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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__(self, in_planes, planes, norm_layer=nn.InstanceNorm2d, stride=1, dilation=1,
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):
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super(ResidualBlock, self).__init__()
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self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3,
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dilation=dilation, padding=dilation, stride=stride, bias=False)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
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dilation=dilation, padding=dilation, bias=False)
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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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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__(self, 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(CNNEncoder, self).__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(3, feature_dims[0], kernel_size=7, stride=2, padding=3, bias=False)
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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(feature_dims[2], 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(output_dim, 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(self.in_planes, dim, norm_layer=norm_layer, stride=stride, dilation=dilation)
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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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