| import math |
|
|
| import torch.nn as nn |
| import torch.utils.model_zoo as model_zoo |
|
|
| def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): |
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, |
| padding=dilation, groups=groups, bias=False, dilation=dilation) |
|
|
|
|
| def conv1x1(in_planes, out_planes, stride=1): |
| return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) |
|
|
|
|
| class BasicBlock(nn.Module): |
| expansion = 1 |
|
|
| def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, |
| base_width=64, dilation=1, norm_layer=None): |
| super(BasicBlock, self).__init__() |
| if norm_layer is None: |
| norm_layer = nn.BatchNorm2d |
| if groups != 1 or base_width != 64: |
| raise ValueError('BasicBlock only supports groups=1 and base_width=64') |
| if dilation > 1: |
| raise NotImplementedError("Dilation > 1 not supported in BasicBlock") |
| self.conv1 = conv3x3(inplanes, planes, stride) |
| self.bn1 = norm_layer(planes) |
| self.relu = nn.ReLU(inplace=True) |
| self.conv2 = conv3x3(planes, planes) |
| 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 Bottleneck(nn.Module): |
| expansion = 4 |
| def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, |
| base_width=64, dilation=1, norm_layer=None): |
| super(Bottleneck, self).__init__() |
| if norm_layer is None: |
| norm_layer = nn.BatchNorm2d |
| width = int(planes * (base_width / 64.)) * groups |
| |
| self.conv1 = conv1x1(inplanes, width) |
| self.bn1 = norm_layer(width) |
| |
| self.conv2 = conv3x3(width, width, stride, groups, dilation) |
| self.bn2 = norm_layer(width) |
| |
| self.conv3 = conv1x1(width, planes * self.expansion) |
| self.bn3 = norm_layer(planes * self.expansion) |
|
|
| self.relu = nn.ReLU(inplace=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 ResNet(nn.Module): |
| def __init__(self, block, layers, num_classes=1000): |
| |
| |
| |
| |
| self.inplanes = 64 |
| super(ResNet, self).__init__() |
| |
| 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=0, 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) |
| self.features = [self.conv1, self.bn1, self.relu, self.maxpool, self.layer1, self.layer2, self.layer3] |
|
|
| self.avgpool = nn.AvgPool2d(7) |
| 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.fc(x) |
|
|
| return x |
|
|
| def resnet50(pretrained=False, **kwargs): |
| model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) |
| if pretrained: |
| model.load_state_dict(model_zoo.load_url('https://s3.amazonaws.com/pytorch/models/resnet50-19c8e357.pth', model_dir='model_data'), strict=False) |
| |
| del model.avgpool |
| del model.fc |
| return model |
|
|
