import torch.nn as nn
import math
#import torch.utils.model_zoo as model_zoo


__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152', 
           'resnet18v1', 'resnet34v1', 'resnet50v1', 'resnet101v1', 'resnet152v1',
           'resnet18v2', 'resnet34v2', 'resnet50v2', 'resnet101v2', 'resnet152v2',
           'resnet18v3', 'resnet34v3', 'resnet50v3', 'resnet101v3', 'resnet152v3']


#model_urls = {
#    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
#    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
#    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
#    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
#    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
#}
#

def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, 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, planes * self.expansion, kernel_size=1, 
                               bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=12, avgPool_kernel=7):
        self.inplanes = 64
        super(ResNet, self).__init__()
            
        
        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=2, padding=1, 
                               bias=False)        
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        
        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
        
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
         
        self.avgpool = nn.AvgPool2d(kernel_size=avgPool_kernel, stride=1)  
        
        self.fc = nn.Linear(512 * block.expansion, num_classes)  
        
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
                
#        for m in self.modules():
#            if isinstance(m, nn.Conv2d):
#                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
#            elif isinstance(m, nn.BatchNorm2d):
#                nn.init.constant_(m.weight, 1)
#                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        
        x = self.maxpool(x)

        x = self.layer1(x)        
        x = self.layer2(x)         
        x = self.layer3(x)       
        x = self.layer4(x)
#        print(x.size())    
        
        x = self.avgpool(x)
#        print(x.size())
        
        x = x.view(x.size(0), -1)        
        x = self.fc(x)

        return x



def resnet18(input_size, label_size, **kwargs):
    """Constructs a ResNet-18 model.
        
    Args:
       
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=label_size, 
                   kernel_size=7, stride=2, padding=3,
                   avgPool_kernelSize=int(input_size[2]/32), **kwargs)
    
    return model



def resnet18v1(input_size, label_size, **kwargs):
    """Constructs a ResNet-18 model.
        
    Args:
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], num_classes=label_size, 
                   avgPool_kernel=int(input_size[2]/32), **kwargs)
    
    return model

def resnet34v1(input_size, label_size, **kwargs):
    """Constructs a ResNet-18 model.
        
    Args:
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], num_classes=label_size, 
                   avgPool_kernel=int(input_size[2]/32), **kwargs)
    
    return model



def resnet50v1(input_size, label_size, **kwargs):
    """Constructs a ResNet-18 model.
        
    Args:
        
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=label_size, 
                   avgPool_kernel=int(input_size[2]/32), **kwargs)
    
    return model



def resnet101v1(input_size, label_size, **kwargs):
    """Constructs a ResNet-18 model.
        
    Args:
        
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes=label_size, 
                   avgPool_kernel=int(input_size[2]/32), **kwargs)
    
    return model


def resnet152v1(input_size, label_size, **kwargs):
    """Constructs a ResNet-18 model.
        
    Args:
        
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], num_classes=label_size, 
                   avgPool_kernel=int(input_size[2]/32), **kwargs)
    
    return model



