init + inference.py патч

data/MBD/model/deep_lab_model/decoder.py

@@ -0,0 +1,59 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from model.deep_lab_model.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
+
+class Decoder(nn.Module):
+    def __init__(self, num_classes, backbone, BatchNorm):
+        super(Decoder, self).__init__()
+        if backbone == 'resnet' or backbone == 'drn':
+            low_level_inplanes = 256
+        elif backbone == 'xception':
+            low_level_inplanes = 128
+        elif backbone == 'mobilenet':
+            low_level_inplanes = 24
+        else:
+            raise NotImplementedError
+
+        self.conv1 = nn.Conv2d(low_level_inplanes, 48, 1, bias=False)
+        self.bn1 = BatchNorm(48)
+        self.relu = nn.ReLU()
+        self.last_conv = nn.Sequential(nn.Conv2d(304, 256, kernel_size=3, stride=1, padding=1, bias=False),
+                                       BatchNorm(256),
+                                       nn.ReLU(),
+                                       nn.Dropout(0.5),
+                                       nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
+                                       BatchNorm(256),
+                                       nn.ReLU(),
+                                       nn.Dropout(0.1),
+                                       nn.Conv2d(256, num_classes, kernel_size=1, stride=1),
+                                       nn.Sigmoid()
+                                       )
+        self._init_weight()
+
+
+    def forward(self, x, low_level_feat):
+        low_level_feat = self.conv1(low_level_feat)
+        low_level_feat = self.bn1(low_level_feat)
+        low_level_feat = self.relu(low_level_feat)
+
+        x = F.interpolate(x, size=low_level_feat.size()[2:], mode='bilinear', align_corners=True)
+        x = torch.cat((x, low_level_feat), dim=1)
+        x = self.last_conv(x)
+
+        return x
+
+    def _init_weight(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                torch.nn.init.kaiming_normal_(m.weight)
+            elif isinstance(m, SynchronizedBatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+def build_decoder(num_classes, backbone, BatchNorm):
+    return Decoder(num_classes, backbone, BatchNorm)