from __future__ import absolute_import
import math

import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from torch.autograd import Variable


__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, cfg, stride=1, downsample=None):
        # cfg should be a number in this case
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, cfg, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(cfg)
        self.conv2 = nn.Conv2d(cfg, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        #####################

        # self.conv1 = conv3x3(inplanes, cfg, stride)
        # self.bn1 = nn.BatchNorm2d(cfg)
        # self.relu = nn.ReLU(inplace=True)
        # self.conv2 = conv3x3(cfg, planes)
        # self.bn2 = nn.BatchNorm2d(planes)

        self.downsample = downsample
        self.stride = stride



    def forward(self, x):

        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        # print(out.size(),self.shortcut(x).size())
        out += self.shortcut(x)
        out = F.relu(out)
        return out
        #######################
        # 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

def downsample_basic_block(x, planes):
    x = nn.AvgPool2d(2,2)(x)
    zero_pads = torch.Tensor(
        x.size(0), planes - x.size(1), x.size(2), x.size(3)).zero_()
    if isinstance(x.data, torch.cuda.FloatTensor):
        zero_pads = zero_pads.cuda()

    out = Variable(torch.cat([x.data, zero_pads], dim=1))

    return out

class ResNet(nn.Module):

    def __init__(self, depth, dataset='cifar10', cfg=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

        block = BasicBlock
        if cfg == None:
            cfg = [[16]*n, [32]*n, [64]*n]
            cfg = [item for sub_list in cfg for item in sub_list]

        self.cfg = cfg

        self.inplanes = 64
        num_blocks = [2,2,2,2]
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], cfg=cfg[0:n], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], cfg=cfg[0:n], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], cfg=cfg[0:n], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], cfg=cfg[0:n], stride=2)
        self.fc = nn.Linear(512*block.expansion, 10)
        ####################

        # self.inplanes = 16
        # self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1,
        #                        bias=False)
        # self.bn1 = nn.BatchNorm2d(16)
        # self.relu = nn.ReLU(inplace=True)
        # self.layer1 = self._make_layer(block, 16, n, cfg=cfg[0:n])
        # self.layer2 = self._make_layer(block, 32, n, cfg=cfg[n:2*n], stride=2)
        # self.layer3 = self._make_layer(block, 64, n, cfg=cfg[2*n:3*n], stride=2)
        # self.avgpool = nn.AvgPool2d(8)
        # if dataset == 'cifar10':
        #     num_classes = 10
        # elif dataset == 'cifar100':
        #     num_classes = 100
        # self.fc = nn.Linear(64 * block.expansion, num_classes)

        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_()

    # def _make_layer(self, block, planes, blocks, cfg, stride=1):
    #     downsample = None
    #     if stride != 1 or self.inplanes != planes * block.expansion:
    #         downsample = partial(downsample_basic_block, planes=planes*block.expansion)
    #
    #     layers = []
    #     layers.append(block(self.inplanes, planes, cfg[0], stride, downsample))
    #     self.inplanes = planes * block.expansion
    #     for i in range(1, blocks):
    #         layers.append(block(self.inplanes, planes, cfg[i]))
    #
    #     return nn.Sequential(*layers)

    def _make_layer(self, block, planes, blocks, cfg, stride):
        strides = [stride] + [1]*(blocks-1)
        layers = []
        k = 0
        for stride in strides:
            layers.append(block(self.inplanes, planes, cfg[k], stride))
            self.inplanes = planes * block.expansion
            k += 1
        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 = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        # x = self.conv1(x)
        # x = self.bn1(x)
        # x = self.relu(x)    # 32x32
        #
        # x = self.layer1(x)  # 32x32
        # x = self.layer2(x)  # 16x16
        # x = self.layer3(x)  # 8x8
        #
        # x = self.avgpool(x)
        # x = x.view(x.size(0), -1)
        # x = self.fc(x)

        return x

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

if __name__ == '__main__':
    net = resnet_prue(depth=56)
    x=Variable(torch.FloatTensor(16, 3, 32, 32))
    y = net(x)
    print(y.data.shape)
