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import torch.nn as nn |
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from .trident_conv import MultiScaleTridentConv |
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class ResidualBlock(nn.Module): |
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def __init__( |
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self, |
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in_planes, |
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planes, |
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norm_layer=nn.InstanceNorm2d, |
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stride=1, |
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dilation=1, |
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): |
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super().__init__() |
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self.conv1 = nn.Conv2d( |
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in_planes, |
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planes, |
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kernel_size=3, |
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dilation=dilation, |
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padding=dilation, |
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stride=stride, |
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bias=False, |
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) |
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self.conv2 = nn.Conv2d( |
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planes, planes, kernel_size=3, dilation=dilation, padding=dilation, bias=False |
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) |
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self.relu = nn.ReLU(inplace=True) |
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self.norm1 = norm_layer(planes) |
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self.norm2 = norm_layer(planes) |
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if not stride == 1 or in_planes != planes: |
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self.norm3 = norm_layer(planes) |
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if stride == 1 and in_planes == planes: |
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self.downsample = None |
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else: |
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self.downsample = nn.Sequential( |
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nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3 |
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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.norm1(self.conv1(y))) |
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y = self.relu(self.norm2(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 CNNEncoder(nn.Module): |
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def __init__( |
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self, |
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output_dim=128, |
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norm_layer=nn.InstanceNorm2d, |
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num_output_scales=1, |
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**kwargs, |
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): |
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super().__init__() |
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self.num_branch = num_output_scales |
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feature_dims = [64, 96, 128] |
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self.conv1 = nn.Conv2d( |
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3, feature_dims[0], kernel_size=7, stride=2, padding=3, bias=False |
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) |
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self.norm1 = norm_layer(feature_dims[0]) |
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self.relu1 = nn.ReLU(inplace=True) |
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self.in_planes = feature_dims[0] |
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self.layer1 = self._make_layer(feature_dims[0], stride=1, norm_layer=norm_layer) |
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self.layer2 = self._make_layer(feature_dims[1], stride=2, norm_layer=norm_layer) |
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stride = 2 if num_output_scales == 1 else 1 |
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self.layer3 = self._make_layer( |
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feature_dims[2], |
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stride=stride, |
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norm_layer=norm_layer, |
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) |
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self.conv2 = nn.Conv2d(feature_dims[2], output_dim, 1, 1, 0) |
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if self.num_branch > 1: |
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if self.num_branch == 4: |
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strides = (1, 2, 4, 8) |
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elif self.num_branch == 3: |
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strides = (1, 2, 4) |
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elif self.num_branch == 2: |
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strides = (1, 2) |
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else: |
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raise ValueError |
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self.trident_conv = MultiScaleTridentConv( |
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output_dim, |
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output_dim, |
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kernel_size=3, |
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strides=strides, |
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paddings=1, |
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num_branch=self.num_branch, |
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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.InstanceNorm2d, nn.GroupNorm)): |
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if m.weight is not None: |
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nn.init.constant_(m.weight, 1) |
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if m.bias is not None: |
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nn.init.constant_(m.bias, 0) |
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def _make_layer(self, dim, stride=1, dilation=1, norm_layer=nn.InstanceNorm2d): |
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layer1 = ResidualBlock( |
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self.in_planes, dim, norm_layer=norm_layer, stride=stride, dilation=dilation |
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) |
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layer2 = ResidualBlock(dim, dim, norm_layer=norm_layer, stride=1, dilation=dilation) |
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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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x = self.conv1(x) |
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x = self.norm1(x) |
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x = self.relu1(x) |
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x = self.layer1(x) |
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x = self.layer2(x) |
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x = self.layer3(x) |
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x = self.conv2(x) |
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if self.num_branch > 1: |
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out = self.trident_conv([x] * self.num_branch) |
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else: |
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out = [x] |
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return out |
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