Source code for torchvision.transforms.v2._temporal

from typing import Any, Dict

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

from torchvision.transforms.v2.utils import is_simple_tensor

[docs]class UniformTemporalSubsample(Transform): """[BETA] Uniformly subsample ``num_samples`` indices from the temporal dimension of the video. .. v2betastatus:: UniformTemporalSubsample transform 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 = (is_simple_tensor, datapoints.Video) def __init__(self, num_samples: int): super().__init__() self.num_samples = num_samples def _transform(self, inpt: datapoints._VideoType, params: Dict[str, Any]) -> datapoints._VideoType: return F.uniform_temporal_subsample(inpt, self.num_samples)


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