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| # coding=utf-8 | |
| # Copyright 2021 The Deeplab2 Authors. | |
| # | |
| # 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. | |
| """Provide utility functions to write simple tests.""" | |
| import functools | |
| import numpy as np | |
| import tensorflow as tf | |
| NORMALIZATION_LAYERS = ( | |
| tf.keras.layers.experimental.SyncBatchNormalization, | |
| tf.keras.layers.BatchNormalization | |
| ) | |
| def create_strategy(): | |
| """Returns a strategy based on available devices. | |
| Does NOT work with local_multiworker_tpu_test tests! | |
| """ | |
| tpus = tf.config.list_logical_devices(device_type='TPU') | |
| gpus = tf.config.list_logical_devices(device_type='GPU') | |
| if tpus: | |
| resolver = tf.distribute.cluster_resolver.TPUClusterResolver('') | |
| tf.config.experimental_connect_to_cluster(resolver) | |
| tf.tpu.experimental.initialize_tpu_system(resolver) | |
| return tf.distribute.TPUStrategy(resolver) | |
| elif gpus: | |
| return tf.distribute.OneDeviceStrategy('/gpu:0') | |
| else: | |
| return tf.distribute.OneDeviceStrategy('/cpu:0') | |
| def test_all_strategies(func): | |
| """Decorator to test CPU, GPU and TPU strategies.""" | |
| def decorator(self): | |
| strategy = create_strategy() | |
| return func(self, strategy) | |
| return decorator | |
| def create_test_input(batch, height, width, channels): | |
| """Creates test input tensor.""" | |
| return tf.convert_to_tensor( | |
| np.tile( | |
| np.reshape( | |
| np.reshape(np.arange(height), [height, 1]) + | |
| np.reshape(np.arange(width), [1, width]), | |
| [1, height, width, 1]), | |
| [batch, 1, 1, channels]), dtype=tf.float32) | |