# -*- coding: utf-8 -*-
"""
@inproceedings{han2018coteaching,
  title={Co-teaching: Robust training of deep neural networks with extremely noisy labels},
  author={Han, Bo and Yao, Quanming and Yu, Xingrui and Niu, Gang and Xu, Miao and Hu, Weihua and Tsang, Ivor and Sugiyama, Masashi},
  booktitle={NeurIPS},
  pages={8535--8545},
  year={2018}
}

Modified by JHe
"""

import math
import torch
import torch.nn as nn
import torch.nn.init as init 
import torch.nn.functional as F
import torch.optim as optim

def call_bn(bn, x):
    return bn(x)

class CNN(nn.Module):
    def __init__(self, input_channel=3, n_outputs=10, dropout_rate=0.25, top_bn=False):
        self.dropout_rate = dropout_rate
        self.top_bn = top_bn
        super(CNN, self).__init__()
        self.c1=nn.Conv2d(input_channel,128,kernel_size=3,stride=1, padding=1)
        self.c2=nn.Conv2d(128,128,kernel_size=3,stride=1, padding=1)
        self.c3=nn.Conv2d(128,128,kernel_size=3,stride=1, padding=1)
        self.c4=nn.Conv2d(128,256,kernel_size=3,stride=1, padding=1)
        self.c5=nn.Conv2d(256,256,kernel_size=3,stride=1, padding=1)
        self.c6=nn.Conv2d(256,256,kernel_size=3,stride=1, padding=1)
        self.c7=nn.Conv2d(256,512,kernel_size=3,stride=1, padding=0)
        self.c8=nn.Conv2d(512,256,kernel_size=3,stride=1, padding=0)
        self.c9=nn.Conv2d(256,128,kernel_size=3,stride=1, padding=0)
        self.l_c1=nn.Linear(128,n_outputs)
        self.bn1=nn.BatchNorm2d(128)        
        self.bn2=nn.BatchNorm2d(128)
        self.bn3=nn.BatchNorm2d(128)
        self.bn4=nn.BatchNorm2d(256)
        self.bn5=nn.BatchNorm2d(256)
        self.bn6=nn.BatchNorm2d(256)
        self.bn7=nn.BatchNorm2d(512)
        self.bn8=nn.BatchNorm2d(256)
        self.bn9=nn.BatchNorm2d(128)

    def forward(self, x,):
        h=x
        h=self.c1(h)
        h=F.leaky_relu(call_bn(self.bn1, h), negative_slope=0.01)
        h=self.c2(h)
        h=F.leaky_relu(call_bn(self.bn2, h), negative_slope=0.01)
        h=self.c3(h)
        h=F.leaky_relu(call_bn(self.bn3, h), negative_slope=0.01)
        h=F.max_pool2d(h, kernel_size=2, stride=2)
        h=F.dropout2d(h, p=self.dropout_rate)

        h=self.c4(h)
        h=F.leaky_relu(call_bn(self.bn4, h), negative_slope=0.01)
        h=self.c5(h)
        h=F.leaky_relu(call_bn(self.bn5, h), negative_slope=0.01)
        h=self.c6(h)
        h=F.leaky_relu(call_bn(self.bn6, h), negative_slope=0.01)
        h=F.max_pool2d(h, kernel_size=2, stride=2)
        h=F.dropout2d(h, p=self.dropout_rate)

        h=self.c7(h)
        h=F.leaky_relu(call_bn(self.bn7, h), negative_slope=0.01)
        h=self.c8(h)
        h=F.leaky_relu(call_bn(self.bn8, h), negative_slope=0.01)
        h=self.c9(h)
        h=F.leaky_relu(call_bn(self.bn9, h), negative_slope=0.01)
        h=F.avg_pool2d(h, kernel_size=h.data.shape[2])

        features = h.view(h.size(0), h.size(1))
        logit=self.l_c1(features)  #changed this
        if self.top_bn:
            logit=call_bn(self.bn_c1, logit)
        return logit, features
