'''Pre-activation ResNet in PyTorch.

Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Identity Mappings in Deep Residual Networks. arXiv:1603.05027
'''
import torch
import torch.nn as nn
import torch.nn.functional as F


class PreActBlock(nn.Module):
    '''Pre-activation version of the BasicBlock.'''
    expansion = 1

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

        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)
            )

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


class PreActBottleneck(nn.Module):
    '''Pre-activation version of the original Bottleneck module.'''
    expansion = 4

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

        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)
            )

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


class PreActResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10, normalize_input=True, use_batchnorm=True):
        super(PreActResNet, self).__init__()
        self.in_planes = 64
        self.normalize_input = normalize_input
        self.use_batchnorm = use_batchnorm

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        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.bn = nn.BatchNorm2d(512 * block.expansion)
        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 _normalize_input(self, x):
        if self.normalize_input:
            mean = torch.as_tensor([0.4914, 0.4822, 0.4465], dtype=x.dtype,
                                device=x.device)
            std = torch.as_tensor([0.2471, 0.2435, 0.2616], dtype=x.dtype,
                                device=x.device)
            x = (x - mean.view(1, 3, 1, 1)) / std.view(1, 3, 1, 1)
        return x

    def forward(self, x, y=None):
        x = self._normalize_input(x)
        out = self.conv1(x)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.relu(self.bn(out))
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        if y is None:
            return out
        return out[torch.arange(out.size(0)), y]

    def train(self, mode=True):
        """
        Override the train method to optionally keep BatchNorm layers in evaluation mode.

        Args:
            mode (bool): Whether to set training mode for the model
        """
        super(PreActResNet, self).train(mode)
        if mode and not self.use_batchnorm:
            # Set all BatchNorm layers to eval mode even when the model is in training mode
            for submodule in self.modules():
                if "batchnorm" in submodule.__class__.__name__.lower():
                    submodule.train(False)
        return self


def PreActResNet18(num_classes=10, normalize_input=True, use_batchnorm=True):
    return PreActResNet(PreActBlock, [2,2,2,2], num_classes=num_classes, normalize_input=normalize_input, use_batchnorm=use_batchnorm)

def PreActResNet34(num_classes=10, normalize_input=True, use_batchnorm=True):
    return PreActResNet(PreActBlock, [3,4,6,3], num_classes=num_classes, normalize_input=normalize_input, use_batchnorm=use_batchnorm)

def PreActResNet50(num_classes=10, normalize_input=True, use_batchnorm=True):
    return PreActResNet(PreActBottleneck, [3,4,6,3], num_classes=num_classes, normalize_input=normalize_input, use_batchnorm=use_batchnorm)

def PreActResNet101(num_classes=10, normalize_input=True, use_batchnorm=True):
    return PreActResNet(PreActBottleneck, [3,4,23,3], num_classes=num_classes, normalize_input=normalize_input, use_batchnorm=use_batchnorm)

def PreActResNet152(num_classes=10, normalize_input=True, use_batchnorm=True):
    return PreActResNet(PreActBottleneck, [3,8,36,3], num_classes=num_classes, normalize_input=normalize_input, use_batchnorm=use_batchnorm)


def test():
    net = PreActResNet18()
    y = net((torch.randn(1,3,32,32)))
    print(y.size())

# test()
