upgrade linters

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.0.1
+    rev: v4.4.0
     hooks:
       - id: check-docstring-first
       - id: check-toml
@@ -13,20 +13,20 @@ repos:
       - id: end-of-file-fixer
 
   - repo: https://github.com/omnilib/ufmt
-    rev: v1.3.3
+    rev: v2.0.1
     hooks:
       - id: ufmt
         additional_dependencies:
-          - black == 22.3.0
-          - usort == 1.0.2
+          - black == 22.12.0
+          - usort == 1.0.5
 
   - repo: https://github.com/PyCQA/flake8
-    rev: 5.0.4
+    rev: 6.0.0
     hooks:
       - id: flake8
         args: [--config=setup.cfg]
 
   - repo: https://github.com/PyCQA/pydocstyle
-    rev: 6.1.1
+    rev: 6.3.0
     hooks:
       - id: pydocstyle

docs/source/conf.py

@@ -235,7 +235,6 @@ def patched_make_field(self, types, domain, items, **kw):
     # `kw` catches `env=None` needed for newer sphinx while maintaining
     #  backwards compatibility when passed along further down!
 
-    # type: (list, unicode, tuple) -> nodes.field  # noqa: F821
     def handle_item(fieldarg, content):
         par = nodes.paragraph()
         par += addnodes.literal_strong("", fieldarg)  # Patch: this line added
@@ -321,7 +320,6 @@ def inject_weight_metadata(app, what, name, obj, options, lines):
     """
 
     if obj.__name__.endswith(("_Weights", "_QuantizedWeights")):
-
         if len(obj) == 0:
             lines[:] = ["There are no available pre-trained weights."]
             return
@@ -468,6 +466,5 @@ def generate_weights_table(module, table_name, metrics, dataset, include_pattern
 
 
 def setup(app):
-
     app.connect("autodoc-process-docstring", inject_minigalleries)
     app.connect("autodoc-process-docstring", inject_weight_metadata)

references/depth/stereo/cascade_evaluation.py

@@ -203,7 +203,6 @@ def evaluate(model, loader, args, writer=None, step=None):
 
     for n_cascades in args.n_cascades:
         for n_iters in args.n_iterations:
-
             config = f"{n_cascades}c_{n_iters}i"
             config_image_folder = os.path.join(base_image_folder, config)
             os.makedirs(config_image_folder, exist_ok=True)

references/depth/stereo/presets.py

@@ -84,7 +84,6 @@ def __init__(
         erase_px_range: Tuple[int, int] = (50, 100),
         erase_num_repeats: int = 1,
     ) -> None:
-
         if scaling_type not in ["linear", "exponential"]:
             raise ValueError(f"Unknown scaling type: {scaling_type}. Available types: linear, exponential")
 

references/depth/stereo/transforms.py

@@ -19,7 +19,6 @@ def rand_float_range(size: Sequence[int], low: float, high: float) -> Tensor:
 
 
 class InterpolationStrategy:
-
     _valid_modes: List[str] = ["mixed", "bicubic", "bilinear"]
 
     def __init__(self, mode: str = "mixed") -> None:
@@ -156,7 +155,6 @@ def forward(
         disparities: Tuple[T_FLOW, T_FLOW],
         masks: Tuple[T_MASK, T_MASK],
     ) -> Tuple[T_STEREO_TENSOR, Tuple[T_FLOW, T_FLOW], Tuple[T_MASK, T_MASK]]:
-
         img_left = F.normalize(images[0], mean=self.mean, std=self.std)
         img_right = F.normalize(images[1], mean=self.mean, std=self.std)
 
@@ -209,7 +207,6 @@ def forward(
         disparities: Tuple[T_FLOW, T_FLOW],
         masks: Tuple[T_MASK, T_MASK],
     ) -> Tuple[T_STEREO_TENSOR, Tuple[T_FLOW, T_FLOW], Tuple[T_MASK, T_MASK]]:
-
         if torch.rand(1) < self.p:
             # asymmetric: different transform for img1 and img2
             img_left = super().forward(images[0])
@@ -236,7 +233,6 @@ def forward(
         disparities: Tuple[T_FLOW, T_FLOW],
         masks: Tuple[T_MASK, T_MASK],
     ) -> Tuple[T_STEREO_TENSOR, Tuple[T_FLOW, T_FLOW], Tuple[T_MASK, T_MASK]]:
-
         gamma = rand_float_range((1,), low=self.gamma_range[0], high=self.gamma_range[1]).item()
 
         if torch.rand(1) < self.p:
@@ -285,7 +281,6 @@ def forward(
         disparities: T_STEREO_TENSOR,
         masks: T_STEREO_TENSOR,
     ) -> Tuple[T_STEREO_TENSOR, Tuple[T_FLOW, T_FLOW], Tuple[T_MASK, T_MASK]]:
-
         if torch.rand(1) < self.p:
             return images, disparities, masks
 
@@ -342,7 +337,6 @@ def forward(
         disparities: T_STEREO_TENSOR,
         masks: T_STEREO_TENSOR,
     ) -> Tuple[T_STEREO_TENSOR, Tuple[T_FLOW, T_FLOW], Tuple[T_MASK, T_MASK]]:
-
         left_image, right_image = images
 
         if torch.rand(1) < self.p:
@@ -421,7 +415,6 @@ def forward(
         disparities: Tuple[T_FLOW, T_FLOW],
         masks: Tuple[T_MASK, T_MASK],
     ) -> Tuple[T_STEREO_TENSOR, Tuple[T_FLOW, T_FLOW], Tuple[T_MASK, T_MASK]]:
-
         img_left, img_right = images
         dsp_left, dsp_right = disparities
         mask_left, mask_right = masks
@@ -522,7 +515,6 @@ def forward(
         disparities: Tuple[T_FLOW, T_FLOW],
         masks: Tuple[T_MASK, T_MASK],
     ) -> Tuple[T_STEREO_TENSOR, Tuple[T_FLOW, T_FLOW], Tuple[T_MASK, T_MASK]]:
-
         img_left, img_right = images
         dsp_left, dsp_right = disparities
         mask_left, mask_right = masks

references/detection/transforms.py

@@ -166,8 +166,7 @@ def __init__(
         self.p = p
 
     @torch.jit.unused
-    def _get_fill_value(self, is_pil):
-        # type: (bool) -> int
+    def _get_fill_value(self, is_pil: bool) -> int:
         # We fake the type to make it work on JIT
         return tuple(int(x) for x in self.fill) if is_pil else 0
 
@@ -447,7 +446,6 @@ def _copy_paste(
     blending: bool = True,
     resize_interpolation: F.InterpolationMode = F.InterpolationMode.BILINEAR,
 ) -> Tuple[torch.Tensor, Dict[str, Tensor]]:
-
     # Random paste targets selection:
     num_masks = len(paste_target["masks"])
 

references/optical_flow/train.py

@@ -181,7 +181,6 @@ def preprocessing(img1, img2, flow, valid_flow_mask):
 def train_one_epoch(model, optimizer, scheduler, train_loader, logger, args):
     device = torch.device(args.device)
     for data_blob in logger.log_every(train_loader):
-
         optimizer.zero_grad()
 
         image1, image2, flow_gt, valid_flow_mask = (x.to(device) for x in data_blob)

references/optical_flow/transforms.py

@@ -6,7 +6,6 @@
 class ValidateModelInput(torch.nn.Module):
     # Pass-through transform that checks the shape and dtypes to make sure the model gets what it expects
     def forward(self, img1, img2, flow, valid_flow_mask):
-
         if not all(isinstance(arg, torch.Tensor) for arg in (img1, img2, flow, valid_flow_mask) if arg is not None):
             raise TypeError("This method expects all input arguments to be of type torch.Tensor.")
         if not all(arg.dtype == torch.float32 for arg in (img1, img2, flow) if arg is not None):
@@ -90,7 +89,6 @@ def __init__(self, brightness=0, contrast=0, saturation=0, hue=0, p=0.2):
         self.p = p
 
     def forward(self, img1, img2, flow, valid_flow_mask):
-
         if torch.rand(1) < self.p:
             # asymmetric: different transform for img1 and img2
             img1 = super().forward(img1)

references/optical_flow/utils.py

@@ -155,7 +155,6 @@ 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()
 

test/test_datasets.py

@@ -278,7 +278,6 @@ class CityScapesTestCase(datasets_utils.ImageDatasetTestCase):
     FEATURE_TYPES = (PIL.Image.Image, (dict, PIL.Image.Image))
 
     def inject_fake_data(self, tmpdir, config):
-
         tmpdir = pathlib.Path(tmpdir)
 
         mode_to_splits = {
@@ -1948,7 +1947,6 @@ def inject_fake_data(self, tmpdir, config):
 
         num_examples = 2 if config["split"] == "train" else 3
         for split_dir in ("training", "testing"):
-
             datasets_utils.create_image_folder(
                 root / split_dir,
                 name="image_2",

test/test_extended_models.py

@@ -182,7 +182,6 @@ def test_naming_conventions(model_fn):
 )
 @run_if_test_with_extended
 def test_schema_meta_validation(model_fn):
-
     if model_fn.__name__ == "maskrcnn_resnet50_fpn_v2":
         pytest.skip(reason="FIXME https://github.com/pytorch/vision/issues/7349")
 

test/test_functional_tensor.py

@@ -67,7 +67,6 @@ def test_scale_channel():
 
 
 class TestRotate:
-
     ALL_DTYPES = [None, torch.float32, torch.float64, torch.float16]
     scripted_rotate = torch.jit.script(F.rotate)
     IMG_W = 26
@@ -153,7 +152,6 @@ def test_rotate_interpolation_type(self):
 
 
 class TestAffine:
-
     ALL_DTYPES = [None, torch.float32, torch.float64, torch.float16]
     scripted_affine = torch.jit.script(F.affine)
 
@@ -379,7 +377,6 @@ def _get_data_dims_and_points_for_perspective():
 @pytest.mark.parametrize("fill", (None, [0, 0, 0], [1, 2, 3], [255, 255, 255], [1], (2.0,)))
 @pytest.mark.parametrize("fn", [F.perspective, torch.jit.script(F.perspective)])
 def test_perspective_pil_vs_tensor(device, dims_and_points, dt, fill, fn):
-
     if dt == torch.float16 and device == "cpu":
         # skip float16 on CPU case
         return
@@ -411,7 +408,6 @@ def test_perspective_pil_vs_tensor(device, dims_and_points, dt, fill, fn):
 @pytest.mark.parametrize("dims_and_points", _get_data_dims_and_points_for_perspective())
 @pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
 def test_perspective_batch(device, dims_and_points, dt):
-
     if dt == torch.float16 and device == "cpu":
         # skip float16 on CPU case
         return
@@ -451,7 +447,6 @@ def test_perspective_interpolation_type():
 @pytest.mark.parametrize("max_size", [None, 34, 40, 1000])
 @pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC, NEAREST, NEAREST_EXACT])
 def test_resize(device, dt, size, max_size, interpolation):
-
     if dt == torch.float16 and device == "cpu":
         # skip float16 on CPU case
         return
@@ -501,7 +496,6 @@ def test_resize(device, dt, size, max_size, interpolation):
 
 @pytest.mark.parametrize("device", cpu_and_cuda())
 def test_resize_asserts(device):
-
     tensor, pil_img = _create_data(26, 36, device=device)
 
     res1 = F.resize(tensor, size=32, interpolation=PIL.Image.BILINEAR)
@@ -521,7 +515,6 @@ def test_resize_asserts(device):
 @pytest.mark.parametrize("size", [[96, 72], [96, 420], [420, 72]])
 @pytest.mark.parametrize("interpolation", [BILINEAR, BICUBIC])
 def test_resize_antialias(device, dt, size, interpolation):
-
     if dt == torch.float16 and device == "cpu":
         # skip float16 on CPU case
         return
@@ -570,7 +563,6 @@ def test_resize_antialias(device, dt, size, interpolation):
 
 
 def test_resize_antialias_default_warning():
-
     img = torch.randint(0, 256, size=(3, 44, 56), dtype=torch.uint8)
 
     match = "The default value of the antialias"
@@ -589,7 +581,6 @@ def test_resize_antialias_default_warning():
 def check_functional_vs_PIL_vs_scripted(
     fn, fn_pil, fn_t, config, device, dtype, channels=3, tol=2.0 + 1e-10, agg_method="max"
 ):
-
     script_fn = torch.jit.script(fn)
     torch.manual_seed(15)
     tensor, pil_img = _create_data(26, 34, channels=channels, device=device)
@@ -1026,7 +1017,6 @@ def test_crop(device, top, left, height, width):
 @pytest.mark.parametrize("sigma", [[0.5, 0.5], (0.5, 0.5), (0.8, 0.8), (1.7, 1.7)])
 @pytest.mark.parametrize("fn", [F.gaussian_blur, torch.jit.script(F.gaussian_blur)])
 def test_gaussian_blur(device, image_size, dt, ksize, sigma, fn):
-
     # true_cv2_results = {
     #     # np_img = np.arange(3 * 10 * 12, dtype="uint8").reshape((10, 12, 3))
     #     # cv2.GaussianBlur(np_img, ksize=(3, 3), sigmaX=0.8)

test/test_image.py

@@ -79,7 +79,6 @@ def normalize_dimensions(img_pil):
     ],
 )
 def test_decode_jpeg(img_path, pil_mode, mode):
-
     with Image.open(img_path) as img:
         is_cmyk = img.mode == "CMYK"
         if pil_mode is not None:
@@ -152,7 +151,6 @@ def test_damaged_corrupt_images(img_path):
     ],
 )
 def test_decode_png(img_path, pil_mode, mode):
-
     with Image.open(img_path) as img:
         if pil_mode is not None:
             img = img.convert(pil_mode)
@@ -402,7 +400,6 @@ def test_decode_jpeg_cuda_errors():
 
 
 def test_encode_jpeg_errors():
-
     with pytest.raises(RuntimeError, match="Input tensor dtype should be uint8"):
         encode_jpeg(torch.empty((3, 100, 100), dtype=torch.float32))
 

test/test_models.py

@@ -634,7 +634,6 @@ def checkOut(out):
 
 
 def test_generalizedrcnn_transform_repr():
-
     min_size, max_size = 224, 299
     image_mean = [0.485, 0.456, 0.406]
     image_std = [0.229, 0.224, 0.225]
@@ -1033,7 +1032,6 @@ def test_detection_model_trainable_backbone_layers(model_fn, disable_weight_load
 @pytest.mark.parametrize("model_fn", list_model_fns(models.optical_flow))
 @pytest.mark.parametrize("scripted", (False, True))
 def test_raft(model_fn, scripted):
-
     torch.manual_seed(0)
 
     # We need very small images, otherwise the pickle size would exceed the 50KB
@@ -1058,7 +1056,6 @@ def test_raft(model_fn, scripted):
 
 
 def test_presets_antialias():
-
     img = torch.randint(0, 256, size=(1, 3, 224, 224), dtype=torch.uint8)
 
     match = "The default value of the antialias parameter"

test/test_models_detection_negative_samples.py

@@ -51,7 +51,6 @@ def test_targets_to_anchors(self):
         assert matched_gt_boxes[0].dtype == torch.float32
 
     def test_assign_targets_to_proposals(self):
-
         proposals = [torch.randint(-50, 50, (20, 4), dtype=torch.float32)]
         gt_boxes = [torch.zeros((0, 4), dtype=torch.float32)]
         gt_labels = [torch.tensor([[0]], dtype=torch.int64)]

test/test_onnx.py

@@ -66,7 +66,6 @@ def run_model(
             self.ort_validate(onnx_io, test_inputs, test_ouputs)
 
     def ort_validate(self, onnx_io, inputs, outputs):
-
         inputs, _ = torch.jit._flatten(inputs)
         outputs, _ = torch.jit._flatten(outputs)
 

test/test_ops.py

@@ -3,7 +3,7 @@
 from abc import ABC, abstractmethod
 from functools import lru_cache
 from itertools import product
-from typing import Callable, List, Tuple
+from typing import Callable, List
 
 import numpy as np
 import pytest