Update PyTorch dependencies (minor)

[renovate]

This PR contains the following updates:

Package	Change	Age	Confidence
torchcodec	==0.13.0 → ==0.15.0
torchvision	==0.27.1 → ==0.28.0

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Release Notes

pytorch/torchcodec (torchcodec)

v0.15.0: TorchCodec 0.15

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TorchCodec 0.15 is out! This is a small release compatible with torch >= 2.11, with the following improvements:

• meta-pytorch#1503 and meta-pytorch#1504 improved decoding coverage on some videos, where a premature "end of file" would otherwise be raised.
• meta-pytorch#1489 optimizes forward seeks - you should see better performance on sparse decoding scenarios, especially on CPU when num_ffmpeg_threads is high.
• Free-threaded wheels are now shipped for MacOS (and Linux, but that was already supported).

v0.14.0: TorchCodec 0.14: HDR Video Decoding for CPU & CUDA, and Fast Wav Decoder

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TorchCodec 0.14 is out! It is compatible with torch >= 2.11. It comes with two major additions: a fast audio WavDecoder, and support for HDR video decoding!

Fast wav decoder

Inspired by SDPL's fast wav decoder, TorchCodec now has a dedicated WavDecoder for decoding WAV files. It bypasses FFmpeg entirely and reads WAV data directly, resulting in significantly faster decoding. It supports multiple sample formats (int16, int32, float32, etc.), and can decode from files, bytes, or file-like objects.

from torchcodec.decoders import WavDecoder

decoder = WavDecoder("audio.wav")
samples = decoder.get_all_samples()  # AudioSamples with data and sample_rate

Read more in our docs.

HDR Video Decoding

VideoDecoder now supports HDR (High Dynamic Range) video decoding without losing precision. When output_dtype=torch.float32 is specified, the decoder outputs RGB float32 frames in [0, 1], preserving the full HDR color range. This is supported for both CPU and CUDA!

import torch
from torchcodec.decoders import VideoDecoder

decoder = VideoDecoder("hdr_video.mp4", output_dtype=torch.float32)
frame = decoder[0]  # Full HDR precision in float32

Read more in our docs.

⚠️ This feature is in beta stage, so behavior may slightly change depending on user feedback. Let us know if you encounter any issue!

Other Improvements

• Improved audio seeking: AudioDecoder seeking is now much faster (#​1449)
• Dropped NPP dependency: TorchCodec no longer depends on NVIDIA's NPP library, which will simplify installing and using TorchCodec for CUDA decoding.

Bug Fixes

• Fix a rare crash scenario during process teardown with the CUDA decoder (#​1441)
• Fix CUDA decoding of videos with odd dimensions(#​1462)

pytorch/vision (torchvision)

v0.28.0: TorchVision 0.28.0 Release

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TorchVision 0.28 is out with some small enhancement and bug-fixes:

Enhancements

• [transforms] Let wrap() preserve metadata for custom TVTensor subclasses (#​9490)
• [transforms] Allow strings for interpolation param in resize transforms (#​9461)

Bug fixes

• [transforms] Fix F.resize on tv_tensors.Mask to honor NEAREST_EXACT interpolation. Previously the interpolation argument was ignored for mask inputs (resize_mask hardcoded NEAREST), so NEAREST_EXACT silently produced plain NEAREST output (#​9497)
• [io] Fix a GIF decoder bug on malformed GIFs that could write outside the allocated tensor's memory (#​9520)

Contributors

🎉 We're grateful for our community, which helps us improve Torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:

Andrey Talman, Benson Ma, Jason Fried, Joanne Yun, Nicolas Hug

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