File size: 4,559 Bytes
74da989 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 | # SPDX-License-Identifier: Apache-2.0
import random
import torch
def _is_tensor_video_clip(clip) -> bool:
if not torch.is_tensor(clip):
raise TypeError(f"clip should be Tensor. Got {type(clip)}")
if not clip.ndimension() == 4:
raise ValueError(f"clip should be 4D. Got {clip.dim()}D")
return True
def crop(clip, i, j, h, w) -> torch.Tensor:
"""
Args:
clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
"""
if len(clip.size()) != 4:
raise ValueError("clip should be a 4D tensor")
return clip[..., i:i + h, j:j + w]
def resize(clip, target_size, interpolation_mode) -> torch.Tensor:
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=True,
antialias=True,
)
def center_crop_th_tw(clip, th, tw, top_crop) -> torch.Tensor:
if not _is_tensor_video_clip(clip):
raise ValueError("clip should be a 4D torch.tensor")
# import ipdb;ipdb.set_trace()
h, w = clip.size(-2), clip.size(-1)
tr = th / tw
if h / w > tr:
new_h = int(w * tr)
new_w = w
else:
new_h = h
new_w = int(h / tr)
i = 0 if top_crop else int(round((h - new_h) / 2.0))
j = int(round((w - new_w) / 2.0))
return crop(clip, i, j, new_h, new_w)
def normalize_video(clip) -> torch.Tensor:
"""
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(
f"clip tensor should have data type uint8. Got {clip.dtype}")
# return clip.float().permute(3, 0, 1, 2) / 255.0
return clip.float() / 255.0
class CenterCropResizeVideo:
"""
First use the short side for cropping length,
center crop video, then resize to the specified size
"""
def __init__(
self,
size,
top_crop=False,
interpolation_mode="bilinear",
) -> None:
if len(size) != 2:
raise ValueError(
f"size should be tuple (height, width), instead got {size}")
self.size = size
self.top_crop = top_crop
self.interpolation_mode = interpolation_mode
def __call__(self, clip) -> torch.Tensor:
"""
Args:
clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
Returns:
torch.tensor: scale resized / center cropped video clip.
size is (T, C, crop_size, crop_size)
"""
clip_center_crop = center_crop_th_tw(clip,
self.size[0],
self.size[1],
top_crop=self.top_crop)
clip_center_crop_resize = resize(
clip_center_crop,
target_size=self.size,
interpolation_mode=self.interpolation_mode,
)
return clip_center_crop_resize
def __repr__(self) -> str:
return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}"
class Normalize255:
"""
Convert tensor data type from uint8 to float, divide value by 255.0 and
"""
def __init__(self) -> None:
pass
def __call__(self, clip) -> torch.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)
"""
return normalize_video(clip)
def __repr__(self) -> str:
return self.__class__.__name__
class TemporalRandomCrop:
"""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) -> None:
self.size = size
def __call__(self, total_frames) -> tuple[int, int]:
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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