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