old_contents: """
  Utilize vgg_fcn8 as encoder.
  ------------------------
  
  The MIT License (MIT)
  
  Copyright (c) 2017 Marvin Teichmann
  
  Details: https://github.com/MarvinTeichmann/KittiSeg/blob/master/LICENSE
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow_fcn import fcn8_vgg
  
  import tensorflow as tf
  
  import os
  
  
  def inference(hypes, images, train=True):
      """Build the MNIST model up to where it may be used for inference.
  
      Args:
        images: Images placeholder, from inputs().
        train: whether the network is used for train of inference
  
      Returns:
        softmax_linear: Output tensor with the computed logits.
      """
      vgg16_npy_path = os.path.join(hypes['dirs']['data_dir'], 'weights',
                                    "vgg16.npy")
      vgg_fcn = fcn8_vgg.FCN8VGG(vgg16_npy_path=vgg16_npy_path)
  
      vgg_fcn.wd = hypes['wd']
  
      vgg_fcn.build(images, train=train, num_classes=2, random_init_fc8=True)
  
      logits = {}
  
      logits['images'] = images
  
      if hypes['arch']['fcn_in'] == 'pool5':
          logits['fcn_in'] = vgg_fcn.pool5
      elif hypes['arch']['fcn_in'] == 'fc7':
          logits['fcn_in'] = vgg_fcn.fc7
      else:
          raise NotImplementedError
  
      logits['feed2'] = vgg_fcn.pool4
      logits['feed4'] = vgg_fcn.pool3
  
      logits['feed4'] = vgg_fcn.pool3
  
      logits['fcn_logits'] = vgg_fcn.upscore32
  
      logits['deep_feat'] = vgg_fcn.pool5
      logits['early_feat'] = vgg_fcn.conv4_3
  
      return logits
  
new_contents: """
  Utilize vgg_fcn8 as encoder.
  ------------------------
  
  The MIT License (MIT)
  
  Copyright (c) 2017 Marvin Teichmann
  
  Details: https://github.com/MarvinTeichmann/KittiSeg/blob/master/LICENSE
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow_fcn import fcn8_vgg
  
  import tensorflow as tf
  
  import os
  
  
  def inference(hypes, images, train=True):
      """Build the MNIST model up to where it may be used for inference.
  
      Args:
        images: Images placeholder, from inputs().
        train: whether the network is used for train of inference
  
      Returns:
        softmax_linear: Output tensor with the computed logits.
      """
      vgg16_npy_path = os.path.join(hypes['dirs']['data_dir'], 'weights',
                                    "vgg16.npy")
      vgg_fcn = fcn8_vgg.FCN8VGG(vgg16_npy_path=vgg16_npy_path)
  
      vgg_fcn.wd = hypes['wd']
  
      vgg_fcn.build(images, train=train, num_classes=2, random_init_fc8=True)
  
      logits = {}
  
      logits['images'] = images
  
      if hypes['arch']['fcn_in'] == 'pool5':
          logits['fcn_in'] = vgg_fcn.pool5
      elif hypes['arch']['fcn_in'] == 'fc7':
          logits['fcn_in'] = vgg_fcn.fc7
      else:
          raise NotImplementedError
  
      logits['feed2'] = vgg_fcn.pool4
      logits['feed4'] = vgg_fcn.pool3
  
      logits['fcn_logits'] = vgg_fcn.upscore32
  
      logits['deep_feat'] = vgg_fcn.pool5
      logits['early_feat'] = vgg_fcn.conv4_3
  
      return logits
  
subject: Update fcn8_vgg.py (#62)