net.py

'''Predict age in one shot.'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.downsample = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.downsample = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.downsample(x)
        out = F.relu(out)
        return out

    def freeze_bn(self):
        '''Freeze BatchNorm layers.'''
        for layer in self.modules():
            if isinstance(layer, nn.BatchNorm2d):
                layer.training = False

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, 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, self.expansion*planes, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion*planes)

        self.downsample = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.downsample = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    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 += self.downsample(x)
        out = F.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block,  64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        # self.avg_pool = nn.AvgPool2d(kernel_size=7)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.max_pool(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        return out

def ResNet18():
    return ResNet(BasicBlock, [2,2,2,2])

def ResNet50():
    return ResNet(Bottleneck, [3,4,6,3])

def ResNet101():
    return ResNet(Bottleneck, [2,4,23,3])


class AGNet(nn.Module):
    def __init__(self):
        super(AGNet, self).__init__()
        self.backbone = ResNet18()
        self.age_head = self._make_head()
        self.linear1 = nn.Linear(256,200)

    def _make_head(self):
        return nn.Sequential(
            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
            nn.ReLU(True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.ReLU(True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
        )

    def forward(self, x):
        N = x.size(0)
        y = self.backbone(x)
        y = F.avg_pool2d(y,5)
        # Age head
        y_age = self.age_head(y)
        y_age = self.linear1(y_age.view(N,-1))

        return y_age


def test():
    net = AGNet()
    age = net(Variable(torch.randn(2,3,150,150)))
    print(age.size())


# test()