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| ''' | |
| Reference: | |
| https://github.com/khurramjaved96/incremental-learning/blob/autoencoders/model/resnet32.py | |
| https://github.com/hshustc/CVPR19_Incremental_Learning/blob/master/cifar100-class-incremental/modified_resnet_cifar.py | |
| ''' | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| # from convs.modified_linear import CosineLinear | |
| class DownsampleA(nn.Module): | |
| def __init__(self, nIn, nOut, stride): | |
| super(DownsampleA, self).__init__() | |
| assert stride == 2 | |
| self.avg = nn.AvgPool2d(kernel_size=1, stride=stride) | |
| def forward(self, x): | |
| x = self.avg(x) | |
| return torch.cat((x, x.mul(0)), 1) | |
| class DownsampleB(nn.Module): | |
| def __init__(self, nIn, nOut, stride): | |
| super(DownsampleB, self).__init__() | |
| self.conv = nn.Conv2d(nIn, nOut, kernel_size=1, stride=stride, padding=0, bias=False) | |
| self.bn = nn.BatchNorm2d(nOut) | |
| def forward(self, x): | |
| x = self.conv(x) | |
| x = self.bn(x) | |
| return x | |
| class DownsampleC(nn.Module): | |
| def __init__(self, nIn, nOut, stride): | |
| super(DownsampleC, self).__init__() | |
| assert stride != 1 or nIn != nOut | |
| self.conv = nn.Conv2d(nIn, nOut, kernel_size=1, stride=stride, padding=0, bias=False) | |
| def forward(self, x): | |
| x = self.conv(x) | |
| return x | |
| class DownsampleD(nn.Module): | |
| def __init__(self, nIn, nOut, stride): | |
| super(DownsampleD, self).__init__() | |
| assert stride == 2 | |
| self.conv = nn.Conv2d(nIn, nOut, kernel_size=2, stride=stride, padding=0, bias=False) | |
| self.bn = nn.BatchNorm2d(nOut) | |
| def forward(self, x): | |
| x = self.conv(x) | |
| x = self.bn(x) | |
| return x | |
| class ResNetBasicblock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None, last=False): | |
| super(ResNetBasicblock, self).__init__() | |
| self.conv_a = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False) | |
| self.bn_a = nn.BatchNorm2d(planes) | |
| self.conv_b = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) | |
| self.bn_b = nn.BatchNorm2d(planes) | |
| self.downsample = downsample | |
| self.last = last | |
| def forward(self, x): | |
| residual = x | |
| basicblock = self.conv_a(x) | |
| basicblock = self.bn_a(basicblock) | |
| basicblock = F.relu(basicblock, inplace=True) | |
| basicblock = self.conv_b(basicblock) | |
| basicblock = self.bn_b(basicblock) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out = residual + basicblock | |
| if not self.last: | |
| out = F.relu(out, inplace=True) | |
| return out | |
| class CifarResNet(nn.Module): | |
| """ | |
| ResNet optimized for the Cifar Dataset, as specified in | |
| https://arxiv.org/abs/1512.03385.pdf | |
| """ | |
| def __init__(self, block, depth, channels=3): | |
| super(CifarResNet, self).__init__() | |
| # Model type specifies number of layers for CIFAR-10 and CIFAR-100 model | |
| assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110' | |
| layer_blocks = (depth - 2) // 6 | |
| self.conv_1_3x3 = nn.Conv2d(channels, 16, kernel_size=3, stride=1, padding=1, bias=False) | |
| self.bn_1 = nn.BatchNorm2d(16) | |
| self.inplanes = 16 | |
| self.stage_1 = self._make_layer(block, 16, layer_blocks, 1) | |
| self.stage_2 = self._make_layer(block, 32, layer_blocks, 2) | |
| self.stage_3 = self._make_layer(block, 64, layer_blocks, 2, last_phase=True) | |
| self.avgpool = nn.AvgPool2d(8) | |
| self.out_dim = 64 * block.expansion | |
| # self.fc = CosineLinear(64*block.expansion, 10) | |
| 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, last_phase=False): | |
| downsample = None | |
| if stride != 1 or self.inplanes != planes * block.expansion: | |
| downsample = DownsampleB(self.inplanes, planes * block.expansion, stride) # DownsampleA => DownsampleB | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, stride, downsample)) | |
| self.inplanes = planes * block.expansion | |
| if last_phase: | |
| for i in range(1, blocks-1): | |
| layers.append(block(self.inplanes, planes)) | |
| layers.append(block(self.inplanes, planes, last=True)) | |
| else: | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| x = self.conv_1_3x3(x) # [bs, 16, 32, 32] | |
| x = F.relu(self.bn_1(x), inplace=True) | |
| x_1 = self.stage_1(x) # [bs, 16, 32, 32] | |
| x_2 = self.stage_2(x_1) # [bs, 32, 16, 16] | |
| x_3 = self.stage_3(x_2) # [bs, 64, 8, 8] | |
| pooled = self.avgpool(x_3) # [bs, 64, 1, 1] | |
| features = pooled.view(pooled.size(0), -1) # [bs, 64] | |
| # out = self.fc(vector) | |
| return { | |
| 'fmaps': [x_1, x_2, x_3], | |
| 'features': features | |
| } | |
| def last_conv(self): | |
| return self.stage_3[-1].conv_b | |
| def resnet20mnist(): | |
| """Constructs a ResNet-20 model for MNIST.""" | |
| model = CifarResNet(ResNetBasicblock, 20, 1) | |
| return model | |
| def resnet32mnist(): | |
| """Constructs a ResNet-32 model for MNIST.""" | |
| model = CifarResNet(ResNetBasicblock, 32, 1) | |
| return model | |
| def resnet20(): | |
| """Constructs a ResNet-20 model for CIFAR-10.""" | |
| model = CifarResNet(ResNetBasicblock, 20) | |
| return model | |
| def resnet32(): | |
| """Constructs a ResNet-32 model for CIFAR-10.""" | |
| model = CifarResNet(ResNetBasicblock, 32) | |
| return model | |
| def resnet44(): | |
| """Constructs a ResNet-44 model for CIFAR-10.""" | |
| model = CifarResNet(ResNetBasicblock, 44) | |
| return model | |
| def resnet56(): | |
| """Constructs a ResNet-56 model for CIFAR-10.""" | |
| model = CifarResNet(ResNetBasicblock, 56) | |
| return model | |
| def resnet110(): | |
| """Constructs a ResNet-110 model for CIFAR-10.""" | |
| model = CifarResNet(ResNetBasicblock, 110) | |
| return model | |