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import torch
from rscd.models.backbones import Decompose
class ResNet3D(torch.nn.Module):
def __init__(self, resnet2d):
super(ResNet3D, self).__init__()
self.conv1 = Decompose.Decompose_conv(resnet2d.conv1, time_dim=3, time_padding=1, center=True)
self.bn1 = Decompose.Decompose_norm(resnet2d.bn1)
self.relu = torch.nn.ReLU(inplace=True)
self.maxpool = Decompose.Decompose_pool(resnet2d.maxpool, time_dim=1, time_padding=0, time_stride=1)
self.layer1 = Decompose_layer(resnet2d.layer1)
self.layer2 = Decompose_layer(resnet2d.layer2)
self.layer3 = Decompose_layer(resnet2d.layer3)
self.layer4 = Decompose_layer(resnet2d.layer4)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
return x
def Decompose_layer(reslayer2d):
reslayers3d = []
for layer2d in reslayer2d:
layer3d = Bottleneck3d(layer2d)
reslayers3d.append(layer3d)
return torch.nn.Sequential(*reslayers3d)
class Bottleneck3d(torch.nn.Module):
def __init__(self, bottleneck2d):
super(Bottleneck3d, self).__init__()
self.conv1 = Decompose.Decompose_conv(bottleneck2d.conv1, time_dim=3, time_padding=1,
time_stride=1, center=True)
self.bn1 = Decompose.Decompose_norm(bottleneck2d.bn1)
self.conv2 = Decompose.Decompose_conv(bottleneck2d.conv2, time_dim=3, time_padding=1,
time_stride=1, center=True)
self.bn2 = Decompose.Decompose_norm(bottleneck2d.bn2)
# self.conv3 = Decompose.Decompose_conv(bottleneck2d.conv3, time_dim=1, center=True)
# self.bn3 = Decompose.Decompose_norm(bottleneck2d.bn3)
self.relu = torch.nn.ReLU(inplace=True)
if bottleneck2d.downsample is not None:
self.downsample = Decompose_downsample(bottleneck2d.downsample, time_stride=1)
else:
self.downsample = None
self.stride = bottleneck2d.stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
# out = self.conv3(out)
# out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
# out += residual
out = out + residual
out = self.relu(out)
return out
def Decompose_downsample(downsample2d, time_stride=1):
downsample3d = torch.nn.Sequential(
Decompose.inflate_conv(downsample2d[0], time_dim=1, time_stride=time_stride, center=True),
Decompose.Decompose_norm(downsample2d[1]))
return downsample3d
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