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# 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.
# ==============================================================================
"""Generate a single synthetic sample."""
import io
import os
import numpy as np
import tensorflow as tf
import synthetic_model
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string(
'sample_filename', None,
"""Output file to store the generated binary code.""")
def GenerateSample(filename, code_shape, layer_depth):
# {0, +1} binary codes.
# No conversion since the output file is expected to store
# codes using {0, +1} codes (and not {-1, +1}).
code = synthetic_model.GenerateSingleCode(code_shape)
code = np.round(code)
# Reformat the code so as to be compatible with what is generated
# by the image encoder.
# The image encoder generates a tensor of size:
# iteration_count x batch_size x height x width x iteration_depth.
# Here: batch_size = 1
if code_shape[-1] % layer_depth != 0:
raise ValueError('Number of layers is not an integer')
height = code_shape[0]
width = code_shape[1]
code = code.reshape([1, height, width, -1, layer_depth])
code = np.transpose(code, [3, 0, 1, 2, 4])
int_codes = code.astype(np.int8)
exported_codes = np.packbits(int_codes.reshape(-1))
output = io.BytesIO()
np.savez_compressed(output, shape=int_codes.shape, codes=exported_codes)
with tf.gfile.FastGFile(filename, 'wb') as code_file:
code_file.write(output.getvalue())
def main(argv=None): # pylint: disable=unused-argument
# Note: the height and the width is different from the training dataset.
# The main purpose is to show that the entropy coder model is fully
# convolutional and can be used on any image size.
layer_depth = 2
GenerateSample(FLAGS.sample_filename, [31, 36, 8], layer_depth)
if __name__ == '__main__':
tf.app.run()
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