import torch
import torch.nn as nn
import torch.nn.functional as F

'''
MNIST简单全连接模型
'''
class Mnist_2NN(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 200)
        self.fc2 = nn.Linear(200, 200)
        self.fc3 = nn.Linear(200, 10)

    def forward(self, inputs):
        tensor = F.relu(self.fc1(inputs))
        tensor = F.relu(self.fc2(tensor))
        tensor = self.fc3(tensor)
        return tensor



'''
Cifar简单全连接模型
'''
class Cifar_2NN(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(3072, 200)
        self.fc2 = nn.Linear(200, 200)
        self.fc3 = nn.Linear(200, 10)

    def forward(self, inputs):
        tensor = F.relu(self.fc1(inputs))
        tensor = F.relu(self.fc2(tensor))
        tensor = self.fc3(tensor)
        return tensor

'''
MNIST简单卷积神经网络模型
'''
class Mnist_CNN(nn.Module):
    def __init__(self):
        super().__init__()
        # 定义每一层模型
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, stride=1, padding=0)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=0)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.conv3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=0)
        self.fc1 = nn.Linear(3*3*64, 64)
        self.fc2 = nn.Linear(64, 10)

    def forward(self, inputs):
        # 构造模型
        tensor = inputs.view(-1, 1, 28, 28)
        tensor = F.relu(self.conv1(tensor))
        tensor = self.pool1(tensor)
        tensor = F.relu(self.conv2(tensor))
        tensor = self.pool2(tensor)
        tensor = F.relu(self.conv3(tensor))     
        tensor = tensor.view(-1, 3*3*64)
        tensor = F.relu(self.fc1(tensor))
        tensor = self.fc2(tensor)
        return tensor

'''
cifar简单卷积神经网络模型
'''
class Cifar_CNN(nn.Module):
    def __init__(self):
        super().__init__()
        # 定义每一层模型
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(8*8*128, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, inputs):
        # 构造模型
        tensor = inputs.view(-1, 3, 32, 32)
        tensor = F.relu(self.conv1(tensor))
        tensor = self.pool1(tensor)
        tensor = F.relu(self.conv2(tensor))
        tensor = self.pool2(tensor)
        tensor = F.relu(self.conv3(tensor))  
        # print(tensor.shape)
        # raise(1)
        tensor = tensor.view(-1, 8*8*128)
        tensor = F.relu(self.fc1(tensor))
        tensor = self.fc2(tensor)
        return tensor



'''
resnet卷积神经网络模型
'''
class RestNetBasicBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride):
        super(RestNetBasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)

    def forward(self, inputs):
        tensor = self.conv1(inputs)
        tensor = F.relu(self.bn1(tensor))
        tensor = self.conv2(tensor)
        tensor = self.bn2(tensor)
        return F.relu(inputs + tensor)


class RestNetDownBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride):
        super(RestNetDownBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride[0], padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride[1], padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.extra = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride[0], padding=0),
            nn.BatchNorm2d(out_channels)
        )

    def forward(self, input):
        extra_x = self.extra(input)
        tensor = self.conv1(input)
        tensor = F.relu(self.bn1(tensor))
        tensor = self.conv2(tensor)
        tensor = self.bn2(tensor)
        return F.relu(extra_x + tensor)


class RestNet18(nn.Module):
    def __init__(self):
        super(RestNet18, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
        self.bn1 = nn.BatchNorm2d(64)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = nn.Sequential(RestNetBasicBlock(64, 64, 1),
                                    RestNetBasicBlock(64, 64, 1))

        self.layer2 = nn.Sequential(RestNetDownBlock(64, 128, [2, 1]),
                                    RestNetBasicBlock(128, 128, 1))

        self.layer3 = nn.Sequential(RestNetDownBlock(128, 256, [2, 1]),
                                    RestNetBasicBlock(256, 256, 1))

        self.layer4 = nn.Sequential(RestNetDownBlock(256, 512, [2, 1]),
                                    RestNetBasicBlock(512, 512, 1))

        self.avgpool = nn.AdaptiveAvgPool2d(output_size=(1, 1))

        self.fc = nn.Linear(512, 10)

    def forward(self, inputs):
        tensor = inputs.view(-1, 3, 32, 32)
        tensor = self.conv1(tensor)
        tensor = self.layer1(tensor)
        tensor = self.layer2(tensor)
        tensor = self.layer3(tensor)
        tensor = self.layer4(tensor)
        tensor = self.avgpool(tensor)
        tensor = tensor.reshape(tensor.shape[0], -1)
        tensor = self.fc(tensor)
        return tensor