
import torch.nn as nn
import math




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 * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        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=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        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(7, 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_()

    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)
        x1 = self.maxpool(x)

        x2 = self.layer1(x1)
        x3 = self.layer2(x2)
        x4 = self.layer3(x3)
        x5 = self.layer4(x4)

        # x = self.avgpool(x)
        # x = x.view(x.size(0), -1)
        # x = self.fc(x)
        return x1, x2, x3, x4, x5




def resnet18(pretrained=False, **kwargs):
    """Constructs a ResNet-18 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    return model

def resnet34(pretrained=False, **kwargs):
    """Constructs a ResNet-34 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    # if pretrained:
    #     model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
    return model



class SubNet(nn.Module):

    def __init__(self, ):
        super(SubNet, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels=256, out_channels=128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(in_channels=128, out_channels=64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
        )
        self.linear = nn.Sequential(
            nn.Linear(in_features=4096, out_features=4096),
            nn.ReLU,
            nn.Dropout(),
            nn.Linear(in_features=4096, out_features=1024),
            nn.ReLU(),
            nn.Dropout(),
            nn.Linear(in_features=1024, out_features=4, bias=False)
        )

    def forward(self, input):
        # minput = torch.mean(input)
        x = self.conv(input)
        # mx = torch.mean(x)
        x = x.view(-1, 4096)
        x = self.linear(x)
        return x




class ResBlock(nn.Module):
    def __init__(self, inch):
        super(ResBlock, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels=inch, out_channels=inch, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(in_channels=inch, out_channels=inch, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
        )
    def forward(self, input):
        x = self.conv(input)
        return x+input


class SubNet2(nn.Module):
    def __init__(self):
        super(SubNet2, self).__init__()
        self.block1 = nn.Sequential(ResBlock(256),
                                    ResBlock(256))
        self.conv1 = nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1)
        self.block2 = nn.Sequential(ResBlock(256),
                                    ResBlock(256))
        self.conv2 = nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1)
        self.linear = nn.Sequential(
            nn.Linear(2*2*256, 1024),
            nn.ReLU(),
            nn.Linear(1024, 1024),
            nn.ReLU(),
            nn.Linear(1024, 4))


    def forward(self, input):
        x = self.block1(input)
        x = self.conv1(x)
        x = self.block2(x)
        x = self.conv2(x)
        x = x.view(-1, 2*2*256)
        x = self.linear(x)
        return x



class SubNet3(nn.Module):
    def __init__(self):
        super(SubNet3, self).__init__()
        self.block2 = nn.Sequential(ResBlock(256),
                                    ResBlock(256),
                                    ResBlock(256))
        self.conv2 = nn.Conv2d(256, 128, kernel_size=1, stride=1, padding=0)
        self.block3 = nn.Sequential(ResBlock(128),
                                    ResBlock(128),
                                    ResBlock(128),
                                    ResBlock(128),
                                    )
        self.conv3 = nn.Conv2d(128, 64, kernel_size=1, stride=1, padding=0)
        self.block4 = nn.Sequential(ResBlock(64),
                                    ResBlock(64),
                                    ResBlock(64),
                                    ResBlock(64),
                                    )
        self.linear = nn.Sequential(
            nn.Linear(10*10*64, 6400),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(6400, 3200),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(3200, 4))


    def forward(self, input):
        x = self.block2(input)
        x = self.conv2(x)
        x = self.block3(x)
        x = self.conv3(x)
        x = self.block4(x)
        x = x.view(-1, 10*10*64)
        x = self.linear(x)
        return x