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import torch.nn as nn |
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import torch |
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class BasicBlock(nn.Module): |
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expansion = 1 |
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def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs): |
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super(BasicBlock, self).__init__() |
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self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, |
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kernel_size=3, stride=stride, padding=1, bias=False) |
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self.bn1 = nn.BatchNorm2d(out_channel) |
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self.relu = nn.ReLU() |
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self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel, |
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kernel_size=3, stride=1, padding=1, bias=False) |
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self.bn2 = nn.BatchNorm2d(out_channel) |
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self.downsample = downsample |
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def forward(self, x): |
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identity = x |
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if self.downsample is not None: |
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identity = self.downsample(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 += identity |
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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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""" |
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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。 |
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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， |
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这么做的好处是能够在top1上提升大概0.5%的准确率。 |
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可参考Resnet v1.5 https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch |
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""" |
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expansion = 4 |
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def __init__(self, in_channel, out_channel, stride=1, downsample=None, |
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groups=1, width_per_group=64): |
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super(Bottleneck, self).__init__() |
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width = int(out_channel * (width_per_group / 64.)) * groups |
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self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width, |
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kernel_size=1, stride=1, bias=False) |
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self.bn1 = nn.BatchNorm2d(width) |
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self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups, |
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kernel_size=3, stride=stride, bias=False, padding=1) |
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self.bn2 = nn.BatchNorm2d(width) |
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self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel*self.expansion, |
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kernel_size=1, stride=1, bias=False) |
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self.bn3 = nn.BatchNorm2d(out_channel*self.expansion) |
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self.relu = nn.ReLU(inplace=True) |
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self.downsample = downsample |
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def forward(self, x): |
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identity = x |
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if self.downsample is not None: |
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identity = self.downsample(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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out += identity |
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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, |
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block, |
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blocks_num, |
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num_classes=1000, |
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include_top=True, |
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groups=1, |
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width_per_group=64): |
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super(ResNet, self).__init__() |
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self.include_top = include_top |
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self.in_channel = 64 |
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self.groups = groups |
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self.width_per_group = width_per_group |
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self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2, |
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padding=3, bias=False) |
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self.bn1 = nn.BatchNorm2d(self.in_channel) |
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self.relu = 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, blocks_num[0]) |
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self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2) |
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self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2) |
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self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2) |
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if self.include_top: |
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self.avgpool = nn.AdaptiveAvgPool2d((1, 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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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') |
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print() |
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def _make_layer(self, block, channel, block_num, stride=1): |
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downsample = None |
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if stride != 1 or self.in_channel != channel * block.expansion: |
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downsample = nn.Sequential( |
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nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False), |
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nn.BatchNorm2d(channel * block.expansion)) |
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layers = [] |
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layers.append(block(self.in_channel, |
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channel, |
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downsample=downsample, |
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stride=stride, |
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groups=self.groups, |
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width_per_group=self.width_per_group)) |
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self.in_channel = channel * block.expansion |
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for _ in range(1, block_num): |
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layers.append(block(self.in_channel, |
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channel, |
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groups=self.groups, |
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width_per_group=self.width_per_group)) |
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return nn.Sequential(*layers) |
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def forward(self, x): |
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x = self.conv1(x) |
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x = self.bn1(x) |
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x = self.relu(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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if self.include_top: |
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x = self.avgpool(x) |
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x = torch.flatten(x, 1) |
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x = self.fc(x) |
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return x |
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def resnet34(num_classes=1000, include_top=True): |
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return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top) |
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def resnet50(num_classes=1000, include_top=True): |
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return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top) |
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def resnet101(num_classes=1000, include_top=True): |
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return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top) |
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def resnext50_32x4d(num_classes=1000, include_top=True): |
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groups = 32 |
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width_per_group = 4 |
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return ResNet(Bottleneck, [3, 4, 6, 3], |
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num_classes=num_classes, |
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include_top=include_top, |
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groups=groups, |
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width_per_group=width_per_group) |
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def resnext101_32x8d(num_classes=1000, include_top=True): |
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groups = 32 |
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width_per_group = 8 |
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return ResNet(Bottleneck, [3, 4, 23, 3], |
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num_classes=num_classes, |
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include_top=include_top, |
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groups=groups, |
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width_per_group=width_per_group) |
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