a +=icT@sdZddlZddlZddlZddlZddlmmZddl m Z ddl Z ddl m Z z ddlZWneyxdZYn0gdZGdddejje jZd/dd Zd d Zd d ZddZd0ddZd1ddZddZddZddZd2ddZddZdd Zd!d"Zd#d$Z d3d%d&Z!d'd(Z"Gd)d*d*ej#jj$j%Z&Gd+d,d,ej#jj$j%Z'ej#jj$j(j)d-Z*ej+ej#jj$j(e*e&e'fd.Z(ej#jj$j,Z,ej#jj$j-Z-ej#jj$j.Z.dS)4z!Utilities for unit-testing Keras.N) parameterized) test_utils functionalsubclass sequentialcseZdZfddZZS)TestCasecstjtdSN)kerasbackendZ clear_sessionsupertearDownself __class__v/home/droni/.local/share/virtualenvs/DPS-5Je3_V2c/lib/python3.9/site-packages/keras/testing_infra/test_combinations.pyr &s zTestCase.tearDown)__name__ __module__ __qualname__r __classcell__rrrrr%srcsDtdurdggd}fdd|Dfdd}t||S)a4 Execute the decorated test with all Keras saved model formats). This decorator is intended to be applied either to individual test methods in a `test_combinations.TestCase` class, or directly to a test class that extends it. Doing so will cause the contents of the individual test method (or all test methods in the class) to be executed multiple times - once for each Keras saved model format. The Keras saved model formats include: 1. HDF5: 'h5' 2. SavedModel: 'tf' Note: if stacking this decorator with absl.testing's parameterized decorators, those should be at the bottom of the stack. Various methods in `testing_utils` to get file path for saved models will auto-generate a string of the two saved model formats. This allows unittests to confirm the equivalence between the two Keras saved model formats. For example, consider the following unittest: ```python class MyTests(test_utils.KerasTestCase): @test_utils.run_with_all_saved_model_formats def test_foo(self): save_format = test_utils.get_save_format() saved_model_dir = '/tmp/saved_model/' model = keras.models.Sequential() model.add(keras.layers.Dense(2, input_shape=(3,))) model.add(keras.layers.Dense(3)) model.compile(loss='mse', optimizer='sgd', metrics=['acc']) keras.models.save_model(model, saved_model_dir, save_format=save_format) model = keras.models.load_model(saved_model_dir) if __name__ == "__main__": tf.test.main() ``` This test tries to save the model into the formats of 'hdf5', 'h5', 'keras', 'tensorflow', and 'tf'. We can also annotate the whole class if we want this to apply to all tests in the class: ```python @test_utils.run_with_all_saved_model_formats class MyTests(test_utils.KerasTestCase): def test_foo(self): save_format = test_utils.get_save_format() saved_model_dir = '/tmp/saved_model/' model = keras.models.Sequential() model.add(keras.layers.Dense(2, input_shape=(3,))) model.add(keras.layers.Dense(3)) model.compile(loss='mse', optimizer='sgd', metrics=['acc']) keras.models.save_model(model, saved_model_dir, save_format=save_format) model = tf.keras.models.load_model(saved_model_dir) if __name__ == "__main__": tf.test.main() ``` Args: test_or_class: test method or class to be annotated. If None, this method returns a decorator that can be applied to a test method or test class. If it is not None this returns the decorator applied to the test or class. exclude_formats: A collection of Keras saved model formats to not run. (May also be a single format not wrapped in a collection). Defaults to None. Returns: Returns a decorator that will run the decorated test method multiple times: once for each desired Keras saved model format. Raises: ImportError: If abseil parameterized is not installed or not included as a target dependency. 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This decorator is intended to be applied either to individual test methods in a `test_combinations.TestCase` class, or directly to a test class that extends it. Doing so will cause the contents of the individual test method (or all test methods in the class) to be executed multiple times - once for each Keras model type. The Keras model types are: ['functional', 'subclass', 'sequential'] Note: if stacking this decorator with absl.testing's parameterized decorators, those should be at the bottom of the stack. Various methods in `testing_utils` to get models will auto-generate a model of the currently active Keras model type. This allows unittests to confirm the equivalence between different Keras models. For example, consider the following unittest: ```python class MyTests(test_utils.KerasTestCase): @test_utils.run_with_all_model_types( exclude_models = ['sequential']) def test_foo(self): model = test_utils.get_small_mlp(1, 4, input_dim=3) optimizer = RMSPropOptimizer(learning_rate=0.001) loss = 'mse' metrics = ['mae'] model.compile(optimizer, loss, metrics=metrics) inputs = np.zeros((10, 3)) targets = np.zeros((10, 4)) dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets)) dataset = dataset.repeat(100) dataset = dataset.batch(10) model.fit(dataset, epochs=1, steps_per_epoch=2, verbose=1) if __name__ == "__main__": tf.test.main() ``` This test tries building a small mlp as both a functional model and as a subclass model. We can also annotate the whole class if we want this to apply to all tests in the class: ```python @test_utils.run_with_all_model_types(exclude_models = ['sequential']) class MyTests(test_utils.KerasTestCase): def test_foo(self): model = test_utils.get_small_mlp(1, 4, input_dim=3) optimizer = RMSPropOptimizer(learning_rate=0.001) loss = 'mse' metrics = ['mae'] model.compile(optimizer, loss, metrics=metrics) inputs = np.zeros((10, 3)) targets = np.zeros((10, 4)) dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets)) dataset = dataset.repeat(100) dataset = dataset.batch(10) model.fit(dataset, epochs=1, steps_per_epoch=2, verbose=1) if __name__ == "__main__": tf.test.main() ``` Args: test_or_class: test method or class to be annotated. If None, this method returns a decorator that can be applied to a test method or test class. If it is not None this returns the decorator applied to the test or class. exclude_models: A collection of Keras model types to not run. (May also be a single model type not wrapped in a collection). Defaults to None. Returns: Returns a decorator that will run the decorated test method multiple times: once for each desired Keras model type. Raises: ImportError: If abseil parameterized is not installed or not included as a target dependency. rcs(g|] }|tjvrd||fqSrr)rmodel)exclude_modelsrrr"sz,run_with_all_model_types..cs$tjtfdd}|S)r#csx|dkr"t|g|Ri|nR|dkrDt|g|Ri|n0|dkrft|g|Ri|ntd|fdS)r$rrrr%N)_test_functional_model_type_test_subclass_model_type_test_sequential_model_typer))r model_typer*r+r,rrr.szLrun_with_all_model_types..single_method_decorator..decoratedr/r2r3r,rr5s z9run_with_all_model_types..single_method_decorator)r8)r9r?Z model_typesr5r)r?r4rrun_with_all_model_typess Z  rDcOsDtd&||g|Ri|Wdn1s60YdS)Nrrmodel_type_scoper<rrrr@,s r@cOsDtd&||g|Ri|Wdn1s60YdS)NrrEr<rrrrA1s rAcOsDtd&||g|Ri|Wdn1s60YdS)NrrEr<rrrrB6s rBFc sX|rtd|dg|s&d|s@tjjs@dfdd}t||S)a Execute the decorated test with all keras execution modes. This decorator is intended to be applied either to individual test methods in a `test_combinations.TestCase` class, or directly to a test class that extends it. Doing so will cause the contents of the individual test method (or all test methods in the class) to be executed multiple times - once executing in legacy graph mode, once running eagerly and with `should_run_eagerly` returning True, and once running eagerly with `should_run_eagerly` returning False. If Tensorflow v2 behavior is enabled, legacy graph mode will be skipped, and the test will only run twice. Note: if stacking this decorator with absl.testing's parameterized decorators, those should be at the bottom of the stack. For example, consider the following unittest: ```python class MyTests(test_utils.KerasTestCase): @test_utils.run_all_keras_modes def test_foo(self): model = test_utils.get_small_functional_mlp(1, 4, input_dim=3) optimizer = RMSPropOptimizer(learning_rate=0.001) loss = 'mse' metrics = ['mae'] model.compile( optimizer, loss, metrics=metrics, run_eagerly=test_utils.should_run_eagerly()) inputs = np.zeros((10, 3)) targets = np.zeros((10, 4)) dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets)) dataset = dataset.repeat(100) dataset = dataset.batch(10) model.fit(dataset, epochs=1, steps_per_epoch=2, verbose=1) if __name__ == "__main__": tf.test.main() ``` This test will try compiling & fitting the small functional mlp using all three Keras execution modes. Args: test_or_class: test method or class to be annotated. If None, this method returns a decorator that can be applied to a test method or test class. If it is not None this returns the decorator applied to the test or class. config: An optional config_pb2.ConfigProto to use to configure the session when executing graphs. always_skip_v1: If True, does not try running the legacy graph mode even when Tensorflow v2 behavior is not enabled. always_skip_eager: If True, does not execute the decorated test with eager execution modes. **kwargs: Additional kwargs for configuring tests for in-progress Keras behaviors/ refactorings that we haven't fully rolled out yet Returns: Returns a decorator that will run the decorated test method multiple times. 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