Pre-commit: Black

.pre-commit-config.yaml

@@ -1,21 +1,28 @@
 repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.0.1
+    hooks:
+      - id: check-docstring-first
+      - id: check-toml
+      - id: check-yaml
+        exclude: packaging/.*
+      - id: end-of-file-fixer
+
+  - repo: https://github.com/psf/black
+    rev: 21.9b0
+    hooks:
+      - id: black
+
   - repo: https://github.com/omnilib/ufmt
     rev: v1.3.0
     hooks:
       - id: ufmt
         additional_dependencies:
           - black == 21.9b0
           - usort == 0.6.4
+
   - repo: https://gitlab.com/pycqa/flake8
     rev: 3.9.2
     hooks:
       - id: flake8
         args: [--config=setup.cfg]
-  - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.0.1
-    hooks:
-      - id: check-docstring-first
-      - id: check-toml
-      - id: check-yaml
-        exclude: packaging/.*
-      - id: end-of-file-fixer

gallery/plot_repurposing_annotations.py

@@ -161,6 +161,7 @@ def show(imgs):
 # Here is an example where we re-purpose the dataset from the
 # `PenFudan Detection Tutorial <https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html>`_.
 
+
 class SegmentationToDetectionDataset(torch.utils.data.Dataset):
     def __init__(self, root, transforms):
         self.root = root

gallery/plot_scripted_tensor_transforms.py

@@ -33,14 +33,14 @@
 from torchvision.io import read_image
 
 
-plt.rcParams["savefig.bbox"] = 'tight'
+plt.rcParams["savefig.bbox"] = "tight"
 torch.manual_seed(1)
 
 
 def show(imgs):
     fix, axs = plt.subplots(ncols=len(imgs), squeeze=False)
     for i, img in enumerate(imgs):
-        img = T.ToPILImage()(img.to('cpu'))
+        img = T.ToPILImage()(img.to("cpu"))
         axs[0, i].imshow(np.asarray(img))
         axs[0, i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
 
@@ -49,8 +49,8 @@ def show(imgs):
 # The :func:`~torchvision.io.read_image` function allows to read an image and
 # directly load it as a tensor
 
-dog1 = read_image(str(Path('assets') / 'dog1.jpg'))
-dog2 = read_image(str(Path('assets') / 'dog2.jpg'))
+dog1 = read_image(str(Path("assets") / "dog1.jpg"))
+dog2 = read_image(str(Path("assets") / "dog2.jpg"))
 show([dog1, dog2])
 
 ####################################
@@ -68,7 +68,7 @@ def show(imgs):
     T.RandomHorizontalFlip(p=0.3),
 )
 
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
+device = "cuda" if torch.cuda.is_available() else "cpu"
 dog1 = dog1.to(device)
 dog2 = dog2.to(device)
 
@@ -89,15 +89,14 @@ def show(imgs):
 
 
 class Predictor(nn.Module):
-
     def __init__(self):
         super().__init__()
         self.resnet18 = resnet18(pretrained=True, progress=False).eval()
         self.transforms = nn.Sequential(
-            T.Resize([256, ]),  # We use single int value inside a list due to torchscript type restrictions
+            T.Resize((256,)),  # We use single int value inside a list due to torchscript type restrictions
             T.CenterCrop(224),
             T.ConvertImageDtype(torch.float),
-            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -125,7 +124,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 import json
 
-with open(Path('assets') / 'imagenet_class_index.json', 'r') as labels_file:
+with open(Path("assets") / "imagenet_class_index.json", "r") as labels_file:
     labels = json.load(labels_file)
 
 for i, (pred, pred_scripted) in enumerate(zip(res, res_scripted)):

gallery/plot_transforms.py

@@ -18,8 +18,8 @@
 import torchvision.transforms as T
 
 
-plt.rcParams["savefig.bbox"] = 'tight'
-orig_img = Image.open(Path('assets') / 'astronaut.jpg')
+plt.rcParams["savefig.bbox"] = "tight"
+orig_img = Image.open(Path("assets") / "astronaut.jpg")
 # if you change the seed, make sure that the randomly-applied transforms
 # properly show that the image can be both transformed and *not* transformed!
 torch.manual_seed(0)
@@ -41,7 +41,7 @@ def plot(imgs, with_orig=True, row_title=None, **imshow_kwargs):
             ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
 
     if with_orig:
-        axs[0, 0].set(title='Original image')
+        axs[0, 0].set(title="Original image")
         axs[0, 0].title.set_size(8)
     if row_title is not None:
         for row_idx in range(num_rows):
@@ -93,7 +93,7 @@ def plot(imgs, with_orig=True, row_title=None, **imshow_kwargs):
 # (see also :func:`~torchvision.transforms.functional.to_grayscale`)
 # converts an image to grayscale
 gray_img = T.Grayscale()(orig_img)
-plot([gray_img], cmap='gray')
+plot([gray_img], cmap="gray")
 
 ####################################
 # Random transforms
@@ -105,7 +105,7 @@ def plot(imgs, with_orig=True, row_title=None, **imshow_kwargs):
 # ~~~~~~~~~~~
 # The :class:`~torchvision.transforms.ColorJitter` transform
 # randomly changes the brightness, saturation, and other properties of an image.
-jitter = T.ColorJitter(brightness=.5, hue=.3)
+jitter = T.ColorJitter(brightness=0.5, hue=0.3)
 jitted_imgs = [jitter(orig_img) for _ in range(4)]
 plot(jitted_imgs)
 
@@ -240,11 +240,8 @@ def plot(imgs, with_orig=True, row_title=None, **imshow_kwargs):
 # See :class:`~torchvision.transforms.AutoAugmentPolicy` for the available policies.
 policies = [T.AutoAugmentPolicy.CIFAR10, T.AutoAugmentPolicy.IMAGENET, T.AutoAugmentPolicy.SVHN]
 augmenters = [T.AutoAugment(policy) for policy in policies]
-imgs = [
-    [augmenter(orig_img) for _ in range(4)]
-    for augmenter in augmenters
-]
-row_title = [str(policy).split('.')[-1] for policy in policies]
+imgs = [[augmenter(orig_img) for _ in range(4)] for augmenter in augmenters]
+row_title = [str(policy).split(".")[-1] for policy in policies]
 plot(imgs, row_title=row_title)
 
 ####################################

gallery/plot_video_api.py

@@ -35,9 +35,7 @@
 
 # Download the sample video
 download_url(
-    "https://github.com/pytorch/vision/blob/main/test/assets/videos/WUzgd7C1pWA.mp4?raw=true",
-    ".",
-    "WUzgd7C1pWA.mp4"
+    "https://github.com/pytorch/vision/blob/main/test/assets/videos/WUzgd7C1pWA.mp4?raw=true", ".", "WUzgd7C1pWA.mp4"
 )
 video_path = "./WUzgd7C1pWA.mp4"
 
@@ -75,12 +73,12 @@
 frames = []  # we are going to save the frames here.
 ptss = []  # pts is a presentation timestamp in seconds (float) of each frame
 for frame in video:
-    frames.append(frame['data'])
-    ptss.append(frame['pts'])
+    frames.append(frame["data"])
+    ptss.append(frame["pts"])
 
 print("PTS for first five frames ", ptss[:5])
 print("Total number of frames: ", len(frames))
-approx_nf = metadata['audio']['duration'][0] * metadata['audio']['framerate'][0]
+approx_nf = metadata["audio"]["duration"][0] * metadata["audio"]["framerate"][0]
 print("Approx total number of datapoints we can expect: ", approx_nf)
 print("Read data size: ", frames[0].size(0) * len(frames))
 
@@ -96,6 +94,7 @@
 
 
 import itertools
+
 video.set_current_stream("video")
 
 frames = []  # we are going to save the frames here.
@@ -116,11 +115,11 @@
 frames = []  # we are going to save the frames here.
 video = video.seek(2)
 
-for frame in itertools.takewhile(lambda x: x['pts'] <= 5, video):
-    frames.append(frame['data'])
+for frame in itertools.takewhile(lambda x: x["pts"] <= 5, video):
+    frames.append(frame["data"])
 
 print("Total number of frames: ", len(frames))
-approx_nf = (5 - 2) * video.get_metadata()['video']['fps'][0]
+approx_nf = (5 - 2) * video.get_metadata()["video"]["fps"][0]
 print("We can expect approx: ", approx_nf)
 print("Tensor size: ", frames[0].size())
 
@@ -136,18 +135,17 @@ def example_read_video(video_object, start=0, end=None, read_video=True, read_au
         end = float("inf")
     if end < start:
         raise ValueError(
-            "end time should be larger than start time, got "
-            "start time={} and end time={}".format(start, end)
+            "end time should be larger than start time, got " "start time={} and end time={}".format(start, end)
         )
 
     video_frames = torch.empty(0)
     video_pts = []
     if read_video:
         video_object.set_current_stream("video")
         frames = []
-        for frame in itertools.takewhile(lambda x: x['pts'] <= end, video_object.seek(start)):
-            frames.append(frame['data'])
-            video_pts.append(frame['pts'])
+        for frame in itertools.takewhile(lambda x: x["pts"] <= end, video_object.seek(start)):
+            frames.append(frame["data"])
+            video_pts.append(frame["pts"])
         if len(frames) > 0:
             video_frames = torch.stack(frames, 0)
 
@@ -156,9 +154,9 @@ def example_read_video(video_object, start=0, end=None, read_video=True, read_au
     if read_audio:
         video_object.set_current_stream("audio")
         frames = []
-        for frame in itertools.takewhile(lambda x: x['pts'] <= end, video_object.seek(start)):
-            frames.append(frame['data'])
-            video_pts.append(frame['pts'])
+        for frame in itertools.takewhile(lambda x: x["pts"] <= end, video_object.seek(start)):
+            frames.append(frame["data"])
+            video_pts.append(frame["pts"])
         if len(frames) > 0:
             audio_frames = torch.cat(frames, 0)
 
@@ -179,36 +177,39 @@ def example_read_video(video_object, start=0, end=None, read_video=True, read_au
 ####################################
 # Make sample dataset
 import os
+
 os.makedirs("./dataset", exist_ok=True)
 os.makedirs("./dataset/1", exist_ok=True)
 os.makedirs("./dataset/2", exist_ok=True)
 
 ####################################
 # Download the videos
 from torchvision.datasets.utils import download_url
+
 download_url(
     "https://github.com/pytorch/vision/blob/main/test/assets/videos/WUzgd7C1pWA.mp4?raw=true",
-    "./dataset/1", "WUzgd7C1pWA.mp4"
+    "./dataset/1",
+    "WUzgd7C1pWA.mp4",
 )
 download_url(
     "https://github.com/pytorch/vision/blob/main/test/assets/videos/RATRACE_wave_f_nm_np1_fr_goo_37.avi?raw=true",
     "./dataset/1",
-    "RATRACE_wave_f_nm_np1_fr_goo_37.avi"
+    "RATRACE_wave_f_nm_np1_fr_goo_37.avi",
 )
 download_url(
     "https://github.com/pytorch/vision/blob/main/test/assets/videos/SOX5yA1l24A.mp4?raw=true",
     "./dataset/2",
-    "SOX5yA1l24A.mp4"
+    "SOX5yA1l24A.mp4",
 )
 download_url(
     "https://github.com/pytorch/vision/blob/main/test/assets/videos/v_SoccerJuggling_g23_c01.avi?raw=true",
     "./dataset/2",
-    "v_SoccerJuggling_g23_c01.avi"
+    "v_SoccerJuggling_g23_c01.avi",
 )
 download_url(
     "https://github.com/pytorch/vision/blob/main/test/assets/videos/v_SoccerJuggling_g24_c01.avi?raw=true",
     "./dataset/2",
-    "v_SoccerJuggling_g24_c01.avi"
+    "v_SoccerJuggling_g24_c01.avi",
 )
 
 ####################################
@@ -231,6 +232,7 @@ def get_samples(root, extensions=(".mp4", ".avi")):
     _, class_to_idx = _find_classes(root)
     return make_dataset(root, class_to_idx, extensions=extensions)
 
+
 ####################################
 # We are going to define the dataset and some basic arguments.
 # We assume the structure of the FolderDataset, and add the following parameters:
@@ -269,23 +271,19 @@ def __iter__(self):
             video_frames = []  # video frame buffer
 
             # Seek and return frames
-            max_seek = metadata["video"]['duration'][0] - (self.clip_len / metadata["video"]['fps'][0])
-            start = random.uniform(0., max_seek)
+            max_seek = metadata["video"]["duration"][0] - (self.clip_len / metadata["video"]["fps"][0])
+            start = random.uniform(0.0, max_seek)
             for frame in itertools.islice(vid.seek(start), self.clip_len):
-                video_frames.append(self.frame_transform(frame['data']))
-                current_pts = frame['pts']
+                video_frames.append(self.frame_transform(frame["data"]))
+                current_pts = frame["pts"]
             # Stack it into a tensor
             video = torch.stack(video_frames, 0)
             if self.video_transform:
                 video = self.video_transform(video)
-            output = {
-                'path': path,
-                'video': video,
-                'target': target,
-                'start': start,
-                'end': current_pts}
+            output = {"path": path, "video": video, "target": target, "start": start, "end": current_pts}
             yield output
 
+
 ####################################
 # Given a path of videos in a folder structure, i.e:
 #
@@ -310,14 +308,15 @@ def __iter__(self):
 
 ####################################
 from torch.utils.data import DataLoader
+
 loader = DataLoader(dataset, batch_size=12)
-data = {"video": [], 'start': [], 'end': [], 'tensorsize': []}
+data = {"video": [], "start": [], "end": [], "tensorsize": []}
 for batch in loader:
-    for i in range(len(batch['path'])):
-        data['video'].append(batch['path'][i])
-        data['start'].append(batch['start'][i].item())
-        data['end'].append(batch['end'][i].item())
-        data['tensorsize'].append(batch['video'][i].size())
+    for i in range(len(batch["path"])):
+        data["video"].append(batch["path"][i])
+        data["start"].append(batch["start"][i].item())
+        data["end"].append(batch["end"][i].item())
+        data["tensorsize"].append(batch["video"][i].size())
 print(data)
 
 ####################################
@@ -337,5 +336,6 @@ def __iter__(self):
 # Cleanup the video and dataset:
 import os
 import shutil
+
 os.remove("./WUzgd7C1pWA.mp4")
 shutil.rmtree("./dataset")