Shortcuts

Source code for torchvision.transforms.v2.functional._temporal

import torch

from torchvision import tv_tensors

from torchvision.utils import _log_api_usage_once

from ._utils import _get_kernel, _register_kernel_internal


[docs]def uniform_temporal_subsample(inpt: torch.Tensor, num_samples: int) -> torch.Tensor: """[BETA] See :class:`~torchvision.transforms.v2.UniformTemporalSubsample` for details.""" if torch.jit.is_scripting(): return uniform_temporal_subsample_video(inpt, num_samples=num_samples) _log_api_usage_once(uniform_temporal_subsample) kernel = _get_kernel(uniform_temporal_subsample, type(inpt)) return kernel(inpt, num_samples=num_samples)
@_register_kernel_internal(uniform_temporal_subsample, torch.Tensor) @_register_kernel_internal(uniform_temporal_subsample, tv_tensors.Video) def uniform_temporal_subsample_video(video: torch.Tensor, num_samples: int) -> torch.Tensor: # Reference: https://github.com/facebookresearch/pytorchvideo/blob/a0a131e/pytorchvideo/transforms/functional.py#L19 t_max = video.shape[-4] - 1 indices = torch.linspace(0, t_max, num_samples, device=video.device).long() return torch.index_select(video, -4, indices)

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources