.

Author: ahmadsharif1

docs/source/index.rst

@@ -42,6 +42,14 @@ We achieve these capabilities through:
 
         A simple video decoding example
 
+     .. grid-item-card:: :octicon:`file-code;1em`
+        GPU decoding using TorchCodec
+        :img-top: _static/img/card-background.svg
+        :link: generated_examples/basic_cuda_example.html
+        :link-type: url
+
+        A GPU decoding example
+
      .. grid-item-card:: :octicon:`file-code;1em`
         API Reference
         :img-top: _static/img/card-background.svg

examples/basic_example.py

@@ -19,8 +19,9 @@
 # :ref:`creating_decoder`.
 
 from typing import Optional
-import torch
+
 import requests
+import torch
 
 
 # Video source: https://www.pexels.com/video/dog-eating-854132/
@@ -33,16 +34,16 @@
 raw_video_bytes = response.content
 
 
-def plot(frames: torch.Tensor, title : Optional[str] = None):
+def plot(frames: torch.Tensor, title: Optional[str] = None):
     try:
-        from torchvision.utils import make_grid
-        from torchvision.transforms.v2.functional import to_pil_image
         import matplotlib.pyplot as plt
+        from torchvision.transforms.v2.functional import to_pil_image
+        from torchvision.utils import make_grid
     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=[])
@@ -76,7 +77,7 @@ 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 = }")
@@ -106,9 +107,10 @@ 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,
     )
 
 # %%