# Source: https://github.com/kenshohara/3D-ResNets-PyTorch/blob/master/models/densenet.py
# Paper Ref: https://arxiv.org/abs/2004.04968

from collections import OrderedDict

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

__author__ = "Soumick Chatterjee"
__copyright__ = "Copyright 2022, Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany"
__credits__ = ["Soumick Chatterjee"]
__license__ = "GPL"
__version__ = "1.0.0"
__maintainer__ = "Soumick Chatterjee"
__email__ = "soumick.chatterjee@ovgu.de"
__status__ = "Untested"


class _DenseLayer(nn.Sequential):

    def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
        super().__init__()
        self.add_module('norm1', nn.BatchNorm3d(num_input_features))
        self.add_module('relu1', nn.ReLU(inplace=True))
        self.add_module(
            'conv1',
            nn.Conv3d(num_input_features,
                      bn_size * growth_rate,
                      kernel_size=1,
                      stride=1,
                      bias=False))
        self.add_module('norm2', nn.BatchNorm3d(bn_size * growth_rate))
        self.add_module('relu2', nn.ReLU(inplace=True))
        self.add_module(
            'conv2',
            nn.Conv3d(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().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().__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{}'.format(i + 1), layer)


class _Transition(nn.Sequential):

    def __init__(self, num_input_features, num_output_features, no_pool=True):
        super().__init__()
        self.add_module('norm', nn.BatchNorm3d(num_input_features))
        self.add_module('relu', nn.ReLU(inplace=True))
        self.add_module(
            'conv',
            nn.Conv3d(num_input_features,
                      num_output_features,
                      kernel_size=1,
                      stride=1,
                      bias=False))
        if not no_pool:
            self.add_module('pool', nn.AvgPool3d(kernel_size=2, stride=2))


class DenseNet(nn.Module):
    """Densenet-BC model class
    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,
                 n_input_channels=3,
                 conv1_kernel=7,
                 conv1_stride=1,
                 no_pool=True,
                 growth_rate=32,
                 block_config=(6, 12, 24, 16),
                 num_init_features=64,
                 bn_size=4,
                 drop_rate=0,
                 num_classes=1000):

        super().__init__()

        # First convolution
        self.model = [('conv1',
                          nn.Conv3d(n_input_channels,
                                    num_init_features,
                                    kernel_size=conv1_kernel,
                                    stride=conv1_stride,
                                    padding=conv1_kernel // 2,
                                    bias=False)),
                         ('norm1', nn.BatchNorm3d(num_init_features)),
                         ('relu1', nn.ReLU(inplace=True))]
        if not no_pool:
            self.model.append(
                ('pool1', nn.MaxPool3d(kernel_size=3, stride=2, padding=1)))
        self.model = nn.Sequential(OrderedDict(self.model))

        # 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.model.add_module('denseblock{}'.format(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, no_pool=no_pool)
                self.model.add_module('transition{}'.format(i + 1), trans)
                num_features = num_features // 2

        # Final batch norm
        self.model.add_module('norm5', nn.BatchNorm3d(num_features))
    
        # Final fully connected layer
        self.model.add_module('finconv', nn.Conv3d(num_features, num_classes, kernel_size=1, stride=1, padding=0))

        for m in self.modules():
            if isinstance(m, nn.Conv3d):
                m.weight = nn.init.kaiming_normal_(m.weight, mode='fan_out')
            elif isinstance(m, nn.BatchNorm3d) or isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

        for m in self.modules():
            if isinstance(m, nn.Conv3d):
                nn.init.kaiming_normal_(m.weight,
                                        mode='fan_out',
                                        nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm3d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def forward(self, x):
        return self.model(x)


def generate_model(model_depth, **kwargs):
    assert model_depth in [121, 169, 201, 264]

    if model_depth == 121:
        model = DenseNet(num_init_features=64,
                         growth_rate=32,
                         block_config=(6, 12, 24, 16),
                         **kwargs)
    elif model_depth == 169:
        model = DenseNet(num_init_features=64,
                         growth_rate=32,
                         block_config=(6, 12, 32, 32),
                         **kwargs)
    elif model_depth == 201:
        model = DenseNet(num_init_features=64,
                         growth_rate=32,
                         block_config=(6, 12, 48, 32),
                         **kwargs)
    elif model_depth == 264:
        model = DenseNet(num_init_features=64,
                         growth_rate=32,
                         block_config=(6, 12, 64, 48),
                         **kwargs)

    return model
