| | import torch.nn as nn
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| |
|
| | from .trident_conv import MultiScaleTridentConv
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| |
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| |
|
| | class ResidualBlock(nn.Module):
|
| | def __init__(self, in_planes, planes, norm_layer=nn.InstanceNorm2d, stride=1, dilation=1,
|
| | ):
|
| | 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:
|
| | self.downsample = None
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| | else:
|
| | 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:
|
| | 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,
|
| | ):
|
| | 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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| |
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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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| |
|
| | self.conv2 = nn.Conv2d(feature_dims[2], output_dim, 1, 1, 0)
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| |
|
| | if self.num_branch > 1:
|
| | if self.num_branch == 4:
|
| | strides = (1, 2, 4, 8)
|
| | elif self.num_branch == 3:
|
| | strides = (1, 2, 4)
|
| | elif self.num_branch == 2:
|
| | strides = (1, 2)
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| | else:
|
| | raise ValueError
|
| |
|
| | 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,
|
| | num_branch=self.num_branch,
|
| | )
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| |
|
| | 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.InstanceNorm2d, nn.GroupNorm)):
|
| | if m.weight is not None:
|
| | nn.init.constant_(m.weight, 1)
|
| | if m.bias is not None:
|
| | nn.init.constant_(m.bias, 0)
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| |
|
| | def _make_layer(self, dim, stride=1, dilation=1, norm_layer=nn.InstanceNorm2d):
|
| | layer1 = ResidualBlock(self.in_planes, dim, norm_layer=norm_layer, stride=stride, dilation=dilation)
|
| | 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
|
| | return nn.Sequential(*layers)
|
| |
|
| | def forward(self, x):
|
| | x = self.conv1(x)
|
| | x = self.norm1(x)
|
| | x = self.relu1(x)
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| |
|
| | x = self.layer1(x)
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| | x = self.layer2(x)
|
| | x = self.layer3(x)
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| |
|
| | x = self.conv2(x)
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| |
|
| | if self.num_branch > 1:
|
| | out = self.trident_conv([x] * self.num_branch)
|
| | else:
|
| | out = [x]
|
| |
|
| | return out
|
| |
|
