70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
# Copyright 2020 The JAX Authors.
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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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# https://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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"""Tests for mnist_lib, saved_model_lib, saved_model_main."""
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import os
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from absl import flags
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from absl.testing import absltest
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from absl.testing import parameterized
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from jax._src import test_util as jtu
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from jax import config
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from jax.experimental.jax2tf.examples import saved_model_main
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from jax.experimental.jax2tf.tests import tf_test_util
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config.parse_flags_with_absl()
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FLAGS = flags.FLAGS
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class SavedModelMainTest(tf_test_util.JaxToTfTestCase):
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def setUp(self):
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super().setUp()
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FLAGS.model_path = os.path.join(absltest.get_default_test_tmpdir(),
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"saved_models")
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FLAGS.num_epochs = 1
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FLAGS.test_savedmodel = True
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FLAGS.mock_data = True
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@parameterized.named_parameters(
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dict(
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testcase_name=f"_{model}_batch={serving_batch_size}",
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model=model,
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serving_batch_size=serving_batch_size)
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for model in ["mnist_pure_jax", "mnist_flax"]
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for serving_batch_size in [1, -1])
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def test_train_and_save_full(self,
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model="mnist_flax",
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serving_batch_size=-1):
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if (serving_batch_size == -1 and
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config.jax2tf_default_native_serialization and
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not config.jax_dynamic_shapes):
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self.skipTest("shape polymorphism but --jax_dynamic_shapes is not set.")
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FLAGS.model = model
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FLAGS.model_classifier_layer = True
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FLAGS.serving_batch_size = serving_batch_size
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saved_model_main.train_and_save()
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@parameterized.named_parameters(
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dict(testcase_name=f"_{model}", model=model)
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for model in ["mnist_pure_jax", "mnist_flax"])
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def test_train_and_save_features(self, model="mnist_flax"):
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FLAGS.model = model
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FLAGS.model_classifier_layer = False
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saved_model_main.train_and_save()
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if __name__ == "__main__":
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absltest.main(testLoader=jtu.JaxTestLoader())
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