Source code for torchvision.transforms.v2._temporal

from typing import Any, Dict

import torch
from torchvision.transforms.v2 import functional as F, Transform

[docs]class UniformTemporalSubsample(Transform): """Uniformly subsample ``num_samples`` indices from the temporal dimension of the video. Videos are expected to be of shape ``[..., T, C, H, W]`` where ``T`` denotes the temporal dimension. When ``num_samples`` is larger than the size of temporal dimension of the video, it will sample frames based on nearest neighbor interpolation. Args: num_samples (int): The number of equispaced samples to be selected """ _transformed_types = (torch.Tensor,) def __init__(self, num_samples: int): super().__init__() self.num_samples = num_samples def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any: return self._call_kernel(F.uniform_temporal_subsample, inpt, self.num_samples)


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