'''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, is_last=False):
        super(Bottleneck, self).__init__()
        self.is_last = is_last
        self.stride = stride

        mid_planes = int(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)
        preact = torch.cat([out, res], 1) if self.stride == 2 else out+res
        out = F.relu(preact)
        # out = F.relu(torch.cat([out, res], 1)) if self.stride == 2 else F.relu(out+res)
        if self.is_last:
            return out, preact
        else:
            return out


class ShuffleNet(nn.Module):
    def __init__(self, cfg, num_classes=10):
        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], num_classes)
        self.last_channel = out_planes[2]

    def get_hint_channel(self):
        return self.last_channel

    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,
                                     is_last=(i == num_blocks - 1)))
            self.in_planes = out_planes
        return nn.Sequential(*layers)

    def get_feat_modules(self):
        feat_m = nn.ModuleList([])
        feat_m.append(self.conv1)
        feat_m.append(self.bn1)
        feat_m.append(self.layer1)
        feat_m.append(self.layer2)
        feat_m.append(self.layer3)
        return feat_m

    def get_bn_before_relu(self):
        raise NotImplementedError('ShuffleNet currently is not supported for "Overhaul" teacher')

    def forward(self, out, is_feat=False, preact=False, begin=0, end=100):
        if begin <= 0:
            out = F.relu(self.bn1(self.conv1(out)))
            f0 = out
        if end==0:
            return out

        if begin <= 1:
            out, f1_pre = self.layer1(out)
            f1 = out
        if end==1:
            return out

        if begin <= 2:
            out, f2_pre = self.layer2(out)
            f2 = out
        if end==2:
            return out

        if begin <= 3:
            out, f3_pre = self.layer3(out)
            f3 = out
        if end==3:
            return out

        if begin <= 4:
            out = F.adaptive_avg_pool2d(out, 1)
            out = out.view(out.size(0), -1)
            f4 = out
        if end==4:
            return out

        if begin<=5:
            out = self.linear(out)

        if is_feat:
            if preact:
                return [f0, f1_pre, f2_pre, f3_pre, f4], out
            else:
                return [f0, f1, f2, f3, f4], out
        else:
            return out


def ShuffleV1(**kwargs):
    cfg = {
        'out_planes': [240, 480, 960],
        'num_blocks': [4, 8, 4],
        'groups': 3
    }
    return ShuffleNet(cfg, **kwargs)


if __name__ == '__main__':

    x = torch.randn(2, 3, 32, 32)
    net = ShuffleV1(num_classes=100)
    import time
    a = time.time()
    feats, logit = net(x, is_feat=True, preact=True)
    b = time.time()
    print(b - a)
    for f in feats:
        print(f.shape, f.min().item())
    print(logit.shape)
