from model.CBAM import CBAM
import torch
import torch.nn as nn
from model.Tool import GeM as gem


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inchannel, outchannel, stride=1, dowsample=None):
        # super(Bottleneck, self).__init__()
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels=inchannel, out_channels=outchannel, kernel_size=1, stride=1, bias=False)
        self.bn1 = nn.BatchNorm2d(outchannel)
        self.conv2 = nn.Conv2d(in_channels=outchannel, out_channels=outchannel, kernel_size=3, bias=False,
                               stride=stride, padding=1)
        self.bn2 = nn.BatchNorm2d(outchannel)
        self.conv3 = nn.Conv2d(in_channels=outchannel, out_channels=outchannel * self.expansion, stride=1, bias=False,
                               kernel_size=1)
        self.bn3 = nn.BatchNorm2d(outchannel * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = dowsample

    def forward(self, x):
        self.identity = x
        # print('>>>>>>>>',type(x))
        if self.downsample is not None:
            # print('>>>>downsample>>>>', type(self.downsample))
            self.identity = self.downsample(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)
        # print('>>>>out>>>identity',out.size(),self.identity.size())
        out = out + self.identity
        out = self.relu(out)
        return out


class resnet(nn.Module):
    def __init__(self, block=Bottleneck, block_num=[3, 4, 6, 3], num_class=1000):
        super().__init__()
        self.in_channel = 64
        self.conv1 = nn.Conv2d(in_channels=3,
                               out_channels=self.in_channel,
                               stride=2,
                               kernel_size=7,
                               padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(self.in_channel)
        self.relu = nn.ReLU(inplace=True)
        self.cbam = CBAM(self.in_channel)
        self.cbam1 = CBAM(2048)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, block_num[0], stride=1)
        self.layer2 = self._make_layer(block, 128, block_num[1], stride=2)
        self.layer3 = self._make_layer(block, 256, block_num[2], stride=2)
        self.layer4 = self._make_layer(block, 512, block_num[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.gem = gem()
        self.fc = nn.Linear(512 * block.expansion, num_class)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal(m.weight, mode='fan_out',
                                       nonlinearity='relu')
            if isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1.0)
                nn.init.constant_(m.bias, 1.0)

    def _make_layer(self, block, channel, block_num, stride=1):
        downsample = None
        if stride != 1 or self.in_channel != channel * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(channel * block.expansion))
        layer = []
        layer.append(block(self.in_channel, channel, stride, downsample))
        self.in_channel = channel * block.expansion
        for _ in range(1, block_num):
            layer.append(block(self.in_channel, channel))
        return nn.Sequential(*layer)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.cbam(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.cbam1(x)
        # x = self.avgpool(x)
        x = self.gem(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)
        return x


class TripletNet(nn.Module):
    def __init__(self, num_class, flag=True):
        super(TripletNet, self).__init__()
        self.initnet = rescbam(num_class)
        self.flag = flag

    def forward(self, x1, x2=None, x3=None):
        if self.flag:
            output1 = self.initnet(x1)
            output2 = self.initnet(x2)
            output3 = self.initnet(x3)
            return output1, output2, output3
        else:
            output = self.initnet(x1)
            return output


def rescbam(num_class):
    return resnet(block=Bottleneck, block_num=[3, 4, 6, 3], num_class=num_class)


if __name__ == '__main__':
    input1 = torch.randn(4, 3, 640, 640)
    input2 = torch.randn(4, 3, 640, 640)
    input3 = torch.randn(4, 3, 640, 640)

    # rescbam测试
    # Resnet50 = rescbam(512)
    # output = Resnet50.forward(input1)
    # print(Resnet50)

    # trnet测试
    trnet = TripletNet(512)
    output = trnet(input1, input2, input3)
    print(output)