#-*- encoding:utf-8 -*-

'''
@Author : dingjiawen
@Date : 2023/12/12 13:36
@Usage : 
@Desc :
'''

# -*- coding: UTF-8 -*-
import torch.nn as nn
import setting


# CNN Model (2 conv layer)
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.Dropout(0.5),  # drop 50% of the neuron
            nn.ReLU(),
            nn.MaxPool2d(2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.Dropout(0.5),  # drop 50% of the neuron
            nn.ReLU(),
            nn.MaxPool2d(2))
        self.layer3 = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.Dropout(0.5),  # drop 50% of the neuron
            nn.ReLU(),
            nn.MaxPool2d(2))
        self.fc = nn.Sequential(
            # 除以8是因为MaxPool2d了3次
            nn.Linear((setting.IMAGE_WIDTH // 8) * (setting.IMAGE_HEIGHT // 8) * 64, 1024),
            nn.Dropout(0.5),  # drop 50% of the neuron
            nn.ReLU())
        self.rfc = nn.Sequential(
            # setting.MAX_CAPTCHA * setting.ALL_CHAR_SET_LEN是字典集的长度
            nn.Linear(1024, setting.MAX_CAPTCHA * setting.ALL_CHAR_SET_LEN),
        )

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = self.layer3(out)
        # flatten展平
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        out = self.rfc(out)
        return out