#!/usr/bin/python
# -*- coding: UTF-8 -*-
"""
@author:andrew
@file:Hybrid_Net014.py
@email:admin@marques22.com
@email:2021022362@m.scnu.edu.cn
@time:2022/10/14
"""

import os

import torch
from torch import nn
from torchinfo import summary
from torch import cat

os.environ["CUDA_VISIBLE_DEVICES"] = "0"

# 修改激活函数
# 提高呼吸采样率

# 输入时长
WHOLE_SEGMENT_SECOND = 30

# 呼吸采样率
RESPIRATORY_FRE = 10

# BCG 时频图大小
BCG_GRAPH_SIZE = (26, 121)


class BasicBlock_1d(nn.Module):
    expansion = 1

    def __init__(self, input_channel, output_channel, stride=1):
        super(BasicBlock_1d, self).__init__()
        self.left = nn.Sequential(
            nn.Conv1d(in_channels=input_channel, out_channels=output_channel,
                      kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm1d(output_channel),
            nn.ReLU(inplace=True),
            nn.Conv1d(in_channels=output_channel, out_channels=output_channel,
                      kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm1d(output_channel)
        )
        self.right = nn.Sequential()

        if stride != 1 or input_channel != self.expansion * output_channel:
            self.right = nn.Sequential(
                nn.Conv1d(in_channels=input_channel, out_channels=output_channel * self.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm1d(self.expansion * output_channel)
            )

        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        out = self.left(x)
        residual = self.right(x)
        out += residual
        out = self.relu(out)
        return out


class ResNet_1d(nn.Module):
    def __init__(self, block, number_block, num_classes=2, init_weights=True):
        super(ResNet_1d, self).__init__()

        self.in_channel = 64

        self.conv1 = nn.Conv1d(in_channels=1, out_channels=self.in_channel,
                               kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm1d(64)
        self.relu = nn.ReLU(inplace=True)
        self.pool1 = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(block=block, out_channel=64, num_block=number_block[0], stride=1)
        self.layer2 = self._make_layer(block=block, out_channel=128, num_block=number_block[1], stride=2)
        self.layer3 = self._make_layer(block=block, out_channel=256, num_block=number_block[2], stride=2)
        self.layer4 = self._make_layer(block=block, out_channel=512, num_block=number_block[3], stride=2)

        self.pool2 = nn.AvgPool1d(4)

        self.linear = nn.Linear(512, num_classes)
        self.features = nn.Sequential(
            # nn.Linear(in_features=1024, out_features=nc),
            nn.Flatten(),
            nn.Linear(in_features=512 * 2, out_features=512),
            nn.Linear(in_features=512, out_features=num_classes)

            # nn.Softmax()
            # nn.Sigmoid()
        )
        # self.linear = nn.Linear(512 * block.expansion, num_classes)
        if init_weights:
            self.initialize_weights()

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

    def initialize_weights(self):
        for m in self.modules():
            if isinstance(m, (nn.Conv2d, nn.Conv1d)):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')  # 何教授方法
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)  # 正态分布赋值
                nn.init.constant_(m.bias, 0)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.pool1(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.pool2(x)
        x = x.view(x.size(0), -1)

        x = self.features(x)
        return x


def HYBRIDNET017():
    return ResNet_1d(BasicBlock_1d, [2, 2, 2, 2])


if __name__ == '__main__':
    model = HYBRIDNET017().cuda()
    summary(model, [(32, 1, 300)])