import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from collections import OrderedDict

__all__ = ['DenseNet', 'densenet121_ibn_a', 'densenet169_ibn_a', 'densenet201_ibn_a', 'densenet161_ibn_a']


model_urls = {
    'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth',
    'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth',
    'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth',
    'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth',
}

class BNIN(nn.Module):
    def __init__(self, planes):
        super(BNIN, self).__init__()
        half1 = int(planes*0.6)
        self.half = half1
        half2 = planes - half1
        self.BN = nn.BatchNorm2d(half1)
        self.IN = nn.InstanceNorm2d(half2, affine=True)
    
    def forward(self, x):
        split = torch.split(x, self.half, 1)
        out1 = self.BN(split[0].contiguous())
        out2 = self.IN(split[1].contiguous())
        out = torch.cat((out1, out2), 1)
        return out

def densenet121_ibn_a(pretrained=False, **kwargs):
    r"""Densenet-121 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16),
                     **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['densenet121']))
    return model


def densenet169_ibn_a(pretrained=True, **kwargs):
    r"""Densenet-169 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32),
                     **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['densenet169']))
    return model


def densenet201_ibn_a(pretrained=False, **kwargs):
    r"""Densenet-201 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32),
                     **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['densenet201']))
    return model


def densenet161_ibn_a(pretrained=False, **kwargs):
    r"""Densenet-161 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = DenseNet(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24),
                     **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['densenet161']))
    return model


class _DenseLayer(nn.Sequential):
    def __init__(self, num_input_features, growth_rate, bn_size, drop_rate, ibn):
        super(_DenseLayer, self).__init__()
        if ibn:
            self.add_module('norm.1', BNIN(num_input_features)),
        else:
            self.add_module('norm.1', nn.BatchNorm2d(num_input_features)),
        self.add_module('relu.1', nn.ReLU(inplace=True)),
        self.add_module('conv.1', nn.Conv2d(num_input_features, bn_size *
                        growth_rate, kernel_size=1, stride=1, bias=False)),
        self.add_module('norm.2', nn.BatchNorm2d(bn_size * growth_rate)),
        self.add_module('relu.2', nn.ReLU(inplace=True)),
        self.add_module('conv.2', nn.Conv2d(bn_size * growth_rate, growth_rate,
                        kernel_size=3, stride=1, padding=1, bias=False)),
        self.drop_rate = drop_rate

    def forward(self, x):
        new_features = super(_DenseLayer, self).forward(x)
        if self.drop_rate > 0:
            new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
        return torch.cat([x, new_features], 1)


class _DenseBlock(nn.Sequential):
    def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate, ibn):
        super(_DenseBlock, self).__init__()
        for i in range(num_layers):
            if ibn and i % 3 == 0:
                layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate, True)
            else:
                layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate, False)
            self.add_module('denselayer%d' % (i + 1), layer)


class _Transition(nn.Sequential):
    def __init__(self, num_input_features, num_output_features):
        super(_Transition, self).__init__()
        self.add_module('norm', nn.BatchNorm2d(num_input_features))
        self.add_module('relu', nn.ReLU(inplace=True))
        self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,
                                          kernel_size=1, stride=1, bias=False))
        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))


class DenseNet(nn.Module):
    r"""Densenet-BC model class, based on
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
    Args:
        growth_rate (int) - how many filters to add each layer (`k` in paper)
        block_config (list of 4 ints) - how many layers in each pooling block
        num_init_features (int) - the number of filters to learn in the first convolution layer
        bn_size (int) - multiplicative factor for number of bottle neck layers
          (i.e. bn_size * k features in the bottleneck layer)
        drop_rate (float) - dropout rate after each dense layer
        num_classes (int) - number of classification classes
    """
    def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
                 num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000,num_features=2048):

        super(DenseNet, self).__init__()
        self.num_classes = num_classes
        self.num_features = num_features
        self.avgpool = nn.AvgPool2d(7)


        # First convolution
        self.features = nn.Sequential(OrderedDict([
            ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
            ('norm0', nn.BatchNorm2d(num_init_features)),
            ('relu0', nn.ReLU(inplace=True)),
            ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
        ]))

        # Each denseblock
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):
            ibn = True
            if i >= 3:
                ibn = False
            block = _DenseBlock(num_layers=num_layers, num_input_features=num_features,
                                bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate, ibn=ibn)
            self.features.add_module('denseblock%d' % (i + 1), block)
            num_features = num_features + num_layers * growth_rate
            if i != len(block_config) - 1:
                trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2)
                self.features.add_module('transition%d' % (i + 1), trans)
                num_features = num_features // 2

        # Final batch norm
        self.features.add_module('norm5', nn.BatchNorm2d(num_features))

################ copy from se_resnet152
        self.dropout = nn.Dropout()
        
        self.dim_red_conv = nn.Conv2d(1664, self.num_features, 1, bias=False)
        #nn.init.kaiming_normal_(self.dim_red_conv.weight.data, mode='fan_out')

        self.dim_red_bn = nn.BatchNorm2d(self.num_features)
        self.dim_red_bn.weight.data.fill_(1)
        self.dim_red_bn.bias.data.zero_()
##################
        # self.fc = nn.Linear(self.num_features, self.num_classes, True)

        #nn.init.normal_(self.fc.weight, std=0.001)
        #nn.init.constant_(self.fc.bias, 0)


        # Linear layer
        self.classifier = nn.Linear(self.num_features, self.num_classes, True)

    def forward(self, x):
        features = self.features(x)
        out = F.relu(features, inplace=True)
        # out = F.avg_pool2d(out, kernel_size=7, stride=1).view(features.size(0), -1)

        x = self.avgpool(out)

        x = self.dropout(x)
        x = self.dim_red_conv(x)

        if self.training:
            # x_g = x
            # x = self.dim_red_bn(x)
            x = self.dim_red_bn(x)
            x_g = x
            x = x.contiguous().view(-1, self.num_features)
            x = self.classifier(x)
            return x, x_g
        else:
            x = x.div(x.norm(2, 1, keepdim=True).add(1e-8).expand_as(x))
            x = x.view(x.size(0), -1)
            return x

        # out = self.classifier(out)
        # return out