'''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



class FilterResponseNormNd(nn.Module):
    def __init__(self, ndim, num_features, eps=1e-6,
                 learnable_eps=False):
        """
        Input Variables:
        ----------------
            ndim: An integer indicating the number of dimensions of the expected input tensor.
            num_features: An integer indicating the number of input feature dimensions.
            eps: A scalar constant or learnable variable.
            learnable_eps: A bool value indicating whether the eps is learnable.
        """
        assert ndim in [3, 4, 5], \
            'FilterResponseNorm only supports 3d, 4d or 5d inputs.'
        super(FilterResponseNormNd, self).__init__()
        shape = (1, num_features) + (1, ) * (ndim - 2)
        self.eps = nn.Parameter(torch.ones(*shape) * eps)
        if not learnable_eps:
            self.eps.requires_grad_(False)
        self.gamma = nn.Parameter(torch.Tensor(*shape))
        self.beta = nn.Parameter(torch.Tensor(*shape))
        self.tau = nn.Parameter(torch.Tensor(*shape))
        self.reset_parameters()



    def forward(self, x):
        avg_dims = tuple(range(2, x.dim()))
        nu2 = torch.pow(x, 2).mean(dim=avg_dims, keepdim=True)
        x = x * torch.rsqrt(nu2 + torch.abs(self.eps))
        return torch.max(self.gamma * x + self.beta, self.tau)

    def reset_parameters(self):
        nn.init.ones_(self.gamma)
        nn.init.zeros_(self.beta)
        nn.init.zeros_(self.tau)

class FilterResponseNorm1d(FilterResponseNormNd):
    def __init__(self, num_features, eps=1e-6, learnable_eps=False):
        super(FilterResponseNorm1d, self).__init__(
            3, num_features, eps=eps, learnable_eps=learnable_eps)

class FilterResponseNorm2d(FilterResponseNormNd):
    def __init__(self, num_features, eps=1e-6, learnable_eps=False):
        super(FilterResponseNorm2d, self).__init__(
            4, num_features, eps=eps, learnable_eps=learnable_eps)

class FilterResponseNorm3d(FilterResponseNormNd):
    def __init__(self, num_features, eps=1e-6, learnable_eps=False):
        super(FilterResponseNorm3d, self).__init__(
            5, num_features, eps=eps, learnable_eps=learnable_eps)


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 = FilterResponseNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = FilterResponseNorm2d(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),
                FilterResponseNorm2d(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 = FilterResponseNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = FilterResponseNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
        self.bn3 = FilterResponseNorm2d(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),
                FilterResponseNorm2d(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_LN(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet_LN, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = FilterResponseNorm2d(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):
        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_LN():
    return ResNet_LN(BasicBlock, [2,2,2,2])

def ResNet34_LN():
    return ResNet_LN(BasicBlock, [3,4,6,3])

def ResNet50_LN():
    return ResNet_LN(Bottleneck, [3,4,6,3])

def ResNet101_LN():
    return ResNet_LN(Bottleneck, [3,4,23,3])

def ResNet152_LN():
    return ResNet_LN(Bottleneck, [3,8,36,3])


def test():
    net = ResNet18_LN()
    y = net(torch.randn(1,3,32,32))
    print(y.size())

# test()
