resnext.py

'''ResNeXt in PyTorch.
See the paper "Aggregated Residual Transformations for Deep Neural Networks" for more details.
https://github.com/kuangliu/pytorch-cifar
'''
import torch
import torch.nn as nn
import torch.nn.functional as F


class Block(nn.Module):
    '''Grouped convolution block.'''
    expansion = 2

    def __init__(self, in_planes, cardinality=32, bottleneck_width=4, stride=1):
        super(Block, self).__init__()
        group_width = cardinality * bottleneck_width
        self.conv1 = nn.Conv2d(in_planes, group_width, kernel_size=1, bias=True)
        self.bn1 = nn.BatchNorm2d(group_width)
        self.conv2 = nn.Conv2d(group_width, group_width, kernel_size=3, stride=stride, padding=1, groups=cardinality, bias=True)
        # self.bn2 = nn.BatchNorm2d(group_width)
        self.conv3 = nn.Conv2d(group_width, self.expansion*group_width, kernel_size=1, bias=True)
        # self.bn3 = nn.BatchNorm2d(self.expansion*group_width)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*group_width:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*group_width, kernel_size=1, stride=stride, bias=True),
                # nn.BatchNorm2d(self.expansion*group_width)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        # out = F.relu(self.bn2(self.conv2(out)))
        # out = self.bn3(self.conv3(out))
        # out = F.relu(self.conv1(x))
        out = F.relu(self.conv2(out))
        out = self.conv3(out)
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class ResNeXt(nn.Module):
    def __init__(self, num_blocks, cardinality, bottleneck_width, num_classes=10):
        super(ResNeXt, self).__init__()
        self.cardinality = cardinality
        self.bottleneck_width = bottleneck_width
        self.in_planes = 16

        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, bias=True, padding=1)
        # self.bn1 = nn.BatchNorm2d(16)
        self.layer1 = self._make_layer(num_blocks[0], 1)
        self.layer2 = self._make_layer(num_blocks[1], 2)
        self.layer3 = self._make_layer(num_blocks[2], 2)
        # self.layer4 = self._make_layer(num_blocks[3], 2)
        self.linear1 = nn.Linear(cardinality*bottleneck_width*512, 512)
        self.linear2 = nn.Linear(512, num_classes)


    def _make_layer(self, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(Block(self.in_planes, self.cardinality, self.bottleneck_width, stride))
            self.in_planes = Block.expansion * self.cardinality * self.bottleneck_width
        # Increase bottleneck_width by 2 after each stage.
        self.bottleneck_width *= 2
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.conv1(x))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = torch.flatten(out, 1)
        out = F.relu(self.linear1(out))
        out = self.linear2(out)
        return out


def ResNeXt29_2x64d():
    return ResNeXt(num_blocks=[3,3,3], cardinality=2, bottleneck_width=64)

def ResNeXt29_4x64d():
    return ResNeXt(num_blocks=[3,3,3], cardinality=4, bottleneck_width=64)

def ResNeXt29_8x64d():
    return ResNeXt(num_blocks=[3,3,3], cardinality=8, bottleneck_width=64)

def ResNeXt29_32x4d():
    return ResNeXt(num_blocks=[3,3,3], cardinality=32, bottleneck_width=4)

def ResNeXt_cifar(in_ch=3, in_dim=32):
    return ResNeXt(num_blocks=[1,1,1], cardinality=2, bottleneck_width=32)

if __name__ == "__main__":
    from thop import profile
    net = ResNeXt_cifar()
    x = torch.randn(1,3,32,32)
    y = net(x)
    print(net)
    macs, params = profile(net, (torch.randn(1, 3, 32, 32),))
    print(macs / 1000000, params / 1000000)  # 6830M, 7M
    print(y)