'''
ResNet for CIFAR10.

Ref:
[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
import torch.nn.init as init

from lrp.linear import Linear
from lrp.conv_cifar import Conv2d
from lrp.sequential import Sequential


__all__ = [
    'ResNet', 'resnet20', 'resnet32', 'resnet44', 'resnet56', 'resnet110',
    'resnet1202'
]


def _weights_init(m):
    classname = m.__class__.__name__
    if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d) \
    or isinstance(m, Linear) or isinstance(m, Conv2d): # <--------------
        init.kaiming_normal_(m.weight)


class LambdaLayer(nn.Module):
    def __init__(self, lambd):
        super(LambdaLayer, self).__init__()
        self.lambd = lambd

    def forward(self, x):
        return self.lambd(x)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, option='A'):
        super(BasicBlock, self).__init__()
        self.conv1 = Conv2d(in_planes, # <------
                               planes,
                               kernel_size=3,
                               stride=stride,
                               padding=1,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = Conv2d(planes, # <------
                               planes,
                               kernel_size=3,
                               stride=1,
                               padding=1,
                               bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = Sequential() # <------
        if stride != 1 or in_planes != planes:
            if option == 'A':
                self.shortcut = LambdaLayer(lambda x: F.pad(
                    x[:, :, ::2, ::2],
                    (0, 0, 0, 0, planes // 4, planes // 4), "constant", 0))
            elif option == 'B':
                self.shortcut = Sequential( # <------
                    Conv2d(in_planes, # <------
                              self.expansion * planes,
                              kernel_size=1,
                              stride=stride,
                              bias=False),
                    nn.BatchNorm2d(self.expansion * planes))

    def forward(self, x, explain=False, rule='epsilon'):
        out = F.relu(self.bn1(self.conv1(x, explain, rule)))
        out = self.bn2(self.conv2(out, explain, rule))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()
        self.in_planes = 16

        self.conv1 = Conv2d(3, # <------
                               16,
                               kernel_size=3,
                               stride=1,
                               padding=1,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(16)
        self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)
        self.linear = Linear(64, num_classes) # <-----------------------

        self.apply(_weights_init)

    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 Sequential(*layers) # <-----------------------

    def forward(self, x, explain=False, rule="epsilon"):
        out = F.relu(self.bn1(self.conv1(x, explain, rule)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = F.avg_pool2d(out, out.size()[3])
        out = out.view(out.size(0), -1)
        out = self.linear(out, explain, rule)
        return out


def resnet20():
    return ResNet(BasicBlock, [3, 3, 3])


def resnet32():
    return ResNet(BasicBlock, [5, 5, 5])


def resnet44():
    return ResNet(BasicBlock, [7, 7, 7])


def resnet56():
    return ResNet(BasicBlock, [9, 9, 9])


def resnet110():
    return ResNet(BasicBlock, [18, 18, 18])


def test(net):
    import numpy as np
    total_params = 0

    for x in filter(lambda p: p.requires_grad, net.parameters()):
        total_params += np.prod(x.data.numpy().shape)
    print("Total number of params", total_params)
    print(
        "Total layers",
        len(
            list(
                filter(lambda p: p.requires_grad and len(p.data.size()) > 1,
                       net.parameters()))))


if __name__ == "__main__":
    for net_name in __all__:
        if net_name.startswith('resnet'):
            print(net_name)
            test(globals()[net_name]())
            print()
