import torch import torch.nn as nn import torch.utils.model_zoo as model_zoo try: from .yolov1_basic import conv1x1, BasicBlock, Bottleneck except: from yolov1_basic import conv1x1, BasicBlock, Bottleneck __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', } # --------------------- ResNet ----------------------- class ResNet(nn.Module): def __init__(self, block, layers, zero_init_residual=False): super(ResNet, self).__init__() self.inplanes = 64 self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=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) self.layer3 = self._make_layer(block, 256, layers[2], stride=2) self.layer4 = self._make_layer(block, 512, layers[3], stride=2) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) # Zero-initialize the last BN in each residual branch, # so that the residual branch starts with zeros, and each residual block behaves like an identity. # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 if zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): nn.init.constant_(m.bn3.weight, 0) elif isinstance(m, BasicBlock): nn.init.constant_(m.bn2.weight, 0) def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): """ Input: x: (Tensor) -> [B, C, H, W] Output: c5: (Tensor) -> [B, C, H/32, W/32] """ c1 = self.conv1(x) # [B, C, H/2, W/2] c1 = self.bn1(c1) # [B, C, H/2, W/2] c1 = self.relu(c1) # [B, C, H/2, W/2] c2 = self.maxpool(c1) # [B, C, H/4, W/4] c2 = self.layer1(c2) # [B, C, H/4, W/4] c3 = self.layer2(c2) # [B, C, H/8, W/8] c4 = self.layer3(c3) # [B, C, H/16, W/16] c5 = self.layer4(c4) # [B, C, H/32, W/32] return c5 # --------------------- Functions ----------------------- def build_resnet(model_name="resnet18", pretrained=False): if model_name == 'resnet18': model = resnet18(pretrained) feat_dim = 512 elif model_name == 'resnet34': model = resnet34(pretrained) feat_dim = 512 elif model_name == 'resnet50': model = resnet50(pretrained) feat_dim = 2048 elif model_name == 'resnet101': model = resnet34(pretrained) feat_dim = 2048 else: raise NotImplementedError("Unknown resnet: {}".format(model_name)) return model, feat_dim 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: # strict = False as we don't need fc layer params. model.load_state_dict(model_zoo.load_url(model_urls['resnet18']), strict=False) 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']), strict=False) 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']), strict=False) 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']), strict=False) 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']), strict=False) return model if __name__=='__main__': import time from thop import profile # Build backbone model, _ = build_resnet(model_name='resnet18') # Inference x = torch.randn(1, 3, 640, 640) t0 = time.time() output = model(x) t1 = time.time() print('Time: ', t1 - t0) print(output.shape) print('==============================') flops, params = profile(model, inputs=(x, ), verbose=False) print('==============================') print('GFLOPs : {:.2f}'.format(flops / 1e9 * 2)) print('Params : {:.2f} M'.format(params / 1e6))