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'], "vgg16.npy")
    vgg_fcn = fcn8_vgg.FCN8VGG(vgg16_npy_path=vgg16_npy_path)

    num_classes = 2  # does not influence training what so ever
    vgg_fcn.wd = hypes['wd']

    vgg_fcn.build(images, train=train, num_classes=num_classes,
                  random_init_fc8=True)

    if hypes['arch']['deep_feat'] == "pool5":
        deep_feat = vgg_fcn.pool5
    elif hypes['arch']['deep_feat'] == "fc7":
        deep_feat = vgg_fcn.fc7
    else:
        raise NotImplementedError

    vgg_dict = {'deep_feat': deep_feat,
                'early_feat': vgg_fcn.conv4_3}

    return vgg_dict

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'], "vgg16.npy")
    vgg_fcn = fcn8_vgg.FCN8VGG(vgg16_npy_path=vgg16_npy_path)

    '''
    num_classes does not influence training when KittiBox is used alone.
    However, if you wish to use MultiNet with custom submodules, 
    e.g., if KittiSeg is customized for != 2 classes, this value must
    reflect num_classes in KittiSeg, since MultiNet will try to share 
    variable score_fr/weights
    ''' 
    num_classes = 2  
    vgg_fcn.wd = hypes['wd']

    vgg_fcn.build(images, train=train, num_classes=num_classes,
                  random_init_fc8=True)

    if hypes['arch']['deep_feat'] == "pool5":
        deep_feat = vgg_fcn.pool5
    elif hypes['arch']['deep_feat'] == "fc7":
        deep_feat = vgg_fcn.fc7
    else:
        raise NotImplementedError

    vgg_dict = {'deep_feat': deep_feat,
                'early_feat': vgg_fcn.conv4_3}

    return vgg_dict

Edit vgg.py comment (#36)