"""
Properly implemented ResNet-s for CIFAR10 as described in paper [1].
The implementation and structure of this file is hugely influenced by [2]
which is implemented for ImageNet and doesn't have option A for identity.
Moreover, most of the implementations on the web is copy-paste from
torchvision's resnet and has wrong number of params.
Proper ResNet-s for CIFAR10 (for fair comparision and etc.) has following
number of layers and parameters:
name      | layers | params
ResNet20  |    20  | 0.27M
ResNet32  |    32  | 0.46M
ResNet44  |    44  | 0.66M
ResNet56  |    56  | 0.85M
ResNet110 |   110  |  1.7M
ResNet1202|  1202  | 19.4m
which this implementation indeed has.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Deep Residual Learning for Image Recognition. arXiv:1512.03385
[2] https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
If you use this implementation in you work, please don't forget to mention the
author, Yerlan Idelbayev.
Borrow from : https://github.com/akamaster/pytorch_resnet_cifar10.git
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from torch.autograd import Variable

__all__ = [
    "ResNets",
    "resnet20s",
    "resnet32s",
    "resnet44s",
    "resnet56s",
    "resnet110s",
    "resnet1202s",
]


class NormalizeByChannelMeanStd(torch.nn.Module):
    def __init__(self, mean, std):
        super(NormalizeByChannelMeanStd, self).__init__()
        if not isinstance(mean, torch.Tensor):
            mean = torch.tensor(mean)
        if not isinstance(std, torch.Tensor):
            std = torch.tensor(std)
        self.register_buffer("mean", mean)
        self.register_buffer("std", std)

    def forward(self, tensor):
        return self.normalize_fn(tensor, self.mean, self.std)

    def extra_repr(self):
        return "mean={}, std={}".format(self.mean, self.std)

    def normalize_fn(self, tensor, mean, std):
        """Differentiable version of torchvision.functional.normalize"""
        # here we assume the color channel is in at dim=1
        mean = mean[None, :, None, None]
        std = std[None, :, None, None]
        return tensor.sub(mean).div(std)


def _weights_init(m):
    classname = m.__class__.__name__
    if isinstance(m, nn.Linear) or isinstance(m, nn.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 = 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 != planes:
            if option == "A":
                """
                For CIFAR10 ResNet paper uses 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 = 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 ResNets(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNets, self).__init__()
        self.in_planes = 16

        print("The normalize layer is contained in the network")
        self.normalize = NormalizeByChannelMeanStd(
            mean=[0.4914, 0.4822, 0.4465], std=[0.2470, 0.2435, 0.2616]
        )

        self.conv1 = nn.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.fc = nn.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 nn.Sequential(*layers)

    def forward(self, x):
        x = self.normalize(x)
        out = F.relu(self.bn1(self.conv1(x)))
        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.fc(out)
        return out


def resnet20s(num_classes=10):
    return ResNets(BasicBlock, [3, 3, 3], num_classes=num_classes)


def resnet32s(num_classes=10):
    return ResNets(BasicBlock, [5, 5, 5], num_classes=num_classes)


def resnet44s(num_classes=10):
    return ResNets(BasicBlock, [7, 7, 7], num_classes=num_classes)


def resnet56s(num_classes=10):
    return ResNets(BasicBlock, [9, 9, 9], num_classes=num_classes)


def resnet110s(num_classes=10):
    return ResNets(BasicBlock, [18, 18, 18], num_classes=num_classes)


def resnet1202s(num_classes=10):
    return ResNets(BasicBlock, [200, 200, 200], num_classes=num_classes)
