ResNet.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np

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

class BasicBlock(nn.Module):

    expansion = 1

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

        self.residual_branch = 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 * self.expansion, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
        )
        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels * self.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels * self.expansion),
            )

    def forward(self, x):
        out = F.relu(self.residual_branch(x) + self.shortcut(x))
        return out

class BottleNeck(nn.Module):

    expansion = 4

    def __init__(self, in_channels, out_channels, stride=1):
        super(BottleNeck, self).__init__()
        
        self.residual_branch = 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, kernel_size=3, stride=stride, 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):
        out = F.relu(self.residual_branch(x) + self.shortcut(x))
        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=10):
        super(ResNet, self).__init__()
        self.in_channels = 64
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias = False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
        )

        self.layer2 = self._make_layer(block, 64, layers[0], 1)
        self.layer3 = self._make_layer(block, 128, layers[1], 2)
        self.layer4 = self._make_layer(block, 256, layers[2], 2)
        self.layer5 = self._make_layer(block, 512, layers[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:
            layers.append(block(self.in_channels, out_channels, stride))
            self.in_channels = out_channels * block.expansion

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.layer5(x)
        x = self.avg_pool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

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

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

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

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

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

def test(netname):
    print("---------{}----------".format(netname))
    net = globals()[netname]()
    #print(net)
    #print("+++++++++++++")
    total_params = 0

    for x in filter(lambda p: p.requires_grad, net.parameters()):
        total_params += np.prod(x.data.numpy().shape)
    print("Total number of params", total_params)
    print("Total layers", len(list(filter(lambda p: p.requires_grad and len(p.data.size())>1, net.parameters()))))
    print()

if __name__ == "__main__":
    for netname in __all__:
        test(netname)