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