File size: 7,127 Bytes
162943d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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

# -*- coding: utf-8 -*-

# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: ps-license@tuebingen.mpg.de

from lib.net.net_util import *
import torch.nn as nn
import torch.nn.functional as F


class HourGlass(nn.Module):
    def __init__(self, num_modules, depth, num_features, opt):
        super(HourGlass, self).__init__()
        self.num_modules = num_modules
        self.depth = depth
        self.features = num_features
        self.opt = opt

        self._generate_network(self.depth)

    def _generate_network(self, level):
        self.add_module('b1_' + str(level),
                        ConvBlock(self.features, self.features, self.opt))

        self.add_module('b2_' + str(level),
                        ConvBlock(self.features, self.features, self.opt))

        if level > 1:
            self._generate_network(level - 1)
        else:
            self.add_module('b2_plus_' + str(level),
                            ConvBlock(self.features, self.features, self.opt))

        self.add_module('b3_' + str(level),
                        ConvBlock(self.features, self.features, self.opt))

    def _forward(self, level, inp):
        # Upper branch
        up1 = inp
        up1 = self._modules['b1_' + str(level)](up1)

        # Lower branch
        low1 = F.avg_pool2d(inp, 2, stride=2)
        low1 = self._modules['b2_' + str(level)](low1)

        if level > 1:
            low2 = self._forward(level - 1, low1)
        else:
            low2 = low1
            low2 = self._modules['b2_plus_' + str(level)](low2)

        low3 = low2
        low3 = self._modules['b3_' + str(level)](low3)

        # NOTE: for newer PyTorch (1.3~), it seems that training results are degraded due to implementation diff in F.grid_sample
        # if the pretrained model behaves weirdly, switch with the commented line.
        # NOTE: I also found that "bicubic" works better.
        up2 = F.interpolate(low3,
                            scale_factor=2,
                            mode='bicubic',
                            align_corners=True)
        # up2 = F.interpolate(low3, scale_factor=2, mode='nearest)

        return up1 + up2

    def forward(self, x):
        return self._forward(self.depth, x)


class HGFilter(nn.Module):
    def __init__(self, opt, num_modules, in_dim):
        super(HGFilter, self).__init__()
        self.num_modules = num_modules

        self.opt = opt
        [k, s, d, p] = self.opt.conv1

        # self.conv1 = nn.Conv2d(in_dim, 64, kernel_size=7, stride=2, padding=3)
        self.conv1 = nn.Conv2d(in_dim,
                               64,
                               kernel_size=k,
                               stride=s,
                               dilation=d,
                               padding=p)

        if self.opt.norm == 'batch':
            self.bn1 = nn.BatchNorm2d(64)
        elif self.opt.norm == 'group':
            self.bn1 = nn.GroupNorm(32, 64)

        if self.opt.hg_down == 'conv64':
            self.conv2 = ConvBlock(64, 64, self.opt)
            self.down_conv2 = nn.Conv2d(64,
                                        128,
                                        kernel_size=3,
                                        stride=2,
                                        padding=1)
        elif self.opt.hg_down == 'conv128':
            self.conv2 = ConvBlock(64, 128, self.opt)
            self.down_conv2 = nn.Conv2d(128,
                                        128,
                                        kernel_size=3,
                                        stride=2,
                                        padding=1)
        elif self.opt.hg_down == 'ave_pool':
            self.conv2 = ConvBlock(64, 128, self.opt)
        else:
            raise NameError('Unknown Fan Filter setting!')

        self.conv3 = ConvBlock(128, 128, self.opt)
        self.conv4 = ConvBlock(128, 256, self.opt)

        # Stacking part
        for hg_module in range(self.num_modules):
            self.add_module('m' + str(hg_module),
                            HourGlass(1, opt.num_hourglass, 256, self.opt))

            self.add_module('top_m_' + str(hg_module),
                            ConvBlock(256, 256, self.opt))
            self.add_module(
                'conv_last' + str(hg_module),
                nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
            if self.opt.norm == 'batch':
                self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256))
            elif self.opt.norm == 'group':
                self.add_module('bn_end' + str(hg_module),
                                nn.GroupNorm(32, 256))

            self.add_module(
                'l' + str(hg_module),
                nn.Conv2d(256,
                          opt.hourglass_dim,
                          kernel_size=1,
                          stride=1,
                          padding=0))

            if hg_module < self.num_modules - 1:
                self.add_module(
                    'bl' + str(hg_module),
                    nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
                self.add_module(
                    'al' + str(hg_module),
                    nn.Conv2d(opt.hourglass_dim,
                              256,
                              kernel_size=1,
                              stride=1,
                              padding=0))

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)), True)
        tmpx = x
        if self.opt.hg_down == 'ave_pool':
            x = F.avg_pool2d(self.conv2(x), 2, stride=2)
        elif self.opt.hg_down in ['conv64', 'conv128']:
            x = self.conv2(x)
            x = self.down_conv2(x)
        else:
            raise NameError('Unknown Fan Filter setting!')

        x = self.conv3(x)
        x = self.conv4(x)

        previous = x

        outputs = []
        for i in range(self.num_modules):
            hg = self._modules['m' + str(i)](previous)

            ll = hg
            ll = self._modules['top_m_' + str(i)](ll)

            ll = F.relu(
                self._modules['bn_end' + str(i)](
                    self._modules['conv_last' + str(i)](ll)), True)

            # Predict heatmaps
            tmp_out = self._modules['l' + str(i)](ll)
            outputs.append(tmp_out)

            if i < self.num_modules - 1:
                ll = self._modules['bl' + str(i)](ll)
                tmp_out_ = self._modules['al' + str(i)](tmp_out)
                previous = previous + ll + tmp_out_

        return outputs