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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch.autograd import Variable | |
| import math | |
| from functools import partial | |
| __all__ = [ | |
| 'ResNet', 'resnet10', 'resnet18', 'resnet34', 'resnet50', 'resnet101', | |
| 'resnet152', 'resnet200' | |
| ] | |
| def conv3x3x3(in_planes, out_planes, stride=1, dilation=1): | |
| # 3x3x3 convolution with padding | |
| return nn.Conv3d( | |
| in_planes, | |
| out_planes, | |
| kernel_size=3, | |
| dilation=dilation, | |
| stride=stride, | |
| padding=dilation, | |
| bias=False) | |
| def downsample_basic_block(x, planes, stride, no_cuda=False): | |
| out = F.avg_pool3d(x, kernel_size=1, stride=stride) | |
| zero_pads = torch.Tensor( | |
| out.size(0), planes - out.size(1), out.size(2), out.size(3), | |
| out.size(4)).zero_() | |
| if not no_cuda: | |
| if isinstance(out.data, torch.cuda.FloatTensor): | |
| zero_pads = zero_pads.cuda() | |
| out = Variable(torch.cat([out.data, zero_pads], dim=1)) | |
| return out | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None): | |
| super(BasicBlock, self).__init__() | |
| self.conv1 = conv3x3x3(inplanes, planes, stride=stride, dilation=dilation) | |
| self.bn1 = nn.BatchNorm3d(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = conv3x3x3(planes, planes, dilation=dilation) | |
| self.bn2 = nn.BatchNorm3d(planes) | |
| self.downsample = downsample | |
| self.stride = stride | |
| self.dilation = dilation | |
| 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) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = self.relu(out) | |
| return out | |
| class Bottleneck(nn.Module): | |
| expansion = 4 | |
| def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None): | |
| super(Bottleneck, self).__init__() | |
| self.conv1 = nn.Conv3d(inplanes, planes, kernel_size=1, bias=False) | |
| self.bn1 = nn.BatchNorm3d(planes) | |
| self.conv2 = nn.Conv3d( | |
| planes, planes, kernel_size=3, stride=stride, dilation=dilation, padding=dilation, bias=False) | |
| self.bn2 = nn.BatchNorm3d(planes) | |
| self.conv3 = nn.Conv3d(planes, planes * 4, kernel_size=1, bias=False) | |
| self.bn3 = nn.BatchNorm3d(planes * 4) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.downsample = downsample | |
| self.stride = stride | |
| self.dilation = dilation | |
| 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 = self.relu(out) | |
| return out | |
| class ResNet(nn.Module): | |
| def __init__(self, | |
| block, | |
| layers, | |
| sample_input_D, | |
| sample_input_H, | |
| sample_input_W, | |
| num_seg_classes, | |
| shortcut_type='B', | |
| no_cuda = False): | |
| self.inplanes = 64 | |
| self.no_cuda = no_cuda | |
| super(ResNet, self).__init__() | |
| self.conv1 = nn.Conv3d( | |
| 1, | |
| 64, | |
| kernel_size=7, | |
| stride=(2, 2, 2), | |
| padding=(3, 3, 3), | |
| bias=False) | |
| self.bn1 = nn.BatchNorm3d(64) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.maxpool = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1) | |
| self.layer1 = self._make_layer(block, 64, layers[0], shortcut_type) | |
| self.layer2 = self._make_layer( | |
| block, 128, layers[1], shortcut_type, stride=2) | |
| self.layer3 = self._make_layer( | |
| block, 256, layers[2], shortcut_type, stride=1, dilation=2) | |
| self.layer4 = self._make_layer( | |
| block, 512, layers[3], shortcut_type, stride=1, dilation=4) | |
| self.conv_seg = nn.Sequential( | |
| nn.ConvTranspose3d( | |
| 512 * block.expansion, | |
| 32, | |
| 2, | |
| stride=2 | |
| ), | |
| nn.BatchNorm3d(32), | |
| nn.ReLU(inplace=True), | |
| nn.Conv3d( | |
| 32, | |
| 32, | |
| kernel_size=3, | |
| stride=(1, 1, 1), | |
| padding=(1, 1, 1), | |
| bias=False), | |
| nn.BatchNorm3d(32), | |
| nn.ReLU(inplace=True), | |
| nn.Conv3d( | |
| 32, | |
| num_seg_classes, | |
| kernel_size=1, | |
| stride=(1, 1, 1), | |
| bias=False) | |
| ) | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv3d): | |
| m.weight = nn.init.kaiming_normal_(m.weight, mode='fan_out') | |
| elif isinstance(m, nn.BatchNorm3d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| def _make_layer(self, block, planes, blocks, shortcut_type, stride=1, dilation=1): | |
| downsample = None | |
| if stride != 1 or self.inplanes != planes * block.expansion: | |
| if shortcut_type == 'A': | |
| downsample = partial( | |
| downsample_basic_block, | |
| planes=planes * block.expansion, | |
| stride=stride, | |
| no_cuda=self.no_cuda) | |
| else: | |
| downsample = nn.Sequential( | |
| nn.Conv3d( | |
| self.inplanes, | |
| planes * block.expansion, | |
| kernel_size=1, | |
| stride=stride, | |
| bias=False), nn.BatchNorm3d(planes * block.expansion)) | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, stride=stride, dilation=dilation, downsample=downsample)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes, dilation=dilation)) | |
| return nn.Sequential(*layers) | |
| 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) | |
| x = self.conv_seg(x) | |
| return x | |
| def resnet10(**kwargs): | |
| """Constructs a ResNet-18 model. | |
| """ | |
| model = ResNet(BasicBlock, [1, 1, 1, 1], **kwargs) | |
| return model | |
| def resnet18(**kwargs): | |
| """Constructs a ResNet-18 model. | |
| """ | |
| model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) | |
| return model | |
| def resnet34(**kwargs): | |
| """Constructs a ResNet-34 model. | |
| """ | |
| model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) | |
| return model | |
| def resnet50(**kwargs): | |
| """Constructs a ResNet-50 model. | |
| """ | |
| model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) | |
| return model | |
| def resnet101(**kwargs): | |
| """Constructs a ResNet-101 model. | |
| """ | |
| model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) | |
| return model | |
| def resnet152(**kwargs): | |
| """Constructs a ResNet-101 model. | |
| """ | |
| model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) | |
| return model | |
| def resnet200(**kwargs): | |
| """Constructs a ResNet-101 model. | |
| """ | |
| model = ResNet(Bottleneck, [3, 24, 36, 3], **kwargs) | |
| return model | |