118 lines
3.7 KiB
Python
118 lines
3.7 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""TensorBoard encoder helper module.
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Encoder depends on TensorFlow.
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"""
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import numpy as np
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from tensorboard.util import op_evaluator
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class _TensorFlowPngEncoder(op_evaluator.PersistentOpEvaluator):
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"""Encode an image to PNG.
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This function is thread-safe, and has high performance when run in
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parallel. See `encode_png_benchmark.py` for details.
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Arguments:
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image: A numpy array of shape `[height, width, channels]`, where
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`channels` is 1, 3, or 4, and of dtype uint8.
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Returns:
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A bytestring with PNG-encoded data.
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"""
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def __init__(self):
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super().__init__()
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self._image_placeholder = None
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self._encode_op = None
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def initialize_graph(self):
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# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
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import tensorflow.compat.v1 as tf
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self._image_placeholder = tf.placeholder(
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dtype=tf.uint8, name="image_to_encode"
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)
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self._encode_op = tf.image.encode_png(self._image_placeholder)
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def run(self, image): # pylint: disable=arguments-differ
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if not isinstance(image, np.ndarray):
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raise ValueError("'image' must be a numpy array: %r" % image)
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if image.dtype != np.uint8:
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raise ValueError(
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"'image' dtype must be uint8, but is %r" % image.dtype
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)
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return self._encode_op.eval(feed_dict={self._image_placeholder: image})
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encode_png = _TensorFlowPngEncoder()
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class _TensorFlowWavEncoder(op_evaluator.PersistentOpEvaluator):
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"""Encode an audio clip to WAV.
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This function is thread-safe and exhibits good parallel performance.
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Arguments:
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audio: A numpy array of shape `[samples, channels]`.
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samples_per_second: A positive `int`, in Hz.
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Returns:
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A bytestring with WAV-encoded data.
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"""
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def __init__(self):
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super().__init__()
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self._audio_placeholder = None
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self._samples_per_second_placeholder = None
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self._encode_op = None
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def initialize_graph(self):
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# TODO(nickfelt): remove on-demand imports once dep situation is fixed.
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import tensorflow.compat.v1 as tf
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self._audio_placeholder = tf.placeholder(
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dtype=tf.float32, name="image_to_encode"
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)
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self._samples_per_second_placeholder = tf.placeholder(
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dtype=tf.int32, name="samples_per_second"
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)
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self._encode_op = tf.audio.encode_wav(
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self._audio_placeholder,
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sample_rate=self._samples_per_second_placeholder,
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)
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def run(
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self, audio, samples_per_second
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): # pylint: disable=arguments-differ
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if not isinstance(audio, np.ndarray):
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raise ValueError("'audio' must be a numpy array: %r" % audio)
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if not isinstance(samples_per_second, int):
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raise ValueError(
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"'samples_per_second' must be an int: %r" % samples_per_second
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)
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feed_dict = {
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self._audio_placeholder: audio,
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self._samples_per_second_placeholder: samples_per_second,
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}
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return self._encode_op.eval(feed_dict=feed_dict)
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encode_wav = _TensorFlowWavEncoder()
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