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
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
|
"""
This file is part of the private API. Please do not use directly these classes as they will be modified on
future versions without warning. The classes should be accessed only via the transforms argument of Weights.
"""
from typing import Optional, Tuple
import torch
from torch import nn, Tensor
from . import functional as F, InterpolationMode
__all__ = [
"ObjectDetection",
"ImageClassification",
"VideoClassification",
"SemanticSegmentation",
"OpticalFlow",
]
class ObjectDetection(nn.Module):
def forward(self, img: Tensor) -> Tensor:
if not isinstance(img, Tensor):
img = F.pil_to_tensor(img)
return F.convert_image_dtype(img, torch.float)
def __repr__(self) -> str:
return self.__class__.__name__ + "()"
def describe(self) -> str:
return (
"Accepts ``PIL.Image``, batched ``(B, C, H, W)`` and single ``(C, H, W)`` image ``torch.Tensor`` objects. "
"The images are rescaled to ``[0.0, 1.0]``."
)
class ImageClassification(nn.Module):
def __init__(
self,
*,
crop_size: int,
resize_size: int = 256,
mean: Tuple[float, ...] = (0.485, 0.456, 0.406),
std: Tuple[float, ...] = (0.229, 0.224, 0.225),
interpolation: InterpolationMode = InterpolationMode.BILINEAR,
) -> None:
super().__init__()
self.crop_size = [crop_size]
self.resize_size = [resize_size]
self.mean = list(mean)
self.std = list(std)
self.interpolation = interpolation
def forward(self, img: Tensor) -> Tensor:
img = F.resize(img, self.resize_size, interpolation=self.interpolation)
img = F.center_crop(img, self.crop_size)
if not isinstance(img, Tensor):
img = F.pil_to_tensor(img)
img = F.convert_image_dtype(img, torch.float)
img = F.normalize(img, mean=self.mean, std=self.std)
return img
def __repr__(self) -> str:
format_string = self.__class__.__name__ + "("
format_string += f"\n crop_size={self.crop_size}"
format_string += f"\n resize_size={self.resize_size}"
format_string += f"\n mean={self.mean}"
format_string += f"\n std={self.std}"
format_string += f"\n interpolation={self.interpolation}"
format_string += "\n)"
return format_string
def describe(self) -> str:
return (
"Accepts ``PIL.Image``, batched ``(B, C, H, W)`` and single ``(C, H, W)`` image ``torch.Tensor`` objects. "
f"The images are resized to ``resize_size={self.resize_size}`` using ``interpolation={self.interpolation}``, "
f"followed by a central crop of ``crop_size={self.crop_size}``. Finally the values are first rescaled to "
f"``[0.0, 1.0]`` and then normalized using ``mean={self.mean}`` and ``std={self.std}``."
)
class VideoClassification(nn.Module):
def __init__(
self,
*,
crop_size: Tuple[int, int],
resize_size: Tuple[int, int],
mean: Tuple[float, ...] = (0.43216, 0.394666, 0.37645),
std: Tuple[float, ...] = (0.22803, 0.22145, 0.216989),
interpolation: InterpolationMode = InterpolationMode.BILINEAR,
) -> None:
super().__init__()
self.crop_size = list(crop_size)
self.resize_size = list(resize_size)
self.mean = list(mean)
self.std = list(std)
self.interpolation = interpolation
def forward(self, vid: Tensor) -> Tensor:
need_squeeze = False
if vid.ndim < 5:
vid = vid.unsqueeze(dim=0)
need_squeeze = True
N, T, C, H, W = vid.shape
vid = vid.view(-1, C, H, W)
vid = F.resize(vid, self.resize_size, interpolation=self.interpolation)
vid = F.center_crop(vid, self.crop_size)
vid = F.convert_image_dtype(vid, torch.float)
vid = F.normalize(vid, mean=self.mean, std=self.std)
H, W = self.crop_size
vid = vid.view(N, T, C, H, W)
vid = vid.permute(0, 2, 1, 3, 4) # (N, T, C, H, W) => (N, C, T, H, W)
if need_squeeze:
vid = vid.squeeze(dim=0)
return vid
def __repr__(self) -> str:
format_string = self.__class__.__name__ + "("
format_string += f"\n crop_size={self.crop_size}"
format_string += f"\n resize_size={self.resize_size}"
format_string += f"\n mean={self.mean}"
format_string += f"\n std={self.std}"
format_string += f"\n interpolation={self.interpolation}"
format_string += "\n)"
return format_string
def describe(self) -> str:
return (
"Accepts batched ``(B, T, C, H, W)`` and single ``(T, C, H, W)`` video frame ``torch.Tensor`` objects. "
f"The frames are resized to ``resize_size={self.resize_size}`` using ``interpolation={self.interpolation}``, "
f"followed by a central crop of ``crop_size={self.crop_size}``. Finally the values are first rescaled to "
f"``[0.0, 1.0]`` and then normalized using ``mean={self.mean}`` and ``std={self.std}``. Finally the output "
"dimensions are permuted to ``(..., C, T, H, W)`` tensors."
)
class SemanticSegmentation(nn.Module):
def __init__(
self,
*,
resize_size: Optional[int],
mean: Tuple[float, ...] = (0.485, 0.456, 0.406),
std: Tuple[float, ...] = (0.229, 0.224, 0.225),
interpolation: InterpolationMode = InterpolationMode.BILINEAR,
) -> None:
super().__init__()
self.resize_size = [resize_size] if resize_size is not None else None
self.mean = list(mean)
self.std = list(std)
self.interpolation = interpolation
def forward(self, img: Tensor) -> Tensor:
if isinstance(self.resize_size, list):
img = F.resize(img, self.resize_size, interpolation=self.interpolation)
if not isinstance(img, Tensor):
img = F.pil_to_tensor(img)
img = F.convert_image_dtype(img, torch.float)
img = F.normalize(img, mean=self.mean, std=self.std)
return img
def __repr__(self) -> str:
format_string = self.__class__.__name__ + "("
format_string += f"\n resize_size={self.resize_size}"
format_string += f"\n mean={self.mean}"
format_string += f"\n std={self.std}"
format_string += f"\n interpolation={self.interpolation}"
format_string += "\n)"
return format_string
def describe(self) -> str:
return (
"Accepts ``PIL.Image``, batched ``(B, C, H, W)`` and single ``(C, H, W)`` image ``torch.Tensor`` objects. "
f"The images are resized to ``resize_size={self.resize_size}`` using ``interpolation={self.interpolation}``. "
f"Finally the values are first rescaled to ``[0.0, 1.0]`` and then normalized using ``mean={self.mean}`` and "
f"``std={self.std}``."
)
class OpticalFlow(nn.Module):
def forward(self, img1: Tensor, img2: Tensor) -> Tuple[Tensor, Tensor]:
if not isinstance(img1, Tensor):
img1 = F.pil_to_tensor(img1)
if not isinstance(img2, Tensor):
img2 = F.pil_to_tensor(img2)
img1 = F.convert_image_dtype(img1, torch.float)
img2 = F.convert_image_dtype(img2, torch.float)
# map [0, 1] into [-1, 1]
img1 = F.normalize(img1, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
img2 = F.normalize(img2, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
img1 = img1.contiguous()
img2 = img2.contiguous()
return img1, img2
def __repr__(self) -> str:
return self.__class__.__name__ + "()"
def describe(self) -> str:
return (
"Accepts ``PIL.Image``, batched ``(B, C, H, W)`` and single ``(C, H, W)`` image ``torch.Tensor`` objects. "
"The images are rescaled to ``[-1.0, 1.0]``."
)
|
