import matplotlib.pyplot as plt
import torch
import torch.nn.functional as F
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets, utils
from torchvision.transforms import ToTensor
from torchvision.transforms import transforms


class BasicBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=[1, 1], padding=1) -> None:
        super(BasicBlock, self).__init__()
        # 残差部分
        self.layer = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride[0], padding=padding, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),  # 原地替换 节省内存开销
            nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride[1], padding=padding, bias=False),
            nn.BatchNorm2d(out_channels)
        )

        # shortcut 部分
        # 由于存在维度不一致的情况 所以分情况
        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != out_channels:
            self.shortcut = nn.Sequential(
                # 卷积核为1 进行升降维
                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride[0], bias=False),
                nn.BatchNorm2d(out_channels)
            )

    def forward(self, x):
        #         print('shape of x: {}'.format(x.shape))
        out = self.layer(x)
        #         print('shape of out: {}'.format(out.shape))
        #         print('After shortcut shape of x: {}'.format(self.shortcut(x).shape))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


# 采用bn的网络中，卷积层的输出并不加偏置
class ResNet18(nn.Module):
    def __init__(self, BasicBlock, num_classes=10) -> None:
        super(ResNet18, self).__init__()
        self.in_channels = 64
        # 第一层作为单独的 因为没有残差快
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
            nn.BatchNorm2d(64),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )
        # conv2_x
        self.conv2 = self._make_layer(BasicBlock, 64, [[1, 1], [1, 1]])
        # self.conv2_2 = self._make_layer(BasicBlock,64,[1,1])

        # conv3_x
        self.conv3 = self._make_layer(BasicBlock, 128, [[2, 1], [1, 1]])
        # self.conv3_2 = self._make_layer(BasicBlock,128,[1,1])

        # conv4_x
        self.conv4 = self._make_layer(BasicBlock, 256, [[2, 1], [1, 1]])
        # self.conv4_2 = self._make_layer(BasicBlock,256,[1,1])

        # conv5_x
        self.conv5 = self._make_layer(BasicBlock, 512, [[2, 1], [1, 1]])
        # self.conv5_2 = self._make_layer(BasicBlock,512,[1,1])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)
        self.senet64 = SEAttention(64)
        self.senet128 = SEAttention(128)
        self.senet256 = SEAttention(256)
        self.senet512 = SEAttention(512)

    # 这个函数主要是用来，重复同一个残差块
    def _make_layer(self, block, out_channels, strides):
        layers = []
        for stride in strides:
            layers.append(block(self.in_channels, out_channels, stride))
            self.in_channels = out_channels
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.conv2(out)
        print("out1.shape:", out.shape)
        out = self.senet64(out)
        out = self.conv3(out)
        out = self.senet128(out)
        print("out2.shape:", out.shape)
        out = self.conv4(out)
        out = self.senet256(out)
        print("out3.shape:", out.shape)
        out = self.conv5(out)
        print("out4.shape:", out.shape)
        out = self.senet512(out)

        out = self.avgpool(out)
        out = out.reshape(x.shape[0], -1)
        out = self.fc(out)
        return out

class SEAttention(nn.Module):
    def __init__(self, in_channels, reduction_ratio=16):
        super(SEAttention, self).__init__()
        # 定义全局均值池化层
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        # 定义全连接层
        self.fc = nn.Sequential(
            nn.Linear(in_channels, in_channels // reduction_ratio),
            nn.ReLU(inplace=True),
            nn.Linear(in_channels // reduction_ratio, in_channels),
            nn.Sigmoid()
        )

    def forward(self, x):
        # 计算全局平均值，并通过全连接层得到每个通道的重要度
        module_input = x  # 用于残差计算
        x = self.avg_pool(x)
        x = torch.flatten(x, start_dim=1)
        x = self.fc(x)
        x = x.view(-1, x.size(1), 1, 1)
        # 通过重要度对每个通道的特征图进行加权
        x = module_input * x.expand_as(module_input)
        return x

x = torch.rand(4,3,512,512)
print("x.shape:", x.shape)
model = ResNet18(BasicBlock)
y = model(x)
print("y.shape:", y.shape)