From c887fd3d700897c3b3a33e06127d038a57a4d463 Mon Sep 17 00:00:00 2001 From: George Pearse Date: Tue, 2 Dec 2025 05:19:53 +0000 Subject: [PATCH 01/12] refactor: Complete visengine to visdet namespace migration and fix bugs - Rename all visengine references to visdet/visdet.engine throughout codebase - Fix YAML config handling: convert [1333, 800] list to tuple for img_scale - Fix COCO dataset config: use img_path instead of img for data_prefix - Fix collect_results for non-distributed mode (world_size=1) - Fix collect_results signature (remove incorrect collect_device param) - Fix scale_factor handling in FCNMaskHead for 2-element arrays - Skip out-of-range category predictions in CocoMetric - Add visualization hooks to engine.hooks exports - Fix circular import in visualization_hook with TYPE_CHECKING - Improve SimpleRunner pipeline handling for train/val - Add justfile with common development commands --- .../datasets/coco_instance_segmentation.yaml | 4 +-- justfile | 25 +++++++++++++++ visdet/cv/transforms/processing.py | 14 +++++--- visdet/engine/__init__.py | 4 +++ visdet/engine/_strategy/single_device.py | 2 +- visdet/engine/config/config.py | 8 ++--- visdet/engine/config/utils.py | 6 ++-- visdet/engine/dist/__init__.py | 4 +++ visdet/engine/evaluator/metric.py | 4 +-- visdet/engine/hooks/__init__.py | 3 ++ visdet/engine/hooks/visualization_hook.py | 7 +++- visdet/engine/logging/logger.py | 6 ++-- visdet/engine/registry/utils.py | 14 ++++---- visdet/engine/runner/runner.py | 10 +++--- visdet/engine/utils/dl_utils/visualize.py | 4 +-- visdet/evaluation/metrics/coco_metric.py | 6 ++++ .../roi_heads/mask_heads/fcn_mask_head.py | 8 ++++- visdet/registry.py | 6 ++-- visdet/runner.py | 32 ++++++++++++++++--- 19 files changed, 124 insertions(+), 43 deletions(-) create mode 100644 justfile diff --git a/configs/presets/datasets/coco_instance_segmentation.yaml b/configs/presets/datasets/coco_instance_segmentation.yaml index f57c5fc8..f7c54bfc 100644 --- a/configs/presets/datasets/coco_instance_segmentation.yaml +++ b/configs/presets/datasets/coco_instance_segmentation.yaml @@ -9,12 +9,12 @@ type: CocoDataset data_root: data/coco/ ann_file: annotations/instances_train2017.json data_prefix: - img: train2017/ + img_path: train2017/ # Validation paths val_ann_file: annotations/instances_val2017.json val_data_prefix: - img: val2017/ + img_path: val2017/ # Data pipeline for training train_pipeline: diff --git a/justfile b/justfile new file mode 100644 index 00000000..8ac4b527 --- /dev/null +++ b/justfile @@ -0,0 +1,25 @@ +# visdet justfile - common development commands + +# Default recipe to list available commands +default: + @just --list + +# Run Mask R-CNN Swin training on COCO instance segmentation (default test) +train: + uv run python scripts/train_simple.py \ + --model mask_rcnn_swin_s \ + --dataset coco_instance_segmentation \ + --epochs 12 \ + --work-dir ./work_dirs/mask_rcnn_swin_coco + +# List available models +list-models: + uv run python scripts/train_simple.py --list-models + +# List available datasets +list-datasets: + uv run python scripts/train_simple.py --list-datasets + +# Show a preset configuration +show-preset preset category="model": + uv run python scripts/train_simple.py --show-preset {{ preset }} --category {{ category }} diff --git a/visdet/cv/transforms/processing.py b/visdet/cv/transforms/processing.py index 029e445b..f520e0b8 100644 --- a/visdet/cv/transforms/processing.py +++ b/visdet/cv/transforms/processing.py @@ -156,11 +156,15 @@ def __init__( assert is_list_of(img_scale, tuple), f"img_scale must be a list of tuples, got {img_scale}" self.img_scale = img_scale elif isinstance(first_elem, (int, float)): - # List of integers like [1333, 800] is invalid - raise AssertionError( - f"img_scale must be a tuple or list of tuples, not a list of integers. " - f"Got {img_scale}. Did you mean ({img_scale[0]}, {img_scale[1]})?" - ) + # List of 2 integers like [1333, 800] - convert to tuple + # This commonly comes from YAML configs which don't have tuple syntax + if len(img_scale) == 2: + self.img_scale = [tuple(img_scale)] + else: + raise AssertionError( + f"img_scale must be a tuple or list of tuples, not a list of integers. " + f"Got {img_scale}. Did you mean ({img_scale[0]}, {img_scale[1]})?" + ) else: raise AssertionError(f"img_scale must be a tuple or list of tuples, got {img_scale}") else: diff --git a/visdet/engine/__init__.py b/visdet/engine/__init__.py index b17e0ef9..c26166da 100644 --- a/visdet/engine/__init__.py +++ b/visdet/engine/__init__.py @@ -6,6 +6,10 @@ # Import version first to ensure it's available from visdet.version import __version__, version_info +# Import Config for convenience +from visdet.engine.config import Config, ConfigDict +from visdet.engine.registry import DefaultScope + # Re-export version info explicitly at module level globals()["__version__"] = __version__ globals()["version_info"] = version_info diff --git a/visdet/engine/_strategy/single_device.py b/visdet/engine/_strategy/single_device.py index 55fad918..4e016ff9 100644 --- a/visdet/engine/_strategy/single_device.py +++ b/visdet/engine/_strategy/single_device.py @@ -275,7 +275,7 @@ def save_checkpoint( extra_ckpt["meta"].update( seed=self.seed, time=time.strftime("%Y%m%d_%H%M%S", time.localtime()), - visengine=visengine.__version__ + get_git_hash(), + visdet_engine=visdet.engine.__version__ + get_git_hash(), ) state_dict.update(extra_ckpt) diff --git a/visdet/engine/config/config.py b/visdet/engine/config/config.py index 3f2c835f..68a9f07f 100644 --- a/visdet/engine/config/config.py +++ b/visdet/engine/config/config.py @@ -1186,9 +1186,9 @@ def is_base_line(c): else: base_files = [] elif file_format in (".yml", ".yaml", ".json"): - import visengine + from visdet.engine.fileio import load as fileio_load - cfg_dict = visengine.load(filename) + cfg_dict = fileio_load(filename) base_files = cfg_dict.get(BASE_KEY, []) else: raise ConfigParsingError(f"The config type should be py, json, yaml or yml, but got {file_format}") @@ -1622,8 +1622,8 @@ def _is_lazy_import(filename: str) -> bool: # relative import -> lazy_import if node.level != 0: return True - # Skip checking when using `visengine.config` in cfg file - if node.module == "visengine" and len(node.names) == 1 and node.names[0].name == "Config": + # Skip checking when using `visdet.engine.config` in cfg file + if node.module == "visdet.engine.config" and len(node.names) == 1 and node.names[0].name == "Config": continue if not isinstance(node.module, str): continue diff --git a/visdet/engine/config/utils.py b/visdet/engine/config/utils.py index b16e40fe..a2bbe42e 100644 --- a/visdet/engine/config/utils.py +++ b/visdet/engine/config/utils.py @@ -165,7 +165,7 @@ def _is_builtin_module(module_name: str) -> bool: """ if module_name.startswith("."): return False - if module_name.startswith("visengine.config"): + if module_name.startswith("visdet.engine.config"): return True if module_name in sys.builtin_module_names: return True @@ -361,13 +361,13 @@ class docstring says. elif alias_node.asname is not None: # case1: # from visdet.engine.dataset import BaseDataset as Dataset -> - # Dataset = LazyObject('visengine.dataset', 'BaseDataset') + # Dataset = LazyObject('visdet.engine.dataset', 'BaseDataset') code = f'{alias_node.asname} = LazyObject("{module}", "{alias_node.name}", "{self.filename}, line {lineno}")' # noqa: E501 self.imported_obj.add(alias_node.asname) else: # case2: # from visdet.engine.model import BaseModel - # BaseModel = LazyObject('visengine.model', 'BaseModel') + # BaseModel = LazyObject('visdet.engine.model', 'BaseModel') code = ( f'{alias_node.name} = LazyObject("{module}", "{alias_node.name}", "{self.filename}, line {lineno}")' # noqa: E501 ) diff --git a/visdet/engine/dist/__init__.py b/visdet/engine/dist/__init__.py index 920ff72c..5fc9df03 100644 --- a/visdet/engine/dist/__init__.py +++ b/visdet/engine/dist/__init__.py @@ -126,6 +126,10 @@ def collect_results(result_part, size, tmpdir=None): """Collect results from all processes and merge them.""" rank, world_size = get_dist_info() + # Non-distributed mode: just return the results directly + if world_size == 1: + return result_part + if tmpdir is None: tmpdir = '.' diff --git a/visdet/engine/evaluator/metric.py b/visdet/engine/evaluator/metric.py index f331d9fa..a27f4ba6 100644 --- a/visdet/engine/evaluator/metric.py +++ b/visdet/engine/evaluator/metric.py @@ -123,9 +123,9 @@ def evaluate(self, size: int) -> dict: ) if self.collect_device == "cpu": - results = collect_results(self.results, size, self.collect_device, tmpdir=self.collect_dir) + results = collect_results(self.results, size, tmpdir=self.collect_dir) else: - results = collect_results(self.results, size, self.collect_device) + results = collect_results(self.results, size) if is_main_process(): # cast all tensors in results list to cpu diff --git a/visdet/engine/hooks/__init__.py b/visdet/engine/hooks/__init__.py index 861ad249..ac287caa 100644 --- a/visdet/engine/hooks/__init__.py +++ b/visdet/engine/hooks/__init__.py @@ -14,13 +14,16 @@ from visdet.engine.hooks.runtime_info_hook import RuntimeInfoHook from visdet.engine.hooks.sampler_seed_hook import DistSamplerSeedHook from visdet.engine.hooks.sync_buffer_hook import SyncBuffersHook +from visdet.engine.hooks.visualization_hook import DetVisualizationHook, GroundingVisualizationHook __all__ = [ "CheckpointHook", + "DetVisualizationHook", "DistSamplerSeedHook", "EMAHook", "EarlyStoppingHook", "EmptyCacheHook", + "GroundingVisualizationHook", "Hook", "IterTimerHook", "LoggerHook", diff --git a/visdet/engine/hooks/visualization_hook.py b/visdet/engine/hooks/visualization_hook.py index 0b016d88..e0db0ae5 100644 --- a/visdet/engine/hooks/visualization_hook.py +++ b/visdet/engine/hooks/visualization_hook.py @@ -1,14 +1,16 @@ # Copyright (c) OpenMMLab. All rights reserved. +from __future__ import annotations + import os.path as osp import warnings from collections.abc import Sequence +from typing import TYPE_CHECKING import numpy as np from visdet.cv import imfrombytes, imwrite from visdet.engine.fileio import get from visdet.engine.hooks import Hook -from visdet.engine.runner import Runner from visdet.engine.utils import mkdir_or_exist from visdet.engine.visualization import Visualizer from visdet.registry import HOOKS @@ -16,6 +18,9 @@ from visdet.structures.bbox import BaseBoxes from visdet.visualization.palette import _get_adaptive_scales +if TYPE_CHECKING: + from visdet.engine.runner import Runner + @HOOKS.register_module() class DetVisualizationHook(Hook): diff --git a/visdet/engine/logging/logger.py b/visdet/engine/logging/logger.py index eb1799fc..52dbed48 100644 --- a/visdet/engine/logging/logger.py +++ b/visdet/engine/logging/logger.py @@ -287,7 +287,7 @@ class MMLogger(Logger, ManagerMixin): Args: name (str): Global instance name. logger_name (str): ``name`` attribute of ``Logging.Logger`` instance. - If `logger_name` is not defined, defaults to 'visengine'. + If `logger_name` is not defined, defaults to 'visdet'. log_file (str, optional): The log filename. If specified, a ``FileHandler`` will be added to the logger. Defaults to None. log_level (str): The log level of the handler. Defaults to @@ -317,7 +317,7 @@ class MMLogger(Logger, ManagerMixin): def __init__( self, name: str, - logger_name="visengine", + logger_name="visdet", log_file: str | None = None, log_level: int | str = "INFO", file_mode: str = "w", @@ -404,7 +404,7 @@ def get_current_instance(cls) -> "MMLogger": MMLogger: Configured logger instance. """ if not cls._instance_dict: - cls.get_instance("visengine") + cls.get_instance("visdet") return super().get_current_instance() def callHandlers(self, record: LogRecord) -> None: diff --git a/visdet/engine/registry/utils.py b/visdet/engine/registry/utils.py index 883d67c5..bf99b593 100644 --- a/visdet/engine/registry/utils.py +++ b/visdet/engine/registry/utils.py @@ -66,13 +66,13 @@ def count_registered_modules(save_path: str | None = None, verbose: bool = True) dict: Statistic results of all registered modules. """ # import modules to trigger registering - import visengine.dataset - import visengine.evaluator - import visengine.hooks - import visengine.model - import visengine.optim - import visengine.runner - import visengine.visualization # noqa: F401 + import visdet.datasets # noqa: F401 + import visdet.evaluation # noqa: F401 + import visdet.core # noqa: F401 + import visdet.models # noqa: F401 + import visdet.engine.optim # noqa: F401 + import visdet.engine.runner # noqa: F401 + import visdet.visualization # noqa: F401 registries_info = {} # traverse all registries in MMEngine diff --git a/visdet/engine/runner/runner.py b/visdet/engine/runner/runner.py index 32e06bfc..8ea5c343 100644 --- a/visdet/engine/runner/runner.py +++ b/visdet/engine/runner/runner.py @@ -217,7 +217,7 @@ class Runner: dict build Visualizer object. Defaults to None. If not specified, default config will be used. default_scope (str): Used to reset registries location. - Defaults to "visengine". + Defaults to "visdet". randomness (dict): Some settings to make the experiment as reproducible as possible like seed and deterministic. Defaults to ``dict(seed=None)``. If seed is None, a random number @@ -319,7 +319,7 @@ def __init__( log_processor: dict | None = None, log_level: str = "INFO", visualizer: Visualizer | dict | None = None, - default_scope: str = "visengine", + default_scope: str = "visdet", randomness: dict | None = None, experiment_name: str | None = None, cfg: ConfigType | None = None, @@ -519,7 +519,7 @@ def from_cfg(cls, cfg: ConfigType) -> "Runner": log_processor=cfg.get("log_processor"), log_level=cfg.get("log_level", "INFO"), visualizer=cfg.get("visualizer"), - default_scope=cfg.get("default_scope", "visengine"), + default_scope=cfg.get("default_scope", "visdet"), randomness=cfg.get("randomness", {"seed": None}), experiment_name=cfg.get("experiment_name"), cfg=cfg, @@ -1999,7 +1999,7 @@ def resume( # check whether the number of GPU used for current experiment # is consistent with resuming from checkpoint if "config" in checkpoint["meta"]: - config = visengine.Config.fromstring(checkpoint["meta"]["config"], file_format=".py") + config = Config.fromstring(checkpoint["meta"]["config"], file_format=".py") previous_gpu_ids = config.get("gpu_ids", None) if previous_gpu_ids is not None and len(previous_gpu_ids) > 0 and len(previous_gpu_ids) != self._world_size: # TODO, should we modify the iteration? @@ -2193,7 +2193,7 @@ def save_checkpoint( seed=self.seed, experiment_name=self.experiment_name, time=time.strftime("%Y%m%d_%H%M%S", time.localtime()), - visengine_version=__version__ + get_git_hash(), + visdet_version=__version__ + get_git_hash(), ) if hasattr(self.train_dataloader.dataset, "metainfo"): diff --git a/visdet/engine/utils/dl_utils/visualize.py b/visdet/engine/utils/dl_utils/visualize.py index e34e9a42..5a831ebe 100644 --- a/visdet/engine/utils/dl_utils/visualize.py +++ b/visdet/engine/utils/dl_utils/visualize.py @@ -35,8 +35,8 @@ def runtimeinfo_step(self, runner, batch_idx, data_batch=None): runner.message_hub.update_scalar(f"train/{name}", momentum[0]) -@patch("visengine.optim.optimizer.OptimWrapper.update_params", update_params_step) -@patch("visengine.hooks.RuntimeInfoHook.before_train_iter", runtimeinfo_step) +@patch("visdet.engine.optim.optimizer.OptimWrapper.update_params", update_params_step) +@patch("visdet.engine.hooks.RuntimeInfoHook.before_train_iter", runtimeinfo_step) def fake_run(cfg): from visdet.engine.runner import Runner diff --git a/visdet/evaluation/metrics/coco_metric.py b/visdet/evaluation/metrics/coco_metric.py index d19a390d..dca3e405 100644 --- a/visdet/evaluation/metrics/coco_metric.py +++ b/visdet/evaluation/metrics/coco_metric.py @@ -236,6 +236,9 @@ def results2json(self, results: Sequence[dict], outfile_prefix: str) -> dict: scores = result["scores"] # bbox results for i, label in enumerate(labels): + # Skip predictions with out-of-range category labels + if label >= len(self.cat_ids): + continue data = dict() data["image_id"] = image_id data["bbox"] = self.xyxy2xywh(bboxes[i]) @@ -250,6 +253,9 @@ def results2json(self, results: Sequence[dict], outfile_prefix: str) -> dict: masks = result["masks"] mask_scores = result.get("mask_scores", scores) for i, label in enumerate(labels): + # Skip predictions with out-of-range category labels + if label >= len(self.cat_ids): + continue data = dict() data["image_id"] = image_id data["bbox"] = self.xyxy2xywh(bboxes[i]) diff --git a/visdet/models/roi_heads/mask_heads/fcn_mask_head.py b/visdet/models/roi_heads/mask_heads/fcn_mask_head.py index d7ebbd05..bf139480 100644 --- a/visdet/models/roi_heads/mask_heads/fcn_mask_head.py +++ b/visdet/models/roi_heads/mask_heads/fcn_mask_head.py @@ -319,7 +319,13 @@ def _predict_by_feat_single( >>> assert encoded_masks.size()[0] == N >>> assert encoded_masks.size()[1:] == ori_shape """ - scale_factor = bboxes.new_tensor(img_meta["scale_factor"]).repeat((1, 2)) + scale_factor_raw = img_meta["scale_factor"] + if len(scale_factor_raw) == 2: + # (w_scale, h_scale) -> (w, h, w, h) + scale_factor = bboxes.new_tensor(scale_factor_raw).repeat((1, 2)) + else: + # Already 4 elements (w, h, w, h) or similar + scale_factor = bboxes.new_tensor(scale_factor_raw[:4]).reshape(1, 4) img_h, img_w = img_meta["ori_shape"][:2] device = bboxes.device diff --git a/visdet/registry.py b/visdet/registry.py index 6194d212..ecf7c4cc 100644 --- a/visdet/registry.py +++ b/visdet/registry.py @@ -33,13 +33,13 @@ from visdet.engine.registry import Registry # manage all kinds of runners like `EpochBasedRunner` and `IterBasedRunner` -RUNNERS = Registry("runner", parent=VISENGINE_RUNNERS, locations=["visengine.runner"]) +RUNNERS = Registry("runner", parent=VISENGINE_RUNNERS, locations=["visdet.engine.runner"]) # manage runner constructors that define how to initialize runners RUNNER_CONSTRUCTORS = Registry( - "runner constructor", parent=VISENGINE_RUNNER_CONSTRUCTORS, locations=["visengine.runner"] + "runner constructor", parent=VISENGINE_RUNNER_CONSTRUCTORS, locations=["visdet.engine.runner"] ) # manage all kinds of loops like `EpochBasedTrainLoop` -LOOPS = Registry("loop", parent=VISENGINE_LOOPS, locations=["visengine.runner"]) +LOOPS = Registry("loop", parent=VISENGINE_LOOPS, locations=["visdet.engine.runner"]) # manage all kinds of hooks like `CheckpointHook` HOOKS = VISENGINE_HOOKS diff --git a/visdet/runner.py b/visdet/runner.py index 8c1f1998..c4bda6ef 100644 --- a/visdet/runner.py +++ b/visdet/runner.py @@ -372,6 +372,10 @@ def _build_config(self) -> None: logger.info(f"Overriding training annotation file with: {self.train_ann_file}") self.dataset_cfg["ann_file"] = self.train_ann_file + # Rename train_pipeline to pipeline for training dataset + if "train_pipeline" in self.dataset_cfg: + self.dataset_cfg["pipeline"] = self.dataset_cfg.pop("train_pipeline") + # --- Train Dataloader --- train_dataloader = { "batch_size": self.batch_size, @@ -405,14 +409,24 @@ def _build_config(self) -> None: has_validation = True if has_validation: - # Use a different pipeline for validation if provided - if "val_pipeline" in val_dataset_cfg: - val_dataset_cfg["pipeline"] = val_dataset_cfg.pop("val_pipeline") - else: # Or remove augmentations from the training pipeline + # Use validation data prefix if provided, otherwise keep training prefix + if "val_data_prefix" in val_dataset_cfg: + val_dataset_cfg["data_prefix"] = val_dataset_cfg.pop("val_data_prefix") + + # Use test_pipeline for validation if provided, otherwise fall back to pipeline + if "test_pipeline" in val_dataset_cfg: + val_dataset_cfg["pipeline"] = val_dataset_cfg.pop("test_pipeline") + elif "train_pipeline" in val_dataset_cfg: + # Remove train_pipeline and use existing pipeline without augmentations + val_dataset_cfg.pop("train_pipeline") val_dataset_cfg["pipeline"] = [ p for p in val_dataset_cfg.get("pipeline", []) if p.get("type") not in ["RandomFlip"] ] + # Remove validation-specific keys that shouldn't be passed to dataset + for key in ["val_ann_file", "val_data_prefix"]: + val_dataset_cfg.pop(key, None) + val_dataloader = { "batch_size": self.batch_size, "num_workers": self.num_workers, @@ -429,6 +443,16 @@ def _build_config(self) -> None: "metric": ["bbox", "segm"], } + # Clean up non-dataset keys from configs + # These are convenience keys in the preset that shouldn't be passed to the dataset + keys_to_remove = [ + "val_ann_file", "val_data_prefix", "train_pipeline", "test_pipeline", + "batch_size", "num_workers", "persistent_workers" + ] + for key in keys_to_remove: + self.dataset_cfg.pop(key, None) + val_dataset_cfg.pop(key, None) if val_dataset_cfg else None + # --- Assemble Final Config --- config_dict = { "default_scope": "visdet", From c9f680b212dd22cd13b5aea968f78ef6885f38aa Mon Sep 17 00:00:00 2001 From: George Pearse Date: Tue, 2 Dec 2025 05:34:35 +0000 Subject: [PATCH 02/12] docs: Add roadmap with SPDL integration plan - Create comprehensive roadmap.md documenting SPDL integration phases - Add references to roadmap in index.md - Update data_pipeline.md with SPDL future enhancement note - Update quick-start.md with SimpleRunner API and visdet imports SPDL (Meta's Scalable and Performant Data Loading) offers: - 74% faster data iteration vs PyTorch DataLoader - 38% less CPU usage - 50GB less memory footprint - Thread-based execution instead of multiprocessing - Additional 33% speedup with Python 3.13t (nogil) Integration planned in 4 phases through 2025: - Phase 1 (Q1): Adapter layer and optional dependency - Phase 2 (Q2): Detection-specific optimizations - Phase 3 (Q3): Distributed training support - Phase 4 (Q4): Python 3.13t and advanced features --- docs/getting-started/quick-start.md | 25 ++- docs/index.md | 7 + docs/roadmap.md | 304 ++++++++++++++++++++++++++++ docs/tutorials/data_pipeline.md | 2 + 4 files changed, 334 insertions(+), 4 deletions(-) create mode 100644 docs/roadmap.md diff --git a/docs/getting-started/quick-start.md b/docs/getting-started/quick-start.md index 071226aa..1da4b4c4 100644 --- a/docs/getting-started/quick-start.md +++ b/docs/getting-started/quick-start.md @@ -2,6 +2,24 @@ This guide provides quick examples to get you started with VisDet. +## Training with SimpleRunner + +The easiest way to train a model is using the `SimpleRunner` API: + +```python +from visdet import SimpleRunner + +# Simple, string-based API - just like Hugging Face or Ultralytics +runner = SimpleRunner( + model='mask_rcnn_swin_s', + dataset='coco_instance_segmentation', + epochs=12, + batch_size=2 +) + +runner.train() +``` + ## Inference with Pre-trained Models ### Using High-level APIs @@ -9,12 +27,11 @@ This guide provides quick examples to get you started with VisDet. You can use high-level APIs to perform inference on images: ```python -from mmdet.apis import init_detector, inference_detector -import mmcv +from visdet.apis import init_detector, inference_detector # Specify the config file and checkpoint file config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' -checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth' +checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco.pth' # Build the model from a config file and a checkpoint file model = init_detector(config_file, checkpoint_file, device='cuda:0') @@ -24,7 +41,7 @@ img = 'demo/demo.jpg' result = inference_detector(model, img) # Show the results -from mmdet.apis import show_result_pyplot +from visdet.apis import show_result_pyplot show_result_pyplot(model, img, result) ``` diff --git a/docs/index.md b/docs/index.md index b41baa3a..d917f4bb 100644 --- a/docs/index.md +++ b/docs/index.md @@ -33,6 +33,13 @@ The master branch works with **PyTorch 1.5+**. +## Roadmap + +See our [Roadmap](roadmap.md) for planned features, including: +- **SPDL Integration**: Thread-based data loading for 74% faster training +- **Kornia**: GPU-accelerated augmentations +- **Python 3.13t**: Free-threaded Python support + ## What's New For the latest updates and improvements to VisDet, please refer to the [changelog](about/changelog.md). diff --git a/docs/roadmap.md b/docs/roadmap.md new file mode 100644 index 00000000..a66a4278 --- /dev/null +++ b/docs/roadmap.md @@ -0,0 +1,304 @@ +# Roadmap + +This document outlines the development roadmap for visdet, with a focus on performance improvements, modern integrations, and developer experience enhancements. + +--- + +## SPDL Integration Plan + +### Overview + +[SPDL (Scalable and Performant Data Loading)](https://github.com/facebookresearch/spdl) is Meta's thread-based data loading library that dramatically outperforms PyTorch's process-based `DataLoader`. Integrating SPDL into visdet will provide: + +- **74% faster iteration** through datasets like ImageNet +- **38% less CPU usage** during training +- **50GB less memory** footprint compared to PyTorch DataLoader +- **Additional 33% speedup** with Free-Threaded Python 3.13t (no code changes needed) + +### Why SPDL? + +PyTorch's `DataLoader` uses multiprocessing, which has fundamental limitations: + +| Aspect | PyTorch DataLoader | SPDL | +|--------|-------------------|------| +| Execution Model | Process-based (multiprocessing) | Thread-based | +| Memory | Copies batch tensors at least twice | Direct memory sharing | +| Scaling | Degrades beyond 8-16 workers | Scales linearly | +| Large Batches | Performance drops | Sustains/improves throughput | +| GIL Impact | N/A (separate processes) | Minimal (I/O bound), eliminated with Python 3.13t | + +SPDL's explicit pipeline architecture also enables independent optimization of each stage: +- **Source**: Index/key generation +- **I/O**: File reading and decoding +- **Transform**: Preprocessing and augmentation +- **Batch**: Collation +- **Prefetch**: GPU transfer + +### Current Architecture + +visdet currently uses PyTorch's standard DataLoader in: + +``` +visdet/datasets/builder.py # build_dataloader() function +visdet/engine/runner/runner.py # Runner dataloader construction +visdet/runner.py # SimpleRunner dataloader config +``` + +Key components: +- `torch.utils.data.DataLoader` with multiprocessing workers +- Group samplers for aspect ratio grouping (`GroupSampler`, `DistributedGroupSampler`) +- Custom collate functions for detection data structures +- Worker initialization for reproducible seeding + +### Integration Phases + +#### Phase 1: Foundation (Target: Q1 2025) + +**Goal**: Create SPDL adapter layer without breaking existing API + +1. **Add SPDL as optional dependency** + ```toml + # pyproject.toml + [project.optional-dependencies] + spdl = ["spdl>=0.1.6"] + ``` + +2. **Create `SPDLDataLoader` wrapper class** + ```python + # visdet/datasets/spdl_loader.py + from spdl.dataloader import PipelineBuilder + + class SPDLDataLoader: + """Drop-in replacement for PyTorch DataLoader using SPDL.""" + + def __init__( + self, + dataset, + batch_size: int, + num_workers: int = 4, + shuffle: bool = True, + collate_fn = None, + prefetch_factor: int = 2, + ): + self.dataset = dataset + self.batch_size = batch_size + self.num_workers = num_workers + self.collate_fn = collate_fn or default_collate + self.prefetch_factor = prefetch_factor + self._build_pipeline(shuffle) + + def _build_pipeline(self, shuffle: bool): + indices = list(range(len(self.dataset))) + if shuffle: + random.shuffle(indices) + + self.pipeline = ( + PipelineBuilder() + .add_source(indices) + .pipe( + self.dataset.__getitem__, + concurrency=self.num_workers, + output_order="input" # Preserve order for reproducibility + ) + .aggregate(self.batch_size) + .pipe(self.collate_fn) + .add_sink(self.prefetch_factor) + .build(num_threads=self.num_workers) + ) + + def __iter__(self): + return iter(self.pipeline) + + def __len__(self): + return (len(self.dataset) + self.batch_size - 1) // self.batch_size + ``` + +3. **Add configuration flag** + ```python + # In SimpleRunner + runner = SimpleRunner( + model='mask_rcnn_swin_s', + dataset='coco_instance_segmentation', + dataloader_backend='spdl', # or 'pytorch' (default) + ) + ``` + +#### Phase 2: Detection-Specific Optimizations (Target: Q2 2025) + +**Goal**: Optimize SPDL pipeline for object detection workloads + +1. **Aspect Ratio Grouping** + - Implement group-aware batching in SPDL pipeline + - Minimize padding waste by grouping similar aspect ratios + + ```python + def aspect_ratio_group_source(dataset, batch_size): + """Generate indices grouped by aspect ratio.""" + # Group images by aspect ratio buckets + groups = defaultdict(list) + for idx in range(len(dataset)): + ar = dataset.get_aspect_ratio(idx) + bucket = round(ar, 1) + groups[bucket].append(idx) + + # Yield batches from same group + for bucket, indices in groups.items(): + random.shuffle(indices) + for i in range(0, len(indices), batch_size): + yield indices[i:i + batch_size] + ``` + +2. **Optimized Image Decoding** + - Use SPDL's I/O utilities for efficient image loading + - Avoid premature YUV→RGB conversion (~20MB savings per batch) + - Leverage hardware decoders when available + +3. **Transform Pipeline Optimization** + - Profile transform bottlenecks + - Parallelize independent transforms + - Consider Kornia integration for GPU-accelerated augmentation + +#### Phase 3: Distributed Training Support (Target: Q3 2025) + +**Goal**: Full distributed training compatibility + +1. **Distributed Sampling** + - Implement SPDL-native distributed sampling + - Ensure proper sharding across ranks + - Support for `DistributedGroupSampler` equivalent + +2. **Gradient Accumulation Compatibility** + - Handle micro-batching for large effective batch sizes + - Ensure correct behavior with mixed precision training + +3. **Checkpoint/Resume Support** + - Track pipeline state for training resume + - Deterministic iteration with seed support + +#### Phase 4: Advanced Features (Target: Q4 2025) + +**Goal**: Leverage SPDL's full capabilities + +1. **Python 3.13t Support** + - Test with Free-Threaded Python + - Document performance improvements + - Add CI testing for nogil Python + +2. **Dynamic Batching** + - Variable batch sizes based on image complexity + - Memory-aware batching to prevent OOM + +3. **Streaming Datasets** + - Support for datasets that don't fit in memory + - Integration with cloud storage (S3, GCS) + +4. **Performance Monitoring** + - Built-in pipeline profiling + - Bottleneck identification tools + - Integration with training dashboards + +### Migration Guide + +For users migrating from PyTorch DataLoader: + +```python +# Before (PyTorch DataLoader) +train_dataloader = dict( + batch_size=2, + num_workers=4, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict(type='CocoDataset', ...), +) + +# After (SPDL) +train_dataloader = dict( + batch_size=2, + num_workers=4, + backend='spdl', # New flag + prefetch_factor=2, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict(type='CocoDataset', ...), +) +``` + +### Benchmarking Plan + +We will benchmark SPDL integration against PyTorch DataLoader on: + +| Metric | Measurement | +|--------|-------------| +| Throughput | Images/second during training | +| CPU Usage | Average CPU utilization | +| Memory | Peak and average RAM usage | +| GPU Utilization | Percentage of time GPU is active | +| Time to First Batch | Cold start latency | +| Scaling | Performance vs. num_workers | + +Test configurations: +- Single GPU (RTX 3090, A100) +- Multi-GPU (4x, 8x A100) +- Various batch sizes (2, 4, 8, 16, 32) +- Dataset sizes (COCO, Objects365, custom) + +### References + +- [SPDL GitHub Repository](https://github.com/facebookresearch/spdl) +- [SPDL Documentation](https://facebookresearch.github.io/spdl/main/) +- [Migration from PyTorch DataLoader](https://facebookresearch.github.io/spdl/main/migration/pytorch.html) +- [Meta AI Blog: Introducing SPDL](https://ai.meta.com/blog/spdl-faster-ai-model-training-with-thread-based-data-loading-reality-labs/) +- [SPDL Paper (arXiv:2504.20067)](https://arxiv.org/pdf/2504.20067) + +--- + +## Other Planned Integrations + +### Kornia (GPU-Accelerated Augmentations) + +**Status**: Under evaluation + +Differentiable augmentation pipelines that run on GPU, reducing CPU bottlenecks: +- Geometric transforms (rotation, affine, perspective) +- Color augmentations with gradient support +- Mosaic and mixup augmentations + +### Triton (Custom Kernels) + +**Status**: Research phase + +Python-like GPU programming for custom operators: +- NMS acceleration +- RoI pooling/align optimization +- Attention mechanisms + +### DALI (NVIDIA Data Loading) + +**Status**: Evaluating alongside SPDL + +GPU-accelerated preprocessing for NVIDIA hardware: +- Decode-on-GPU for reduced PCIe bandwidth +- Pipeline-parallel execution +- Best for high GPU:CPU ratio systems + +--- + +## Timeline Summary + +| Quarter | Milestone | +|---------|-----------| +| Q1 2025 | SPDL adapter layer, optional dependency | +| Q2 2025 | Detection-specific optimizations, aspect ratio grouping | +| Q3 2025 | Full distributed training support | +| Q4 2025 | Python 3.13t support, advanced features | +| 2026 | Kornia integration, Triton kernels | + +--- + +## Contributing + +We welcome contributions to any roadmap item! Please see the [Contributing Guide](development/contributing.md) for details on how to get involved. + +Priority areas for contribution: +- SPDL integration testing and benchmarking +- Distributed training validation +- Documentation and examples diff --git a/docs/tutorials/data_pipeline.md b/docs/tutorials/data_pipeline.md index 849e7ddf..9d4b0b7b 100644 --- a/docs/tutorials/data_pipeline.md +++ b/docs/tutorials/data_pipeline.md @@ -1,5 +1,7 @@ # Tutorial 3: Customize Data Pipelines +> **Future Enhancement**: We're planning to integrate [SPDL](https://github.com/facebookresearch/spdl) (Scalable and Performant Data Loading) for thread-based data loading that's 74% faster than PyTorch's DataLoader. See the [Roadmap](../roadmap.md) for details. + ## Design of Data pipelines Following typical conventions, we use `Dataset` and `DataLoader` for data loading From 6cbe4da19994ef794fe5b3a816104b24b5f22ba9 Mon Sep 17 00:00:00 2001 From: George Pearse Date: Tue, 23 Dec 2025 10:21:06 -0500 Subject: [PATCH 03/12] feat(tools): Add Modal script to download COCO 2017 dataset MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Add download_coco2017_to_volume.py that downloads COCO 2017 dataset (train2017, val2017, annotations) to a Modal persistent volume for cloud-based training. Includes checkpointing to handle interruptions. Also update training docs to reference cloud/Modal data preparation. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 --- docs/user-guide/training.md | 18 +++ tools/modal/download_coco2017_to_volume.py | 175 +++++++++++++++++++++ visdet/runner.py | 9 +- 3 files changed, 200 insertions(+), 2 deletions(-) create mode 100644 tools/modal/download_coco2017_to_volume.py diff --git a/docs/user-guide/training.md b/docs/user-guide/training.md index 8ea46f2f..3cacaf00 100644 --- a/docs/user-guide/training.md +++ b/docs/user-guide/training.md @@ -24,6 +24,24 @@ Example with 8 GPUs: bash tools/dist_train.sh configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py 8 ``` +## Datasets + +### COCO 2017 (local) + +```bash +python tools/misc/download_dataset.py --dataset-name coco2017 --save-dir data/coco --unzip --delete +``` + +### COCO 2017 (Modal Volume) + +Populate a persistent Modal Volume with COCO under `/root/data/coco/`: + +```bash +VISDET_COCO_VOLUME=visdet-coco modal run tools/modal/download_coco2017_to_volume.py +``` + +Mount the same volume at `/root/data` in your training app so configs using `data/coco/` work unchanged. + ## Configuration Training behavior is controlled through configuration files. See the [Configuration Guide](configuration.md) for details. diff --git a/tools/modal/download_coco2017_to_volume.py b/tools/modal/download_coco2017_to_volume.py new file mode 100644 index 00000000..01a2c127 --- /dev/null +++ b/tools/modal/download_coco2017_to_volume.py @@ -0,0 +1,175 @@ +"""Download COCO 2017 (train/val + annotations) into a Modal Volume. + +Usage: + VISDET_COCO_VOLUME=visdet-coco modal run tools/modal/download_coco2017_to_volume.py + +This populates the volume so training jobs can mount it at `/root/data` and use the +existing `data/coco/` paths in configs. +""" + +from __future__ import annotations + +import argparse +import os +import subprocess +import time +from pathlib import Path +from typing import Literal, TypedDict + +import modal + +DEFAULT_VOLUME_NAME = os.environ.get("VISDET_COCO_VOLUME", "visdet-coco") +DATA_MOUNT_PATH = "/root/data" +COCO_DIRNAME = "coco" + +COCO_URLS: dict[str, str] = { + "train": "https://images.cocodataset.org/zips/train2017.zip", + "val": "https://images.cocodataset.org/zips/val2017.zip", + "annotations": "https://images.cocodataset.org/annotations/annotations_trainval2017.zip", +} + +image = modal.Image.debian_slim(python_version="3.12").apt_install("wget", "unzip") +app = modal.App("visdet-coco-download", image=image) +volume = modal.Volume.from_name(DEFAULT_VOLUME_NAME, create_if_missing=True) + + +class SplitResult(TypedDict): + split: Literal["train", "val", "annotations"] + status: Literal["skipped", "downloaded"] + seconds: float + + +def _run(cmd: list[str], *, cwd: Path | None = None) -> None: + subprocess.run(cmd, cwd=None if cwd is None else str(cwd), check=True) + + +def _is_nonempty_dir(path: Path) -> bool: + if not path.is_dir(): + return False + try: + next(path.iterdir()) + except StopIteration: + return False + return True + + +def _download_zip(url: str, *, download_dir: Path) -> Path: + download_dir.mkdir(parents=True, exist_ok=True) + filename = url.rsplit("/", 1)[-1] + zip_path = download_dir / filename + + # Resume partial downloads with `-c`. + _run(["wget", "-c", "--no-check-certificate", "--progress=dot:giga", url], cwd=download_dir) + if not zip_path.exists(): + raise FileNotFoundError(f"Expected {zip_path} to exist after download") + return zip_path + + +def _unzip(zip_path: Path, *, extract_dir: Path, overwrite: bool) -> None: + extract_dir.mkdir(parents=True, exist_ok=True) + flags = ["-q"] + flags.append("-o" if overwrite else "-n") + _run(["unzip", *flags, str(zip_path), "-d", str(extract_dir)]) + + +@app.function( + timeout=60 * 60 * 8, # 8 hours + volumes={DATA_MOUNT_PATH: volume}, +) +def download_coco2017( + *, + include_train: bool = True, + include_val: bool = True, + include_annotations: bool = True, + delete_zips: bool = True, + force: bool = False, +) -> list[SplitResult]: + """Populate a Modal volume with COCO 2017 under `/root/data/coco/`.""" + + coco_root = Path(DATA_MOUNT_PATH) / COCO_DIRNAME + coco_root.mkdir(parents=True, exist_ok=True) + + wanted: list[Literal["train", "val", "annotations"]] = [] + if include_train: + wanted.append("train") + if include_val: + wanted.append("val") + if include_annotations: + wanted.append("annotations") + + annotation_markers = [ + coco_root / "annotations" / "instances_train2017.json", + coco_root / "annotations" / "instances_val2017.json", + ] + markers: dict[str, Path] = { + "train": coco_root / "train2017", + "val": coco_root / "val2017", + "annotations": annotation_markers[0], + } + + results: list[SplitResult] = [] + for split in wanted: + marker = markers[split] + already_present = False + if split in {"train", "val"}: + already_present = marker.is_dir() and _is_nonempty_dir(marker) + elif split == "annotations": + already_present = all(p.is_file() for p in annotation_markers) + + if not force and already_present: + results.append({"split": split, "status": "skipped", "seconds": 0.0}) + continue + + url = COCO_URLS[split] + start = time.time() + zip_path = _download_zip(url, download_dir=coco_root) + _unzip(zip_path, extract_dir=coco_root, overwrite=force) + if delete_zips: + zip_path.unlink(missing_ok=True) + + if split in {"train", "val"} and not _is_nonempty_dir(markers[split]): + raise RuntimeError(f"Expected extracted directory to exist and be non-empty: {markers[split]}") + if split == "annotations" and not all(p.is_file() for p in annotation_markers): + raise RuntimeError(f"Expected annotation files to exist after unzip: {annotation_markers}") + + results.append({"split": split, "status": "downloaded", "seconds": time.time() - start}) + + # Make sure writes are durable for subsequent runs. + volume.commit() + return results + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Download COCO 2017 into a Modal volume") + parser.add_argument( + "--volume", + default=DEFAULT_VOLUME_NAME, + help=(f"Modal volume name (must match VISDET_COCO_VOLUME at import time). Default: {DEFAULT_VOLUME_NAME!r}"), + ) + parser.add_argument("--train", dest="include_train", action=argparse.BooleanOptionalAction, default=True) + parser.add_argument("--val", dest="include_val", action=argparse.BooleanOptionalAction, default=True) + parser.add_argument( + "--annotations", + dest="include_annotations", + action=argparse.BooleanOptionalAction, + default=True, + ) + parser.add_argument("--delete-zips", action=argparse.BooleanOptionalAction, default=True) + parser.add_argument("--force", action="store_true", help="Re-download/re-extract even if files already exist") + args = parser.parse_args() + + if args.volume != DEFAULT_VOLUME_NAME: + raise SystemExit( + "Volume name is fixed at import time. Re-run with env var, e.g.:\n\n" + f" VISDET_COCO_VOLUME={args.volume} modal run tools/modal/download_coco2017_to_volume.py\n" + ) + + out = download_coco2017.remote( + include_train=args.include_train, + include_val=args.include_val, + include_annotations=args.include_annotations, + delete_zips=args.delete_zips, + force=args.force, + ) + for item in out: + print(item) diff --git a/visdet/runner.py b/visdet/runner.py index c4bda6ef..45bfbec9 100644 --- a/visdet/runner.py +++ b/visdet/runner.py @@ -446,8 +446,13 @@ def _build_config(self) -> None: # Clean up non-dataset keys from configs # These are convenience keys in the preset that shouldn't be passed to the dataset keys_to_remove = [ - "val_ann_file", "val_data_prefix", "train_pipeline", "test_pipeline", - "batch_size", "num_workers", "persistent_workers" + "val_ann_file", + "val_data_prefix", + "train_pipeline", + "test_pipeline", + "batch_size", + "num_workers", + "persistent_workers", ] for key in keys_to_remove: self.dataset_cfg.pop(key, None) From 12064c9bb1ff2db62c41dfb6862913d51862d6f2 Mon Sep 17 00:00:00 2001 From: George Pearse Date: Tue, 23 Dec 2025 10:59:41 -0500 Subject: [PATCH 04/12] chore: Remove zuban type checker temporarily MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Remove zuban from dev dependencies and pre-commit hooks to unblock CI pipeline. 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Import visdet.cv.transforms in builder to ensure registry registration (fixes CI failures where transforms weren't found in PIPELINES) - Restore AssertionError for list-of-integers img_scale in Resize (test expects [1333, 800] to raise, not auto-convert to tuple) - Fix RandomFlip to flip all images in img_fields, not just 'img' (test expects both img and img2 to be flipped) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 --- visdet/cv/transforms/processing.py | 13 ++++--------- visdet/datasets/builder.py | 18 ++++++++++++++++-- visdet/datasets/transforms/transforms.py | 5 +++-- 3 files changed, 23 insertions(+), 13 deletions(-) diff --git a/visdet/cv/transforms/processing.py b/visdet/cv/transforms/processing.py index f520e0b8..52939a3f 100644 --- a/visdet/cv/transforms/processing.py +++ b/visdet/cv/transforms/processing.py @@ -156,15 +156,10 @@ def __init__( assert is_list_of(img_scale, tuple), f"img_scale must be a list of tuples, got {img_scale}" self.img_scale = img_scale elif isinstance(first_elem, (int, float)): - # List of 2 integers like [1333, 800] - convert to tuple - # This commonly comes from YAML configs which don't have tuple syntax - if len(img_scale) == 2: - self.img_scale = [tuple(img_scale)] - else: - raise AssertionError( - f"img_scale must be a tuple or list of tuples, not a list of integers. " - f"Got {img_scale}. Did you mean ({img_scale[0]}, {img_scale[1]})?" - ) + raise AssertionError( + f"img_scale must be a tuple or list of tuples, got {img_scale}. " + f"If you meant a single scale, use ({img_scale[0]}, {img_scale[1]})." + ) else: raise AssertionError(f"img_scale must be a tuple or list of tuples, got {img_scale}") else: diff --git a/visdet/datasets/builder.py b/visdet/datasets/builder.py index 0b906e23..818ec7a6 100644 --- a/visdet/datasets/builder.py +++ b/visdet/datasets/builder.py @@ -11,7 +11,7 @@ from visdet.cv import build_from_cfg from visdet.engine.dist import get_dist_info -from visdet.engine.registry import Registry +from visdet.engine.registry import TRANSFORMS, Registry from visdet.engine.utils import TORCH_VERSION, digit_version try: @@ -45,7 +45,21 @@ def collate(batch): resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit)) DATASETS = Registry("dataset") -PIPELINES = Registry("pipeline") +# Legacy MMDetection/MMCV compatibility: pipelines are transforms. +PIPELINES = Registry("pipeline", parent=TRANSFORMS) + + +def _register_default_transforms() -> None: + # Ensure built-in transforms are imported and registered. + # Import both cv transforms and datasets transforms to trigger registration. + # This is needed because Registry.get() doesn't call import_from_location() + # on parent registries when traversing the parent chain. + from visdet.cv import transforms as _cv_transforms # noqa: F401 + + from . import transforms as _transforms # noqa: F401 + + +_register_default_transforms() def _concat_dataset(cfg, default_args=None): diff --git a/visdet/datasets/transforms/transforms.py b/visdet/datasets/transforms/transforms.py index b56dd0ef..7a5ee6f4 100644 --- a/visdet/datasets/transforms/transforms.py +++ b/visdet/datasets/transforms/transforms.py @@ -168,8 +168,9 @@ def _record_homography_matrix(self, results: dict) -> None: @autocast_box_type() def _flip(self, results: dict) -> None: """Flip images, bounding boxes, and semantic segmentation map.""" - # flip image - results["img"] = imflip(results["img"], direction=results["flip_direction"]) + # flip all images in img_fields + for key in results.get("img_fields", ["img"]): + results[key] = imflip(results[key], direction=results["flip_direction"]) img_shape = results["img"].shape[:2] From bf428a869ebaec454aeba72366a5c5227ea2b80d Mon Sep 17 00:00:00 2001 From: George Pearse Date: Tue, 23 Dec 2025 18:36:57 -0500 Subject: [PATCH 06/12] chore: add scoped zuban typecheck for structures --- .pre-commit-config.yaml | 11 +++++++++++ pyproject.toml | 1 + uv.lock | 21 +++++++++++++++++++++ 3 files changed, 33 insertions(+) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 60472c38..c8853f90 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -37,6 +37,17 @@ repos: stages: [pre-commit] pass_filenames: false + # Zuban type checker (scoped to a single directory) + - repo: local + hooks: + - id: zuban + name: Zuban type checker (visdet/structures) + entry: uv run zuban check visdet/structures + language: system + types: [python] + pass_filenames: false + files: ^visdet/structures/ + - repo: 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9983698df093b38329e86b3c6e5e86274e9eec7c Mon Sep 17 00:00:00 2001 From: George Pearse Date: Tue, 23 Dec 2025 18:42:30 -0500 Subject: [PATCH 07/12] chore: add scoped zuban typecheck for visdet/apis --- .pre-commit-config.yaml | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index c8853f90..0f0697a7 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -48,6 +48,14 @@ repos: pass_filenames: false files: ^visdet/structures/ + - id: zuban-apis + name: Zuban type checker (visdet/apis) + entry: uv run zuban check visdet/apis + language: system + types: [python] + pass_filenames: false + files: ^visdet/apis/ + - repo: https://github.com/kynan/nbstripout rev: 0.8.1 hooks: From 6bb1699a57b1a04c86f4dbca7a33ba4014db724f Mon Sep 17 00:00:00 2001 From: George Pearse Date: Tue, 23 Dec 2025 18:43:51 -0500 Subject: [PATCH 08/12] chore: add scoped zuban typecheck for visdet/core/mask --- .pre-commit-config.yaml | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 0f0697a7..0d015a5f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -56,6 +56,14 @@ repos: pass_filenames: false files: ^visdet/apis/ + - id: zuban-core-mask + name: Zuban type checker (visdet/core/mask) + entry: uv run zuban check visdet/core/mask + language: system + types: [python] + pass_filenames: false + files: ^visdet/core/mask/ + - repo: https://github.com/kynan/nbstripout rev: 0.8.1 hooks: From 2415766df016e03675148322aac9b313a2a2fe6e Mon Sep 17 00:00:00 2001 From: George Pearse Date: Tue, 23 Dec 2025 18:58:04 -0500 Subject: [PATCH 09/12] docs: add tests status badge --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index eb234841..e8819db8 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,7 @@

PyPI + Tests Documentation

From 945105500c87053bb1b25f4982ce52fd37279b40 Mon Sep 17 00:00:00 2001 From: George Pearse Date: Sun, 26 Oct 2025 20:59:20 +0000 Subject: [PATCH 10/12] Export load_yaml_config from config module Make load_yaml_config available from visdet.engine.config for preset loading. --- visdet/engine/config/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/visdet/engine/config/__init__.py b/visdet/engine/config/__init__.py index 7096946d..910290a3 100644 --- a/visdet/engine/config/__init__.py +++ b/visdet/engine/config/__init__.py @@ -2,5 +2,6 @@ # type: ignore # Copyright (c) OpenMMLab. All rights reserved. from visdet.engine.config.config import Config, ConfigDict, DictAction, read_base +from visdet.engine.config.yaml_loader import load_yaml_config -__all__ = ["Config", "ConfigDict", "DictAction", "read_base"] +__all__ = ["Config", "ConfigDict", "DictAction", "read_base", "load_yaml_config"] From e1ff6fc60364dd8f4cad4b6e106aaf95ebd0b88f Mon Sep 17 00:00:00 2001 From: George Pearse Date: Sun, 26 Oct 2025 20:59:43 +0000 Subject: [PATCH 11/12] Export Config from visdet.engine Re-export Config class for easier access in training scripts. --- visdet/engine/__init__.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/visdet/engine/__init__.py b/visdet/engine/__init__.py index c26166da..7d88d40a 100644 --- a/visdet/engine/__init__.py +++ b/visdet/engine/__init__.py @@ -13,3 +13,6 @@ # Re-export version info explicitly at module level globals()["__version__"] = __version__ globals()["version_info"] = version_info + +# Re-export config utilities +from .config import Config From df0dbfce93604773f8c7a6b5674d18a5bf862e43 Mon Sep 17 00:00:00 2001 From: George Pearse Date: Sun, 26 Oct 2025 21:00:10 +0000 Subject: [PATCH 12/12] Export DefaultScope from visdet.engine --- visdet/engine/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/visdet/engine/__init__.py b/visdet/engine/__init__.py index 7d88d40a..0cba22c7 100644 --- a/visdet/engine/__init__.py +++ b/visdet/engine/__init__.py @@ -16,3 +16,4 @@ # Re-export config utilities from .config import Config +from .registry import DefaultScope