'''
Modified from https://github.com/chengyangfu/pytorch-vgg-cifar10
'''
import math

import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from quantizer_gpu import Quantizer

__all__ = [
    'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
    'vgg19_bn', 'vgg19',
]


class VGG(nn.Module):
    '''
    VGG model 
    '''
    def __init__(self):
        super(VGG, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size = 3, padding = 1)             #'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']
        self.bn1 = nn.BatchNorm2d(64)
        self.conv2 = nn.Conv2d(64, 128, kernel_size = 3, padding = 1)
        self.bn2 = nn.BatchNorm2d(128)
        self.conv3 = nn.Conv2d(128, 256, kernel_size = 3, padding = 1)
        self.bn3 = nn.BatchNorm2d(256)
        self.conv4 = nn.Conv2d(256, 256, kernel_size = 3, padding = 1)
        self.bn4 = nn.BatchNorm2d(256)
        self.conv5 = nn.Conv2d(256, 512, kernel_size = 3, padding = 1)
        self.bn5 = nn.BatchNorm2d(512)
        self.conv6 = nn.Conv2d(512, 512, kernel_size = 3, padding = 1)
        self.bn6 = nn.BatchNorm2d(512)
        self.conv7 = nn.Conv2d(512, 512, kernel_size = 3, padding = 1)
        self.bn7 = nn.BatchNorm2d(512)
        self.conv8 = nn.Conv2d(512, 512, kernel_size = 3, padding = 1)
        self.bn8 = nn.BatchNorm2d(512)
        self.activation = nn.ReLU(inplace = True)
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(512, 512),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(512, 512),
            nn.ReLU(True),
            nn.Linear(512, 10),
        )
         # Initialize weights
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
                m.bias.data.zero_()


    def forward(self, x, n = 0, tree = None):
        
                                                                                #'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']
        if n >= 4:
            if n==4:
                x = self.quantize_activation(x, True, tree[n-4], 'lookup_table', True)
            else:
                tree0 = tree[0]
                x = self.quantize_activation(x, True, tree0[0], 'lookup_table', True)

        layer1 = F.max_pool2d(self.activation(self.bn1(self.conv1(x))), 2)
        if n == 1:
            layer1 = self.quantize_activation(layer1, True, tree[n-1], 'lookup_table', False)
        elif n == 5:
            tree1 = tree[1]
            layer1 = self.quantize_activation(layer1, True, tree1[n-5], 'lookup_table', False)
        
        layer2 = F.max_pool2d(self.activation(self.bn2(self.conv2(layer1))), 2)
        layer3 = self.activation(self.bn3(self.conv3(layer2)))
        layer4 = F.max_pool2d(self.activation(self.bn4(self.conv4(layer3))), 2)
        layer5 = self.activation(self.bn5(self.conv5(layer4)))
        layer6 = F.max_pool2d(self.activation(self.bn6(self.conv6(layer5))), 2)
        layer7 = self.activation(self.bn7(self.conv7(layer6)))
        layer8 = F.max_pool2d(self.activation(self.bn8(self.conv8(layer7))), 2)
        '''out = self.features(x)
        print(out.shape)
        exit(0)'''
        out = layer8.view(layer8.size(0), -1)
        out = self.classifier(out)
        return out, [x, layer1]

    def quantize_activation(self, input, ifTraining, tree, lookup_table, isInput):
        # return Quantizer(ifQuantizing, ifTraining, tree, lookup_table).apply(input)
        return Quantizer().apply(input, ifTraining, tree, lookup_table, isInput)


def make_layers(cfg, batch_norm=False):
    layers = []
    in_channels = 3
    for v in cfg:
        if v == 'M':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    return nn.Sequential(*layers)


cfg = {
    'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 
          512, 512, 512, 512, 'M'],
}


def vgg11():
    """VGG 11-layer model (configuration "A")"""
    return VGG(make_layers(cfg['A']))


def vgg11_bn():
    """VGG 11-layer model (configuration "A") with batch normalization"""
    return VGG(make_layers(cfg['A'], batch_norm=True))


def vgg13():
    """VGG 13-layer model (configuration "B")"""
    return VGG(make_layers(cfg['B']))


def vgg13_bn():
    """VGG 13-layer model (configuration "B") with batch normalization"""
    return VGG(make_layers(cfg['B'], batch_norm=True))


def vgg16():
    """VGG 16-layer model (configuration "D")"""
    return VGG(make_layers(cfg['D']))


def vgg16_bn():
    """VGG 16-layer model (configuration "D") with batch normalization"""
    return VGG(make_layers(cfg['D'], batch_norm=True))


def vgg19():
    """VGG 19-layer model (configuration "E")"""
    return VGG(make_layers(cfg['E']))


def vgg19_bn():
    """VGG 19-layer model (configuration 'E') with batch normalization"""
    return VGG(make_layers(cfg['E'], batch_norm=True))