| | import torch
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| | from rscd.models.backbones import Decompose
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| |
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| |
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| | class ResNet3D(torch.nn.Module):
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| | def __init__(self, resnet2d):
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| | super(ResNet3D, self).__init__()
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| | self.conv1 = Decompose.Decompose_conv(resnet2d.conv1, time_dim=3, time_padding=1, center=True)
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| | self.bn1 = Decompose.Decompose_norm(resnet2d.bn1)
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| | self.relu = torch.nn.ReLU(inplace=True)
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| | self.maxpool = Decompose.Decompose_pool(resnet2d.maxpool, time_dim=1, time_padding=0, time_stride=1)
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| |
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| | self.layer1 = Decompose_layer(resnet2d.layer1)
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| | self.layer2 = Decompose_layer(resnet2d.layer2)
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| | self.layer3 = Decompose_layer(resnet2d.layer3)
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| | self.layer4 = Decompose_layer(resnet2d.layer4)
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| |
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| | def forward(self, x):
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| | x = self.conv1(x)
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| | x = self.bn1(x)
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| | x = self.relu(x)
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| |
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| | x = self.maxpool(x)
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| |
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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.layer4(x)
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| | return x
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| |
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| | def Decompose_layer(reslayer2d):
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| | reslayers3d = []
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| | for layer2d in reslayer2d:
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| | layer3d = Bottleneck3d(layer2d)
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| | reslayers3d.append(layer3d)
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| | return torch.nn.Sequential(*reslayers3d)
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| |
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| |
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| | class Bottleneck3d(torch.nn.Module):
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| | def __init__(self, bottleneck2d):
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| | super(Bottleneck3d, self).__init__()
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| |
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| | self.conv1 = Decompose.Decompose_conv(bottleneck2d.conv1, time_dim=3, time_padding=1,
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| | time_stride=1, center=True)
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| | self.bn1 = Decompose.Decompose_norm(bottleneck2d.bn1)
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| |
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| | self.conv2 = Decompose.Decompose_conv(bottleneck2d.conv2, time_dim=3, time_padding=1,
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| | time_stride=1, center=True)
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| | self.bn2 = Decompose.Decompose_norm(bottleneck2d.bn2)
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| |
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| | self.relu = torch.nn.ReLU(inplace=True)
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| |
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| | if bottleneck2d.downsample is not None:
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| | self.downsample = Decompose_downsample(bottleneck2d.downsample, time_stride=1)
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| | else:
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| | self.downsample = None
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| |
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| | self.stride = bottleneck2d.stride
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| |
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| | def forward(self, x):
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| | residual = x
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| | out = self.conv1(x)
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| | out = self.bn1(out)
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| | out = self.relu(out)
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| |
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| | out = self.conv2(out)
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| | out = self.bn2(out)
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| | out = self.relu(out)
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| |
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| | if self.downsample is not None:
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| | residual = self.downsample(x)
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| |
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| |
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| | out = out + residual
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| | out = self.relu(out)
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| | return out
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| |
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| |
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| | def Decompose_downsample(downsample2d, time_stride=1):
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| | downsample3d = torch.nn.Sequential(
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| | Decompose.inflate_conv(downsample2d[0], time_dim=1, time_stride=time_stride, center=True),
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| | Decompose.Decompose_norm(downsample2d[1]))
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| | return downsample3d
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