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import torch
import random
import numbers
from torchvision.transforms import RandomCrop, RandomResizedCrop
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 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, C, H, W)
Return:
clip (torch.tensor, dtype=torch.float): Size is (T, C, 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
return clip.float() / 255.0
def resize(clip, target_size, interpolation_mode):
if len(target_size) != 2:
raise ValueError(f"target size should be tuple (height, width), instead got {target_size}")
return torch.nn.functional.interpolate(clip, size=target_size, mode=interpolation_mode, align_corners=False)
class ToTensorVideo:
"""
Convert tensor data type from uint8 to float, divide value by 255.0 and
permute the dimensions of clip tensor
"""
def __init__(self):
pass
def __call__(self, clip):
"""
Args:
clip (torch.tensor, dtype=torch.uint8): Size is (T, C, H, W)
Return:
clip (torch.tensor, dtype=torch.float): Size is (T, C, H, W)
"""
return to_tensor(clip)
def __repr__(self) -> str:
return self.__class__.__name__
class ResizeVideo:
'''
Resize to the specified size
'''
def __init__(
self,
size,
interpolation_mode="bilinear",
):
if isinstance(size, tuple):
if len(size) != 2:
raise ValueError(f"size should be tuple (height, width), instead got {size}")
self.size = size
else:
self.size = (size, size)
self.interpolation_mode = interpolation_mode
def __call__(self, clip):
"""
Args:
clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
Returns:
torch.tensor: scale resized video clip.
size is (T, C, h, w)
"""
clip_resize = resize(clip, target_size=self.size, interpolation_mode=self.interpolation_mode)
return clip_resize
def __repr__(self) -> str:
return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}"
class TemporalRandomCrop(object):
"""Temporally crop the given frame indices at a random location.
Args:
size (int): Desired length of frames will be seen in the model.
"""
def __init__(self, size):
self.size = size
def __call__(self, total_frames):
rand_end = max(0, total_frames - self.size - 1)
begin_index = random.randint(0, rand_end)
end_index = min(begin_index + self.size, total_frames)
return begin_index, end_index
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