110 lines
3.6 KiB
Python
110 lines
3.6 KiB
Python
# Copyright 2020 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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# ==============================================================================
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# pylint: disable=protected-access
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"""Tests for saving/loading function for keras Model."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import shutil
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from absl.testing import parameterized
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import numpy as np
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from tensorflow.python import keras
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from tensorflow.python.keras import keras_parameterized
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from tensorflow.python.keras import testing_utils
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from tensorflow.python.keras.tests import model_architectures
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from tensorflow.python.platform import test
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@keras_parameterized.run_with_all_saved_model_formats
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class TestModelArchitectures(keras_parameterized.TestCase):
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def _save_model_dir(self, dirname='saved_model'):
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temp_dir = self.get_temp_dir()
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self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
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return os.path.join(temp_dir, dirname)
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def get_test_data(self, input_shape, target_shape):
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"""Generate test dataset for testing."""
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if isinstance(input_shape, list):
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x = [
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np.random.random((2,) + input_shape[i][1:])
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for i in range(len(input_shape))
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]
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else:
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x = np.random.random((2,) + input_shape[1:])
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if isinstance(target_shape, list):
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y = [
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np.random.random((2,) + target_shape[i][1:])
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for i in range(len(target_shape))
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]
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else:
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y = np.random.random((2,) + target_shape[1:])
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return x, y
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def get_custom_objects(self):
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"""Define custom_objects."""
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class CustomOpt(keras.optimizers.SGD):
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pass
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def custom_loss(y_true, y_pred):
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return keras.losses.mse(y_true, y_pred)
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return {'CustomOpt': CustomOpt,
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'custom_loss': custom_loss}
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@parameterized.named_parameters(*model_architectures.ALL_MODELS)
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def test_basic_saving_and_loading(self, model_fn):
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save_format = testing_utils.get_save_format()
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custom_objects = self.get_custom_objects()
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if 'subclassed_in_functional' in model_fn.__name__:
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subclass_custom_objects = {
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'MySubclassModel':
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model_architectures.MySubclassModel,
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}
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custom_objects.update(subclass_custom_objects)
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elif ('subclassed' in model_fn.__name__ and save_format == 'h5'):
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self.skipTest('Saving the model to HDF5 format requires the model to be '
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'a Functional model or a Sequential model.')
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saved_model_dir = self._save_model_dir()
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model_data = model_fn()
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model = model_data.model
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x_test, y_test = self.get_test_data(
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model_data.input_shape, model_data.target_shape)
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model.compile('rmsprop', 'mse')
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model.train_on_batch(x_test, y_test)
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# Save model.
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out1 = model.predict(x_test)
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keras.models.save_model(model, saved_model_dir, save_format=save_format)
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# Load model.
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loaded_model = keras.models.load_model(
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saved_model_dir,
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custom_objects=custom_objects)
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out2 = loaded_model.predict(x_test)
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self.assertAllClose(out1, out2, atol=1e-05)
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if __name__ == '__main__':
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test.main()
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