import pdb

import torch
import torch.nn as nn
import torch.nn.init as init
from model.resnet_pre import resnet18, conv1x1, BasicBlock, load_state_dict_from_url, model_urls

class MLP(nn.Module):
    def __init__(self, input_dim=256, output_dim=1):
        super(MLP, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.fc1 = nn.Linear(self.input_dim, 128)  # 32
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 32)
        self.fc4 = nn.Linear(32, 16)
        self.fc5 = nn.Linear(16, self.output_dim)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()
        self.dropout = nn.Dropout(0.5)
        self.bn1 = nn.BatchNorm1d(128)
        self.bn2 = nn.BatchNorm1d(64)
        self.bn3 = nn.BatchNorm1d(32)
        self.bn4 = nn.BatchNorm1d(16)
        for m in self.modules():
            if isinstance(m, nn.Linear):
                init.kaiming_normal_(m.weight)
                if m.bias is not None:
                    init.constant_(m.bias, 0)

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(self.bn1(x))
        x = self.fc2(x)
        x = self.relu(self.bn2(x))
        x = self.fc3(x)
        x = self.relu(self.bn3(x))
        x = self.fc4(x)
        x = self.relu(self.bn4(x))
        x = self.sigmoid(self.fc5(x))
        return x


class Net2(nn.Module):  # 该网络部署有风险，dnn推理有障碍
    def __init__(self, input_dim=960, output_dim=1):
        super(Net2, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.conv1 = nn.Conv1d(1, 16, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv1d(16, 32, kernel_size=3, stride=2, padding=1)
        # self.conv3 = nn.Conv1d(32, 64, kernel_size=3, stride=2, padding=1)
        # self.conv4 = nn.Conv1d(64, 64, kernel_size=5, stride=2, padding=1)
        self.maxPool1 = nn.MaxPool1d(kernel_size=3, stride=2)
        self.conv5 = nn.Conv1d(32, 64, kernel_size=5, stride=2, padding=1)
        self.maxPool2 = nn.MaxPool1d(kernel_size=3, stride=2)

        self.avgPool = nn.AdaptiveAvgPool1d(1)
        self.MaxPool = nn.AdaptiveMaxPool1d(1)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()
        self.dropout = nn.Dropout(0.5)
        self.flatten = nn.Flatten()
        # self.conv6 = nn.Conv1d(128, 128, kernel_size=5, stride=2, padding=1)
        self.fc1 = nn.Linear(960, 128)
        self.fc21 = nn.Linear(960, 32)
        self.fc22 = nn.Linear(32, 128)
        self.fc3 = nn.Linear(128, 1)
        self.bn1 = nn.BatchNorm1d(16)
        self.bn2 = nn.BatchNorm1d(32)
        self.bn3 = nn.BatchNorm1d(64)
        self.bn4 = nn.BatchNorm1d(128)
        for m in self.modules():
            if isinstance(m, nn.Linear):
                init.kaiming_normal_(m.weight)
                if m.bias is not None:
                    init.constant_(m.bias, 0)

    def conv1x1(in_planes, out_planes, stride=1):
        """1x1 convolution"""
        return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)

    def forward(self, x):
        x = self.conv1(x)  # 16
        x = self.relu(x)
        x = self.conv2(x)  # 32
        x = self.relu(x)
        # x = self.conv3(x)
        # x = self.relu(x)
        # x = self.conv4(x)  # 64
        # x = self.relu(x)
        # x = self.maxPool1(x)

        x = self.conv5(x)
        x = self.relu(x)
        # x = self.conv6(x)
        # x = self.relu(x)
        # x = self.maxPool2(x)
        # x = self.MaxPool(x)

        x = x.view(x.size(0), -1)
        x = self.dropout(x)
        x = self.flatten(x)

        # pdb.set_trace()
        x1 = self.fc1(x)
        x2 = self.fc22(self.fc21(x))
        x = self.fc3(x1 + x2)
        x = self.sigmoid(x)
        return x

class Net3(nn.Module):  # 目前较合适的网络结构，相较于Net2，Net3的输出结果更加准确
    def __init__(self, pretrained=True, progress=True, num_classes=1, scale=0.75):
        super(Net3, self).__init__()
        self.resnet18 = resnet18(pretrained=pretrained, progress=progress)

        # Remove the last three layers (layer3, layer4, avgpool, fc)
        # self.resnet18.layer3 = nn.Identity()
        # self.resnet18.layer4 = nn.Identity()
        self.resnet18.avgpool = nn.Identity()
        self.resnet18.fc = nn.Identity()
        self.flatten = nn.Flatten()
        # Calculate the output size after layer2
        # Assuming input size is 224x224, layer2 will have output size of 56x56
        # So, the flattened size will be 128 * scale * 56 * 56
        self.flattened_size = int(128 * (56 * 56) * scale * scale)

        # Add new layers for classification
        self.classifier = nn.Sequential(
            nn.AdaptiveAvgPool2d((1, 1)),
            nn.Flatten(),
            nn.Linear(384, num_classes),  #  layer1, layer2 in_features=96   # layer1 in_features=48  #layer3 in_features=192
            # nn.ReLU(),
            nn.Dropout(0.6),
            # nn.Linear(256, num_classes),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.resnet18.layer1(x)
        x = self.resnet18.layer2(x)
        x = self.resnet18.layer3(x)
        x = self.resnet18.layer4(x)

        # Debugging: Print the shape of the tensor before flattening
        # print("Shape before flattening:", x.shape)

        # Ensure the tensor is flattened correctly
        # x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None, scale=0.75):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(block, int(64 * scale), layers[0])
        self.layer2 = self._make_layer(block, int(128 * scale), layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, int(256 * scale), layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, int(512 * scale), layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(int(512 * block.expansion * scale), num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
        self.sigmoid = nn.Sigmoid()

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))
        return nn.Sequential(*layers)

    def _forward_impl(self, x):
        # See note [TorchScript super()]
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

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

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)
        x = self.sigmoid(x)
        return x

    def forward(self, x):
        return self._forward_impl(x)

def Net4(arch, pretrained, progress, **kwargs):
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained:
        state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
        src_state_dict = state_dict
        target_state_dict = model.state_dict()
        skip_keys = []
        # skip mismatch size tensors in case of pretraining
        for k in src_state_dict.keys():
            if k not in target_state_dict:
                continue
            if src_state_dict[k].size() != target_state_dict[k].size():
                skip_keys.append(k)
        for k in skip_keys:
            del src_state_dict[k]
        missing_keys, unexpected_keys = model.load_state_dict(src_state_dict, strict=False)
    return model


if __name__ == '__main__':
    '''
    net2 = Net2()
    input_tensor = torch.randn(10, 1, 64)
    # 前向传播
    output_tensor = net2(input_tensor)
    # pdb.set_trace()
    print("输入张量形状:", input_tensor.shape)
    print("输出张量形状:", output_tensor.shape)
    '''

    # model = Net3(pretrained=True, num_classes=1)  # 预训练从resnet中间结果获取数据训练模型
    model = Net4('resnet18', True, True)
    input_tensor = torch.randn(1, 3, 224, 244)  # Adjust batch size to 10
    output = model(input_tensor)
    print(output.shape)  # Should be [10, 2]