tf-image-segmentation computed output size would be negative

I am getting following error when I am running sticker example in Windows 10/Python 3.5/TF 0.1.

computed output size would be negative

	 [[Node: fcn_8s/vgg_16/fc6/convolution = Conv2D[T=DT_FLOAT, data_format="NHWC", padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/cpu:0"](fcn_8s/vgg_16/pool5/MaxPool, fcn_8s/vgg_16/fc6/weights/read)]]



Caused by op 'fcn_8s/vgg_16/fc6/convolution', defined at:

  File "C:\Program Files (x86)\Microsoft Visual Studio 14.0\Common7\IDE\Extensions\Microsoft\Python Tools for Visual Studio\2.2\visualstudio_py_launcher.py", line 78, in <module>

    vspd.debug(filename, port_num, debug_id, debug_options, run_as)

  File "C:\Program Files (x86)\Microsoft Visual Studio 14.0\Common7\IDE\Extensions\Microsoft\Python Tools for Visual Studio\2.2\visualstudio_py_debugger.py", line 2483, in debug

    exec_file(file, globals_obj)

  File "C:\Program Files (x86)\Microsoft Visual Studio 14.0\Common7\IDE\Extensions\Microsoft\Python Tools for Visual Studio\2.2\visualstudio_py_util.py", line 111, in exec_file

    exec_code(code, file, global_variables)

  File "C:\Program Files (x86)\Microsoft Visual Studio 14.0\Common7\IDE\Extensions\Microsoft\Python Tools for Visual Studio\2.2\visualstudio_py_util.py", line 87, in exec_code

    exec(code_obj, global_variables)

  File "C:\Users\vijay\Documents\Visual Studio 2015\Projects\PythonApplication3\PythonApplication3\sticker.py", line 48, in <module>

    is_training=False)

  File "C:\TensorFlow\tf-image-segmentation-master\tf_image_segmentation\utils\inference.py", line 53, in new_network_definition

    all_outputs = network_definition(*args, **kwargs)

  File "C:\TensorFlow\tf-image-segmentation-master\tf_image_segmentation\models\fcn_8s.py", line 78, in FCN_8s

    spatial_squeeze=False)

  File "C:\TensorFlow\models-master\slim\nets\vgg.py", line 165, in vgg_16

    net = slim.conv2d(net, 4096, [7, 7], padding='VALID', scope='fc6')

  File "C:\Users\vijay\Miniconda3\lib\site-packages\tensorflow\contrib\framework\python\ops\arg_scope.py", line 177, in func_with_args

    return func(*args, **current_args)

  File "C:\Users\vijay\Miniconda3\lib\site-packages\tensorflow\contrib\layers\python\layers\layers.py", line 907, in convolution

    outputs = layer.apply(inputs)

  File "C:\Users\vijay\Miniconda3\lib\site-packages\tensorflow\python\layers\base.py", line 303, in apply

    return self.__call__(inputs, **kwargs)

  File "C:\Users\vijay\Miniconda3\lib\site-packages\tensorflow\python\layers\base.py", line 273, in __call__

    outputs = self.call(inputs, **kwargs)

  File "C:\Users\vijay\Miniconda3\lib\site-packages\tensorflow\python\layers\convolutional.py", line 156, in call

    data_format=utils.convert_data_format(self.data_format, self.rank + 2))

  File "C:\Users\vijay\Miniconda3\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 639, in convolution

    op=op)

  File "C:\Users\vijay\Miniconda3\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 308, in with_space_to_batch

    return op(input, num_spatial_dims, padding)

  File "C:\Users\vijay\Miniconda3\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 631, in op

    name=name)

  File "C:\Users\vijay\Miniconda3\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 129, in _non_atrous_convolution

    name=name)

  File "C:\Users\vijay\Miniconda3\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 396, in conv2d

    data_format=data_format, name=name)

  File "C:\Users\vijay\Miniconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 763, in apply_op

    op_def=op_def)

  File "C:\Users\vijay\Miniconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 2327, in create_op

    original_op=self._default_original_op, op_def=op_def)

  File "C:\Users\vijay\Miniconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1226, in __init__

    self._traceback = _extract_stack()



InvalidArgumentError (see above for traceback): computed output size would be negative

	 [[Node: fcn_8s/vgg_16/fc6/convolution = Conv2D[T=DT_FLOAT, data_format="NHWC", padding="VALID", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/cpu:0"](fcn_8s/vgg_16/pool5/MaxPool, fcn_8s/vgg_16/fc6/weights/read)]]

My 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.global_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:
    

    sess.run(initializer)
    #saver.restore(sess,fcn_16s_checkpoint_path)
    saver = tf.train.Saver()
    saver.restore(sess, "C:/temp/model_fcn8s_final.ckpt")
    #path = 'C:\\temp\\model_fcn8s_final.ckpt'
    #saver = tf.train.import_meta_graph(path + '.meta')
    #saver.restore(sess, tf.train.latest_checkpoint("C:\\temp\\"))
    all_vars = tf.get_collection('vars')
    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()