Try to format code as in #5106

Author: vfdev-5

references/classification/train_quantization.py

@@ -9,7 +9,7 @@
 import torchvision
 import utils
 from torch import nn
-from train import train_one_epoch, evaluate, load_data
+from train import evaluate, load_data, train_one_epoch
 
 
 def main(args):

references/detection/group_by_aspect_ratio.py

@@ -2,7 +2,7 @@
 import copy
 import math
 from collections import defaultdict
-from itertools import repeat, chain
+from itertools import chain, repeat
 
 import numpy as np
 import torch

references/detection/train.py

@@ -29,8 +29,8 @@
 import torchvision.models.detection.mask_rcnn
 import utils
 from coco_utils import get_coco, get_coco_kp
-from engine import train_one_epoch, evaluate
-from group_by_aspect_ratio import GroupedBatchSampler, create_aspect_ratio_groups
+from engine import evaluate, train_one_epoch
+from group_by_aspect_ratio import create_aspect_ratio_groups, GroupedBatchSampler
 from torchvision.transforms import InterpolationMode
 from transforms import SimpleCopyPaste
 

references/detection/transforms.py

@@ -1,11 +1,10 @@
-from typing import List, Tuple, Dict, Optional, Union
+from typing import Dict, List, Optional, Tuple, Union
 
 import torch
 import torchvision
 from torch import nn, Tensor
 from torchvision import ops
-from torchvision.transforms import functional as F
-from torchvision.transforms import transforms as T, InterpolationMode
+from torchvision.transforms import functional as F, InterpolationMode, transforms as T
 
 
 def _flip_coco_person_keypoints(kps, width):

references/optical_flow/train.py

@@ -6,8 +6,8 @@
 import torch
 import torchvision.models.optical_flow
 import utils
-from presets import OpticalFlowPresetTrain, OpticalFlowPresetEval
-from torchvision.datasets import KittiFlow, FlyingChairs, FlyingThings3D, Sintel, HD1K
+from presets import OpticalFlowPresetEval, OpticalFlowPresetTrain
+from torchvision.datasets import FlyingChairs, FlyingThings3D, HD1K, KittiFlow, Sintel
 
 
 def get_train_dataset(stage, dataset_root):

references/optical_flow/utils.py

@@ -1,8 +1,7 @@
 import datetime
 import os
 import time
-from collections import defaultdict
-from collections import deque
+from collections import defaultdict, deque
 
 import torch
 import torch.distributed as dist
@@ -158,7 +157,7 @@ def log_every(self, iterable, print_freq=5, header=None):
 def compute_metrics(flow_pred, flow_gt, valid_flow_mask=None):
 
     epe = ((flow_pred - flow_gt) ** 2).sum(dim=1).sqrt()
-    flow_norm = (flow_gt ** 2).sum(dim=1).sqrt()
+    flow_norm = (flow_gt**2).sum(dim=1).sqrt()
 
     if valid_flow_mask is not None:
         epe = epe[valid_flow_mask]
@@ -183,7 +182,7 @@ def sequence_loss(flow_preds, flow_gt, valid_flow_mask, gamma=0.8, max_flow=400)
         raise ValueError(f"Gamma should be < 1, got {gamma}.")
 
     # exlude invalid pixels and extremely large diplacements
-    flow_norm = torch.sum(flow_gt ** 2, dim=1).sqrt()
+    flow_norm = torch.sum(flow_gt**2, dim=1).sqrt()
     valid_flow_mask = valid_flow_mask & (flow_norm < max_flow)
 
     valid_flow_mask = valid_flow_mask[:, None, :, :]

references/segmentation/utils.py

@@ -75,7 +75,7 @@ def update(self, a, b):
         with torch.inference_mode():
             k = (a >= 0) & (a < n)
             inds = n * a[k].to(torch.int64) + b[k]
-            self.mat += torch.bincount(inds, minlength=n ** 2).reshape(n, n)
+            self.mat += torch.bincount(inds, minlength=n**2).reshape(n, n)
 
     def reset(self):
         self.mat.zero_()

test/builtin_dataset_mocks.py

@@ -14,12 +14,12 @@
 import unittest.mock
 import warnings
 import xml.etree.ElementTree as ET
-from collections import defaultdict, Counter
+from collections import Counter, defaultdict
 
 import numpy as np
 import pytest
 import torch
-from datasets_utils import make_zip, make_tar, create_image_folder, create_image_file, combinations_grid
+from datasets_utils import combinations_grid, create_image_file, create_image_folder, make_tar, make_zip
 from torch.nn.functional import one_hot
 from torch.testing import make_tensor as _make_tensor
 from torchvision.prototype import datasets

test/conftest.py

@@ -3,7 +3,7 @@
 import numpy as np
 import pytest
 import torch
-from common_utils import IN_CIRCLE_CI, CIRCLECI_GPU_NO_CUDA_MSG, IN_FBCODE, IN_RE_WORKER, CUDA_NOT_AVAILABLE_MSG
+from common_utils import CIRCLECI_GPU_NO_CUDA_MSG, CUDA_NOT_AVAILABLE_MSG, IN_CIRCLE_CI, IN_FBCODE, IN_RE_WORKER
 
 
 def pytest_configure(config):

test/datasets_utils.py

@@ -22,7 +22,7 @@
 import torch
 import torchvision.datasets
 import torchvision.io
-from common_utils import get_tmp_dir, disable_console_output
+from common_utils import disable_console_output, get_tmp_dir
 
 
 __all__ = [

test/test_datasets_download.py

@@ -9,15 +9,15 @@
 from os import path
 from urllib.error import HTTPError, URLError
 from urllib.parse import urlparse
-from urllib.request import urlopen, Request
+from urllib.request import Request, urlopen
 
 import pytest
 from torchvision import datasets
 from torchvision.datasets.utils import (
-    download_url,
+    _get_redirect_url,
     check_integrity,
     download_file_from_google_drive,
-    _get_redirect_url,
+    download_url,
     USER_AGENT,
 )
 

test/test_datasets_samplers.py

@@ -1,12 +1,8 @@
 import pytest
 import torch
-from common_utils import get_list_of_videos, assert_equal
+from common_utils import assert_equal, get_list_of_videos
 from torchvision import io
-from torchvision.datasets.samplers import (
-    DistributedSampler,
-    RandomClipSampler,
-    UniformClipSampler,
-)
+from torchvision.datasets.samplers import DistributedSampler, RandomClipSampler, UniformClipSampler
 from torchvision.datasets.video_utils import VideoClips
 
 

test/test_datasets_video_utils.py

@@ -1,8 +1,8 @@
 import pytest
 import torch
-from common_utils import get_list_of_videos, assert_equal
+from common_utils import assert_equal, get_list_of_videos
 from torchvision import io
-from torchvision.datasets.video_utils import VideoClips, unfold
+from torchvision.datasets.video_utils import unfold, VideoClips
 
 
 class TestVideo:

test/test_extended_models.py

@@ -5,7 +5,7 @@
 import test_models as TM
 import torch
 from torchvision import models
-from torchvision.models._api import WeightsEnum, Weights
+from torchvision.models._api import Weights, WeightsEnum
 from torchvision.models._utils import handle_legacy_interface
 
 

test/test_functional_tensor.py

@@ -14,14 +14,14 @@
 import torchvision.transforms.functional_pil as F_pil
 import torchvision.transforms.functional_tensor as F_t
 from common_utils import (
-    cpu_and_gpu,
-    needs_cuda,
+    _assert_approx_equal_tensor_to_pil,
+    _assert_equal_tensor_to_pil,
     _create_data,
     _create_data_batch,
-    _assert_equal_tensor_to_pil,
-    _assert_approx_equal_tensor_to_pil,
     _test_fn_on_batch,
     assert_equal,
+    cpu_and_gpu,
+    needs_cuda,
 )
 from torchvision.transforms import InterpolationMode
 

test/test_image.py

@@ -8,21 +8,21 @@
 import pytest
 import torch
 import torchvision.transforms.functional as F
-from common_utils import needs_cuda, assert_equal
-from PIL import Image, __version__ as PILLOW_VERSION
+from common_utils import assert_equal, needs_cuda
+from PIL import __version__ as PILLOW_VERSION, Image
 from torchvision.io.image import (
-    decode_png,
+    _read_png_16,
+    decode_image,
     decode_jpeg,
+    decode_png,
     encode_jpeg,
-    write_jpeg,
-    decode_image,
-    read_file,
     encode_png,
-    write_png,
-    write_file,
     ImageReadMode,
+    read_file,
     read_image,
-    _read_png_16,
+    write_file,
+    write_jpeg,
+    write_png,
 )
 
 IMAGE_ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets")
@@ -168,7 +168,7 @@ def test_decode_png(img_path, pil_mode, mode):
         img_lpng = _read_png_16(img_path, mode=mode)
         assert img_lpng.dtype == torch.int32
         # PIL converts 16 bits pngs in uint8
-        img_lpng = torch.round(img_lpng / (2 ** 16 - 1) * 255).to(torch.uint8)
+        img_lpng = torch.round(img_lpng / (2**16 - 1) * 255).to(torch.uint8)
     else:
         data = read_file(img_path)
         img_lpng = decode_image(data, mode=mode)

test/test_models.py

@@ -14,7 +14,7 @@
 import torch.fx
 import torch.nn as nn
 from _utils_internal import get_relative_path
-from common_utils import map_nested_tensor_object, freeze_rng_state, set_rng_seed, cpu_and_gpu, needs_cuda
+from common_utils import cpu_and_gpu, freeze_rng_state, map_nested_tensor_object, needs_cuda, set_rng_seed
 from torchvision import models
 
 ACCEPT = os.getenv("EXPECTTEST_ACCEPT", "0") == "1"

test/test_models_detection_negative_samples.py

@@ -4,7 +4,7 @@
 from common_utils import assert_equal
 from torchvision.models.detection.faster_rcnn import FastRCNNPredictor, TwoMLPHead
 from torchvision.models.detection.roi_heads import RoIHeads
-from torchvision.models.detection.rpn import AnchorGenerator, RPNHead, RegionProposalNetwork
+from torchvision.models.detection.rpn import AnchorGenerator, RegionProposalNetwork, RPNHead
 from torchvision.ops import MultiScaleRoIAlign
 
 
@@ -60,7 +60,7 @@ def test_assign_targets_to_proposals(self):
 
         resolution = box_roi_pool.output_size[0]
         representation_size = 1024
-        box_head = TwoMLPHead(4 * resolution ** 2, representation_size)
+        box_head = TwoMLPHead(4 * resolution**2, representation_size)
 
         representation_size = 1024
         box_predictor = FastRCNNPredictor(representation_size, 2)

test/test_onnx.py

@@ -4,13 +4,12 @@
 
 import pytest
 import torch
-from common_utils import set_rng_seed, assert_equal
-from torchvision import models
-from torchvision import ops
+from common_utils import assert_equal, set_rng_seed
+from torchvision import models, ops
 from torchvision.models.detection.faster_rcnn import FastRCNNPredictor, TwoMLPHead
 from torchvision.models.detection.image_list import ImageList
 from torchvision.models.detection.roi_heads import RoIHeads
-from torchvision.models.detection.rpn import AnchorGenerator, RPNHead, RegionProposalNetwork
+from torchvision.models.detection.rpn import AnchorGenerator, RegionProposalNetwork, RPNHead
 from torchvision.models.detection.transform import GeneralizedRCNNTransform
 from torchvision.ops._register_onnx_ops import _onnx_opset_version
 
@@ -265,7 +264,7 @@ def _init_test_roi_heads_faster_rcnn(self):
 
         resolution = box_roi_pool.output_size[0]
         representation_size = 1024
-        box_head = TwoMLPHead(out_channels * resolution ** 2, representation_size)
+        box_head = TwoMLPHead(out_channels * resolution**2, representation_size)
 
         representation_size = 1024
         box_predictor = FastRCNNPredictor(representation_size, num_classes)

test/test_ops.py

@@ -79,7 +79,7 @@ def test_forward(self, device, contiguous, x_dtype=None, rois_dtype=None, **kwar
         rois_dtype = self.dtype if rois_dtype is None else rois_dtype
         pool_size = 5
         # n_channels % (pool_size ** 2) == 0 required for PS opeartions.
-        n_channels = 2 * (pool_size ** 2)
+        n_channels = 2 * (pool_size**2)
         x = torch.rand(2, n_channels, 10, 10, dtype=x_dtype, device=device)
         if not contiguous:
             x = x.permute(0, 1, 3, 2)
@@ -115,7 +115,7 @@ def test_is_leaf_node(self, device):
     def test_backward(self, seed, device, contiguous):
         torch.random.manual_seed(seed)
         pool_size = 2
-        x = torch.rand(1, 2 * (pool_size ** 2), 5, 5, dtype=self.dtype, device=device, requires_grad=True)
+        x = torch.rand(1, 2 * (pool_size**2), 5, 5, dtype=self.dtype, device=device, requires_grad=True)
         if not contiguous:
             x = x.permute(0, 1, 3, 2)
         rois = torch.tensor(

test/test_prototype_builtin_datasets.py

@@ -5,14 +5,14 @@
 
 import pytest
 import torch
-from builtin_dataset_mocks import parametrize_dataset_mocks, DATASET_MOCKS
-from torch.testing._comparison import assert_equal, TensorLikePair, ObjectPair
+from builtin_dataset_mocks import DATASET_MOCKS, parametrize_dataset_mocks
+from torch.testing._comparison import assert_equal, ObjectPair, TensorLikePair
 from torch.utils.data import DataLoader
 from torch.utils.data.graph import traverse
 from torch.utils.data.graph_settings import get_all_graph_pipes
-from torchdata.datapipes.iter import Shuffler, ShardingFilter
+from torchdata.datapipes.iter import ShardingFilter, Shuffler
 from torchvision._utils import sequence_to_str
-from torchvision.prototype import transforms, datasets
+from torchvision.prototype import datasets, transforms
 from torchvision.prototype.datasets.utils._internal import INFINITE_BUFFER_SIZE
 from torchvision.prototype.features import Image, Label
 

test/test_prototype_datasets_utils.py

@@ -9,8 +9,8 @@
 from torchdata.datapipes.iter import FileOpener, TarArchiveLoader
 from torchvision.datasets._optical_flow import _read_flo as read_flo_ref
 from torchvision.datasets.utils import _decompress
-from torchvision.prototype.datasets.utils import HttpResource, GDriveResource, Dataset, OnlineResource
-from torchvision.prototype.datasets.utils._internal import read_flo, fromfile
+from torchvision.prototype.datasets.utils import Dataset, GDriveResource, HttpResource, OnlineResource
+from torchvision.prototype.datasets.utils._internal import fromfile, read_flo
 
 
 @pytest.mark.filterwarnings("error:The given NumPy array is not writeable:UserWarning")

test/test_prototype_models.py

@@ -2,7 +2,7 @@
 import test_models as TM
 import torch
 import torchvision.prototype.models.depth.stereo.raft_stereo as raft_stereo
-from common_utils import set_rng_seed, cpu_and_gpu
+from common_utils import cpu_and_gpu, set_rng_seed
 
 
 @pytest.mark.parametrize("model_builder", (raft_stereo.raft_stereo_base, raft_stereo.raft_stereo_realtime))

test/test_prototype_transforms.py

@@ -4,15 +4,15 @@
 import torch
 from common_utils import assert_equal
 from test_prototype_transforms_functional import (
-    make_images,
-    make_bounding_boxes,
     make_bounding_box,
-    make_one_hot_labels,
+    make_bounding_boxes,
+    make_images,
     make_label,
+    make_one_hot_labels,
     make_segmentation_mask,
 )
-from torchvision.prototype import transforms, features
-from torchvision.transforms.functional import to_pil_image, pil_to_tensor, InterpolationMode
+from torchvision.prototype import features, transforms
+from torchvision.transforms.functional import InterpolationMode, pil_to_tensor, to_pil_image
 
 
 def make_vanilla_tensor_images(*args, **kwargs):

test/test_transforms.py

@@ -24,7 +24,7 @@
 except ImportError:
     stats = None
 
-from common_utils import cycle_over, int_dtypes, float_dtypes, assert_equal
+from common_utils import assert_equal, cycle_over, float_dtypes, int_dtypes
 
 
 GRACE_HOPPER = get_file_path_2(