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import torch.nn as nn |
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import torch.nn.functional as F |
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def conv1x1(in_planes, out_planes, stride=1): |
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"""1x1 convolution without padding""" |
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return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False) |
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def conv3x3(in_planes, out_planes, stride=1): |
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"""3x3 convolution with padding""" |
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) |
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class BasicBlock(nn.Module): |
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def __init__(self, in_planes, planes, stride=1): |
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super().__init__() |
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self.conv1 = conv3x3(in_planes, planes, stride) |
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self.conv2 = conv3x3(planes, planes) |
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self.bn1 = nn.BatchNorm2d(planes) |
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self.bn2 = nn.BatchNorm2d(planes) |
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self.relu = nn.ReLU(inplace=True) |
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if stride == 1: |
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self.downsample = None |
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else: |
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self.downsample = nn.Sequential( |
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conv1x1(in_planes, planes, stride=stride), |
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nn.BatchNorm2d(planes) |
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) |
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def forward(self, x): |
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y = x |
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y = self.relu(self.bn1(self.conv1(y))) |
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y = self.bn2(self.conv2(y)) |
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if self.downsample is not None: |
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x = self.downsample(x) |
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return self.relu(x+y) |
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class ResNetFPN_8_2(nn.Module): |
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""" |
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ResNet+FPN, output resolution are 1/8 and 1/2. |
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Each block has 2 layers. |
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""" |
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def __init__(self, config): |
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super().__init__() |
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block = BasicBlock |
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initial_dim = config['initial_dim'] |
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block_dims = config['block_dims'] |
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self.in_planes = initial_dim |
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self.conv1 = nn.Conv2d(1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False) |
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self.bn1 = nn.BatchNorm2d(initial_dim) |
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self.relu = nn.ReLU(inplace=True) |
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self.layer1 = self._make_layer(block, block_dims[0], stride=1) |
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self.layer2 = self._make_layer(block, block_dims[1], stride=2) |
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self.layer3 = self._make_layer(block, block_dims[2], stride=2) |
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self.layer3_outconv = conv1x1(block_dims[2], block_dims[2]) |
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self.layer2_outconv = conv1x1(block_dims[1], block_dims[2]) |
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self.layer2_outconv2 = nn.Sequential( |
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conv3x3(block_dims[2], block_dims[2]), |
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nn.BatchNorm2d(block_dims[2]), |
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nn.LeakyReLU(), |
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conv3x3(block_dims[2], block_dims[1]), |
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) |
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self.layer1_outconv = conv1x1(block_dims[0], block_dims[1]) |
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self.layer1_outconv2 = nn.Sequential( |
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conv3x3(block_dims[1], block_dims[1]), |
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nn.BatchNorm2d(block_dims[1]), |
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nn.LeakyReLU(), |
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conv3x3(block_dims[1], block_dims[0]), |
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) |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') |
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elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): |
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nn.init.constant_(m.weight, 1) |
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nn.init.constant_(m.bias, 0) |
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def _make_layer(self, block, dim, stride=1): |
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layer1 = block(self.in_planes, dim, stride=stride) |
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layer2 = block(dim, dim, stride=1) |
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layers = (layer1, layer2) |
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self.in_planes = dim |
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return nn.Sequential(*layers) |
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def forward(self, x): |
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x0 = self.relu(self.bn1(self.conv1(x))) |
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x1 = self.layer1(x0) |
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x2 = self.layer2(x1) |
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x3 = self.layer3(x2) |
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x3_out = self.layer3_outconv(x3) |
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x3_out_2x = F.interpolate(x3_out, scale_factor=2., mode='bilinear', align_corners=True) |
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x2_out = self.layer2_outconv(x2) |
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x2_out = self.layer2_outconv2(x2_out+x3_out_2x) |
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x2_out_2x = F.interpolate(x2_out, scale_factor=2., mode='bilinear', align_corners=True) |
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x1_out = self.layer1_outconv(x1) |
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x1_out = self.layer1_outconv2(x1_out+x2_out_2x) |
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return [x3_out, x1_out] |
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class ResNetFPN_16_4(nn.Module): |
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""" |
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ResNet+FPN, output resolution are 1/16 and 1/4. |
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Each block has 2 layers. |
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""" |
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def __init__(self, config): |
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super().__init__() |
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block = BasicBlock |
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initial_dim = config['initial_dim'] |
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block_dims = config['block_dims'] |
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self.in_planes = initial_dim |
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self.conv1 = nn.Conv2d(1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False) |
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self.bn1 = nn.BatchNorm2d(initial_dim) |
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self.relu = nn.ReLU(inplace=True) |
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self.layer1 = self._make_layer(block, block_dims[0], stride=1) |
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self.layer2 = self._make_layer(block, block_dims[1], stride=2) |
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self.layer3 = self._make_layer(block, block_dims[2], stride=2) |
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self.layer4 = self._make_layer(block, block_dims[3], stride=2) |
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self.layer4_outconv = conv1x1(block_dims[3], block_dims[3]) |
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self.layer3_outconv = conv1x1(block_dims[2], block_dims[3]) |
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self.layer3_outconv2 = nn.Sequential( |
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conv3x3(block_dims[3], block_dims[3]), |
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nn.BatchNorm2d(block_dims[3]), |
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nn.LeakyReLU(), |
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conv3x3(block_dims[3], block_dims[2]), |
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) |
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self.layer2_outconv = conv1x1(block_dims[1], block_dims[2]) |
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self.layer2_outconv2 = nn.Sequential( |
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conv3x3(block_dims[2], block_dims[2]), |
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nn.BatchNorm2d(block_dims[2]), |
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nn.LeakyReLU(), |
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conv3x3(block_dims[2], block_dims[1]), |
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) |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') |
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elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): |
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nn.init.constant_(m.weight, 1) |
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nn.init.constant_(m.bias, 0) |
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def _make_layer(self, block, dim, stride=1): |
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layer1 = block(self.in_planes, dim, stride=stride) |
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layer2 = block(dim, dim, stride=1) |
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layers = (layer1, layer2) |
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self.in_planes = dim |
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return nn.Sequential(*layers) |
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def forward(self, x): |
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x0 = self.relu(self.bn1(self.conv1(x))) |
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x1 = self.layer1(x0) |
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x2 = self.layer2(x1) |
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x3 = self.layer3(x2) |
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x4 = self.layer4(x3) |
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x4_out = self.layer4_outconv(x4) |
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x4_out_2x = F.interpolate(x4_out, scale_factor=2., mode='bilinear', align_corners=True) |
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x3_out = self.layer3_outconv(x3) |
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x3_out = self.layer3_outconv2(x3_out+x4_out_2x) |
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x3_out_2x = F.interpolate(x3_out, scale_factor=2., mode='bilinear', align_corners=True) |
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x2_out = self.layer2_outconv(x2) |
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x2_out = self.layer2_outconv2(x2_out+x3_out_2x) |
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return [x4_out, x2_out] |
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