'''Dual Path Networks in PyTorch.'''
import torch
import torch.nn as nn
import torch.nn.functional as F

from .build import META_ARCH_REGISTRY

__all__ = [
    'DPN',
    'DPN26',
    'DPN92'
]

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

@META_ARCH_REGISTRY.register()
class DPN(nn.Module):
    def __init__(self, cfg, num_classes=10):
        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.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.last_linear = nn.Linear(out_planes[3]+(num_blocks[3]+1)*dense_depth[3], num_classes)

    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 features(self, input):
        x = F.relu(self.bn1(self.conv1(input)))
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        return x

    def logits(self, features):
        x = F.adaptive_avg_pool2d(features, 1)
        x = self.last_linear(x.view(x.size(0), -1))
        return x

    def forward(self, input):
        x = self.features(input)
        x = self.logits(x)
        return x

@META_ARCH_REGISTRY.register()
class DPN26(DPN):
    def __init__(self, cfg):
        num_classes = cfg.model.classes
        net_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)
        }
        super(DPN26, self).__init__(net_cfg, num_classes)

@META_ARCH_REGISTRY.register()
class DPN92(DPN):
    def __init__(self, cfg):
        num_classes = cfg.model.classes
        net_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)
        }
        super(DPN92, self).__init__(net_cfg, num_classes)
