'''DPN in PyTorch.

Reference
    Dual Path Networks
    https://arxiv.org/abs/1707.01629
'''


import torch
import torch.nn as nn
import torch.nn.functional as F


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

        self.conv1 = nn.Conv2d(last_planes, in_planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=32, bias=False)
        self.bn2 = nn.BatchNorm2d(in_planes)
        self.conv3 = nn.Conv2d(in_planes, out_planes+dense_depth, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_planes+dense_depth)

        self.shortcut = nn.Sequential()
        if first_layer:
            self.shortcut = nn.Sequential(
                nn.Conv2d(last_planes, out_planes+dense_depth, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_planes+dense_depth)
            )


    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        x = self.shortcut(x)
        d = self.out_planes
        out = torch.cat([x[:,:d,:,:]+out[:,:d,:,:], x[:,d:,:,:], out[:,d:,:,:]], 1)
        out = F.relu(out)
        return out


class DPN(nn.Module):
    def __init__(self, cfg):
        super(DPN, self).__init__()
        in_planes, out_planes = cfg['in_planes'], cfg['out_planes']
        num_blocks, dense_depth = cfg['num_blocks'], cfg['dense_depth']
        self.embDim = out_planes[3]+(num_blocks[3]+1)*dense_depth[3]
        
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.last_planes = 64
        self.layer1 = self._make_layer(in_planes[0], out_planes[0], num_blocks[0], dense_depth[0], stride=1)
        self.layer2 = self._make_layer(in_planes[1], out_planes[1], num_blocks[1], dense_depth[1], stride=2)
        self.layer3 = self._make_layer(in_planes[2], out_planes[2], num_blocks[2], dense_depth[2], stride=2)
        self.layer4 = self._make_layer(in_planes[3], out_planes[3], num_blocks[3], dense_depth[3], stride=2)
        self.linear = nn.Linear(out_planes[3]+(num_blocks[3]+1)*dense_depth[3], 10)


    def _make_layer(self, in_planes, out_planes, num_blocks, dense_depth, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for i,stride in enumerate(strides):
            layers.append(Bottleneck(self.last_planes, in_planes, out_planes, dense_depth, stride, i==0))
            self.last_planes = out_planes + (i+2) * dense_depth
        return nn.Sequential(*layers)


    def forward(self, x, last=False, freeze=False):
        if freeze:
            with torch.no_grad():
                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)
                e = out.view(out.size(0), -1)
        else:
            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)
            e = out.view(out.size(0), -1)
        out = self.linear(e)
        if last:
            return out, e
        else:
            return out
      
      
    def get_embedding_dim(self):
        return self.embDim


def DPN26():
    cfg = {
        'in_planes': (96,192,384,768),
        'out_planes': (256,512,1024,2048),
        'num_blocks': (2,2,2,2),
        'dense_depth': (16,32,24,128)
    }
    return DPN(cfg)


def DPN92():
    cfg = {
        'in_planes': (96,192,384,768),
        'out_planes': (256,512,1024,2048),
        'num_blocks': (3,4,20,3),
        'dense_depth': (16,32,24,128)
    }
    return DPN(cfg)


def test():
    net = DPN92()
    x = torch.randn(1,3,32,32)
    y = net(x)
    print(y)

# test()
