reapply linter & upgrade black to 24.3.0

Summary:
Pull Request resolved: #5275

Upgrade the `black` and `flake8` versions to match FB internal. Re-run the `./vision/fair/detectron2/dev/linter.sh` to reapply linter. Manually fix lint errors.

Differential Revision: D56913399

fbshipit-source-id: 0e33fd3a888596871ad641a1c43562a61b50556a

Author: Yanghan Wang

.github/workflows/workflow.yml

@@ -18,8 +18,8 @@ jobs:
         # flake8-bugbear flake8-comprehensions are useful but not available internally
         run: |
           python -m pip install --upgrade pip
-          python -m pip install flake8==3.8.1 isort==4.3.21
-          python -m pip install black==22.3.0
+          python -m pip install flake8==6.1.0 isort==4.3.21
+          python -m pip install black==24.3.0
           flake8 --version
       - name: Lint
         run: |

configs/Misc/torchvision_imagenet_R_50.py

@@ -10,7 +10,6 @@
 
 """
 
-
 import torch
 from torch import nn
 from torch.nn import functional as F

configs/common/train.py

@@ -13,6 +13,6 @@
     checkpointer=dict(period=5000, max_to_keep=100),  # options for PeriodicCheckpointer
     eval_period=5000,
     log_period=20,
-    device="cuda"
+    device="cuda",
     # ...
 )

demo/demo.py

@@ -2,13 +2,12 @@
 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
@@ -32,9 +31,7 @@ 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
 
@@ -47,9 +44,7 @@ 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",
@@ -118,9 +113,11 @@ def main() -> None:
             logger.info(
                 "{}: {} in {:.2f}s".format(
                     path,
-                    "detected {} instances".format(len(predictions["instances"]))
-                    if "instances" in predictions
-                    else "finished",
+                    (
+                        "detected {} instances".format(len(predictions["instances"]))
+                        if "instances" in predictions
+                        else "finished"
+                    ),
                     time.time() - start_time,
                 )
             )
@@ -130,9 +127,7 @@ def main() -> None:
                     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:
@@ -159,9 +154,7 @@ def main() -> None:
         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")

detectron2/checkpoint/c2_model_loading.py

@@ -4,7 +4,6 @@
 import re
 from typing import Dict, List
 import torch
-from tabulate import tabulate
 
 
 def convert_basic_c2_names(original_keys):

detectron2/data/build.py

@@ -364,7 +364,7 @@ def build_batch_data_loader(
             collate_fn=operator.itemgetter(0),  # don't batch, but yield individual elements
             worker_init_fn=worker_init_reset_seed,
             generator=generator,
-            **kwargs
+            **kwargs,
         )  # yield individual mapped dict
         data_loader = AspectRatioGroupedDataset(data_loader, batch_size)
         if collate_fn is None:
@@ -408,12 +408,14 @@ def _build_weighted_sampler(cfg, enable_category_balance=False):
             name: get_detection_dataset_dicts(
                 [name],
                 filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
-                min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
-                if cfg.MODEL.KEYPOINT_ON
-                else 0,
-                proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN
-                if cfg.MODEL.LOAD_PROPOSALS
-                else None,
+                min_keypoints=(
+                    cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
+                    if cfg.MODEL.KEYPOINT_ON
+                    else 0
+                ),
+                proposal_files=(
+                    cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None
+                ),
             )
             for name in cfg.DATASETS.TRAIN
         }
@@ -466,9 +468,9 @@ def _train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None):
         dataset = get_detection_dataset_dicts(
             cfg.DATASETS.TRAIN,
             filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
-            min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
-            if cfg.MODEL.KEYPOINT_ON
-            else 0,
+            min_keypoints=(
+                cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE if cfg.MODEL.KEYPOINT_ON else 0
+            ),
             proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
         )
         _log_api_usage("dataset." + cfg.DATASETS.TRAIN[0])
@@ -522,7 +524,7 @@ def build_detection_train_loader(
     aspect_ratio_grouping=True,
     num_workers=0,
     collate_fn=None,
-    **kwargs
+    **kwargs,
 ):
     """
     Build a dataloader for object detection with some default features.
@@ -574,7 +576,7 @@ def build_detection_train_loader(
         aspect_ratio_grouping=aspect_ratio_grouping,
         num_workers=num_workers,
         collate_fn=collate_fn,
-        **kwargs
+        **kwargs,
     )
 
 
@@ -589,21 +591,26 @@ def _test_loader_from_config(cfg, dataset_name, mapper=None):
     dataset = get_detection_dataset_dicts(
         dataset_name,
         filter_empty=False,
-        proposal_files=[
-            cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(x)] for x in dataset_name
-        ]
-        if cfg.MODEL.LOAD_PROPOSALS
-        else None,
+        proposal_files=(
+            [
+                cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(x)]
+                for x in dataset_name
+            ]
+            if cfg.MODEL.LOAD_PROPOSALS
+            else None
+        ),
     )
     if mapper is None:
         mapper = DatasetMapper(cfg, False)
     return {
         "dataset": dataset,
         "mapper": mapper,
         "num_workers": cfg.DATALOADER.NUM_WORKERS,
-        "sampler": InferenceSampler(len(dataset))
-        if not isinstance(dataset, torchdata.IterableDataset)
-        else None,
+        "sampler": (
+            InferenceSampler(len(dataset))
+            if not isinstance(dataset, torchdata.IterableDataset)
+            else None
+        ),
     }
 
 

detectron2/data/datasets/cityscapes.py

@@ -3,17 +3,16 @@
 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
@@ -40,9 +39,7 @@ 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")
 
@@ -79,9 +76,7 @@ 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))
@@ -110,9 +105,7 @@ 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:
@@ -216,9 +209,7 @@ 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:
@@ -272,9 +263,9 @@ 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:
@@ -318,9 +309,7 @@ def main() -> None:
         )
         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: