import torch.nn as nn import torch from torch.autograd import Variable import math import torch.utils.model_zoo as model_zoo from models.features import Features __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152'] model_urls = { 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', } def conv3x3(in_planes, out_planes, stride=1): "3x3 convolution with padding" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = nn.BatchNorm2d(planes) self.downsample = downsample self.stride = 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) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class Bottleneck(Features): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) # padding = (2 - stride) + (dilation // 2 - 1) padding = 2 - stride assert stride==1 or dilation==1, "stride and dilation must have one equals to zero at least" if dilation > 1: padding = dilation self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=padding, bias=False, dilation=dilation) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * 4) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = 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) if out.size() != residual.size(): print(out.size(), residual.size()) out += residual out = self.relu(out) return out class Bottleneck_nop(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None): super(Bottleneck_nop, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=0, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * 4) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = 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) s = residual.size(3) residual = residual[:, :, 1:s-1, 1:s-1] out += residual out = self.relu(out) return out class ResNet(nn.Module): def __init__(self, block, layers, layer4=False, layer3=False): self.inplanes = 64 super(ResNet, self).__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=0, # 3 bias=False) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2) # 31x31, 15x15 self.feature_size = 128 * block.expansion if layer3: self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2) # 15x15, 7x7 self.feature_size = (256 + 128) * block.expansion else: self.layer3 = lambda x:x # identity if layer4: self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4) # 7x7, 3x3 self.feature_size = 512 * block.expansion else: self.layer4 = lambda x:x # identity for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() def _make_layer(self, block, planes, blocks, stride=1, dilation=1): downsample = None dd = dilation if stride != 1 or self.inplanes != planes * block.expansion: if stride == 1 and dilation == 1: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion), ) else: if dilation > 1: dd = dilation // 2 padding = dd else: dd = 1 padding = 0 downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=3, stride=stride, bias=False, padding=padding, dilation=dd), nn.BatchNorm2d(planes * block.expansion), ) layers = [] # layers.append(block(self.inplanes, planes, stride, downsample, dilation=dilation)) layers.append(block(self.inplanes, planes, stride, downsample, dilation=dd)) 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) p0 = self.relu(x) x = self.maxpool(p0) p1 = self.layer1(x) p2 = self.layer2(p1) p3 = self.layer3(p2) return p0, p1, p2, p3 class ResAdjust(nn.Module): def __init__(self, block=Bottleneck, out_channels=256, adjust_number=1, fuse_layers=[2,3,4]): super(ResAdjust, self).__init__() self.fuse_layers = set(fuse_layers) if 2 in self.fuse_layers: self.layer2 = self._make_layer(block, 128, 1, out_channels, adjust_number) if 3 in self.fuse_layers: self.layer3 = self._make_layer(block, 256, 2, out_channels, adjust_number) if 4 in self.fuse_layers: self.layer4 = self._make_layer(block, 512, 4, out_channels, adjust_number) self.feature_size = out_channels * len(self.fuse_layers) def _make_layer(self, block, plances, dilation, out, number=1): layers = [] for _ in range(number): layer = block(plances * block.expansion, plances, dilation=dilation) layers.append(layer) downsample = nn.Sequential( nn.Conv2d(plances * block.expansion, out, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(out) ) layers.append(downsample) return nn.Sequential(*layers) def forward(self, p2, p3, p4): outputs = [] if 2 in self.fuse_layers: outputs.append(self.layer2(p2)) if 3 in self.fuse_layers: outputs.append(self.layer3(p3)) if 4 in self.fuse_layers: outputs.append(self.layer4(p4)) # return torch.cat(outputs, 1) return outputs def resnet18(pretrained=False, **kwargs): """Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])) return model def resnet34(pretrained=False, **kwargs): """Constructs a ResNet-34 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet34'])) return model def resnet50(pretrained=False, **kwargs): """Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) return model def resnet101(pretrained=False, **kwargs): """Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet101'])) return model def resnet152(pretrained=False, **kwargs): """Constructs a ResNet-152 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet152'])) return model if __name__ == '__main__': net = resnet50() print(net) net = net.cuda() var = torch.FloatTensor(1,3,127,127).cuda() var = Variable(var) net(var) print('*************') var = torch.FloatTensor(1,3,255,255).cuda() var = Variable(var) net(var)