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| import torch | |
| import torch.nn as nn | |
| import math | |
| import torch.utils.model_zoo as model_zoo | |
| import torch.nn.functional as F | |
| __all__ = ['ResNet', 'resnet18_rep', 'resnet34_rep' ] | |
| def conv3x3(in_planes, out_planes, stride=1): | |
| "3x3 convolution with padding" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, | |
| padding=1, bias=True) | |
| def conv1x1(in_planes, out_planes, stride=1): | |
| """1x1 convolution""" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=True) | |
| class conv_block(nn.Module): | |
| def __init__(self, in_planes, planes, mode, stride=1): | |
| super(conv_block, self).__init__() | |
| self.conv = conv3x3(in_planes, planes, stride) | |
| self.mode = mode | |
| if mode == 'parallel_adapters': | |
| self.adapter = conv1x1(in_planes, planes, stride) | |
| def re_init_conv(self): | |
| nn.init.kaiming_normal_(self.adapter.weight, mode='fan_out', nonlinearity='relu') | |
| return | |
| def forward(self, x): | |
| y = self.conv(x) | |
| if self.mode == 'parallel_adapters': | |
| y = y + self.adapter(x) | |
| return y | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, inplanes, planes, mode, stride=1, downsample=None): | |
| super(BasicBlock, self).__init__() | |
| self.conv1 = conv_block(inplanes, planes, mode, stride) | |
| self.norm1 = nn.BatchNorm2d(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = conv_block(planes, planes, mode) | |
| self.norm2 = nn.BatchNorm2d(planes) | |
| self.mode = mode | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| residual = x | |
| out = self.conv1(x) | |
| out = self.norm1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.norm2(out) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = self.relu(out) | |
| return out | |
| class ResNet(nn.Module): | |
| def __init__(self, block, layers, num_classes=100, args = None): | |
| self.inplanes = 64 | |
| super(ResNet, self).__init__() | |
| assert args is not None | |
| self.mode = args["mode"] | |
| if 'cifar' in args["dataset"]: | |
| self.conv1 = nn.Sequential(nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False), | |
| nn.BatchNorm2d(self.inplanes), nn.ReLU(inplace=True)) | |
| print("use cifar") | |
| elif 'imagenet' in args["dataset"] or 'stanfordcar' in args["dataset"]: | |
| if args["init_cls"] == args["increment"]: | |
| self.conv1 = nn.Sequential( | |
| nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False), | |
| nn.BatchNorm2d(self.inplanes), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool2d(kernel_size=3, stride=2, padding=1), | |
| ) | |
| else: | |
| # Following PODNET implmentation | |
| self.conv1 = nn.Sequential( | |
| nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False), | |
| nn.BatchNorm2d(self.inplanes), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool2d(kernel_size=3, stride=2, padding=1), | |
| ) | |
| 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.feature = nn.AvgPool2d(4, stride=1) | |
| self.out_dim = 512 | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') | |
| elif isinstance(m, nn.BatchNorm2d): | |
| nn.init.constant_(m.weight, 1) | |
| nn.init.constant_(m.bias, 0) | |
| 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=True), | |
| ) | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, self.mode, stride, downsample)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes, self.mode)) | |
| return nn.Sequential(*layers) | |
| def switch(self, mode='normal'): | |
| for name, module in self.named_modules(): | |
| if hasattr(module, 'mode'): | |
| module.mode = mode | |
| def re_init_params(self): | |
| for name, module in self.named_modules(): | |
| if hasattr(module, 're_init_conv'): | |
| module.re_init_conv() | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| dim = x.size()[-1] | |
| pool = nn.AvgPool2d(dim, stride=1) | |
| x = pool(x) | |
| x = x.view(x.size(0), -1) | |
| return {"features": x} | |
| def resnet18_rep(pretrained=False, **kwargs): | |
| """Constructs a ResNet-18 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| """ | |
| model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) | |
| if pretrained: | |
| pretrained_state_dict = model_zoo.load_url(model_urls['resnet18']) | |
| now_state_dict = model.state_dict() | |
| now_state_dict.update(pretrained_state_dict) | |
| model.load_state_dict(now_state_dict) | |
| return model | |
| def resnet34_rep(pretrained=False, **kwargs): | |
| """Constructs a ResNet-34 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| """ | |
| model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) | |
| if pretrained: | |
| pretrained_state_dict = model_zoo.load_url(model_urls['resnet34']) | |
| now_state_dict = model.state_dict() | |
| now_state_dict.update(pretrained_state_dict) | |
| model.load_state_dict(now_state_dict) | |
| return model |