'''
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 .conv import SparseConv2D
from torch.autograd import Variable
from advertorch.utils import NormalizeByChannelMeanStd

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


def _weights_init(m):
    classname = m.__class__.__name__
    #print(classname)
    if isinstance(m, nn.Linear):
        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'):
        if True:
            conv = nn.Conv2d
        else:
            conv = SparseConv2D
        super(BasicBlock, self).__init__()
        self.conv1 = conv(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv(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(
                     conv(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                     nn.BatchNorm2d(self.expansion * planes)
                )

    def forward(self, x):
        #print(self.bn1.weight)
        #print(self.bn1.num_batches_tracked)
        #print(self.bn1.running_mean)
        #print(self.bn1.running_var)
        out = F.relu(self.bn1(self.conv1(x)))
        #print(out.shape)
        out = self.conv2(out)
        #print(out.shape)
        #print(self.bn2.weight)
        #print(self.bn2.num_batches_tracked)
        #print(self.bn2.running_mean)
        #print(self.bn2.running_var)


        out = self.bn2(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.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)))
        #print(out)
        out = self.layer1(out)
        #print(out)
        out = self.layer2(out)
        #print(out)
        out = self.layer3(out)
        #print(out)
        out = F.avg_pool2d(out, out.size()[3])
        #print(out)
        out = out.view(out.size(0), -1)
        #print(out)
        out = self.fc(out)
        return out


def resnet20(number_class=10):
    return ResNet(BasicBlock, [3, 3, 3], num_classes=number_class)


def resnet32(number_class=10):
    return ResNet(BasicBlock, [5, 5, 5], num_classes=number_class)


def resnet44(number_class=10):
    return ResNet(BasicBlock, [7, 7, 7], num_classes=number_class)


def resnet56(number_class=10):
    return ResNet(BasicBlock, [9, 9, 9], num_classes=number_class)


def resnet110(number_class=10):
    return ResNet(BasicBlock, [18, 18, 18], num_classes=number_class)


def resnet1202(number_class=10):
    return ResNet(BasicBlock, [200, 200, 200], num_classes=number_class)


if __name__ == '__main__':
    import torch.nn as nn
    import torch.nn.utils.prune as prune
    def prune_model_custom(model, mask_dict):
        for name,m in model.named_modules():
            if isinstance(m, SparseConv2D):
                print('pruning layer with custom mask:', name)
                try:
                    prune.CustomFromMask.apply(m, 'weight', mask=mask_dict[name+'.weight_mask'])
                except:
                    pass
    model=resnet20(10)
    model.eval()
    model.load_state_dict(torch.load('init.pt'))
    mask = torch.load('21-mask.pt')
    prune_model_custom(model, mask)
    print(model(torch.ones(1,3,28,28)))