| import torch.nn as nn |
| import torch |
|
|
|
|
| class BasicBlock(nn.Module): |
| expansion = 1 |
|
|
| def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs): |
| super(BasicBlock, self).__init__() |
| self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, |
| kernel_size=3, stride=stride, padding=1, bias=False) |
| self.bn1 = nn.BatchNorm2d(out_channel) |
| self.relu = nn.ReLU() |
| self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel, |
| kernel_size=3, stride=1, padding=1, bias=False) |
| self.bn2 = nn.BatchNorm2d(out_channel) |
| self.downsample = downsample |
|
|
| def forward(self, x): |
| identity = x |
| if self.downsample is not None: |
| identity = self.downsample(x) |
|
|
| out = self.conv1(x) |
| out = self.bn1(out) |
| out = self.relu(out) |
|
|
| out = self.conv2(out) |
| out = self.bn2(out) |
|
|
| out += identity |
| out = self.relu(out) |
|
|
| return out |
|
|
|
|
| class Bottleneck(nn.Module): |
| """ |
| 注意：原论文中，在虚线残差结构的主分支上，第一个1x1卷积层的步距是2，第二个3x3卷积层步距是1。 |
| 但在pytorch官方实现过程中是第一个1x1卷积层的步距是1，第二个3x3卷积层步距是2， |
| 这么做的好处是能够在top1上提升大概0.5%的准确率。 |
| 可参考Resnet v1.5 https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch |
| """ |
| expansion = 4 |
|
|
| def __init__(self, in_channel, out_channel, stride=1, downsample=None, |
| groups=1, width_per_group=64): |
| super(Bottleneck, self).__init__() |
|
|
| width = int(out_channel * (width_per_group / 64.)) * groups |
|
|
| self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width, |
| kernel_size=1, stride=1, bias=False) |
| self.bn1 = nn.BatchNorm2d(width) |
| |
| self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups, |
| kernel_size=3, stride=stride, bias=False, padding=1) |
| self.bn2 = nn.BatchNorm2d(width) |
| |
| self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel*self.expansion, |
| kernel_size=1, stride=1, bias=False) |
| self.bn3 = nn.BatchNorm2d(out_channel*self.expansion) |
| self.relu = nn.ReLU(inplace=True) |
| self.downsample = downsample |
|
|
| def forward(self, x): |
| identity = x |
| if self.downsample is not None: |
| identity = self.downsample(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) |
|
|
| out += identity |
| out = self.relu(out) |
|
|
| return out |
|
|
|
|
| class ResNet(nn.Module): |
|
|
| def __init__(self, |
| block, |
| blocks_num, |
| num_classes=1000, |
| include_top=True, |
| groups=1, |
| width_per_group=64): |
| super(ResNet, self).__init__() |
| self.include_top = include_top |
| self.in_channel = 64 |
|
|
| self.groups = groups |
| self.width_per_group = width_per_group |
|
|
| self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2, |
| padding=3, bias=False) |
| self.bn1 = nn.BatchNorm2d(self.in_channel) |
| self.relu = nn.ReLU(inplace=True) |
| self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) |
| self.layer1 = self._make_layer(block, 64, blocks_num[0]) |
| self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2) |
| self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2) |
| self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2) |
| if self.include_top: |
| 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') |
| print() |
|
|
| def _make_layer(self, block, channel, block_num, stride=1): |
| downsample = None |
| if stride != 1 or self.in_channel != channel * block.expansion: |
| downsample = nn.Sequential( |
| nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False), |
| nn.BatchNorm2d(channel * block.expansion)) |
|
|
| layers = [] |
| layers.append(block(self.in_channel, |
| channel, |
| downsample=downsample, |
| stride=stride, |
| groups=self.groups, |
| width_per_group=self.width_per_group)) |
| self.in_channel = channel * block.expansion |
|
|
| for _ in range(1, block_num): |
| layers.append(block(self.in_channel, |
| channel, |
| groups=self.groups, |
| width_per_group=self.width_per_group)) |
|
|
| 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) |
|
|
| if self.include_top: |
| x = self.avgpool(x) |
| x = torch.flatten(x, 1) |
| x = self.fc(x) |
|
|
| return x |
|
|
|
|
| def resnet34(num_classes=1000, include_top=True): |
| |
| return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top) |
|
|
|
|
| def resnet50(num_classes=1000, include_top=True): |
| |
| return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top) |
|
|
|
|
| def resnet101(num_classes=1000, include_top=True): |
| |
| return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top) |
|
|
|
|
| def resnext50_32x4d(num_classes=1000, include_top=True): |
| |
| groups = 32 |
| width_per_group = 4 |
| return ResNet(Bottleneck, [3, 4, 6, 3], |
| num_classes=num_classes, |
| include_top=include_top, |
| groups=groups, |
| width_per_group=width_per_group) |
|
|
|
|
| def resnext101_32x8d(num_classes=1000, include_top=True): |
| |
| groups = 32 |
| width_per_group = 8 |
| return ResNet(Bottleneck, [3, 4, 23, 3], |
| num_classes=num_classes, |
| include_top=include_top, |
| groups=groups, |
| width_per_group=width_per_group) |
|
|
