tf 1.0 changes tf.pack -> tf.stack

[ahundt]

tf.pack should become tf.stack. There are a number of other changes that need to be applied for tf 1.0.

https://github.com/tensorflow/tensorflow/releases

[warmspringwinds]

@ahundt, I guess I will create a separate branch since tf 1.0 is not backwards compatible.

[ahundt]

the changes were quite minor

[vijtad]

I am getting following error after changing tf.stack when I am running fcn_32s_train.ipynb

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-2-f9a75342d227> in <module>()
     68 upsampled_logits_batch, vgg_16_variables_mapping = FCN_32s(image_batch_tensor=image_batch,
     69                                                            number_of_classes=number_of_classes,
---> 70                                                            is_training=True)
     71 
     72 

/notebooks/tf-image-segmentation/tf_image_segmentation/models/fcn_32s.pyc in FCN_32s(image_batch_tensor, number_of_classes, is_training)
    110                                             is_training=is_training,
    111                                             spatial_squeeze=False,
--> 112                                             fc_conv_padding='SAME')
    113 
    114         downsampled_logits_shape = tf.shape(logits)

/notebooks/models/slim/nets/vgg.pyc in vgg_16(inputs, num_classes, is_training, dropout_keep_prob, spatial_squeeze, scope, fc_conv_padding)
    164     with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
    165                         outputs_collections=end_points_collection):
--> 166       net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
    167       net = slim.max_pool2d(net, [2, 2], scope='pool1')
    168       net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')

/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/layers.pyc in repeat(inputs, repetitions, layer, *args, **kwargs)
   1718     for i in range(repetitions):
   1719       kwargs['scope'] = scope + '_' + str(i+1)
-> 1720       outputs = layer(outputs, *args, **kwargs)
   1721     return outputs
   1722 

/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.pyc in func_with_args(*args, **kwargs)
    175       current_args = current_scope[key_func].copy()
    176       current_args.update(kwargs)
--> 177     return func(*args, **current_args)
    178   _add_op(func)
    179   setattr(func_with_args, '_key_op', _key_op(func))

/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/layers.pyc in convolution(inputs, num_outputs, kernel_size, stride, padding, data_format, rate, activation_fn, normalizer_fn, normalizer_params, weights_initializer, weights_regularizer, biases_initializer, biases_regularizer, reuse, variables_collections, outputs_collections, trainable, scope)
    905                         _scope=sc,
    906                         _reuse=reuse)
--> 907     outputs = layer.apply(inputs)
    908 
    909     # Add variables to collections.

/usr/local/lib/python2.7/dist-packages/tensorflow/python/layers/base.pyc in apply(self, inputs, **kwargs)
    301       Output tensor(s).
    302     """
--> 303     return self.__call__(inputs, **kwargs)
    304 
    305 

/usr/local/lib/python2.7/dist-packages/tensorflow/python/layers/base.pyc in __call__(self, inputs, **kwargs)
    267           input_shapes = [x.get_shape() for x in input_list]
    268           if len(input_shapes) == 1:
--> 269             self.build(input_shapes[0])
    270           else:
    271             self.build(input_shapes)

/usr/local/lib/python2.7/dist-packages/tensorflow/python/layers/convolutional.pyc in build(self, input_shape)
    143                                   regularizer=self.bias_regularizer,
    144                                   trainable=True,
--> 145                                   dtype=self.dtype)
    146     else:
    147       self.bias = None

/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.pyc in get_variable(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, custom_getter)
    986       collections=collections, caching_device=caching_device,
    987       partitioner=partitioner, validate_shape=validate_shape,
--> 988       custom_getter=custom_getter)
    989 get_variable_or_local_docstring = (
    990     """%s

/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.pyc in get_variable(self, var_store, name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, custom_getter)
    888           collections=collections, caching_device=caching_device,
    889           partitioner=partitioner, validate_shape=validate_shape,
--> 890           custom_getter=custom_getter)
    891 
    892   def _get_partitioned_variable(self,

/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.pyc in get_variable(self, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, custom_getter)
    339           reuse=reuse, trainable=trainable, collections=collections,
    340           caching_device=caching_device, partitioner=partitioner,
--> 341           validate_shape=validate_shape)
    342     else:
    343       return _true_getter(

/usr/local/lib/python2.7/dist-packages/tensorflow/python/layers/base.pyc in variable_getter(getter, name, shape, dtype, initializer, regularizer, trainable, **kwargs)
    256           name, shape, initializer=initializer, regularizer=regularizer,
    257           dtype=dtype, trainable=trainable,
--> 258           variable_getter=functools.partial(getter, **kwargs))
    259 
    260     # Build (if necessary) and call the layer, inside a variable scope.

/usr/local/lib/python2.7/dist-packages/tensorflow/python/layers/base.pyc in _add_variable(self, name, shape, dtype, initializer, regularizer, trainable, variable_getter)
    206                                initializer=initializer,
    207                                dtype=dtype,
--> 208                                trainable=trainable and self.trainable)
    209     # TODO(sguada) fix name = variable.op.name
    210     if variable in existing_variables:

/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/layers.pyc in layer_variable_getter(getter, *args, **kwargs)
   1308       getter = functools.partial(current_custom_getter, getter)
   1309     kwargs['rename'] = rename
-> 1310     return _model_variable_getter(getter, *args, **kwargs)
   1311   return layer_variable_getter
   1312 

/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/layers/python/layers/layers.pyc in _model_variable_getter(getter, name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, rename, **_)
   1297       regularizer=regularizer, collections=collections, trainable=trainable,
   1298       caching_device=caching_device, partitioner=partitioner,
-> 1299       custom_getter=getter)
   1300 
   1301 

/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.pyc in func_with_args(*args, **kwargs)
    175       current_args = current_scope[key_func].copy()
    176       current_args.update(kwargs)
--> 177     return func(*args, **current_args)
    178   _add_op(func)
    179   setattr(func_with_args, '_key_op', _key_op(func))

/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/variables.pyc in model_variable(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, device, partitioner, custom_getter)
    266                  trainable=trainable, collections=collections,
    267                  caching_device=caching_device, device=device,
--> 268                  partitioner=partitioner, custom_getter=custom_getter)
    269   return var
    270 

/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.pyc in func_with_args(*args, **kwargs)
    175       current_args = current_scope[key_func].copy()
    176       current_args.update(kwargs)
--> 177     return func(*args, **current_args)
    178   _add_op(func)
    179   setattr(func_with_args, '_key_op', _key_op(func))

/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/framework/python/ops/variables.pyc in variable(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, device, partitioner, custom_getter)
    223                   collections=collections,
    224                   caching_device=caching_device,
--> 225                   partitioner=partitioner)
    226 
    227 

/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.pyc in _true_getter(name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape)
    331           initializer=initializer, regularizer=regularizer, reuse=reuse,
    332           trainable=trainable, collections=collections,
--> 333           caching_device=caching_device, validate_shape=validate_shape)
    334 
    335     if custom_getter is not None:

/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.pyc in _get_single_variable(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape)
    682         caching_device=caching_device,
    683         dtype=variable_dtype,
--> 684         validate_shape=validate_shape)
    685     self._vars[name] = v
    686     logging.vlog(1, "Created variable %s with shape %s and init %s", v.name,

/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.pyc in __init__(self, initial_value, trainable, collections, validate_shape, caching_device, name, variable_def, dtype, expected_shape, import_scope)
    224           name=name,
    225           dtype=dtype,
--> 226           expected_shape=expected_shape)
    227 
    228   def __str__(self):

/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.pyc in _init_from_args(self, initial_value, trainable, collections, validate_shape, caching_device, name, dtype, expected_shape)
    301             with ops.name_scope("Initializer"),  ops.device(None):
    302               self._initial_value = ops.convert_to_tensor(
--> 303                   initial_value(), name="initial_value", dtype=dtype)
    304               shape = (self._initial_value.get_shape()
    305                        if validate_shape else tensor_shape.unknown_shape())

/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.pyc in <lambda>()
    671       else:
    672         init_val = lambda: initializer(
--> 673             shape.as_list(), dtype=dtype, partition_info=partition_info)
    674         variable_dtype = dtype.base_dtype
    675 

TypeError: __init__() got multiple values for keyword argument 'dtype'

[FredHaa]

I think that is a general problem with tf 1.0
Might be related to tensorflow/models#672

[vijtad]

You are correct, it was due to tf.zeros_initializer.
Due to api change, I should put parenthesis () to make it work in fc_8s.py
Thanks

[ghost]

Is tensorflow 1.0 backend available?

I am using windows python3.5 tensorflow 1.0, and it always reports errors that I cannot fix myself.

[vijtad]

I was able to run sticker example successfully on TF 1.0/Python 3.5 /Windows 10.
I used VS 2015 to debug Python code.

Few changes has to be done to make it work.

Segmentation of sticker cat is not producing good result as described in the article.
I have attached the images.
Please give me suggestion on how to improve segmentation quality.

I have done following changes

1. In latest source code of tensorflow/models/slim/nets/vgg.py, parameter fc_conv_padding='VALID' is missing in vgg_16 method. I have to add to make it run.

2. Changed xRange to range in one of the source code upsampling.py as XRange has been replaced with range in Python 3.5.

3. tf.pack should become tf.stack.

4. tf.zeros_initializer should be replaced with tf.zeros_initializer() in vgg.py.

Source code is as follows

from __future__ import division

import os
import sys
import tensorflow as tf
import skimage.io as io
import numpy as np

sys.path.append("tf-image-segmentation/")
sys.path.append("/home/dpakhom1/workspace/my_models/slim/")

fcn_16s_checkpoint_path = \
 'C:/TensorFlow/checkpoints/vgg_16.ckpt'

#os.environ["CUDA_VISIBLE_DEVICES"] = '1'

slim = tf.contrib.slim

from tf_image_segmentation.models.fcn_8s import FCN_8s
from tf_image_segmentation.utils.inference import adapt_network_for_any_size_input
from tf_image_segmentation.utils.pascal_voc import pascal_segmentation_lut

number_of_classes = 21

#image_filename = 'C:/Tensorflow/sticker/me.jpg'

image_filename = 'C:/Tensorflow/sticker/small_cat.jpg'

image_filename_placeholder = tf.placeholder(tf.string)

feed_dict_to_use = {image_filename_placeholder: image_filename}

image_tensor = tf.read_file(image_filename_placeholder)

image_tensor = tf.image.decode_jpeg(image_tensor, channels=3)

# Fake batch for image and annotation by adding
# leading empty axis.
image_batch_tensor = tf.expand_dims(image_tensor, axis=0)

# Be careful: after adaptation, network returns final labels
# and not logits
FCN_8s = adapt_network_for_any_size_input(FCN_8s, 32)


pred, fcn_16s_variables_mapping = FCN_8s(image_batch_tensor=image_batch_tensor,
                                          number_of_classes=number_of_classes,
                                          is_training=False)

# The op for initializing the variables.
initializer = tf.local_variables_initializer()

#saver = tf.train.Saver()
#saver = tf.train.import_meta_graph('C:/TensorFlow/checkpoints/fcn_8s_checkpoint/model_fcn8s_final.ckpt.meta', clear_devices=True)

with tf.Session() as sess:
    

    #saver.restore(sess,fcn_16s_checkpoint_path)
    saver = tf.train.Saver()
    saver.restore(sess, "C:/temp/model_fcn8s_final.ckpt")
    sess.run(initializer)
    #path = 'C:\\temp\\model_fcn8s_final.ckpt'
    #saver = tf.train.import_meta_graph(path + '.meta')
    #saver.restore(sess, tf.train.latest_checkpoint("C:\\temp\\"))
    print("Model restored.") 
    print('Initialized')
    image_np, pred_np = sess.run([image_tensor, pred], feed_dict=feed_dict_to_use)
    
    io.imshow(image_np)
    io.show()
    
    io.imshow(pred_np.squeeze())
    io.show()
    # Eroding countour

import skimage.morphology

prediction_mask = (pred_np.squeeze() == 8)

# Let's apply some morphological operations to
# create the contour for our sticker

cropped_object = image_np * np.dstack((prediction_mask,) * 3)

square = skimage.morphology.square(5)

temp = skimage.morphology.binary_erosion(prediction_mask, square)

negative_mask = (temp != True)

eroding_countour = negative_mask * prediction_mask

eroding_countour_img = np.dstack((eroding_countour, ) * 3)

cropped_object[eroding_countour_img] = 248

png_transparancy_mask = np.uint8(prediction_mask * 255)

image_shape = cropped_object.shape

png_array = np.zeros(shape=[image_shape[0], image_shape[1], 4], dtype=np.uint8)

png_array[:, :, :3] = cropped_object

png_array[:, :, 3] = png_transparancy_mask

io.imshow(cropped_object)

io.imsave('C:/Tensorflow/sticker/sticker_cat.png', png_array)

vgg.py code is as follows

# Copyright 2016 The TensorFlow 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.
# ==============================================================================
"""Contains model definitions for versions of the Oxford VGG network.

These model definitions were introduced in the following technical report:

  Very Deep Convolutional Networks For Large-Scale Image Recognition
  Karen Simonyan and Andrew Zisserman
  arXiv technical report, 2015
  PDF: http://arxiv.org/pdf/1409.1556.pdf
  ILSVRC 2014 Slides: http://www.robots.ox.ac.uk/~karen/pdf/ILSVRC_2014.pdf
  CC-BY-4.0

More information can be obtained from the VGG website:
www.robots.ox.ac.uk/~vgg/research/very_deep/

Usage:
  with slim.arg_scope(vgg.vgg_arg_scope()):
    outputs, end_points = vgg.vgg_a(inputs)

  with slim.arg_scope(vgg.vgg_arg_scope()):
    outputs, end_points = vgg.vgg_16(inputs)

@@vgg_a
@@vgg_16
@@vgg_19
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

slim = tf.contrib.slim


def vgg_arg_scope(weight_decay=0.0005):
  """Defines the VGG arg scope.

  Args:
    weight_decay: The l2 regularization coefficient.

  Returns:
    An arg_scope.
  """
  with slim.arg_scope([slim.conv2d, slim.fully_connected],
                      activation_fn=tf.nn.relu,
                      weights_regularizer=slim.l2_regularizer(weight_decay),
                      biases_initializer=tf.zeros_initializer()):
    with slim.arg_scope([slim.conv2d], padding='SAME') as arg_sc:
      return arg_sc


def vgg_a(inputs,
          num_classes=1000,
          is_training=True,
          dropout_keep_prob=0.5,
          spatial_squeeze=True,
          scope='vgg_a'):
  """Oxford Net VGG 11-Layers version A Example.

  Note: All the fully_connected layers have been transformed to conv2d layers.
        To use in classification mode, resize input to 224x224.

  Args:
    inputs: a tensor of size [batch_size, height, width, channels].
    num_classes: number of predicted classes.
    is_training: whether or not the model is being trained.
    dropout_keep_prob: the probability that activations are kept in the dropout
      layers during training.
    spatial_squeeze: whether or not should squeeze the spatial dimensions of the
      outputs. Useful to remove unnecessary dimensions for classification.
    scope: Optional scope for the variables.

  Returns:
    the last op containing the log predictions and end_points dict.
  """
  with tf.variable_scope(scope, 'vgg_a', [inputs]) as sc:
    end_points_collection = sc.name + '_end_points'
    # Collect outputs for conv2d, fully_connected and max_pool2d.
    with slim.arg_scope([slim.conv2d, slim.max_pool2d],
                        outputs_collections=end_points_collection):
      net = slim.repeat(inputs, 1, slim.conv2d, 64, [3, 3], scope='conv1')
      net = slim.max_pool2d(net, [2, 2], scope='pool1')
      net = slim.repeat(net, 1, slim.conv2d, 128, [3, 3], scope='conv2')
      net = slim.max_pool2d(net, [2, 2], scope='pool2')
      net = slim.repeat(net, 2, slim.conv2d, 256, [3, 3], scope='conv3')
      net = slim.max_pool2d(net, [2, 2], scope='pool3')
      net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv4')
      net = slim.max_pool2d(net, [2, 2], scope='pool4')
      net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv5')
      net = slim.max_pool2d(net, [2, 2], scope='pool5')
      # Use conv2d instead of fully_connected layers.
      net = slim.conv2d(net, 4096, [7, 7], padding='VALID', scope='fc6')
      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                         scope='dropout6')
      net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                         scope='dropout7')
      net = slim.conv2d(net, num_classes, [1, 1],
                        activation_fn=None,
                        normalizer_fn=None,
                        scope='fc8')
      # Convert end_points_collection into a end_point dict.
      end_points = slim.utils.convert_collection_to_dict(end_points_collection)
      if spatial_squeeze:
        net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
        end_points[sc.name + '/fc8'] = net
      return net, end_points
vgg_a.default_image_size = 224


def vgg_16(inputs,
           num_classes=1000,
           is_training=True,
           dropout_keep_prob=0.5,
           spatial_squeeze=True,
           scope='vgg_16',
           fc_conv_padding='VALID'):
  """Oxford Net VGG 16-Layers version D Example.

  Note: All the fully_connected layers have been transformed to conv2d layers.
        To use in classification mode, resize input to 224x224.

  Args:
    inputs: a tensor of size [batch_size, height, width, channels].
    num_classes: number of predicted classes.
    is_training: whether or not the model is being trained.
    dropout_keep_prob: the probability that activations are kept in the dropout
      layers during training.
    spatial_squeeze: whether or not should squeeze the spatial dimensions of the
      outputs. Useful to remove unnecessary dimensions for classification.
    scope: Optional scope for the variables.

  Returns:
    the last op containing the log predictions and end_points dict.
  """
  with tf.variable_scope(scope, 'vgg_16', [inputs]) as sc:
    end_points_collection = sc.name + '_end_points'
    # Collect outputs for conv2d, fully_connected and max_pool2d.6
    with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
                        outputs_collections=end_points_collection):
      net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
      net = slim.max_pool2d(net, [2, 2], scope='pool1')
      net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
      net = slim.max_pool2d(net, [2, 2], scope='pool2')
      net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
      net = slim.max_pool2d(net, [2, 2], scope='pool3')
      net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
      net = slim.max_pool2d(net, [2, 2], scope='pool4')
      net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
      net = slim.max_pool2d(net, [2, 2], scope='pool5')
      # Use conv2d instead of fully_connected layers.
      net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6')
      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                         scope='dropout6')
      net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                         scope='dropout7')
      net = slim.conv2d(net, num_classes, [1, 1],
                        activation_fn=None,
                        normalizer_fn=None,
                        scope='fc8')
      # Convert end_points_collection into a end_point dict.
      end_points = slim.utils.convert_collection_to_dict(end_points_collection)
      if spatial_squeeze:
        net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
        end_points[sc.name + '/fc8'] = net
      return net, end_points
vgg_16.default_image_size = 224


def vgg_19(inputs,
           num_classes=1000,
           is_training=True,
           dropout_keep_prob=0.5,
           spatial_squeeze=True,
           scope='vgg_19'):
  """Oxford Net VGG 19-Layers version E Example.

  Note: All the fully_connected layers have been transformed to conv2d layers.
        To use in classification mode, resize input to 224x224.

  Args:
    inputs: a tensor of size [batch_size, height, width, channels].
    num_classes: number of predicted classes.
    is_training: whether or not the model is being trained.
    dropout_keep_prob: the probability that activations are kept in the dropout
      layers during training.
    spatial_squeeze: whether or not should squeeze the spatial dimensions of the
      outputs. Useful to remove unnecessary dimensions for classification.
    scope: Optional scope for the variables.

  Returns:
    the last op containing the log predictions and end_points dict.
  """
  with tf.variable_scope(scope, 'vgg_19', [inputs]) as sc:
    end_points_collection = sc.name + '_end_points'
    # Collect outputs for conv2d, fully_connected and max_pool2d.
    with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
                        outputs_collections=end_points_collection):
      net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
      net = slim.max_pool2d(net, [2, 2], scope='pool1')
      net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
      net = slim.max_pool2d(net, [2, 2], scope='pool2')
      net = slim.repeat(net, 4, slim.conv2d, 256, [3, 3], scope='conv3')
      net = slim.max_pool2d(net, [2, 2], scope='pool3')
      net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv4')
      net = slim.max_pool2d(net, [2, 2], scope='pool4')
      net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv5')
      net = slim.max_pool2d(net, [2, 2], scope='pool5')
      # Use conv2d instead of fully_connected layers.
      net = slim.conv2d(net, 4096, [7, 7], padding='VALID', scope='fc6')
      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                         scope='dropout6')
      net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                         scope='dropout7')
      net = slim.conv2d(net, num_classes, [1, 1],
                        activation_fn=None,
                        normalizer_fn=None,
                        scope='fc8')
      # Convert end_points_collection into a end_point dict.
      end_points = slim.utils.convert_collection_to_dict(end_points_collection)
      if spatial_squeeze:
        net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
        end_points[sc.name + '/fc8'] = net
      return net, end_points
vgg_19.default_image_size = 224

# Alias
vgg_d = vgg_16
vgg_e = vgg_19

[ghost]

Thanks for the reply. I just find a way to use tensorflow upgrade tool to upgrade the folder models and tf-image-segmentation automatically. And it works fine now.

[ghost]

Also I think if you use fcn_8s_checkpoint_path = "C:/TensorFlow/checkpoints/vgg_8.ckpt" instead of fcn_16s_checkpoint_path = "C:/TensorFlow/checkpoints/vgg_16.ckpt", it may produce better segmentation result. But I am not sure.

[vijtad]

fcn_16s_checkpoint_path variable is not being used in the code. I am using model_fcn8s_final.ckpt provided in dropbox. saver.restore(sess, "C:/temp/model_fcn8s_final.ckpt") What is the image size of this cat ? I have this cat size as width = 144 and height = 85. I am getting good segmentation of me.jpg and image size is 400x400. I would like to whether segmentation depends on image size.

…

On Sat, Mar 11, 2017 at 9:36 PM, zhaozj89 ***@***.***> wrote: Also I think if you use fcn_8s_checkpoint_path = 'C:/TensorFlow/checkpoints/vgg_8.ckpt' instead of ``fcn_16s_checkpoint_path = 'C:/TensorFlow/checkpoints/vgg_16.ckpt', it may produce better segmentation result. But I am not sure. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#8 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AFye3To4b2Aulvg9VRT8Z4cnxW0gHoyYks5rk1oqgaJpZM4MACdU> .

[ahundt]

@vijtad can you please submit a pull request with your changes rather than including them in comments? That allows us to review and merge the code.

Here are two documents that explain how:
https://gist.github.com/Chaser324/ce0505fbed06b947d962
https://help.github.com/categories/collaborating-with-issues-and-pull-requests/

[barryridge]

Hi @warmspringwinds, @vijtad, @ahundt,

I took the liberty of submitting a PR that adds forward/backward compatibility for tf-1.0.x with version checks at all the fail points in the code based on the output of tf_upgrade_tool.py as suggested by @zhaozj89.

Please have a look and consider merging. As I say in the PR though, I'm unsure if this type of forward/backward compatibility version checking is the best approach for future maintainability. E.g. it would probably break future applications of tf_upgrade_tool.py. In order to retain that option, maintaining separate branches might be better. In any case, I'll leave it up to you.

[shivam-kotwalia]

Hi @barryridge Just tested for the tf_upgrade_tool.py and it worked like a charm. :) 👍

[caxton]

@vijtad I refer your code and got an error below, any idea? I also found that you use prediction_mask = (pred_np.squeeze() == 8) instead of prediction_mask = (pred_np.squeeze() == 15), why?
Any suggestion is appreciated, thanks.

/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:77: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 1 but corresponding boolean dimension is 360

ValueError                                Traceback (most recent call last)
<ipython-input-1-16f3a3a662ba> in <module>()
     83 png_array = np.zeros(shape=[image_shape[0], image_shape[1], 4], dtype=np.uint8)
     84 
---> 85 png_array[:, :, :3] = cropped_object
     86 
     87 png_array[:, :, 3] = png_transparancy_mask

ValueError: could not broadcast input array from shape (360,480,3) into shape (1,360,3)