# 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.
# ==============================================================================
import os
import torch
from collections import OrderedDict
from torch import nn as nn
from torchvision.models import vgg as vgg

VGG_PRETRAIN_PATH = '_models/vgg19-dcbb9e9d.pth'

NAMES = {
    'vgg11': [
        'conv1_1', 'relu1_1', 'pool1', 'conv2_1', 'relu2_1', 'pool2',
        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'pool3', 'conv4_1',
        'relu4_1', 'conv4_2', 'relu4_2', 'pool4', 'conv5_1', 'relu5_1',
        'conv5_2', 'relu5_2', 'pool5'
    ],
    'vgg13': [
        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1',
        'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1',
        'conv3_2', 'relu3_2', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2',
        'relu4_2', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'pool5'
    ],
    'vgg16': [
        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1',
        'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1',
        'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1',
        'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'pool4',
        'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3',
        'pool5'
    ],
    'vgg19': [
        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1',
        'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1',
        'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4',
        'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
        'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 'conv5_1', 'relu5_1',
        'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4',
        'pool5'
    ]
}


def insert_bn(names):
  """Insert bn layer after each conv.

  Args:
      names (list): The list of layer names.

  Returns:
      list: The list of layer names with bn layers.
  """
  names_bn = []
  for name in names:
    names_bn.append(name)
    if 'conv' in name:
      position = name.replace('conv', '')
      names_bn.append('bn' + position)
  return names_bn


class VGGFeatureExtractor(nn.Module):
  """VGG network for feature extraction.

  In this implementation, we allow users to choose whether use normalization
  in the input feature and the type of vgg network. Note that the pretrained
  path must fit the vgg type.

  Args:
      layer_name_list (list[str]): Forward function returns the corresponding
          features according to the layer_name_list.
          Example: {'relu1_1', 'relu2_1', 'relu3_1'}.
      vgg_type (str): Set the type of vgg network. Default: 'vgg19'.
      use_input_norm (bool): If True, normalize the input image. Importantly,
          the input feature must in the range [0, 1]. Default: True.
      range_norm (bool): If True, norm images with range [-1, 1] to [0, 1].
          Default: False.
      requires_grad (bool): If true, the parameters of VGG network will be
          optimized. Default: False.
      remove_pooling (bool): If true, the max pooling operations in VGG net
          will be removed. Default: False.
      pooling_stride (int): The stride of max pooling operation. Default: 2.
  """

  def __init__(self,
               layer_name_list,
               vgg_type='vgg19',
               use_input_norm=True,
               range_norm=False,
               requires_grad=False,
               remove_pooling=False,
               pooling_stride=2):
    super(VGGFeatureExtractor, self).__init__()

    self.layer_name_list = layer_name_list
    self.use_input_norm = use_input_norm
    self.range_norm = range_norm

    self.names = NAMES[vgg_type.replace('_bn', '')]
    if 'bn' in vgg_type:
      self.names = insert_bn(self.names)

    # only borrow layers that will be used to avoid unused params
    max_idx = 0
    for v in layer_name_list:
      idx = self.names.index(v)
      if idx > max_idx:
        max_idx = idx

    if os.path.exists(VGG_PRETRAIN_PATH):
      vgg_net = getattr(vgg, vgg_type)(pretrained=False)
      state_dict = torch.load(VGG_PRETRAIN_PATH, map_location=lambda storage, loc: storage)
      vgg_net.load_state_dict(state_dict)
    else:
      vgg_net = getattr(vgg, vgg_type)(pretrained=True)

    features = vgg_net.features[:max_idx + 1]

    modified_net = OrderedDict()
    for k, v in zip(self.names, features):
      if 'pool' in k:
        # if remove_pooling is true, pooling operation will be removed
        if remove_pooling:
          continue
        else:
          # in some cases, we may want to change the default stride
          modified_net[k] = nn.MaxPool2d(
              kernel_size=2, stride=pooling_stride)
      else:
        modified_net[k] = v

    self.vgg_net = nn.Sequential(modified_net)

    if not requires_grad:
      self.vgg_net.eval()
      for param in self.parameters():
        param.requires_grad = False
    else:
      self.vgg_net.train()
      for param in self.parameters():
        param.requires_grad = True

    if self.use_input_norm:
      # the mean is for image with range [0, 1]
      self.register_buffer(
          'mean',
          torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
      # the std is for image with range [0, 1]
      self.register_buffer(
          'std',
          torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))

  def forward(self, x):
    """Forward function.

    Args:
        x (Tensor): Input tensor with shape (n, c, h, w).

    Returns:
        Tensor: Forward results.
    """
    if self.range_norm:
      x = (x + 1) / 2
    if self.use_input_norm:
      x = (x - self.mean) / self.std

    output = {}
    for key, layer in self.vgg_net._modules.items():
      x = layer(x)
      if key in self.layer_name_list:
        output[key] = x.clone()

    return output
