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import torch |
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import torch.nn as nn |
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import math |
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import torch.utils.model_zoo as model_zoo |
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import torch.nn.functional as F |
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__all__ = ['ResNet', 'resnet18_rep', 'resnet34_rep' ] |
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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(in_planes, out_planes, kernel_size=3, stride=stride, |
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padding=1, bias=True) |
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def conv1x1(in_planes, out_planes, stride=1): |
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"""1x1 convolution""" |
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return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=True) |
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class conv_block(nn.Module): |
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def __init__(self, in_planes, planes, mode, stride=1): |
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super(conv_block, self).__init__() |
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self.conv = conv3x3(in_planes, planes, stride) |
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self.mode = mode |
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if mode == 'parallel_adapters': |
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self.adapter = conv1x1(in_planes, planes, stride) |
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def re_init_conv(self): |
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nn.init.kaiming_normal_(self.adapter.weight, mode='fan_out', nonlinearity='relu') |
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return |
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def forward(self, x): |
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y = self.conv(x) |
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if self.mode == 'parallel_adapters': |
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y = y + self.adapter(x) |
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return y |
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class BasicBlock(nn.Module): |
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expansion = 1 |
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def __init__(self, inplanes, planes, mode, stride=1, downsample=None): |
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super(BasicBlock, self).__init__() |
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self.conv1 = conv_block(inplanes, planes, mode, stride) |
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self.norm1 = nn.BatchNorm2d(planes) |
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self.relu = nn.ReLU(inplace=True) |
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self.conv2 = conv_block(planes, planes, mode) |
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self.norm2 = nn.BatchNorm2d(planes) |
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self.mode = mode |
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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.norm1(out) |
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out = self.relu(out) |
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out = self.conv2(out) |
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out = self.norm2(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=100, args = None): |
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self.inplanes = 64 |
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super(ResNet, self).__init__() |
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assert args is not None |
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self.mode = args["mode"] |
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if 'cifar' in args["dataset"]: |
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self.conv1 = nn.Sequential(nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False), |
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nn.BatchNorm2d(self.inplanes), nn.ReLU(inplace=True)) |
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print("use cifar") |
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elif 'imagenet' in args["dataset"]: |
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if args["init_cls"] == args["increment"]: |
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self.conv1 = nn.Sequential( |
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nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False), |
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nn.BatchNorm2d(self.inplanes), |
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nn.ReLU(inplace=True), |
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nn.MaxPool2d(kernel_size=3, stride=2, padding=1), |
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) |
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else: |
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self.conv1 = nn.Sequential( |
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nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False), |
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nn.BatchNorm2d(self.inplanes), |
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nn.ReLU(inplace=True), |
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nn.MaxPool2d(kernel_size=3, stride=2, padding=1), |
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) |
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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.feature = nn.AvgPool2d(4, stride=1) |
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self.out_dim = 512 |
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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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elif isinstance(m, nn.BatchNorm2d): |
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nn.init.constant_(m.weight, 1) |
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nn.init.constant_(m.bias, 0) |
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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(self.inplanes, planes * block.expansion, |
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kernel_size=1, stride=stride, bias=True), |
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) |
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layers = [] |
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layers.append(block(self.inplanes, planes, self.mode, 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, self.mode)) |
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return nn.Sequential(*layers) |
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def switch(self, mode='normal'): |
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for name, module in self.named_modules(): |
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if hasattr(module, 'mode'): |
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module.mode = mode |
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def re_init_params(self): |
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for name, module in self.named_modules(): |
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if hasattr(module, 're_init_conv'): |
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module.re_init_conv() |
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def forward(self, x): |
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x = self.conv1(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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dim = x.size()[-1] |
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pool = nn.AvgPool2d(dim, stride=1) |
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x = pool(x) |
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x = x.view(x.size(0), -1) |
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return {"features": x} |
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def resnet18_rep(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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pretrained_state_dict = model_zoo.load_url(model_urls['resnet18']) |
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now_state_dict = model.state_dict() |
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now_state_dict.update(pretrained_state_dict) |
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model.load_state_dict(now_state_dict) |
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return model |
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def resnet34_rep(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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pretrained_state_dict = model_zoo.load_url(model_urls['resnet34']) |
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now_state_dict = model.state_dict() |
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now_state_dict.update(pretrained_state_dict) |
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model.load_state_dict(now_state_dict) |
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return model |
