'''

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.

'''

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

def _weights_init(m):

    classname = m.__class__.__name__

    print(classname)

    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 ResNet(nn.Module):

    def __init__(self, block, num_blocks, num_classes=10):

        super(ResNet, self).__init__()

        self.in_planes = 16

        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.linear = 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):

        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)

        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 resnet1202():

    return ResNet(BasicBlock, [200, 200, 200])

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()