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__ = [