## https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py
'''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import random
import sys, os
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from utils import mixup_data
from load_data import per_image_standardization


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion*planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10, per_img_std = False):
        super(ResNet, self).__init__()
        self.per_img_std = per_img_std
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear = nn.Linear(512*block.expansion, num_classes)

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

    def forward(self, x, target=None, mixup_hidden = False,  mixup_alpha = 0.1, layer_mix=None):
        if self.per_img_std:
            x = per_image_standardization(x)
        
        if mixup_hidden == True:
            if layer_mix == None:
                layer_mix = random.randint(0,2)
            
            out = x
            
            if layer_mix == 0:
                #out = lam * out + (1 - lam) * out[index,:]
                out, y_a, y_b, lam = mixup_data(out, out, target, mixup_alpha)
            #print (out)       
            
            out = F.relu(self.bn1(self.conv1(x)))
            
            out = self.layer1(out)
    
            if layer_mix == 1:
                #out = lam * out + (1 - lam) * out[index,:]
                out, y_a, y_b, lam = mixup_data(out, out, target, mixup_alpha)
            
            #print (out)

            out = self.layer2(out)
    
            if layer_mix == 2:
                #out = lam * out + (1 - lam) * out[index,:]
                out, y_a, y_b, lam = mixup_data(out, out, target, mixup_alpha)
           #print (out)

            out = self.layer3(out)
            
            if layer_mix == 3:
                #out = lam * out + (1 - lam) * out[index,:]
                out, y_a, y_b, lam = mixup_data(out, out, target, mixup_alpha)
            #print (out)

            out = self.layer4(out)
            
            if layer_mix == 4:
                #out = lam * out + (1 - lam) * out[index,:]
                out, y_a, y_b, lam = mixup_data(out, out, target, mixup_alpha)

            #print (out)
            out = F.avg_pool2d(out, 4)
            out = out.view(out.size(0), -1)
            out = self.linear(out)
            
            if layer_mix == 5:
                #out = lam * out + (1 - lam) * out[index,:]
                out, y_a, y_b, lam = mixup_data(out, out, target, mixup_alpha)
            
            lam = torch.tensor(lam).cuda()
            lam = lam.repeat(y_a.size())
            #d = {}
            #d['out'] = out
            #d['target_a'] = y_a
            #d['target_b'] = y_b
            #d['lam'] = lam
            #print (out.shape)
            #print (y_a.shape)
            #print (y_b.size()) 
            #print (lam.size())
            return out, y_a, y_b, lam

        
        else:
            out = x
            out = F.relu(self.bn1(self.conv1(x)))
            out = self.layer1(out)
            out = self.layer2(out)
            out = self.layer3(out)
            out = self.layer4(out)
            out = F.avg_pool2d(out, 4)
            out = out.view(out.size(0), -1)
            out = self.linear(out)
            return out
        

def resnet18(num_classes=10, dropout = False, per_img_std = False):
    """Constructs a ResNet-18 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], num_classes, per_img_std = per_img_std)
    return model


def resnet34(num_classes=10, dropout = False, per_img_std = False):
    """Constructs a ResNet-34 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], num_classes, per_img_std = per_img_std)
    return model


def resnet50(num_classes=10, dropout = False, per_img_std = False):
    """Constructs a ResNet-50 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes, per_img_std = per_img_std)
    return model


def resnet101(num_classes=10, dropout = False, per_img_std = False):
    """Constructs a ResNet-101 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes, per_img_std = per_img_std)
    return model


def resnet152(num_classes=10, dropout = False, per_img_std = False):
    """Constructs a ResNet-152 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes, per_img_std = per_img_std)
    return model