25BlhmJiiM1Jz15KQrpNuS9uu7eB8+24Idehw/4DiyoOHrEhqZHj/orcTuM1tYP+tu66fJGt9sH76Pg2h7n97vg8jVt13Ol7ZcRz3ogeScrVVpq5xT/64Hx9/zMboOKKmWitlXXi06r7ba/nn1/apue/SN/+8oLLMrD4cDUHTdqK2hC429+v77LmYlgMpIfCPIQI2Ls311sLkiyZDqe08sJ0Hprw9zQNH88CVeeS56YDXvPyJQgQujlsCSTQJotxxcJmXby4XW4qo9bs0x8A0B2KUbFMRiDEgAQ6y4dV4y3MSDSqT4pA0y+IfiDrZP3/JzEryRNiXz9oxmKx93JfDvugmrpTJ79r1k8rsBUQWewfP3bj72EFwX/e2nybnB6nsAyqscSSur96Uu/RdpFqZEOMO2sl+Ery44fthhFi07ls7AcNCMMUR7UrH488JUpSS7b0XJeXY6HF2xE0wfYk/V6wttwRaXeM47rPpxxrHqGWip9WJOQaenQ749FNfy0GYeHZ7yJZEPExJ+eR8kaM4cBBm5hgYQuTKnMSTSQPPbzdpmTQuS/rTdiBOwuUi3pz+DM83J2Fwgyruq7fjH7AmFsT0gNU/IM9FrHwZqqWzYyIQ73zZ17gM22+4IGknw1qcwfbFPyvWvlzWXxKhrYKwtpmgTniR1+qv+VQc168d1t/rS9q6J+G4t7Z9Bs2x9n0p43RanMeW6MMu9+n7sDaeDVQSqx+ydaWJGSFENsPM4TixyVaSz24PufvCcwwhlmVOExMOQuIIhnz+KJHbN1cYJXI4TowhIk602BxMhFEZhpjOG251TsKendnkh7D7UsW4q6wriqi87OYpu1cyemVSWa50ZsitbsK/3LC8NNOc5GJj01tRyH+9vRKwldsbmVzEadNFs8HSUK2aVFr9VrFo/+5XafOL7ibWZuPWrincSlg112bOTJ04mLB7j55g+/7aM/J98JPenkkh8rosW+bj0j4PbyAXFZnjopOwvmpeYXCGdDuKcDNMG4Za3IjUBEY1cWwm7tn5pfIKF2H9yPckEeY5ZFNrG5bEIUQVtvOQRItxy5aBp44ucMfBZb74/MsKgTDOYSQSQloFmTRweU5cwhQDUxYxIF1PFbxtxFlI/PkmEnYHftLa/hyT9t8bq3gq3zrc2Hkti+iVjWtfh7WvU0VYWPoQWP5nXX9ZvCyNq98qCmm8VduvvFeugTNMqm08dvq80/+Gk7DJGYRVazxVkmPH0p9KfPLt+LDtfpXJ921NfxAEkJOzYNuScHtdNDlkeR3NcRxG6af1SRaOY4UD3O1DU74WCs4Ic6Ov0EBx1hqCMkdhyEr2kDmDiPD8tOHKPDJo5IvPv4yXb65weU7T9kr+F1G288Am6yoAZg1ppWOc2W5m5lkK93D5+SHpKVTYvigsLj21NrSToP3iG9qv1Jqsfxr8pFlr316gIqeyu4xm99G4hBeuZk00gfpL1173pJe3/Wq3Ythx51j95DSyTPT2K+q3WwLj63hOwl/KE5WogNaRlqw9OF7UWvtSr+2/kGfuiZ7jJAr31a60+PPM2Oy0PhWRk8IRpMcpRE2enWO2mRgkcpAtJU3EuDyP/FsXn+apo4scONuJqMIokS3KhSH5nz9PWuWK2fw6hKSDMP3EZjOdiZM43zqJwsG1X1l2zJ6Po/qVzLwmp1t+xeYFqORVq9e+eK3eoJhJNzhuUpt+xK+wrE1wODYKUfGjsHaMcLXEwaPlStZEKr8EKbKsGq0QjYrbaVBWJNw51ZfaVoD8mM5xIbrOrbv+6jeT1Pe/JV5uu9LXrBHetn9rXEh7nj//OAIZa4KrIREJ4yTGITIE5eLBtnADNvm9DgLgqaOL3HHwfBFLjEBAEkFslWMMkXGYGYZIGGKm+UoYI8MQ02rHGTiJ800kDK3PwdoX3bO3DsVdGnZfLJ/i3X39qxd7beIfR3jaCW7nrrUR3EvojbqsrJkMlUzslKk6yK59iENFXNr+tmV+4noxbE1kcvXV97epsyouVR1cOe6Xjl1burZcvNYva/c4IpmPVe25Z9QqY3c+NP55+qVTWfkNtaK4EFtN5tXbeWA7DVyZBqY5cGWbmHtb5bhtPMqnJo5hGwciwpev3M49h88yhFjZQ0QVIlIIzdE0MM+BeUrOX/O8rHSYsvQ0nG8i4Z+VKX7K9srXuVnbxgKVtD4QVheWSeomxY6S6rgXzR/3bdvX2fsR+F++TkWQotYTMtb3UV2zlcVV69UbVzeZF891H60fdk2DNP12Y7QTKs33x+1La4dyGrsfm7FpCZ1ZWxrH4g2z2vuxe2jh+lKZvrd9aM9pOYOTuMrj7rUps3uQmCb0PC/KxSkGZhWOpkQoZk1lRzHpOkwHYRzEly6/jLsOkpPYs9MBl+eRbRwYZebKPLLN7WqUooMAyn6M4UVAJJTqxdHBggpIzV14pdCaLLvK6gfHgbhzs2xcWRGuvXitYY9nUa1sbfm0eUElxoWb8dfMBM6ISMUm+3ZbZzc/idRtr3ETTsRS369y3LHHa9f218i2JurtTTxXdJxxVm5HvZJzpZ4249JyhWtcxpoR007IOeuf91UxrjSfX7jR06wpPWflq7TjqgqSxIGDcWYz1L+Lm20xr748b8oy5ygxWV6KFl+Orx5d5JUXn+b28YjbxiMOh4mogUGi7wDDEBnHOekhguauasWFHIdTa4jIu0TkCRH5hCv7n0Xk90Xk4yLyz0TkLnfsHSJySUQ+IyLf6crfmMsuicjbT+1ZGs26L2eh9ob8law9M+Pul8orGdf8C/x12useh7UvTts/X2Zf9Gry11/POnbF8tLZi36cB+Wx/W5f5JYLa/RAlc6jfPVjfe0Vv4dS3phyFwRJS7CtM5gfm7Xx05X+NahiPORzdt6HQkhl+WCsvVNlVV2XNlvOL49J5Ufi77Mpm1VKTAjTPaSqSiDZUJhFpBGG4goukQvDxDYOfOXKbdx9+FzFFRwMc7G/SEMuRdyQHKjGfqfhLJzEPwbe2JR9GPjzqvrvAX8AvANARF4LvAX4d/M5/6uIDCIyAP8L8F3Aa4G35ronQ+T4l8SOt5R87Qt0mvLJ4L8U/iX0v5OIgxcDjuNC1r5e/kvrrr0jQvh9SZNr56vqX+Lq3hpuovVaPE5nkNvUdlK4+jtxL3w/Wk6tvaZvy+quKDt3+mj1T3Lsap6ZtkrX1n8nE+nW/LuKhen77WH+Lv6avt4cK/FNAxyOMxc2E7cfHnG4mTjcTGzMyClERpmL45YpMTfDXJSUQPbjiDy9PeTug+c5CDNBksPXxXGbOJXNzDDOSQcRFLIidDgDgYCz5QL9FyLy6qbs/3a7vwl8X95+M/BeVb0C/KGIXAK+KR+7pKqfS2Mk7811P3VqDx37emxU59SpwnYmFp48GVniF8CuctA/0LWv7Nq1WvHC+miTxQiAC1nuo1YXmwYf4xIoRlPBDDBYyj2768WIAMQcNVuXl7zS0Pv7sJe+xJlg6ZPdW2zsmiti1Ux4ae7ZWPXmC+7rlvt3zywdbia+X1HJz3HVytbu2XQvdq4d90rJNghy6X9zr/n9sAA1GpugNKq1AlN1Vdnq79fEZcGGMfV1imH50sfA5WmsfCquzCmy1KRJh7DNhCOqsI0Dl+dNbmfgtvGIZ6bDosD0SMZUgsbUh+083DSz7L8O/J95+z7gUXfssVx2XPkORORhEfmYiHzsaH4uFeYHfyxL7ewVdtlmFrbd0IoLK9yKti/40sHl622/VkFp285foQpi484t1/FfKRMx/JcYFpZ9RXFZlLOtl6T1xROos4gmth8awrzmph6bMfTn+21jx5tysbIV+d7fX7Xc296/ESf/Jbf7ju79aYzQqn62CkkT88ziMjbPw5+7xl00H7XyHqqCwnYamKOUiNgxf+EvjBOHQ4oqdRDmYmp9OCRT7ds3V9KxYebOzD0YB/HMdMi9F5/kIExFhDHiEEI2xw7pWkOI3H7haLffDa6JSIjIjwET8EvX0o6Hqj6iqg+q6oMH4badF3dVoejlyBUuQOUYxZx/6GvXOUm0OO5F88fbZcGWAMEu8fLV7YVvdRutiOG4rGNZ4nzOKhe2RjjsnNjknfDEs3y1m/4fx33JCaLEGmFruJ9yaE3MtHO8AnhFxFkloMeJWdZ2YEcZufMOnsTdrrVfDXOyuIQUm9K8QCcN5ffs9rDiHI5iso94enuBy3OKPBU12T188fLLuG3ccjhMy+XyikZaAl26cGW6gQmDReSHgO8G3qBaRuhx4FWu2v25jBPKT7gI5eUuSWByujKZ55pFBsxtuS5j4dxdW+LFBEP7kvv6uwNQ/3t5txCssIg9vq5ro7QdBCQssQv8Sze6VR0TU2xCZE/K8qXL6/IVR+X8FSqRzbH/NtxYX1jGTfwYeJbb4n/OjtU+WnwXKrNyN3l1qO+vqufH3nMFg6Dk+JDG/ssyDlU8SmvXyloHPp8MyD0L4rwQBR8FTQKEhgD5OBhrMSkqwkWJNwqk5c9NyothHMCz20MOx4lntwc8e3TAlWlMS6IxeXMejik+xBgiz7Mp4sg2cyFDdiU3U+7L84ZvvueP+L0nE8N+ZTvy7PMHyeoyB6GJUYjz6XzCVREJEXkj8LeB/0hVn3OHPgD8ExH5KeDrgNcA/x/pWbxGRB4gEYe3AH/tDBeqczsAOgqm+DF5U0NI7r/2gMNCHfUgJ69RTeWT5IhPQ3mJSqBVSD4XXhGX7fmVcZmckl5YdIk07Y+l/A0m84cl8Gpwpseay8ewfFQsEpUqaGpTZZngAiWgrgyKDkNpo/pqDjmC8yaNn+TrYXU1h5CHcu8lorMqJXJ4djCLB2MOHzgsRE82ZVs3YcmtsRlzchs3TrPsyvxWL5K/1FkvMbFMJpGlLVsK3kh5zhaHT+09mcljM6TzJFsr5hwflTu89U8pfRCG0lb6GNmjyomhciwfmRXdjMs9bTY1oTAiZPk8Qsrubcl8dUiZte44uJweew4iY7kxnr5yyPPbrGeYk+3EHIUpBg7HVGceZo6moThvhRALwVAVnuWA33vyPr7m8DmePLqY2n3mIjEqbIXtdkDnlJfmNJxKJETkPcC3Aa8QkceAHyetZhwCH86xFn5TVf8zVf2kiPwKSSE5AT+imsKAisjfAD5Einf9LlX95Km9S3e8xDdMDbEkltHFK9DiP0aqFHmLbEr64lo+C11yWIi6vAyqJfx9OSbUAU0toGoTu8EyQFXxEI2Q2GTPL2Qpr/qgSzwI67ftDzYWLPKy5X1rZHVVXMj5FXZYBNG49GEGIZZxaieSzPNSnvOZlDG2a9u+3bM9M3cesPiM+fH1QY6hWm6UKa/gmCZ+VkrkaifrFwJh/XGJe1KbLlmQvQ8h31+cS2oDgfJ+lPbyaoKPHSpQUiuUDke7n6Q4X4LkxkVZmp+TRHhuOkjRpAY4iiNHMYecO0oh51K3UzwICcmMeruZiTE5axXryexJaiHpZjfxnzy6yOEw8dX5AvOVARTkSmAeR5gEma4DkVDVt64Uv/OE+n8X+Lsr5R8EPnhqj+qTikIuJYmRMmlKNqUY05dKdQnG6gLFaFweruWu2HE1FoHocnI6AuLdqL2bcfUiW6IdHIEwD0a3Pl5eXNzk2uIIwiJS+fqIwKR1iDyW88oLaF8zMzqLusSztP7kLF+lLDS5I7y+o7io14SmTESfg1Rzvgxz2/YEzT9PwDw4xRPRuIhI4vuQr6eEauJJjEuA3+xlusSijMvYlGc2IBrrIESWwMnuKbcnlvXME8tIvQqV/U0khPRs8ntX8naouzeW981yjcgMTx8dchBmnjwKyVBqmFNUaxWmaVjSesacggGYZwEVoiRRwQhGjMLkQuFplGLi/dX5ArcfbPmypgZlFjS3U4UaOAbn3wvUTWbLmFS+mCEgw1A5CGkIlf7AlsX8suCSR2Nh0XcMb3wXBknJWlt9h5oeoFlRWNF37JiGe32HfbG3U+0N6b6CZfKayGKemk0fU/3lXA0BOdpW/SmTxJZoXe6NQjyacHw+GKxuxtRXqzIG5PJc2thRLntCUczQIzAsHKAhamb3l/bs+mWyiUUlWyZtFcm8WG+Gqv8lD4i/L9y+cxpc9BHunKIncQTbP6uxEWkK15LvYYolmtrBM8qjT9yTz01ERURRYBgiBwfJjdzECcsFOgzJ1kGV/C+MYzKyCiFxF+bt+ezzBzz9zEXmKwNfVjh8+RWOLm/QTebGNnGxkD0B12MJ9MbDm8xayHbcizu7L4etlU+zE0syeyzLw1p0A+5rYfu+nDypotYZzP2vDc0G2XhGK0u88sLbF9ffg7+ub8dNioVzctyBcVZTLEvBhZtRF0V6mpdlVd9Hf88x7o6DP8cvJeY6oo5bCY6bsUnpx8j6vzLWvl9Lzs38rPOzs3wlYve1NnaTs5UwWL/NfmbtHBuPeXlnKgMov4LjgxD7ep64WF0rywRG7PnRdDMnzlmLXj1l0SM1kwhAGlrT3Wi+lPPPyEueiQABCkeXN7z8judTQJ4AbMOLgJPwD8RYS38MUmTkzbhkgPbHNX/5/cttL2xrT1Hk1bhYz/nJYQRnXFj5avJvm7bATRipy8vLLUvbrQikusjpMSbFbIz5nOXJJn2Bm+Qx1suM+V5ljiAxjU9rIjzNyOCILVQy/44b+9ZNTn9P05yiQuVnJtO8SxggjbEn7v7Zqi65RG18LZZpIRpSfRgqDkhzZCppCPrWvQPlo+PuY5rTuzSEZUznGRgSl2IJlbxY5wPP+gQ+rfn6yBK9LPdxuKLEpzfphc3NzJAS5wRFxpjMpwPESVDdQA4Yo1EIY0RzbIijK2OKCm+GUiENcdwOsBXkSkgixkZ56rkRxpjKp4B6XdAxON9Ewqz77EXw8rjpGbJbs5rIYQ8ClhevdcyxYKnSTF7brlY7HOtt7PmaW/QKAassD6tzZHlRfQQoszC0YyXwrOunV775iFiArcRUkZZC0reovwebKO34GHH0RMtfz/pg4+DFqnxcgzteRCe3PJyXejlaeUaQFZOhHkM7tpZTpHW0sz6Ua0v9DEOAeVrGaV7aKBGt7PmvfUTyeFZ9t3p2TbOYdWJfOX8cYJqJG5DbkrMVQl7STdsSlM0mmVEfjDOXjzYlFqVZSPr91HT2xYASI2KaBrbbgXkckw4iGAch3PaK53j+mUNu/QxeQpV3cUdfEAJFEG9jA2QUc2j3sHQY8pfVvXwuZ4ItzUmOn1mWwLy+I1gHWSZXazZtCq6m36mdebmn1rkrv2PluM894idlkMXMGBYux15UC3Qicfdl9Wy3P+ZNy3ObRZ/jrSqd522KpK3L9dTdj10rUo+FTZi48vxw999GMbf+2HO3DwMk3cqazYILZ6ciiSuz51BN9LCMqdl+BGd3UvLGNkRcGqLWjqU941K+xJhIqxMkQh6AWQg5q5ZIMtk2cSJ1U9hspuTmHZLytwSvyWHshhBzCP2soJzyh2gTYRuQKfD8M4ccXNyyPTqdBJxvIqHseg/6xDHE5as3O023IwACtRZcs27Bs+VFxncsqZvsMs+JHbV+eW9HY/E3Yx2T0SZgZGcCiLHGfsJ7OddY2ibxcYkL4X7iv7r++nGu+idzXLgtXx92o3zNc1rtmSOqWibVMgFDNX7ix7wQZMc5GOvdBurdTvVz8M/Kj+FQ36OM1ONgCl97jhb41rgi1bLiI23cEJF0fnm25BULGz9dzmnFDUMgvxMr9wwNMUxjNWwVPTIjucwt5pUkPZgzAUheoLaCIcA2i9DGNWyGubh7z5qsKi8ezFyZBuIc0DkRBWLmlJQkYkRhezTyspdd5jTIYix5/iAiXwSeBb60774Ar6D3w+O89APOT19u1X78WVX9M8cdPNdEAkBEPqaqD/Z+9H4ch/PSlxdrP26NJdCOjo69oROJjo6OE3ErEIlH9t2BjN6PGuelH3B++vKi7Me510l0dHTsF7cCJ9HR0bFHdCLR0dFxIs4tkbi6EPxXfa1XichviMinROSTIvKjufweEfmwiHw2/9+dy0VEfjb37eMi8rrr3J9BRH5HRH4t7z8gIh/N1/tlETnI5Yd5/1I+/urr3I+7ROR9OX3Cp0XkW/YxJiLyt/Jz+YSIvEdELtysMZH1lBIveAxE5KFc/7Mi8tB16sfNSW2hqufuRwqx8q+BrwcOgN8DXnsDr3cv8Lq8/XJSmoDXAv8T8PZc/nbgJ/P2m0jBfwV4PfDR69yf/xr4J8Cv5f1fAd6St38e+M/z9n8B/Hzefgvwy9e5H+8G/tO8fQDcdbPHhBQw+Q+Bi24sfuhmjQnwF4HXAZ9wZS9oDIB7gM/l/7vz9t3XoR/fAYx5+yddP16b58wh8ECeS8PVzqsbNtGv8cF8C/Aht/8O4B038frvB/4y8Bng3lx2L/CZvP0PgLe6+qXedbj2/cBHgG8Hfi2/cF9yL0MZG1Kkr2/J22OuJ9epH3fmySlN+U0dE5ZI6/fke/w14Dtv5pgAr24m5wsaA+CtwD9w5VW9q+1Hc+yvAL+Ut6v5YmNytfPqvIobZw7Bf72R2dNvBD4KvFJVP58PfQF45U3o398nxQ81B4GvAb6qquZg4K9V+pGPP5nrXw88AHwR+EdZ9PmHInI7N3lMVPVx4O8B/wb4POkef5v9jInhhY7BzXif/zrXMbWFx3klEnuBiLwM+KfA31TVp/wxTaT3hq4Xi8h3A0+o6m/fyOucESOJvf05Vf1Gkg9NJcPepDG5m5TI6QFScOXb2c0otzfcjDE4DXIDUlt4nFcicVJo/hsCEdmQCMQvqeqv5uI/EZF78/F7gSducP++FfgeEfkj4L0kkeNngLtExDx2/bVKP/LxO4E/vQ79gPSVeUxVP5r330ciGjd7TP4S8Ieq+kVV3QK/ShqnfYyJ4YWOwQ17n2VJbfEDmWBd936cVyLxW+QQ/Flr/RZSuP4bAhERUnDfT6vqT7lDHwBME/0QSVdh5T+YtdmvB5507OdVQ1Xfoar3q+qrSff866r6A8BvsKRSbPth/fu+XP+6fNVU9QvAoyLy53LRG0hR0G/qmJDEjNeLyG35OVk/bvqYOLzQMfgQ8B0icnfmjL4jl10TZElt8T26m9riLXml5wGW1BZXN6+uVbF0o34kTfEfkLSxP3aDr/UfkljGjwO/m39vIsmyHwE+C/w/wD25vpASIP9r4F8BD96APn0by+rG1+eHfAn434DDXH4h71/Kx7/+Ovfh3wc+lsflfydp5m/6mAD/PfD7wCeAXyRp7W/KmADvIelCtiTu6m1XMwYkncGl/Pvh69SPSyQdg72zP+/q/1jux2eA77qWedXNsjs6Ok7EeRU3Ojo6zgk6kejo6DgRnUh0dHSciE4kOjo6TkQnEh0dHSeiE4mOjo4T0YlER0fHifj/AWjIGi09fZ2MAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -731,306 +760,306 @@ "name": "stdout", "output_type": "stream", "text": [ - "goat : ['scorpion', 'trilobite', 'bulldozer', 'turtle', 'camel']\n", - "grave : ['rhinoceros', 'snake', 'frog_2', 'frog_1', 'trilobite']\n", - "post : ['rhinoceros', 'scorpion', 'ostrich', 'golfball', 'turtle']\n", - "videoplayer : ['trilobite', 'turtle', 'rhinoceros', 'frog_1', 'Goldfish']\n", - "knob : ['ostrich', 'camel', 'scorpion', 'snake', 'man_with_hammock']\n", - "nan : ['trilobite', 'snake', 'ostrich', 'frog_2', 'turtle']\n", - "socks : ['frog_1', 'frog_2', 'crab', 'rhinoceros', 'nan']\n", - "socks : ['frog_2', 'scorpion', 'nan', 'frog_1', 'snake']\n", - "carrier : ['turtle', 'snake', 'frog_1', 'frog_2', 'crab']\n", - "leopard : ['ostrich', 'trilobite', 'coin', 'frog_2', 'frog_1']\n", - "hat : ['scorpion', 'frog_1', 'frog_2', 'ostrich', 'crab']\n", - "owl : ['nan', 'snake', 'frog_2', 'horn', 'rhinoceros']\n", - "carrier : ['frog_1', 'buttery?', 'ostrich', 'coin', 'owl']\n", - "crab : ['scorpion', 'crab', 'turtle', 'trilobite', 'camel']\n", - "fire_extinguisher : ['trilobite', 'frog_2', 'frog_1', 'turtle', 'ostrich']\n", - "washing_machine : ['man_with_hat', 'turtle', 'frog_1', 'frog_2', 'scorpion']\n", - "nan : ['scorpion', 'camel', 'ostrich', 'trilobite', 'crab']\n", - "coin : ['snake', 'frog_1', 'kitchen', 'lama', 'nan']\n", - "Snowmobile : ['frog_1', 'snake', 'nan', 'turtle', 'frog_2']\n", - "goat : ['frog_1', 'frog_2', 'nan', 'scorpion', 'owl']\n", - "fly : ['scorpion', 'frog_1', 'frog_2', 'coffin', 'bulldozer']\n", - "iPod : ['ostrich', 'snake', 'trilobite', 'lama', 'nan']\n", - "socks : ['frog_2', 'trilobite', 'ostrich', 'frog_1', 'basketball_ring']\n", - "knob : ['turtle', 'frog_1', 'rhinoceros', 'trilobite', 'Fern']\n", - "iPod : ['frog_1', 'frog_2', 'submachine_gun', 'man_with_hammock', 'canoe']\n", - "owl : ['scorpion', 'crab', 'coin', 'rhinoceros', 'trilobite']\n", - "beermug : ['frog_1', 'frog_2', 'man_with_hammock', 'rhinoceros', 'trilobite']\n", - "umbrella : ['rhinoceros', 'trilobite', 'crab', 'ostrich', 'swan']\n", - "crab : ['frog_1', 'frog_2', 'Goldfish', 'deer', 'rhinoceros']\n", - "harp : ['snake', 'bulldozer', 'camel', 'crab', 'trilobite']\n", - "nan : ['kitchen', 'snake', 'rhinoceros', 'trilobite', 'turtle']\n", - "grave : ['ostrich', 'frog_1', 'snake', 'man_with_hat', 'frog_2']\n", - "lama : ['frog_1', 'snake', 'knob', 'nan', 'man_with_hat']\n", - "nan : ['trilobite', 'turtle', 'rhinoceros', 'duck', 'Goldfish']\n", - "lama : ['rhinoceros', 'ostrich', 'frog_1', 'snake', 'trilobite']\n", - "shredder : ['crab', 'camel', 'trilobite', 'scorpion', 'bulldozer']\n", - "tower : ['turtle', 'coin', 'frog_2', 'frog_1', 'trilobite']\n", - "Snowmobile : ['trilobite', 'coin', 'rhinoceros', 'snake', 'nan']\n", - "buttery? : ['ostrich', 'coin', 'scorpion', 'nan', 'frog_2']\n", - "Stained glass : ['frog_1', 'rhinoceros', 'turtle', 'coin', 'ostrich']\n", - "carrier : ['snake', 'rhinoceros', 'nan', 'frog_1', 'nan']\n", - "duck : ['rhinoceros', 'trilobite', 'turtle', 'frog_2', 'frog_1']\n", - "swan : ['scorpion', 'nan', 'frog_1', 'snake', 'frog_2']\n", - "swan : ['rhinoceros', 'frog_2', 'frog_1', 'nan', 'man_with_hammock']\n", - "fly : ['rhinoceros', 'nan', 'ostrich', 'turtle', 'snake']\n", - "kitchen : ['trilobite', 'frog_1', 'rhinoceros', 'turtle', 'scorpion']\n", - "umbrella : ['ostrich', 'nan', 'frog_1', 'corn_flakes', 'snake']\n", - "swan : ['frog_2', 'trilobite', 'turtle', 'camel', 'ostrich']\n", - "tower : ['frog_2', 'frog_1', 'turtle', 'swan', 'nan']\n", - "nan : ['frog_2', 'frog_1', 'nan', 'snake', 'ostrich']\n", - "airplane : ['ostrich', 'snake', 'camel', 'scorpion', 'trilobite']\n", - "leopard : ['ostrich', 'frog_1', 'frog_2', 'camel', 'scorpion']\n", - "orca : ['scorpion', 'rhinoceros', 'turtle', 'bulldozer', 'ostrich']\n", - "bulldozer : ['frog_2', 'snake', 'frog_1', 'coin', 'rhinoceros']\n", - "owl : ['coffin', 'snake', 'ostrich', 'frog_2', 'frog_1']\n", - "fly : ['scorpion', 'frog_1', 'snake', 'frog_2', 'trilobite']\n", - "Goldfish : ['snake', 'nan', 'rhinoceros', 'frog_1', 'turtle']\n", - "iPod : ['coin', 'frog_1', 'frog_2', 'trilobite', 'snake']\n", - "piano : ['trilobite', 'coin', 'ostrich', 'bulldozer', 'rhinoceros']\n", - "nan : ['frog_2', 'ostrich', 'frog_1', 'snake', 'scorpion']\n", - "airplane : ['scorpion', 'turtle', 'crab', 'helmet', 'coin']\n", - "buttery? : ['trilobite', 'turtle', 'rhinoceros', 'man_with_hammock', 'frog_2']\n", - "videoplayer : ['snake', 'scorpion', 'ostrich', 'frog_1', 'rhinoceros']\n", - "hat : ['frog_1', 'frog_2', 'scorpion', 'nan', 'turtle']\n", - "harp : ['scorpion', 'ostrich', 'nan', 'snake', 'trilobite']\n", - "orca : ['frog_1', 'frog_2', 'nan', 'penguin', 'trilobite']\n", - "bulldozer : ['scorpion', 'turtle', 'nan', 'bag', 'frog_1']\n", - "fire_extinguisher : ['trilobite', 'frog_1', 'scorpion', 'rhinoceros', 'turtle']\n", - "butterfly : ['trilobite', 'snake', 'frog_1', 'nan', 'bus']\n", - "leopard : ['trilobite', 'rhinoceros', 'frog_1', 'snake', 'nan']\n", - "shredder : ['scorpion', 'trilobite', 'windmill', 'snake', 'post']\n", - "bowlingball : ['man_with_hat', 'snake', 'frog_2', 'frog_1', 'nan']\n", - "butterfly : ['frog_1', 'rhinoceros', 'scorpion', 'nan', 'frog_2']\n", - "Bat : ['frog_1', 'golfball', 'turtle', 'frog_2', 'earphone']\n", - "Goldfish : ['snake', 'frog_2', 'turtle', 'injection', 'starfish']\n", - "bowlingball : ['frog_1', 'snake', 'nan', 'frog_2', 'scorpion']\n", - "socks : ['trilobite', 'turtle', 'camel', 'ostrich', 'goat']\n", - "carrier : ['turtle', 'snake', 'frog_2', 'frog_1', 'injection']\n", - "iPod : ['frog_2', 'nan', 'turtle', 'ostrich', 'frog_1']\n", - "kitchen : ['frog_2', 'frog_1', 'rhinoceros', 'turtle', 'trilobite']\n", - "lama : ['turtle', 'trilobite', 'nan', 'frog_1', 'bulldozer']\n", - "nan : ['frog_1', 'frog_2', 'trilobite', 'nan', 'Loudspeaker']\n", - "harp : ['rhinoceros', 'crab', 'frog_2', 'frog_1', 'turtle']\n", - "helmet : ['frog_1', 'frog_2', 'snake', 'nan', 'man_with_hat']\n", - "helmet : ['swan', 'turtle', 'frog_2', 'crab', 'trilobite']\n", - "nan : ['trilobite', 'turtle', 'frog_2', 'crab', 'fungi']\n", - "umbrella : ['turtle', 'crab', 'ostrich', 'trilobite', 'camel']\n", - "goat : ['frog_1', 'lama', 'Goldfish', 'scorpion', 'rhinoceros']\n", - "harp : ['frog_2', 'frog_1', 'turtle', 'snake', 'nan']\n", - "grave : ['ostrich', 'scorpion', 'swan', 'snake', 'knob']\n", - "nan : ['coin', 'turtle', 'scorpion', 'trilobite', 'helmet']\n", - "fire_extinguisher : ['ostrich', 'tent', 'scorpion', 'snake', 'nan']\n", - "umbrella : ['snake', 'rhinoceros', 'nan', 'frog_2', 'trilobite']\n", - "bowlingball : ['frog_2', 'snake', 'frog_1', 'turtle', 'owl']\n", - "coin : ['frog_2', 'scorpion', 'frog_1', 'crab', 'nan']\n", - "coffin : ['frog_1', 'turtle', 'frog_2', 'scorpion', 'crab']\n", - "owl : ['frog_1', 'rhinoceros', 'frog_2', 'duck', 'nan']\n", - "helmet : ['ostrich', 'turtle', 'coin', 'frog_2', 'crab']\n", - "mandarin : ['frog_1', 'frog_2', 'rhinoceros', 'scorpion', 'turtle']\n", - "Snowmobile : ['trilobite', 'snake', 'nan', 'man_with_hat', 'camel']\n", - "nan : ['trilobite', 'ostrich', 'socks', 'rhinoceros', 'teapot']\n", - "owl : ['turtle', 'rhinoceros', 'Goldfish', 'scorpion', 'kangaroo']\n", - "nan : ['scorpion', 'rhinoceros', 'frog_1', 'frog_2', 'nan']\n", - "beermug : ['ostrich', 'trilobite', 'scorpion', 'turtle', 'camel']\n", - "crab : ['scorpion', 'turtle', 'nan', 'frog_2', 'snake']\n", - "harp : ['trilobite', 'snake', 'Goldfish', 'bowlingball', 'frog_1']\n", - "hat : ['rhinoceros', 'ostrich', 'scorpion', 'crab', 'turtle']\n", - "butterfly : ['rhinoceros', 'frog_2', 'frog_1', 'turtle', 'snake']\n", - "nan : ['ostrich', 'trilobite', 'crab', 'camel', 'scorpion']\n", - "nan : ['trilobite', 'rhinoceros', 'ostrich', 'scorpion', 'crab']\n", - "fire_extinguisher : ['coin', 'rhinoceros', 'frog_2', 'turtle', 'snake']\n", - "fly : ['ostrich', 'trilobite', 'rhinoceros', 'lama', 'nan']\n", - "boat : ['coin', 'frog_1', 'frog_2', 'scorpion', 'nan']\n", - "Stained glass : ['frog_2', 'frog_1', 'snake', 'coin', 'rhinoceros']\n", - "duck : ['ostrich', 'scorpion', 'turtle', 'rhinoceros', 'tent']\n", - "nan : ['ostrich', 'scorpion', 'buttery?', 'frog_2', 'frog_1']\n", - "washing_machine : ['frog_1', 'snake', 'scorpion', 'turtle', 'frog_2']\n", - "knob : ['frog_1', 'rhinoceros', 'trilobite', 'snake', 'coin']\n", - "piano : ['frog_2', 'trilobite', 'nan', 'snake', 'coin']\n", - "Goldfish : ['snake', 'ostrich', 'nan', 'fighter', 'frog_2']\n", - "iPod : ['coin', 'trilobite', 'frog_2', 'snake', 'rhinoceros']\n", - "buttery? : ['turtle', 'snake', 'frog_1', 'coffin', 'binocular']\n", - "lama : ['crab', 'scorpion', 'ostrich', 'frog_1', 'frog_2']\n", - "coffin : ['scorpion', 'turtle', 'swan', 'orca', 'Goldfish']\n", - "mandarin : ['scorpion', 'frog_2', 'frog_1', 'nan', 'buttery?']\n", - "bulldozer : ['scorpion', 'frog_1', 'man_with_hat', 'rhinoceros', 'shredder']\n", - "socks : ['crab', 'scorpion', 'ostrich', 'horseshoe_crab', 'snake']\n", - "owl : ['rhinoceros', 'frog_1', 'ostrich', 'turtle', 'trilobite']\n", - "butterfly : ['snake', 'scorpion', 'trilobite', 'frog_2', 'frog_1']\n", - "nan : ['trilobite', 'turtle', 'frog_2', 'ostrich', 'man_with_hammock']\n", - "Cannon : ['rhinoceros', 'boat', 'turtle', 'man_with_hammock', 'crab']\n", - "butterfly : ['frog_2', 'frog_1', 'trilobite', 'rhinoceros', 'nan']\n", - "nan : ['ostrich', 'camel', 'kitchen', 'trilobite', 'snake']\n", - "man_with_hat : ['rhinoceros', 'turtle', 'scorpion', 'frog_1', 'nan']\n", - "leopard : ['scorpion', 'swan', 'turtle', 'snake', 'frog_1']\n", - "post : ['ostrich', 'turtle', 'frog_2', 'nan', 'snake']\n", - "coin : ['ostrich', 'scorpion', 'turtle', 'swan', 'trilobite']\n", - "nan : ['frog_1', 'ostrich', 'frog_2', 'trilobite', 'snake']\n", - "leopard : ['rhinoceros', 'trilobite', 'turtle', 'snake', 'goat']\n", - "orca : ['frog_1', 'rhinoceros', 'snake', 'frog_2', 'dolphin']\n", - "Snowmobile : ['scorpion', 'ostrich', 'rhinoceros', 'snake', 'nan']\n", - "coffin : ['frog_1', 'scorpion', 'owl', 'nan', 'crab']\n", - "hat : ['bulldozer', 'frog_2', 'scorpion', 'frog_1', 'nan']\n", - "fire_extinguisher : ['frog_2', 'scorpion', 'crab', 'bulldozer', 'trilobite']\n", - "shredder : ['snake', 'frog_2', 'frog_1', 'nan', 'shredder']\n", - "crab : ['frog_1', 'turtle', 'trilobite', 'frog_2', 'crab']\n", - "airplane : ['rhinoceros', 'snake', 'trilobite', 'scorpion', 'ostrich']\n", - "mandarin : ['trilobite', 'ostrich', 'rhinoceros', 'nan', 'coin']\n", - "post : ['scorpion', 'crab', 'rhinoceros', 'frog_2', 'frog_1']\n", - "shredder : ['turtle', 'rhinoceros', 'bulldozer', 'scorpion', 'frog_2']\n", - "nan : ['turtle', 'frog_1', 'rhinoceros', 'frog_2', 'snake']\n", - "mandarin : ['scorpion', 'trilobite', 'frog_1', 'man_with_hat', 'crab']\n", - "washing_machine : ['frog_1', 'swan', 'frog_2', 'rhinoceros', 'turtle']\n", - "man_with_hammock : ['nan', 'trilobite', 'ostrich', 'snake', 'rhinoceros']\n", - "Goldfish : ['trilobite', 'frog_2', 'turtle', 'coin', 'ostrich']\n", - "man_with_hat : ['snake', 'ostrich', 'turtle', 'trilobite', 'Goldfish']\n", - "coffin : ['frog_1', 'scorpion', 'frog_2', 'nan', 'bulldozer']\n", - "helmet : ['rhinoceros', 'swan', 'trilobite', 'scorpion', 'frog_1']\n", - "Goldfish : ['trilobite', 'frog_2', 'man_with_hammock', 'babycar', 'coin']\n", - "Snowmobile : ['rhinoceros', 'ostrich', 'crab', 'coin', 'snake']\n", - "nan : ['frog_2', 'nan', 'trilobite', 'turtle', 'frog_1']\n", - "duck : ['trilobite', 'kitchen', 'rhinoceros', 'turtle', 'centipede']\n", - "man_with_hammock : ['ostrich', 'trilobite', 'snake', 'frog_2', 'rhinoceros']\n", - "goat : ['ostrich', 'camel', 'frog_2', 'swan', 'turtle']\n", - "videoplayer : ['trilobite', 'turtle', 'rhinoceros', 'frog_2', 'coin']\n", - "Cannon : ['frog_2', 'frog_1', 'owl', 'rhinoceros', 'trilobite']\n", - "orca : ['frog_1', 'frog_2', 'man_with_hat', 'turtle', 'knob']\n", - "tower : ['frog_2', 'frog_1', 'turtle', 'nan', 'snake']\n", - "coin : ['turtle', 'scorpion', 'camel', 'snake', 'ostrich']\n", - "kitchen : ['ostrich', 'trilobite', 'scorpion', 'frog_2', 'buttery?']\n", - "knob : ['nan', 'ostrich', 'trilobite', 'scorpion', 'shirt']\n", - "helmet : ['trilobite', 'frog_1', 'nan', 'frog_2', 'camel']\n", - "videoplayer : ['rhinoceros', 'snake', 'trilobite', 'frog_1', 'frog_2']\n", - "camel : ['frog_2', 'frog_1', 'scorpion', 'nan', 'turtle']\n", - "Bat : ['frog_2', 'nan', 'frog_1', 'owl', 'bus']\n", - "mandarin : ['ostrich', 'scorpion', 'antenna', 'trilobite', 'rhinoceros']\n", - "coffin : ['turtle', 'frog_2', 'snake', 'ostrich', 'frog_1']\n", - "washing_machine : ['rhinoceros', 'ostrich', 'swan', 'snake', 'trilobite']\n", - "nan : ['frog_1', 'turtle', 'frog_2', 'nan', 'snake']\n", - "nan : ['frog_1', 'snake', 'scorpion', 'golfball', 'frog_2']\n", - "airplane : ['rhinoceros', 'trilobite', 'ostrich', 'frog_2', 'scorpion']\n", - "Stained glass : ['frog_1', 'coin', 'rhinoceros', 'scorpion', 'turtle']\n", - "man_with_hammock : ['lama', 'snake', 'ostrich', 'rhinoceros', 'turtle']\n", - "Stained glass : ['ostrich', 'coin', 'trilobite', 'rhinoceros', 'frog_1']\n", - "crab : ['ostrich', 'trilobite', 'turtle', 'rhinoceros', 'nan']\n", - "post : ['trilobite', 'rhinoceros', 'ostrich', 'snake', 'coin']\n", - "bowlingball : ['scorpion', 'trilobite', 'frog_1', 'ostrich', 'frog_2']\n", - "man_with_hammock : ['frog_1', 'turtle', 'hourglass', 'scorpion', 'nan']\n", - "Bat : ['frog_2', 'crab', 'nan', 'coin', 'frog_1']\n", - "piano : ['man_with_hammock', 'trilobite', 'rhinoceros', 'frog_1', 'ostrich']\n", - "iPod : ['trilobite', 'rhinoceros', 'coin', 'ostrich', 'frog_2']\n", - "crab : ['ostrich', 'rhinoceros', 'frog_1', 'frog_2', 'bowlingball']\n", - "tower : ['frog_1', 'trilobite', 'snake', 'ostrich', 'frog_2']\n", - "camel : ['scorpion', 'nan', 'swan', 'turtle', 'auto_bike']\n", - "lama : ['snake', 'frog_1', 'nan', 'trilobite', 'horn']\n", - "camel : ['scorpion', 'frog_2', 'crab', 'frog_1', 'nan']\n", - "Cannon : ['frog_1', 'frog_2', 'nan', 'snake', 'crab']\n", - "swan : ['frog_1', 'ostrich', 'rhinoceros', 'camel', 'frog_2']\n", - "goat : ['trilobite', 'turtle', 'rhinoceros', 'owl', 'giraffe']\n", - "leopard : ['rhinoceros', 'trilobite', 'snake', 'frog_1', 'frog_2']\n", - "kitchen : ['frog_1', 'frog_2', 'turtle', 'snake', 'crab']\n", - "beermug : ['scorpion', 'ostrich', 'snake', 'frog_2', 'frog_1']\n", - "tower : ['trilobite', 'ostrich', 'crab', 'frog_2', 'scorpion']\n", - "shredder : ['ostrich', 'lama', 'owl', 'camel', 'swan']\n", - "nan : ['frog_1', 'snake', 'scorpion', 'ostrich', 'frog_2']\n", - "knob : ['turtle', 'snake', 'frog_2', 'nan', 'camel']\n", - "helmet : ['snake', 'scorpion', 'kitchen', 'crab', 'nan']\n", - "butterfly : ['coin', 'frog_2', 'ostrich', 'owl', 'frog_1']\n", - "nan : ['ostrich', 'coin', 'frog_2', 'crab', 'snake']\n", - "airplane : ['ostrich', 'nan', 'snake', 'camel', 'turtle']\n", - "socks : ['trilobite', 'scorpion', 'rhinoceros', 'frog_1', 'ostrich']\n", - "bowlingball : ['frog_1', 'frog_2', 'rhinoceros', 'snake', 'buttery?']\n", - "nan : ['rhinoceros', 'scorpion', 'trilobite', 'frog_2', 'glass']\n", - "nan : ['frog_2', 'frog_1', 'nan', 'scorpion', 'owl']\n", - "bulldozer : ['frog_2', 'ostrich', 'coin', 'frog_1', 'trilobite']\n", - "nan : ['scorpion', 'frog_2', 'frog_1', 'crab', 'swan']\n", - "buttery? : ['snake', 'man_with_hat', 'rhinoceros', 'scorpion', 'nan']\n", - "carrier : ['scorpion', 'ostrich', 'trilobite', 'man_with_hat', 'tower']\n", - "buttery? : ['frog_2', 'snake', 'ostrich', 'frog_1', 'turtle']\n", - "duck : ['ostrich', 'rhinoceros', 'snake', 'elephant', 'swan']\n", - "umbrella : ['frog_1', 'frog_2', 'ostrich', 'snake', 'nan']\n", - "buttery? : ['frog_1', 'frog_2', 'scorpion', 'trilobite', 'man_with_hat']\n", - "boat : ['coin', 'nan', 'snake', 'scorpion', 'frog_1']\n", - "lama : ['frog_1', 'frog_2', 'nan', 'scorpion', 'owl']\n", - "nan : ['rhinoceros', 'frog_1', 'nan', 'nan', 'frog_2']\n", - "kitchen : ['frog_1', 'snake', 'trilobite', 'nan', 'videoplayer']\n", - "Bat : ['scorpion', 'frog_2', 'frog_1', 'trilobite', 'coin']\n", - "duck : ['ostrich', 'owl', 'frog_2', 'frog_1', 'coin']\n", - "coin : ['snake', 'buttery?', 'scorpion', 'frog_1', 'nan']\n", - "fly : ['turtle', 'scorpion', 'ostrich', 'swan', 'nan']\n", - "bulldozer : ['snake', 'ostrich', 'scorpion', 'nan', 'crab']\n", - "man_with_hat : ['trilobite', 'turtle', 'ostrich', 'bulldozer', 'rhinoceros']\n", - "Bat : ['rhinoceros', 'frog_1', 'scorpion', 'crab', 'turtle']\n", - "bulldozer : ['nan', 'frog_1', 'turtle', 'snake', 'man_with_hat']\n", - "nan : ['trilobite', 'frog_1', 'frog_2', 'rhinoceros', 'snake']\n", - "kitchen : ['frog_1', 'rhinoceros', 'knob', 'scorpion', 'frog_2']\n", - "boat : ['trilobite', 'rhinoceros', 'frog_1', 'frog_2', 'microscope']\n", - "Stained glass : ['rhinoceros', 'frog_1', 'nan', 'scorpion', 'frog_2']\n", - "fly : ['rhinoceros', 'frog_1', 'trilobite', 'frog_2', 'buttery?']\n", - "man_with_hammock : ['turtle', 'scorpion', 'camel', 'ostrich', 'crab']\n", - "coin : ['man_with_hat', 'turtle', 'crab', 'snake', 'scorpion']\n", - "washing_machine : ['ostrich', 'trilobite', 'snake', 'frog_2', 'turtle']\n", - "Cannon : ['trilobite', 'frog_1', 'ostrich', 'snake', 'rhinoceros']\n", - "coffin : ['scorpion', 'frog_1', 'crab', 'frog_2', 'rhinoceros']\n", - "man_with_hat : ['frog_1', 'frog_2', 'snake', 'scorpion', 'nan']\n", - "swan : ['nan', 'frog_2', 'frog_1', 'scorpion', 'snake']\n", - "hat : ['scorpion', 'frog_2', 'crab', 'frog_1', 'snake']\n", - "washing_machine : ['frog_2', 'frog_1', 'rhinoceros', 'nan', 'turtle']\n", - "Bat : ['frog_1', 'ostrich', 'rhinoceros', 'frog_2', 'snake']\n", - "carrier : ['frog_1', 'nan', 'frog_2', 'kitchen', 'owl']\n", - "man_with_hat : ['frog_2', 'snake', 'frog_1', 'nan', 'owl']\n", - "grave : ['frog_1', 'rhinoceros', 'frog_2', 'bulldozer', 'turtle']\n", - "boat : ['frog_1', 'ostrich', 'frog_2', 'owl', 'swan']\n", - "tower : ['rhinoceros', 'snake', 'ostrich', 'swan', 'turtle']\n", - "nan : ['snake', 'trilobite', 'ostrich', 'nan', 'frog_2']\n", - "camel : ['frog_1', 'scorpion', 'frog_2', 'ostrich', 'refrigerator']\n", - "mandarin : ['turtle', 'trilobite', 'ostrich', 'crab', 'camel']\n", - "man_with_hammock : ['turtle', 'swan', 'ostrich', 'frog_1', 'frog_2']\n", - "piano : ['trilobite', 'scorpion', 'snake', 'frog_2', 'nan']\n", - "post : ['ostrich', 'crab', 'camel', 'trilobite', 'frog_2']\n", - "nan : ['frog_2', 'trilobite', 'frog_1', 'turtle', 'coin']\n", - "goat : ['ostrich', 'snake', 'crab', 'owl', 'nan']\n", - "Cannon : ['snake', 'ostrich', 'crab', 'camel', 'turtle']\n", - "Snowmobile : ['ostrich', 'scorpion', 'rhinoceros', 'chimpanzee', 'coin']\n", - "duck : ['trilobite', 'snake', 'ostrich', 'frog_2', 'frog_1']\n", - "nan : ['trilobite', 'snake', 'nan', 'camel', 'frog_2']\n", - "nan : ['owl', 'nan', 'rhinoceros', 'turtle', 'airship']\n", - "boat : ['snake', 'turtle', 'nan', 'man_with_hat', 'frog_2']\n", - "fire_extinguisher : ['ostrich', 'frog_1', 'coin', 'scorpion', 'Snowmobile']\n", - "piano : ['frog_2', 'frog_1', 'scorpion', 'rhinoceros', 'nan']\n", - "videoplayer : ['frog_1', 'scorpion', 'frog_2', 'rhinoceros', 'keyboard']\n", - "beermug : ['frog_1', 'turtle', 'rhinoceros', 'scorpion', 'frog_2']\n", - "piano : ['rhinoceros', 'frog_2', 'trilobite', 'frog_1', 'coin']\n", - "harp : ['turtle', 'snake', 'trilobite', 'man_with_hammock', 'ostrich']\n", - "umbrella : ['ostrich', 'rhinoceros', 'snake', 'frog_2', 'injection']\n", - "videoplayer : ['rhinoceros', 'snake', 'scorpion', 'frog_1', 'trilobite']\n", - "man_with_hat : ['turtle', 'frog_1', 'Goldfish', 'ostrich', 'trilobite']\n", - "hat : ['ostrich', 'turtle', 'trilobite', 'helmet', 'rhinoceros']\n", - "orca : ['rhinoceros', 'frog_2', 'scorpion', 'nan', 'ostrich']\n", - "orca : ['scorpion', 'turtle', 'trilobite', 'camel', 'nan']\n", - "grave : ['scorpion', 'frog_1', 'fly', 'ostrich', 'frog_2']\n", - "airplane : ['scorpion', 'frog_1', 'nan', 'coin', 'trilobite']\n", - "camel : ['nan', 'frog_2', 'rhinoceros', 'nan', 'trilobite']\n", - "Cannon : ['frog_1', 'snake', 'rhinoceros', 'scorpion', 'kitchen']\n", - "Stained glass : ['snake', 'trilobite', 'rhinoceros', 'frog_1', 'kitchen']\n", - "grave : ['frog_1', 'frog_2', 'scorpion', 'owl', 'nan']\n", - "nan : ['trilobite', 'rhinoceros', 'scorpion', 'ostrich', 'frog_2']\n", - "bowlingball : ['frog_2', 'ostrich', 'trilobite', 'turtle', 'snake']\n", - "knob : ['turtle', 'trilobite', 'swan', 'snake', 'scorpion']\n", - "nan : ['trilobite', 'frog_1', 'snake', 'owl', 'buttery?']\n", - "nan : ['scorpion', 'trilobite', 'snake', 'frog_2', 'rhinoceros']\n", - "Goldfish : ['frog_2', 'ostrich', 'camel', 'crab', 'nan']\n", - "camel : ['frog_1', 'scorpion', 'Goldfish', 'frog_2', 'buttery?']\n", - "shredder : ['trilobite', 'rhinoceros', 'frog_2', 'owl', 'frog_1']\n", - "nan : ['frog_1', 'bulldozer', 'trilobite', 'rhinoceros', 'coin']\n", - "beermug : ['ostrich', 'rhinoceros', 'coin', 'bulldozer', 'crab']\n", - "post : ['snake', 'frog_2', 'scorpion', 'trilobite', 'swan']\n", - "swan : ['snake', 'scorpion', 'trilobite', 'swan', 'ostrich']\n", - "beermug : ['trilobite', 'frog_2', 'coin', 'rhinoceros', 'turtle']\n", - "boat : ['coin', 'turtle', 'frog_2', 'nan', 'trilobite']\n" + "Bat : ['man_with_hammock', 'tent', 'camel', 'segway', 'grasshopper']\n", + "camel : ['turtle', 'watermelon', 'earphone', 'glass', 'octopus']\n", + "grave : ['basketball_ring', 'tricycle', 'telescope', 'bottle', 'camera']\n", + "knob : ['watermelon', 'tennis_ball', 'glass', 'tv', 'chimpanzee']\n", + "nan : ['nan', 'guitar_pick', 'antenna', 'lama', 'sunflower']\n", + "iPod : ['TuningFork', 'camel', 'light_bulb', 'trilobite', 'hourglass']\n", + "leopard : ['umbrella', 'bottle', 'man_with_hammock', 'pool', 'fire_extinguisher']\n", + "buttery? : ['pedal', 'bear', 'frog_1', 'watermelon', 'roulette']\n", + "washing_machine : ['nan', 'Cannon', 'sunflower', 'umbrella', 'toaster2']\n", + "carrier : ['praying_mantis', 'person', 'antenna', 'Loudspeaker', 'tent']\n", + "beermug : ['bear', 'basketball_ring', 'Bat', 'segway', 'basket_clam']\n", + "nan : ['frying_pan', 'shredder', 'cochlea', 'mouse_PC', 'teapot']\n", + "tower : ['knife', 'knife2', 'penguin', 'skunk', 'pedal']\n", + "washing_machine : ['light_bulb', 'tie', 'hat', 'pinetree', 'building']\n", + "Stained glass : ['nan', 'harmonica', 'grape', 'airplane', 'lama']\n", + "Bat : ['bowling', 'harmonica', 'xylophone', 'boxing', 'light_bulb']\n", + "fire_extinguisher : ['building', 'Stained glass', 'basketball_ring', 'pinetree', 'bottle']\n", + "piano : ['harp', 'keyboard', 'radio', 'beermug', 'roulette']\n", + "hat : ['segway', 'tricycle', 'Cannon', 'water_bowl', 'nan']\n", + "coin : ['lawn mower', 'dumbbell', 'babycar', 'teapot', 'shredder']\n", + "lama : ['sunflower', 'nan', 'Cannon', 'light_bulb', 'gas_station']\n", + "shredder : ['lawn mower', 'watermelon', 'mouse_PC', 'light_bulb', 'keyboard']\n", + "mandarin : ['golfball', 'light_bulb', 'chimpanzee', 'horseshoe_crab', 'sunflower']\n", + "boat : ['nan', 'pedal', 'bear', 'porcupine', 'orca']\n", + "crab : ['nan', 'sunflower', 'glass', 'building', 'lama']\n", + "grave : ['nan', 'Copier', 'man_with_hammock', 'tricycle', 'nan']\n", + "harp : ['nan', 'hourglass', 'person', 'owl', 'horse']\n", + "nan : ['lama', 'lawn mower', 'tire', 'ostrich', 'basketball_ring']\n", + "Bat : ['pedal', 'bowling', 'hourglass', 'raccoon', 'nan']\n", + "fire_extinguisher : ['frog_1', 'frog_2', 'nan', 'gun2', 'watermelon']\n", + "nan : ['clip', 'nan', 'telescope', 'bear', 'pedal']\n", + "coin : ['nan', 'watermelon', 'owl', 'lama', 'bird_1']\n", + "lama : ['nan', 'leopard', 'tricycle', 'radio', 'tomato']\n", + "nan : ['keyboard', 'antenna', 'clip', 'camera', 'horse']\n", + "Snowmobile : ['knob', 'laptop', 'projector', 'mouse_PC', 'pinetree']\n", + "piano : ['watermelon', 'giraffe', 'penguin', 'golfball', 'snake']\n", + "post : ['nan', 'goat', 'tennis_ball', 'owl', 'sward']\n", + "nan : ['basket_clam', 'lawn mower', 'boxing', 'xylophone', 'Snowmobile']\n", + "helmet : ['duck', 'bird_2', 'duck', 'nan', 'knob']\n", + "nan : ['penguin', 'sunflower', 'duck', 'ostrich', 'camel']\n", + "nan : ['bottle', 'knife', 'saddle', 'pinetree', 'shoes']\n", + "nan : ['keyboard', 'silver', 'clip', 'knife2', 'sunflower']\n", + "washing_machine : ['horse', 'glass', 'man_with_hat', 'roulette', 'knob']\n", + "harp : ['harmonica', 'Stained glass', 'chimpanzee', 'watermelon', 'yacht']\n", + "umbrella : ['chimpanzee', 'nan', 'tricycle', 'tomato', 'bowling']\n", + "fly : ['bear', 'ostrich', 'sunflower', 'horse', 'light_bulb']\n", + "tower : ['tennis_ball', 'swan', 'mouse_PC', 'billiard', 'Fern']\n", + "fly : ['ostrich', 'hourglass', 'umbrella', 'shoes', 'nan']\n", + "coffin : ['frog_2', 'frog_1', 'snake', 'fire_extinguisher', 'glass']\n", + "goat : ['cochlea', 'microscope', 'airplane', 'trilobite', 'porcupine']\n", + "Stained glass : ['camera', 'boat', 'soccer_ball', 'Fern', 'gun2']\n", + "nan : ['basketball_ring', 'clip', 'bowling', 'watermelon', 'umbrella']\n", + "camel : ['nan', 'bulldozer', 'watermelon', 'trilobite', 'tricycle']\n", + "tower : ['golfball', 'kangaroo', 'nan', 'duck', 'porcupine']\n", + "Stained glass : ['praying_mantis', 'clip', 'centipede', 'keyboard', 'deer']\n", + "airplane : ['sunflower', 'hourglass', 'man_with_hat', 'bear', 'tricycle']\n", + "bulldozer : ['light_bulb', 'cup', 'flower_pot', 'hat', 'teapot']\n", + "bulldozer : ['watermelon', 'xylophone', 'toaster', 'nan', 'radio']\n", + "goat : ['pedal', 'watermelon', 'frog_2', 'frog_1', 'chimpanzee']\n", + "fly : ['man_with_hammock', 'sunflower', 'rhinoceros', 'porcupine', 'camel']\n", + "coin : ['silver', 'clip', 'light_bulb', 'horse', 'sunflower']\n", + "videoplayer : ['tricycle', 'dumbbell', 'sunflower', 'nan', 'frisbee']\n", + "carrier : ['man_with_hammock', 'shredder', 'raccoon', 'telephonebox', 'man_with_hat']\n", + "kitchen : ['cochlea', 'snake', 'knife', 'sunflower', 'trilobite']\n", + "videoplayer : ['basketball_ring', 'Cannon', 'horse', 'tricycle', 'crested_ibis']\n", + "nan : ['nan', 'corn_flakes', 'nan', 'roulette', 'soccer_ball']\n", + "beermug : ['Cannon', 'Snowmobile', 'sunflower', 'man_with_hammock', 'frisbee']\n", + "helmet : ['light_bulb', 'gorilla', 'binocular', 'camera', 'chimpanzee']\n", + "bowlingball : ['segway', 'person', 'tv', 'Stained glass', 'treadmill']\n", + "nan : ['basket_clam', 'floppy', 'penguin', 'camera', 'driver']\n", + "swan : ['skunk', 'snake', 'praying_mantis', 'keyboard', 'raccoon']\n", + "harp : ['frisbee', 'harmonica', 'laptop', 'lama', 'Cannon']\n", + "man_with_hat : ['porcupine', 'kangaroo', 'snake', 'starfish', 'frog_2']\n", + "nan : ['basketball_ring', 'crested_ibis', 'duck', 'chimpanzee', 'sunflower']\n", + "camel : ['clip', 'basketball_ring', 'basket_clam', 'xylophone', 'bowling']\n", + "nan : ['pedal', 'chimpanzee', 'saddle', 'gorilla', 'toaster']\n", + "airplane : ['knob', 'building', 'roulette', 'crab', 'pedal']\n", + "iPod : ['light_bulb', 'harmonica', 'can', 'toaster', 'post']\n", + "umbrella : ['crab', 'stearing', 'horseshoe_crab', 'ostrich', 'spider']\n", + "bowlingball : ['gun', 'silver', 'roulette', 'pedal', 'nan']\n", + "helmet : ['cochlea', 'man_with_hammock', 'nan', 'praying_mantis', 'sunflower']\n", + "fly : ['bottle', 'harmonica', 'bowling', 'flashlight', 'Bat']\n", + "butterfly : ['airship', 'man_with_hat', 'coffin', 'balloon', 'duck']\n", + "man_with_hammock : ['sunflower', 'tricycle', 'nan', 'porcupine', 'balloon']\n", + "post : ['knob', 'light_bulb', 'can', 'bottle', 'hourglass']\n", + "owl : ['Fern', 'pinetree', 'sunflower', 'light_bulb', 'goat']\n", + "socks : ['xylophone', 'nan', 'driver', 'harmonica', 'harp']\n", + "iPod : ['TuningFork', 'frog_1', 'frog_2', 'kangaroo', 'driver']\n", + "duck : ['horse', 'bear', 'tennis_ball', 'basketball_ring', 'swan']\n", + "bulldozer : ['watermelon', 'harmonica', 'projector', 'binocular', 'glass']\n", + "tower : ['laptop', 'Personal_Digital_Assistant', 'binocular', 'floppy', 'flashlight']\n", + "mandarin : ['snake', 'praying_mantis', 'keyboard', 'grasshopper', 'laptop']\n", + "man_with_hammock : ['zebra', 'man_with_hat', 'cochlea', 'raccoon', 'camel']\n", + "washing_machine : ['bear', 'leopard', 'frog_2', 'toaster', 'tennis_ball']\n", + "shredder : ['golfball', 'light_bulb', 'watermelon', 'nan', 'ostrich']\n", + "orca : ['bottle', 'fungi', 'Bat', 'frog_2', 'frog_1']\n", + "coffin : ['ostrich', 'knob', 'penguin', 'crested_ibis', 'gorilla']\n", + "mandarin : ['nan', 'turtle', 'hourglass', 'Fern', 'snake']\n", + "videoplayer : ['pinetree', 'owl', 'knob', 'hourglass', 'Fern']\n", + "iPod : ['light_bulb', 'tennis_ball', 'telephone', 'knob', 'hourglass']\n", + "hat : ['harmonica', 'hourglass', 'Goldfish', 'bear', 'man_with_hat']\n", + "mandarin : ['frog_1', 'tv', 'chimpanzee', 'frog_2', 'nan']\n", + "butterfly : ['crested_ibis', 'bowling', 'pool', 'roulette', 'elephant']\n", + "helmet : ['praying_mantis', 'Fern', 'bowling', 'light_bulb', 'knob']\n", + "duck : ['chimpanzee', 'watermelon', 'harmonica', 'tennis_ball', 'billiard']\n", + "beermug : ['hourglass', 'telephone', 'nan', 'nan', 'tent']\n", + "leopard : ['nan', 'hourglass', 'chimpanzee', 'fungi', 'butterfly']\n", + "helmet : ['cochlea', 'horse', 'bear', 'porcupine', 'dolphin']\n", + "carrier : ['mandarin', 'nan', 'camera', 'watermelon', 'tent']\n", + "orca : ['light_bulb', 'sunflower', 'projector', 'laptop', 'crab']\n", + "nan : ['pedal', 'mouse_PC', 'keyboard', 'knife2', 'crab']\n", + "coffin : ['coffin', 'light_bulb', 'golfball', 'man_with_hammock', 'harp']\n", + "socks : ['man_with_hammock', 'horseshoe_crab', 'crab', 'tent', 'camel']\n", + "fire_extinguisher : ['giraffe', 'coffin', 'zebra', 'binocular', 'harmonica']\n", + "nan : ['toaster', 'pedal', 'watermelon', 'crab', 'babycar']\n", + "Cannon : ['ostrich', 'lama', 'starfish', 'tie', 'porcupine']\n", + "Bat : ['camel', 'horseshoe_crab', 'clip', 'elephant', 'roulette']\n", + "Cannon : ['horse', 'nan', 'knob', 'frisbee', 'sunflower']\n", + "buttery? : ['fungi', 'umbrella', 'man_with_hammock', 'babycar', 'praying_mantis']\n", + "iPod : ['frisbee', 'sunflower', 'Cannon', 'laptop', 'keyboard']\n", + "Snowmobile : ['clip', 'Fern', 'camera', 'man_with_hat', 'horse']\n", + "nan : ['basketball_ring', 'golfball', 'ostrich', 'harmonica', 'tent']\n", + "shredder : ['xylophone', 'treadmill', 'billiard', 'boat', 'basketball_ring']\n", + "nan : ['saddle', 'knob', 'pedal', 'treadmill', 'gun']\n", + "duck : ['sunflower', 'bowling', 'frisbee', 'golfball', 'bowlingball']\n", + "buttery? : ['nan', 'light_bulb', 'ostrich', 'cup', 'building']\n", + "nan : ['man_with_hammock', 'helicopter', 'gun2', 'nan', 'submachine_gun']\n", + "Goldfish : ['snake', 'iPod', 'keyboard', 'earphone', 'clip']\n", + "umbrella : ['lama', 'frog_2', 'watermelon', 'camel', 'frog_1']\n", + "piano : ['lama', 'nan', 'balloon', 'camel', 'ostrich']\n", + "leopard : ['knob', 'porcupine', 'deer', 'gas_station', 'telephonebox']\n", + "socks : ['sunflower', 'segway', 'light_bulb', 'Cannon', 'knob']\n", + "bulldozer : ['elephant', 'skateboard', 'knob', 'saddle', 'nan']\n", + "man_with_hat : ['crab', 'soccer_ball', 'watermelon', 'leopard', 'knob']\n", + "boat : ['raccoon', 'xylophone', 'knob', 'dog_2', 'skunk']\n", + "nan : ['antenna', 'tent', 'roulette', 'nan', 'nan']\n", + "Goldfish : ['Fern', 'gas_station', 'tricycle', 'Snowmobile', 'man_with_hammock']\n", + "butterfly : ['pinetree', 'Fern', 'light_bulb', 'tie', 'tent']\n", + "Stained glass : ['roulette', 'praying_mantis', 'pedal', 'tomato', 'Cannon']\n", + "lama : ['light_bulb', 'building', 'sunflower', 'knob', 'hat']\n", + "orca : ['nan', 'praying_mantis', 'bowling', 'cochlea', 'snake']\n", + "nan : ['light_bulb', 'fungi', 'bear', 'knob', 'harmonica']\n", + "carrier : ['bowling', 'bird_1', 'camel', 'lama', 'gorilla']\n", + "nan : ['bear', 'camel', 'crab', 'pedal', 'frog_2']\n", + "crab : ['penguin', 'nan', 'hourglass', 'man_with_hat', 'raccoon']\n", + "knob : ['chimpanzee', 'nan', 'nan', 'soccer_ball', 'light_bulb']\n", + "Goldfish : ['sunflower', 'chimpanzee', 'praying_mantis', 'nan', 'knob']\n", + "crab : ['light_bulb', 'sunflower', 'golfball', 'windmill', 'turtle']\n", + "kitchen : ['snake', 'watermelon', 'tennis_ball', 'cochlea', 'nan']\n", + "kitchen : ['horseshoe_crab', 'hourglass', 'knob', 'porcupine', 'goat']\n", + "nan : ['xylophone', 'glass', 'tennis_ball', 'nan', 'billiard']\n", + "post : ['frog_1', 'harmonica', 'frog_2', 'nan', 'clip']\n", + "fire_extinguisher : ['nan', 'xylophone', 'pedal', 'clip', 'nan']\n", + "swan : ['light_bulb', 'glass', 'knob', 'praying_mantis', 'socks']\n", + "Snowmobile : ['Fern', 'tent', 'umbrella', 'pinetree', 'light_bulb']\n", + "nan : ['raccoon', 'pinetree', 'trilobite', 'porcupine', 'shredder']\n", + "lama : ['elephant', 'camel', 'bear', 'pedal', 'babycar']\n", + "shredder : ['glass', 'helmet', 'camera', 'sunflower', 'light_bulb']\n", + "knob : ['keyboard', 'snake', 'silver', 'knife2', 'clip']\n", + "grave : ['light_bulb', 'watermelon', 'sunflower', 'tent', 'tomato']\n", + "beermug : ['chimpanzee', 'bear', 'frog_2', 'xylophone', 'elephant']\n", + "nan : ['sunflower', 'cochlea', 'horse', 'knob', 'Cannon']\n", + "videoplayer : ['antenna', 'Fern', 'laptop', 'bulldozer', 'tv']\n", + "Goldfish : ['cochlea', 'knob', 'lama', 'praying_mantis', 'sunflower']\n", + "washing_machine : ['butterfly', 'antenna', 'chimpanzee', 'tomato', 'airship']\n", + "boat : ['knob', 'tent', 'sunflower', 'antenna', 'Snowmobile']\n", + "iPod : ['pinetree', 'tie', 'lama', 'astronomical_telescope', 'lighthouse']\n", + "carrier : ['camel', 'projector', 'frog_1', 'frog_2', 'pedal']\n", + "Bat : ['injection', 'penguin', 'silver', 'raccoon', 'skunk']\n", + "swan : ['glass', 'horn', 'socks', 'frog_2', 'beermug']\n", + "coin : ['nan', 'pool', 'xylophone', 'TuningFork', 'floppy']\n", + "post : ['helicopter', 'glass', 'stearing', 'owl', 'horseshoe_crab']\n", + "crab : ['nan', 'praying_mantis', 'lama', 'basketball_ring', 'tricycle']\n", + "nan : ['Loudspeaker', 'nan', 'TuningFork', 'fungi', 'airplane']\n", + "coin : ['nan', 'light_bulb', 'cup', 'sunflower', 'coffin']\n", + "Bat : ['light_bulb', 'man_with_hammock', 'nan', 'roulette', 'gorilla']\n", + "umbrella : ['bird_1', 'grape', 'hourglass', 'praying_mantis', 'umbrella']\n", + "man_with_hat : ['goat', 'nan', 'raccoon', 'porcupine', 'balloon']\n", + "grave : ['watermelon', 'frog_1', 'building', 'antenna', 'frog_2']\n", + "bowlingball : ['building', 'man_with_hammock', 'knob', 'horn', 'pinetree']\n", + "fly : ['watermelon', 'cochlea', 'nan', 'bowling', 'gorilla']\n", + "airplane : ['nan', 'shredder', 'tricycle', 'xylophone', 'man_with_hammock']\n", + "harp : ['horseshoe_crab', 'man_with_hat', 'water_bowl', 'glass', 'hourglass']\n", + "harp : ['man_with_hammock', 'sunflower', 'hourglass', 'cochlea', 'giraffe']\n", + "man_with_hat : ['trilobite', 'gorilla', 'nan', 'penguin', 'camel']\n", + "beermug : ['sunflower', 'Fern', 'man_with_hammock', 'tent', 'giraffe']\n", + "man_with_hammock : ['horse', 'bear', 'hourglass', 'lama', 'lawn mower']\n", + "crab : ['Fern', 'pinetree', 'keyboard', 'shirt', 'sunflower']\n", + "hat : ['frying_pan', 'projector', 'knife2', 'person', 'knife']\n", + "nan : ['tricycle', 'chimpanzee', 'lama', 'ostrich', 'nan']\n", + "camel : ['gorilla', 'elephant', 'leopard', 'fly', 'chimpanzee']\n", + "orca : ['zebra', 'roulette', 'ostrich', 'post', 'giraffe']\n", + "fly : ['knife', 'toaster2', 'shredder', 'knife2', 'tricycle']\n", + "buttery? : ['lawn mower', 'roulette', 'nan', 'telephone', 'glass']\n", + "man_with_hat : ['antenna', 'frog_2', 'frog_1', 'man_with_hat', 'grasshopper']\n", + "leopard : ['dog_1', 'bird_1', 'dog_2', 'airplane', 'porcupine']\n", + "lama : ['light_bulb', 'tent', 'antenna', 'praying_mantis', 'man_with_hat']\n", + "butterfly : ['bear', 'can', 'toaster', 'giraffe', 'leopard']\n", + "Snowmobile : ['tie', 'sunflower', 'Fern', 'shirt', 'penguin']\n", + "bowlingball : ['light_bulb', 'cochlea', 'penguin', 'keyboard', 'soccer_ball']\n", + "owl : ['roulette', 'rhinoceros', 'harmonica', 'harp', 'deer']\n", + "Cannon : ['billiard', 'dumbbell', 'xylophone', 'glass', 'nan']\n", + "man_with_hammock : ['treadmill', 'harp', 'shredder', 'boat', 'segway']\n", + "kitchen : ['billiard', 'glass', 'man_with_hat', 'boat', 'keyboard']\n", + "nan : ['praying_mantis', 'knife', 'telescope', 'TuningFork', 'snake']\n", + "Snowmobile : ['nan', 'tricycle', 'knob', 'lathe_machine', 'Cannon']\n", + "leopard : ['knob', 'lama', 'crested_ibis', 'praying_mantis', 'roulette']\n", + "beermug : ['knob', 'crested_ibis', 'giraffe', 'duck', 'bird_2']\n", + "piano : ['man_with_hammock', 'praying_mantis', 'segway', 'knob', 'boat']\n", + "butterfly : ['knob', 'nan', 'bear', 'pedal', 'floppy']\n", + "owl : ['clip', 'keyboard', 'knob', 'xylophone', 'glass']\n", + "fire_extinguisher : ['antenna', 'tomato', 'telescope', 'praying_mantis', 'grape']\n", + "helmet : ['pedal', 'saddle', 'sward', 'toaster', 'shredder']\n", + "coffin : ['frying_pan', 'watermelon', 'babycar', 'camera', 'crab']\n", + "carrier : ['light_bulb', 'starfish', 'basketball_ring', 'man_with_hammock', 'sunflower']\n", + "Cannon : ['basketball_ring', 'segway', 'tennis_ball', 'swan', 'duck']\n", + "swan : ['frog_2', 'frog_1', 'cochlea', 'nan', 'snake']\n", + "hat : ['pinetree', 'antenna', 'lama', 'roulette', 'astronomical_telescope']\n", + "videoplayer : ['nan', 'praying_mantis', 'giraffe', 'porcupine', 'boat']\n", + "piano : ['treadmill', 'xylophone', 'deer', 'bear', 'pool']\n", + "man_with_hammock : ['mandarin', 'tent', 'pedal', 'penguin', 'hourglass']\n", + "swan : ['chimpanzee', 'bear', 'gorilla', 'toaster', 'elephant']\n", + "shredder : ['xylophone', 'TuningFork', 'frisbee', 'harmonica', 'Fern']\n", + "goat : ['sunflower', 'cup', 'horseshoe_crab', 'hourglass', 'mouse_PC']\n", + "bulldozer : ['man_with_hammock', 'lawn mower', 'nan', 'scorpion', 'Cannon']\n", + "leopard : ['sunflower', 'nan', 'glass', 'frying_pan', 'man_with_hat']\n", + "piano : ['airship', 'light_bulb', 'sunflower', 'building', 'crab']\n", + "nan : ['umbrella', 'man_with_hammock', 'Snowmobile', 'nan', 'nan']\n", + "orca : ['chimpanzee', 'gorilla', 'snake', 'praying_mantis', 'fly']\n", + "camel : ['grape', 'butterfly', 'fungi', 'umbrella', 'swan']\n", + "knob : ['Snowmobile', 'chimpanzee', 'segway', 'tricycle', 'lawn mower']\n", + "post : ['clip', 'light_bulb', 'harmonica', 'cd', 'glass']\n", + "buttery? : ['xylophone', 'praying_mantis', 'floppy', 'Fern', 'nan']\n", + "boat : ['man_with_hammock', 'starfish', 'centipede', 'gas_station', 'sunflower']\n", + "crab : ['nan', 'yacht', 'antenna', 'helicopter', 'telephonebox']\n", + "mandarin : ['glass', 'golfball', 'harmonica', 'turtle', 'trilobite']\n", + "bowlingball : ['praying_mantis', 'knob', 'sunflower', 'nan', 'giraffe']\n", + "socks : ['lama', 'kangaroo', 'hourglass', 'praying_mantis', 'sunflower']\n", + "goat : ['tent', 'basketball_ring', 'carrier', 'pinetree', 'light_bulb']\n", + "tower : ['frog_2', 'nan', 'chimpanzee', 'gorilla', 'nan']\n", + "swan : ['antenna', 'shredder', 'helicopter', 'elephant', 'mouse_PC']\n", + "man_with_hammock : ['nan', 'Snowmobile', 'tricycle', 'nan', 'toaster']\n", + "Stained glass : ['turtle', 'astronomical_telescope', 'frog_2', 'nan', 'microscope']\n", + "Cannon : ['cochlea', 'horseshoe_crab', 'basket_clam', 'iPod', 'light_bulb']\n", + "videoplayer : ['glass', 'keyboard', 'praying_mantis', 'sward', 'laptop']\n", + "hat : ['frog_2', 'frog_1', 'nan', 'snake', 'kangaroo']\n", + "Goldfish : ['tennis_ball', 'nan', 'glass', 'pedal', 'frying_pan']\n", + "bulldozer : ['pedal', 'tricycle', 'leopard', 'chimpanzee', 'astronomical_telescope']\n", + "socks : ['nan', 'pool', 'horn', 'gun2', 'sward']\n", + "bowlingball : ['knife2', 'gun2', 'watermelon', 'pinetree', 'saddle']\n", + "washing_machine : ['camel', 'tent', 'porcupine', 'raccoon', 'skunk']\n", + "socks : ['giraffe', 'airplane', 'chimpanzee', 'goat', 'praying_mantis']\n", + "nan : ['watermelon', 'keyboard', 'mandarin', 'hourglass', 'cochlea']\n", + "man_with_hat : ['knob', 'sward', 'praying_mantis', 'sunflower', 'grasshopper']\n", + "owl : ['horseshoe_crab', 'turtle', 'watermelon', 'light_bulb', 'astronomical_telescope']\n", + "duck : ['airship', 'nan', 'nan', 'telephonebox', 'tent']\n", + "shredder : ['frog_2', 'lama', 'sunflower', 'gas_station', 'Copier']\n", + "nan : ['knob', 'airplane', 'roulette', 'telephone', 'pedal']\n", + "post : ['Fern', 'clip', 'boat', 'pinetree', 'shredder']\n", + "coffin : ['frisbee', 'praying_mantis', 'bear', 'teapot', 'horse']\n", + "butterfly : ['bird_1', 'roulette', 'frog_2', 'bowling', 'chimpanzee']\n", + "Cannon : ['giraffe', 'nan', 'man_with_hammock', 'shredder', 'lathe_machine']\n", + "kitchen : ['nan', 'harmonica', 'floppy', 'xylophone', 'person']\n", + "airplane : ['turtle', 'watermelon', 'light_bulb', 'chimpanzee', 'Loudspeaker']\n", + "mandarin : ['cup', 'bear', 'harmonica', 'fungi', 'umbrella']\n", + "camel : ['nan', 'tent', 'golfball', 'frisbee', 'Fern']\n", + "Snowmobile : ['billiard', 'water_bowl', 'tennis_ball', 'xylophone', 'skateboard']\n", + "boat : ['lathe_machine', 'frog_2', 'tricycle', 'astronomical_telescope', 'Snowmobile']\n", + "nan : ['golfball', 'basketball_ring', 'crab', 'antenna', 'umbrella']\n", + "nan : ['chimpanzee', 'nan', 'dumbbell', 'mouse_PC', 'praying_mantis']\n", + "airplane : ['chimpanzee', 'praying_mantis', 'tricycle', 'snake', 'driver']\n", + "Stained glass : ['lama', 'zebra', 'penguin', 'porcupine', 'building']\n", + "knob : ['laptop', 'nan', 'tv', 'keyboard', 'porcupine']\n", + "boat : ['tent', 'can', 'man_with_hammock', 'man_with_hat', 'xylophone']\n", + "grave : ['gas_station', 'airplane', 'nan', 'man_with_hammock', 'corn_flakes']\n", + "kitchen : ['bear', 'gorilla', 'nan', 'lama', 'knob']\n", + "goat : ['raccoon', 'tent', 'porcupine', 'roulette', 'horseshoe_crab']\n", + "harp : ['man_with_hammock', 'building', 'nan', 'telephonebox', 'tent']\n", + "duck : ['nan', 'mouse_PC', 'projector', 'frying_pan', 'crab']\n", + "Goldfish : ['watermelon', 'segway', 'gorilla', 'chimpanzee', 'grape']\n", + "orca : ['cup', 'knob', 'pedal', 'keyboard', 'sward']\n", + "goat : ['tie', 'pinetree', 'light_bulb', 'building', 'post']\n", + "grave : ['chimpanzee', 'gorilla', 'hourglass', 'bear', 'yacht']\n", + "lama : ['sunflower', 'elephant', 'gorilla', 'crab', 'rhinoceros']\n", + "hat : ['microscope', 'injection', 'airplane', 'trilobite', 'sward']\n", + "umbrella : ['nan', 'knob', 'tent', 'light_bulb', 'antenna']\n", + "owl : ['sunflower', 'light_bulb', 'building', 'man_with_hammock', 'knob']\n", + "nan : ['elephant', 'nan', 'bear', 'skateboard', 'basket_clam']\n", + "nan : ['kangaroo', 'frog_2', 'bear', 'frog_1', 'camel']\n", + "airplane : ['clip', 'nan', 'fly', 'tv', 'leopard']\n", + "nan : ['nan', 'praying_mantis', 'golfball', 'pinetree', 'light_bulb']\n", + "coffin : ['light_bulb', 'golfball', 'carrier', 'tent', 'fungi']\n", + "buttery? : ['horseshoe_crab', 'trilobite', 'glass', 'turtle', 'frog_2']\n", + "owl : ['mandarin', 'nan', 'pedal', 'glass', 'saddle']\n", + "duck : ['giraffe', 'tent', 'horse', 'zebra', 'cup']\n", + "coin : ['building', 'lighthouse', 'frisbee', 'post', 'Fern']\n", + "umbrella : ['golfball', 'harmonica', 'xylophone', 'nan', 'nan']\n", + "tower : ['water_bowl', 'toaster', 'antenna', 'refrigerator', 'light_bulb']\n", + "knob : ['horseshoe_crab', 'centipede', 'sunflower', 'cochlea', 'umbrella']\n", + "fire_extinguisher : ['frog_2', 'turtle', 'man_with_hammock', 'horn', 'bear']\n" ] } ], @@ -1051,63 +1080,63 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "horn : ['kitchen', 'turtle', 'crab', 'ostrich', 'trilobite']\n", - "kangaroo : ['frog_2', 'trilobite', 'nan', 'fighter', 'kangaroo']\n", - "grasshopper : ['snake', 'frog_1', 'duck', 'frog_2', 'turtle']\n", - "tent : ['frog_2', 'scorpion', 'nan', 'tent', 'frog_1']\n", - "bear : ['frog_2', 'duck', 'frog_1', 'owl', 'bear']\n", - "refrigerator : ['ostrich', 'shredder', 'scorpion', 'snake', 'duck']\n", - "treadmill : ['rhinoceros', 'trilobite', 'crab', 'treadmill', 'socks']\n", - "horseshoe_crab : ['scorpion', 'ostrich', 'trilobite', 'frog_1', 'horseshoe_crab']\n", - "Loudspeaker : ['turtle', 'camel', 'snake', 'frog_2', 'tie']\n", - "bustab : ['frog_1', 'frog_2', 'nan', 'man_with_hat', 'owl']\n", - "flashlight : ['scorpion', 'turtle', 'rhinoceros', 'frog_1', 'yacht']\n", - "keyboard : ['frog_2', 'frog_1', 'keyboard', 'man_with_hammock', 'nan']\n", - "game : ['rhinoceros', 'coin', 'frog_1', 'game', 'frog_2']\n", - "hourglass : ['hourglass', 'turtle', 'snake', 'camel', 'ostrich']\n", - "dog_1 : ['scorpion', 'nan', 'frog_1', 'frog_2', 'tricycle']\n", - "dumbbell : ['snake', 'frog_2', 'frog_1', 'nan', 'buttery?']\n", - "crested_ibis : ['snake', 'crested_ibis', 'nan', 'swan', 'ostrich']\n", - "kangaroo : ['rhinoceros', 'frog_1', 'frog_2', 'trilobite', 'nan']\n", - "teapot : ['snake', 'turtle', 'nan', 'auto_bike', 'nan']\n", - "game : ['trilobite', 'ostrich', 'frog_1', 'snake', 'scorpion']\n", - "Copier : ['rhinoceros', 'ostrich', 'crab', 'swan', 'snake']\n", - "balloon : ['trilobite', 'ostrich', 'rhinoceros', 'snake', 'coin']\n", - "roulette : ['frog_2', 'frog_1', 'snake', 'roulette', 'ostrich']\n", - "clip : ['turtle', 'helmet', 'camel', 'crab', 'clip']\n", - "submachine_gun : ['trilobite', 'rhinoceros', 'crab', 'frog_1', 'scorpion']\n", - "tricycle : ['trilobite', 'camel', 'tricycle', 'lawn mower', 'nan']\n", - "tent : ['tent', 'turtle', 'ostrich', 'scorpion', 'camel']\n", - "driver : ['turtle', 'camel', 'frog_2', 'snake', 'goat']\n", - "giraffe : ['ostrich', 'lama', 'giraffe', 'nan', 'trilobite']\n", - "stearing : ['frog_2', 'nan', 'owl', 'frog_1', 'rhinoceros']\n", - "flashlight : ['turtle', 'flashlight', 'trilobite', 'ostrich', 'frog_2']\n", - "light_bulb : ['frog_1', 'nan', 'owl', 'frog_2', 'snake']\n", - "saddle : ['frog_1', 'turtle', 'scorpion', 'frog_2', 'crab']\n", - "Copier : ['rhinoceros', 'crab', 'ostrich', 'snake', 'shredder']\n", - "knife : ['turtle', 'knife', 'trilobite', 'rhinoceros', 'glass']\n", - "Fern : ['scorpion', 'frog_1', 'trilobite', 'crab', 'Fern']\n", - "knit_hat : ['frog_2', 'turtle', 'frog_1', 'owl', 'nan']\n", - "billiard : ['frog_2', 'snake', 'owl', 'frog_1', 'roulette']\n", - "canoe : ['turtle', 'frog_1', 'canoe', 'snake', 'swan']\n", - "deer : ['ostrich', 'trilobite', 'rhinoceros', 'frog_1', 'coin']\n", - "penguin : ['frog_2', 'frog_1', 'nan', 'owl', 'rhinoceros']\n", - "camera : ['trilobite', 'frog_1', 'rhinoceros', 'nan', 'coin']\n", - "skateboard : ['rhinoceros', 'crab', 'snake', 'ostrich', 'trilobite']\n", - "bike : ['bike', 'turtle', 'frog_1', 'trilobite', 'nan']\n", - "bus : ['bus', 'frog_2', 'nan', 'ostrich', 'frog_1']\n", - "centipede : ['ostrich', 'trilobite', 'rhinoceros', 'clip', 'snake']\n", - "centipede : ['frog_2', 'frog_1', 'turtle', 'snake', 'owl']\n", - "ostrich : ['ostrich', 'scorpion', 'rhinoceros', 'bowlingball', 'frisbee']\n", - "gun : ['frog_1', 'rhinoceros', 'scorpion', 'frog_2', 'nan']\n", - "horn : ['turtle', 'swan', 'ostrich', 'nan', 'horn']\n" + "turtle : ['turtle', 'octopus', 'watermelon', 'horseshoe_crab', 'cochlea']\n", + "bonsai : ['cochlea', 'mouse_PC', 'fungi', 'penguin', 'skunk']\n", + "knife2 : ['keyboard', 'nan', 'man_with_hammock', 'goat', 'zebra']\n", + "glass : ['sunflower', 'light_bulb', 'cochlea', 'mouse_PC', 'glass']\n", + "submachine_gun : ['nan', 'frisbee', 'tent', 'tie', 'lighthouse']\n", + "shirt : ['tent', 'person', 'light_bulb', 'praying_mantis', 'man_with_hat']\n", + "bike : ['Snowmobile', 'chimpanzee', 'tricycle', 'lama', 'toaster']\n", + "TuningFork : ['glass', 'harmonica', 'nan', 'TuningFork', 'driver']\n", + "game : ['umbrella', 'binocular', 'light_bulb', 'flashlight', 'tire']\n", + "tv : ['praying_mantis', 'nan', 'boat', 'helicopter', 'camera']\n", + "sward : ['sward', 'cochlea', 'knob', 'pedal', 'lathe_machine']\n", + "can : ['light_bulb', 'cd', 'horn', 'can', 'nan']\n", + "can : ['nan', 'nan', 'butterfly', 'tricycle', 'balloon']\n", + "stearing : ['ostrich', 'bear', 'deer', 'basketball_ring', 'leopard']\n", + "teapot : ['basket_clam', 'raccoon', 'hourglass', 'nan', 'flower_pot']\n", + "building : ['frog_2', 'frog_1', 'nan', 'building', 'hourglass']\n", + "light_bulb : ['light_bulb', 'nan', 'golfball', 'trilobite', 'coffin']\n", + "penguin : ['penguin', 'duck', 'bowling', 'sunflower', 'orca']\n", + "telescope : ['glass', 'telescope', 'frog_2', 'horn', 'Stained glass']\n", + "babycar : ['frying_pan', 'gorilla', 'babycar', 'toaster', 'chimpanzee']\n", + "tennis : ['nan', 'man_with_hammock', 'frisbee', 'cochlea', 'fungi']\n", + "dumbbell : ['knob', 'tennis_ball', 'dumbbell', 'frisbee', 'duck']\n", + "horn : ['tent', 'tricycle', 'nan', 'telephonebox', 'building']\n", + "scorpion : ['bear', 'deer', 'bottle', 'harmonica', 'nan']\n", + "pool : ['pinetree', 'projector', 'knob', 'gas_station', 'binocular']\n", + "horse : ['horse', 'lama', 'Fern', 'clip', 'centipede']\n", + "injection : ['nan', 'horseshoe_crab', 'watermelon', 'man_with_hammock', 'Cannon']\n", + "keyboard : ['camera', 'keyboard', 'antenna', 'post', 'zebra']\n", + "boat : ['camel', 'gun', 'gas_station', 'saddle', 'porcupine']\n", + "bottle : ['leopard', 'giraffe', 'chimpanzee', 'microscope', 'telescope']\n", + "tent : ['tent', 'nan', 'giraffe', 'frisbee', 'hourglass']\n", + "frog_1 : ['frog_1', 'frog_2', 'beermug', 'watermelon', 'nan']\n", + "gun : ['cochlea', 'trilobite', 'sward', 'lawn mower', 'snake']\n", + "building : ['building', 'pinetree', 'tent', 'nan', 'crab']\n", + "tire : ['nan', 'mandarin', 'leopard', 'airplane', 'dumbbell']\n", + "soccer_ball : ['soccer_ball', 'xylophone', 'harmonica', 'bear', 'chimpanzee']\n", + "gun2 : ['pinetree', 'praying_mantis', 'laptop', 'telephone', 'Fern']\n", + "mouse_PC : ['cup', 'nan', 'man_with_hammock', 'balloon', 'frog_2']\n", + "bag : ['light_bulb', 'golfball', 'pinetree', 'harp', 'horn']\n", + "dolphin : ['horse', 'dolphin', 'camel', 'elephant', 'porcupine']\n", + "mouse_PC : ['nan', 'radio', 'butterfly', 'soccer_ball', 'babycar']\n", + "flower_pot : ['teapot', 'rhinoceros', 'flower_pot', 'tie', 'gun2']\n", + "driver : ['bowling', 'light_bulb', 'watermelon', 'building', 'bus']\n", + "elephant : ['snake', 'elephant', 'skunk', 'chimpanzee', 'keyboard']\n", + "telescope : ['nan', 'gorilla', 'chimpanzee', 'bear', 'lama']\n", + "boat : ['xylophone', 'raccoon', 'owl', 'keyboard', 'dog_2']\n", + "camera : ['tent', 'camera', 'giraffe', 'building', 'roulette']\n", + "octopus : ['knob', 'nan', 'horse', 'chimpanzee', 'fungi']\n", + "turtle : ['Fern', 'pinetree', 'turtle', 'owl', 'hourglass']\n", + "ostrich : ['crested_ibis', 'duck', 'ostrich', 'owl', 'bowling']\n" ] } ], @@ -1120,7 +1149,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -1138,23 +1167,15 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 59, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "TRAIN: Acc Top1/10 0.5924479365348816 0.94140625\n", - "TEST: Similarity Acc 0.9981326053889612\n" + "TRAIN: Acc Top1/10 0.6666666865348816 0.9375\n", + "TRAIN: Similarity Acc 0.9853021378708552\n" ] } ], @@ -1164,7 +1185,11 @@ "sys.path.append('../')\n", "from meg_decoding.models import get_model, Classifier\n", "\n", - "classifier = Classifier(None)\n", + "class Args:\n", + " normalize_image_features= False\n", + "args = Args()\n", + "\n", + "classifier = Classifier(args)\n", "Z = torch.Tensor(train_pred_features).to('cuda')\n", "\n", "Y = []\n", @@ -1180,29 +1205,40 @@ "acc_tmp = np.zeros(len(similarity))\n", "for i in range(len(similarity)):\n", " acc_tmp[i] = np.sum(similarity[i,:] < similarity[i,i]) / (len(similarity)-1)\n", - "print('TEST: Similarity Acc', np.mean(acc_tmp))" + "print('TRAIN: Similarity Acc', np.mean(acc_tmp))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "191" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.sum(similarity[i,:] < similarity[i,i])" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "TEST(AVG): Acc Top1/10/K 0.03999999910593033 0.32 0.66\n", - "TEST(AVG): Similarity Acc 0.5604081632653062\n" + "TEST(AVG): Acc Top1/10/K 0.019999999552965164 0.32 0.54\n", + "TEST(AVG): Similarity Acc 0.5155102040816326\n" ] } ], @@ -1228,31 +1264,34 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "TEST: Acc Top1/10/K 0.03999999910593033 0.12 0.5\n", - "TEST: Similarity Acc 0.5281632653061226\n", - "TEST: Acc Top1/10/K 0.019999999552965164 0.22 0.58\n", - "TEST: Similarity Acc 0.5485714285714286\n", - "TEST: Acc Top1/10/K 0.05999999865889549 0.14 0.42\n", - "TEST: Similarity Acc 0.46693877551020413\n", - "TEST: Acc Top1/10/K 0.019999999552965164 0.3 0.6\n", - "TEST: Similarity Acc 0.58\n", - "TEST: Acc Top1/10/K 0.0 0.24 0.66\n", - "TEST: Similarity Acc 0.5714285714285714\n", - "TEST: Acc Top1/10/K 0.03999999910593033 0.26 0.68\n", - "TEST: Similarity Acc 0.5893877551020408\n", - "{'testTop1acc': '0.030', 'testTop10acc': '0.213', 'testTopKacc': '0.573', 'test Similarityacc': '0.547'}\n" + "TEST: Acc Top1/10/K 0.0 0.16 0.46\n", + "TEST: Similarity Acc 0.5077551020408163\n", + "TEST: Acc Top1/10/K 0.0 0.18 0.52\n", + "TEST: Similarity Acc 0.49795918367346936\n", + "TEST: Acc Top1/10/K 0.019999999552965164 0.18 0.42\n", + "TEST: Similarity Acc 0.4706122448979591\n", + "TEST: Acc Top1/10/K 0.03999999910593033 0.2 0.56\n", + "TEST: Similarity Acc 0.5453061224489797\n", + "TEST: Acc Top1/10/K 0.05999999865889549 0.34 0.6\n", + "TEST: Similarity Acc 0.550204081632653\n", + "TEST: Acc Top1/10/K 0.0 0.2 0.56\n", + "TEST: Similarity Acc 0.5073469387755102\n", + "{'testTop1acc': '0.020', 'testTop10acc': '0.210', 'testTopKacc': '0.520', 'test Similarityacc': '0.513'}\n", + "TEST: (AVG)Similarity Acc 0.5155102040816326\n" ] } ], "source": [ "mean_Acc = {'testTop1acc': [], 'testTop10acc':[], 'testTopKacc':[],'test Similarityacc':[]}\n", + "Zs=[]\n", + "Ys=[]\n", "for sess in range(6):\n", " Z = []\n", " Y = []\n", @@ -1264,6 +1303,8 @@ " Y = torch.Tensor(Y).to('cuda')\n", " Z = np.stack(Z, axis=0)\n", " Z = torch.Tensor(Z).to('cuda')\n", + " Zs.append(Z.unsqueeze(0))\n", + " Ys.append(Y.unsqueeze(0))\n", "\n", " testTop1acc, testTop10acc, testTopKacc = classifier(Z, Y, test=False, top_k=25)\n", " print('TEST: Acc Top1/10/K', testTop1acc, testTop10acc, testTopKacc)\n", @@ -1279,33 +1320,42 @@ " mean_Acc['testTopKacc'].append(testTopKacc)\n", " mean_Acc['test Similarityacc'].append(np.mean(acc_tmp))\n", "\n", - "print({k:'{:.3f}'.format(np.mean(v)) for k, v in mean_Acc.items()})" + "print({k:'{:.3f}'.format(np.mean(v)) for k, v in mean_Acc.items()})\n", + "\n", + "\n", + "Z = torch.cat(Zs, axis=0).mean(dim=0)\n", + "Y = torch.cat(Ys, axis=0).mean(dim=0)\n", + "similarity = calc_similarity(Z, Y)\n", + "acc_tmp = np.zeros(len(similarity))\n", + "for i in range(len(similarity)):\n", + " acc_tmp[i] = np.sum(similarity[i,:] < similarity[i,i]) / (len(similarity)-1)\n", + "print('TEST: (AVG)Similarity Acc', np.mean(acc_tmp))" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[-0.02829436, 0.021185 , 0.03548387, ..., -0.03496352,\n", - " -0.0449915 , -0.03700984],\n", - " [ 0.00924444, 0.0474132 , 0.048658 , ..., -0.00230785,\n", - " 0.02518838, 0.04114052],\n", - " [ 0.03573305, 0.10414703, 0.09177056, ..., 0.03593158,\n", - " 0.04384382, 0.10292213],\n", + "array([[-0.05286709, -0.0330622 , -0.02971671, ..., 0.00778109,\n", + " -0.05837752, -0.02244263],\n", + " [-0.00175766, -0.04036688, -0.06846678, ..., -0.01900831,\n", + " -0.025181 , -0.0361926 ],\n", + " [ 0.01132893, -0.01116514, 0.0068013 , ..., -0.02158575,\n", + " -0.07323602, -0.02004133],\n", " ...,\n", - " [-0.00134944, 0.01704587, 0.03670729, ..., -0.0302972 ,\n", - " -0.0075427 , -0.0084782 ],\n", - " [ 0.03754435, 0.05194615, 0.06902866, ..., 0.00701771,\n", - " 0.00141289, -0.0053163 ],\n", - " [ 0.01080816, 0.04355977, 0.03786531, ..., 0.00411169,\n", - " -0.00474188, 0.03192064]], dtype=float32)" + " [ 0.00744497, -0.00774237, -0.02196336, ..., -0.00637732,\n", + " -0.02821182, -0.07562105],\n", + " [-0.00865966, -0.06237145, -0.01914276, ..., 0.00587261,\n", + " -0.0176643 , -0.00213297],\n", + " [ 0.01403732, -0.04614489, -0.03762517, ..., -0.02528448,\n", + " -0.06757275, 0.01953033]], dtype=float32)" ] }, - "execution_count": 19, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -1316,22 +1366,22 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1349,26 +1399,26 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[ 0.04108303 0.04088428 0.03548387 ... -0.05713566 -0.06061254\n", - " -0.06656156]\n", - " [ 0.05157361 0.048658 0.0474132 ... -0.02635343 -0.02831518\n", - " -0.0399184 ]\n", - " [ 0.10414703 0.10292213 0.09177056 ... -0.00605685 -0.01238619\n", - " -0.01508587]\n", + "[[ 0.07811476 0.0762556 0.06676015 ... -0.06404212 -0.06637167\n", + " -0.06727783]\n", + " [ 0.10484349 0.09008161 0.08452075 ... -0.05853303 -0.06846678\n", + " -0.10877159]\n", + " [ 0.1067533 0.0742688 0.07134086 ... -0.07323602 -0.09312805\n", + " -0.10860907]\n", " ...\n", - " [ 0.04187033 0.03670729 0.03446947 ... -0.04422709 -0.05808438\n", - " -0.05826079]\n", - " [ 0.06902866 0.05455588 0.05391106 ... -0.01362184 -0.0174164\n", - " -0.02178394]\n", - " [ 0.04902737 0.04355977 0.03786531 ... -0.03734122 -0.04704913\n", - " -0.06549982]]\n" + " [ 0.09223431 0.07800243 0.07307668 ... -0.05624556 -0.07562105\n", + " -0.07929761]\n", + " [ 0.09314198 0.09209663 0.07670547 ... -0.07457788 -0.07910059\n", + " -0.0961978 ]\n", + " [ 0.13051413 0.07288393 0.06907146 ... -0.06757275 -0.07942246\n", + " -0.09006844]]\n" ] } ], @@ -1379,63 +1429,63 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "horn : ['treadmill', 'hourglass', 'grasshopper', 'penguin', 'tent'] [1085, 644, 198, 155, 1052]\n", - "kangaroo : ['treadmill', 'grasshopper', 'kangaroo', 'horn', 'hourglass'] [1085, 198, 107, 532, 644]\n", - "grasshopper : ['kangaroo', 'horn', 'grasshopper', 'penguin', 'game'] [107, 532, 198, 155, 655]\n", - "tent : ['kangaroo', 'Fern', 'deer', 'bustab', 'kangaroo'] [109, 1192, 255, 362, 107]\n", - "bear : ['bear', 'camera', 'grasshopper', 'treadmill', 'game'] [187, 415, 198, 1085, 655]\n", - "refrigerator : ['grasshopper', 'bear', 'treadmill', 'game', 'tent'] [198, 187, 1085, 655, 1052]\n", - "treadmill : ['dog_1', 'teapot', 'penguin', 'centipede', 'hourglass'] [176, 1022, 155, 75, 644]\n", - "horseshoe_crab : ['hourglass', 'grasshopper', 'dog_1', 'clip', 'driver'] [644, 198, 176, 796, 900]\n", - "Loudspeaker : ['refrigerator', 'dog_1', 'Copier', 'grasshopper', 'game'] [849, 176, 813, 198, 655]\n", - "bustab : ['grasshopper', 'hourglass', 'refrigerator', 'horn', 'knife'] [198, 644, 849, 532, 436]\n", - "flashlight : ['horn', 'penguin', 'game', 'roulette', 'treadmill'] [532, 155, 655, 875, 1085]\n", - "keyboard : ['penguin', 'kangaroo', 'grasshopper', 'tent', 'submachine_gun'] [155, 107, 198, 1051, 663]\n", - "game : ['treadmill', 'hourglass', 'tent', 'grasshopper', 'dog_1'] [1085, 644, 1052, 198, 176]\n", - "hourglass : ['horn', 'horn', 'grasshopper', 'bear', 'teapot'] [532, 532, 198, 187, 1022]\n", - "dog_1 : ['grasshopper', 'kangaroo', 'horn', 'roulette', 'treadmill'] [198, 107, 532, 875, 1085]\n", - "dumbbell : ['penguin', 'grasshopper', 'game', 'treadmill', 'hourglass'] [155, 198, 653, 1085, 644]\n", - "crested_ibis : ['treadmill', 'grasshopper', 'game', 'light_bulb', 'refrigerator'] [1085, 198, 655, 714, 849]\n", - "kangaroo : ['penguin', 'grasshopper', 'bike', 'kangaroo', 'refrigerator'] [155, 198, 764, 107, 849]\n", - "teapot : ['refrigerator', 'dog_1', 'camera', 'hourglass', 'grasshopper'] [849, 176, 415, 644, 198]\n", - "game : ['grasshopper', 'knife', 'submachine_gun', 'dog_1', 'roulette'] [198, 436, 663, 176, 875]\n", - "Copier : ['billiard', 'grasshopper', 'knife', 'horn', 'bustab'] [827, 198, 436, 532, 362]\n", - "balloon : ['penguin', 'grasshopper', 'tent', 'game', 'dog_1'] [155, 198, 1052, 653, 176]\n", - "roulette : ['treadmill', 'billiard', 'knife', 'game', 'grasshopper'] [1085, 827, 436, 655, 198]\n", - "clip : ['grasshopper', 'billiard', 'kangaroo', 'tent', 'penguin'] [198, 827, 107, 1051, 155]\n", - "submachine_gun : ['horn', 'grasshopper', 'kangaroo', 'tent', 'bear'] [532, 198, 107, 1051, 187]\n", - "tricycle : ['giraffe', 'tent', 'tent', 'submachine_gun', 'balloon'] [257, 1051, 1052, 663, 631]\n", - "tent : ['grasshopper', 'kangaroo', 'gun', 'horn', 'camera'] [198, 107, 858, 532, 415]\n", - "driver : ['penguin', 'skateboard', 'light_bulb', 'treadmill', 'game'] [155, 933, 714, 1085, 653]\n", - "giraffe : ['billiard', 'treadmill', 'grasshopper', 'giraffe', 'horn'] [827, 1085, 198, 257, 532]\n", - "stearing : ['bike', 'penguin', 'knit_hat', 'skateboard', 'kangaroo'] [764, 155, 1131, 933, 107]\n", - "flashlight : ['kangaroo', 'grasshopper', 'treadmill', 'dumbbell', 'penguin'] [107, 198, 1085, 494, 155]\n", - "light_bulb : ['bear', 'grasshopper', 'horn', 'Fern', 'penguin'] [187, 198, 532, 1192, 155]\n", - "saddle : ['light_bulb', 'hourglass', 'tent', 'submachine_gun', 'dog_1'] [714, 644, 1052, 663, 176]\n", - "Copier : ['penguin', 'light_bulb', 'kangaroo', 'bike', 'treadmill'] [155, 714, 107, 764, 1085]\n", - "knife : ['bear', 'horn', 'treadmill', 'billiard', 'teapot'] [187, 532, 1085, 827, 1022]\n", - "Fern : ['bear', 'camera', 'grasshopper', 'refrigerator', 'Fern'] [187, 415, 198, 849, 1192]\n", - "knit_hat : ['treadmill', 'hourglass', 'bear', 'game', 'light_bulb'] [1085, 644, 187, 655, 714]\n", - "billiard : ['knife', 'treadmill', 'horn', 'grasshopper', 'bear'] [436, 1085, 532, 198, 187]\n", - "canoe : ['bear', 'horn', 'dog_1', 'roulette', 'penguin'] [187, 532, 176, 875, 155]\n", - "deer : ['game', 'penguin', 'bus', 'bike', 'kangaroo'] [655, 155, 891, 764, 109]\n", - "penguin : ['submachine_gun', 'treadmill', 'knife', 'bear', 'grasshopper'] [663, 1085, 436, 187, 198]\n", - "camera : ['refrigerator', 'bike', 'kangaroo', 'game', 'bear'] [849, 764, 107, 653, 187]\n", - "skateboard : ['penguin', 'treadmill', 'grasshopper', 'bear', 'giraffe'] [155, 1085, 198, 187, 257]\n", - "bike : ['bear', 'submachine_gun', 'treadmill', 'dog_1', 'light_bulb'] [187, 663, 1085, 176, 714]\n", - "bus : ['billiard', 'balloon', 'canoe', 'ostrich', 'bus'] [827, 631, 668, 8, 891]\n", - "centipede : ['hourglass', 'grasshopper', 'penguin', 'dog_1', 'game'] [644, 198, 155, 176, 653]\n", - "centipede : ['refrigerator', 'dog_1', 'bear', 'grasshopper', 'hourglass'] [849, 176, 187, 198, 644]\n", - "ostrich : ['treadmill', 'grasshopper', 'game', 'tent', 'knife'] [1085, 198, 655, 1052, 436]\n", - "gun : ['grasshopper', 'bus', 'canoe', 'kangaroo', 'roulette'] [198, 891, 668, 107, 875]\n", - "horn : ['submachine_gun', 'kangaroo', 'grasshopper', 'dumbbell', 'treadmill'] [663, 107, 198, 494, 1085]\n" + "turtle : ['light_bulb', 'tire', 'elephant', 'teapot', 'flower_pot'] [716, 423, 303, 1021, 823]\n", + "bonsai : ['frog_1', 'light_bulb', 'bike', 'elephant', 'flower_pot'] [16, 716, 765, 303, 823]\n", + "knife2 : ['light_bulb', 'teapot', 'submachine_gun', 'camera', 'flower_pot'] [716, 1021, 660, 413, 823]\n", + "glass : ['light_bulb', 'injection', 'flower_pot', 'building', 'driver'] [716, 1011, 823, 943, 897]\n", + "submachine_gun : ['flower_pot', 'elephant', 'building', 'teapot', 'tire'] [823, 303, 937, 1021, 423]\n", + "shirt : ['tire', 'light_bulb', 'injection', 'flower_pot', 'building'] [423, 716, 1011, 823, 943]\n", + "bike : ['tire', 'light_bulb', 'teapot', 'building', 'stearing'] [423, 716, 1021, 943, 986]\n", + "TuningFork : ['teapot', 'can', 'tire', 'stearing', 'soccer_ball'] [1021, 957, 423, 986, 950]\n", + "game : ['tire', 'building', 'light_bulb', 'teapot', 'flower_pot'] [423, 943, 716, 1021, 823]\n", + "tv : ['stearing', 'flower_pot', 'injection', 'tire', 'frog_1'] [986, 823, 1011, 423, 16]\n", + "sward : ['submachine_gun', 'camera', 'light_bulb', 'frog_1', 'teapot'] [660, 413, 716, 16, 1021]\n", + "can : ['tire', 'submachine_gun', 'building', 'light_bulb', 'stearing'] [423, 660, 943, 716, 986]\n", + "can : ['flower_pot', 'frog_1', 'tire', 'light_bulb', 'horse'] [823, 16, 423, 716, 233]\n", + "stearing : ['light_bulb', 'tire', 'bike', 'injection', 'frog_1'] [716, 423, 765, 1011, 16]\n", + "teapot : ['tire', 'bike', 'light_bulb', 'soccer_ball', 'flower_pot'] [423, 765, 716, 950, 823]\n", + "building : ['teapot', 'light_bulb', 'flower_pot', 'building', 'injection'] [1021, 716, 823, 943, 1011]\n", + "light_bulb : ['flower_pot', 'teapot', 'injection', 'tire', 'bike'] [823, 1021, 1011, 423, 765]\n", + "penguin : ['stearing', 'teapot', 'tire', 'bike', 'frog_1'] [986, 1021, 423, 765, 16]\n", + "telescope : ['tire', 'flower_pot', 'frog_1', 'building', 'bike'] [423, 823, 16, 937, 765]\n", + "babycar : ['bike', 'light_bulb', 'flower_pot', 'injection', 'stearing'] [765, 716, 823, 1011, 986]\n", + "tennis : ['light_bulb', 'tire', 'flower_pot', 'teapot', 'frog_1'] [716, 423, 823, 1021, 16]\n", + "dumbbell : ['flower_pot', 'light_bulb', 'frog_1', 'tire', 'teapot'] [823, 716, 16, 423, 1021]\n", + "horn : ['bike', 'tire', 'submachine_gun', 'stearing', 'light_bulb'] [765, 423, 660, 986, 716]\n", + "scorpion : ['flower_pot', 'frog_1', 'injection', 'tire', 'stearing'] [823, 16, 1011, 423, 986]\n", + "pool : ['tire', 'teapot', 'bike', 'can', 'stearing'] [423, 1021, 765, 957, 986]\n", + "horse : ['teapot', 'flower_pot', 'can', 'tire', 'soccer_ball'] [1021, 823, 957, 423, 950]\n", + "injection : ['camera', 'stearing', 'teapot', 'flower_pot', 'tire'] [413, 986, 1021, 823, 423]\n", + "keyboard : ['frog_1', 'injection', 'flower_pot', 'building', 'bottle'] [16, 1011, 823, 937, 1124]\n", + "boat : ['tire', 'injection', 'teapot', 'can', 'flower_pot'] [423, 1011, 1021, 957, 823]\n", + "bottle : ['frog_1', 'flower_pot', 'bike', 'gun', 'stearing'] [16, 823, 765, 858, 986]\n", + "tent : ['injection', 'flower_pot', 'tire', 'light_bulb', 'frog_1'] [1011, 823, 423, 716, 16]\n", + "frog_1 : ['flower_pot', 'light_bulb', 'tire', 'teapot', 'building'] [823, 716, 423, 1021, 943]\n", + "gun : ['injection', 'frog_1', 'teapot', 'tire', 'flower_pot'] [1011, 16, 1021, 423, 823]\n", + "building : ['tire', 'bike', 'flower_pot', 'stearing', 'can'] [423, 765, 823, 986, 957]\n", + "tire : ['flower_pot', 'light_bulb', 'gun2', 'can', 'elephant'] [823, 716, 869, 957, 303]\n", + "soccer_ball : ['flower_pot', 'tire', 'teapot', 'injection', 'bike'] [823, 423, 1021, 1011, 765]\n", + "gun2 : ['light_bulb', 'bike', 'stearing', 'teapot', 'gun2'] [716, 765, 986, 1021, 869]\n", + "mouse_PC : ['tire', 'injection', 'light_bulb', 'pool', 'bottle'] [423, 1011, 716, 634, 1124]\n", + "bag : ['tire', 'flower_pot', 'light_bulb', 'submachine_gun', 'teapot'] [423, 823, 716, 660, 1021]\n", + "dolphin : ['tire', 'flower_pot', 'light_bulb', 'building', 'TuningFork'] [423, 823, 716, 943, 1100]\n", + "mouse_PC : ['flower_pot', 'tire', 'frog_1', 'injection', 'camera'] [823, 423, 16, 1011, 413]\n", + "flower_pot : ['bike', 'flower_pot', 'injection', 'dumbbell', 'keyboard'] [765, 823, 1011, 493, 457]\n", + "driver : ['tire', 'light_bulb', 'flower_pot', 'injection', 'soccer_ball'] [423, 716, 823, 1011, 950]\n", + "elephant : ['bike', 'frog_1', 'flower_pot', 'gun', 'teapot'] [765, 16, 823, 858, 1021]\n", + "telescope : ['tire', 'building', 'can', 'teapot', 'injection'] [423, 943, 957, 1021, 1011]\n", + "boat : ['light_bulb', 'flower_pot', 'teapot', 'bike', 'gun2'] [716, 823, 1021, 765, 869]\n", + "camera : ['light_bulb', 'tire', 'teapot', 'camera', 'flower_pot'] [716, 423, 1021, 413, 823]\n", + "octopus : ['tire', 'light_bulb', 'tennis', 'injection', 'building'] [423, 716, 1042, 1011, 943]\n", + "turtle : ['light_bulb', 'tire', 'flower_pot', 'soccer_ball', 'building'] [716, 423, 823, 950, 943]\n", + "ostrich : ['light_bulb', 'injection', 'camera', 'bike', 'flower_pot'] [716, 1011, 413, 765, 823]\n" ] } ], @@ -1446,22 +1496,22 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1477,6 +1527,13 @@ "plt.imshow(similarity)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/notebooks/image_featuers_check.ipynb b/notebooks/image_featuers_check.ipynb new file mode 100644 index 0000000..a003c76 --- /dev/null +++ b/notebooks/image_featuers_check.ipynb @@ -0,0 +1,47 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "231b2509", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "faf15eff", + "metadata": {}, + "outputs": [], + "source": [ + "train_image_path = '/home/yainoue/meg2image/codes/MEG-decoding/data/GOD/image_features_train.npy'\n", + "test_image_path = '/home/yainoue/meg2image/codes/MEG-decoding/data/GOD/image_features.npy'" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/train_wowandb.py b/train_wowandb.py index 31dadd9..e299924 100644 --- a/train_wowandb.py +++ b/train_wowandb.py @@ -267,6 +267,7 @@ def run(args: DictConfig) -> None: loss.backward() optimizer.step() # get_grad(brain_encoder) + # break # Accumulate gradients for Gwilliams for the whole epoch if args.dataset == "Brennan2018": @@ -351,7 +352,7 @@ def run(args: DictConfig) -> None: if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230417_sbj01_seq2stat') + args = compose(config_name='20230424_sbj01_seq2stat') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) run(args) diff --git a/train_wowandb_cv.py b/train_wowandb_cv.py new file mode 100644 index 0000000..4f56208 --- /dev/null +++ b/train_wowandb_cv.py @@ -0,0 +1,368 @@ +import os, sys, random +import numpy as np +import torch +import torch.nn as nn +from time import time +from tqdm import tqdm, trange +from termcolor import cprint +# import wandb + +from omegaconf import DictConfig, open_dict +import hydra +from hydra.utils import get_original_cwd + +from constants import device +# from speech_decoding.dataclass.brennan2018 import Brennan2018Dataset +# from speech_decoding.dataclass.gwilliams2022 import ( +# Gwilliams2022SentenceSplit, +# Gwilliams2022ShallowSplit, +# Gwilliams2022DeepSplit, +# Gwilliams2022Collator, +# ) +from torch.utils.data import DataLoader, RandomSampler, BatchSampler + +from meg_decoding.models import get_model, Classifier +from meg_decoding.utils.get_dataloaders import get_dataloaders, get_samplers +from meg_decoding.utils.loss import * +from meg_decoding.dataclass.god import GODDatasetBase, GODCollator +from meg_decoding.utils.loggers import Pickleogger +from meg_decoding.utils.vis_grad import get_grad +from torch.utils.data.dataset import Subset + + +def run(args: DictConfig) -> None: + + from meg_decoding.utils.reproducibility import seed_worker + # NOTE: We do need it (IMHO). + if args.reproducible: + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + torch.use_deterministic_algorithms(True) + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + g = torch.Generator() + g.manual_seed(0) + seed_worker = seed_worker + else: + g = None + seed_worker = None + + pkl_logger = Pickleogger(os.path.join(args.save_root, 'runs')) + + # with open_dict(args): + # args.root_dir = get_original_cwd() + cprint(f"Current working directory : {os.getcwd()}") + cprint(args, color="white") + + # ----------------------- + # Dataloader + # ----------------------- + # NOTE: Segmentation should always be by word onsets, not just every 3 seconds + if args.dataset == "Gwilliams2022": + + if args.split_mode == "sentence": + + train_set = Gwilliams2022SentenceSplit(args) + test_set = Gwilliams2022SentenceSplit(args, train_set.test_word_idxs_dict) + + assert train_set.num_subjects == test_set.num_subjects + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + elif args.split_mode == "shallow": + + dataset = Gwilliams2022ShallowSplit(args) + + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(dataset.Y.shape[0] * args.split_ratio) + test_size = dataset.Y.shape[0] - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + + elif args.split_mode == "deep": + + train_set = Gwilliams2022DeepSplit(args, train=True) + test_set = Gwilliams2022DeepSplit(args, train=False) + assert train_set.num_subjects == test_set.num_subjects + + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + cprint(f"Test segments: {test_size}", "cyan") + + if args.use_sampler: + # NOTE: currently not supporting reproducibility + train_loader, test_loader = get_samplers( + train_set, + test_set, + args, + test_bsz=test_size, + collate_fn=Gwilliams2022Collator(args), + ) + else: + # FIXME: maybe either get rid of reproducibility, or remove this? + if args.reproducible: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, seed_worker, g, test_bsz=test_size + ) + else: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, test_bsz=test_size + ) + + elif args.dataset == "Brennan2018": + # NOTE: takes an optional debug param force_recompute to pre-process the EEG even if it exists + dataset = Brennan2018Dataset(args) + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(len(dataset) * args.split_ratio) + test_size = len(dataset) - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + cprint( + f"Number of samples: {len(train_set)} (train), {len(test_set)} (test)", color="blue", + ) + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, g, seed_worker, test_bsz=test_size + ) + + elif args.dataset == "GOD": + source_dataset = GODDatasetBase(args, 'train') + # val_dataset = GODDatasetBase(args, 'val') + # train_size = int(np.round(len(source_dataset)*0.8)) + # val_size = len(source_dataset) - train_size + + # train_dataset, val_dataset = torch.utils.data.random_split(source_dataset, [train_size, val_size]) + ind_tr = list(range(0, 6000)) + list(range(7200, 21600)) # + list(range(7200, 13200)) + list(range(14400, 20400)) + ind_te = list(range(6000,7200)) # + list(range(13200, 14400)) + list(range(20400, 21600)) + train_dataset = Subset(source_dataset, ind_tr) + val_dataset = Subset(source_dataset, ind_te) + + with open_dict(args): + args.num_subjects = source_dataset.num_subjects + print('num subject is {}'.format(args.num_subjects)) + + + if args.use_sampler: + test_size = 50# 重複サンプルが存在するのでval_dataset.Y.shape[0] + train_loader, test_loader = get_samplers( + train_dataset, + val_dataset, + args, + test_bsz=test_size, + collate_fn=GODCollator(args),) + + else: + train_loader = DataLoader( + train_dataset, + batch_size= args.batch_size, + drop_last=True, + shuffle=True, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + test_loader = DataLoader( + val_dataset, + batch_size=50, # args.batch_size, + drop_last=True, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + + else: + raise ValueError("Unknown dataset") + + if args.use_wandb: + wandb.config = {k: v for k, v in dict(args).items() if k not in ["root_dir", "wandb"]} + wandb.init( + project=args.wandb.project, + entity=args.wandb.entity, + config=wandb.config, + save_code=True, + ) + wandb.run.name = args.wandb.run_name + "_" + args.split_mode + wandb.run.save() + + # --------------------- + # Models + # --------------------- + brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) + + classifier = Classifier(args) + + # --------------- + # Loss + # --------------- + loss_func = CLIPLoss(args).to(device) + loss_func.train() + + # -------------------- + # Optimizer + # -------------------- + optimizer = torch.optim.Adam( + list(brain_encoder.parameters()) + list(loss_func.parameters()), lr=float(args.lr), + ) + + if args.lr_scheduler == "cosine": + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, T_max=args.epochs, eta_min=args.lr * 0.1 + ) + elif args.lr_scheduler == "multistep": + scheduler = torch.optim.lr_scheduler.MultiStepLR( + optimizer, + milestones=[int(m * args.epochs) for m in args.lr_multistep_mlstns], + gamma=args.lr_step_gamma, + ) + else: + scheduler = None + + # ====================================== + best_acc = 0 + pbar = tqdm(range(args.epochs)) + for epoch in pbar: + pbar.set_description("training {}/{} epoch".format(epoch, args.epochs)) + train_losses = [] + test_losses = [] + trainTop1accs = [] + trainTop10accs = [] + testTop1accs = [] + testTop10accs = [] + + brain_encoder.train() + pbar2 = tqdm(train_loader) + for i, batch in enumerate(pbar2): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, chunkIDs = batch + assert ( + len(chunkIDs.unique()) == X.shape[0] + ), "Duplicate segments in batch are not allowed. Aborting." + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y = X.to(device), Y.to(device) + Z = brain_encoder(X, subject_idxs) + loss = loss_func(Y, Z) + # import pdb; pdb.set_trace() + with torch.no_grad(): + trainTop1acc, trainTop10acc = classifier(Z, Y) + + train_losses.append(loss.item()) + trainTop1accs.append(trainTop1acc) + trainTop10accs.append(trainTop10acc) + + pbar.set_description("training {}/{} iters Train/Loss: {}, Train/Top1Acc: {}, Train/Top10Acc: {}".format(i, len(train_loader), loss.item(), trainTop1acc, trainTop10acc)) + if args.dataset == "Gwilliams2022": + optimizer.zero_grad() + loss.backward() + optimizer.step() + if args.dataset == "GOD": + optimizer.zero_grad() + loss.backward() + optimizer.step() + # get_grad(brain_encoder) + # break + + # Accumulate gradients for Gwilliams for the whole epoch + if args.dataset == "Brennan2018": + optimizer.zero_grad() + loss.backward() + optimizer.step() + + brain_encoder.eval() + for batch in test_loader: + + with torch.no_grad(): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, chunkIDs = batch + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y = X.to(device), Y.to(device) + + Z = brain_encoder(X, subject_idxs) # 0.96 GB + + loss = loss_func(Y, Z) + + testTop1acc, testTop10acc = classifier(Z, Y, test=True) # ( 250, 1024, 360 ) + + test_losses.append(loss.item()) + testTop1accs.append(testTop1acc) + testTop10accs.append(testTop10acc) + + print( + f"Ep {epoch}/{args.epochs} | ", + f"train l: {np.mean(train_losses):.3f} | ", + f"test l: {np.mean(test_losses):.3f} | ", + f"trainTop10acc: {np.mean(trainTop10accs):.3f} | ", + f"testTop10acc: {np.mean(testTop10accs):.3f} | ", + f"lr: {optimizer.param_groups[0]['lr']:.5f}", + f"temp: {loss_func.temp.item():.3f}", + ) + pkl_logger.log({ + "epoch": epoch, + "train_loss": np.mean(train_losses), + "test_loss": np.mean(test_losses), + "trainTop1acc": np.mean(trainTop1accs), + "trainTop10acc": np.mean(trainTop10accs), + "testTop1acc": np.mean(testTop1accs), + "testTop10acc": np.mean(testTop10accs), + "lrate": optimizer.param_groups[0]["lr"], + "temp": loss_func.temp.item(), + }, 'logs') + + if args.use_wandb: + performance_now = { + "epoch": epoch, + "train_loss": np.mean(train_losses), + "test_loss": np.mean(test_losses), + "trainTop1acc": np.mean(trainTop1accs), + "trainTop10acc": np.mean(trainTop10accs), + "testTop1acc": np.mean(testTop1accs), + "testTop10acc": np.mean(testTop10accs), + "lrate": optimizer.param_groups[0]["lr"], + "temp": loss_func.temp.item(), + } + wandb.log(performance_now) + + if scheduler is not None: + scheduler.step() + + savedir = os.path.join(args.save_root, 'weights') + last_weight_file = os.path.join(savedir, "model_last.pt") + torch.save(brain_encoder.state_dict(), last_weight_file) + print('model is saved as ', last_weight_file) + if best_acc < np.mean(testTop10accs): + best_weight_file = os.path.join(savedir, "model_best.pt") + torch.save(brain_encoder.state_dict(), best_weight_file) + best_acc = np.mean(testTop10accs) + print('best model is updated !!, {}'.format(best_acc), best_weight_file) + + + +if __name__ == "__main__": + from hydra import initialize, compose + with initialize(version_base=None, config_path="../configs/"): + args = compose(config_name='20230426_all_seq2stat') + if not os.path.exists(os.path.join(args.save_root, 'weights')): + os.makedirs(os.path.join(args.save_root, 'weights')) + run(args) From b84698104a293af0a37083bfb3762e4c1d11ed51 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Thu, 27 Apr 2023 23:48:00 +0900 Subject: [PATCH 38/71] ADD: corr of corr --- examples/check_corrof_corr.py | 237 ++++++++++++++++++++++++++++++++++ 1 file changed, 237 insertions(+) create mode 100644 examples/check_corrof_corr.py diff --git a/examples/check_corrof_corr.py b/examples/check_corrof_corr.py new file mode 100644 index 0000000..d0a2bf8 --- /dev/null +++ b/examples/check_corrof_corr.py @@ -0,0 +1,237 @@ +'''Generic Object Decoding: Feature prediction +Analysis summary +---------------- +- Learning method: Sparse linear regression +- Preprocessing: Normalization and voxel selection +- Data: GenericDecoding_demo +- Results format: Pandas dataframe +''' + + +from __future__ import print_function + +import os +import sys +sys.path.append('.') +import pickle +from itertools import product +from time import time +import itertools +import numpy as np +from scipy import stats + +from meg_decoding.kamitani_lab.slir import SparseLinearRegression +from sklearn.linear_model import LinearRegression # For quick demo + +from meg_decoding.kamitani_lab.ml import add_bias +from meg_decoding.kamitani_lab.preproc import select_top +from meg_decoding.kamitani_lab.stats import corrcoef, corrmat + +from meg_decoding.matlab_utils.load_meg import get_meg_data, roi, time_window, get_baseline +import tqdm +import random +import hydra +import matplotlib.pyplot as plt +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +import pandas as pd + + + +def prepare_dataset(args, split, manual_ch=None, onsets:dict=None): + DATAROOT = args.data_root + processed_meg_path_pattern = os.path.join(DATAROOT, '{sub}/mat/{name}') + label_path_pattern = os.path.join(DATAROOT, '{sub}/labels/{name}') + trigger_meg_path_pattern = os.path.join(DATAROOT, '{sub}/trigger/{name}') + processed_rest_meg_path_pattern = os.path.join(DATAROOT, '{sub}/mat/{name}') + + sub_list = list(args.subjects.keys()) + sub_id_map = {s:i for i, s in enumerate(sub_list)} + + roi_channels = roi(args) if manual_ch is None else manual_ch # if onsets is specified. this setting is ignored. + + def epoching(meg, window, preprocess_pipeline=[]): + assert len(labels) == len(window) + # n_epochs x n_chs x time_smaples + meg_epochs = np.zeros([len(window), len(meg), window[0][1]-window[0][0]]) + for i, w in enumerate(window): + window_meg = meg[:, w[0]:w[1]] # ch x time_samples + for func_ in preprocess_pipeline: + window_meg = func_(window_meg) + meg_epochs[i] = window_meg + return meg_epochs + + meg_epochs = [] + sub_epochs = [] + label_epochs = [] + image_feature_epochs = [] + rest_mean, rest_std = None, None + pbar = tqdm.tqdm(sub_list) + for sub in pbar: + pbar.set_description("load subject data -- current: {}".format(sub)) + fs = args.subjects[sub]['fs'] + for meg_name, label_name, trigger_name, rest_name in zip(args.subjects[sub][split]['mat'], args.subjects[sub][split]['labels'], args.subjects[sub][split]['trigger'], args.subjects[sub][split]['rest']): + processed_meg_path = processed_meg_path_pattern.format(sub=sub, name=meg_name) + label_path = label_path_pattern.format(sub=sub, name=label_name) + trigger_path = trigger_meg_path_pattern.format(sub=sub, name=trigger_name) + processed_rest_meg_path = processed_rest_meg_path_pattern.format(sub=sub, name=rest_name) + if args.z_scoring: + print('z_scoring start') + rest_mean, rest_std = get_baseline(processed_rest_meg_path, fs, args.rest_duration) + MEG_Data, image_features, labels, triggers = get_meg_data(processed_meg_path, label_path, trigger_path, rest_mean=rest_mean, rest_std=rest_std, split=split) + if onsets is None: + ROI_MEG_Data = MEG_Data[roi_channels, :] # num_roi_channels x time_samples + window = time_window(args, triggers, fs) + ROI_MEG_epochs = epoching(ROI_MEG_Data, window) + else: + ROI_MEG_epochs = [] + for r, o in onsets.items(): + args.region = r if isinstance(r, list) else [r] + roi_channels = roi(args) + ROI_MEG_Data = MEG_Data[roi_channels, :] + duration = args.window.end - args.window.start + args.window.start = o + args.window.end = o + duration + window = time_window(args, triggers, fs) + single_ROI_MEG_epoch = epoching(ROI_MEG_Data, window) # n_epochs x n_chs x time_smaples + # print('DEBUG: ', window[-1], ROI_MEG_Data.shape) + ROI_MEG_epochs.append(single_ROI_MEG_epoch) + ROI_MEG_epochs = np.concatenate(ROI_MEG_epochs, axis=1) + + meg_epochs.append(ROI_MEG_epochs) # array [epoch x ch x time_stamp] + sub_epochs+=[sub_id_map[sub]] * len(ROI_MEG_epochs) # list [epoch] + label_epochs.append(labels) # array [epoch] + image_feature_epochs.append(image_features) # array [epoch x dim] + meg_epochs = np.concatenate(meg_epochs, axis=0) + label_epochs = np.concatenate(label_epochs, axis=0) + image_feature_epochs = np.concatenate(image_feature_epochs, axis=0) + print('dataset is created. size is ', meg_epochs.shape) + return meg_epochs, sub_epochs, label_epochs, image_feature_epochs + + +def get_average_features(predicted_y, val_index): + test_labels_unique = np.unique(val_index) + test_pred_features_avg = [] + for i in range(len(test_labels_unique)): + target_ids = val_index== i + test_pred_features_avg.append(predicted_y[target_ids].mean(axis=0, keepdims=True)) + test_pred_features_avg = np.concatenate(test_pred_features_avg, axis=0) + return test_pred_features_avg, np.arange(len(test_labels_unique)) + +def metric_kamitani(results:dict, label:list): + pred_percept = [results['predicted_feature_averaged_percept']] # n_preds x n_units + cat_feature_percept = [results['category_feature_averaged_percept']] # n_sample x n_units + + pwident_cr_pt = [] # Prop correct in pair-wise identification (perception) + # cnt = 0 + for fpt, pred_pt in zip(cat_feature_percept, pred_percept): + # ind_cat_other = list(range(len(cat_feature_percept))).remove(cnt) + # feat_other = cat_feature_percept[ind_cat_other,:] + + # n_unit = fpt.shape[1] + # feat_other = feat_other[:, :n_unit] + + feat_candidate_pt = fpt # np.vstack([fpt, feat_other]) + + simmat_pt = corrmat(pred_pt, feat_candidate_pt) + + cr_pt = get_pwident_correctrate(simmat_pt) + + pwident_cr_pt.append(np.mean(cr_pt)) + # cnt += 1 + assert len(pwident_cr_pt) == 1 + return np.mean(cr_pt), {k:v for k, v in zip(label, cr_pt)} + + +# Functions ############################################################ + +def get_pwident_correctrate(simmat): + ''' + Returns correct rate in pairwise identification + Parameters + ---------- + simmat : numpy array [num_prediction * num_category] + Similarity matrix + Returns + ------- + correct_rate : correct rate of pair-wise identification + ''' + + num_pred = simmat.shape[0] + labels = range(num_pred) + + correct_rate = [] + for i in range(num_pred): + pred_feat = simmat[i, :] + correct_feat = pred_feat[labels[i]] + pred_num = len(pred_feat) - 1 + correct_rate.append((pred_num - np.sum(pred_feat > correct_feat)) / float(pred_num)) + + return correct_rate + +def corr_of_corr(corrmat1, corrmat2): + ''' + Returns correlation of correlation matrices + Parameters + ---------- + corrmat1 : numpy array [num_category * num_category] + Correlation matrix 1 + corrmat2 : numpy array [num_category * num_category] + Correlation matrix 2 + Returns + ------- + corr_of_corr : correlation of correlation matrices + ''' + + corr_of_corr = np.corrcoef(corrmat1.flatten(), corrmat2.flatten())[0, 1] + + return corr_of_corr + +def calc_corr_of_corr(meg_data:np.ndarray, image_data:np.ndarray, savedir:str)->np.ndarray: + """_summary_ + + Args: + meg_data (np.ndarray): n_epochs x n_ch x time_samples + image_data (np.ndarray): n_epochs x n_dims + + Returns: + np.ndarray: _description_ + """ + meg_corr = np.corrcoef(meg_data.reshape(meg_data.shape[0], -1)) + vis_corr(meg_corr, os.path.join(savedir, 'meg_corr.png')) + image_corr = np.corrcoef(image_data) + vis_corr(image_corr, os.path.join(savedir, 'image_corr.png')) + corr_of_corr = corr_of_corr(meg_corr, image_corr) + vis_corr(corr_of_corr, os.path.join(savedir, 'corr_of_corr.png')) + +def vis_corr(corr, savefile=None): + sns.heatmap(corr, square=True, annot=False) + plt.savefig(savefile) + plt.close() + print('saved to ', savefile) + +def run_meg_fit_and_evaluate(args, ch_ratios=1, manual_ch:list=None, onsets:dict=None): + prefix='sbj01' + random.seed(0) + ## prepare dataset + train_X, train_subs, train_label, train_Y = prepare_dataset(args, split='train', manual_ch=manual_ch, onsets=onsets) + test_X, test_subs, test_label, test_Y = prepare_dataset(args, split='val', manual_ch=manual_ch, onsets=onsets) + # import pdb; pdb.set_trace() + + s1_d1_train_X, s1_d1_train_label, s1_d1_train_Y = train_X[:3600], train_label[:3600], train_Y[:3600] + s1_d2_train_X, s1_d2_train_label, s1_d2_train_Y = train_X[3600:7200], train_label[3600:7200], train_Y[3600:7200] + + + ## preprocess + train_X = np.mean(train_X, axis=-1) # get SCP + test_X = np.mean(test_X, axis=-1) + + + + +if __name__ == '__main__': + # To avoid any use of global variables, + # do nothing except calling main() here + # main() + # main_meg_repetiton_roi() \ No newline at end of file From 79975734dd9c73f5ca23c6d8d85ec7e1eab07dec Mon Sep 17 00:00:00 2001 From: inoue26 Date: Fri, 28 Apr 2023 03:04:48 +0900 Subject: [PATCH 39/71] ADD: corr_corr of image and meg is higher when 200-400, 2-5Hz occipital --- examples/check_corrof_corr.py | 153 ++++++++++++++++++-------------- examples/kamitani_regression.py | 32 ++++--- requirements.txt | 1 + 3 files changed, 105 insertions(+), 81 deletions(-) diff --git a/examples/check_corrof_corr.py b/examples/check_corrof_corr.py index d0a2bf8..3b247b0 100644 --- a/examples/check_corrof_corr.py +++ b/examples/check_corrof_corr.py @@ -35,6 +35,7 @@ import pandas as pd import matplotlib.pyplot as plt import seaborn as sns +import mne import pandas as pd @@ -82,7 +83,18 @@ def epoching(meg, window, preprocess_pipeline=[]): MEG_Data, image_features, labels, triggers = get_meg_data(processed_meg_path, label_path, trigger_path, rest_mean=rest_mean, rest_std=rest_std, split=split) if onsets is None: ROI_MEG_Data = MEG_Data[roi_channels, :] # num_roi_channels x time_samples - window = time_window(args, triggers, fs) + if args.preprocs.brain_filter is not None: + brain_filter_low = args.preprocs.brain_filter[0] + brain_filter_high = args.preprocs.brain_filter[1] + ROI_MEG_Data = mne.filter.filter_data(ROI_MEG_Data, sfreq=fs, l_freq=brain_filter_low, h_freq=brain_filter_high,) + print(f'band path filter: {brain_filter_low}-{brain_filter_high}') + if args.preprocs.brain_resample_rate is not None: + ROI_MEG_Data = mne.filter.resample(ROI_MEG_Data, down=fs / args.preprocs.brain_resample_rate) + print('resample {} to {} Hz'.format(fs,args.preprocs.brain_resample_rate)) + window = time_window(args, triggers, args.preprocs.brain_resample_rate) + else: + window = time_window(args, triggers, fs) + # import pdb; pdb.set_trace() ROI_MEG_epochs = epoching(ROI_MEG_Data, window) else: ROI_MEG_epochs = [] @@ -110,65 +122,6 @@ def epoching(meg, window, preprocess_pipeline=[]): return meg_epochs, sub_epochs, label_epochs, image_feature_epochs -def get_average_features(predicted_y, val_index): - test_labels_unique = np.unique(val_index) - test_pred_features_avg = [] - for i in range(len(test_labels_unique)): - target_ids = val_index== i - test_pred_features_avg.append(predicted_y[target_ids].mean(axis=0, keepdims=True)) - test_pred_features_avg = np.concatenate(test_pred_features_avg, axis=0) - return test_pred_features_avg, np.arange(len(test_labels_unique)) - -def metric_kamitani(results:dict, label:list): - pred_percept = [results['predicted_feature_averaged_percept']] # n_preds x n_units - cat_feature_percept = [results['category_feature_averaged_percept']] # n_sample x n_units - - pwident_cr_pt = [] # Prop correct in pair-wise identification (perception) - # cnt = 0 - for fpt, pred_pt in zip(cat_feature_percept, pred_percept): - # ind_cat_other = list(range(len(cat_feature_percept))).remove(cnt) - # feat_other = cat_feature_percept[ind_cat_other,:] - - # n_unit = fpt.shape[1] - # feat_other = feat_other[:, :n_unit] - - feat_candidate_pt = fpt # np.vstack([fpt, feat_other]) - - simmat_pt = corrmat(pred_pt, feat_candidate_pt) - - cr_pt = get_pwident_correctrate(simmat_pt) - - pwident_cr_pt.append(np.mean(cr_pt)) - # cnt += 1 - assert len(pwident_cr_pt) == 1 - return np.mean(cr_pt), {k:v for k, v in zip(label, cr_pt)} - - -# Functions ############################################################ - -def get_pwident_correctrate(simmat): - ''' - Returns correct rate in pairwise identification - Parameters - ---------- - simmat : numpy array [num_prediction * num_category] - Similarity matrix - Returns - ------- - correct_rate : correct rate of pair-wise identification - ''' - - num_pred = simmat.shape[0] - labels = range(num_pred) - - correct_rate = [] - for i in range(num_pred): - pred_feat = simmat[i, :] - correct_feat = pred_feat[labels[i]] - pred_num = len(pred_feat) - 1 - correct_rate.append((pred_num - np.sum(pred_feat > correct_feat)) / float(pred_num)) - - return correct_rate def corr_of_corr(corrmat1, corrmat2): ''' @@ -184,7 +137,7 @@ def corr_of_corr(corrmat1, corrmat2): corr_of_corr : correlation of correlation matrices ''' - corr_of_corr = np.corrcoef(corrmat1.flatten(), corrmat2.flatten())[0, 1] + corr_of_corr = np.corrcoef(corrmat1.flatten(), corrmat2.flatten()) return corr_of_corr @@ -200,10 +153,22 @@ def calc_corr_of_corr(meg_data:np.ndarray, image_data:np.ndarray, savedir:str)-> """ meg_corr = np.corrcoef(meg_data.reshape(meg_data.shape[0], -1)) vis_corr(meg_corr, os.path.join(savedir, 'meg_corr.png')) + vis_corr(meg_corr[:100,:100], os.path.join(savedir, 'meg_corr_zoom.png')) image_corr = np.corrcoef(image_data) - vis_corr(image_corr, os.path.join(savedir, 'image_corr.png')) - corr_of_corr = corr_of_corr(meg_corr, image_corr) - vis_corr(corr_of_corr, os.path.join(savedir, 'corr_of_corr.png')) + # vis_corr(image_corr, os.path.join(savedir, 'image_corr.png')) + # vis_corr(image_corr[:100,:100], os.path.join(savedir, 'image_corr_zoom.png')) + meg_corr = meg_corr[np.triu(meg_corr, k=1)!=0] + image_corr = image_corr[np.triu(image_corr, k=1)!=0] + meg_image_corr_corr = corr_of_corr(meg_corr, image_corr) + # vis_corr(meg_image_corr_corr, os.path.join(savedir, 'corr_of_corr.png')) + print('correlation: ', meg_image_corr_corr[0,1]) + + plt.scatter(meg_corr, image_corr, s=2) + plt.xlabel('meg_corr') + plt.ylabel('image_corr') + plt.savefig(os.path.join(savedir, 'meg_image_scatter.png')) + print('save to ', os.path.join(savedir, 'meg_image_scatter.png')) + plt.close() def vis_corr(corr, savefile=None): sns.heatmap(corr, square=True, annot=False) @@ -211,7 +176,45 @@ def vis_corr(corr, savefile=None): plt.close() print('saved to ', savefile) -def run_meg_fit_and_evaluate(args, ch_ratios=1, manual_ch:list=None, onsets:dict=None): +def same_image2neighbor(X, Y, label): + same_image_indices = [] + for i in range(1,1201): + same_image_indices += list(np.where(label==i)[0]) + assert len(list(np.where(label==i)[0])) > 0 + return X[same_image_indices], Y[same_image_indices] + + +def run_original(X, Y, saveroot, subdir): + # そのまま、corrを計算 + savedir = os.path.join(saveroot, subdir) + if not os.path.exists(savedir): + os.makedirs(savedir) + calc_corr_of_corr(X, Y, savedir) + +def run_normalize_trial(X, Y, saveroot, subdir): + # epoch方向にnormalizeしてcorrを計算 + savedir = os.path.join(saveroot, subdir) + if not os.path.exists(savedir): + os.makedirs(savedir) + X = X - X.mean(axis=0, keepdims=True) + X = X / X.std(axis=0, keepdims=True) + Y = Y - Y.mean(axis=0, keepdims=True) + Y = Y / Y.std(axis=0, keepdims=True) + calc_corr_of_corr(X, Y, savedir) + +def run_SCP(X, Y, saveroot, subdir): + # SCPを計算してcorrを計算 + savedir = os.path.join(saveroot, subdir) + if not os.path.exists(savedir): + os.makedirs(savedir) + X = X - X.mean(axis=0, keepdims=True) + X = X / X.std(axis=0, keepdims=True) + X = np.mean(X, axis=-1) + Y = Y - Y.mean(axis=0, keepdims=True) + Y = Y / Y.std(axis=0, keepdims=True) + calc_corr_of_corr(X, Y, savedir) + +def run(args, ch_ratios=1, manual_ch:list=None, onsets:dict=None, saveroot:str='/home/yainoue/meg2image/results/20230427_corr_corr_resample120'): prefix='sbj01' random.seed(0) ## prepare dataset @@ -219,19 +222,35 @@ def run_meg_fit_and_evaluate(args, ch_ratios=1, manual_ch:list=None, onsets:dict test_X, test_subs, test_label, test_Y = prepare_dataset(args, split='val', manual_ch=manual_ch, onsets=onsets) # import pdb; pdb.set_trace() + same_image_indices = sum([[i, i+1200, i+2400] for i in range(1200)], []) s1_d1_train_X, s1_d1_train_label, s1_d1_train_Y = train_X[:3600], train_label[:3600], train_Y[:3600] + s1_d1_train_X, s1_d1_train_Y = same_image2neighbor(s1_d1_train_X, s1_d1_train_Y, s1_d1_train_label) s1_d2_train_X, s1_d2_train_label, s1_d2_train_Y = train_X[3600:7200], train_label[3600:7200], train_Y[3600:7200] + s1_d2_train_X, s1_d2_train_Y = same_image2neighbor(s1_d2_train_X, s1_d2_train_Y, s1_d2_train_label) + + # run_original(s1_d1_train_X, s1_d1_train_Y, saveroot, 's1d1') # # 0.00644 # 0.02039 # 0.00442 (8-13Hz) # 0.0193 (2-8Hz) # 0.0113 (20-40) # 0.011(30-40) # 0.116(40-60) # 0.012 + # run_original(s1_d2_train_X, s1_d2_train_Y, saveroot, 's1d2') # # 0.00432 # 0.007867 # 0.00567 # 0.0067 # 0.0106 # 0.010 # 0.011 # 0.0118 + + run_normalize_trial(s1_d1_train_X, s1_d1_train_Y, saveroot, 's1d1_norm_unit') # 0.01078 # 0.01068 # 0.03266 (2-13 Hz) # 0.0160 # 0.032 # 0.021 # 0.21(30-40) # 0.023 # 0.024(30-60) # 0.0265(30-70) # 0.028(60-70) # 0.043(60-100) # 0.046 + # run_normalize_trial(s1_d2_train_X, s1_d2_train_Y, saveroot, 's1d2_norm_unit') # 0.00818 # 0.00799 # 0.025034 # 0.0160 # 0.023 # 0.021 # 0.23 # 0.025 # 0.026 # 0.028 # 0.03 (60-70) # 0.0466 # 0.0490(60-120) + + # run_SCP(s1_d1_train_X, s1_d1_train_Y, saveroot, 's1d1_scp') # 0.008361 # 0.008417 # 0.0226 # 0.00987 # 0.022 # 0.00779 # 0.008 # 0.009 + # run_SCP(s1_d2_train_X, s1_d2_train_Y, saveroot, 's1d2_scp') # 0.006416 # 0.0064630 # 0.0160 # 0.00911 # 0.016 # 0.0089 # 0.0099 # 0.01 + ## preprocess train_X = np.mean(train_X, axis=-1) # get SCP test_X = np.mean(test_X, axis=-1) +@hydra.main(version_base=None, config_path="../../configs", config_name="20230421_sbj01_kamitani_regression.yaml") +def main(args): + run(args) + if __name__ == '__main__': # To avoid any use of global variables, # do nothing except calling main() here - # main() - # main_meg_repetiton_roi() \ No newline at end of file + main() \ No newline at end of file diff --git a/examples/kamitani_regression.py b/examples/kamitani_regression.py index d96a4e5..46638a5 100644 --- a/examples/kamitani_regression.py +++ b/examples/kamitani_regression.py @@ -78,17 +78,17 @@ def epoching(meg, window, preprocess_pipeline=[]): MEG_Data, image_features, labels, triggers = get_meg_data(processed_meg_path, label_path, trigger_path, rest_mean=rest_mean, rest_std=rest_std, split=split) if onsets is None: ROI_MEG_Data = MEG_Data[roi_channels, :] # num_roi_channels x time_samples - # if args.preprocs.brain_filter is not None: - # brain_filter_low = args.preprocs.brain_filter[0] - # brain_filter_high = args.preprocs.brain_filter[1] - # ROI_MEG_Data = mne.filter.filter_data(ROI_MEG_Data, sfreq=fs, l_freq=brain_filter_low, h_freq=brain_filter_high,) - # print(f'band path filter: {brain_filter_low}-{brain_filter_high}') - # if args.preprocs.brain_resample_rate is not None: - # ROI_MEG_Data = mne.filter.resample(ROI_MEG_Data, down=fs / args.preprocs.brain_resample_rate) - # print('resample {} to {} Hz'.format(fs,args.preprocs.brain_resample_rate)) - # window = time_window(args, triggers, args.preprocs.brain_resample_rate) - # else: - # window = time_window(args, triggers, fs) + if args.preprocs.brain_filter is not None: + brain_filter_low = args.preprocs.brain_filter[0] + brain_filter_high = args.preprocs.brain_filter[1] + ROI_MEG_Data = mne.filter.filter_data(ROI_MEG_Data, sfreq=fs, l_freq=brain_filter_low, h_freq=brain_filter_high,) + print(f'band path filter: {brain_filter_low}-{brain_filter_high}') + if args.preprocs.brain_resample_rate is not None: + ROI_MEG_Data = mne.filter.resample(ROI_MEG_Data, down=fs / args.preprocs.brain_resample_rate) + print('resample {} to {} Hz'.format(fs,args.preprocs.brain_resample_rate)) + window = time_window(args, triggers, args.preprocs.brain_resample_rate) + else: + window = time_window(args, triggers, fs) window = time_window(args, triggers, fs) # import pdb; pdb.set_trace() ROI_MEG_epochs = epoching(ROI_MEG_Data, window) @@ -212,7 +212,7 @@ def get_pwident_correctrate(simmat): return correct_rate -def run_meg_fit_and_evaluate(args, ch_ratios=1, manual_ch:list=None, onsets:dict=None): +def run_meg_fit_and_evaluate(args, ch_ratios=1, manual_ch:list=None, onsets:dict=None, normalize=False): prefix='sbj01' random.seed(0) ## prepare dataset @@ -220,6 +220,8 @@ def run_meg_fit_and_evaluate(args, ch_ratios=1, manual_ch:list=None, onsets:dict test_X, test_subs, test_label, test_Y = prepare_dataset(args, split='val', manual_ch=manual_ch, onsets=onsets) # import pdb; pdb.set_trace() + + ## preprocess train_X = np.mean(train_X, axis=-1) # get SCP test_X = np.mean(test_X, axis=-1) @@ -251,7 +253,9 @@ def run_meg_fit_and_evaluate(args, ch_ratios=1, manual_ch:list=None, onsets:dict # import pdb; pdb.set_trace() return acc - +@hydra.main(version_base=None, config_path="../../configs", config_name="20230421_sbj01_kamitani_regression.yaml") +def main_meg_repetiton_roi(args): + run_meg_fit_and_evaluate(args, normalize=True) @hydra.main(version_base=None, config_path="../../configs", config_name="20230421_sbj01_kamitani_regression.yaml") def main_meg_repetiton_roi(args): @@ -634,5 +638,5 @@ def get_averaged_feature(pred_y, true_y, labels): # main_meg_repetiton_roi() # main_meg_run_manual_ch() # main_meg_repetiton_N() - main_meg_repetiton_onsets_per_ch() + # main_meg_repetiton_onsets_per_ch() # main_meg() \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 7d48dc2..a308ba3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,3 +13,4 @@ torch==1.12.1+cu113 torchaudio==0.12.1+cu113 tqdm==4.64.0 transformers==4.24.0 +seaborn \ No newline at end of file From 2017504430b06b49a9602c5de87c1020fd937642 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Fri, 28 Apr 2023 03:04:25 +0900 Subject: [PATCH 40/71] ADD: EEGNet --- examples/kamitani_regression.py | 20 +++++------ meg_decoding/models.py | 64 +++++++++++++++++++++++++++++++++ 2 files changed, 74 insertions(+), 10 deletions(-) diff --git a/examples/kamitani_regression.py b/examples/kamitani_regression.py index 46638a5..fa6a062 100644 --- a/examples/kamitani_regression.py +++ b/examples/kamitani_regression.py @@ -95,7 +95,7 @@ def epoching(meg, window, preprocess_pipeline=[]): else: ROI_MEG_epochs = [] for r, o in onsets.items(): - args.region = r if isinstance(r, list) else [r] + args.region = r if isinstance(r, list) else [r] roi_channels = roi(args) ROI_MEG_Data = MEG_Data[roi_channels, :] duration = args.window.end - args.window.start @@ -224,16 +224,16 @@ def run_meg_fit_and_evaluate(args, ch_ratios=1, manual_ch:list=None, onsets:dict ## preprocess train_X = np.mean(train_X, axis=-1) # get SCP - test_X = np.mean(test_X, axis=-1) + test_X = np.mean(test_X, axis=-1) - ## feature prediction + ## feature prediction n_voxel = int(ch_ratios*(train_X.shape[1])) print('n_voxel: ', n_voxel) pred_y, true_y = feature_prediction(train_X, train_Y, test_X, test_Y, n_voxel=n_voxel, n_iter=200) - + ## evaluate pred_y_avg, true_y_avg, test_label_set = get_averaged_feature(pred_y, true_y, test_label) results = {} @@ -261,7 +261,7 @@ def main_meg_repetiton_roi(args): def main_meg_repetiton_roi(args): onsets = [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4] duration = 0.2 - + roi_names = ['occipital', 'parietal', 'frontal', 'temporal', 'central'] for roi_name in roi_names: acc_list = [] @@ -269,7 +269,7 @@ def main_meg_repetiton_roi(args): for start_ in onsets: args.window.start = start_ args.window.end = start_ + duration - acc = run_meg_fit_and_evaluate(args) + acc = run_meg_fit_and_evaluate(args) acc_list.append(acc) plt.plot(onsets, acc_list, label=roi_name) @@ -299,7 +299,7 @@ def main_meg_repetiton_N(args): args.region = region for ch_ratio in ch_ratios: - acc = run_meg_fit_and_evaluate(args, ch_ratio) + acc = run_meg_fit_and_evaluate(args, ch_ratio) acc_list.append(acc) label = '-'.join(roi_name_pair) plt.plot(ch_ratios, acc_list, label=label) @@ -345,9 +345,9 @@ def main_meg_repetiton_onsets_per_ch(args): results['acc'].append(acc) for k, v in zip(roi_names, onset_list): results[k].append(v) - + df = pd.DataFrame(results) - + df.to_csv(savefile) print('results is saved as ', savefile) @@ -365,7 +365,7 @@ def main_meg_run_manual_ch(args): for manual_ch in manual_ch_list: if manual_ch is not None: manual_ch = [c - 1 for c in manual_ch] # matlab -> python - acc = run_meg_fit_and_evaluate(args, manual_ch=manual_ch) + acc = run_meg_fit_and_evaluate(args, manual_ch=manual_ch) acc_list.append(acc) print(acc_list) diff --git a/meg_decoding/models.py b/meg_decoding/models.py index 41d9402..bc6cb46 100644 --- a/meg_decoding/models.py +++ b/meg_decoding/models.py @@ -21,9 +21,73 @@ def get_model(args): return BrainEncoderSeq2Static(args) elif args.model == 'linear': return LinearEncoder(args) + elif args.model == 'eegnet': + return EEGNet(args) else: raise ValueError('no model named {} is prepared'.format(args.model)) +class EEGNet(nn.Module): + def __init__(self, args): + super(EEGNet, self).__init__() + T = int((args.window.end - args.window.start) * args.preprocs.brain_resample_rate) + # DownSampling + # self.down1 = nn.Sequential(nn.AvgPool2d((1, p0))) + + # Conv2d(in,out,kernel,stride,padding,bias) #k1 30 + self.conv1 = nn.Sequential( + nn.Conv2d(1, args.F1, (1, args.k1), padding="same", bias=False), nn.BatchNorm2d(args.F1) + ) + + self.conv2 = nn.Sequential( + nn.Conv2d( + args.F1, args.D * args.F1, (args.num_channels, 1), groups=args.F1, bias=False + ), + nn.BatchNorm2d(args.D * args.F1), + nn.ELU(), + nn.AvgPool2d((1, args.p1)), # 2 + nn.Dropout(args.dr1), + ) + + self.conv3 = nn.Sequential( + nn.Conv2d( + args.D * args.F1, + args.D * args.F1, + (1, args.k2), # 4 + padding="same", + groups=args.D * args.F1, + bias=False, + ), + nn.Conv2d(args.D * args.F1, args.F2, (1, 1), bias=False), + nn.BatchNorm2d(args.F2), + nn.ELU(), + nn.AvgPool2d((1, args.p2)), # 4 + nn.Dropout(args.dr2), + ) + + self.n_dim = self.compute_dim(args.num_channels, T) + self.classifier = nn.Linear(self.n_dim, 512, bias=True) + + def forward(self, x): + # 1, 1, 128, 300 + # x = self.down1(x) + x = self.conv1(x) # 1, 16, 128, 300 + x = self.conv2(x) # 1, 32, 1, 150 + x = self.conv3(x) # 1, 32, 1, 37 + x = x.view(-1, self.n_dim) + x = self.classifier(x) + return x + + def compute_dim(self, num_channels, T): + x = torch.zeros((1, 1, num_channels, T)) + + # x = self.down1(x) + x = self.conv1(x) + x = self.conv2(x) + x = self.conv3(x) + + return x.size()[1] * x.size()[2] * x.size()[3] + + class SpatialAttention(nn.Module): """Same as SpatialAttentionVer2, but a little more concise""" From 58c72f67fd9f0999458b38b12ff4fb212159cd6d Mon Sep 17 00:00:00 2001 From: inoue26 Date: Fri, 28 Apr 2023 15:22:47 +0900 Subject: [PATCH 41/71] ADD: prevent overfitting --- examples/view_training_curve.py | 4 ++-- meg_decoding/dataclass/god.py | 2 ++ meg_decoding/models.py | 12 ++++++++---- train_wowandb_cv.py | 8 ++++---- 4 files changed, 16 insertions(+), 10 deletions(-) diff --git a/examples/view_training_curve.py b/examples/view_training_curve.py index ce5b932..3acc705 100644 --- a/examples/view_training_curve.py +++ b/examples/view_training_curve.py @@ -69,9 +69,9 @@ def parse_data(filepath): # filepath = '/home/yainoue/meg2image/results/20230425_sbj01_seq2stat_cv/runs/2023-04-25 21:00:55.483213' # filepath = '/home/yainoue/meg2image/results/20230425_sbj01_seq2stat_cv_norm_wo_dilation/runs/2023-04-26 11:27:01.295948' # filepath = '/home/yainoue/meg2image/results/20230426_all_seq2stat_cv_norm_wo_dilation/runs/2023-04-26 16:05:36.849896' - filepath = '/home/yainoue/meg2image/results/20230426_all_seq2stat_cv_norm_wo_dilation/runs/2023-04-26 16:19:27.018492' + # filepath = '/home/yainoue/meg2image/results/20230426_all_seq2stat_cv_norm_wo_dilation/runs/2023-04-26 16:19:27.018492' # filepath = '/home/yainoue/meg2image/results/20230413_sbj01/runs/2023-04-13 21:52:17.333087'#'home/yainoue/meg2image/results/test/2023-04-11 18:06:44.273986' # filepath = '/home/yainoue/meg2image/results/20230413_sbj01_seq2stat/runs/2023-04-16 19:21:02.983480' # filepath = '/home/yainoue/meg2image/results/20230412_mini/runs/2023-04-13 15:17:53.310665' - + filepath = '/home/yainoue/meg2image/results/20230427_sbj01_eegnet_cv_norm/runs/2023-04-28 03:53:04.866741' parse_data(filepath) diff --git a/meg_decoding/dataclass/god.py b/meg_decoding/dataclass/god.py index 17aa2c2..2fe153d 100644 --- a/meg_decoding/dataclass/god.py +++ b/meg_decoding/dataclass/god.py @@ -37,6 +37,8 @@ def __init__(self, args, split, preprocess_pipleine:list=[], return_label:bool=F self.Y = image_feature_epochs.astype(np.float32) # epochs x dims if args.normalize_image_features: self.Y = normalize_per_unit(self.Y) + if args.normalize_meg: + self.X = normalize_per_unit(self.X) self.subs = sub_epochs # epochs (x 1) self.labels = label_epochs # epochs (x 1) diff --git a/meg_decoding/models.py b/meg_decoding/models.py index bc6cb46..b88f045 100644 --- a/meg_decoding/models.py +++ b/meg_decoding/models.py @@ -8,6 +8,7 @@ from termcolor import cprint from einops import rearrange from tqdm import tqdm +from meg_decoding.matlab_utils.load_meg import roi from constants import device from meg_decoding.utils.layout import ch_locations_2d @@ -37,10 +38,11 @@ def __init__(self, args): self.conv1 = nn.Sequential( nn.Conv2d(1, args.F1, (1, args.k1), padding="same", bias=False), nn.BatchNorm2d(args.F1) ) - + roi_channels = roi(args) + num_channels = len(roi_channels) self.conv2 = nn.Sequential( nn.Conv2d( - args.F1, args.D * args.F1, (args.num_channels, 1), groups=args.F1, bias=False + args.F1, args.D * args.F1, (num_channels, 1), groups=args.F1, bias=False ), nn.BatchNorm2d(args.D * args.F1), nn.ELU(), @@ -64,10 +66,12 @@ def __init__(self, args): nn.Dropout(args.dr2), ) - self.n_dim = self.compute_dim(args.num_channels, T) + self.n_dim = self.compute_dim(num_channels, T) self.classifier = nn.Linear(self.n_dim, 512, bias=True) - def forward(self, x): + def forward(self, x, sbj_idxs): + # import pdb; pdb.set_trace() + x = x.unsqueeze(1) # 1, 1, 128, 300 # x = self.down1(x) x = self.conv1(x) # 1, 16, 128, 300 diff --git a/train_wowandb_cv.py b/train_wowandb_cv.py index 4f56208..152edae 100644 --- a/train_wowandb_cv.py +++ b/train_wowandb_cv.py @@ -142,8 +142,8 @@ def run(args: DictConfig) -> None: # val_size = len(source_dataset) - train_size # train_dataset, val_dataset = torch.utils.data.random_split(source_dataset, [train_size, val_size]) - ind_tr = list(range(0, 6000)) + list(range(7200, 21600)) # + list(range(7200, 13200)) + list(range(14400, 20400)) - ind_te = list(range(6000,7200)) # + list(range(13200, 14400)) + list(range(20400, 21600)) + ind_tr = list(range(0, 3000)) + list(range(3600, 6600)) #+ list(range(7200, 21600)) # + list(range(7200, 13200)) + list(range(14400, 20400)) + ind_te = list(range(3000,3600)) + list(range(6600, 7200)) # + list(range(13200, 14400)) + list(range(20400, 21600)) train_dataset = Subset(source_dataset, ind_tr) val_dataset = Subset(source_dataset, ind_te) @@ -257,9 +257,9 @@ def run(args: DictConfig) -> None: raise ValueError("Unexpected number of items from dataloader.") X, Y = X.to(device), Y.to(device) + # import pdb; pdb.set_trace() Z = brain_encoder(X, subject_idxs) loss = loss_func(Y, Z) - # import pdb; pdb.set_trace() with torch.no_grad(): trainTop1acc, trainTop10acc = classifier(Z, Y) @@ -362,7 +362,7 @@ def run(args: DictConfig) -> None: if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230426_all_seq2stat') + args = compose(config_name='20230427_sbj01_eegnet') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) run(args) From c8af3fd2692da87ac0a9be584aad09afb84a194c Mon Sep 17 00:00:00 2001 From: inoue26 Date: Fri, 28 Apr 2023 17:02:38 +0900 Subject: [PATCH 42/71] ADD: evaluate --- eval_wowandb_cv.py | 331 ++++++++++++++++++++++++++++++++++++++++++++ train_wowandb_cv.py | 5 +- 2 files changed, 335 insertions(+), 1 deletion(-) create mode 100644 eval_wowandb_cv.py diff --git a/eval_wowandb_cv.py b/eval_wowandb_cv.py new file mode 100644 index 0000000..3c15f9d --- /dev/null +++ b/eval_wowandb_cv.py @@ -0,0 +1,331 @@ +import os, sys, random +import numpy as np +import torch +import torch.nn as nn +from time import time +from tqdm import tqdm, trange +from termcolor import cprint +# import wandb + +from omegaconf import DictConfig, open_dict +import hydra +from hydra.utils import get_original_cwd + +from constants import device +# from speech_decoding.dataclass.brennan2018 import Brennan2018Dataset +# from speech_decoding.dataclass.gwilliams2022 import ( +# Gwilliams2022SentenceSplit, +# Gwilliams2022ShallowSplit, +# Gwilliams2022DeepSplit, +# Gwilliams2022Collator, +# ) +from torch.utils.data import DataLoader, RandomSampler, BatchSampler + +from meg_decoding.models import get_model, Classifier +from meg_decoding.utils.get_dataloaders import get_dataloaders, get_samplers +from meg_decoding.utils.loss import * +from meg_decoding.dataclass.god import GODDatasetBase, GODCollator +from meg_decoding.utils.loggers import Pickleogger +from meg_decoding.utils.vis_grad import get_grad +from torch.utils.data.dataset import Subset + + +def run(args: DictConfig) -> None: + + from meg_decoding.utils.reproducibility import seed_worker + # NOTE: We do need it (IMHO). + if args.reproducible: + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + torch.use_deterministic_algorithms(True) + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + g = torch.Generator() + g.manual_seed(0) + seed_worker = seed_worker + else: + g = None + seed_worker = None + + pkl_logger = Pickleogger(os.path.join(args.save_root, 'runs')) + + # with open_dict(args): + # args.root_dir = get_original_cwd() + cprint(f"Current working directory : {os.getcwd()}") + cprint(args, color="white") + + # ----------------------- + # Dataloader + # ----------------------- + # NOTE: Segmentation should always be by word onsets, not just every 3 seconds + if args.dataset == "Gwilliams2022": + + if args.split_mode == "sentence": + + train_set = Gwilliams2022SentenceSplit(args) + test_set = Gwilliams2022SentenceSplit(args, train_set.test_word_idxs_dict) + + assert train_set.num_subjects == test_set.num_subjects + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + elif args.split_mode == "shallow": + + dataset = Gwilliams2022ShallowSplit(args) + + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(dataset.Y.shape[0] * args.split_ratio) + test_size = dataset.Y.shape[0] - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + + elif args.split_mode == "deep": + + train_set = Gwilliams2022DeepSplit(args, train=True) + test_set = Gwilliams2022DeepSplit(args, train=False) + assert train_set.num_subjects == test_set.num_subjects + + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + cprint(f"Test segments: {test_size}", "cyan") + + if args.use_sampler: + # NOTE: currently not supporting reproducibility + train_loader, test_loader = get_samplers( + train_set, + test_set, + args, + test_bsz=test_size, + collate_fn=Gwilliams2022Collator(args), + ) + else: + # FIXME: maybe either get rid of reproducibility, or remove this? + if args.reproducible: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, seed_worker, g, test_bsz=test_size + ) + else: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, test_bsz=test_size + ) + + elif args.dataset == "Brennan2018": + # NOTE: takes an optional debug param force_recompute to pre-process the EEG even if it exists + dataset = Brennan2018Dataset(args) + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(len(dataset) * args.split_ratio) + test_size = len(dataset) - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + cprint( + f"Number of samples: {len(train_set)} (train), {len(test_set)} (test)", color="blue", + ) + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, g, seed_worker, test_bsz=test_size + ) + + elif args.dataset == "GOD": + source_dataset = GODDatasetBase(args, 'train') + # val_dataset = GODDatasetBase(args, 'val') + # train_size = int(np.round(len(source_dataset)*0.8)) + # val_size = len(source_dataset) - train_size + + # train_dataset, val_dataset = torch.utils.data.random_split(source_dataset, [train_size, val_size]) + ind_tr = list(range(0, 3000)) + list(range(3600, 6600)) #+ list(range(7200, 21600)) # + list(range(7200, 13200)) + list(range(14400, 20400)) + ind_te = list(range(3000,3600)) + list(range(6600, 7200)) # + list(range(13200, 14400)) + list(range(20400, 21600)) + train_dataset = Subset(source_dataset, ind_tr) + val_dataset = Subset(source_dataset, ind_te) + + with open_dict(args): + args.num_subjects = source_dataset.num_subjects + print('num subject is {}'.format(args.num_subjects)) + + + if args.use_sampler: + test_size = 50# 重複サンプルが存在するのでval_dataset.Y.shape[0] + train_loader, test_loader = get_samplers( + train_dataset, + val_dataset, + args, + test_bsz=test_size, + collate_fn=GODCollator(args),) + + else: + train_loader = DataLoader( + train_dataset, + batch_size= args.batch_size, + drop_last=True, + shuffle=True, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + test_loader = DataLoader( + val_dataset, + batch_size=50, # args.batch_size, + drop_last=True, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + + else: + raise ValueError("Unknown dataset") + + if args.use_wandb: + wandb.config = {k: v for k, v in dict(args).items() if k not in ["root_dir", "wandb"]} + wandb.init( + project=args.wandb.project, + entity=args.wandb.entity, + config=wandb.config, + save_code=True, + ) + wandb.run.name = args.wandb.run_name + "_" + args.split_mode + wandb.run.save() + + # --------------------- + # Models + # --------------------- + brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) + + classifier = Classifier(args) + + # --------------- + # Loss + # --------------- + loss_func = CLIPLoss(args).to(device) + loss_func.train() + + # -------------------- + # Optimizer + # -------------------- + optimizer = torch.optim.Adam( + list(brain_encoder.parameters()) + list(loss_func.parameters()), lr=float(args.lr), + ) + + if args.lr_scheduler == "cosine": + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, T_max=args.epochs, eta_min=args.lr * 0.1 + ) + elif args.lr_scheduler == "multistep": + scheduler = torch.optim.lr_scheduler.MultiStepLR( + optimizer, + milestones=[int(m * args.epochs) for m in args.lr_multistep_mlstns], + gamma=args.lr_step_gamma, + ) + else: + scheduler = None + + # ====================================== + train_losses = [] + test_losses = [] + trainTop1accs = [] + trainTop10accs = [] + testTop1accs = [] + testTop10accs = [] + brain_encoder.eval() + pbar2 = tqdm(train_loader) + for i, batch in enumerate(pbar2): + with torch.no_grad(): + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, chunkIDs = batch + assert ( + len(chunkIDs.unique()) == X.shape[0] + ), "Duplicate segments in batch are not allowed. Aborting." + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y = X.to(device), Y.to(device) + # import pdb; pdb.set_trace() + Z = brain_encoder(X, subject_idxs) + loss = loss_func(Y, Z) + with torch.no_grad(): + trainTop1acc, trainTop10acc = classifier(Z, Y) + train_losses.append(loss.item()) + trainTop1accs.append(trainTop1acc) + trainTop10accs.append(trainTop10acc) + + Zs = [] + Ys = [] + brain_encoder.eval() + for batch in test_loader: + with torch.no_grad(): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, chunkIDs = batch + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y = X.to(device), Y.to(device) + + Z = brain_encoder(X, subject_idxs) # 0.96 GB + Zs.append(Z) + Ys.append(Y) + + loss = loss_func(Y, Z) + + testTop1acc, testTop10acc = classifier(Z, Y, test=True) # ( 250, 1024, 360 ) + + test_losses.append(loss.item()) + testTop1accs.append(testTop1acc) + testTop10accs.append(testTop10acc) + Zs = torch.cat(Zs, dim=0) + Ys = torch.cat(Ys, dim=0) + + print( + f"train l: {np.mean(train_losses):.3f} | ", + f"test l: {np.mean(test_losses):.3f} | ", + f"trainTop10acc: {np.mean(trainTop10accs):.3f} | ", + f"testTop10acc: {np.mean(testTop10accs):.3f} | ", + f"lr: {optimizer.param_groups[0]['lr']:.5f}", + f"temp: {loss_func.temp.item():.3f}", + ) + evaluate(Zs, Ys) + +def calc_similarity(x, y): + batch_size = len(x) + gt_size = len(y) + + similarity = torch.empty(batch_size, gt_size).to('cuda') + for i in range(batch_size): + for j in range(gt_size): + similarity[i, j] = (x[i] @ y[j]) / max((x[i].norm() * y[j].norm()), 1e-8) + return similarity.cpu().numpy() + +def evaluate(Z, Y): + # Z: (batch_size, 512) + # Y: (gt_size, 512) + similarity = calc_similarity(Z, Y) + acc_tmp = np.zeros(len(similarity)) + for i in range(len(similarity)): + acc_tmp[i] = np.sum(similarity[i,:] < similarity[i,i]) / (len(similarity)-1) + similarity_acc = np.mean(acc_tmp) + print('Similarity Acc', similarity_acc) + return similarity_acc + + +if __name__ == "__main__": + from hydra import initialize, compose + with initialize(version_base=None, config_path="../configs/"): + args = compose(config_name='20230427_sbj01_eegnet') + if not os.path.exists(os.path.join(args.save_root, 'weights')): + os.makedirs(os.path.join(args.save_root, 'weights')) + run(args) diff --git a/train_wowandb_cv.py b/train_wowandb_cv.py index 152edae..4e6977d 100644 --- a/train_wowandb_cv.py +++ b/train_wowandb_cv.py @@ -146,7 +146,7 @@ def run(args: DictConfig) -> None: ind_te = list(range(3000,3600)) + list(range(6600, 7200)) # + list(range(13200, 14400)) + list(range(20400, 21600)) train_dataset = Subset(source_dataset, ind_tr) val_dataset = Subset(source_dataset, ind_te) - + with open_dict(args): args.num_subjects = source_dataset.num_subjects print('num subject is {}'.format(args.num_subjects)) @@ -358,6 +358,9 @@ def run(args: DictConfig) -> None: print('best model is updated !!, {}'.format(best_acc), best_weight_file) +def evaluate() + + if __name__ == "__main__": from hydra import initialize, compose From b77e5b0b3a56ef8c30063a7ee144dd6e0fb6a428 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Fri, 28 Apr 2023 17:05:10 +0900 Subject: [PATCH 43/71] MOD: can load weight --- eval_wowandb_cv.py | 34 ++++++++++++---------------------- 1 file changed, 12 insertions(+), 22 deletions(-) diff --git a/eval_wowandb_cv.py b/eval_wowandb_cv.py index 3c15f9d..bb0876d 100644 --- a/eval_wowandb_cv.py +++ b/eval_wowandb_cv.py @@ -202,34 +202,24 @@ def run(args: DictConfig) -> None: # --------------------- brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) + weight_dir = os.path.join(args.save_root, 'weights') + last_weight_file = os.path.join(weight_dir, "model_last.pt") + best_weight_file = os.path.join(weight_dir, "model_last.pt") + if os.path.exists(best_weight_file): + brain_encoder.load_state_dict(torch.load(best_weight_file)) + print('weight is loaded from ', best_weight_file) + else: + brain_encoder.load_state_dict(torch.load(last_weight_file)) + print('weight is loaded from ', last_weight_file) + + classifier = Classifier(args) # --------------- # Loss # --------------- loss_func = CLIPLoss(args).to(device) - loss_func.train() - - # -------------------- - # Optimizer - # -------------------- - optimizer = torch.optim.Adam( - list(brain_encoder.parameters()) + list(loss_func.parameters()), lr=float(args.lr), - ) - - if args.lr_scheduler == "cosine": - scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( - optimizer, T_max=args.epochs, eta_min=args.lr * 0.1 - ) - elif args.lr_scheduler == "multistep": - scheduler = torch.optim.lr_scheduler.MultiStepLR( - optimizer, - milestones=[int(m * args.epochs) for m in args.lr_multistep_mlstns], - gamma=args.lr_step_gamma, - ) - else: - scheduler = None - + loss_func.eval() # ====================================== train_losses = [] test_losses = [] From 3482d512dd7b0f3ca6b8c70cbb2243e064560447 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Sun, 30 Apr 2023 18:54:27 +0900 Subject: [PATCH 44/71] ADD: outlier analysis --- eval_wowandb_cv.py | 78 ++++++- examples/view_training_curve.py | 2 + meg_decoding/dataclass/god.py | 74 ++++++- train_wowandb_cv.py | 7 +- train_wowandb_cv_regression.py | 366 ++++++++++++++++++++++++++++++++ 5 files changed, 504 insertions(+), 23 deletions(-) create mode 100644 train_wowandb_cv_regression.py diff --git a/eval_wowandb_cv.py b/eval_wowandb_cv.py index bb0876d..066e154 100644 --- a/eval_wowandb_cv.py +++ b/eval_wowandb_cv.py @@ -28,6 +28,8 @@ from meg_decoding.utils.loggers import Pickleogger from meg_decoding.utils.vis_grad import get_grad from torch.utils.data.dataset import Subset +import matplotlib.pyplot as plt +import seaborn as sns def run(args: DictConfig) -> None: @@ -137,7 +139,12 @@ def run(args: DictConfig) -> None: elif args.dataset == "GOD": source_dataset = GODDatasetBase(args, 'train') - # val_dataset = GODDatasetBase(args, 'val') + outlier_dataset = GODDatasetBase(args, 'val', + mean_X= source_dataset.mean_X, + mean_Y=source_dataset.mean_Y, + std_X=source_dataset.std_X, + std_Y=source_dataset.std_Y + ) # train_size = int(np.round(len(source_dataset)*0.8)) # val_size = len(source_dataset) - train_size @@ -166,14 +173,15 @@ def run(args: DictConfig) -> None: train_dataset, batch_size= args.batch_size, drop_last=True, - shuffle=True, + shuffle=False, num_workers=args.num_workers, pin_memory=True, worker_init_fn=seed_worker, generator=g, ) test_loader = DataLoader( - val_dataset, + # val_dataset, # + outlier_dataset, # val_dataset batch_size=50, # args.batch_size, drop_last=True, shuffle=False, @@ -285,11 +293,48 @@ def run(args: DictConfig) -> None: f"test l: {np.mean(test_losses):.3f} | ", f"trainTop10acc: {np.mean(trainTop10accs):.3f} | ", f"testTop10acc: {np.mean(testTop10accs):.3f} | ", - f"lr: {optimizer.param_groups[0]['lr']:.5f}", f"temp: {loss_func.temp.item():.3f}", ) - evaluate(Zs, Ys) - + acc, mat = evaluate(Zs, Ys) + vis_confusion_mat(mat, acc, os.path.join(args.save_root, 'confusion_mat.png')) + n_database_hits = mat.sum(axis=0) + print('Num of hits of dataset \n', n_database_hits) + + miss_detection = np.sum(mat>0, axis=-1)/(len(mat)-1) + print('Num miss detection: \n', miss_detection) + + true_detection = np.sum(mat > 0, axis=1) / (len(mat)-1) + print('Num query detection: \n', true_detection) + + + N=1 + plot_array_label = np.argsort(miss_detection)[::-1][:N] + plot_array_value = np.sort(miss_detection)[::-1][:N] + print(plot_array_label) + print(plot_array_value) + fig, axes = plt.subplots(nrows=2, figsize=(32,8)) + boxplot_and_plot(Zs.detach().cpu().numpy(), plot_array_label, axes[0]) + boxplot_and_plot(Ys.detach().cpu().numpy(), plot_array_label, axes[1]) + plt.savefig(os.path.join(args.save_root, 'boxplot_and_plot.png'),bbox_inches='tight') + plt.close() + import pdb; pdb.set_trace() + + mask = np.tril(mat, k=1) > 0 + bias_detection = np.abs(mat - mat.T) + biased_judge = np.sum(bias_detection==2 * mask) + fair_judge = np.sum(bias_detection==0 * mask) + print('num biased {} vs num fair judged {}'.format(biased_judge, fair_judge)) + +def boxplot_and_plot(bp_array, plot_array_label, ax): + # array: n_image x dims(512) + plot_array = bp_array[plot_array_label] + ax.boxplot(bp_array) + for l, ar in zip(plot_array_label, plot_array): + ax.plot(np.arange(len(ar)), ar, label=str(l)) + ax.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left') + ax.set_xlabel('unit id') + ax.set_ylabel('logits') + def calc_similarity(x, y): batch_size = len(x) gt_size = len(y) @@ -303,19 +348,38 @@ def calc_similarity(x, y): def evaluate(Z, Y): # Z: (batch_size, 512) # Y: (gt_size, 512) + # 仮説1:判定に偏りがある。-> あるサンプルのimageの特徴量がMEGの潜在空間ににているかどうかを判定するだけの基準になっているのではないか? + Z = Z - Z.mean(dim=0, keepdims=True) + Z = Z / Z.std(dim=0, keepdims=True) + binary_confusion_matrix = np.zeros([len(Z), len(Y)]) similarity = calc_similarity(Z, Y) acc_tmp = np.zeros(len(similarity)) for i in range(len(similarity)): acc_tmp[i] = np.sum(similarity[i,:] < similarity[i,i]) / (len(similarity)-1) + binary_confusion_matrix[i,similarity[i,:] < similarity[i,i]] = -1 + binary_confusion_matrix[i,similarity[i,:] > similarity[i,i]] = 1 similarity_acc = np.mean(acc_tmp) + + print('Similarity Acc', similarity_acc) - return similarity_acc + + return similarity_acc, binary_confusion_matrix + +def vis_confusion_mat(mat, acc, savefile=None): + sns.heatmap(mat, square=True, annot=False) + plt.xlabel('database data') + plt.ylabel('query data') + plt.title('similaruty acc: {}'.format(acc)) + plt.savefig(savefile) + plt.close() + print('saved to ', savefile) if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): args = compose(config_name='20230427_sbj01_eegnet') + # args = compose(config_name='20230425_sbj01_seq2stat') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) run(args) diff --git a/examples/view_training_curve.py b/examples/view_training_curve.py index 3acc705..2ece911 100644 --- a/examples/view_training_curve.py +++ b/examples/view_training_curve.py @@ -74,4 +74,6 @@ def parse_data(filepath): # filepath = '/home/yainoue/meg2image/results/20230413_sbj01_seq2stat/runs/2023-04-16 19:21:02.983480' # filepath = '/home/yainoue/meg2image/results/20230412_mini/runs/2023-04-13 15:17:53.310665' filepath = '/home/yainoue/meg2image/results/20230427_sbj01_eegnet_cv_norm/runs/2023-04-28 03:53:04.866741' + filepath = '/home/yainoue/meg2image/results/20230428_sbj01_eegnet_cv_norm/runs/2023-04-28 21:05:42.739003' + filepath = '/home/yainoue/meg2image/results/20230429_sbj01_eegnet_cv_norm_regression/runs/2023-04-30 04:16:13.736655' parse_data(filepath) diff --git a/meg_decoding/dataclass/god.py b/meg_decoding/dataclass/god.py index 2fe153d..6c34a0b 100644 --- a/meg_decoding/dataclass/god.py +++ b/meg_decoding/dataclass/god.py @@ -17,15 +17,21 @@ mne.set_log_level(verbose="WARNING") -def normalize_per_unit(tensor): +def normalize_per_unit(tensor, return_stats=False): print('normalize image_feature along unit dim') # array: n_samples x n_units(512) - tensor = tensor - np.mean(tensor, axis=0, keepdims=True) - tensor = tensor / np.std(tensor, axis=0, keepdims=True) - return tensor + mean = np.mean(tensor, axis=0, keepdims=True) + std = np.std(tensor, axis=0, keepdims=True) + tensor = tensor - mean + tensor = tensor / std + if return_stats: + return tensor, mean, std + else: + return tensor class GODDatasetBase(Dataset): - def __init__(self, args, split, preprocess_pipleine:list=[], return_label:bool=False): + def __init__(self, args, split, preprocess_pipleine:list=[], return_label:bool=False, + mean_X=None, mean_Y=None, std_X=None, std_Y=None): self.args = args self.sub_id_map = {s:i for i, s in enumerate(list(self.args.subjects.keys()))} self.preprocess_pipeline = preprocess_pipleine @@ -35,14 +41,36 @@ def __init__(self, args, split, preprocess_pipleine:list=[], return_label:bool=F self.X = meg_epochs.astype(np.float32) # epochs x ch x time_samples self.Y = image_feature_epochs.astype(np.float32) # epochs x dims - if args.normalize_image_features: - self.Y = normalize_per_unit(self.Y) - if args.normalize_meg: - self.X = normalize_per_unit(self.X) - + if mean_X is not None: + print('MEG is normalized by given stats') + self.mean_X, self.std_X= mean_X, std_X + self.X = self.X - self.mean_X + self.X = self.X / self.std_X + else: + if args.normalize_meg: + print('MEG is normalized by self stats') + self.X, self.mean_X, self.std_X = normalize_per_unit(self.X, return_stats=True) + else: + self.mean_X, self.std_X = None, None + if mean_Y is not None: + print('image features is normalized by self stats') + self.mean_Y, self.std_Y = mean_Y, std_Y + self.Y = self.Y - self.mean_Y + self.Y = self.Y / self.std_Y + else: + if args.normalize_image_features: + print('Image features is normalized by self stats') + self.Y, self.mean_Y, self.std_Y = normalize_per_unit(self.Y, return_stats=True) + else: + self.mean_Y, self.std_Y = None, None + self.subs = sub_epochs # epochs (x 1) self.labels = label_epochs # epochs (x 1) + if split == 'val': + self.X, self.Y, self.subs, self.labels = self.avg_same_image_sub_epochs(self.X, self.Y, self.subs, self.labels) + + self.num_subjects = len(np.unique(self.subs)) self.return_label = return_label @@ -118,8 +146,34 @@ def epoching(meg, window): label_epochs = np.concatenate(label_epochs, axis=0) image_feature_epochs = np.concatenate(image_feature_epochs, axis=0) print('dataset is created. size is ', meg_epochs.shape) + # if split == 'val': + # meg_epochs, image_feature_epochs, sub_epochs, label_epochs = self.avg_same_image_sub_epochs(meg_epochs, image_feature_epochs, sub_epochs, label_epochs) + return meg_epochs, sub_epochs, label_epochs, image_feature_epochs + def avg_same_image_sub_epochs(self, Xs, Ys, subs, labels): + subs = np.array(subs) + avg_Xs = [] + avg_Ys = [] + new_subs = [] + new_labels = [] + for i in np.unique(labels): + for s in np.unique(subs): + sub_and_label_equivalent_flag = (labels==i) * (subs==s) + avg_Xs.append(np.mean(Xs[sub_and_label_equivalent_flag], axis=0, keepdims=True)) + avg_Ys.append(np.mean(Ys[sub_and_label_equivalent_flag], axis=0, keepdims=True)) + new_subs.append(s) + new_labels.append(i) + return np.concatenate(avg_Xs), np.concatenate(avg_Ys), new_subs, np.asarray(new_labels) + +def same_image2neighbor(X, Y, label): + same_image_indices = [] + for i in range(1,1201): + same_image_indices += list(np.where(label==i)[0]) + assert len(list(np.where(label==i)[0])) > 0 + return X[same_image_indices], Y[same_image_indices] + + class GODCollator(torch.nn.Module): """ diff --git a/train_wowandb_cv.py b/train_wowandb_cv.py index 4e6977d..5f126e5 100644 --- a/train_wowandb_cv.py +++ b/train_wowandb_cv.py @@ -357,15 +357,10 @@ def run(args: DictConfig) -> None: best_acc = np.mean(testTop10accs) print('best model is updated !!, {}'.format(best_acc), best_weight_file) - -def evaluate() - - - if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230427_sbj01_eegnet') + args = compose(config_name='20230428_sbj01_eegnet') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) run(args) diff --git a/train_wowandb_cv_regression.py b/train_wowandb_cv_regression.py new file mode 100644 index 0000000..18f8971 --- /dev/null +++ b/train_wowandb_cv_regression.py @@ -0,0 +1,366 @@ +import os, sys, random +import numpy as np +import torch +import torch.nn as nn +from time import time +from tqdm import tqdm, trange +from termcolor import cprint +# import wandb + +from omegaconf import DictConfig, open_dict +import hydra +from hydra.utils import get_original_cwd + +from constants import device +# from speech_decoding.dataclass.brennan2018 import Brennan2018Dataset +# from speech_decoding.dataclass.gwilliams2022 import ( +# Gwilliams2022SentenceSplit, +# Gwilliams2022ShallowSplit, +# Gwilliams2022DeepSplit, +# Gwilliams2022Collator, +# ) +from torch.utils.data import DataLoader, RandomSampler, BatchSampler + +from meg_decoding.models import get_model, Classifier +from meg_decoding.utils.get_dataloaders import get_dataloaders, get_samplers +from meg_decoding.utils.loss import * +from meg_decoding.dataclass.god import GODDatasetBase, GODCollator +from meg_decoding.utils.loggers import Pickleogger +from meg_decoding.utils.vis_grad import get_grad +from torch.utils.data.dataset import Subset + + +def run(args: DictConfig) -> None: + + from meg_decoding.utils.reproducibility import seed_worker + # NOTE: We do need it (IMHO). + if args.reproducible: + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + torch.use_deterministic_algorithms(True) + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + g = torch.Generator() + g.manual_seed(0) + seed_worker = seed_worker + else: + g = None + seed_worker = None + + pkl_logger = Pickleogger(os.path.join(args.save_root, 'runs')) + + # with open_dict(args): + # args.root_dir = get_original_cwd() + cprint(f"Current working directory : {os.getcwd()}") + cprint(args, color="white") + + # ----------------------- + # Dataloader + # ----------------------- + # NOTE: Segmentation should always be by word onsets, not just every 3 seconds + if args.dataset == "Gwilliams2022": + + if args.split_mode == "sentence": + + train_set = Gwilliams2022SentenceSplit(args) + test_set = Gwilliams2022SentenceSplit(args, train_set.test_word_idxs_dict) + + assert train_set.num_subjects == test_set.num_subjects + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + elif args.split_mode == "shallow": + + dataset = Gwilliams2022ShallowSplit(args) + + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(dataset.Y.shape[0] * args.split_ratio) + test_size = dataset.Y.shape[0] - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + + elif args.split_mode == "deep": + + train_set = Gwilliams2022DeepSplit(args, train=True) + test_set = Gwilliams2022DeepSplit(args, train=False) + assert train_set.num_subjects == test_set.num_subjects + + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + cprint(f"Test segments: {test_size}", "cyan") + + if args.use_sampler: + # NOTE: currently not supporting reproducibility + train_loader, test_loader = get_samplers( + train_set, + test_set, + args, + test_bsz=test_size, + collate_fn=Gwilliams2022Collator(args), + ) + else: + # FIXME: maybe either get rid of reproducibility, or remove this? + if args.reproducible: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, seed_worker, g, test_bsz=test_size + ) + else: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, test_bsz=test_size + ) + + elif args.dataset == "Brennan2018": + # NOTE: takes an optional debug param force_recompute to pre-process the EEG even if it exists + dataset = Brennan2018Dataset(args) + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(len(dataset) * args.split_ratio) + test_size = len(dataset) - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + cprint( + f"Number of samples: {len(train_set)} (train), {len(test_set)} (test)", color="blue", + ) + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, g, seed_worker, test_bsz=test_size + ) + + elif args.dataset == "GOD": + source_dataset = GODDatasetBase(args, 'train') + # val_dataset = GODDatasetBase(args, 'val') + # train_size = int(np.round(len(source_dataset)*0.8)) + # val_size = len(source_dataset) - train_size + + # train_dataset, val_dataset = torch.utils.data.random_split(source_dataset, [train_size, val_size]) + ind_tr = list(range(0, 3000)) + list(range(3600, 6600)) #+ list(range(7200, 21600)) # + list(range(7200, 13200)) + list(range(14400, 20400)) + ind_te = list(range(3000,3600)) + list(range(6600, 7200)) # + list(range(13200, 14400)) + list(range(20400, 21600)) + train_dataset = Subset(source_dataset, ind_tr) + val_dataset = Subset(source_dataset, ind_te) + + with open_dict(args): + args.num_subjects = source_dataset.num_subjects + print('num subject is {}'.format(args.num_subjects)) + + + if args.use_sampler: + test_size = 50# 重複サンプルが存在するのでval_dataset.Y.shape[0] + train_loader, test_loader = get_samplers( + train_dataset, + val_dataset, + args, + test_bsz=test_size, + collate_fn=GODCollator(args),) + + else: + train_loader = DataLoader( + train_dataset, + batch_size= args.batch_size, + drop_last=True, + shuffle=True, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + test_loader = DataLoader( + val_dataset, + batch_size=50, # args.batch_size, + drop_last=True, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + + else: + raise ValueError("Unknown dataset") + + if args.use_wandb: + wandb.config = {k: v for k, v in dict(args).items() if k not in ["root_dir", "wandb"]} + wandb.init( + project=args.wandb.project, + entity=args.wandb.entity, + config=wandb.config, + save_code=True, + ) + wandb.run.name = args.wandb.run_name + "_" + args.split_mode + wandb.run.save() + + # --------------------- + # Models + # --------------------- + brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) + + classifier = Classifier(args) + + # --------------- + # Loss + # --------------- + loss_func = torch.nn.MSELoss(reduction="mean") #CLIPLoss(args).to(device) + loss_func.train() + + # -------------------- + # Optimizer + # -------------------- + optimizer = torch.optim.Adam( + list(brain_encoder.parameters()) + list(loss_func.parameters()), lr=float(args.lr), + ) + + if args.lr_scheduler == "cosine": + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, T_max=args.epochs, eta_min=args.lr * 0.1 + ) + elif args.lr_scheduler == "multistep": + scheduler = torch.optim.lr_scheduler.MultiStepLR( + optimizer, + milestones=[int(m * args.epochs) for m in args.lr_multistep_mlstns], + gamma=args.lr_step_gamma, + ) + else: + scheduler = None + + # ====================================== + best_acc = 0 + pbar = tqdm(range(args.epochs)) + for epoch in pbar: + pbar.set_description("training {}/{} epoch".format(epoch, args.epochs)) + train_losses = [] + test_losses = [] + trainTop1accs = [] + trainTop10accs = [] + testTop1accs = [] + testTop10accs = [] + + brain_encoder.train() + pbar2 = tqdm(train_loader) + for i, batch in enumerate(pbar2): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, chunkIDs = batch + assert ( + len(chunkIDs.unique()) == X.shape[0] + ), "Duplicate segments in batch are not allowed. Aborting." + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y = X.to(device), Y.to(device) + # import pdb; pdb.set_trace() + Z = brain_encoder(X, subject_idxs) + loss = loss_func(Y, Z) + with torch.no_grad(): + trainTop1acc, trainTop10acc = classifier(Z, Y) + + train_losses.append(loss.item()) + trainTop1accs.append(trainTop1acc) + trainTop10accs.append(trainTop10acc) + + pbar.set_description("training {}/{} iters Train/Loss: {}, Train/Top1Acc: {}, Train/Top10Acc: {}".format(i, len(train_loader), loss.item(), trainTop1acc, trainTop10acc)) + if args.dataset == "Gwilliams2022": + optimizer.zero_grad() + loss.backward() + optimizer.step() + if args.dataset == "GOD": + optimizer.zero_grad() + loss.backward() + optimizer.step() + # get_grad(brain_encoder) + # break + + # Accumulate gradients for Gwilliams for the whole epoch + if args.dataset == "Brennan2018": + optimizer.zero_grad() + loss.backward() + optimizer.step() + + brain_encoder.eval() + for batch in test_loader: + + with torch.no_grad(): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, chunkIDs = batch + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y = X.to(device), Y.to(device) + + Z = brain_encoder(X, subject_idxs) # 0.96 GB + + loss = loss_func(Y, Z) + + testTop1acc, testTop10acc = classifier(Z, Y, test=True) # ( 250, 1024, 360 ) + + test_losses.append(loss.item()) + testTop1accs.append(testTop1acc) + testTop10accs.append(testTop10acc) + + print( + f"Ep {epoch}/{args.epochs} | ", + f"train l: {np.mean(train_losses):.3f} | ", + f"test l: {np.mean(test_losses):.3f} | ", + f"trainTop10acc: {np.mean(trainTop10accs):.3f} | ", + f"testTop10acc: {np.mean(testTop10accs):.3f} | ", + f"lr: {optimizer.param_groups[0]['lr']:.5f}", + f"temp: {0.00:.3f}", + ) + pkl_logger.log({ + "epoch": epoch, + "train_loss": np.mean(train_losses), + "test_loss": np.mean(test_losses), + "trainTop1acc": np.mean(trainTop1accs), + "trainTop10acc": np.mean(trainTop10accs), + "testTop1acc": np.mean(testTop1accs), + "testTop10acc": np.mean(testTop10accs), + "lrate": optimizer.param_groups[0]["lr"], + "temp": 0, + }, 'logs') + + if args.use_wandb: + performance_now = { + "epoch": epoch, + "train_loss": np.mean(train_losses), + "test_loss": np.mean(test_losses), + "trainTop1acc": np.mean(trainTop1accs), + "trainTop10acc": np.mean(trainTop10accs), + "testTop1acc": np.mean(testTop1accs), + "testTop10acc": np.mean(testTop10accs), + "lrate": optimizer.param_groups[0]["lr"], + "temp": 0, + } + wandb.log(performance_now) + + if scheduler is not None: + scheduler.step() + + savedir = os.path.join(args.save_root, 'weights') + last_weight_file = os.path.join(savedir, "model_last.pt") + torch.save(brain_encoder.state_dict(), last_weight_file) + print('model is saved as ', last_weight_file) + if best_acc < np.mean(testTop10accs): + best_weight_file = os.path.join(savedir, "model_best.pt") + torch.save(brain_encoder.state_dict(), best_weight_file) + best_acc = np.mean(testTop10accs) + print('best model is updated !!, {}'.format(best_acc), best_weight_file) + +if __name__ == "__main__": + from hydra import initialize, compose + with initialize(version_base=None, config_path="../configs/"): + args = compose(config_name='20230429_sbj01_eegnet regression') + if not os.path.exists(os.path.join(args.save_root, 'weights')): + os.makedirs(os.path.join(args.save_root, 'weights')) + run(args) From 5cde6170af458e8828870284f8817644131ca9be Mon Sep 17 00:00:00 2001 From: inoue26 Date: Sun, 30 Apr 2023 19:34:16 +0900 Subject: [PATCH 45/71] ADD: same label loss --- meg_decoding/utils/loss.py | 25 +++ train_wowandb_cv_contrastive.py | 365 ++++++++++++++++++++++++++++++++ 2 files changed, 390 insertions(+) create mode 100644 train_wowandb_cv_contrastive.py diff --git a/meg_decoding/utils/loss.py b/meg_decoding/utils/loss.py index f6eba88..37788df 100644 --- a/meg_decoding/utils/loss.py +++ b/meg_decoding/utils/loss.py @@ -14,6 +14,31 @@ def torch_exp(x: torch.Tensor): # x: ( N, ) def torch_log(x: torch.Tensor): return torch.log(x.clamp(min=1e-10)) +class SameLabelLoss(nn.Module): + def __init__(self): + super().__init__() + self.mse = nn.MSELoss(reduction="mean") + + def forward(self, Z, labels): + """ + Z: ( B, 512 ) torch.tensor + labels: ( B, ) np.ndarray + """ + loss = [] + for i,l in enumerate(labels): + indices = np.where(labels==l)[0] + anchor_Z = Z[l,:] + for ind in indices: + if ind == i: + continue + else: + ref_Z = Z[ind,:] + loss.append(self.mse(anchor_Z, ref_Z)) + + return torch.mean(torch.stack(loss)) + + + class MSELoss(nn.Module): """Takes reduction mean only for batch direction""" diff --git a/train_wowandb_cv_contrastive.py b/train_wowandb_cv_contrastive.py new file mode 100644 index 0000000..e320f31 --- /dev/null +++ b/train_wowandb_cv_contrastive.py @@ -0,0 +1,365 @@ +import os, sys, random +import numpy as np +import torch +import torch.nn as nn +from time import time +from tqdm import tqdm, trange +from termcolor import cprint +# import wandb + +from omegaconf import DictConfig, open_dict +import hydra +from hydra.utils import get_original_cwd + +from constants import device +# from speech_decoding.dataclass.brennan2018 import Brennan2018Dataset +# from speech_decoding.dataclass.gwilliams2022 import ( +# Gwilliams2022SentenceSplit, +# Gwilliams2022ShallowSplit, +# Gwilliams2022DeepSplit, +# Gwilliams2022Collator, +# ) +from torch.utils.data import DataLoader, RandomSampler, BatchSampler + +from meg_decoding.models import get_model, Classifier +from meg_decoding.utils.get_dataloaders import get_dataloaders, get_samplers +from meg_decoding.utils.loss import * +from meg_decoding.dataclass.god import GODDatasetBase, GODCollator +from meg_decoding.utils.loggers import Pickleogger +from meg_decoding.utils.vis_grad import get_grad +from torch.utils.data.dataset import Subset + + +def run(args: DictConfig) -> None: + + from meg_decoding.utils.reproducibility import seed_worker + # NOTE: We do need it (IMHO). + if args.reproducible: + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + torch.use_deterministic_algorithms(True) + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + g = torch.Generator() + g.manual_seed(0) + seed_worker = seed_worker + else: + g = None + seed_worker = None + + pkl_logger = Pickleogger(os.path.join(args.save_root, 'runs')) + + # with open_dict(args): + # args.root_dir = get_original_cwd() + cprint(f"Current working directory : {os.getcwd()}") + cprint(args, color="white") + + # ----------------------- + # Dataloader + # ----------------------- + # NOTE: Segmentation should always be by word onsets, not just every 3 seconds + if args.dataset == "Gwilliams2022": + + if args.split_mode == "sentence": + + train_set = Gwilliams2022SentenceSplit(args) + test_set = Gwilliams2022SentenceSplit(args, train_set.test_word_idxs_dict) + + assert train_set.num_subjects == test_set.num_subjects + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + elif args.split_mode == "shallow": + + dataset = Gwilliams2022ShallowSplit(args) + + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(dataset.Y.shape[0] * args.split_ratio) + test_size = dataset.Y.shape[0] - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + + elif args.split_mode == "deep": + + train_set = Gwilliams2022DeepSplit(args, train=True) + test_set = Gwilliams2022DeepSplit(args, train=False) + assert train_set.num_subjects == test_set.num_subjects + + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + cprint(f"Test segments: {test_size}", "cyan") + + if args.use_sampler: + # NOTE: currently not supporting reproducibility + train_loader, test_loader = get_samplers( + train_set, + test_set, + args, + test_bsz=test_size, + collate_fn=Gwilliams2022Collator(args), + ) + else: + # FIXME: maybe either get rid of reproducibility, or remove this? + if args.reproducible: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, seed_worker, g, test_bsz=test_size + ) + else: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, test_bsz=test_size + ) + + elif args.dataset == "Brennan2018": + # NOTE: takes an optional debug param force_recompute to pre-process the EEG even if it exists + dataset = Brennan2018Dataset(args) + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(len(dataset) * args.split_ratio) + test_size = len(dataset) - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + cprint( + f"Number of samples: {len(train_set)} (train), {len(test_set)} (test)", color="blue", + ) + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, g, seed_worker, test_bsz=test_size + ) + + elif args.dataset == "GOD": + source_dataset = GODDatasetBase(args, 'train', return_label=True) + # val_dataset = GODDatasetBase(args, 'val') + # train_size = int(np.round(len(source_dataset)*0.8)) + # val_size = len(source_dataset) - train_size + + # train_dataset, val_dataset = torch.utils.data.random_split(source_dataset, [train_size, val_size]) + ind_tr = list(range(0, 3000)) + list(range(3600, 6600)) #+ list(range(7200, 21600)) # + list(range(7200, 13200)) + list(range(14400, 20400)) + ind_te = list(range(3000,3600)) + list(range(6600, 7200)) # + list(range(13200, 14400)) + list(range(20400, 21600)) + train_dataset = Subset(source_dataset, ind_tr) + val_dataset = Subset(source_dataset, ind_te) + + with open_dict(args): + args.num_subjects = source_dataset.num_subjects + print('num subject is {}'.format(args.num_subjects)) + + + if args.use_sampler: + test_size = 50# 重複サンプルが存在するのでval_dataset.Y.shape[0] + train_loader, test_loader = get_samplers( + train_dataset, + val_dataset, + args, + test_bsz=test_size, + collate_fn=GODCollator(args),) + + else: + train_loader = DataLoader( + train_dataset, + batch_size= args.batch_size, + drop_last=True, + shuffle=True, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + test_loader = DataLoader( + val_dataset, + batch_size=50, # args.batch_size, + drop_last=True, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + + else: + raise ValueError("Unknown dataset") + + if args.use_wandb: + wandb.config = {k: v for k, v in dict(args).items() if k not in ["root_dir", "wandb"]} + wandb.init( + project=args.wandb.project, + entity=args.wandb.entity, + config=wandb.config, + save_code=True, + ) + wandb.run.name = args.wandb.run_name + "_" + args.split_mode + wandb.run.save() + + # --------------------- + # Models + # --------------------- + brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) + + classifier = Classifier(args) + + # --------------- + # Loss + # --------------- + loss_func = CLIPLoss(args).to(device) + loss_func.train() + loss_func2 = SameLabelLoss(args).to(device) + loss_func2.train() + + # -------------------- + # Optimizer + # -------------------- + optimizer = torch.optim.Adam( + list(brain_encoder.parameters()) + list(loss_func.parameters()), lr=float(args.lr), + ) + + if args.lr_scheduler == "cosine": + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, T_max=args.epochs, eta_min=args.lr * 0.1 + ) + elif args.lr_scheduler == "multistep": + scheduler = torch.optim.lr_scheduler.MultiStepLR( + optimizer, + milestones=[int(m * args.epochs) for m in args.lr_multistep_mlstns], + gamma=args.lr_step_gamma, + ) + else: + scheduler = None + + # ====================================== + best_acc = 0 + pbar = tqdm(range(args.epochs)) + for epoch in pbar: + pbar.set_description("training {}/{} epoch".format(epoch, args.epochs)) + train_losses = [] + test_losses = [] + trainTop1accs = [] + trainTop10accs = [] + testTop1accs = [] + testTop10accs = [] + + brain_encoder.train() + pbar2 = tqdm(train_loader) + for i, batch in enumerate(pbar2): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, labels = batch + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y = X.to(device), Y.to(device) + # import pdb; pdb.set_trace() + Z = brain_encoder(X, subject_idxs) + loss = loss_func(Y, Z) + loss_func2(Z, labels.detach().cpu().numpy()) + with torch.no_grad(): + trainTop1acc, trainTop10acc = classifier(Z, Y) + + train_losses.append(loss.item()) + trainTop1accs.append(trainTop1acc) + trainTop10accs.append(trainTop10acc) + + pbar.set_description("training {}/{} iters Train/Loss: {}, Train/Top1Acc: {}, Train/Top10Acc: {}".format(i, len(train_loader), loss.item(), trainTop1acc, trainTop10acc)) + if args.dataset == "Gwilliams2022": + optimizer.zero_grad() + loss.backward() + optimizer.step() + if args.dataset == "GOD": + optimizer.zero_grad() + loss.backward() + optimizer.step() + # get_grad(brain_encoder) + # break + + # Accumulate gradients for Gwilliams for the whole epoch + if args.dataset == "Brennan2018": + optimizer.zero_grad() + loss.backward() + optimizer.step() + + brain_encoder.eval() + for batch in test_loader: + + with torch.no_grad(): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, labels = batch + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y = X.to(device), Y.to(device) + + Z = brain_encoder(X, subject_idxs) # 0.96 GB + + loss = loss_func(Y, Z) + loss_func2(Z, labels.detach().cpu().numpy()) + + testTop1acc, testTop10acc = classifier(Z, Y, test=True) # ( 250, 1024, 360 ) + + test_losses.append(loss.item()) + testTop1accs.append(testTop1acc) + testTop10accs.append(testTop10acc) + + print( + f"Ep {epoch}/{args.epochs} | ", + f"train l: {np.mean(train_losses):.3f} | ", + f"test l: {np.mean(test_losses):.3f} | ", + f"trainTop10acc: {np.mean(trainTop10accs):.3f} | ", + f"testTop10acc: {np.mean(testTop10accs):.3f} | ", + f"lr: {optimizer.param_groups[0]['lr']:.5f}", + f"temp: {loss_func.temp.item():.3f}", + ) + pkl_logger.log({ + "epoch": epoch, + "train_loss": np.mean(train_losses), + "test_loss": np.mean(test_losses), + "trainTop1acc": np.mean(trainTop1accs), + "trainTop10acc": np.mean(trainTop10accs), + "testTop1acc": np.mean(testTop1accs), + "testTop10acc": np.mean(testTop10accs), + "lrate": optimizer.param_groups[0]["lr"], + "temp": loss_func.temp.item(), + }, 'logs') + + if args.use_wandb: + performance_now = { + "epoch": epoch, + "train_loss": np.mean(train_losses), + "test_loss": np.mean(test_losses), + "trainTop1acc": np.mean(trainTop1accs), + "trainTop10acc": np.mean(trainTop10accs), + "testTop1acc": np.mean(testTop1accs), + "testTop10acc": np.mean(testTop10accs), + "lrate": optimizer.param_groups[0]["lr"], + "temp": loss_func.temp.item(), + } + wandb.log(performance_now) + + if scheduler is not None: + scheduler.step() + + savedir = os.path.join(args.save_root, 'weights') + last_weight_file = os.path.join(savedir, "model_last.pt") + torch.save(brain_encoder.state_dict(), last_weight_file) + print('model is saved as ', last_weight_file) + if best_acc < np.mean(testTop10accs): + best_weight_file = os.path.join(savedir, "model_best.pt") + torch.save(brain_encoder.state_dict(), best_weight_file) + best_acc = np.mean(testTop10accs) + print('best model is updated !!, {}'.format(best_acc), best_weight_file) + +if __name__ == "__main__": + from hydra import initialize, compose + with initialize(version_base=None, config_path="../configs/"): + args = compose(config_name='20230428_sbj01_eegnet') + if not os.path.exists(os.path.join(args.save_root, 'weights')): + os.makedirs(os.path.join(args.save_root, 'weights')) + run(args) From 25a32a882fcbeb7711bf47251c5ff68442436403 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 1 May 2023 15:56:56 +0900 Subject: [PATCH 46/71] ADD: regression. this is the best model I have ever seen, including Ridge regression after resource construction --- eval_wowandb_cv.py | 54 ++++++++++++++++++++++++---------- train_wowandb_cv_regression.py | 2 +- 2 files changed, 40 insertions(+), 16 deletions(-) diff --git a/eval_wowandb_cv.py b/eval_wowandb_cv.py index 066e154..9ad905a 100644 --- a/eval_wowandb_cv.py +++ b/eval_wowandb_cv.py @@ -212,7 +212,7 @@ def run(args: DictConfig) -> None: weight_dir = os.path.join(args.save_root, 'weights') last_weight_file = os.path.join(weight_dir, "model_last.pt") - best_weight_file = os.path.join(weight_dir, "model_last.pt") + best_weight_file = os.path.join(weight_dir, "model_best.pt") if os.path.exists(best_weight_file): brain_encoder.load_state_dict(torch.load(best_weight_file)) print('weight is loaded from ', best_weight_file) @@ -295,35 +295,62 @@ def run(args: DictConfig) -> None: f"testTop10acc: {np.mean(testTop10accs):.3f} | ", f"temp: {loss_func.temp.item():.3f}", ) + + + # 仮説1:判定に偏りがある。-> あるサンプルのimageの特徴量がMEGの潜在空間ににているかどうかを判定するだけの基準になっているのではないか? + Zs = Zs - Zs.mean(dim=0, keepdims=True) + Zs = Zs / Zs.std(dim=0, keepdims=True) + Zs = Zs - Zs.mean(dim=1, keepdims=True) + Zs = Zs / Zs.std(dim=1, keepdims=True) + acc, mat = evaluate(Zs, Ys) vis_confusion_mat(mat, acc, os.path.join(args.save_root, 'confusion_mat.png')) n_database_hits = mat.sum(axis=0) print('Num of hits of dataset \n', n_database_hits) - miss_detection = np.sum(mat>0, axis=-1)/(len(mat)-1) + miss_detection = np.sum(mat < 0, axis=0)/(len(mat)-1) # FP print('Num miss detection: \n', miss_detection) - true_detection = np.sum(mat > 0, axis=1) / (len(mat)-1) + true_detection = np.sum(mat > 0, axis=1) / (len(mat)-1) # TP print('Num query detection: \n', true_detection) N=1 plot_array_label = np.argsort(miss_detection)[::-1][:N] plot_array_value = np.sort(miss_detection)[::-1][:N] - print(plot_array_label) - print(plot_array_value) + print('miss detaction id', plot_array_label) + print('miss detection value', plot_array_value) fig, axes = plt.subplots(nrows=2, figsize=(32,8)) boxplot_and_plot(Zs.detach().cpu().numpy(), plot_array_label, axes[0]) boxplot_and_plot(Ys.detach().cpu().numpy(), plot_array_label, axes[1]) plt.savefig(os.path.join(args.save_root, 'boxplot_and_plot.png'),bbox_inches='tight') plt.close() - import pdb; pdb.set_trace() - mask = np.tril(mat, k=1) > 0 + N=1 + plot_array_label = np.argsort(true_detection)[:N] + plot_array_value = np.sort(true_detection)[:N] + print(plot_array_label) + print(plot_array_value) + fig, axes = plt.subplots(nrows=2, figsize=(32,8)) + boxplot_and_plot(Zs.detach().cpu().numpy(), plot_array_label, axes[0]) + boxplot_and_plot(Ys.detach().cpu().numpy(), plot_array_label, axes[1]) + plt.savefig(os.path.join(args.save_root, 'boxplot_and_plot_weak.png'),bbox_inches='tight') + plt.close() + + mask = np.tril(np.ones_like(mat), k=-1) > 0 bias_detection = np.abs(mat - mat.T) - biased_judge = np.sum(bias_detection==2 * mask) - fair_judge = np.sum(bias_detection==0 * mask) + biased_judge = np.sum((bias_detection==2) * mask) + fair_judge = np.sum((bias_detection==0) * mask) print('num biased {} vs num fair judged {}'.format(biased_judge, fair_judge)) + + Zs_std = Zs.std(dim=1) + plt.scatter(Zs_std.detach().cpu().numpy(), true_detection) + plt.xlabel('std of Z') + plt.ylabel('TP ratio') + plt.savefig(os.path.join(args.save_root, 'std_vs_tp.png'),bbox_inches='tight') + + similarity = calc_similarity(Zs, Ys) + import pdb; pdb.set_trace() def boxplot_and_plot(bp_array, plot_array_label, ax): # array: n_image x dims(512) @@ -348,16 +375,13 @@ def calc_similarity(x, y): def evaluate(Z, Y): # Z: (batch_size, 512) # Y: (gt_size, 512) - # 仮説1:判定に偏りがある。-> あるサンプルのimageの特徴量がMEGの潜在空間ににているかどうかを判定するだけの基準になっているのではないか? - Z = Z - Z.mean(dim=0, keepdims=True) - Z = Z / Z.std(dim=0, keepdims=True) binary_confusion_matrix = np.zeros([len(Z), len(Y)]) similarity = calc_similarity(Z, Y) acc_tmp = np.zeros(len(similarity)) for i in range(len(similarity)): acc_tmp[i] = np.sum(similarity[i,:] < similarity[i,i]) / (len(similarity)-1) - binary_confusion_matrix[i,similarity[i,:] < similarity[i,i]] = -1 - binary_confusion_matrix[i,similarity[i,:] > similarity[i,i]] = 1 + binary_confusion_matrix[i,similarity[i,:] < similarity[i,i]] = 1 + binary_confusion_matrix[i,similarity[i,:] > similarity[i,i]] = -1 similarity_acc = np.mean(acc_tmp) @@ -378,7 +402,7 @@ def vis_confusion_mat(mat, acc, savefile=None): if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230427_sbj01_eegnet') + args = compose(config_name='20230429_sbj01_eegnet_regression') # args = compose(config_name='20230425_sbj01_seq2stat') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) diff --git a/train_wowandb_cv_regression.py b/train_wowandb_cv_regression.py index 18f8971..0549ae2 100644 --- a/train_wowandb_cv_regression.py +++ b/train_wowandb_cv_regression.py @@ -360,7 +360,7 @@ def run(args: DictConfig) -> None: if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230429_sbj01_eegnet regression') + args = compose(config_name='20230429_sbj01_eegnet_regression') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) run(args) From fff87ce0b6a6fdd1025b7b2baa447c8d4732a89c Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 1 May 2023 16:03:39 +0900 Subject: [PATCH 47/71] ADD: regression cvfor all subj --- meg_decoding/models.py | 72 ++++ train_wowandb_cv_regression_all_subject.py | 366 +++++++++++++++++++++ 2 files changed, 438 insertions(+) create mode 100644 train_wowandb_cv_regression_all_subject.py diff --git a/meg_decoding/models.py b/meg_decoding/models.py index b88f045..e87754d 100644 --- a/meg_decoding/models.py +++ b/meg_decoding/models.py @@ -24,6 +24,8 @@ def get_model(args): return LinearEncoder(args) elif args.model == 'eegnet': return EEGNet(args) + elif args.model == 'eegnet_sub': + return EEGNet(args) else: raise ValueError('no model named {} is prepared'.format(args.model)) @@ -91,6 +93,76 @@ def compute_dim(self, num_channels, T): return x.size()[1] * x.size()[2] * x.size()[3] +class EEGNetSub(nn.Module): + def __init__(self, args): + super(EEGNet, self).__init__() + T = int((args.window.end - args.window.start) * args.preprocs.brain_resample_rate) + # DownSampling + # self.down1 = nn.Sequential(nn.AvgPool2d((1, p0))) + + # Conv2d(in,out,kernel,stride,padding,bias) #k1 30 + self.num_subjects = args.num_subjects + + self.conv1_sub = nn.ModuleList( + nn.Sequential( + nn.Conv2d(1, args.F1, (1, args.k1), padding="same", bias=False), nn.BatchNorm2d(args.F1) + ) for _ in range(self.num_subjects) + ) + roi_channels = roi(args) + num_channels = len(roi_channels) + self.conv2 = nn.Sequential( + nn.Conv2d( + args.F1, args.D * args.F1, (num_channels, 1), groups=args.F1, bias=False + ), + nn.BatchNorm2d(args.D * args.F1), + nn.ELU(), + nn.AvgPool2d((1, args.p1)), # 2 + nn.Dropout(args.dr1), + ) + + self.conv3 = nn.Sequential( + nn.Conv2d( + args.D * args.F1, + args.D * args.F1, + (1, args.k2), # 4 + padding="same", + groups=args.D * args.F1, + bias=False, + ), + nn.Conv2d(args.D * args.F1, args.F2, (1, 1), bias=False), + nn.BatchNorm2d(args.F2), + nn.ELU(), + nn.AvgPool2d((1, args.p2)), # 4 + nn.Dropout(args.dr2), + ) + + self.n_dim = self.compute_dim(num_channels, T) + self.classifier = nn.Linear(self.n_dim, 512, bias=True) + + def forward(self, x, sbj_idxs): + # import pdb; pdb.set_trace() + x = x.unsqueeze(1) + # 1, 1, 128, 300 + # x = self.down1(x) + # x = self.conv1(x) # 1, 16, 128, 300 + x = torch.cat( + [self.conv1_sub[i](x.unsqueeze(dim=0)) for i, x in zip(sbj_idxs, x)] + )# self.conv1_sub[sbj_idxs](x) + x = self.conv2(x) # 1, 32, 1, 150 + x = self.conv3(x) # 1, 32, 1, 37 + x = x.view(-1, self.n_dim) + x = self.classifier(x) + return x + + def compute_dim(self, num_channels, T): + x = torch.zeros((1, 1, num_channels, T)) + + # x = self.down1(x) + x = self.conv1(x) + x = self.conv2(x) + x = self.conv3(x) + + return x.size()[1] * x.size()[2] * x.size()[3] class SpatialAttention(nn.Module): """Same as SpatialAttentionVer2, but a little more concise""" diff --git a/train_wowandb_cv_regression_all_subject.py b/train_wowandb_cv_regression_all_subject.py new file mode 100644 index 0000000..c537b6c --- /dev/null +++ b/train_wowandb_cv_regression_all_subject.py @@ -0,0 +1,366 @@ +import os, sys, random +import numpy as np +import torch +import torch.nn as nn +from time import time +from tqdm import tqdm, trange +from termcolor import cprint +# import wandb + +from omegaconf import DictConfig, open_dict +import hydra +from hydra.utils import get_original_cwd + +from constants import device +# from speech_decoding.dataclass.brennan2018 import Brennan2018Dataset +# from speech_decoding.dataclass.gwilliams2022 import ( +# Gwilliams2022SentenceSplit, +# Gwilliams2022ShallowSplit, +# Gwilliams2022DeepSplit, +# Gwilliams2022Collator, +# ) +from torch.utils.data import DataLoader, RandomSampler, BatchSampler + +from meg_decoding.models import get_model, Classifier +from meg_decoding.utils.get_dataloaders import get_dataloaders, get_samplers +from meg_decoding.utils.loss import * +from meg_decoding.dataclass.god import GODDatasetBase, GODCollator +from meg_decoding.utils.loggers import Pickleogger +from meg_decoding.utils.vis_grad import get_grad +from torch.utils.data.dataset import Subset + + +def run(args: DictConfig) -> None: + + from meg_decoding.utils.reproducibility import seed_worker + # NOTE: We do need it (IMHO). + if args.reproducible: + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + torch.use_deterministic_algorithms(True) + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + g = torch.Generator() + g.manual_seed(0) + seed_worker = seed_worker + else: + g = None + seed_worker = None + + pkl_logger = Pickleogger(os.path.join(args.save_root, 'runs')) + + # with open_dict(args): + # args.root_dir = get_original_cwd() + cprint(f"Current working directory : {os.getcwd()}") + cprint(args, color="white") + + # ----------------------- + # Dataloader + # ----------------------- + # NOTE: Segmentation should always be by word onsets, not just every 3 seconds + if args.dataset == "Gwilliams2022": + + if args.split_mode == "sentence": + + train_set = Gwilliams2022SentenceSplit(args) + test_set = Gwilliams2022SentenceSplit(args, train_set.test_word_idxs_dict) + + assert train_set.num_subjects == test_set.num_subjects + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + elif args.split_mode == "shallow": + + dataset = Gwilliams2022ShallowSplit(args) + + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(dataset.Y.shape[0] * args.split_ratio) + test_size = dataset.Y.shape[0] - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + + elif args.split_mode == "deep": + + train_set = Gwilliams2022DeepSplit(args, train=True) + test_set = Gwilliams2022DeepSplit(args, train=False) + assert train_set.num_subjects == test_set.num_subjects + + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + cprint(f"Test segments: {test_size}", "cyan") + + if args.use_sampler: + # NOTE: currently not supporting reproducibility + train_loader, test_loader = get_samplers( + train_set, + test_set, + args, + test_bsz=test_size, + collate_fn=Gwilliams2022Collator(args), + ) + else: + # FIXME: maybe either get rid of reproducibility, or remove this? + if args.reproducible: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, seed_worker, g, test_bsz=test_size + ) + else: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, test_bsz=test_size + ) + + elif args.dataset == "Brennan2018": + # NOTE: takes an optional debug param force_recompute to pre-process the EEG even if it exists + dataset = Brennan2018Dataset(args) + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(len(dataset) * args.split_ratio) + test_size = len(dataset) - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + cprint( + f"Number of samples: {len(train_set)} (train), {len(test_set)} (test)", color="blue", + ) + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, g, seed_worker, test_bsz=test_size + ) + + elif args.dataset == "GOD": + source_dataset = GODDatasetBase(args, 'train') + # val_dataset = GODDatasetBase(args, 'val') + # train_size = int(np.round(len(source_dataset)*0.8)) + # val_size = len(source_dataset) - train_size + + # train_dataset, val_dataset = torch.utils.data.random_split(source_dataset, [train_size, val_size]) + ind_tr = list(range(0, 3000)) + list(range(3600, 6600)) + list(range(7200, 10200)) + list(range(10800, 13800)) + list(range(14400, 17400)) + list(range(18000, 21000)) + ind_te = list(range(3000,3600)) + list(range(6600, 7200)) +list(range(10200,10800)) + list(range(13800, 14400)) + list(range(17400, 18000)) + list(range(21000, 21600)) + train_dataset = Subset(source_dataset, ind_tr) + val_dataset = Subset(source_dataset, ind_te) + + with open_dict(args): + args.num_subjects = source_dataset.num_subjects + print('num subject is {}'.format(args.num_subjects)) + + + if args.use_sampler: + test_size = 50# 重複サンプルが存在するのでval_dataset.Y.shape[0] + train_loader, test_loader = get_samplers( + train_dataset, + val_dataset, + args, + test_bsz=test_size, + collate_fn=GODCollator(args),) + + else: + train_loader = DataLoader( + train_dataset, + batch_size= args.batch_size, + drop_last=True, + shuffle=True, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + test_loader = DataLoader( + val_dataset, + batch_size=50, # args.batch_size, + drop_last=True, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + + else: + raise ValueError("Unknown dataset") + + if args.use_wandb: + wandb.config = {k: v for k, v in dict(args).items() if k not in ["root_dir", "wandb"]} + wandb.init( + project=args.wandb.project, + entity=args.wandb.entity, + config=wandb.config, + save_code=True, + ) + wandb.run.name = args.wandb.run_name + "_" + args.split_mode + wandb.run.save() + + # --------------------- + # Models + # --------------------- + brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) + + classifier = Classifier(args) + + # --------------- + # Loss + # --------------- + loss_func = torch.nn.MSELoss(reduction="mean") #CLIPLoss(args).to(device) + loss_func.train() + + # -------------------- + # Optimizer + # -------------------- + optimizer = torch.optim.Adam( + list(brain_encoder.parameters()) + list(loss_func.parameters()), lr=float(args.lr), + ) + + if args.lr_scheduler == "cosine": + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, T_max=args.epochs, eta_min=args.lr * 0.1 + ) + elif args.lr_scheduler == "multistep": + scheduler = torch.optim.lr_scheduler.MultiStepLR( + optimizer, + milestones=[int(m * args.epochs) for m in args.lr_multistep_mlstns], + gamma=args.lr_step_gamma, + ) + else: + scheduler = None + + # ====================================== + best_acc = 0 + pbar = tqdm(range(args.epochs)) + for epoch in pbar: + pbar.set_description("training {}/{} epoch".format(epoch, args.epochs)) + train_losses = [] + test_losses = [] + trainTop1accs = [] + trainTop10accs = [] + testTop1accs = [] + testTop10accs = [] + + brain_encoder.train() + pbar2 = tqdm(train_loader) + for i, batch in enumerate(pbar2): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, chunkIDs = batch + assert ( + len(chunkIDs.unique()) == X.shape[0] + ), "Duplicate segments in batch are not allowed. Aborting." + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y = X.to(device), Y.to(device) + # import pdb; pdb.set_trace() + Z = brain_encoder(X, subject_idxs) + loss = loss_func(Y, Z) + with torch.no_grad(): + trainTop1acc, trainTop10acc = classifier(Z, Y) + + train_losses.append(loss.item()) + trainTop1accs.append(trainTop1acc) + trainTop10accs.append(trainTop10acc) + + pbar.set_description("training {}/{} iters Train/Loss: {}, Train/Top1Acc: {}, Train/Top10Acc: {}".format(i, len(train_loader), loss.item(), trainTop1acc, trainTop10acc)) + if args.dataset == "Gwilliams2022": + optimizer.zero_grad() + loss.backward() + optimizer.step() + if args.dataset == "GOD": + optimizer.zero_grad() + loss.backward() + optimizer.step() + # get_grad(brain_encoder) + # break + + # Accumulate gradients for Gwilliams for the whole epoch + if args.dataset == "Brennan2018": + optimizer.zero_grad() + loss.backward() + optimizer.step() + + brain_encoder.eval() + for batch in test_loader: + + with torch.no_grad(): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, chunkIDs = batch + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y = X.to(device), Y.to(device) + + Z = brain_encoder(X, subject_idxs) # 0.96 GB + + loss = loss_func(Y, Z) + + testTop1acc, testTop10acc = classifier(Z, Y, test=True) # ( 250, 1024, 360 ) + + test_losses.append(loss.item()) + testTop1accs.append(testTop1acc) + testTop10accs.append(testTop10acc) + + print( + f"Ep {epoch}/{args.epochs} | ", + f"train l: {np.mean(train_losses):.3f} | ", + f"test l: {np.mean(test_losses):.3f} | ", + f"trainTop10acc: {np.mean(trainTop10accs):.3f} | ", + f"testTop10acc: {np.mean(testTop10accs):.3f} | ", + f"lr: {optimizer.param_groups[0]['lr']:.5f}", + f"temp: {0.00:.3f}", + ) + pkl_logger.log({ + "epoch": epoch, + "train_loss": np.mean(train_losses), + "test_loss": np.mean(test_losses), + "trainTop1acc": np.mean(trainTop1accs), + "trainTop10acc": np.mean(trainTop10accs), + "testTop1acc": np.mean(testTop1accs), + "testTop10acc": np.mean(testTop10accs), + "lrate": optimizer.param_groups[0]["lr"], + "temp": 0, + }, 'logs') + + if args.use_wandb: + performance_now = { + "epoch": epoch, + "train_loss": np.mean(train_losses), + "test_loss": np.mean(test_losses), + "trainTop1acc": np.mean(trainTop1accs), + "trainTop10acc": np.mean(trainTop10accs), + "testTop1acc": np.mean(testTop1accs), + "testTop10acc": np.mean(testTop10accs), + "lrate": optimizer.param_groups[0]["lr"], + "temp": 0, + } + wandb.log(performance_now) + + if scheduler is not None: + scheduler.step() + + savedir = os.path.join(args.save_root, 'weights') + last_weight_file = os.path.join(savedir, "model_last.pt") + torch.save(brain_encoder.state_dict(), last_weight_file) + print('model is saved as ', last_weight_file) + if best_acc < np.mean(testTop10accs): + best_weight_file = os.path.join(savedir, "model_best.pt") + torch.save(brain_encoder.state_dict(), best_weight_file) + best_acc = np.mean(testTop10accs) + print('best model is updated !!, {}'.format(best_acc), best_weight_file) + +if __name__ == "__main__": + from hydra import initialize, compose + with initialize(version_base=None, config_path="../configs/"): + args = compose(config_name='20230501_sbj01_eegnet_regression') + if not os.path.exists(os.path.join(args.save_root, 'weights')): + os.makedirs(os.path.join(args.save_root, 'weights')) + run(args) From 922ba798758227f32962693db16578ea051c8f26 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Mon, 1 May 2023 22:37:14 +0900 Subject: [PATCH 48/71] ADD: regression for subs --- train_wowandb_cv_regression_all_subject.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/train_wowandb_cv_regression_all_subject.py b/train_wowandb_cv_regression_all_subject.py index c537b6c..9acae0c 100644 --- a/train_wowandb_cv_regression_all_subject.py +++ b/train_wowandb_cv_regression_all_subject.py @@ -360,7 +360,7 @@ def run(args: DictConfig) -> None: if __name__ == "__main__": from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): - args = compose(config_name='20230501_sbj01_eegnet_regression') + args = compose(config_name='20230501_all_eegnet_regression') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) run(args) From 5922861e8011e5c99753c01c3ba10634676baf65 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Tue, 9 May 2023 11:24:35 +0900 Subject: [PATCH 49/71] Report --- configs/configs_bu0501/20230411.yaml | 60 ++++++++++++ configs/configs_bu0501/20230412.yaml | 60 ++++++++++++ configs/configs_bu0501/20230413_loc.yaml | 64 +++++++++++++ configs/configs_bu0501/20230413_sbj01.yaml | 62 +++++++++++++ .../20230414_sbj01_seq2stat.yaml | 63 +++++++++++++ .../20230417_sbj01_seq2stat.yaml | 67 ++++++++++++++ .../20230419_sbj01_seq2stat.yaml | 66 ++++++++++++++ .../configs_bu0501/20230420_sbj01_linear.yaml | 56 ++++++++++++ .../20230420_sbj01_seq2stat.yaml | 66 ++++++++++++++ .../20230421_sbj01_kamitani_regression.yaml | 65 +++++++++++++ .../20230423_sbj01_seq2stat_regression.yaml | 68 ++++++++++++++ .../20230424_sbj01_seq2stat.yaml | 76 ++++++++++++++++ .../20230425_sbj01_seq2stat.yaml | 77 ++++++++++++++++ .../configs_bu0501/20230426_all_seq2stat.yaml | 76 ++++++++++++++++ .../configs_bu0501/20230427_sbj01_eegnet.yaml | 84 +++++++++++++++++ .../configs_bu0501/20230428_sbj01_eegnet.yaml | 84 +++++++++++++++++ .../20230429_sbj01_eegnet_regression.yaml | 84 +++++++++++++++++ .../20230501_all_eegnet_regression.yaml | 84 +++++++++++++++++ configs/configs_bu0501/analysis.yaml | 2 + configs/configs_bu0501/subjects/patternA.yaml | 4 + .../configs_bu0501/subjects/pattern_mini.yaml | 2 + .../subjects/pattern_sbj01.yaml | 2 + .../subjects/pattern_small.yaml | 3 + .../configs_bu0501/subjects/sbj01/sbj01.yaml | 86 ++++++++++++++++++ .../subjects/sbj01/sbj01_mini.yaml | 26 ++++++ .../subjects/sbj01/sbj01_small.yaml | 54 +++++++++++ .../subjects/sbj01/sbj01_train_split.yaml | 86 ++++++++++++++++++ .../configs_bu0501/subjects/sbj02/sbj02.yaml | 86 ++++++++++++++++++ .../subjects/sbj02/sbj02_small.yaml | 55 +++++++++++ .../configs_bu0501/subjects/sbj03/sbj03.yaml | 86 ++++++++++++++++++ configs/configs_bu0501/test.yaml | 60 ++++++++++++ eval_wowandb_cv.py | 33 +++++-- examples/view_training_curve.py | 1 + meg_decoding/dataclass/god.py | 4 +- out.png | Bin 109507 -> 0 bytes tmps/top5.csv | 51 +++++++++++ 36 files changed, 1893 insertions(+), 10 deletions(-) create mode 100644 configs/configs_bu0501/20230411.yaml create mode 100644 configs/configs_bu0501/20230412.yaml create mode 100644 configs/configs_bu0501/20230413_loc.yaml create mode 100644 configs/configs_bu0501/20230413_sbj01.yaml create mode 100644 configs/configs_bu0501/20230414_sbj01_seq2stat.yaml create mode 100644 configs/configs_bu0501/20230417_sbj01_seq2stat.yaml create mode 100644 configs/configs_bu0501/20230419_sbj01_seq2stat.yaml create mode 100644 configs/configs_bu0501/20230420_sbj01_linear.yaml create mode 100644 configs/configs_bu0501/20230420_sbj01_seq2stat.yaml create mode 100644 configs/configs_bu0501/20230421_sbj01_kamitani_regression.yaml create mode 100644 configs/configs_bu0501/20230423_sbj01_seq2stat_regression.yaml create mode 100644 configs/configs_bu0501/20230424_sbj01_seq2stat.yaml create mode 100644 configs/configs_bu0501/20230425_sbj01_seq2stat.yaml create mode 100644 configs/configs_bu0501/20230426_all_seq2stat.yaml create mode 100644 configs/configs_bu0501/20230427_sbj01_eegnet.yaml create mode 100644 configs/configs_bu0501/20230428_sbj01_eegnet.yaml create mode 100644 configs/configs_bu0501/20230429_sbj01_eegnet_regression.yaml create mode 100644 configs/configs_bu0501/20230501_all_eegnet_regression.yaml create mode 100644 configs/configs_bu0501/analysis.yaml create mode 100644 configs/configs_bu0501/subjects/patternA.yaml create mode 100644 configs/configs_bu0501/subjects/pattern_mini.yaml create mode 100644 configs/configs_bu0501/subjects/pattern_sbj01.yaml create mode 100644 configs/configs_bu0501/subjects/pattern_small.yaml create mode 100644 configs/configs_bu0501/subjects/sbj01/sbj01.yaml create mode 100644 configs/configs_bu0501/subjects/sbj01/sbj01_mini.yaml create mode 100644 configs/configs_bu0501/subjects/sbj01/sbj01_small.yaml create mode 100644 configs/configs_bu0501/subjects/sbj01/sbj01_train_split.yaml create mode 100644 configs/configs_bu0501/subjects/sbj02/sbj02.yaml create mode 100644 configs/configs_bu0501/subjects/sbj02/sbj02_small.yaml create mode 100644 configs/configs_bu0501/subjects/sbj03/sbj03.yaml create mode 100644 configs/configs_bu0501/test.yaml delete mode 100644 out.png create mode 100644 tmps/top5.csv diff --git a/configs/configs_bu0501/20230411.yaml b/configs/configs_bu0501/20230411.yaml new file mode 100644 index 0000000..ec1d1ef --- /dev/null +++ b/configs/configs_bu0501/20230411.yaml @@ -0,0 +1,60 @@ +defaults: + - subjects: patternA + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/test' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + +# ====== Time Region settings ====== +window: + start: 0.25 # [s] + end: 0.45 # [s] + +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + # brain_resample_rate: 120 # Hz + # brain_filter_low: 1.0 # Hz + # brain_filter_high: 60 # Hz + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + # clamp: True + # clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +z_scoring: true +rest_duration: 60 +num_workers: 6 +batch_size: 64 +updates: 1200 +lr: 3e-4 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +model: brain_encoder +D1: 270 +D2: 320 +F: 512 # NOTE: because if you set last4layers=False, then it's set to 1024 in the dataset class +K: 32 +d_drop: 0.1 # for spatial attention, drop channels within d_drop of a randomly selected channel +seq2seq: false # これは、MEGが時系列をもち、ペアデータが静止画の時に必要な操作 or 時系列モデルなら他の方法も可能 +init_temperature: 5.1 + +memory_efficient: True + diff --git a/configs/configs_bu0501/20230412.yaml b/configs/configs_bu0501/20230412.yaml new file mode 100644 index 0000000..b029d31 --- /dev/null +++ b/configs/configs_bu0501/20230412.yaml @@ -0,0 +1,60 @@ +defaults: + - subjects: pattern_mini + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230412_mini' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + +# ====== Time Region settings ====== +window: + start: 0.25 # [s] + end: 0.45 # [s] + +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + # brain_resample_rate: 120 # Hz + # brain_filter_low: 1.0 # Hz + # brain_filter_high: 60 # Hz + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + # clamp: True + # clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +z_scoring: true +rest_duration: 60 +num_workers: 6 +batch_size: 64 +updates: 1200 +lr: 3e-1 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +model: brain_encoder +D1: 270 +D2: 320 +F: 512 # NOTE: because if you set last4layers=False, then it's set to 1024 in the dataset class +K: 32 +d_drop: 0.1 # for spatial attention, drop channels within d_drop of a randomly selected channel +seq2seq: false # これは、MEGが時系列をもち、ペアデータが静止画の時に必要な操作 or 時系列モデルなら他の方法も可能 +init_temperature: 5.1 + +memory_efficient: True + diff --git a/configs/configs_bu0501/20230413_loc.yaml b/configs/configs_bu0501/20230413_loc.yaml new file mode 100644 index 0000000..44ee9ef --- /dev/null +++ b/configs/configs_bu0501/20230413_loc.yaml @@ -0,0 +1,64 @@ +defaults: + - subjects: pattern_mini + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230412_mini' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + - frontal/left + - frontal/right + - temporal/left + - temporal/right + - parietal/left + - parietal/right +# ====== Time Region settings ====== +window: + start: 0.25 # [s] + end: 0.45 # [s] + +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + # brain_resample_rate: 120 # Hz + # brain_filter_low: 1.0 # Hz + # brain_filter_high: 60 # Hz + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + # clamp: True + # clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +z_scoring: true +rest_duration: 60 +num_workers: 6 +batch_size: 64 +updates: 1200 +lr: 3e-1 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +D1: 270 +D2: 320 +F: 512 # NOTE: because if you set last4layers=False, then it's set to 1024 in the dataset class +K: 32 +d_drop: 0.1 # for spatial attention, drop channels within d_drop of a randomly selected channel +seq2seq: false # これは、MEGが時系列をもち、ペアデータが静止画の時に必要な操作 or 時系列モデルなら他の方法も可能 +init_temperature: 5.1 + +memory_efficient: True + diff --git a/configs/configs_bu0501/20230413_sbj01.yaml b/configs/configs_bu0501/20230413_sbj01.yaml new file mode 100644 index 0000000..3aa857b --- /dev/null +++ b/configs/configs_bu0501/20230413_sbj01.yaml @@ -0,0 +1,62 @@ +defaults: + - subjects: pattern_sbj01 + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230413_sbj01' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + +# ====== Time Region settings ====== +window: + start: 0.25 # [s] + end: 0.45 # [s] + +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + brain_resample_rate: null # Hz + brain_filter: null + # brain_filter_low: 1.0 # Hz + # brain_filter_high: 60 # Hz + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + # clamp: True + # clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +use_sampler: False +z_scoring: true +rest_duration: 60 +num_workers: 6 +batch_size: 64 +updates: 1200 +lr: 3e-1 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +model: brain_encoder +D1: 270 +D2: 320 +F: 512 # NOTE: because if you set last4layers=False, then it's set to 1024 in the dataset class +K: 32 +d_drop: 0.1 # for spatial attention, drop channels within d_drop of a randomly selected channel +seq2seq: false # これは、MEGが時系列をもち、ペアデータが静止画の時に必要な操作 or 時系列モデルなら他の方法も可能 +init_temperature: 5.1 + +memory_efficient: True + diff --git a/configs/configs_bu0501/20230414_sbj01_seq2stat.yaml b/configs/configs_bu0501/20230414_sbj01_seq2stat.yaml new file mode 100644 index 0000000..435ecb1 --- /dev/null +++ b/configs/configs_bu0501/20230414_sbj01_seq2stat.yaml @@ -0,0 +1,63 @@ +defaults: + - subjects: pattern_sbj01 + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230413_sbj01_seq2stat' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + +# ====== Time Region settings ====== +window: + start: 0.25 # [s] + end: 0.45 # [s] +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + brain_resample_rate: null # 120 # Hz + brain_filter: null + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + # clamp: True + # clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +use_sampler: false +z_scoring: true +rest_duration: 60 +num_workers: 6 +batch_size: 64 +updates: 1200 +lr: 3e-4 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +model: brain_endcoder_seq2static +ConvBlocks: + ks: [11, 11, 5, 3, 3] +avgpools: + ks: [2,2,2,2] +D1: 270 +D2: 320 +F: 512 # NOTE: because if you set last4layers=False, then it's set to 1024 in the dataset class +K: 32 +d_drop: 0.1 # for spatial attention, drop channels within d_drop of a randomly selected channel +seq2seq: false # これは、MEGが時系列をもち、ペアデータが静止画の時に必要な操作 or 時系列モデルなら他の方法も可能 +init_temperature: 5.1 + +memory_efficient: True + diff --git a/configs/configs_bu0501/20230417_sbj01_seq2stat.yaml b/configs/configs_bu0501/20230417_sbj01_seq2stat.yaml new file mode 100644 index 0000000..8e25474 --- /dev/null +++ b/configs/configs_bu0501/20230417_sbj01_seq2stat.yaml @@ -0,0 +1,67 @@ +defaults: + - subjects: pattern_sbj01 + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230417_sbj01_seq2stat' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + +# ====== Time Region settings ====== +window: + start: 0.05 # [s] + end: 0.5 # [s] +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + brain_resample_rate: 120 # 1000 # Hz + baseline_len_sec: 0.2 + brain_filter: [1.0, 60] # null is allowed + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + clamp: True + clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +use_sampler: true +z_scoring: false +rest_duration: 60 +num_workers: 6 +batch_size: 64 # original: 64, paper: 128 +updates: 1200 +lr: 3e-4 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +model: brain_endcoder_seq2static +ConvBlocks: + ks: [11, 11, 5, 3, 3] +avgpools: + ks: [2,2,2,2] +D1: 270 +D2: 320 +F: 512 # NOTE: because if you set last4layers=False, then it's set to 1024 in the dataset class +K: 32 +d_drop: 0.2 # for spatial attention, drop channels within d_drop of a randomly selected channel +seq2seq: false # これは、MEGが時系列をもち、ペアデータが静止画の時に必要な操作 or 時系列モデルなら他の方法も可能 +init_temperature: 5.1 +temp_trainable: true +criterion: crossentropy + + +memory_efficient: True + diff --git a/configs/configs_bu0501/20230419_sbj01_seq2stat.yaml b/configs/configs_bu0501/20230419_sbj01_seq2stat.yaml new file mode 100644 index 0000000..8c8297c --- /dev/null +++ b/configs/configs_bu0501/20230419_sbj01_seq2stat.yaml @@ -0,0 +1,66 @@ +defaults: + - subjects: pattern_sbj01 + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230419_sbj01_seq2stat2' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + +# ====== Time Region settings ====== +window: + start: 0.05 # [s] + end: 0.5 # [s] +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + brain_resample_rate: 120 # 1000 # Hz + baseline_len_sec: 0.2 + brain_filter: [1.0, 60] # null is allowed + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + clamp: True + clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +use_sampler: true +z_scoring: false +rest_duration: 60 +num_workers: 6 +batch_size: 64 # original: 64, paper: 128 +updates: 200 # 1200 +lr: 3e-4 # 3e-2 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +model: brain_endcoder_seq2static +ConvBlocks: + ks: [11, 11, 5, 3, 3] +avgpools: + ks: [2,2,2,2] +D1: 270 +D2: 320 +F: 512 # NOTE: because if you set last4layers=False, then it's set to 1024 in the dataset class +K: 32 +d_drop: 0.2 # for spatial attention, drop channels within d_drop of a randomly selected channel +seq2seq: false # これは、MEGが時系列をもち、ペアデータが静止画の時に必要な操作 or 時系列モデルなら他の方法も可能 +temp_trainable: false +init_temperature: 4.5 # 0 +criterion: binary_crossentropy # crossentropy + +memory_efficient: True + diff --git a/configs/configs_bu0501/20230420_sbj01_linear.yaml b/configs/configs_bu0501/20230420_sbj01_linear.yaml new file mode 100644 index 0000000..7bab7e2 --- /dev/null +++ b/configs/configs_bu0501/20230420_sbj01_linear.yaml @@ -0,0 +1,56 @@ +defaults: + - subjects: pattern_sbj01 + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230420_sbj01_linear' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + +# ====== Time Region settings ====== +window: + start: 0.25 # [s] + end: 0.45 # [s] +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + brain_resample_rate: 1000 # 1000 # Hz + baseline_len_sec: 0 + brain_filter: null # null is allowed + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + clamp: False + clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +use_sampler: true +z_scoring: false +rest_duration: 60 +num_workers: 6 +batch_size: 64 # original: 64, paper: 128 +updates: 200 # 1200 +lr: 3e-1 # 3e-2 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean +l2_weight: 1e-4 + +# ==== Architecture ==== # +model: linear +scp: true +channel_size: null + +memory_efficient: True + diff --git a/configs/configs_bu0501/20230420_sbj01_seq2stat.yaml b/configs/configs_bu0501/20230420_sbj01_seq2stat.yaml new file mode 100644 index 0000000..824177b --- /dev/null +++ b/configs/configs_bu0501/20230420_sbj01_seq2stat.yaml @@ -0,0 +1,66 @@ +defaults: + - subjects: pattern_sbj01 + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230420_sbj01_seq2stat2' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + +# ====== Time Region settings ====== +window: + start: 0.25 # [s] + end: 0.45 # [s] +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + brain_resample_rate: 240 # 1000 # Hz + baseline_len_sec: 0 + brain_filter: null # [1.0, 60] # null is allowed + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + clamp: False + clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +use_sampler: true +z_scoring: false +rest_duration: 60 +num_workers: 6 +batch_size: 64 # original: 64, paper: 128 +updates: 200 # 1200 +lr: 3e-3 # 3e-2 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +model: brain_endcoder_seq2static +ConvBlocks: + ks: [11, 11, 5, 3, 3] +avgpools: + ks: [2,2,2,2] +D1: 270 +D2: 320 +F: 512 # NOTE: because if you set last4layers=False, then it's set to 1024 in the dataset class +K: 32 +d_drop: 0.2 # for spatial attention, drop channels within d_drop of a randomly selected channel +seq2seq: false # これは、MEGが時系列をもち、ペアデータが静止画の時に必要な操作 or 時系列モデルなら他の方法も可能 +temp_trainable: true +init_temperature: 3 # 0 +criterion: similarity_crossentropy # crossentropy +normalize_image_features: True +memory_efficient: True + diff --git a/configs/configs_bu0501/20230421_sbj01_kamitani_regression.yaml b/configs/configs_bu0501/20230421_sbj01_kamitani_regression.yaml new file mode 100644 index 0000000..1838d4c --- /dev/null +++ b/configs/configs_bu0501/20230421_sbj01_kamitani_regression.yaml @@ -0,0 +1,65 @@ +defaults: + - subjects: pattern_sbj01 + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230421_sbj01_kamitani_regression' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + # - frontal/left + # - frontal/right + - temporal/left + - temporal/right + # - parietal/left + # - parietal/right + # - central/left + # - central/right + + +# ====== Time Region settings ====== +window: + start: 0.2 # [s] + end: 0.4 # [s] +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + brain_resample_rate: 120 # 1000 # Hz + baseline_len_sec: 0 + brain_filter: [2, 5] # null is allowed + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + clamp: False + clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +use_sampler: true +z_scoring: true +rest_duration: 60 +num_workers: 6 +batch_size: 64 # original: 64, paper: 128 +updates: 200 # 1200 +lr: 3e-1 # 3e-2 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean +l2_weight: 1e-4 + +# ==== Architecture ==== # +model: linear +scp: true +channel_size: null + +memory_efficient: True + diff --git a/configs/configs_bu0501/20230423_sbj01_seq2stat_regression.yaml b/configs/configs_bu0501/20230423_sbj01_seq2stat_regression.yaml new file mode 100644 index 0000000..86c4ca7 --- /dev/null +++ b/configs/configs_bu0501/20230423_sbj01_seq2stat_regression.yaml @@ -0,0 +1,68 @@ +defaults: + - subjects: patternA + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230423_sbj010203_seq2stats_regression' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + +# ====== Time Region settings ====== +window: + start: 0.25 # [s] + end: 0.45 # [s] +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + brain_resample_rate: 240 # 1000 # Hz + baseline_len_sec: 0 + brain_filter: null # [1.0, 60] # null is allowed + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + clamp: False + clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +use_sampler: true +z_scoring: true +rest_duration: 60 +num_workers: 6 +batch_size: 64 # original: 64, paper: 128 +updates: 200 # 1200 +lr: 3e-3 # 3e-2 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean +l2_weight: 1e-7 + +# ==== Architecture ==== # +model: brain_endcoder_seq2static +ConvBlocks: + ks: [11, 11, 5, 3, 3] +avgpools: + ks: [2,2,2,2] +D1: 270 +D2: 320 +F: 512 # NOTE: because if you set last4layers=False, then it's set to 1024 in the dataset class +K: 32 +d_drop: 0.2 # for spatial attention, drop channels within d_drop of a randomly selected channel +seq2seq: false # これは、MEGが時系列をもち、ペアデータが静止画の時に必要な操作 or 時系列モデルなら他の方法も可能 +temp_trainable: true +init_temperature: 3 # 0 +criterion: similarity_crossentropy # crossentropy +normalize_image_features: False +memory_efficient: True +channel_size: null # dummy not use + diff --git a/configs/configs_bu0501/20230424_sbj01_seq2stat.yaml b/configs/configs_bu0501/20230424_sbj01_seq2stat.yaml new file mode 100644 index 0000000..eb8ee2d --- /dev/null +++ b/configs/configs_bu0501/20230424_sbj01_seq2stat.yaml @@ -0,0 +1,76 @@ +defaults: + - subjects: pattern_sbj01 + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230424_sbj01_seq2stat' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + - frontal/left + - frontal/right + - temporal/left + - temporal/right + - parietal/left + - parietal/right + - central/left + - central/right + +# ====== Time Region settings ====== +window: + start: 0.25 # [s] + end: 0.45 # [s] +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + brain_resample_rate: 240 # 1000 # Hz + baseline_len_sec: 0 + brain_filter: null #[1.0, 60] # null is allowed + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + clamp: True + clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +use_sampler: true +z_scoring: true +rest_duration: 60 +num_workers: 6 +batch_size: 16 # original: 64, paper: 128 +updates: 1200 +lr: 3e-4 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +model: brain_endcoder_seq2static +ConvBlocks: + ks: [3, 3, 3, 3, 3] +avgpools: + ks: [2,2,2,2] +D1: 128 +D2: 256 +F: 512 # NOTE: because if you set last4layers=False, then it's set to 1024 in the dataset class +K: 32 +d_drop: 0.2 # for spatial attention, drop channels within d_drop of a randomly selected channel +seq2seq: false # これは、MEGが時系列をもち、ペアデータが静止画の時に必要な操作 or 時系列モデルなら他の方法も可能 +init_temperature: 5.1 +temp_trainable: true +criterion: crossentropy + +normalize_image_features: False +memory_efficient: True +channel_size: null # dummy not use + diff --git a/configs/configs_bu0501/20230425_sbj01_seq2stat.yaml b/configs/configs_bu0501/20230425_sbj01_seq2stat.yaml new file mode 100644 index 0000000..8af25fc --- /dev/null +++ b/configs/configs_bu0501/20230425_sbj01_seq2stat.yaml @@ -0,0 +1,77 @@ +defaults: + - subjects: pattern_sbj01 + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230425_sbj01_seq2stat_cv_norm_wo_dilation' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + - frontal/left + - frontal/right + - temporal/left + - temporal/right + - parietal/left + - parietal/right + - central/left + - central/right + +# ====== Time Region settings ====== +window: + start: 0.25 # [s] + end: 0.45 # [s] +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + brain_resample_rate: 240 # 1000 # Hz + baseline_len_sec: 0 + brain_filter: null #[1.0, 60] # null is allowed + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + clamp: True + clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +use_sampler: true +z_scoring: true +rest_duration: 60 +num_workers: 6 +batch_size: 16 # original: 64, paper: 128 +updates: 1200 +lr: 3e-4 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +model: brain_endcoder_seq2static +ConvBlocks: + ks: [5, 5, 3, 3, 3] +avgpools: + ks: [2,2,2,2] +D1: 128 +D2: 256 +F: 512 # NOTE: because if you set last4layers=False, then it's set to 1024 in the dataset class +K: 32 +d_drop: 0.2 # for spatial attention, drop channels within d_drop of a randomly selected channel +seq2seq: false # これは、MEGが時系列をもち、ペアデータが静止画の時に必要な操作 or 時系列モデルなら他の方法も可能 +init_temperature: 5.1 +temp_trainable: true +criterion: crossentropy + +normalize_image_features: True +normalize_meg: False +memory_efficient: True +channel_size: null # dummy not use + diff --git a/configs/configs_bu0501/20230426_all_seq2stat.yaml b/configs/configs_bu0501/20230426_all_seq2stat.yaml new file mode 100644 index 0000000..e89a1b5 --- /dev/null +++ b/configs/configs_bu0501/20230426_all_seq2stat.yaml @@ -0,0 +1,76 @@ +defaults: + - subjects: patternA + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230426_all_seq2stat_cv_norm_wo_dilation' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + - frontal/left + - frontal/right + - temporal/left + - temporal/right + - parietal/left + - parietal/right + - central/left + - central/right + +# ====== Time Region settings ====== +window: + start: 0.25 # [s] + end: 0.45 # [s] +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + brain_resample_rate: 240 # 1000 # Hz + baseline_len_sec: 0 + brain_filter: null #[1.0, 60] # null is allowed + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + clamp: True + clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +use_sampler: true +z_scoring: true +rest_duration: 60 +num_workers: 6 +batch_size: 256 # original: 64, paper: 128 +updates: 1200 +lr: 3e-4 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +model: brain_endcoder_seq2static +ConvBlocks: + ks: [5, 5, 3, 3, 3] +avgpools: + ks: [2,2,2,2] +D1: 128 +D2: 256 +F: 512 # NOTE: because if you set last4layers=False, then it's set to 1024 in the dataset class +K: 32 +d_drop: 0.1 # for spatial attention, drop channels within d_drop of a randomly selected channel +seq2seq: false # これは、MEGが時系列をもち、ペアデータが静止画の時に必要な操作 or 時系列モデルなら他の方法も可能 +init_temperature: 5.1 +temp_trainable: true +criterion: crossentropy + +normalize_image_features: True +memory_efficient: True +channel_size: null # dummy not use + diff --git a/configs/configs_bu0501/20230427_sbj01_eegnet.yaml b/configs/configs_bu0501/20230427_sbj01_eegnet.yaml new file mode 100644 index 0000000..7c5f4a9 --- /dev/null +++ b/configs/configs_bu0501/20230427_sbj01_eegnet.yaml @@ -0,0 +1,84 @@ +defaults: + - subjects: pattern_sbj01 + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230427_sbj01_eegnet_cv_norm' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + # - frontal/left + # - frontal/right + # - temporal/left + # - temporal/right + # - parietal/left + # - parietal/right + # - central/left + # - central/right + +# ====== Time Region settings ====== +window: + start: 0.2 # [s] + end: 0.4 # [s] +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + brain_resample_rate: 120 # 1000 # Hz + baseline_len_sec: 0 + brain_filter: [2,5] #[1.0, 60] # null is allowed + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + clamp: True + clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +use_sampler: false +z_scoring: true +rest_duration: 60 +num_workers: 6 +batch_size: 256 # original: 64, paper: 128 +updates: 200 +lr: 3e-4 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +model: eegnet +k1: 10 # 960 +k2: 4 # 96 +F1: 16 +F2: 32 +D: 2 +# p0: 10 +p1: 2 # 40 +p2: 4 # 80 +dr1: 0.50 +dr2: 0.75 +# for EEGNet_Multi +n_head: 8 +dim_per_head: 64 +fc1: 256 +fc2: 128 +# num_channels: 22 + +init_temperature: 5.1 +temp_trainable: true +criterion: crossentropy + +normalize_image_features: True +normalize_meg : True +memory_efficient: True +channel_size: null # dummy not use + diff --git a/configs/configs_bu0501/20230428_sbj01_eegnet.yaml b/configs/configs_bu0501/20230428_sbj01_eegnet.yaml new file mode 100644 index 0000000..5985b7b --- /dev/null +++ b/configs/configs_bu0501/20230428_sbj01_eegnet.yaml @@ -0,0 +1,84 @@ +defaults: + - subjects: pattern_sbj01 + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230428_sbj01_eegnet_cv_norm' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + # - frontal/left + # - frontal/right + # - temporal/left + # - temporal/right + # - parietal/left + # - parietal/right + # - central/left + # - central/right + +# ====== Time Region settings ====== +window: + start: 0.2 # [s] + end: 0.4 # [s] +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + brain_resample_rate: 120 # 1000 # Hz + baseline_len_sec: 0 + brain_filter: [2,5] #[1.0, 60] # null is allowed + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + clamp: True + clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +use_sampler: false +z_scoring: true +rest_duration: 60 +num_workers: 6 +batch_size: 256 # original: 64, paper: 128 +updates: 200 +lr: 3e-4 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +model: eegnet +k1: 10 # 960 +k2: 4 # 96 +F1: 16 +F2: 32 +D: 2 +# p0: 10 +p1: 2 # 40 +p2: 4 # 80 +dr1: 0.25 +dr2: 0.38 +# for EEGNet_Multi +n_head: 8 +dim_per_head: 64 +fc1: 256 +fc2: 128 +# num_channels: 22 + +init_temperature: 5.1 +temp_trainable: true +criterion: crossentropy + +normalize_image_features: True +normalize_meg : True +memory_efficient: True +channel_size: null # dummy not use + diff --git a/configs/configs_bu0501/20230429_sbj01_eegnet_regression.yaml b/configs/configs_bu0501/20230429_sbj01_eegnet_regression.yaml new file mode 100644 index 0000000..f1a1e07 --- /dev/null +++ b/configs/configs_bu0501/20230429_sbj01_eegnet_regression.yaml @@ -0,0 +1,84 @@ +defaults: + - subjects: pattern_sbj01 + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230429_sbj01_eegnet_cv_norm_regression' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + # - frontal/left + # - frontal/right + # - temporal/left + # - temporal/right + # - parietal/left + # - parietal/right + # - central/left + # - central/right + +# ====== Time Region settings ====== +window: + start: 0.2 # [s] + end: 0.4 # [s] +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + brain_resample_rate: 120 # 1000 # Hz + baseline_len_sec: 0 + brain_filter: [2,5] #[1.0, 60] # null is allowed + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + clamp: True + clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +use_sampler: false +z_scoring: true +rest_duration: 60 +num_workers: 6 +batch_size: 256 # original: 64, paper: 128 +updates: 200 +lr: 3e-4 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +model: eegnet +k1: 10 # 960 +k2: 4 # 96 +F1: 16 +F2: 32 +D: 2 +# p0: 10 +p1: 2 # 40 +p2: 4 # 80 +dr1: 0.50 +dr2: 0.75 +# for EEGNet_Multi +n_head: 8 +dim_per_head: 64 +fc1: 256 +fc2: 128 +# num_channels: 22 + +init_temperature: 5.1 +temp_trainable: true +criterion: crossentropy + +normalize_image_features: True +normalize_meg : True +memory_efficient: True +channel_size: null # dummy not use + diff --git a/configs/configs_bu0501/20230501_all_eegnet_regression.yaml b/configs/configs_bu0501/20230501_all_eegnet_regression.yaml new file mode 100644 index 0000000..d9ffffb --- /dev/null +++ b/configs/configs_bu0501/20230501_all_eegnet_regression.yaml @@ -0,0 +1,84 @@ +defaults: + - subjects: patternA + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/20230429_sbj01_eegnet_cv_norm_regression' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + # - frontal/left + # - frontal/right + # - temporal/left + # - temporal/right + # - parietal/left + # - parietal/right + # - central/left + # - central/right + +# ====== Time Region settings ====== +window: + start: 0.2 # [s] + end: 0.4 # [s] +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + brain_resample_rate: 120 # 1000 # Hz + baseline_len_sec: 0 + brain_filter: [2,5] #[1.0, 60] # null is allowed + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + clamp: True + clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +use_sampler: false +z_scoring: true +rest_duration: 60 +num_workers: 6 +batch_size: 256 # original: 64, paper: 128 +updates: 200 +lr: 3e-4 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +model: eegnet_sub +k1: 10 # 960 +k2: 4 # 96 +F1: 16 +F2: 32 +D: 2 +# p0: 10 +p1: 2 # 40 +p2: 4 # 80 +dr1: 0.50 +dr2: 0.75 +# for EEGNet_Multi +n_head: 8 +dim_per_head: 64 +fc1: 256 +fc2: 128 +# num_channels: 22 + +init_temperature: 5.1 +temp_trainable: true +criterion: crossentropy + +normalize_image_features: True +normalize_meg : True +memory_efficient: True +channel_size: null # dummy not use + diff --git a/configs/configs_bu0501/analysis.yaml b/configs/configs_bu0501/analysis.yaml new file mode 100644 index 0000000..cee8733 --- /dev/null +++ b/configs/configs_bu0501/analysis.yaml @@ -0,0 +1,2 @@ +defaults: + - subjects: patternA diff --git a/configs/configs_bu0501/subjects/patternA.yaml b/configs/configs_bu0501/subjects/patternA.yaml new file mode 100644 index 0000000..51f6aaf --- /dev/null +++ b/configs/configs_bu0501/subjects/patternA.yaml @@ -0,0 +1,4 @@ +defaults: + - sbj01: sbj01 + - sbj02: sbj02 + - sbj03: sbj03 \ No newline at end of file diff --git a/configs/configs_bu0501/subjects/pattern_mini.yaml b/configs/configs_bu0501/subjects/pattern_mini.yaml new file mode 100644 index 0000000..c1bf80b --- /dev/null +++ b/configs/configs_bu0501/subjects/pattern_mini.yaml @@ -0,0 +1,2 @@ +defaults: + - sbj01: sbj01_mini diff --git a/configs/configs_bu0501/subjects/pattern_sbj01.yaml b/configs/configs_bu0501/subjects/pattern_sbj01.yaml new file mode 100644 index 0000000..6f13980 --- /dev/null +++ b/configs/configs_bu0501/subjects/pattern_sbj01.yaml @@ -0,0 +1,2 @@ +defaults: + - sbj01: sbj01 \ No newline at end of file diff --git a/configs/configs_bu0501/subjects/pattern_small.yaml b/configs/configs_bu0501/subjects/pattern_small.yaml new file mode 100644 index 0000000..8836462 --- /dev/null +++ b/configs/configs_bu0501/subjects/pattern_small.yaml @@ -0,0 +1,3 @@ +defaults: + - sbj01: sbj01_small + - sbj02: sbj02_small diff --git a/configs/configs_bu0501/subjects/sbj01/sbj01.yaml b/configs/configs_bu0501/subjects/sbj01/sbj01.yaml new file mode 100644 index 0000000..504ffe5 --- /dev/null +++ b/configs/configs_bu0501/subjects/sbj01/sbj01.yaml @@ -0,0 +1,86 @@ +# === experiment metadata === # +subject: sbj01 +fs: 1000 +train: + labels: + - ses_1.mat + - ses_2.mat + - ses_3.mat + - ses_4.mat + - ses_5.mat + - ses_6.mat + - ses_7.mat + - ses_8.mat + - ses_9.mat + - ses_10.mat + - ses_11.mat + - ses_12.mat + mat: + - data_block001.mat + - data_block002.mat + - data_block003.mat + - data_block004.mat + - data_block005.mat + - data_block006.mat + - data_block007.mat + - data_block008.mat + - data_block009.mat + - data_block010.mat + - data_block011.mat + - data_block012.mat + trigger: + - ses01.mat + - ses02.mat + - ses03.mat + - ses04.mat + - ses05.mat + - ses06.mat + - ses07.mat + - ses08.mat + - ses09.mat + - ses10.mat + - ses11.mat + - ses12.mat + rest: + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat +val: + labels: + - val_1.mat + - val_2.mat + - val_3.mat + - val_4.mat + - val_5.mat + - val_6.mat + mat: + - data_val001.mat + - data_val002.mat + - data_val003.mat + - data_val004.mat + - data_val005.mat + - data_val006.mat + + trigger: + - val01.mat + - val02.mat + - val03.mat + - val04.mat + - val05.mat + - val06.mat + rest: + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat diff --git a/configs/configs_bu0501/subjects/sbj01/sbj01_mini.yaml b/configs/configs_bu0501/subjects/sbj01/sbj01_mini.yaml new file mode 100644 index 0000000..4989c7c --- /dev/null +++ b/configs/configs_bu0501/subjects/sbj01/sbj01_mini.yaml @@ -0,0 +1,26 @@ +# === experiment metadata === # +subject: sbj01 +fs: 1000 +train: + labels: + - ses_1.mat + mat: + - data_block001.mat + trigger: + - ses01.mat + rest: + - data_rest001.mat +val: + labels: + - val_1.mat + - val_2.mat + mat: + - data_val001.mat + - data_val002.mat + + trigger: + - val01.mat + - val02.mat + rest: + - data_rest001.mat + - data_rest001.mat \ No newline at end of file diff --git a/configs/configs_bu0501/subjects/sbj01/sbj01_small.yaml b/configs/configs_bu0501/subjects/sbj01/sbj01_small.yaml new file mode 100644 index 0000000..fe0bca5 --- /dev/null +++ b/configs/configs_bu0501/subjects/sbj01/sbj01_small.yaml @@ -0,0 +1,54 @@ +# === experiment metadata === # +subject: sbj01 +fs: 1000 +train: + labels: + - ses_1.mat + - ses_2.mat + - ses_3.mat + - ses_4.mat + mat: + - data_block001.mat + - data_block002.mat + - data_block003.mat + - data_block004.mat + trigger: + - ses01.mat + - ses02.mat + - ses03.mat + - ses04.mat + rest: + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat +val: + labels: + - val_1.mat + - val_2.mat + - val_3.mat + - val_4.mat + - val_5.mat + - val_6.mat + mat: + - data_val001.mat + - data_val002.mat + - data_val003.mat + - data_val004.mat + - data_val005.mat + - data_val006.mat + + trigger: + - val01.mat + - val02.mat + - val03.mat + - val04.mat + - val05.mat + - val06.mat + rest: + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat \ No newline at end of file diff --git a/configs/configs_bu0501/subjects/sbj01/sbj01_train_split.yaml b/configs/configs_bu0501/subjects/sbj01/sbj01_train_split.yaml new file mode 100644 index 0000000..85f08d1 --- /dev/null +++ b/configs/configs_bu0501/subjects/sbj01/sbj01_train_split.yaml @@ -0,0 +1,86 @@ +# === experiment metadata === # +subject: sbj01 +fs: 1000 +train: + labels: + - ses_1.mat + - ses_2.mat + - ses_3.mat + - ses_4.mat + - ses_5.mat + - ses_6.mat + - ses_7.mat + - ses_8.mat + - ses_9.mat + - ses_10.mat + # - ses_11.mat + # - ses_12.mat + mat: + - data_block001.mat + - data_block002.mat + - data_block003.mat + - data_block004.mat + - data_block005.mat + - data_block006.mat + - data_block007.mat + - data_block008.mat + - data_block009.mat + - data_block010.mat + # - data_block011.mat + # - data_block012.mat + trigger: + - ses01.mat + - ses02.mat + - ses03.mat + - ses04.mat + - ses05.mat + - ses06.mat + - ses07.mat + - ses08.mat + - ses09.mat + - ses10.mat + # - ses11.mat + # - ses12.mat + rest: + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat + # - data_rest002.mat + # - data_rest002.mat +val: + labels: + - val_1.mat + - val_2.mat + - val_3.mat + - val_4.mat + - val_5.mat + - val_6.mat + mat: + - data_val001.mat + - data_val002.mat + - data_val003.mat + - data_val004.mat + - data_val005.mat + - data_val006.mat + + trigger: + - val01.mat + - val02.mat + - val03.mat + - val04.mat + - val05.mat + - val06.mat + rest: + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat diff --git a/configs/configs_bu0501/subjects/sbj02/sbj02.yaml b/configs/configs_bu0501/subjects/sbj02/sbj02.yaml new file mode 100644 index 0000000..013a6f4 --- /dev/null +++ b/configs/configs_bu0501/subjects/sbj02/sbj02.yaml @@ -0,0 +1,86 @@ +# === experiment metadata === # +subject: sbj02 +fs: 1000 +train: + labels: + - ses_1.mat + - ses_2.mat + - ses_3.mat + - ses_4.mat + - ses_5.mat + - ses_6.mat + - ses_7.mat + - ses_8.mat + - ses_9.mat + - ses_10.mat + - ses_11.mat + - ses_12.mat + mat: + - data_block001.mat + - data_block002.mat + - data_block003.mat + - data_block004.mat + - data_block005.mat + - data_block006.mat + - data_block007.mat + - data_block008.mat + - data_block009.mat + - data_block010.mat + - data_block011.mat + - data_block012.mat + trigger: + - ses01.mat + - ses02.mat + - ses03.mat + - ses04.mat + - ses05.mat + - ses06.mat + - ses07.mat + - ses08.mat + - ses09.mat + - ses10.mat + - ses11.mat + - ses12.mat + rest: + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat +val: + labels: + - val_1.mat + - val_2.mat + - val_3.mat + - val_4.mat + - val_5.mat + - val_6.mat + mat: + - data_val001.mat + - data_val002.mat + - data_val003.mat + - data_val004.mat + - data_val005.mat + - data_val006.mat + + trigger: + - val01.mat + - val02.mat + - val03.mat + - val04.mat + - val05.mat + - val06.mat + rest: + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat \ No newline at end of file diff --git a/configs/configs_bu0501/subjects/sbj02/sbj02_small.yaml b/configs/configs_bu0501/subjects/sbj02/sbj02_small.yaml new file mode 100644 index 0000000..39f1a1e --- /dev/null +++ b/configs/configs_bu0501/subjects/sbj02/sbj02_small.yaml @@ -0,0 +1,55 @@ +# === experiment metadata === # +subject: sbj02 +fs: 1000 +train: + labels: + - ses_1.mat + - ses_2.mat + - ses_3.mat + - ses_4.mat + mat: + - data_block001.mat + - data_block002.mat + - data_block003.mat + - data_block004.mat + trigger: + - ses01.mat + - ses02.mat + - ses03.mat + - ses04.mat + rest: + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat +val: + labels: + - val_1.mat + - val_2.mat + - val_3.mat + - val_4.mat + - val_5.mat + - val_6.mat + mat: + - data_val001.mat + - data_val002.mat + - data_val003.mat + - data_val004.mat + - data_val005.mat + - data_val006.mat + + trigger: + - val01.mat + - val02.mat + - val03.mat + - val04.mat + - val05.mat + - val06.mat + + rest: + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat \ No newline at end of file diff --git a/configs/configs_bu0501/subjects/sbj03/sbj03.yaml b/configs/configs_bu0501/subjects/sbj03/sbj03.yaml new file mode 100644 index 0000000..0862716 --- /dev/null +++ b/configs/configs_bu0501/subjects/sbj03/sbj03.yaml @@ -0,0 +1,86 @@ +# === experiment metadata === # +subject: sbj03 +fs: 1000 +train: + labels: + - ses_1.mat + - ses_2.mat + - ses_3.mat + - ses_4.mat + - ses_5.mat + - ses_6.mat + - ses_7.mat + - ses_8.mat + - ses_9.mat + - ses_10.mat + - ses_11.mat + - ses_12.mat + mat: + - data_block001.mat + - data_block002.mat + - data_block003.mat + - data_block004.mat + - data_block005.mat + - data_block006.mat + - data_block007.mat + - data_block008.mat + - data_block009.mat + - data_block010.mat + - data_block011.mat + - data_block012.mat + trigger: + - ses01.mat + - ses02.mat + - ses03.mat + - ses04.mat + - ses05.mat + - ses06.mat + - ses07.mat + - ses08.mat + - ses09.mat + - ses10.mat + - ses11.mat + - ses12.mat + rest: + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat +val: + labels: + - val_1.mat + - val_2.mat + - val_3.mat + - val_4.mat + - val_5.mat + - val_6.mat + mat: + - data_val001.mat + - data_val002.mat + - data_val003.mat + - data_val004.mat + - data_val005.mat + - data_val006.mat + + trigger: + - val01.mat + - val02.mat + - val03.mat + - val04.mat + - val05.mat + - val06.mat + rest: + - data_rest001.mat + - data_rest001.mat + - data_rest001.mat + - data_rest002.mat + - data_rest002.mat + - data_rest002.mat \ No newline at end of file diff --git a/configs/configs_bu0501/test.yaml b/configs/configs_bu0501/test.yaml new file mode 100644 index 0000000..027bd0a --- /dev/null +++ b/configs/configs_bu0501/test.yaml @@ -0,0 +1,60 @@ +defaults: + - subjects: pattern_small + + +# ====== Path settings ======= +data_root: '/work/project/MEG_GOD/GOD_dataset/' +save_root: '/home/yainoue/meg2image/results/test' +montage_path: './data/GOD/montage.csv' +ch_region_path: './data/GOD/ch_region.json' +root_dir: # dummy +# ====== Spatial Region settings ======= +region: + - occipital/left + - occipital/right + +# ====== Time Region settings ====== +window: + start: 0.25 # [s] + end: 0.45 # [s] + +baseline: rest + +# == Data pre-processing parameters === # +preprocs: + # brain_resample_rate: 120 # Hz + # brain_filter_low: 1.0 # Hz + # brain_filter_high: 60 # Hz + last4layers: False # if True, the brain_encoder's emsize will be 1024, not 512 + # subject_wise: True # whether to scale each subject's EEG dataset individually (only for Brennan2018) + # clamp: True + # clamp_lim: 20 + +# ===== Training settings ==== +use_wandb: false +reproducible: true +dataset: GOD +z_scoring: true +rest_duration: 60 +num_workers: 6 +batch_size: 64 +updates: 1200 +lr: 3e-4 +lr_scheduler: none # cosine or multistep or none +lr_multistep_mlstns: [0.4, 0.6, 0.8, 0.9] +lr_step_gamma: 0.5 +epochs: 300 +reduction: mean + +# ==== Architecture ==== # +model: brain_encoder +D1: 270 +D2: 320 +F: 512 # NOTE: because if you set last4layers=False, then it's set to 1024 in the dataset class +K: 32 +d_drop: 0.1 # for spatial attention, drop channels within d_drop of a randomly selected channel +seq2seq: false # これは、MEGが時系列をもち、ペアデータが静止画の時に必要な操作 or 時系列モデルなら他の方法も可能 +init_temperature: 5.1 + +memory_efficient: True + diff --git a/eval_wowandb_cv.py b/eval_wowandb_cv.py index 9ad905a..f6b1a45 100644 --- a/eval_wowandb_cv.py +++ b/eval_wowandb_cv.py @@ -6,7 +6,7 @@ from tqdm import tqdm, trange from termcolor import cprint # import wandb - +import pandas as pd from omegaconf import DictConfig, open_dict import hydra from hydra.utils import get_original_cwd @@ -138,8 +138,8 @@ def run(args: DictConfig) -> None: ) elif args.dataset == "GOD": - source_dataset = GODDatasetBase(args, 'train') - outlier_dataset = GODDatasetBase(args, 'val', + source_dataset = GODDatasetBase(args, 'train', return_label=True) + outlier_dataset = GODDatasetBase(args, 'val', return_label=True, mean_X= source_dataset.mean_X, mean_Y=source_dataset.mean_Y, std_X=source_dataset.std_X, @@ -243,9 +243,6 @@ def run(args: DictConfig) -> None: X, Y, subject_idxs = batch elif len(batch) == 4: X, Y, subject_idxs, chunkIDs = batch - assert ( - len(chunkIDs.unique()) == X.shape[0] - ), "Duplicate segments in batch are not allowed. Aborting." else: raise ValueError("Unexpected number of items from dataloader.") @@ -261,6 +258,7 @@ def run(args: DictConfig) -> None: Zs = [] Ys = [] + Ls = [] brain_encoder.eval() for batch in test_loader: with torch.no_grad(): @@ -268,7 +266,7 @@ def run(args: DictConfig) -> None: if len(batch) == 3: X, Y, subject_idxs = batch elif len(batch) == 4: - X, Y, subject_idxs, chunkIDs = batch + X, Y, subject_idxs, Labels = batch else: raise ValueError("Unexpected number of items from dataloader.") @@ -277,6 +275,7 @@ def run(args: DictConfig) -> None: Z = brain_encoder(X, subject_idxs) # 0.96 GB Zs.append(Z) Ys.append(Y) + Ls.append(Labels) loss = loss_func(Y, Z) @@ -287,6 +286,7 @@ def run(args: DictConfig) -> None: testTop10accs.append(testTop10acc) Zs = torch.cat(Zs, dim=0) Ys = torch.cat(Ys, dim=0) + Ls = torch.cat(Ls, dim=0).detach().cpu().numpy() print( f"train l: {np.mean(train_losses):.3f} | ", @@ -350,8 +350,24 @@ def run(args: DictConfig) -> None: plt.savefig(os.path.join(args.save_root, 'std_vs_tp.png'),bbox_inches='tight') similarity = calc_similarity(Zs, Ys) + # output top5 similarity + top5_similarity = {'query_image_id':[], 'acc(scene_id)':[], + 'top1_image_id':[], 'top2_image_id':[], 'top3_image_id':[], 'top4_image_id':[], 'top5_image_id':[]} + acc_per_sample = np.round((mat>0).sum(axis=1)/49, decimals=3) + for i, l in enumerate(Ls): + sim_vec = similarity[i,:] + top5_similarity['query_image_id'].append(l) + top5_similarity['acc(scene_id)'].append(acc_per_sample[i]) + ranking = np.argsort(sim_vec)[::-1][:5] + 1 # 1始まりにする + for k in range(1,6): + key = f'top{k}_image_id' + top5_similarity[key].append(ranking[k-1]) + top5_similarity = pd.DataFrame(top5_similarity) + top5_similarity.to_csv(os.path.join(args.save_root, 'top5.csv')) import pdb; pdb.set_trace() + + def boxplot_and_plot(bp_array, plot_array_label, ax): # array: n_image x dims(512) plot_array = bp_array[plot_array_label] @@ -393,7 +409,7 @@ def vis_confusion_mat(mat, acc, savefile=None): sns.heatmap(mat, square=True, annot=False) plt.xlabel('database data') plt.ylabel('query data') - plt.title('similaruty acc: {}'.format(acc)) + plt.title('similarity acc: {}'.format(acc)) plt.savefig(savefile) plt.close() print('saved to ', savefile) @@ -403,6 +419,7 @@ def vis_confusion_mat(mat, acc, savefile=None): from hydra import initialize, compose with initialize(version_base=None, config_path="../configs/"): args = compose(config_name='20230429_sbj01_eegnet_regression') + # args = compose(config_name='20230501_all_eegnet_regression') # args = compose(config_name='20230425_sbj01_seq2stat') if not os.path.exists(os.path.join(args.save_root, 'weights')): os.makedirs(os.path.join(args.save_root, 'weights')) diff --git a/examples/view_training_curve.py b/examples/view_training_curve.py index 2ece911..bc41a5f 100644 --- a/examples/view_training_curve.py +++ b/examples/view_training_curve.py @@ -76,4 +76,5 @@ def parse_data(filepath): filepath = '/home/yainoue/meg2image/results/20230427_sbj01_eegnet_cv_norm/runs/2023-04-28 03:53:04.866741' filepath = '/home/yainoue/meg2image/results/20230428_sbj01_eegnet_cv_norm/runs/2023-04-28 21:05:42.739003' filepath = '/home/yainoue/meg2image/results/20230429_sbj01_eegnet_cv_norm_regression/runs/2023-04-30 04:16:13.736655' + filepath = '/home/yainoue/meg2image/results/20230501_all_eegnet_cv_norm_regression/runs/2023-05-01 17:01:17.163323' parse_data(filepath) diff --git a/meg_decoding/dataclass/god.py b/meg_decoding/dataclass/god.py index 6c34a0b..4dbc9f9 100644 --- a/meg_decoding/dataclass/god.py +++ b/meg_decoding/dataclass/god.py @@ -30,7 +30,7 @@ def normalize_per_unit(tensor, return_stats=False): return tensor class GODDatasetBase(Dataset): - def __init__(self, args, split, preprocess_pipleine:list=[], return_label:bool=False, + def __init__(self, args, split, preprocess_pipleine:list=[], return_label:bool=False, mean_X=None, mean_Y=None, std_X=None, std_Y=None): self.args = args self.sub_id_map = {s:i for i, s in enumerate(list(self.args.subjects.keys()))} @@ -70,7 +70,7 @@ def __init__(self, args, split, preprocess_pipleine:list=[], return_label:bool=F if split == 'val': self.X, self.Y, self.subs, self.labels = self.avg_same_image_sub_epochs(self.X, self.Y, self.subs, self.labels) - + self.labels = self.labels.astype(np.int16) self.num_subjects = len(np.unique(self.subs)) self.return_label = return_label diff --git a/out.png b/out.png deleted file mode 100644 index 7bbca962917a2e4f68adbce6ae3d8fe6e4eaacf1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 109507 zcmeFZ=+-Q@Xpm`yTv$?!Dvw z1^3PG1w+Ojo3-bfYsNF5IhWz8%CZ=!B&YxYV933FqXqzA2mpYVkr81}ct_{gVgCra zNo%{QJ6gJVnz&d1N+xbjc8+d#)}~Y*7A~&Vjt<=HyzE@8R90?oPOd^69QOau3+#?A zA2^u#D5YVGpg6tNaRmS@ljk4M5Ai~400aPXZzMIm()S-c)AhIfmm*tL(hO_)&*gbg zS!tNQeznr+G>EY47j;?vdcS@x6jQGEVrji=s-Y&(gN)&@#;&_6!oK$_osI@AQ#d*N z=S2dGO8*ZZJbgqr>H|eSSbBi4p&ua?BVtEWXypb2>4InJu9jDRmuL2=*izZ5jCiOa 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+20,21,0.857,7,11,12,1,4 +21,22,0.551,50,43,39,37,46 +22,23,0.796,49,48,30,40,45 +23,24,0.551,11,7,10,4,12 +24,25,0.612,4,10,7,3,2 +25,26,0.878,17,42,24,39,36 +26,27,0.633,9,3,2,15,13 +27,28,0.531,43,50,29,49,30 +28,29,0.612,15,3,2,9,10 +29,30,0.939,50,45,49,30,37 +30,31,0.429,21,36,50,45,46 +31,32,0.694,7,11,4,3,10 +32,33,0.531,15,2,43,9,8 +33,34,0.714,12,7,11,35,6 +34,35,0.98,40,35,25,33,41 +35,36,0.857,50,42,49,48,43 +36,37,0.918,50,40,47,18,37 +37,38,0.837,49,50,30,48,42 +38,39,0.388,50,37,46,47,43 +39,40,1.0,40,35,6,33,23 +40,41,0.816,50,42,49,48,18 +41,42,0.796,8,1,7,4,9 +42,43,0.49,42,49,50,48,30 +43,44,0.531,2,15,13,3,10 +44,45,0.918,50,46,18,37,45 +45,46,0.959,50,48,46,37,42 +46,47,0.776,50,42,49,36,43 +47,48,0.816,40,35,7,11,6 +48,49,0.571,40,48,35,42,23 +49,50,1.0,50,18,47,37,46 From 19dbd96b25e1f26fc1c180b93909f28f4028f293 Mon Sep 17 00:00:00 2001 From: inoue26 Date: Wed, 10 May 2023 04:11:53 +0900 Subject: [PATCH 50/71] pred from database including imagenet cal --- .gitignore | 3 +- eval_wowandb_cv.py | 2 +- eval_wowandb_cv_imagenet_val.py | 483 +++++++++++++++++++++++ examples/create_imagenet_features.py | 52 +++ notebooks/create_imagenet_features.ipynb | 118 ++++++ tmps/top5_with_imagenet_val-0.png | Bin 0 -> 1314757 bytes tmps/top5_with_imagenet_val-1.png | Bin 0 -> 1084131 bytes tmps/top5_with_imagenet_val-2.png | Bin 0 -> 1222679 bytes tmps/top5_with_imagenet_val-3.png | Bin 0 -> 1073518 bytes tmps/top5_with_imagenet_val-4.png | Bin 0 -> 914681 bytes tmps/top5_with_imagenet_val.csv | 51 +++ 11 files changed, 707 insertions(+), 2 deletions(-) create mode 100644 eval_wowandb_cv_imagenet_val.py create mode 100644 examples/create_imagenet_features.py create mode 100644 notebooks/create_imagenet_features.ipynb create mode 100644 tmps/top5_with_imagenet_val-0.png create mode 100644 tmps/top5_with_imagenet_val-1.png create mode 100644 tmps/top5_with_imagenet_val-2.png create mode 100644 tmps/top5_with_imagenet_val-3.png create mode 100644 tmps/top5_with_imagenet_val-4.png create mode 100644 tmps/top5_with_imagenet_val.csv diff --git a/.gitignore b/.gitignore index 48e00e3..a6730ab 100644 --- a/.gitignore +++ b/.gitignore @@ -38,4 +38,5 @@ train.sh *_debug* nohup.out logs/* -data/GOD/*.npy \ No newline at end of file +data/GOD/*.npy +data/ImageNet/* \ No newline at end of file diff --git a/eval_wowandb_cv.py b/eval_wowandb_cv.py index f6b1a45..8993682 100644 --- a/eval_wowandb_cv.py +++ b/eval_wowandb_cv.py @@ -318,7 +318,7 @@ def run(args: DictConfig) -> None: N=1 plot_array_label = np.argsort(miss_detection)[::-1][:N] plot_array_value = np.sort(miss_detection)[::-1][:N] - print('miss detaction id', plot_array_label) + print('miss detection id', plot_array_label) print('miss detection value', plot_array_value) fig, axes = plt.subplots(nrows=2, figsize=(32,8)) boxplot_and_plot(Zs.detach().cpu().numpy(), plot_array_label, axes[0]) diff --git a/eval_wowandb_cv_imagenet_val.py b/eval_wowandb_cv_imagenet_val.py new file mode 100644 index 0000000..8a866f2 --- /dev/null +++ b/eval_wowandb_cv_imagenet_val.py @@ -0,0 +1,483 @@ +import os, sys, random +import numpy as np +import torch +import torch.nn as nn +from time import time +from tqdm import tqdm, trange +from termcolor import cprint +# import wandb +import pandas as pd +from omegaconf import DictConfig, open_dict +import hydra +from hydra.utils import get_original_cwd + +from constants import device +# from speech_decoding.dataclass.brennan2018 import Brennan2018Dataset +# from speech_decoding.dataclass.gwilliams2022 import ( +# Gwilliams2022SentenceSplit, +# Gwilliams2022ShallowSplit, +# Gwilliams2022DeepSplit, +# Gwilliams2022Collator, +# ) +from torch.utils.data import DataLoader, RandomSampler, BatchSampler + +from meg_decoding.models import get_model, Classifier +from meg_decoding.utils.get_dataloaders import get_dataloaders, get_samplers +from meg_decoding.utils.loss import * +from meg_decoding.dataclass.god import GODDatasetBase, GODCollator +from meg_decoding.utils.loggers import Pickleogger +from meg_decoding.utils.vis_grad import get_grad +from torch.utils.data.dataset import Subset +import matplotlib.pyplot as plt +import seaborn as sns +import pickle +from PIL import Image + +def run(args: DictConfig) -> None: + + from meg_decoding.utils.reproducibility import seed_worker + # NOTE: We do need it (IMHO). + if args.reproducible: + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + torch.use_deterministic_algorithms(True) + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + g = torch.Generator() + g.manual_seed(0) + seed_worker = seed_worker + else: + g = None + seed_worker = None + + pkl_logger = Pickleogger(os.path.join(args.save_root, 'runs')) + + # with open_dict(args): + # args.root_dir = get_original_cwd() + cprint(f"Current working directory : {os.getcwd()}") + cprint(args, color="white") + + # ----------------------- + # Dataloader + # ----------------------- + # NOTE: Segmentation should always be by word onsets, not just every 3 seconds + if args.dataset == "Gwilliams2022": + + if args.split_mode == "sentence": + + train_set = Gwilliams2022SentenceSplit(args) + test_set = Gwilliams2022SentenceSplit(args, train_set.test_word_idxs_dict) + + assert train_set.num_subjects == test_set.num_subjects + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + elif args.split_mode == "shallow": + + dataset = Gwilliams2022ShallowSplit(args) + + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(dataset.Y.shape[0] * args.split_ratio) + test_size = dataset.Y.shape[0] - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + + elif args.split_mode == "deep": + + train_set = Gwilliams2022DeepSplit(args, train=True) + test_set = Gwilliams2022DeepSplit(args, train=False) + assert train_set.num_subjects == test_set.num_subjects + + with open_dict(args): + args.num_subjects = train_set.num_subjects + + test_size = test_set.Y.shape[0] + + cprint(f"Test segments: {test_size}", "cyan") + + if args.use_sampler: + # NOTE: currently not supporting reproducibility + train_loader, test_loader = get_samplers( + train_set, + test_set, + args, + test_bsz=test_size, + collate_fn=Gwilliams2022Collator(args), + ) + else: + # FIXME: maybe either get rid of reproducibility, or remove this? + if args.reproducible: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, seed_worker, g, test_bsz=test_size + ) + else: + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, test_bsz=test_size + ) + + elif args.dataset == "Brennan2018": + # NOTE: takes an optional debug param force_recompute to pre-process the EEG even if it exists + dataset = Brennan2018Dataset(args) + with open_dict(args): + args.num_subjects = dataset.num_subjects + + train_size = int(len(dataset) * args.split_ratio) + test_size = len(dataset) - train_size + train_set, test_set = torch.utils.data.random_split( + dataset, lengths=[train_size, test_size], generator=g, + ) + cprint( + f"Number of samples: {len(train_set)} (train), {len(test_set)} (test)", color="blue", + ) + train_loader, test_loader = get_dataloaders( + train_set, test_set, args, g, seed_worker, test_bsz=test_size + ) + + elif args.dataset == "GOD": + source_dataset = GODDatasetBase(args, 'train', return_label=True) + outlier_dataset = GODDatasetBase(args, 'val', return_label=True, + mean_X= source_dataset.mean_X, + mean_Y=source_dataset.mean_Y, + std_X=source_dataset.std_X, + std_Y=source_dataset.std_Y + ) + with open("/home/yainoue/meg2image/codes/MEG-decoding/data/ImageNet/val_features.pkl", "rb") as f: + imagenet_data = pickle.load(f) + imagenet_Y = np.zeros((len(imagenet_data), 512)) + imagenet_name = [None] * len(imagenet_data) + cnt = 0 + for k, v in imagenet_data.items(): + imagenet_Y[cnt] = v # v: 512 + imagenet_name[cnt] = k + cnt += 1 + imagenet_Y -= source_dataset.mean_Y + imagenet_Y /= source_dataset.std_Y + imagenet_Y = torch.Tensor(imagenet_Y).to(device) + + # train_size = int(np.round(len(source_dataset)*0.8)) + # val_size = len(source_dataset) - train_size + + # train_dataset, val_dataset = torch.utils.data.random_split(source_dataset, [train_size, val_size]) + ind_tr = list(range(0, 3000)) + list(range(3600, 6600)) #+ list(range(7200, 21600)) # + list(range(7200, 13200)) + list(range(14400, 20400)) + ind_te = list(range(3000,3600)) + list(range(6600, 7200)) # + list(range(13200, 14400)) + list(range(20400, 21600)) + train_dataset = Subset(source_dataset, ind_tr) + val_dataset = Subset(source_dataset, ind_te) + + with open_dict(args): + args.num_subjects = source_dataset.num_subjects + print('num subject is {}'.format(args.num_subjects)) + + + if args.use_sampler: + test_size = 50# 重複サンプルが存在するのでval_dataset.Y.shape[0] + train_loader, test_loader = get_samplers( + train_dataset, + val_dataset, + args, + test_bsz=test_size, + collate_fn=GODCollator(args),) + + else: + train_loader = DataLoader( + train_dataset, + batch_size= args.batch_size, + drop_last=True, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + test_loader = DataLoader( + # val_dataset, # + outlier_dataset, # val_dataset + batch_size=50, # args.batch_size, + drop_last=True, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True, + worker_init_fn=seed_worker, + generator=g, + ) + + else: + raise ValueError("Unknown dataset") + + if args.use_wandb: + wandb.config = {k: v for k, v in dict(args).items() if k not in ["root_dir", "wandb"]} + wandb.init( + project=args.wandb.project, + entity=args.wandb.entity, + config=wandb.config, + save_code=True, + ) + wandb.run.name = args.wandb.run_name + "_" + args.split_mode + wandb.run.save() + + # --------------------- + # Models + # --------------------- + brain_encoder = get_model(args).to(device) #BrainEncoder(args).to(device) + + weight_dir = os.path.join(args.save_root, 'weights') + last_weight_file = os.path.join(weight_dir, "model_last.pt") + best_weight_file = os.path.join(weight_dir, "model_best.pt") + if os.path.exists(best_weight_file): + brain_encoder.load_state_dict(torch.load(best_weight_file)) + print('weight is loaded from ', best_weight_file) + else: + brain_encoder.load_state_dict(torch.load(last_weight_file)) + print('weight is loaded from ', last_weight_file) + + + classifier = Classifier(args) + + # --------------- + # Loss + # --------------- + loss_func = CLIPLoss(args).to(device) + loss_func.eval() + # ====================================== + # train_losses = [] + test_losses = [] + # trainTop1accs = [] + # trainTop10accs = [] + testTop1accs = [] + testTop10accs = [] + # brain_encoder.eval() + # pbar2 = tqdm(train_loader) + # for i, batch in enumerate(pbar2): + # with torch.no_grad(): + # if len(batch) == 3: + # X, Y, subject_idxs = batch + # elif len(batch) == 4: + # X, Y, subject_idxs, chunkIDs = batch + # else: + # raise ValueError("Unexpected number of items from dataloader.") + + # X, Y = X.to(device), Y.to(device) + # # import pdb; pdb.set_trace() + # Z = brain_encoder(X, subject_idxs) + # loss = loss_func(Y, Z) + # with torch.no_grad(): + # trainTop1acc, trainTop10acc = classifier(Z, Y) + # train_losses.append(loss.item()) + # trainTop1accs.append(trainTop1acc) + # trainTop10accs.append(trainTop10acc) + + Zs = [] + Ys = [] + Ls = [] + brain_encoder.eval() + for batch in test_loader: + with torch.no_grad(): + + if len(batch) == 3: + X, Y, subject_idxs = batch + elif len(batch) == 4: + X, Y, subject_idxs, Labels = batch + else: + raise ValueError("Unexpected number of items from dataloader.") + + X, Y = X.to(device), Y.to(device) + + Z = brain_encoder(X, subject_idxs) # 0.96 GB + Zs.append(Z) + Ys.append(Y) + Ls.append(Labels) + + loss = loss_func(Y, Z) + + testTop1acc, testTop10acc = classifier(Z, Y, test=True) # ( 250, 1024, 360 ) + + test_losses.append(loss.item()) + testTop1accs.append(testTop1acc) + testTop10accs.append(testTop10acc) + Zs = torch.cat(Zs, dim=0) + Ys = torch.cat(Ys, dim=0) + Ls = torch.cat(Ls, dim=0).detach().cpu().numpy() + + print( + # f"train l: {np.mean(train_losses):.3f} | ", + f"test l: {np.mean(test_losses):.3f} | ", + # f"trainTop10acc: {np.mean(trainTop10accs):.3f} | ", + f"testTop10acc: {np.mean(testTop10accs):.3f} | ", + # f"temp: {loss_func.temp.item():.3f}", + ) + + + # 仮説1:判定に偏りがある。-> あるサンプルのimageの特徴量がMEGの潜在空間ににているかどうかを判定するだけの基準になっているのではないか? + Zs = Zs - Zs.mean(dim=0, keepdims=True) + Zs = Zs / Zs.std(dim=0, keepdims=True) + Zs = Zs - Zs.mean(dim=1, keepdims=True) + Zs = Zs / Zs.std(dim=1, keepdims=True) + + acc, mat = evaluate(Zs, Ys) + vis_confusion_mat(mat, acc, os.path.join(args.save_root, 'confusion_mat.png')) + n_database_hits = mat.sum(axis=0) + print('Num of hits of dataset \n', n_database_hits) + + miss_detection = np.sum(mat < 0, axis=0)/(len(mat)-1) # FP + print('Num miss detection: \n', miss_detection) + + true_detection = np.sum(mat > 0, axis=1) / (len(mat)-1) # TP + print('Num query detection: \n', true_detection) + + + N=1 + plot_array_label = np.argsort(miss_detection)[::-1][:N] + plot_array_value = np.sort(miss_detection)[::-1][:N] + print('miss detection id', plot_array_label) + print('miss detection value', plot_array_value) + fig, axes = plt.subplots(nrows=2, figsize=(32,8)) + boxplot_and_plot(Zs.detach().cpu().numpy(), plot_array_label, axes[0]) + boxplot_and_plot(Ys.detach().cpu().numpy(), plot_array_label, axes[1]) + plt.savefig(os.path.join(args.save_root, 'boxplot_and_plot.png'),bbox_inches='tight') + plt.close() + + N=1 + plot_array_label = np.argsort(true_detection)[:N] + plot_array_value = np.sort(true_detection)[:N] + print(plot_array_label) + print(plot_array_value) + fig, axes = plt.subplots(nrows=2, figsize=(32,8)) + boxplot_and_plot(Zs.detach().cpu().numpy(), plot_array_label, axes[0]) + boxplot_and_plot(Ys.detach().cpu().numpy(), plot_array_label, axes[1]) + plt.savefig(os.path.join(args.save_root, 'boxplot_and_plot_weak.png'),bbox_inches='tight') + plt.close() + + mask = np.tril(np.ones_like(mat), k=-1) > 0 + bias_detection = np.abs(mat - mat.T) + biased_judge = np.sum((bias_detection==2) * mask) + fair_judge = np.sum((bias_detection==0) * mask) + print('num biased {} vs num fair judged {}'.format(biased_judge, fair_judge)) + + Zs_std = Zs.std(dim=1) + plt.scatter(Zs_std.detach().cpu().numpy(), true_detection) + plt.xlabel('std of Z') + plt.ylabel('TP ratio') + plt.savefig(os.path.join(args.save_root, 'std_vs_tp.png'),bbox_inches='tight') + + Ys_with_imagenet = torch.cat([Ys, imagenet_Y], dim=0) + similarity = calc_similarity(Zs, Ys_with_imagenet) + # output top5 similarity + top5_similarity = {'query_image_id':[], 'acc(scene_id)':[], + 'top1_image_id':[], 'top2_image_id':[], 'top3_image_id':[], 'top4_image_id':[], 'top5_image_id':[]} + + acc_per_sample = np.zeros(len(similarity)) + for i in range(len(similarity)): + acc_per_sample[i] = np.sum(similarity[i,:] < similarity[i,i]) / (similarity.shape[1]-1) + + print('scene identification acc with imagenet_val: ', acc_per_sample.mean()) + # import pdb; pdb.set_trace() + for i, l in enumerate(Ls): + sim_vec = similarity[i,:] + top5_similarity['query_image_id'].append(l) + top5_similarity['acc(scene_id)'].append(acc_per_sample[i]) + ranking = np.argsort(sim_vec)[::-1][:5] + 1 # 1始まりにする + for k in range(1,6): + key = f'top{k}_image_id' + if ranking[k-1] <= 50: + image_name = str(ranking[k-1]) + else: + image_name = imagenet_name[ranking[k-1]-50-1] + top5_similarity[key].append(image_name) + top5_similarity = pd.DataFrame(top5_similarity) + top5_similarity.to_csv(os.path.join(args.save_root, 'top5_with_imagenet_val.csv')) + # import pdb; pdb.set_trace() + + + +def save_top5_prediction(): + top5_similarity = pd.read_csv(os.path.join(args.save_root, 'top5_with_imagenet_val.csv')) + split = 5 + unit = int(len(top5_similarity) / split) + imagenet_val_root = '/storage/dataset/image/ImageNet/ILSVRC2012_val/' + for i in range(split): + image_tiles = [] + for j in range(i*unit, (i+1)*unit): + row = top5_similarity.iloc[j] + row_image_list = [] + for key in ['top1_image_id', 'top2_image_id', 'top3_image_id', 'top4_image_id', 'top5_image_id']: + image_file_name = os.path.join(imagenet_val_root, str(row[key])) + if os.path.exists(image_file_name): + image = Image.open(image_file_name) + image = image.resize((112,112)) + image = np.array(image) + assert image.shape[0] == 112, 'image has shape {}'.format(image.shape) + else: + image = np.ones([112,112,3]).astype(np.uint8) + row_image_list.append(image) + row_image = np.concatenate(row_image_list, axis=0) + image_tiles.append(row_image) + # import pdb; pdb.set_trace() + image_tiles = np.concatenate(image_tiles, axis=1) + pil_img = Image.fromarray(image_tiles) + pil_img.save(os.path.join(args.save_root, f'top5_with_imagenet_val-{i}.png')) + # cv2.write_image(os.path.join(args.save_root, f'top5_with_imagenet_val-{i}.png'), image_tiles) + + + +def boxplot_and_plot(bp_array, plot_array_label, ax): + # array: n_image x dims(512) + plot_array = bp_array[plot_array_label] + ax.boxplot(bp_array) + for l, ar in zip(plot_array_label, plot_array): + ax.plot(np.arange(len(ar)), ar, label=str(l)) + ax.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left') + ax.set_xlabel('unit id') + ax.set_ylabel('logits') + +def calc_similarity(x, y): + batch_size = len(x) + gt_size = len(y) + + similarity = torch.empty(batch_size, gt_size).to('cuda') + for i in range(batch_size): + for j in range(gt_size): + similarity[i, j] = (x[i] @ y[j]) / max((x[i].norm() * y[j].norm()), 1e-8) + return similarity.cpu().numpy() + +def evaluate(Z, Y): + # Z: (batch_size, 512) + # Y: (gt_size, 512) + binary_confusion_matrix = np.zeros([len(Z), len(Y)]) + similarity = calc_similarity(Z, Y) + acc_tmp = np.zeros(len(similarity)) + for i in range(len(similarity)): + acc_tmp[i] = np.sum(similarity[i,:] < similarity[i,i]) / (similarity.shape[1]-1) + binary_confusion_matrix[i,similarity[i,:] < similarity[i,i]] = 1 + binary_confusion_matrix[i,similarity[i,:] > similarity[i,i]] = -1 + similarity_acc = np.mean(acc_tmp) + + + print('Similarity Acc', similarity_acc) + + return similarity_acc, binary_confusion_matrix + +def vis_confusion_mat(mat, acc, savefile=None): + sns.heatmap(mat, square=True, annot=False) + plt.xlabel('database data') + plt.ylabel('query data') + plt.title('similarity acc: {}'.format(acc)) + plt.savefig(savefile) + plt.close() + print('saved to ', savefile) + + +if __name__ == "__main__": + from hydra import initialize, compose + with initialize(version_base=None, config_path="../configs/"): + args = compose(config_name='20230429_sbj01_eegnet_regression') + # args = compose(config_name='20230501_all_eegnet_regression') + # args = compose(config_name='20230425_sbj01_seq2stat') + if not os.path.exists(os.path.join(args.save_root, 'weights')): + os.makedirs(os.path.join(args.save_root, 'weights')) + # run(args) + + save_top5_prediction() diff --git a/examples/create_imagenet_features.py b/examples/create_imagenet_features.py new file mode 100644 index 0000000..dd5ec66 --- /dev/null +++ b/examples/create_imagenet_features.py @@ -0,0 +1,52 @@ +#from importlib.resources import path +import os +import torch +import clip +import numpy as np +from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize +from tqdm import tqdm +import pandas as pd +import time +from PIL import Image +from PIL import ImageFile # 大きな画像もロード +import pickle +ImageFile.LOAD_TRUNCATED_IMAGES = True + +device = "cuda" if torch.cuda.is_available() else "cpu" +model, preprocess = clip.load("ViT-B/32", device=device) +model = model.eval() + +# images_dir = "./imagenet_fall2011_oneImagePerCat_21-2k_20230309/" +images_dir = "/storage/dataset/image/ImageNet/ILSVRC2012_val/" +list_images = os.listdir(images_dir) +len(list_images) + +image_names_list = [] +image_feats_list = [] + +for img in tqdm(list_images): + img_dir = images_dir + img + image = preprocess(Image.open(img_dir)).unsqueeze(0).to(device) + with torch.no_grad(): + image_features = model.encode_image(image) + + + image_features = image_features.to('cpu').detach().numpy().copy() + + image_feats_list.append(image_features) + image_names_list.append(img) + +image_names_np = np.array(image_names_list) +image_feats_np = np.array(image_feats_list) + +image_feats_np = np.squeeze(image_feats_np) + +name_feat_dict = {} +# for img_name in image_names_list: +# for feat_val in image_feats_list: +for img_name, feat_val in zip(image_names_list, image_feats_list): + name_feat_dict[img_name] = np.squeeze(feat_val) + +a_file = open("/home/yainoue/meg2image/codes/MEG-decoding/data/ImageNet/val_features.pkl", "wb") +pickle.dump(name_feat_dict, a_file) +a_file.close() \ No newline at end of file diff --git a/notebooks/create_imagenet_features.ipynb b/notebooks/create_imagenet_features.ipynb new file mode 100644 index 0000000..b3d0b3f --- /dev/null +++ b/notebooks/create_imagenet_features.ipynb @@ -0,0 +1,118 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#from importlib.resources import path\n", + "import os\n", + "import torch\n", + "import clip\n", + "import numpy as np\n", + "from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize\n", + "from tqdm import tqdm\n", + "import pandas as pd\n", + "import time\n", + "from PIL import Image\n", + "from PIL import ImageFile # 大きな画像もロード\n", + "ImageFile.LOAD_TRUNCATED_IMAGES = True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "model, preprocess = clip.load(\"ViT-B/32\", device=device)\n", + "model = model.eval()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "images_dir = \"./imagenet_fall2011_oneImagePerCat_21-2k_20230309/\"\n", + "list_images = os.listdir(images_dir)\n", + "len(list_images)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "image_names_list = []\n", + "image_feats_list = []\n", + "\n", + "for img in list_images:\n", + " img_dir = images_dir + img\n", + " image = preprocess(Image.open(img_dir)).unsqueeze(0).to(device)\n", + " with torch.no_grad():\n", + " image_features = model.encode_image(image)\n", + "\n", + " \n", + " image_features = image_features.to('cpu').detach().numpy().copy()\n", + " \n", + " image_feats_list.append(image_features)\n", + " image_names_list.append(img)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "image_names_np = np.array(image_names_list)\n", + "image_feats_np = np.array(image_feats_list)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "image_feats_np = np.squeeze(image_feats_np)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "name_feat_dict = {}\n", + "for img_name in image_names_list:\n", + " for feat_val in image_feats_list:\n", + " name_feat_dict[img_name] = feat_val" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a_file = open(\"./normal_semantic_features/stim_ID_clip_imagenet19k_map_20230318.pkl\", \"wb\")\n", + "pickle.dump(name_feat_dict, a_file)\n", + "a_file.close()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tmps/top5_with_imagenet_val-0.png 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