# Copyright 2018 The KaiJIN Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""SqueezeNet form torchvision.model """
from collections import OrderedDict
import re
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.hub import load_state_dict_from_url

import torchvision
torchvision.models.densenet


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 _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):
  """Densenet-BC model class, based on
  `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_

  Arguments:
      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
  """

  MEAN = [0.485, 0.456, 0.406]
  STD = [0.229, 0.224, 0.225]
  SIZE = [224, 224]
  SCALE = 255
  CROP = 0.875

  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, num_classes)

    # Official init from torch repo.
    for m in self.modules():
      if isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight)
      elif isinstance(m, nn.BatchNorm2d):
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)
      elif isinstance(m, nn.Linear):
        nn.init.constant_(m.bias, 0)

  def forward(self, x):
    features = self.features(x)
    out = F.relu(features, inplace=True)
    out = F.adaptive_avg_pool2d(out, (1, 1)).view(features.size(0), -1)
    out = self.classifier(out)
    return out


def _load_state_dict(model, model_url, progress):
  # '.'s are no longer allowed in module names, but previous _DenseLayer
  # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
  # They are also in the checkpoints in model_urls. This pattern is used
  # to find such keys.
  pattern = re.compile(
      r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
  state_dict = load_state_dict_from_url(model_url, model_dir='_models/', progress=progress)
  for key in list(state_dict.keys()):
    res = pattern.match(key)
    if res:
      new_key = res.group(1) + res.group(2)
      state_dict[new_key] = state_dict[key]
      del state_dict[key]
  model.load_state_dict(state_dict)


def _densenet(arch, growth_rate, block_config, num_init_features, **kwargs):
  model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
  return model


def densenet121(pretrained=False, **kwargs):
  model = _densenet('densenet121', 32, (6, 12, 24, 16), 64, **kwargs)
  if pretrained:
    _load_state_dict(model, model_urls['densenet121'], progress=True)
  return model


def densenet161(pretrained=False, **kwargs):
  model = _densenet('densenet161', 48, (6, 12, 36, 24), 96, **kwargs)
  if pretrained:
    _load_state_dict(model, model_urls['densenet161'], progress=True)
  return model


def densenet169(pretrained=False, **kwargs):
  model = _densenet('densenet169', 32, (6, 12, 32, 32), 64, **kwargs)
  if pretrained:
    _load_state_dict(model, model_urls['densenet169'], progress=True)
  return model


def densenet201(pretrained=False, **kwargs):
  model = _densenet('densenet201', 32, (6, 12, 48, 32), 64, **kwargs)
  if pretrained:
    _load_state_dict(model, model_urls['densenet201'], progress=True)
  return model
