run pre-commit

Author: NicolasHug

hubconf.py

@@ -3,8 +3,8 @@
 
 from torchvision.models import get_weight
 from torchvision.models.alexnet import alexnet
-from torchvision.models.convnext import convnext_tiny, convnext_small, convnext_base, convnext_large
-from torchvision.models.densenet import densenet121, densenet169, densenet201, densenet161
+from torchvision.models.convnext import convnext_base, convnext_large, convnext_small, convnext_tiny
+from torchvision.models.densenet import densenet121, densenet161, densenet169, densenet201
 from torchvision.models.efficientnet import (
     efficientnet_b0,
     efficientnet_b1,
@@ -14,9 +14,9 @@
     efficientnet_b5,
     efficientnet_b6,
     efficientnet_b7,
-    efficientnet_v2_s,
-    efficientnet_v2_m,
     efficientnet_v2_l,
+    efficientnet_v2_m,
+    efficientnet_v2_s,
 )
 from torchvision.models.googlenet import googlenet
 from torchvision.models.inception import inception_v3
@@ -25,39 +25,39 @@
 from torchvision.models.mobilenetv3 import mobilenet_v3_large, mobilenet_v3_small
 from torchvision.models.optical_flow import raft_large, raft_small
 from torchvision.models.regnet import (
-    regnet_y_400mf,
-    regnet_y_800mf,
-    regnet_y_1_6gf,
-    regnet_y_3_2gf,
-    regnet_y_8gf,
-    regnet_y_16gf,
-    regnet_y_32gf,
-    regnet_y_128gf,
-    regnet_x_400mf,
-    regnet_x_800mf,
+    regnet_x_16gf,
     regnet_x_1_6gf,
+    regnet_x_32gf,
     regnet_x_3_2gf,
+    regnet_x_400mf,
+    regnet_x_800mf,
     regnet_x_8gf,
-    regnet_x_16gf,
-    regnet_x_32gf,
+    regnet_y_128gf,
+    regnet_y_16gf,
+    regnet_y_1_6gf,
+    regnet_y_32gf,
+    regnet_y_3_2gf,
+    regnet_y_400mf,
+    regnet_y_800mf,
+    regnet_y_8gf,
 )
 from torchvision.models.resnet import (
+    resnet101,
+    resnet152,
     resnet18,
     resnet34,
     resnet50,
-    resnet101,
-    resnet152,
-    resnext50_32x4d,
     resnext101_32x8d,
-    wide_resnet50_2,
+    resnext50_32x4d,
     wide_resnet101_2,
+    wide_resnet50_2,
 )
 from torchvision.models.segmentation import (
-    fcn_resnet50,
-    fcn_resnet101,
-    deeplabv3_resnet50,
-    deeplabv3_resnet101,
     deeplabv3_mobilenet_v3_large,
+    deeplabv3_resnet101,
+    deeplabv3_resnet50,
+    fcn_resnet101,
+    fcn_resnet50,
     lraspp_mobilenet_v3_large,
 )
 from torchvision.models.shufflenetv2 import (
@@ -67,11 +67,5 @@
     shufflenet_v2_x2_0,
 )
 from torchvision.models.squeezenet import squeezenet1_0, squeezenet1_1
-from torchvision.models.vgg import vgg11, vgg13, vgg16, vgg19, vgg11_bn, vgg13_bn, vgg16_bn, vgg19_bn
-from torchvision.models.vision_transformer import (
-    vit_b_16,
-    vit_b_32,
-    vit_l_16,
-    vit_l_32,
-    vit_h_14,
-)
+from torchvision.models.vgg import vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, vgg19_bn
+from torchvision.models.vision_transformer import vit_b_16, vit_b_32, vit_h_14, vit_l_16, vit_l_32

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
 
 
 def get_dataset(name, image_set, transform, data_path):

references/detection/transforms.py

@@ -1,10 +1,9 @@
-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.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_()

references/video_classification/presets.py

@@ -1,6 +1,6 @@
 import torch
 from torchvision.transforms import transforms
-from transforms import ConvertBHWCtoBCHW, ConvertBCHWtoCBHW
+from transforms import ConvertBCHWtoCBHW, ConvertBHWCtoBCHW
 
 
 class VideoClassificationPresetTrain:

references/video_classification/train.py

@@ -11,7 +11,7 @@
 import utils
 from torch import nn
 from torch.utils.data.dataloader import default_collate
-from torchvision.datasets.samplers import DistributedSampler, UniformClipSampler, RandomClipSampler
+from torchvision.datasets.samplers import DistributedSampler, RandomClipSampler, UniformClipSampler
 
 
 def train_one_epoch(model, criterion, optimizer, lr_scheduler, data_loader, device, epoch, print_freq, scaler=None):