resnet.py

'''For CIFAR10/100 Training'''
import torch
import torch.nn as nn

__all__ = ['ResNet', 'resnet18', 'resnet34',
           'resnet50', 'resnet101', 'resnet152']


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()

        # residual function
        self.residual_function = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3,
                      stride=stride, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels * BasicBlock.expansion,
                      kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels * BasicBlock.expansion)
        )
        self.shortcut = nn.Sequential()

        # the shortcut output dimension is not the same with residual function
        # use 1*1 convolution to match the dimension
        if stride != 1 or in_channels != BasicBlock.expansion * out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels * BasicBlock.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels * BasicBlock.expansion)
            )

    def forward(self, x):
        return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))


class BottleNeck(nn.Module):
    expansion = 4

    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()
        self.residual_function = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, stride=stride,
                      kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels *
                      BottleNeck.expansion, kernel_size=1, bias=False),
            nn.BatchNorm2d(out_channels * BottleNeck.expansion)
        )
        self.shortcut = nn.Sequential()

        if stride != 1 or in_channels != out_channels*BottleNeck.expansion:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels * BottleNeck.expansion,
                          stride=stride, kernel_size=1, bias=False),
                nn.BatchNorm2d(out_channels * BottleNeck.expansion)
            )

    def forward(self, x):
        return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))


class ResNet(nn.Module):
    def __init__(self, dataset, block, num_block):
        super().__init__()
        self.in_channels = 64
        if dataset == 'cifar10':
            num_classes = 10
        elif dataset == 'cifar100':
            num_classes = 100
        else:
            raise ValueError('Incorrect Dataset Input.')

        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True)
        )

        self.layer1 = self._make_layer(block, 64, num_block[0], 1)
        self.layer2 = self._make_layer(block, 128, num_block[1], 2)
        self.layer3 = self._make_layer(block, 256, num_block[2], 2)
        self.layer4 = self._make_layer(block, 512, num_block[3], 2)
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512*block.expansion, num_classes)

    def _make_layer(self, block, out_channels, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            # print(stride)
            layers.append(block(self.in_channels, out_channels, stride))
            self.in_channels = out_channels * block.expansion

        return nn.Sequential(*layers)

    def forward(self, x, if_feature=False):
        output = self.conv1(x)
        output = self.layer1(output)
        output = self.layer2(output)
        output = self.layer3(output)
        output = self.layer4(output)
        output = self.avg_pool(output)

        output_ = output.view(output.size(0), -1)
        output = self.fc(output_)
        if if_feature:
            return output_, output
        else:
            return output


def resnet18(Dataset):
    return ResNet(Dataset, BasicBlock, [2, 2, 2, 2])


def resnet34(Dataset):
    return ResNet(Dataset, BasicBlock, [3, 4, 6, 3])


def resnet50(Dataset):
    return ResNet(Dataset, BottleNeck, [3, 4, 6, 3])


def resnet101(Dataset):
    return ResNet(Dataset, BottleNeck, [3, 4, 23, 3])


def resnet152(Dataset):
    return ResNet(Dataset, BottleNeck, [3, 8, 36, 3])

def r_t():
    n = resnet18('cifar10').cuda()
    f,_ = n(torch.rand((1,3,32,32)).cuda(),True)
    print(f.size())