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
|
import math
from typing import Optional, Tuple
import torch
from torch import nn, Tensor
from torch.nn import init
from torch.nn.modules.utils import _pair
from torch.nn.parameter import Parameter
from torchvision.extension import _assert_has_ops
from ..utils import _log_api_usage_once
def deform_conv2d(
input: Tensor,
offset: Tensor,
weight: Tensor,
bias: Optional[Tensor] = None,
stride: Tuple[int, int] = (1, 1),
padding: Tuple[int, int] = (0, 0),
dilation: Tuple[int, int] = (1, 1),
mask: Optional[Tensor] = None,
) -> Tensor:
r"""
Performs Deformable Convolution v2, described in
`Deformable ConvNets v2: More Deformable, Better Results
<https://arxiv.org/abs/1811.11168>`__ if :attr:`mask` is not ``None`` and
Performs Deformable Convolution, described in
`Deformable Convolutional Networks
<https://arxiv.org/abs/1703.06211>`__ if :attr:`mask` is ``None``.
Args:
input (Tensor[batch_size, in_channels, in_height, in_width]): input tensor
offset (Tensor[batch_size, 2 * offset_groups * kernel_height * kernel_width, out_height, out_width]):
offsets to be applied for each position in the convolution kernel.
weight (Tensor[out_channels, in_channels // groups, kernel_height, kernel_width]): convolution weights,
split into groups of size (in_channels // groups)
bias (Tensor[out_channels]): optional bias of shape (out_channels,). Default: None
stride (int or Tuple[int, int]): distance between convolution centers. Default: 1
padding (int or Tuple[int, int]): height/width of padding of zeroes around
each image. Default: 0
dilation (int or Tuple[int, int]): the spacing between kernel elements. Default: 1
mask (Tensor[batch_size, offset_groups * kernel_height * kernel_width, out_height, out_width]):
masks to be applied for each position in the convolution kernel. Default: None
Returns:
Tensor[batch_sz, out_channels, out_h, out_w]: result of convolution
Examples::
>>> input = torch.rand(4, 3, 10, 10)
>>> kh, kw = 3, 3
>>> weight = torch.rand(5, 3, kh, kw)
>>> # offset and mask should have the same spatial size as the output
>>> # of the convolution. In this case, for an input of 10, stride of 1
>>> # and kernel size of 3, without padding, the output size is 8
>>> offset = torch.rand(4, 2 * kh * kw, 8, 8)
>>> mask = torch.rand(4, kh * kw, 8, 8)
>>> out = deform_conv2d(input, offset, weight, mask=mask)
>>> print(out.shape)
>>> # returns
>>> torch.Size([4, 5, 8, 8])
"""
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
_log_api_usage_once(deform_conv2d)
_assert_has_ops()
out_channels = weight.shape[0]
use_mask = mask is not None
if mask is None:
mask = torch.zeros((input.shape[0], 1), device=input.device, dtype=input.dtype)
if bias is None:
bias = torch.zeros(out_channels, device=input.device, dtype=input.dtype)
stride_h, stride_w = _pair(stride)
pad_h, pad_w = _pair(padding)
dil_h, dil_w = _pair(dilation)
weights_h, weights_w = weight.shape[-2:]
_, n_in_channels, _, _ = input.shape
n_offset_grps = offset.shape[1] // (2 * weights_h * weights_w)
n_weight_grps = n_in_channels // weight.shape[1]
if n_offset_grps == 0:
raise RuntimeError(
"the shape of the offset tensor at dimension 1 is not valid. It should "
"be a multiple of 2 * weight.size[2] * weight.size[3].\n"
f"Got offset.shape[1]={offset.shape[1]}, while 2 * weight.size[2] * weight.size[3]={2 * weights_h * weights_w}"
)
return torch.ops.torchvision.deform_conv2d(
input,
weight,
offset,
mask,
bias,
stride_h,
stride_w,
pad_h,
pad_w,
dil_h,
dil_w,
n_weight_grps,
n_offset_grps,
use_mask,
)
class DeformConv2d(nn.Module):
"""
See :func:`deform_conv2d`.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
padding: int = 0,
dilation: int = 1,
groups: int = 1,
bias: bool = True,
):
super().__init__()
_log_api_usage_once(self)
if in_channels % groups != 0:
raise ValueError("in_channels must be divisible by groups")
if out_channels % groups != 0:
raise ValueError("out_channels must be divisible by groups")
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = _pair(kernel_size)
self.stride = _pair(stride)
self.padding = _pair(padding)
self.dilation = _pair(dilation)
self.groups = groups
self.weight = Parameter(
torch.empty(out_channels, in_channels // groups, self.kernel_size[0], self.kernel_size[1])
)
if bias:
self.bias = Parameter(torch.empty(out_channels))
else:
self.register_parameter("bias", None)
self.reset_parameters()
def reset_parameters(self) -> None:
init.kaiming_uniform_(self.weight, a=math.sqrt(5))
if self.bias is not None:
fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
bound = 1 / math.sqrt(fan_in)
init.uniform_(self.bias, -bound, bound)
def forward(self, input: Tensor, offset: Tensor, mask: Optional[Tensor] = None) -> Tensor:
"""
Args:
input (Tensor[batch_size, in_channels, in_height, in_width]): input tensor
offset (Tensor[batch_size, 2 * offset_groups * kernel_height * kernel_width, out_height, out_width]):
offsets to be applied for each position in the convolution kernel.
mask (Tensor[batch_size, offset_groups * kernel_height * kernel_width, out_height, out_width]):
masks to be applied for each position in the convolution kernel.
"""
return deform_conv2d(
input,
offset,
self.weight,
self.bias,
stride=self.stride,
padding=self.padding,
dilation=self.dilation,
mask=mask,
)
def __repr__(self) -> str:
s = (
f"{self.__class__.__name__}("
f"{self.in_channels}"
f", {self.out_channels}"
f", kernel_size={self.kernel_size}"
f", stride={self.stride}"
)
s += f", padding={self.padding}" if self.padding != (0, 0) else ""
s += f", dilation={self.dilation}" if self.dilation != (1, 1) else ""
s += f", groups={self.groups}" if self.groups != 1 else ""
s += ", bias=False" if self.bias is None else ""
s += ")"
return s
|
