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| 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 | |
| from utils.log_helper import log_once | |
| __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) | |
| x = self.relu(x) | |
| # print x.size() | |
| x = self.maxpool(x) | |
| # print x.size() | |
| p1 = self.layer1(x) | |
| p2 = self.layer2(p1) | |
| p3 = self.layer3(p2) | |
| # p3 = torch.cat([p2, p3], 1) | |
| log_once("p3 {}".format(p3.size())) | |
| p4 = self.layer4(p3) | |
| return p2, p3, p4 | |
| 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) | |