text: #! /usr/bin/python
  # -*- coding: utf-8 -*-
  """
  VGG for ImageNet.
  
  Introduction
  ----------------
  VGG is a convolutional neural network model proposed by K. Simonyan and A. Zisserman
  from the University of Oxford in the paper "Very Deep Convolutional Networks for
  Large-Scale Image Recognition"  . The model achieves 92.7% top-5 test accuracy in ImageNet,
  which is a dataset of over 14 million images belonging to 1000 classes.
  
  Download Pre-trained Model
  ----------------------------
  - Model weights in this example - vgg16_weights.npz : http://www.cs.toronto.edu/~frossard/post/vgg16/
  - Model weights in this example - vgg19.npy : https://media.githubusercontent.com/media/tensorlayer/pretrained-models/master/models/
  - Caffe VGG 16 model : https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md
  - Tool to convert the Caffe model to TensorFlow's : https://github.com/ethereon/caffe-tensorflow
  
  Note
  ------
  - For simplified CNN layer see "Convolutional layer (Simplified)"
  in read the docs website.
  - When feeding other images to the model be sure to properly resize or crop them
  beforehand. Distorted images might end up being misclassified. One way of safely
  feeding images of multiple sizes is by doing center cropping.
  
  """
  
  import os
  
  import numpy as np
  
  import tensorlayerx as tlx
  from tensorlayerx import logging
  from tensorlayerx.files import assign_weights, maybe_download_and_extract
  from tensorlayerx.nn import (BatchNorm, Conv2d, Linear, Flatten, Sequential, MaxPool2d)
  from tensorlayerx.nn import Module
  
  __all__ = [
      'VGG',
      'vgg16',
      'vgg19',
      'VGG16',
      'VGG19',
      #    'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
      #    'vgg19_bn', 'vgg19',
  ]
  
  layer_names = [
      ['conv1_1', 'conv1_2'], 'pool1', ['conv2_1', 'conv2_2'], 'pool2',
      ['conv3_1', 'conv3_2', 'conv3_3', 'conv3_4'], 'pool3', ['conv4_1', 'conv4_2', 'conv4_3', 'conv4_4'], 'pool4',
      ['conv5_1', 'conv5_2', 'conv5_3', 'conv5_4'], 'pool5', 'flatten', 'fc1_relu', 'fc2_relu', 'outputs'
  ]
  
  cfg = {
      'A': [[64], 'M', [128], 'M', [256, 256], 'M', [512, 512], 'M', [512, 512], 'M', 'F', 'fc1', 'fc2', 'O'],
      'B': [[64, 64], 'M', [128, 128], 'M', [256, 256], 'M', [512, 512], 'M', [512, 512], 'M', 'F', 'fc1', 'fc2', 'O'],
      'D':
          [
              [64, 64], 'M', [128, 128], 'M', [256, 256, 256], 'M', [512, 512, 512], 'M', [512, 512, 512], 'M', 'F',
              'fc1', 'fc2', 'O'
          ],
      'E':
          [
              [64, 64], 'M', [128, 128], 'M', [256, 256, 256, 256], 'M', [512, 512, 512, 512], 'M', [512, 512, 512, 512],
              'M', 'F', 'fc1', 'fc2', 'O'
          ],
  }
  
  mapped_cfg = {
      'vgg11': 'A',
      'vgg11_bn': 'A',
      'vgg13': 'B',
      'vgg13_bn': 'B',
      'vgg16': 'D',
      'vgg16_bn': 'D',
      'vgg19': 'E',
      'vgg19_bn': 'E'
  }
  
  model_urls = {
      'vgg16': 'http://www.cs.toronto.edu/~frossard/vgg16/',
      'vgg19': 'https://media.githubusercontent.com/media/tensorlayer/pretrained-models/master/models/'
  }
  
  model_saved_name = {'vgg16': 'vgg16_weights.npz', 'vgg19': 'vgg19.npy'}
  
  
  class VGG(Module):
  
      def __init__(self, layer_type, batch_norm=False, end_with='outputs', name=None):
          super(VGG, self).__init__(name=name)
          self.end_with = end_with
  
          config = cfg[mapped_cfg[layer_type]]
          self.make_layer = make_layers(config, batch_norm, end_with)
  
      def forward(self, inputs):
          """
          inputs : tensor
              Shape [None, 224, 224, 3], value range [0, 1].
          """
  
          inputs = inputs * 255 - tlx.convert_to_tensor(np.array([123.68, 116.779, 103.939], dtype=np.float32).reshape([1, 1, 1, 3]))
          out = self.make_layer(inputs)
          return out
  
  
  def make_layers(config, batch_norm=False, end_with='outputs'):
      layer_list = []
      is_end = False
      for layer_group_idx, layer_group in enumerate(config):
          if isinstance(layer_group, list):
              for idx, layer in enumerate(layer_group):
                  layer_name = layer_names[layer_group_idx][idx]
                  n_filter = layer
                  if idx == 0:
                      if layer_group_idx > 0:
                          in_channels = config[layer_group_idx - 2][-1]
                      else:
                          in_channels = 3
                  else:
                      in_channels = layer_group[idx - 1]
                  layer_list.append(
                      Conv2d(
                          out_channels=n_filter, kernel_size=(3, 3), stride=(1, 1), act=tlx.ReLU, padding='SAME',
                          in_channels=in_channels, name=layer_name
                      )
                  )
                  if batch_norm:
                      layer_list.append(BatchNorm(num_features=n_filter))
                  if layer_name == end_with:
                      is_end = True
                      break
          else:
              layer_name = layer_names[layer_group_idx]
              if layer_group == 'M':
                  layer_list.append(MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding='SAME', name=layer_name))
              elif layer_group == 'O':
                  layer_list.append(Linear(out_features=1000, in_features=4096, name=layer_name))
              elif layer_group == 'F':
                  layer_list.append(Flatten(name='flatten'))
              elif layer_group == 'fc1':
                  layer_list.append(Linear(out_features=4096, act=tlx.ReLU, in_features=512 * 7 * 7, name=layer_name))
              elif layer_group == 'fc2':
                  layer_list.append(Linear(out_features=4096, act=tlx.ReLU, in_features=4096, name=layer_name))
              if layer_name == end_with:
                  is_end = True
          if is_end:
              break
      return Sequential(layer_list)
  
  
  def restore_model(model, layer_type):
      logging.info("Restore pre-trained weights")
      # download weights
      maybe_download_and_extract(model_saved_name[layer_type], 'model', model_urls[layer_type])
      weights = []
      if layer_type == 'vgg16':
          npz = np.load(os.path.join('model', model_saved_name[layer_type]), allow_pickle=True)
          # get weight list
          for val in sorted(npz.items()):
              logging.info("  Loading weights %s in %s" % (str(val[1].shape), val[0]))
              weights.append(val[1])
              if len(model.all_weights) == len(weights):
                  break
      elif layer_type == 'vgg19':
          npz = np.load(os.path.join('model', model_saved_name[layer_type]), allow_pickle=True, encoding='latin1').item()
          # get weight list
          for val in sorted(npz.items()):
              logging.info("  Loading %s in %s" % (str(val[1][0].shape), val[0]))
              logging.info("  Loading %s in %s" % (str(val[1][1].shape), val[0]))
              weights.extend(val[1])
              if len(model.all_weights) == len(weights):
                  break
      # assign weight values
      assign_weights(weights, model)
      del weights
  
  
  def vgg16(pretrained=False, end_with='outputs', mode='dynamic', name=None):
      """Pre-trained VGG16 model.
  
      Parameters
      ------------
      pretrained : boolean
          Whether to load pretrained weights. Default False.
      end_with : str
          The end point of the model. Default ``fc3_relu`` i.e. the whole model.
      mode : str.
          Model building mode, 'dynamic' or 'static'. Default 'dynamic'.
      name : None or str
          A unique layer name.
  
      Examples
      ---------
      Classify ImageNet classes with VGG16, see `tutorial_models_vgg.py <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_models_vgg.py>`__
      With TensorLayer
      TODO Modify the usage example according to the model storage location
  
      >>> # get the whole model, without pre-trained VGG parameters
      >>> vgg = vgg16()
      >>> # get the whole model, restore pre-trained VGG parameters
      >>> vgg = vgg16(pretrained=True)
      >>> # use for inferencing
      >>> output = vgg(img)
      >>> probs = tlx.softmax(output)[0].numpy()
  
      """
  
      if mode == 'dynamic':
          model = VGG(layer_type='vgg16', batch_norm=False, end_with=end_with, name=name)
      elif mode == 'static':
          raise NotImplementedError
      else:
          raise Exception("No such mode %s" % mode)
      if pretrained:
          restore_model(model, layer_type='vgg16')
      return model
  
  
  def vgg19(pretrained=False, end_with='outputs', mode='dynamic', name=None):
      """Pre-trained VGG19 model.
  
      Parameters
      ------------
      pretrained : boolean
          Whether to load pretrained weights. Default False.
      end_with : str
          The end point of the model. Default ``fc3_relu`` i.e. the whole model.
      mode : str.
          Model building mode, 'dynamic' or 'static'. Default 'dynamic'.
      name : None or str
          A unique layer name.
  
      Examples
      ---------
      Classify ImageNet classes with VGG19, see `tutorial_models_vgg.py <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_models_vgg.py>`__
      With TensorLayer
  
      >>> # get the whole model, without pre-trained VGG parameters
      >>> vgg = vgg19()
      >>> # get the whole model, restore pre-trained VGG parameters
      >>> vgg = vgg19(pretrained=True)
      >>> # use for inferencing
      >>> output = vgg(img)
      >>> probs = tlx.softmax(output)[0].numpy()
  
      """
      if mode == 'dynamic':
          model = VGG(layer_type='vgg19', batch_norm=False, end_with=end_with, name=name)
      elif mode == 'static':
          raise NotImplementedError
      else:
          raise Exception("No such mode %s" % mode)
      if pretrained:
          restore_model(model, layer_type='vgg19')
      return model
  
  
  VGG16 = vgg16
  VGG19 = vgg19
  
  # model without pretrained parameters
  # def vgg11(pretrained=False, end_with='outputs'):
  #     model = VGG(layer_type='vgg11', batch_norm=False, end_with=end_with)
  #     if pretrained:
  #         model.restore_weights()
  #     return model
  #
  #
  # def vgg11_bn(pretrained=False, end_with='outputs'):
  #     model = VGG(layer_type='vgg11_bn', batch_norm=True, end_with=end_with)
  #     if pretrained:
  #         model.restore_weights()
  #     return model
  #
  #
  # def vgg13(pretrained=False, end_with='outputs'):
  #     model = VGG(layer_type='vgg13', batch_norm=False, end_with=end_with)
  #     if pretrained:
  #         model.restore_weights()
  #     return model
  #
  #
  # def vgg13_bn(pretrained=False, end_with='outputs'):
  #     model = VGG(layer_type='vgg13_bn', batch_norm=True, end_with=end_with)
  #     if pretrained:
  #         model.restore_weights()
  #     return model
  #
  #
  # def vgg16_bn(pretrained=False, end_with='outputs'):
  #     model = VGG(layer_type='vgg16_bn', batch_norm=True, end_with=end_with)
  #     if pretrained:
  #         model.restore_weights()
  #     return model
  #
  #
  # def vgg19_bn(pretrained=False, end_with='outputs'):
  #     model = VGG(layer_type='vgg19_bn', batch_norm=True, end_with=end_with)
  #     if pretrained:
  #         model.restore_weights()
  #     return model
  