'''
Pytorch implementation of ResNet models.

Reference:
[1] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, 2016.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F


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, temp=1.0, feature_clamp=1e6):
        super(ResNet, self).__init__()
        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.fc = nn.Linear(512*block.expansion, num_classes)
        self.temp = temp
        self.feature_clamp = feature_clamp  

    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, return_feature=False):
        feature1 = F.relu(self.bn1(self.conv1(x)))
        feature2 = self.layer1(feature1)
        feature3 = self.layer2(feature2)
        feature4 = self.layer3(feature3)
        out = self.layer4(feature4)
        out = F.avg_pool2d(out, 4)
        feature = out.view(out.size(0), -1)

        out = self.fc(feature) / self.temp
        if return_feature:
            return out, out
        return out
    
    def classifier(self, x):
        return self.fc(x)
    
    def feature_1_classifier(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        feature = out.view(out.size(0), -1)

        out = self.fc(feature) / self.temp
        return out
    
    def feature_2_classifier(self, x):
        out = self.layer2(x)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        feature = out.view(out.size(0), -1)

        out = self.fc(feature) / self.temp
        return out
    
    def feature_3_classifier(self, x):
        out = self.layer3(x)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        feature = out.view(out.size(0), -1)

        out = self.fc(feature) / self.temp
        return out

    def feature_4_classifier(self, x):
        out = self.layer4(x)
        out = F.avg_pool2d(out, 4)
        feature = out.view(out.size(0), -1)

        out = self.fc(feature) / self.temp
        return out

def resnet18(temp=1.0, **kwargs):
    model = ResNet(BasicBlock, [2, 2, 2, 2], temp=temp, **kwargs)
    return model


def resnet34(temp=1.0, **kwargs):
    model = ResNet(BasicBlock, [3, 4, 6, 3], temp=temp, **kwargs)
    return model


def resnet50_debug(temp=1.0, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 6, 3], temp=temp, **kwargs)
    return model


def resnet101(temp=1.0, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 23, 3], temp=temp, **kwargs)
    return model


def resnet110(temp=1.0, **kwargs):
    model = ResNet(Bottleneck, [3, 4, 26, 3], temp=temp, **kwargs)
    return model


def resnet152(temp=1.0, **kwargs):
    model = ResNet(Bottleneck, [3, 8, 36, 3], temp=temp, **kwargs)
    return model