171 lines
5.2 KiB
Python
171 lines
5.2 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# Copyright (c) 2018, NVIDIA CORPORATION. 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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import sys
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import tensorflow as tf
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import horovod.tensorflow as hvd
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from utils import image_processing
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from utils import hvd_utils
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from utils import dali_utils
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__all__ = ["get_synth_input_fn", "normalized_inputs"]
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_R_MEAN = 123.68
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_G_MEAN = 116.78
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_B_MEAN = 103.94
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_CHANNEL_MEANS = [_R_MEAN, _G_MEAN, _B_MEAN]
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_NUM_CHANNELS = 3
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def get_synth_input_fn(batch_size, height, width, num_channels, data_format, num_classes, dtype=tf.float32):
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"""Returns an input function that returns a dataset with random data.
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This input_fn returns a data set that iterates over a set of random data and
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bypasses all preprocessing, e.g. jpeg decode and copy. The host to device
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copy is still included. This used to find the upper throughput bound when
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tunning the full input pipeline.
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Args:
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height: Integer height that will be used to create a fake image tensor.
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width: Integer width that will be used to create a fake image tensor.
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num_channels: Integer depth that will be used to create a fake image tensor.
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num_classes: Number of classes that should be represented in the fake labels
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tensor
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dtype: Data type for features/images.
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Returns:
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An input_fn that can be used in place of a real one to return a dataset
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that can be used for iteration.
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"""
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if data_format not in ["NHWC", "NCHW"]:
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raise ValueError("Unknown data_format: %s" % str(data_format))
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if data_format == "NHWC":
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input_shape = [batch_size, height, width, num_channels]
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else:
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input_shape = [batch_size, num_channels, height, width]
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# Convert the inputs to a Dataset.
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inputs = tf.truncated_normal(input_shape, dtype=dtype, mean=127, stddev=60, name='synthetic_inputs')
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labels = tf.random_uniform([batch_size], minval=0, maxval=num_classes - 1, dtype=tf.int32, name='synthetic_labels')
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data = tf.data.Dataset.from_tensors((inputs, labels))
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data = data.repeat()
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data = data.prefetch(buffer_size=tf.contrib.data.AUTOTUNE)
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return data
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def get_tfrecords_input_fn(filenames, batch_size, height, width, training, distort_color, num_threads, deterministic):
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shuffle_buffer_size = 4096
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if deterministic:
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if hvd_utils.is_using_hvd():
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seed = 13 * (1 + hvd.rank())
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else:
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seed = 13
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else:
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seed = None
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ds = tf.data.Dataset.from_tensor_slices(filenames)
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if hvd_utils.is_using_hvd() and training:
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ds = ds.shard(hvd.size(), hvd.rank())
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ds = ds.apply(
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tf.data.experimental.parallel_interleave(
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tf.data.TFRecordDataset,
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cycle_length=10,
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block_length=8,
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sloppy=not deterministic,
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prefetch_input_elements=16
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)
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)
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counter = tf.data.Dataset.range(sys.maxsize)
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ds = tf.data.Dataset.zip((ds, counter))
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def preproc_func(record, counter_):
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return image_processing.preprocess_image_record(record, height, width, _NUM_CHANNELS, training)
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ds = ds.cache()
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if training:
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ds = ds.apply(tf.data.experimental.shuffle_and_repeat(buffer_size=shuffle_buffer_size, seed=seed))
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else:
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ds = ds.repeat()
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ds = ds.apply(
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tf.data.experimental.map_and_batch(
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map_func=preproc_func,
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num_parallel_calls=num_threads,
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batch_size=batch_size,
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drop_remainder=True,
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)
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)
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ds = ds.prefetch(buffer_size=tf.contrib.data.AUTOTUNE)
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return ds
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def get_dali_input_fn(filenames, idx_filenames, batch_size, height, width, training, distort_color, num_threads, deterministic):
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if idx_filenames is None:
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raise ValueError("Must provide idx_filenames for DALI's reader")
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preprocessor = dali_utils.DALIPreprocessor(
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filenames,
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idx_filenames,
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height, width,
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batch_size,
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num_threads,
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dali_cpu=False,
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deterministic=deterministic,
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training=training)
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images, labels = preprocessor.get_device_minibatches()
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return (images, labels)
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def normalized_inputs(inputs):
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num_channels = inputs.get_shape()[-1]
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if inputs.get_shape().ndims != 4:
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raise ValueError('Input must be of size [batch_size, height, width, C>0]')
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if len(_CHANNEL_MEANS) != num_channels:
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raise ValueError('len(means) must match the number of channels')
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# We have a 1-D tensor of means; convert to 3-D.
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means_per_channel = tf.reshape(_CHANNEL_MEANS, [1, 1, num_channels])
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means_per_channel = tf.cast(means_per_channel, dtype=inputs.dtype)
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inputs = tf.subtract(inputs, means_per_channel)
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return tf.divide(inputs, 255.0)
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