.

Author: ahmadsharif1

examples/basic_example.py

@@ -19,9 +19,8 @@
 # :ref:`creating_decoder`.
 
 from typing import Optional
-
-import requests
 import torch
+import requests
 
 
 # Video source: https://www.pexels.com/video/dog-eating-854132/
@@ -34,16 +33,16 @@
 raw_video_bytes = response.content
 
 
-def plot(frames: torch.Tensor, title: Optional[str] = None):
+def plot(frames: torch.Tensor, title : Optional[str] = None):
     try:
-        import matplotlib.pyplot as plt
-        from torchvision.transforms.v2.functional import to_pil_image
         from torchvision.utils import make_grid
+        from torchvision.transforms.v2.functional import to_pil_image
+        import matplotlib.pyplot as plt
     except ImportError:
         print("Cannot plot, please run `pip install torchvision matplotlib`")
         return
 
-    plt.rcParams["savefig.bbox"] = "tight"
+    plt.rcParams["savefig.bbox"] = 'tight'
     fig, ax = plt.subplots()
     ax.imshow(to_pil_image(make_grid(frames)))
     ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
@@ -77,7 +76,7 @@ def plot(frames: torch.Tensor, title: Optional[str] = None):
 # ---------------------------------------
 
 first_frame = decoder[0]  # using a single int index
-every_twenty_frame = decoder[0:-1:20]  # using slices
+every_twenty_frame = decoder[0 : -1 : 20]  # using slices
 
 print(f"{first_frame.shape = }")
 print(f"{first_frame.dtype = }")
@@ -107,10 +106,9 @@ def plot(frames: torch.Tensor, title: Optional[str] = None):
 # The decoder is a normal iterable object and can be iterated over like so:
 
 for frame in decoder:
-    assert isinstance(frame, torch.Tensor) and frame.shape == (
-        3,
-        decoder.metadata.height,
-        decoder.metadata.width,
+    assert (
+        isinstance(frame, torch.Tensor)
+        and frame.shape == (3, decoder.metadata.height, decoder.metadata.width)
     )
 
 # %%