Merge branch 'main' into main

demo/demo.py

@@ -2,12 +2,13 @@
 import argparse
 import glob
 import multiprocessing as mp
-import numpy as np
 import os
 import tempfile
 import time
 import warnings
+
 import cv2
+import numpy as np
 import tqdm
 
 from detectron2.config import get_cfg
@@ -31,7 +32,9 @@ def setup_cfg(args):
     # Set score_threshold for builtin models
     cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold
     cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold
-    cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold
+    cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = (
+        args.confidence_threshold
+    )
     cfg.freeze()
     return cfg
 
@@ -44,7 +47,9 @@ def get_parser():
         metavar="FILE",
         help="path to config file",
     )
-    parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.")
+    parser.add_argument(
+        "--webcam", action="store_true", help="Take inputs from webcam."
+    )
     parser.add_argument("--video-input", help="Path to video file.")
     parser.add_argument(
         "--input",
@@ -90,7 +95,7 @@ def test_opencv_video_format(codec, file_ext):
         return False
 
 
-if __name__ == "__main__":
+def main() -> None:
     mp.set_start_method("spawn", force=True)
     args = get_parser().parse_args()
     setup_logger(name="fvcore")
@@ -125,7 +130,9 @@ def test_opencv_video_format(codec, file_ext):
                     assert os.path.isdir(args.output), args.output
                     out_filename = os.path.join(args.output, os.path.basename(path))
                 else:
-                    assert len(args.input) == 1, "Please specify a directory with args.output"
+                    assert (
+                        len(args.input) == 1
+                    ), "Please specify a directory with args.output"
                     out_filename = args.output
                 visualized_output.save(out_filename)
             else:
@@ -152,7 +159,9 @@ def test_opencv_video_format(codec, file_ext):
         num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
         basename = os.path.basename(args.video_input)
         codec, file_ext = (
-            ("x264", ".mkv") if test_opencv_video_format("x264", ".mkv") else ("mp4v", ".mp4")
+            ("x264", ".mkv")
+            if test_opencv_video_format("x264", ".mkv")
+            else ("mp4v", ".mp4")
         )
         if codec == ".mp4v":
             warnings.warn("x264 codec not available, switching to mp4v")
@@ -186,3 +195,7 @@ def test_opencv_video_format(codec, file_ext):
             output_file.release()
         else:
             cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    main()  # pragma: no cover

detectron2/config/defaults.py

@@ -121,6 +121,8 @@
 _C.DATALOADER.SAMPLER_TRAIN = "TrainingSampler"
 # Repeat threshold for RepeatFactorTrainingSampler
 _C.DATALOADER.REPEAT_THRESHOLD = 0.0
+# if True, take square root when computing repeating factor
+_C.DATALOADER.REPEAT_SQRT = True
 # Tf True, when working on datasets that have instance annotations, the
 # training dataloader will filter out images without associated annotations
 _C.DATALOADER.FILTER_EMPTY_ANNOTATIONS = True

detectron2/data/build.py

@@ -301,6 +301,7 @@ def build_batch_data_loader(
     collate_fn=None,
     drop_last: bool = True,
     single_gpu_batch_size=None,
+    seed=None,
     **kwargs,
 ):
     """
@@ -347,13 +348,19 @@ def build_batch_data_loader(
     else:
         dataset = ToIterableDataset(dataset, sampler, shard_chunk_size=batch_size)
 
+    generator = None
+    if seed is not None:
+        generator = torch.Generator()
+        generator.manual_seed(seed)
+
     if aspect_ratio_grouping:
         assert drop_last, "Aspect ratio grouping will drop incomplete batches."
         data_loader = torchdata.DataLoader(
             dataset,
             num_workers=num_workers,
             collate_fn=operator.itemgetter(0),  # don't batch, but yield individual elements
             worker_init_fn=worker_init_reset_seed,
+            generator=generator,
             **kwargs
         )  # yield individual mapped dict
         data_loader = AspectRatioGroupedDataset(data_loader, batch_size)
@@ -368,6 +375,7 @@ def build_batch_data_loader(
             num_workers=num_workers,
             collate_fn=trivial_batch_collator if collate_fn is None else collate_fn,
             worker_init_fn=worker_init_reset_seed,
+            generator=generator,
             **kwargs
         )
 
@@ -422,7 +430,7 @@ def _build_weighted_sampler(cfg, enable_category_balance=False):
         """
         category_repeat_factors = [
             RepeatFactorTrainingSampler.repeat_factors_from_category_frequency(
-                dataset_dict, cfg.DATALOADER.REPEAT_THRESHOLD
+                dataset_dict, cfg.DATALOADER.REPEAT_THRESHOLD, sqrt=cfg.DATALOADER.REPEAT_SQRT
             )
             for dataset_dict in dataset_name_to_dicts.values()
         ]
@@ -474,7 +482,7 @@ def _train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None):
                 sampler = TrainingSampler(len(dataset))
             elif sampler_name == "RepeatFactorTrainingSampler":
                 repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency(
-                    dataset, cfg.DATALOADER.REPEAT_THRESHOLD
+                    dataset, cfg.DATALOADER.REPEAT_THRESHOLD, sqrt=cfg.DATALOADER.REPEAT_SQRT
                 )
                 sampler = RepeatFactorTrainingSampler(repeat_factors)
             elif sampler_name == "RandomSubsetTrainingSampler":

detectron2/data/datasets/cityscapes.py

@@ -3,16 +3,17 @@
 import json
 import logging
 import multiprocessing as mp
-import numpy as np
 import os
 from itertools import chain
+
+import numpy as np
 import pycocotools.mask as mask_util
-from PIL import Image
 
 from detectron2.structures import BoxMode
 from detectron2.utils.comm import get_world_size
 from detectron2.utils.file_io import PathManager
 from detectron2.utils.logger import setup_logger
+from PIL import Image
 
 try:
     import cv2  # noqa
@@ -39,7 +40,9 @@ def _get_cityscapes_files(image_dir, gt_dir):
             assert basename.endswith(suffix), basename
             basename = basename[: -len(suffix)]
 
-            instance_file = os.path.join(city_gt_dir, basename + "gtFine_instanceIds.png")
+            instance_file = os.path.join(
+                city_gt_dir, basename + "gtFine_instanceIds.png"
+            )
             label_file = os.path.join(city_gt_dir, basename + "gtFine_labelIds.png")
             json_file = os.path.join(city_gt_dir, basename + "gtFine_polygons.json")
 
@@ -76,7 +79,9 @@ def load_cityscapes_instances(image_dir, gt_dir, from_json=True, to_polygons=Tru
     pool = mp.Pool(processes=max(mp.cpu_count() // get_world_size() // 2, 4))
 
     ret = pool.map(
-        functools.partial(_cityscapes_files_to_dict, from_json=from_json, to_polygons=to_polygons),
+        functools.partial(
+            _cityscapes_files_to_dict, from_json=from_json, to_polygons=to_polygons
+        ),
         files,
     )
     logger.info("Loaded {} images from {}".format(len(ret), image_dir))
@@ -105,7 +110,9 @@ def load_cityscapes_semantic(image_dir, gt_dir):
     ret = []
     # gt_dir is small and contain many small files. make sense to fetch to local first
     gt_dir = PathManager.get_local_path(gt_dir)
-    for image_file, _, label_file, json_file in _get_cityscapes_files(image_dir, gt_dir):
+    for image_file, _, label_file, json_file in _get_cityscapes_files(
+        image_dir, gt_dir
+    ):
         label_file = label_file.replace("labelIds", "labelTrainIds")
 
         with PathManager.open(json_file, "r") as f:
@@ -209,7 +216,9 @@ def _cityscapes_files_to_dict(files, from_json, to_polygons):
             elif isinstance(poly_wo_overlaps, MultiPolygon):
                 poly_list = poly_wo_overlaps.geoms
             else:
-                raise NotImplementedError("Unknown geometric structure {}".format(poly_wo_overlaps))
+                raise NotImplementedError(
+                    "Unknown geometric structure {}".format(poly_wo_overlaps)
+                )
 
             poly_coord = []
             for poly_el in poly_list:
@@ -263,9 +272,9 @@ def _cityscapes_files_to_dict(files, from_json, to_polygons):
             if to_polygons:
                 # This conversion comes from D4809743 and D5171122,
                 # when Mask-RCNN was first developed.
-                contours = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[
-                    -2
-                ]
+                contours = cv2.findContours(
+                    mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
+                )[-2]
                 polygons = [c.reshape(-1).tolist() for c in contours if len(c) >= 3]
                 # opencv's can produce invalid polygons
                 if len(polygons) == 0:
@@ -278,7 +287,8 @@ def _cityscapes_files_to_dict(files, from_json, to_polygons):
     return ret
 
 
-if __name__ == "__main__":
+def main() -> None:
+    global logger, labels
     """
     Test the cityscapes dataset loader.
 
@@ -293,9 +303,9 @@ def _cityscapes_files_to_dict(files, from_json, to_polygons):
     parser.add_argument("gt_dir")
     parser.add_argument("--type", choices=["instance", "semantic"], default="instance")
     args = parser.parse_args()
+    from cityscapesscripts.helpers.labels import labels
     from detectron2.data.catalog import Metadata
     from detectron2.utils.visualizer import Visualizer
-    from cityscapesscripts.helpers.labels import labels
 
     logger = setup_logger(name=__name__)
 
@@ -308,7 +318,9 @@ def _cityscapes_files_to_dict(files, from_json, to_polygons):
         )
         logger.info("Done loading {} samples.".format(len(dicts)))
 
-        thing_classes = [k.name for k in labels if k.hasInstances and not k.ignoreInEval]
+        thing_classes = [
+            k.name for k in labels if k.hasInstances and not k.ignoreInEval
+        ]
         meta = Metadata().set(thing_classes=thing_classes)
 
     else:
@@ -327,3 +339,7 @@ def _cityscapes_files_to_dict(files, from_json, to_polygons):
         # cv2.waitKey()
         fpath = os.path.join(dirname, os.path.basename(d["file_name"]))
         vis.save(fpath)
+
+
+if __name__ == "__main__":
+    main()  # pragma: no cover