# coding=utf-8，一定要在最顶行添加
from flask_migrate import MigrateCommand
from App import create_app
from flask_script import Manager
from flask_script import Server
from PIL import Image
import torchvision.transforms as transforms
from torch import nn
import torch as t
import torch.utils.data
from torch.nn import functional as F


class ResNet(nn.Module):

    # 实现主module：ResNet34
    # ResNet34 包含多个layer，每个layer又包含多个residual block
    # 用子module来实现residual block，用_make_layer函数来实现layer

    def __init__(self, num_classes=4):
        super(ResNet, self).__init__()
        # 前几层图像转换
        self.pre = nn.Sequential(
            nn.Conv2d(3, 16, 3, 1, 1, bias=False),
            nn.BatchNorm2d(16),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(3, 2, 1))
        # 重复的layer，分别有3，4，6，3个residual block
        self.layer1 = self._make_layer(16, 16, 3)
        self.layer2 = self._make_layer(16, 32, 4, stride=1)
        self.layer3 = self._make_layer(32, 64, 6, stride=1)
        self.layer4 = self._make_layer(64, 64, 3, stride=1)
        self.fc = nn.Linear(256, num_classes)  # 分类用的全连接

    def _make_layer(self, inchannel, outchannel, block_num, stride=1):
        # 构建layer,包含多个residual block
        shortcut = nn.Sequential(nn.Conv2d(inchannel, outchannel, 1, stride, bias=False), nn.BatchNorm2d(outchannel))
        layers = []
        layers.append(ResidualBlock(inchannel, outchannel, stride, shortcut))
        for i in range(1, block_num):
            layers.append(ResidualBlock(outchannel, outchannel))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.pre(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = F.avg_pool2d(x, 7)
        x = x.view(x.size(0), -1)
        return self.fc(x)

class ResidualBlock(nn.Module):

    # 实现子module: Residual Block

    def __init__(self, inchannel, outchannel, stride=1, shortcut=None):
        super(ResidualBlock, self).__init__()
        self.left = nn.Sequential(
            nn.Conv2d(inchannel, outchannel, 3, stride, 1, bias=False),
            nn.BatchNorm2d(outchannel),
            nn.ReLU(inplace=True),
            nn.Conv2d(outchannel, outchannel, 3, 1, 1, bias=False),
            nn.BatchNorm2d(outchannel))
        self.right = shortcut

    def forward(self, x):
        out = self.left(x)
        residual = x if self.right is None else self.right(x)
        out += residual
        return F.relu(out)



app = create_app()
manager = Manager(app=app)
manager.add_command('runserver', Server(host='0.0.0.0', port="8080", use_debugger=True, use_reloader=True))
manager.add_command('db', MigrateCommand)
if __name__ == '__main__':
    manager.run()
