Bump torchcodec from 0.6.0+metax3.8.1.0 to 0.16.0

[dependabot]

Bumps torchcodec from 0.6.0+metax3.8.1.0 to 0.16.0.

Release notes

Sourced from torchcodec's releases.

TorchCodec 0.16 - Image decoding and encoding

TorchCodec 0.16 is out! It is compatible with torch >= 2.11. The headline feature of this release is image decoding and encoding: TorchCodec now natively decodes and encodes JPEG (CPU and CUDA), PNG, WebP, GIF, AVIF and HEIC. These image decoders and encoders replace their torchvision counterparts, which are now deprecated.

TorchCodec is the recommended way to decode and encode images in the PyTorch ecosystem. If you are coming from torchvision, we wrote a migration guide

Image decoding

TorchCodec exposes one entry-point per format, plus a generic decode_image() that automatically detects the format. The API is largely backward-compatible with TorchVision:

from torchcodec.decoders import decode_image, decode_jpeg, decode_png
img = decode_image("image.jpg")  # CHW uint8 tensor, format auto-detected
img = decode_image("image.avif")
img = decode_image("image.heic")
Or use the format-specific decoders for format-specific options
img = decode_jpeg("image.jpg", device="cuda")

Sources can be a path (str or pathlib.Path), bytes, or a 1D uint8 tensor of encoded bytes:

img = decode_image(open("image.png", "rb").read())
img = decode_image(torch.frombuffer(encoded_bytes, dtype=torch.uint8))

Animated and multi-image formats (WebP, GIF, AVIF, HEIC) decode into an (N, C, H, W) tensor:

from torchcodec.decoders import decode_gif
frames = decode_gif("animated.gif")  # (N, C, H, W)

JPEG decoding is also supported on CUDA, through nvJPEG. For CUDA, prefer passing a batch of sources: the whole batch is decoded in a single nvJPEG call, which is much faster than decoding images one at a time.

from torchcodec.decoders import decode_jpeg
imgs = decode_jpeg(["a.jpg", "b.jpg", "c.jpg"], device="cuda")  # list of CUDA tensors

Read more in our image decoding tutorial

Image encoding

Image encoders follow the same class-based design as our video and audio encoders: build the encoder from a CHW uint8 tensor, then choose where the encoded bytes go: a file, a file-like object, or a tensor.

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[Dongxu-H]

原版本 torchcodec 有 vendor-specific 后缀（如 +metax3.8.1.0、+torch.2.11.0），新版本 0.16.0 是标准版本。需要确认 enflame-tops1106 和 metax-maca3810 后端是否与标准版本兼容。

[dependabot]

OK, I won't notify you again about this release, but will get in touch when a new version is available. If you'd rather skip all updates until the next major or minor version, let me know by commenting @dependabot ignore this major version or @dependabot ignore this minor version. You can also ignore all major, minor, or patch releases for a dependency by adding an ignore condition with the desired update_types to your config file.

If you change your mind, just re-open this PR and I'll resolve any conflicts on it.

[Dongxu-H]

/test | functional_sym_constrain_range_for_size:mthreads

[github-actions]

❌ On-demand test failed

Operator: | functional_sym_constrain_range_for_size
Runner: mthreads
Backend: mthreads-musa520

The test failed to complete. Check the workflow run for details.