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import math |
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import torch.nn as nn |
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from .lib.nn import SynchronizedBatchNorm2d |
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from .utils import load_url |
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BatchNorm2d = SynchronizedBatchNorm2d |
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__all__ = ["ResNet", "resnet18", "resnet50", "resnet101"] |
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model_urls = { |
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"resnet18": "http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet18-imagenet.pth", |
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"resnet50": "http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet50-imagenet.pth", |
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"resnet101": "http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet101-imagenet.pth", |
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} |
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def conv3x3(in_planes, out_planes, stride=1): |
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"3x3 convolution with padding" |
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return nn.Conv2d( |
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in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False |
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) |
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class BasicBlock(nn.Module): |
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expansion = 1 |
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def __init__(self, inplanes, planes, stride=1, downsample=None): |
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super(BasicBlock, self).__init__() |
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self.conv1 = conv3x3(inplanes, planes, stride) |
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self.bn1 = BatchNorm2d(planes) |
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self.relu = nn.ReLU(inplace=True) |
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self.conv2 = conv3x3(planes, planes) |
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self.bn2 = BatchNorm2d(planes) |
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self.downsample = downsample |
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self.stride = stride |
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def forward(self, x): |
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residual = x |
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out = self.conv1(x) |
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out = self.bn1(out) |
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out = self.relu(out) |
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out = self.conv2(out) |
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out = self.bn2(out) |
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if self.downsample is not None: |
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residual = self.downsample(x) |
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out += residual |
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out = self.relu(out) |
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return out |
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class Bottleneck(nn.Module): |
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expansion = 4 |
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def __init__(self, inplanes, planes, stride=1, downsample=None): |
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super(Bottleneck, self).__init__() |
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) |
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self.bn1 = BatchNorm2d(planes) |
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self.conv2 = nn.Conv2d( |
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planes, planes, kernel_size=3, stride=stride, padding=1, bias=False |
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) |
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self.bn2 = BatchNorm2d(planes) |
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self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) |
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self.bn3 = BatchNorm2d(planes * 4) |
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self.relu = nn.ReLU(inplace=True) |
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self.downsample = downsample |
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self.stride = stride |
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def forward(self, x): |
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residual = x |
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out = self.conv1(x) |
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out = self.bn1(out) |
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out = self.relu(out) |
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out = self.conv2(out) |
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out = self.bn2(out) |
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out = self.relu(out) |
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out = self.conv3(out) |
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out = self.bn3(out) |
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if self.downsample is not None: |
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residual = self.downsample(x) |
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out += residual |
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out = self.relu(out) |
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return out |
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class ResNet(nn.Module): |
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def __init__(self, block, layers, num_classes=1000): |
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self.inplanes = 128 |
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super(ResNet, self).__init__() |
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self.conv1 = conv3x3(3, 64, stride=2) |
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self.bn1 = BatchNorm2d(64) |
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self.relu1 = nn.ReLU(inplace=True) |
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self.conv2 = conv3x3(64, 64) |
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self.bn2 = BatchNorm2d(64) |
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self.relu2 = nn.ReLU(inplace=True) |
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self.conv3 = conv3x3(64, 128) |
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self.bn3 = BatchNorm2d(128) |
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self.relu3 = nn.ReLU(inplace=True) |
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
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self.layer1 = self._make_layer(block, 64, layers[0]) |
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2) |
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2) |
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2) |
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self.avgpool = nn.AvgPool2d(7, stride=1) |
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self.fc = nn.Linear(512 * block.expansion, num_classes) |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
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m.weight.data.normal_(0, math.sqrt(2.0 / n)) |
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elif isinstance(m, BatchNorm2d): |
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m.weight.data.fill_(1) |
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m.bias.data.zero_() |
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def _make_layer(self, block, planes, blocks, stride=1): |
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downsample = None |
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if stride != 1 or self.inplanes != planes * block.expansion: |
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downsample = nn.Sequential( |
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nn.Conv2d( |
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self.inplanes, |
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planes * block.expansion, |
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kernel_size=1, |
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stride=stride, |
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bias=False, |
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), |
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BatchNorm2d(planes * block.expansion), |
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) |
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layers = [] |
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layers.append(block(self.inplanes, planes, stride, downsample)) |
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self.inplanes = planes * block.expansion |
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for i in range(1, blocks): |
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layers.append(block(self.inplanes, planes)) |
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return nn.Sequential(*layers) |
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def forward(self, x): |
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x = self.relu1(self.bn1(self.conv1(x))) |
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x = self.relu2(self.bn2(self.conv2(x))) |
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x = self.relu3(self.bn3(self.conv3(x))) |
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x = self.maxpool(x) |
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x = self.layer1(x) |
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x = self.layer2(x) |
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x = self.layer3(x) |
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x = self.layer4(x) |
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x = self.avgpool(x) |
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x = x.view(x.size(0), -1) |
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x = self.fc(x) |
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return x |
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def resnet18(pretrained=False, **kwargs): |
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"""Constructs a ResNet-18 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) |
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if pretrained: |
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model.load_state_dict(load_url(model_urls["resnet18"])) |
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return model |
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''' |
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def resnet34(pretrained=False, **kwargs): |
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"""Constructs a ResNet-34 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) |
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if pretrained: |
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model.load_state_dict(load_url(model_urls['resnet34'])) |
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return model |
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''' |
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def resnet50(pretrained=False, **kwargs): |
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"""Constructs a ResNet-50 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) |
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if pretrained: |
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model.load_state_dict(load_url(model_urls["resnet50"]), strict=False) |
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return model |
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def resnet101(pretrained=False, **kwargs): |
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"""Constructs a ResNet-101 model. |
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Args: |
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pretrained (bool): If True, returns a model pre-trained on ImageNet |
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""" |
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model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) |
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if pretrained: |
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model.load_state_dict(load_url(model_urls["resnet101"]), strict=False) |
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return model |
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