From e629737fe0d67e78bc4353e42be2dd1089824c70 Mon Sep 17 00:00:00 2001 From: Pranjal Kole Date: Thu, 16 Apr 2026 11:18:03 +0530 Subject: [PATCH] patch for python3.12+ --- .../datasets/head_tracking_challenge.py | 2 +- .../trackeval/datasets/kitti_2d_box.py | 4 +-- .../TrackEval/trackeval/metrics/track_map.py | 12 ++++----- external/deep-person-reid/pyproject.toml | 26 +++++++++++++++++++ external/deep-person-reid/setup.py | 13 +--------- models/diffusion.py | 4 +-- requirement.txt => requirements.txt | 1 - tracker/DiffMOTtracker.py | 2 +- tracker/embedding.py | 2 +- tracker/matching.py | 12 ++++----- 10 files changed, 46 insertions(+), 32 deletions(-) create mode 100644 external/deep-person-reid/pyproject.toml rename requirement.txt => requirements.txt (91%) diff --git a/external/TrackEval/trackeval/datasets/head_tracking_challenge.py b/external/TrackEval/trackeval/datasets/head_tracking_challenge.py index 9db9bd4..dd9a0ec 100644 --- a/external/TrackEval/trackeval/datasets/head_tracking_challenge.py +++ b/external/TrackEval/trackeval/datasets/head_tracking_challenge.py @@ -291,7 +291,7 @@ def _load_raw_file(self, tracker, seq, is_gt): time_key = str(t + 1) if time_key in read_data.keys(): try: - time_data = np.asarray(read_data[time_key], dtype=np.float) + time_data = np.asarray(read_data[time_key], dtype=float) except ValueError: if is_gt: raise TrackEvalException( diff --git a/external/TrackEval/trackeval/datasets/kitti_2d_box.py b/external/TrackEval/trackeval/datasets/kitti_2d_box.py index 4efbe63..952ccf6 100644 --- a/external/TrackEval/trackeval/datasets/kitti_2d_box.py +++ b/external/TrackEval/trackeval/datasets/kitti_2d_box.py @@ -246,7 +246,7 @@ def _load_raw_file(self, tracker, seq, is_gt): for t in range(num_timesteps): time_key = str(t) if time_key in read_data.keys(): - time_data = np.asarray(read_data[time_key], dtype=np.float) + time_data = np.asarray(read_data[time_key], dtype=float) raw_data["dets"][t] = np.atleast_2d(time_data[:, 6:10]) raw_data["ids"][t] = np.atleast_1d(time_data[:, 1]).astype(int) raw_data["classes"][t] = np.atleast_1d(time_data[:, 2]).astype(int) @@ -277,7 +277,7 @@ def _load_raw_file(self, tracker, seq, is_gt): raw_data["tracker_confidences"][t] = np.empty(0) if is_gt: if time_key in ignore_data.keys(): - time_ignore = np.asarray(ignore_data[time_key], dtype=np.float) + time_ignore = np.asarray(ignore_data[time_key], dtype=float) raw_data["gt_crowd_ignore_regions"][t] = np.atleast_2d( time_ignore[:, 6:10] ) diff --git a/external/TrackEval/trackeval/metrics/track_map.py b/external/TrackEval/trackeval/metrics/track_map.py index 8962d74..1286602 100644 --- a/external/TrackEval/trackeval/metrics/track_map.py +++ b/external/TrackEval/trackeval/metrics/track_map.py @@ -276,8 +276,8 @@ def combine_sequences(self, all_res): tps = np.logical_and(dt_m != -1, np.logical_not(dt_ig)) fps = np.logical_and(dt_m == -1, np.logical_not(dt_ig)) - tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) - fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float) + tp_sum = np.cumsum(tps, axis=1).astype(dtype=float) + fp_sum = np.cumsum(fps, axis=1).astype(dtype=float) for iou_thr_idx, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): tp = np.array(tp) @@ -315,8 +315,8 @@ def combine_sequences(self, all_res): # compute the precision and recall averages for the respective alpha thresholds and ignore masks for lbl in self.lbls: - res["AP_" + lbl] = np.zeros((len(self.array_labels)), dtype=np.float) - res["AR_" + lbl] = np.zeros((len(self.array_labels)), dtype=np.float) + res["AP_" + lbl] = np.zeros((len(self.array_labels)), dtype=float) + res["AR_" + lbl] = np.zeros((len(self.array_labels)), dtype=float) for a_id, alpha in enumerate(self.array_labels): for lbl_idx, lbl in enumerate(self.lbls): @@ -336,7 +336,7 @@ def combine_classes_class_averaged(self, all_res, ignore_empty_classes=True): """ res = {} for field in self.fields: - res[field] = np.zeros((len(self.array_labels)), dtype=np.float) + res[field] = np.zeros((len(self.array_labels)), dtype=float) field_stacked = np.array([res[field] for res in all_res.values()]) for a_id, alpha in enumerate(self.array_labels): @@ -353,7 +353,7 @@ def combine_classes_det_averaged(self, all_res): res = {} for field in self.fields: - res[field] = np.zeros((len(self.array_labels)), dtype=np.float) + res[field] = np.zeros((len(self.array_labels)), dtype=float) field_stacked = np.array([res[field] for res in all_res.values()]) for a_id, alpha in enumerate(self.array_labels): diff --git a/external/deep-person-reid/pyproject.toml b/external/deep-person-reid/pyproject.toml new file mode 100644 index 0000000..52448f2 --- /dev/null +++ b/external/deep-person-reid/pyproject.toml @@ -0,0 +1,26 @@ +[build-system] +requires = [ + "setuptools", + "wheel", + "numpy", + "cython", +] +build-backend = "setuptools.build_meta" + +[project] +name = "torchreid" +dynamic = [ + "version", + "description", + "authors", + "readme", + "dependencies", + "keywords", +] + +[tool.setuptools.dynamic] +version = {attr = "torchreid.__version__"} +dependencies = {file = ["requirements.txt"]} + +[tool.setuptools.packages.find] +include = ["torchreid"] diff --git a/external/deep-person-reid/setup.py b/external/deep-person-reid/setup.py index a661b97..d9522dd 100644 --- a/external/deep-person-reid/setup.py +++ b/external/deep-person-reid/setup.py @@ -1,23 +1,14 @@ import numpy as np import os.path as osp -from setuptools import setup, find_packages -from distutils.extension import Extension +from setuptools import setup, find_packages, Extension from Cython.Build import cythonize - def readme(): with open("README.rst") as f: content = f.read() return content -def find_version(): - version_file = "torchreid/__init__.py" - with open(version_file, "r") as f: - exec(compile(f.read(), version_file, "exec")) - return locals()["__version__"] - - def numpy_include(): try: numpy_include = np.get_include() @@ -43,8 +34,6 @@ def get_requirements(filename="requirements.txt"): setup( - name="torchreid", - version=find_version(), description="A library for deep learning person re-ID in PyTorch", author="Kaiyang Zhou", license="MIT", diff --git a/models/diffusion.py b/models/diffusion.py index da2d6ec..9d21daa 100644 --- a/models/diffusion.py +++ b/models/diffusion.py @@ -65,9 +65,9 @@ def __init__(self, point_dim=4, context_dim=256, tf_layer=3, residual=False): self.concat1 = MFL(4, context_dim // 2, context_dim+3) self.concat1_2 = MFL(context_dim // 2, context_dim, context_dim + 3) self.concat1_3 = MFL(context_dim, 2 * context_dim, context_dim + 3) - self.layer = nn.TransformerEncoderLayer(d_model=2*context_dim, nhead=4, dim_feedforward=4*context_dim) + self.layer = nn.TransformerEncoderLayer(d_model=2*context_dim, nhead=4, dim_feedforward=4*context_dim, batch_first=True) self.transformer_encoder = nn.TransformerEncoder(self.layer, num_layers=tf_layer) - self.layer2 = nn.TransformerEncoderLayer(d_model=context_dim, nhead=4, dim_feedforward=2 * context_dim) + self.layer2 = nn.TransformerEncoderLayer(d_model=context_dim, nhead=4, dim_feedforward=2 * context_dim, batch_first=True) self.transformer_encoder2 = nn.TransformerEncoder(self.layer2, num_layers=tf_layer) self.concat3 = MFL(2*context_dim,context_dim, context_dim+3) self.concat4 = MFL(context_dim,context_dim//2, context_dim+3) diff --git a/requirement.txt b/requirements.txt similarity index 91% rename from requirement.txt rename to requirements.txt index c28143e..cfbf296 100644 --- a/requirement.txt +++ b/requirements.txt @@ -1,6 +1,5 @@ einops pyyaml -argparse easydict numpy tensorboardX diff --git a/tracker/DiffMOTtracker.py b/tracker/DiffMOTtracker.py index 7bc6618..191a0f3 100644 --- a/tracker/DiffMOTtracker.py +++ b/tracker/DiffMOTtracker.py @@ -35,7 +35,7 @@ def __init__(self, tlwh, score, temp_feat=None, buffer_size=30): self.conds = deque([], maxlen=5) - self._tlwh = np.asarray(tlwh, dtype=np.float) + self._tlwh = np.asarray(tlwh, dtype=float) self.kalman_filter = None self.mean, self.covariance = None, None self.is_activated = False diff --git a/tracker/embedding.py b/tracker/embedding.py index ac459b5..6b4f1aa 100644 --- a/tracker/embedding.py +++ b/tracker/embedding.py @@ -177,7 +177,7 @@ def initialize_model(self): path = "external/weights/dance_sbs_S50.pth" # path = "/home/estar/lwy/DiffMOT/external/weights/dancetrack_sbs_S50_hybtid.pth" elif self.dataset == "sports": - path = "/home/estar/lwy/BoT-SORT-main/fast_reid/tools/logs/SportsMOT/sbs_S50/model_0058.pth" + path = "external/weights/sports_sbs_S50.pth" else: raise RuntimeError("Need the path for a new ReID model.") diff --git a/tracker/matching.py b/tracker/matching.py index 0ac4101..e00d8c7 100644 --- a/tracker/matching.py +++ b/tracker/matching.py @@ -92,13 +92,13 @@ def ious(atlbrs, btlbrs): :rtype ious np.ndarray """ - ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float) + ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=float) if ious.size == 0: return ious ious = bbox_ious( - np.ascontiguousarray(atlbrs, dtype=np.float), - np.ascontiguousarray(btlbrs, dtype=np.float) + np.ascontiguousarray(atlbrs, dtype=float), + np.ascontiguousarray(btlbrs, dtype=float) ) return ious @@ -189,13 +189,13 @@ def embedding_distance(tracks, detections, metric='cosine'): :return: cost_matrix np.ndarray """ - cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float) + cost_matrix = np.zeros((len(tracks), len(detections)), dtype=float) if cost_matrix.size == 0: return cost_matrix - det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float) + det_features = np.asarray([track.curr_feat for track in detections], dtype=float) #for i, track in enumerate(tracks): #cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric)) - track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float) + track_features = np.asarray([track.smooth_feat for track in tracks], dtype=float) cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features return cost_matrix