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| import torch.nn as nn | |
| class ConvBnReLU3D(nn.Module): | |
| def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, pad=1, norm_act=nn.BatchNorm3d): | |
| super(ConvBnReLU3D, self).__init__() | |
| self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=stride, padding=pad, bias=False) | |
| self.bn = norm_act(out_channels) | |
| self.relu = nn.ReLU(inplace=True) | |
| def forward(self, x): | |
| return self.relu(self.bn(self.conv(x))) | |
| class CostRegNet(nn.Module): | |
| def __init__(self, in_channels, norm_act=nn.BatchNorm3d): | |
| super(CostRegNet, self).__init__() | |
| self.conv0 = ConvBnReLU3D(in_channels, 8, norm_act=norm_act) | |
| self.conv1 = ConvBnReLU3D(8, 16, stride=2, norm_act=norm_act) | |
| self.conv2 = ConvBnReLU3D(16, 16, norm_act=norm_act) | |
| self.conv3 = ConvBnReLU3D(16, 32, stride=2, norm_act=norm_act) | |
| self.conv4 = ConvBnReLU3D(32, 32, norm_act=norm_act) | |
| self.conv5 = ConvBnReLU3D(32, 64, stride=2, norm_act=norm_act) | |
| self.conv6 = ConvBnReLU3D(64, 64, norm_act=norm_act) | |
| self.conv7 = nn.Sequential( | |
| nn.ConvTranspose3d(64, 32, 3, padding=1, output_padding=1, stride=2, bias=False), | |
| norm_act(32) | |
| ) | |
| self.conv9 = nn.Sequential( | |
| nn.ConvTranspose3d(32, 16, 3, padding=1, output_padding=1, stride=2, bias=False), | |
| norm_act(16) | |
| ) | |
| self.conv11 = nn.Sequential( | |
| nn.ConvTranspose3d(16, 8, 3, padding=1, output_padding=1,stride=2, bias=False), | |
| norm_act(8) | |
| ) | |
| self.depth_conv = nn.Sequential(nn.Conv3d(8, 1, 3, padding=1, bias=False)) | |
| self.feat_conv = nn.Sequential(nn.Conv3d(8, 8, 3, padding=1, bias=False)) | |
| def forward(self, x): | |
| conv0 = self.conv0(x) | |
| conv2 = self.conv2(self.conv1(conv0)) | |
| conv4 = self.conv4(self.conv3(conv2)) | |
| x = self.conv6(self.conv5(conv4)) | |
| x = conv4 + self.conv7(x) | |
| del conv4 | |
| x = conv2 + self.conv9(x) | |
| del conv2 | |
| x = conv0 + self.conv11(x) | |
| del conv0 | |
| feat = self.feat_conv(x) | |
| depth = self.depth_conv(x) | |
| return feat, depth | |
| class MinCostRegNet(nn.Module): | |
| def __init__(self, in_channels, norm_act=nn.BatchNorm3d): | |
| super(MinCostRegNet, self).__init__() | |
| self.conv0 = ConvBnReLU3D(in_channels, 8, norm_act=norm_act) | |
| self.conv1 = ConvBnReLU3D(8, 16, stride=2, norm_act=norm_act) | |
| self.conv2 = ConvBnReLU3D(16, 16, norm_act=norm_act) | |
| self.conv3 = ConvBnReLU3D(16, 32, stride=2, norm_act=norm_act) | |
| self.conv4 = ConvBnReLU3D(32, 32, norm_act=norm_act) | |
| self.conv9 = nn.Sequential( | |
| nn.ConvTranspose3d(32, 16, 3, padding=1, output_padding=1, | |
| stride=2, bias=False), | |
| norm_act(16)) | |
| self.conv11 = nn.Sequential( | |
| nn.ConvTranspose3d(16, 8, 3, padding=1, output_padding=1, | |
| stride=2, bias=False), | |
| norm_act(8)) | |
| self.depth_conv = nn.Sequential(nn.Conv3d(8, 1, 3, padding=1, bias=False)) | |
| self.feat_conv = nn.Sequential(nn.Conv3d(8, 8, 3, padding=1, bias=False)) | |
| def forward(self, x): | |
| conv0 = self.conv0(x) | |
| conv2 = self.conv2(self.conv1(conv0)) | |
| conv4 = self.conv4(self.conv3(conv2)) | |
| x = conv4 | |
| x = conv2 + self.conv9(x) | |
| del conv2 | |
| x = conv0 + self.conv11(x) | |
| del conv0 | |
| feat = self.feat_conv(x) | |
| depth = self.depth_conv(x) | |
| return feat, depth | |