File size: 10,594 Bytes
0b756df
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
#!/usr/bin/python
# -*- encoding: utf-8 -*-


import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision

from .resnet import Resnet18
# from modules.bn import InPlaceABNSync as BatchNorm2d


class ConvBNReLU(nn.Module):
    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
        super(ConvBNReLU, self).__init__()
        self.conv = nn.Conv2d(in_chan,
                out_chan,
                kernel_size = ks,
                stride = stride,
                padding = padding,
                bias = False)
        self.bn = nn.BatchNorm2d(out_chan)
        self.init_weight()

    def forward(self, x):
        x = self.conv(x)
        x = F.relu(self.bn(x))
        return x

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)

class BiSeNetOutput(nn.Module):
    def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
        super(BiSeNetOutput, self).__init__()
        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
        self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
        self.init_weight()

    def forward(self, x):
        x = self.conv(x)
        x = self.conv_out(x)
        return x

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)

    def get_params(self):
        wd_params, nowd_params = [], []
        for name, module in self.named_modules():
            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
                wd_params.append(module.weight)
                if not module.bias is None:
                    nowd_params.append(module.bias)
            elif isinstance(module, nn.BatchNorm2d):
                nowd_params += list(module.parameters())
        return wd_params, nowd_params


class AttentionRefinementModule(nn.Module):
    def __init__(self, in_chan, out_chan, *args, **kwargs):
        super(AttentionRefinementModule, self).__init__()
        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
        self.bn_atten = nn.BatchNorm2d(out_chan)
        self.sigmoid_atten = nn.Sigmoid()
        self.init_weight()

    def forward(self, x):
        feat = self.conv(x)
        atten = F.avg_pool2d(feat, feat.size()[2:])
        atten = self.conv_atten(atten)
        atten = self.bn_atten(atten)
        atten = self.sigmoid_atten(atten)
        out = torch.mul(feat, atten)
        return out

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)


class ContextPath(nn.Module):
    def __init__(self, *args, **kwargs):
        super(ContextPath, self).__init__()
        self.resnet = Resnet18()
        self.arm16 = AttentionRefinementModule(256, 128)
        self.arm32 = AttentionRefinementModule(512, 128)
        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)

        self.init_weight()

    def forward(self, x):
        H0, W0 = x.size()[2:]
        feat8, feat16, feat32 = self.resnet(x)
        H8, W8 = feat8.size()[2:]
        H16, W16 = feat16.size()[2:]
        H32, W32 = feat32.size()[2:]

        avg = F.avg_pool2d(feat32, feat32.size()[2:])
        avg = self.conv_avg(avg)
        avg_up = F.interpolate(avg, (H32, W32), mode='nearest')

        feat32_arm = self.arm32(feat32)
        feat32_sum = feat32_arm + avg_up
        feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
        feat32_up = self.conv_head32(feat32_up)

        feat16_arm = self.arm16(feat16)
        feat16_sum = feat16_arm + feat32_up
        feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
        feat16_up = self.conv_head16(feat16_up)

        return feat8, feat16_up, feat32_up  # x8, x8, x16

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)

    def get_params(self):
        wd_params, nowd_params = [], []
        for name, module in self.named_modules():
            if isinstance(module, (nn.Linear, nn.Conv2d)):
                wd_params.append(module.weight)
                if not module.bias is None:
                    nowd_params.append(module.bias)
            elif isinstance(module, nn.BatchNorm2d):
                nowd_params += list(module.parameters())
        return wd_params, nowd_params


### This is not used, since I replace this with the resnet feature with the same size
class SpatialPath(nn.Module):
    def __init__(self, *args, **kwargs):
        super(SpatialPath, self).__init__()
        self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
        self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
        self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
        self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
        self.init_weight()

    def forward(self, x):
        feat = self.conv1(x)
        feat = self.conv2(feat)
        feat = self.conv3(feat)
        feat = self.conv_out(feat)
        return feat

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)

    def get_params(self):
        wd_params, nowd_params = [], []
        for name, module in self.named_modules():
            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
                wd_params.append(module.weight)
                if not module.bias is None:
                    nowd_params.append(module.bias)
            elif isinstance(module, nn.BatchNorm2d):
                nowd_params += list(module.parameters())
        return wd_params, nowd_params


class FeatureFusionModule(nn.Module):
    def __init__(self, in_chan, out_chan, *args, **kwargs):
        super(FeatureFusionModule, self).__init__()
        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
        self.conv1 = nn.Conv2d(out_chan,
                out_chan//4,
                kernel_size = 1,
                stride = 1,
                padding = 0,
                bias = False)
        self.conv2 = nn.Conv2d(out_chan//4,
                out_chan,
                kernel_size = 1,
                stride = 1,
                padding = 0,
                bias = False)
        self.relu = nn.ReLU(inplace=True)
        self.sigmoid = nn.Sigmoid()
        self.init_weight()

    def forward(self, fsp, fcp):
        fcat = torch.cat([fsp, fcp], dim=1)
        feat = self.convblk(fcat)
        atten = F.avg_pool2d(feat, feat.size()[2:])
        atten = self.conv1(atten)
        atten = self.relu(atten)
        atten = self.conv2(atten)
        atten = self.sigmoid(atten)
        feat_atten = torch.mul(feat, atten)
        feat_out = feat_atten + feat
        return feat_out

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)

    def get_params(self):
        wd_params, nowd_params = [], []
        for name, module in self.named_modules():
            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
                wd_params.append(module.weight)
                if not module.bias is None:
                    nowd_params.append(module.bias)
            elif isinstance(module, nn.BatchNorm2d):
                nowd_params += list(module.parameters())
        return wd_params, nowd_params


class BiSeNet(nn.Module):
    def __init__(self, n_classes, *args, **kwargs):
        super(BiSeNet, self).__init__()
        self.cp = ContextPath()
        ## here self.sp is deleted
        self.ffm = FeatureFusionModule(256, 256)
        self.conv_out = BiSeNetOutput(256, 256, n_classes)
        self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
        self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
        self.init_weight()

    def forward(self, x):
        H, W = x.size()[2:]
        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # here return res3b1 feature
        feat_sp = feat_res8  # use res3b1 feature to replace spatial path feature
        feat_fuse = self.ffm(feat_sp, feat_cp8)

        feat_out = self.conv_out(feat_fuse)
        feat_out16 = self.conv_out16(feat_cp8)
        feat_out32 = self.conv_out32(feat_cp16)

        feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
        feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
        feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
        return feat_out, feat_out16, feat_out32

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)

    def get_params(self):
        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
        for name, child in self.named_children():
            child_wd_params, child_nowd_params = child.get_params()
            if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
                lr_mul_wd_params += child_wd_params
                lr_mul_nowd_params += child_nowd_params
            else:
                wd_params += child_wd_params
                nowd_params += child_nowd_params
        return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params


if __name__ == "__main__":
    net = BiSeNet(19)
    net.cuda()
    net.eval()
    in_ten = torch.randn(16, 3, 640, 480).cuda()
    out, out16, out32 = net(in_ten)
    print(out.shape)

    net.get_params()