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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() | |