import torch.nn as nn import torchvision.transforms as transforms import math __all__ = ['resnet'] 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=False) def init_model(model): for m in model.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = nn.BatchNorm2d(planes) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * 4) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): residual = 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) 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): super(ResNet, self).__init__() 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=False), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) 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) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) return x class ResNet_imagenet(ResNet): def __init__(self, num_classes=1000, block=Bottleneck, layers=[3, 4, 23, 3]): super(ResNet_imagenet, self).__init__() self.inplanes = 64 self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) self.maxpool = 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.avgpool = nn.AvgPool2d(7) self.fc = nn.Linear(512 * block.expansion, num_classes) init_model(self) self.regime = { 0: {'optimizer': 'SGD', 'lr': 1e-1, 'weight_decay': 1e-4, 'momentum': 0.9}, 30: {'lr': 1e-2}, 60: {'lr': 1e-3, 'weight_decay': 0}, 90: {'lr': 1e-4} } class ResNet_cifar10(ResNet): def __init__(self, num_classes=10, block=BasicBlock, depth=18): super(ResNet_cifar10, self).__init__() self.inplanes = 16 n = int((depth - 2) / 6) self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(16) self.relu = nn.ReLU(inplace=True) self.maxpool = lambda x: x self.layer1 = self._make_layer(block, 16, n) self.layer2 = self._make_layer(block, 32, n, stride=2) self.layer3 = self._make_layer(block, 64, n, stride=2) self.layer4 = lambda x: x self.avgpool = nn.AvgPool2d(8) self.fc = nn.Linear(64, num_classes) init_model(self) self.regime = { 0: {'optimizer': 'SGD', 'lr': 1e-1, 'weight_decay': 1e-4, 'momentum': 0.9}, 81: {'lr': 1e-2}, 122: {'lr': 1e-3, 'weight_decay': 0}, 164: {'lr': 1e-4} } def resnet(**kwargs): num_classes, depth, dataset = map( kwargs.get, ['num_classes', 'depth', 'dataset']) if dataset == 'imagenet': num_classes = num_classes or 1000 depth = depth or 50 if depth == 18: return ResNet_imagenet(num_classes=num_classes, block=BasicBlock, layers=[2, 2, 2, 2]) if depth == 34: return ResNet_imagenet(num_classes=num_classes, block=BasicBlock, layers=[3, 4, 6, 3]) if depth == 50: return ResNet_imagenet(num_classes=num_classes, block=Bottleneck, layers=[3, 4, 6, 3]) if depth == 101: return ResNet_imagenet(num_classes=num_classes, block=Bottleneck, layers=[3, 4, 23, 3]) if depth == 152: return ResNet_imagenet(num_classes=num_classes, block=Bottleneck, layers=[3, 8, 36, 3]) elif dataset == 'cifar10': num_classes = num_classes or 10 depth = depth or 18 #56 return ResNet_cifar10(num_classes=num_classes, block=BasicBlock, depth=depth)