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', 'densenet169']

model_urls = {

    'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth',

}

def densenet169(pretrained=False, **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']), strict=False)

    return model

class _DenseLayer(nn.Sequential):

    def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):

        super(_DenseLayer, self).__init__()

        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),

        self.add_module('relu1', nn.ReLU(inplace=True)),

        self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *

                        growth_rate, kernel_size=1, stride=1, bias=False)),

        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),

        self.add_module('relu2', nn.ReLU(inplace=True)),

        self.add_module('conv2', 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):

        super(_DenseBlock, self).__init__()

        for i in range(num_layers):

            layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate)

            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):

        super(DenseNet, self).__init__()

        # 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):

            block = _DenseBlock(num_layers=num_layers, num_input_features=num_features,

                                bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)

            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))

        # Linear layer

        # self.classifier = nn.Linear(num_features, 1000)

        # self.fc = nn.Linear(1000, 1)

        self.fc = nn.Linear(num_features, 1)

        # Official init from torch repo.

        for m in self.modules():

            if isinstance(m, nn.Conv2d):

                nn.init.kaiming_normal(m.weight.data)

            elif isinstance(m, nn.BatchNorm2d):

                m.weight.data.fill_(1)

                m.bias.data.zero_()

            elif isinstance(m, nn.Linear):

                m.bias.data.zero_()

    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)

        # out = F.relu(self.classifier(out))

        out = F.sigmoid(self.fc(out))

        return out