140 lines
4.2 KiB
Python
140 lines
4.2 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# ==============================================================================
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#
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# Copyright (c) 2019, 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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#
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# ==============================================================================
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from __future__ import print_function
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import os
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from abc import ABC, abstractmethod
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import math
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import tensorflow as tf
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__all__ = ["BaseDataset"]
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class BaseDataset(ABC):
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authorized_normalization_methods = [None, "zero_centered", "zero_one"]
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def __init__(self, data_dir):
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self.data_dir = data_dir
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if not os.path.exists(data_dir):
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raise FileNotFoundError("The dataset directory `%s` does not exist." % data_dir)
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@staticmethod
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def _count_steps(iter_unit, num_samples, num_iter, global_batch_size):
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if iter_unit not in ["batch", "epoch"]:
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raise ValueError("Invalid `iter_unit` value: %s" % iter_unit)
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if iter_unit == 'epoch':
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num_steps = (num_samples // global_batch_size) * num_iter
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num_epochs = num_iter
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else:
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num_steps = num_iter
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num_epochs = math.ceil(num_steps / (num_samples // global_batch_size))
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return num_steps, num_epochs
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@abstractmethod
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def dataset_name(self):
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raise NotImplementedError
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@abstractmethod
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def get_dataset_runtime_specs(self, training, iter_unit, num_iter, global_batch_size):
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# return filenames, num_samples, num_steps, num_epochs
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raise NotImplementedError
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@abstractmethod
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def dataset_fn(
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self,
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batch_size,
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training,
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input_shape,
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mask_shape,
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num_threads,
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use_gpu_prefetch,
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normalize_data_method,
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only_defective_images,
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augment_data,
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seed=None
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):
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if normalize_data_method not in BaseDataset.authorized_normalization_methods:
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raise ValueError(
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'Unknown `normalize_data_method`: %s - Authorized: %s' %
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(normalize_data_method, BaseDataset.authorized_normalization_methods)
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)
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def synth_dataset_fn(
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self,
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batch_size,
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training,
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input_shape,
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mask_shape,
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num_threads,
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use_gpu_prefetch,
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normalize_data_method,
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only_defective_images,
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augment_data,
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seed=None
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):
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if normalize_data_method not in BaseDataset.authorized_normalization_methods:
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raise ValueError(
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'Unknown `normalize_data_method`: %s - Authorized: %s' %
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(normalize_data_method, BaseDataset.authorized_normalization_methods)
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)
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input_shape = [batch_size] + list(input_shape)
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mask_shape = [batch_size] + list(mask_shape)
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# Convert the inputs to a Dataset
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if normalize_data_method is None:
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mean_val = 127.5
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elif normalize_data_method == "zero_centered":
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mean_val = 0
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else:
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mean_val = 0.5
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inputs = tf.truncated_normal(
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input_shape, dtype=tf.float32, mean=mean_val, stddev=1, seed=seed, name='synth_inputs'
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)
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masks = tf.truncated_normal(mask_shape, dtype=tf.float32, mean=0.01, stddev=0.1, seed=seed, name='synth_masks')
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labels = tf.random_uniform([batch_size], minval=0, maxval=1, dtype=tf.int32, name='synthetic_labels')
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dataset = tf.data.Dataset.from_tensors(((inputs, masks), labels))
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dataset = dataset.cache()
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dataset = dataset.repeat()
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dataset = dataset.prefetch(buffer_size=tf.contrib.data.AUTOTUNE)
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if use_gpu_prefetch:
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dataset.apply(tf.data.experimental.prefetch_to_device(device="/gpu:0", buffer_size=batch_size * 8))
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return dataset
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