import torch import torch.nn as nn import torch.utils.model_zoo as model_zoo __all__ = ['ResNetV1b', 'resnet18_v1b', 'resnet34_v1b', 'resnet50_v1b', 'resnet101_v1b', 'resnet152_v1b', 'resnet152_v1s', 'resnet101_v1s', 'resnet50_v1s'] 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', } class BasicBlockV1b(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, previous_dilation=1, norm_layer=nn.BatchNorm2d): super(BasicBlockV1b, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, 3, stride, dilation, dilation, bias=False) self.bn1 = norm_layer(planes) self.relu = nn.ReLU(True) self.conv2 = nn.Conv2d(planes, planes, 3, 1, previous_dilation, dilation=previous_dilation, bias=False) self.bn2 = norm_layer(planes) self.downsample = downsample self.stride = stride def forward(self, x): identity = 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: identity = self.downsample(x) out += identity out = self.relu(out) return out class BottleneckV1b(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, previous_dilation=1, norm_layer=nn.BatchNorm2d): super(BottleneckV1b, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) self.bn1 = norm_layer(planes) self.conv2 = nn.Conv2d(planes, planes, 3, stride, dilation, dilation, bias=False) self.bn2 = norm_layer(planes) self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) self.bn3 = norm_layer(planes * self.expansion) self.relu = nn.ReLU(True) self.downsample = downsample self.stride = stride def forward(self, x): identity = 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: identity = self.downsample(x) out += identity out = self.relu(out) return out class ResNetV1b(nn.Module): def __init__(self, block, layers, num_classes=1000, dilated=True, deep_stem=False, zero_init_residual=False, norm_layer=nn.BatchNorm2d): self.inplanes = 128 if deep_stem else 64 super(ResNetV1b, self).__init__() if deep_stem: self.conv1 = nn.Sequential( nn.Conv2d(3, 64, 3, 2, 1, bias=False), norm_layer(64), nn.ReLU(True), nn.Conv2d(64, 64, 3, 1, 1, bias=False), norm_layer(64), nn.ReLU(True), nn.Conv2d(64, 128, 3, 1, 1, bias=False) ) else: self.conv1 = nn.Conv2d(3, 64, 7, 2, 3, bias=False) self.bn1 = norm_layer(self.inplanes) self.relu = nn.ReLU(True) self.maxpool = nn.MaxPool2d(3, 2, 1) self.layer1 = self._make_layer(block, 64, layers[0], norm_layer=norm_layer) self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer) if dilated: self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2, norm_layer=norm_layer) self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4, norm_layer=norm_layer) else: self.layer3 = self._make_layer(block, 256, layers[2], stride=2, norm_layer=norm_layer) self.layer4 = self._make_layer(block, 512, layers[3], stride=2, norm_layer=norm_layer) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) 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) if zero_init_residual: for m in self.modules(): if isinstance(m, BottleneckV1b): nn.init.constant_(m.bn3.weight, 0) elif isinstance(m, BasicBlockV1b): nn.init.constant_(m.bn2.weight, 0) def _make_layer(self, block, planes, blocks, stride=1, dilation=1, norm_layer=nn.BatchNorm2d): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, 1, stride, bias=False), norm_layer(planes * block.expansion), ) layers = [] if dilation in (1, 2): layers.append(block(self.inplanes, planes, stride, dilation=1, downsample=downsample, previous_dilation=dilation, norm_layer=norm_layer)) elif dilation == 4: layers.append(block(self.inplanes, planes, stride, dilation=2, downsample=downsample, previous_dilation=dilation, norm_layer=norm_layer)) else: raise RuntimeError("=> unknown dilation size: {}".format(dilation)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes, dilation=dilation, previous_dilation=dilation, norm_layer=norm_layer)) 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.fc(x) return x def resnet18_v1b(pretrained=False, **kwargs): model = ResNetV1b(BasicBlockV1b, [2, 2, 2, 2], **kwargs) if pretrained: old_dict = model_zoo.load_url(model_urls['resnet18']) model_dict = model.state_dict() old_dict = {k: v for k, v in old_dict.items() if (k in model_dict)} model_dict.update(old_dict) model.load_state_dict(model_dict) return model def resnet34_v1b(pretrained=False, **kwargs): model = ResNetV1b(BasicBlockV1b, [3, 4, 6, 3], **kwargs) if pretrained: old_dict = model_zoo.load_url(model_urls['resnet34']) model_dict = model.state_dict() old_dict = {k: v for k, v in old_dict.items() if (k in model_dict)} model_dict.update(old_dict) model.load_state_dict(model_dict) return model def resnet50_v1b(pretrained=False, **kwargs): model = ResNetV1b(BottleneckV1b, [3, 4, 6, 3], **kwargs) if pretrained: old_dict = model_zoo.load_url(model_urls['resnet50']) model_dict = model.state_dict() old_dict = {k: v for k, v in old_dict.items() if (k in model_dict)} model_dict.update(old_dict) model.load_state_dict(model_dict) return model def resnet101_v1b(pretrained=False, **kwargs): model = ResNetV1b(BottleneckV1b, [3, 4, 23, 3], **kwargs) if pretrained: old_dict = model_zoo.load_url(model_urls['resnet101']) model_dict = model.state_dict() old_dict = {k: v for k, v in old_dict.items() if (k in model_dict)} model_dict.update(old_dict) model.load_state_dict(model_dict) return model def resnet152_v1b(pretrained=False, **kwargs): model = ResNetV1b(BottleneckV1b, [3, 8, 36, 3], **kwargs) if pretrained: old_dict = model_zoo.load_url(model_urls['resnet152']) model_dict = model.state_dict() old_dict = {k: v for k, v in old_dict.items() if (k in model_dict)} model_dict.update(old_dict) model.load_state_dict(model_dict) return model def resnet50_v1s(pretrained=False, root='~/.torch/models', **kwargs): model = ResNetV1b(BottleneckV1b, [3, 4, 6, 3], deep_stem=True, **kwargs) if pretrained: from ..model_store import get_resnet_file model.load_state_dict(torch.load(get_resnet_file('resnet50', root=root)), strict=False) return model def resnet101_v1s(pretrained=False, root='~/.torch/models', **kwargs): model = ResNetV1b(BottleneckV1b, [3, 4, 23, 3], deep_stem=True, **kwargs) if pretrained: from ..model_store import get_resnet_file model.load_state_dict(torch.load(get_resnet_file('resnet101', root=root)), strict=False) return model def resnet152_v1s(pretrained=False, root='~/.torch/models', **kwargs): model = ResNetV1b(BottleneckV1b, [3, 8, 36, 3], deep_stem=True, **kwargs) if pretrained: from ..model_store import get_resnet_file model.load_state_dict(torch.load(get_resnet_file('resnet152', root=root)), strict=False) return model if __name__ == '__main__': import torch img = torch.randn(4, 3, 224, 224) model = resnet50_v1b(True) output = model(img)