Spaces:
kadirnar
/
Runtime error

File size: 7,420 Bytes
938e515
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
from collections import OrderedDict

import torch.nn as nn

from .bn import ABN, ACT_LEAKY_RELU, ACT_ELU, ACT_NONE
import torch.nn.functional as functional


class ResidualBlock(nn.Module):
    """Configurable residual block

    Parameters
    ----------
    in_channels : int
        Number of input channels.
    channels : list of int
        Number of channels in the internal feature maps. Can either have two or three elements: if three construct
        a residual block with two `3 x 3` convolutions, otherwise construct a bottleneck block with `1 x 1`, then
        `3 x 3` then `1 x 1` convolutions.
    stride : int
        Stride of the first `3 x 3` convolution
    dilation : int
        Dilation to apply to the `3 x 3` convolutions.
    groups : int
        Number of convolution groups. This is used to create ResNeXt-style blocks and is only compatible with
        bottleneck blocks.
    norm_act : callable
        Function to create normalization / activation Module.
    dropout: callable
        Function to create Dropout Module.
    """

    def __init__(self,
                 in_channels,
                 channels,
                 stride=1,
                 dilation=1,
                 groups=1,
                 norm_act=ABN,
                 dropout=None):
        super(ResidualBlock, self).__init__()

        # Check parameters for inconsistencies
        if len(channels) != 2 and len(channels) != 3:
            raise ValueError("channels must contain either two or three values")
        if len(channels) == 2 and groups != 1:
            raise ValueError("groups > 1 are only valid if len(channels) == 3")

        is_bottleneck = len(channels) == 3
        need_proj_conv = stride != 1 or in_channels != channels[-1]

        if not is_bottleneck:
            bn2 = norm_act(channels[1])
            bn2.activation = ACT_NONE
            layers = [
                ("conv1", nn.Conv2d(in_channels, channels[0], 3, stride=stride, padding=dilation, bias=False,
                                    dilation=dilation)),
                ("bn1", norm_act(channels[0])),
                ("conv2", nn.Conv2d(channels[0], channels[1], 3, stride=1, padding=dilation, bias=False,
                                    dilation=dilation)),
                ("bn2", bn2)
            ]
            if dropout is not None:
                layers = layers[0:2] + [("dropout", dropout())] + layers[2:]
        else:
            bn3 = norm_act(channels[2])
            bn3.activation = ACT_NONE
            layers = [
                ("conv1", nn.Conv2d(in_channels, channels[0], 1, stride=1, padding=0, bias=False)),
                ("bn1", norm_act(channels[0])),
                ("conv2", nn.Conv2d(channels[0], channels[1], 3, stride=stride, padding=dilation, bias=False,
                                    groups=groups, dilation=dilation)),
                ("bn2", norm_act(channels[1])),
                ("conv3", nn.Conv2d(channels[1], channels[2], 1, stride=1, padding=0, bias=False)),
                ("bn3", bn3)
            ]
            if dropout is not None:
                layers = layers[0:4] + [("dropout", dropout())] + layers[4:]
        self.convs = nn.Sequential(OrderedDict(layers))

        if need_proj_conv:
            self.proj_conv = nn.Conv2d(in_channels, channels[-1], 1, stride=stride, padding=0, bias=False)
            self.proj_bn = norm_act(channels[-1])
            self.proj_bn.activation = ACT_NONE

    def forward(self, x):
        if hasattr(self, "proj_conv"):
            residual = self.proj_conv(x)
            residual = self.proj_bn(residual)
        else:
            residual = x
        x = self.convs(x) + residual

        if self.convs.bn1.activation == ACT_LEAKY_RELU:
            return functional.leaky_relu(x, negative_slope=self.convs.bn1.slope, inplace=True)
        elif self.convs.bn1.activation == ACT_ELU:
            return functional.elu(x, inplace=True)
        else:
            return x


class IdentityResidualBlock(nn.Module):
    def __init__(self,
                 in_channels,
                 channels,
                 stride=1,
                 dilation=1,
                 groups=1,
                 norm_act=ABN,
                 dropout=None):
        """Configurable identity-mapping residual block

        Parameters
        ----------
        in_channels : int
            Number of input channels.
        channels : list of int
            Number of channels in the internal feature maps. Can either have two or three elements: if three construct
            a residual block with two `3 x 3` convolutions, otherwise construct a bottleneck block with `1 x 1`, then
            `3 x 3` then `1 x 1` convolutions.
        stride : int
            Stride of the first `3 x 3` convolution
        dilation : int
            Dilation to apply to the `3 x 3` convolutions.
        groups : int
            Number of convolution groups. This is used to create ResNeXt-style blocks and is only compatible with
            bottleneck blocks.
        norm_act : callable
            Function to create normalization / activation Module.
        dropout: callable
            Function to create Dropout Module.
        """
        super(IdentityResidualBlock, self).__init__()

        # Check parameters for inconsistencies
        if len(channels) != 2 and len(channels) != 3:
            raise ValueError("channels must contain either two or three values")
        if len(channels) == 2 and groups != 1:
            raise ValueError("groups > 1 are only valid if len(channels) == 3")

        is_bottleneck = len(channels) == 3
        need_proj_conv = stride != 1 or in_channels != channels[-1]

        self.bn1 = norm_act(in_channels)
        if not is_bottleneck:
            layers = [
                ("conv1", nn.Conv2d(in_channels, channels[0], 3, stride=stride, padding=dilation, bias=False,
                                    dilation=dilation)),
                ("bn2", norm_act(channels[0])),
                ("conv2", nn.Conv2d(channels[0], channels[1], 3, stride=1, padding=dilation, bias=False,
                                    dilation=dilation))
            ]
            if dropout is not None:
                layers = layers[0:2] + [("dropout", dropout())] + layers[2:]
        else:
            layers = [
                ("conv1", nn.Conv2d(in_channels, channels[0], 1, stride=stride, padding=0, bias=False)),
                ("bn2", norm_act(channels[0])),
                ("conv2", nn.Conv2d(channels[0], channels[1], 3, stride=1, padding=dilation, bias=False,
                                    groups=groups, dilation=dilation)),
                ("bn3", norm_act(channels[1])),
                ("conv3", nn.Conv2d(channels[1], channels[2], 1, stride=1, padding=0, bias=False))
            ]
            if dropout is not None:
                layers = layers[0:4] + [("dropout", dropout())] + layers[4:]
        self.convs = nn.Sequential(OrderedDict(layers))

        if need_proj_conv:
            self.proj_conv = nn.Conv2d(in_channels, channels[-1], 1, stride=stride, padding=0, bias=False)

    def forward(self, x):
        if hasattr(self, "proj_conv"):
            bn1 = self.bn1(x)
            shortcut = self.proj_conv(bn1)
        else:
            shortcut = x.clone()
            bn1 = self.bn1(x)

        out = self.convs(bn1)
        out.add_(shortcut)

        return out