Create ResNet

ResNet

@@ -0,0 +1,199 @@
+import torch
+import torch.nn as nn
+
+class BasicBlockV1b(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
+                 previous_dilation=1, norm_layer=nn.BatchNorm2d):
+        super(BasicBlockV1b, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, 3, stride,
+                               dilation, dilation, bias=False)
+        self.bn1 = norm_layer(planes)
+        self.relu = nn.ReLU(True)
+        self.conv2 = nn.Conv2d(planes, planes, 3, 1, previous_dilation,
+                               dilation=previous_dilation, bias=False)
+        self.bn2 = norm_layer(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class BottleneckV1b(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None,
+                 previous_dilation=1, norm_layer=nn.BatchNorm2d):
+        super(BottleneckV1b, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
+        self.bn1 = norm_layer(planes)
+        self.conv2 = nn.Conv2d(planes, planes, 3, stride,
+                               dilation, dilation, bias=False)
+        self.bn2 = norm_layer(planes)
+        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
+        self.bn3 = norm_layer(planes * self.expansion)
+        self.relu = nn.ReLU(True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class ResNetV1(nn.Module):
+
+    def __init__(self, block, layers, num_classes=1000, deep_stem=False,
+                 zero_init_residual=False, norm_layer=nn.BatchNorm2d):
+        output_stride = 16
+        scale = 1.0
+        if output_stride == 32:
+            dilations = [1, 1]
+            strides = [2, 2]
+        elif output_stride == 16:
+            dilations = [1, 2]
+            strides = [2, 1]
+        elif output_stride == 8:
+            dilations = [2, 4]
+            strides = [1, 1]
+        else:
+            raise NotImplementedError
+        self.inplanes = int((128 if deep_stem else 64) * scale)
+        super(ResNetV1, self).__init__()
+        if deep_stem:
+            # resnet vc
+            mid_channel = int(64 * scale)
+            self.conv1 = nn.Sequential(
+                nn.Conv2d(3, mid_channel, 3, 2, 1, bias=False),
+                norm_layer(mid_channel),
+                nn.ReLU(True),
+                nn.Conv2d(mid_channel, mid_channel, 3, 1, 1, bias=False),
+                norm_layer(mid_channel),
+                nn.ReLU(True),
+                nn.Conv2d(mid_channel, self.inplanes, 3, 1, 1, bias=False)
+            )
+        else:
+            self.conv1 = nn.Conv2d(3, self.inplanes, 7, 2, 3, bias=False)
+        self.bn1 = norm_layer(self.inplanes)
+        self.relu = nn.ReLU(True)
+        self.maxpool = nn.MaxPool2d(3, 2, 1)
+        self.layer1 = self._make_layer(block, int(64 * scale), layers[0], norm_layer=norm_layer)
+        self.layer2 = self._make_layer(block, int(128 * scale), layers[1], stride=2, norm_layer=norm_layer)
+
+        self.layer3 = self._make_layer(block, int(256 * scale), layers[2], stride=strides[0], dilation=dilations[0],
+                                       norm_layer=norm_layer)
+        self.layer4 = self._make_layer(block, int(512 * scale), layers[3], stride=strides[1], dilation=dilations[1],
+                                       norm_layer=norm_layer, multi_grid=True,
+                                       multi_dilation=[4,8,16])
+
+        self.last_inp_channels = int(512 * block.expansion * scale)
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(int(512 * block.expansion * scale), num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, BottleneckV1b):
+                    nn.init.constant_(m.bn3.weight, 0)
+                elif isinstance(m, BasicBlockV1b):
+                    nn.init.constant_(m.bn2.weight, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1, dilation=1, norm_layer=nn.BatchNorm2d,
+                    multi_grid=False, multi_dilation=None):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion, 1, stride, bias=False),
+                norm_layer(planes * block.expansion),
+            )
+
+        layers = []
+        if not multi_grid:
+            if dilation in (1, 2):
+                layers.append(block(self.inplanes, planes, stride, dilation=1, downsample=downsample,
+                                    previous_dilation=dilation, norm_layer=norm_layer))
+            elif dilation == 4:
+                layers.append(block(self.inplanes, planes, stride, dilation=2, downsample=downsample,
+                                    previous_dilation=dilation, norm_layer=norm_layer))
+            else:
+                raise RuntimeError("=> unknown dilation size: {}".format(dilation))
+        else:
+            layers.append(block(self.inplanes, planes, stride, dilation=multi_dilation[0],
+                                downsample=downsample, previous_dilation=dilation, norm_layer=norm_layer))
+        self.inplanes = planes * block.expansion
+
+        if multi_grid:
+            div = len(multi_dilation)
+            for i in range(1, blocks):
+                layers.append(block(self.inplanes, planes, dilation=multi_dilation[i % div],
+                                    previous_dilation=dilation, norm_layer=norm_layer))
+        else:
+            for _ in range(1, blocks):
+                layers.append(block(self.inplanes, planes, dilation=dilation,
+                                    previous_dilation=dilation, norm_layer=norm_layer))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x1 = self.layer1(x)
+        x2 = self.layer2(x1)
+        x3 = self.layer3(x2)
+        x4 = self.layer4(x3)
+
+        # for classification
+        # x = self.avgpool(c4)
+        # x = x.view(x.size(0), -1)
+        # x = self.fc(x)
+
+        return x1, x2, x3, x4
+
+def resnet50():
+    num_block = [3, 4, 6, 3]
+    model = ResNetV1(BottleneckV1b, num_block)
+    return model