_functional_video.py

import warnings

import torch


warnings.warn(
    "The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in 0.14. "
    "Please use the 'torchvision.transforms.functional' module instead."
)


def _is_tensor_video_clip(clip):
    if not torch.is_tensor(clip):
        raise TypeError("clip should be Tensor. Got %s" % type(clip))

    if not clip.ndimension() == 4:
        raise ValueError("clip should be 4D. Got %dD" % clip.dim())

    return True


def crop(clip, i, j, h, w):
    """
    Args:
        clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
    """
    assert len(clip.size()) == 4, "clip should be a 4D tensor"
    return clip[..., i : i + h, j : j + w]


def resize(clip, target_size, interpolation_mode):
    assert len(target_size) == 2, "target size should be tuple (height, width)"
    return torch.nn.functional.interpolate(clip, size=target_size, mode=interpolation_mode, align_corners=False)


def resized_crop(clip, i, j, h, w, size, interpolation_mode="bilinear"):
    """
    Do spatial cropping and resizing to the video clip
    Args:
        clip (torch.tensor): Video clip to be cropped. Size is (C, T, H, W)
        i (int): i in (i,j) i.e coordinates of the upper left corner.
        j (int): j in (i,j) i.e coordinates of the upper left corner.
        h (int): Height of the cropped region.
        w (int): Width of the cropped region.
        size (tuple(int, int)): height and width of resized clip
    Returns:
        clip (torch.tensor): Resized and cropped clip. Size is (C, T, H, W)
    """
    assert _is_tensor_video_clip(clip), "clip should be a 4D torch.tensor"
    clip = crop(clip, i, j, h, w)
    clip = resize(clip, size, interpolation_mode)
    return clip


def center_crop(clip, crop_size):
    assert _is_tensor_video_clip(clip), "clip should be a 4D torch.tensor"
    h, w = clip.size(-2), clip.size(-1)
    th, tw = crop_size
    assert h >= th and w >= tw, "height and width must be no smaller than crop_size"

    i = int(round((h - th) / 2.0))
    j = int(round((w - tw) / 2.0))
    return crop(clip, i, j, th, tw)


def to_tensor(clip):
    """
    Convert tensor data type from uint8 to float, divide value by 255.0 and
    permute the dimensions of clip tensor
    Args:
        clip (torch.tensor, dtype=torch.uint8): Size is (T, H, W, C)
    Return:
        clip (torch.tensor, dtype=torch.float): Size is (C, T, H, W)
    """
    _is_tensor_video_clip(clip)
    if not clip.dtype == torch.uint8:
        raise TypeError("clip tensor should have data type uint8. Got %s" % str(clip.dtype))
    return clip.float().permute(3, 0, 1, 2) / 255.0


def normalize(clip, mean, std, inplace=False):
    """
    Args:
        clip (torch.tensor): Video clip to be normalized. Size is (C, T, H, W)
        mean (tuple): pixel RGB mean. Size is (3)
        std (tuple): pixel standard deviation. Size is (3)
    Returns:
        normalized clip (torch.tensor): Size is (C, T, H, W)
    """
    assert _is_tensor_video_clip(clip), "clip should be a 4D torch.tensor"
    if not inplace:
        clip = clip.clone()
    mean = torch.as_tensor(mean, dtype=clip.dtype, device=clip.device)
    std = torch.as_tensor(std, dtype=clip.dtype, device=clip.device)
    clip.sub_(mean[:, None, None, None]).div_(std[:, None, None, None])
    return clip


def hflip(clip):
    """
    Args:
        clip (torch.tensor): Video clip to be normalized. Size is (C, T, H, W)
    Returns:
        flipped clip (torch.tensor): Size is (C, T, H, W)
    """
    assert _is_tensor_video_clip(clip), "clip should be a 4D torch.tensor"
    return clip.flip(-1)