import torch import torch.nn as nn import math import torch.utils.model_zoo as model_zoo __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(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None): super(Bottleneck, 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=1, 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) out += residual out = self.relu(out) return out class ResNet(nn.Module): def __init__(self, block, layers, num_classes=1000, img_size=224,dropout_factor = 1.): self.inplanes = 64 self.dropout_factor = dropout_factor super(ResNet, self).__init__() self.conv1 = nn.Conv2d(24, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) # see this issue: https://github.com/xxradon/PytorchToCaffe/issues/16 # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2) self.layer3 = self._make_layer(block, 256, layers[2], stride=2) self.layer4 = self._make_layer(block, 512, layers[3], stride=2) assert img_size % 32 == 0 pool_kernel = int(img_size / 32) self.avgpool = nn.AvgPool2d(pool_kernel, stride=1, ceil_mode=True) self.dropout = nn.Dropout(self.dropout_factor) self.fc = nn.Linear(512 * block.expansion, num_classes) 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): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) 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.avgpool(x) x = x.view(x.size(0), -1) x = self.dropout(x) x = self.fc(x) return x def load_model(model, pretrained_state_dict): model_dict = model.state_dict() pretrained_dict = {k: v for k, v in pretrained_state_dict.items() if k in model_dict and model_dict[k].size() == pretrained_state_dict[k].size()} model.load_state_dict(pretrained_dict, strict=False) if len(pretrained_dict) == 0: print("[INFO] No params were loaded ...") else: for k, v in pretrained_state_dict.items(): if k in pretrained_dict: print("==>> Load {} {}".format(k, v.size())) else: print("[INFO] Skip {} {}".format(k, v.size())) return model 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'])) print("Load pretrained model from {}".format(model_urls['resnet18'])) pretrained_state_dict = model_zoo.load_url(model_urls['resnet18']) model = load_model(model, pretrained_state_dict) 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'])) print("Load pretrained model from {}".format(model_urls['resnet34'])) pretrained_state_dict = model_zoo.load_url(model_urls['resnet34']) model = load_model(model, pretrained_state_dict) 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'])) print("Load pretrained model from {}".format(model_urls['resnet50'])) pretrained_state_dict = model_zoo.load_url(model_urls['resnet50']) model = load_model(model, pretrained_state_dict) 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'])) print("Load pretrained model from {}".format(model_urls['resnet101'])) pretrained_state_dict = model_zoo.load_url(model_urls['resnet101']) model = load_model(model, pretrained_state_dict) 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'])) print("Load pretrained model from {}".format(model_urls['resnet152'])) pretrained_state_dict = model_zoo.load_url(model_urls['resnet152']) model = load_model(model, pretrained_state_dict) return model if __name__ == "__main__": input = torch.randn([1, 24, 256,256]) model = resnet34(True, num_classes=63, img_size=256) output = model(input) print(output.size())