'''ShuffleNet in PyTorch.

See the paper "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F


class ShuffleBlock(nn.Module):
  def __init__(self, groups):
    super(ShuffleBlock, self).__init__()
    self.groups = groups

  def forward(self, x):
    '''Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]'''
    N, C, H, W = x.size()
    g = self.groups
    return x.view(N, g, C//g, H, W).permute(0, 2, 1, 3, 4).reshape(N, C, H, W)


class Bottleneck(nn.Module):
  def __init__(self, in_planes, out_planes, stride, groups):
    super(Bottleneck, self).__init__()
    self.stride = stride

    mid_planes = out_planes/4
    g = 1 if in_planes == 24 else groups
    self.conv1 = nn.Conv2d(in_planes, mid_planes,
                           kernel_size=1, groups=g, bias=False)
    self.bn1 = nn.BatchNorm2d(mid_planes)
    self.shuffle1 = ShuffleBlock(groups=g)
    self.conv2 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3,
                           stride=stride, padding=1, groups=mid_planes, bias=False)
    self.bn2 = nn.BatchNorm2d(mid_planes)
    self.conv3 = nn.Conv2d(mid_planes, out_planes,
                           kernel_size=1, groups=groups, bias=False)
    self.bn3 = nn.BatchNorm2d(out_planes)

    self.shortcut = nn.Sequential()
    if stride == 2:
      self.shortcut = nn.Sequential(nn.AvgPool2d(3, stride=2, padding=1))

  def forward(self, x):
    out = F.relu(self.bn1(self.conv1(x)))
    out = self.shuffle1(out)
    out = F.relu(self.bn2(self.conv2(out)))
    out = self.bn3(self.conv3(out))
    res = self.shortcut(x)
    out = F.relu(torch.cat([out, res], 1)
                 ) if self.stride == 2 else F.relu(out+res)
    return out


class ShuffleNet(nn.Module):
  def __init__(self, cfg):
    super(ShuffleNet, self).__init__()
    out_planes = cfg['out_planes']
    num_blocks = cfg['num_blocks']
    groups = cfg['groups']

    self.conv1 = nn.Conv2d(3, 24, kernel_size=1, bias=False)
    self.bn1 = nn.BatchNorm2d(24)
    self.in_planes = 24
    self.layer1 = self._make_layer(out_planes[0], num_blocks[0], groups)
    self.layer2 = self._make_layer(out_planes[1], num_blocks[1], groups)
    self.layer3 = self._make_layer(out_planes[2], num_blocks[2], groups)
    self.linear = nn.Linear(out_planes[2], 10)

  def _make_layer(self, out_planes, num_blocks, groups):
    layers = []
    for i in range(num_blocks):
      stride = 2 if i == 0 else 1
      cat_planes = self.in_planes if i == 0 else 0
      layers.append(Bottleneck(self.in_planes, out_planes -
                               cat_planes, stride=stride, groups=groups))
      self.in_planes = out_planes
    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, 4)
    out = out.view(out.size(0), -1)
    out = self.linear(out)
    return out


def shufflenet_g2():
  cfg = {
      'out_planes': [200, 400, 800],
      'num_blocks': [4, 8, 4],
      'groups': 2
  }
  return ShuffleNet(cfg)


def shufflenet_g3():
  cfg = {
      'out_planes': [240, 480, 960],
      'num_blocks': [4, 8, 4],
      'groups': 3
  }
  return ShuffleNet(cfg)


def test():
  net = shufflenet_g2()
  x = torch.randn(1, 3, 32, 32)
  y = net(x)
  print(y)

# test()
