#
# ResNet implementation for CIFAR-10 and CIFAR-100.
# Modified to work with polar prototype networks.
#
# Basis code from: https://github.com/facebook/fb.resnet.torch and
# https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
#

from __future__ import absolute_import
import torch.nn as nn
import math
import torch
import torch.nn.functional as F


__all__ = ['resnet']

def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, depth, output_dims=10, multiplier=1, polars=None):
        super(ResNet, self).__init__()
        # Model type specifies number of layers for CIFAR-10 model
        assert (depth - 2) % 6 == 0, 'depth should be 6n+2'
        n = (depth - 2) // 6
        
        # Store a copy of the class prototypes.
        self.polars = polars

        #if depth >= 44:
        #    block  = Bottleneck
        #    layers = [3,4,6]
        #else:
        #    block  = BasicBlock
        #    layers = [2,2,2]
        block = BasicBlock

        self.inplanes = 16 * multiplier
        self.conv1 = nn.Conv2d(3, 16 * multiplier, kernel_size=3, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(16 * multiplier)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(block, 16 * multiplier, n)
        self.layer2 = self._make_layer(block, 32 * multiplier, n, stride=2)
        self.layer3 = self._make_layer(block, 64 * multiplier, n, stride=2)
        self.avgpool = nn.AvgPool2d(16)
        self.fc = nn.Linear(64 * multiplier * block.expansion, output_dims)

        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))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            #elif isinstance(m, nn.Linear):
            #    n = m.out_features
            #    m.weight.data.normal_(0, (2. / n) ** 0.5)
            #    m.weight.data = m.weight.div(m.weight.norm()).data

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                #nn.Conv2d(self.inplanes, planes * block.expansion,
                #          kernel_size=1, stride=stride, bias=False),
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=3, padding=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        x = self.layer1(x)
        x = self.layer2(x) 
        x = self.layer3(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x
    
    def predict(self, x):
        x = F.normalize(x, p=2, dim=1)
        x = torch.mm(x, self.polars.t().cuda())
        return x


def resnet(**kwargs):
    """
    Constructs a ResNet model.
    """
    return ResNet(**kwargs)
