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How to use test_runner method in Molotov

Best Python code snippet using molotov_python
controller_test.py
Source:controller_test.py
1# Copyright 2021 The Orbit Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14"""Tests for orbit.controller."""15import os16from absl import logging17from absl.testing import parameterized18import numpy as np19from orbit import controller20from orbit import runner21from orbit import standard_runner22import tensorflow as tf23def create_model():24  x = tf.keras.layers.Input(shape=(3,), name="input")25  y = tf.keras.layers.Dense(4, name="dense")(x)26  model = tf.keras.Model(x, y)27  return model28def summaries_with_matching_keyword(keyword, summary_dir):29  """Returns summary protos matching given keyword from event file."""30  matches = []31  event_paths = tf.io.gfile.glob(os.path.join(summary_dir, "events*"))32  for event in tf.compat.v1.train.summary_iterator(event_paths[-1]):33    if event.summary is not None:34      for value in event.summary.value:35        if keyword in value.tag:36          matches.append(event.summary)37  return matches38def dataset_fn(ctx):39  del ctx40  inputs = np.zeros((10, 3), dtype=np.float32)41  targets = np.ones((10, 4), dtype=np.float32)42  dataset = tf.data.Dataset.from_tensor_slices((inputs, targets))43  dataset = dataset.repeat(100)44  dataset = dataset.batch(10, drop_remainder=True)45  return dataset46class TestRunner(standard_runner.StandardTrainer,47                 standard_runner.StandardEvaluator):48  """Implements the training and evaluation APIs for the test model."""49  def __init__(self, return_numpy=False):50    self.strategy = tf.distribute.get_strategy()51    self.model = create_model()52    self.optimizer = tf.keras.optimizers.RMSprop(learning_rate=0.1)53    self.global_step = self.optimizer.iterations54    self.train_loss = tf.keras.metrics.Mean("train_loss", dtype=tf.float32)55    self.eval_loss = tf.keras.metrics.Mean("eval_loss", dtype=tf.float32)56    self.return_numpy = return_numpy57    train_dataset = self.strategy.distribute_datasets_from_function(dataset_fn)58    eval_dataset = self.strategy.distribute_datasets_from_function(dataset_fn)59    standard_runner.StandardTrainer.__init__(self, train_dataset)60    standard_runner.StandardEvaluator.__init__(self, eval_dataset)61  def train_step(self, iterator):62    def _replicated_step(inputs):63      """Replicated training step."""64      inputs, targets = inputs65      with tf.GradientTape() as tape:66        outputs = self.model(inputs)67        loss = tf.reduce_mean(tf.keras.losses.MSE(targets, outputs))68      grads = tape.gradient(loss, self.model.variables)69      self.optimizer.apply_gradients(zip(grads, self.model.variables))70      self.train_loss.update_state(loss)71    self.strategy.run(_replicated_step, args=(next(iterator),))72  def train_loop_end(self):73    train_loss = self.train_loss.result()74    return {75        "loss": train_loss.numpy() if self.return_numpy else train_loss,76    }77  def build_eval_dataset(self):78    return self.strategy.distribute_datasets_from_function(dataset_fn)79  def eval_begin(self):80    self.eval_loss.reset_states()81  def eval_step(self, iterator):82    def _replicated_step(inputs):83      """Replicated evaluation step."""84      inputs, targets = inputs85      outputs = self.model(inputs)86      loss = tf.reduce_mean(tf.keras.losses.MSE(targets, outputs))87      self.eval_loss.update_state(loss)88    self.strategy.run(_replicated_step, args=(next(iterator),))89  def eval_end(self):90    eval_loss = self.eval_loss.result()91    return {92        "eval_loss": eval_loss.numpy() if self.return_numpy else eval_loss,93    }94class TestEvaluator(standard_runner.StandardEvaluator):95  """Implements the training and evaluation APIs for the test model."""96  def __init__(self):97    self.strategy = tf.distribute.get_strategy()98    self.model = create_model()99    eval_dataset = self.strategy.distribute_datasets_from_function(dataset_fn)100    standard_runner.StandardEvaluator.__init__(self, eval_dataset)101  def eval_reduce(self, state, output):102    state.append(output)103    return state104  def eval_begin(self):105    return []106  def eval_step(self, iterator):107    def _replicated_step(inputs):108      """Replicated evaluation step."""109      inputs, targets = inputs110      outputs = self.model(inputs)111      loss = tf.reduce_mean(tf.keras.losses.MSE(targets, outputs))112      return loss113    per_replica_losses = self.strategy.run(114        _replicated_step, args=(next(iterator),))115    mean_loss = self.strategy.reduce(116        tf.distribute.ReduceOp.MEAN, per_replica_losses, axis=None)117    return mean_loss118  def eval_end(self, outputs):119    return {120        "eval_loss": tf.reduce_mean(outputs),121    }122class TestEvaluatorNoOutput(runner.AbstractEvaluator):123  def evaluate(self, num_steps):124    pass125class TestEvaluatorWithNestedSummary(standard_runner.StandardEvaluator):126  """Implements the training and evaluation APIs for the test model."""127  def __init__(self):128    self.strategy = tf.distribute.get_strategy()129    self.model = create_model()130    dataset = self.strategy.distribute_datasets_from_function(dataset_fn)131    dataset2 = self.strategy.distribute_datasets_from_function(dataset_fn)132    self.loss = tf.keras.metrics.Mean("loss", dtype=tf.float32)133    self.accuracy = tf.keras.metrics.CategoricalAccuracy(134        "accuracy", dtype=tf.float32)135    self.loss2 = tf.keras.metrics.Mean("loss", dtype=tf.float32)136    self.accuracy2 = tf.keras.metrics.CategoricalAccuracy(137        "accuracy", dtype=tf.float32)138    standard_runner.StandardEvaluator.__init__(139        self, eval_dataset={140            "dataset": dataset,141            "dataset2": dataset2142        })143  def eval_step(self, iterator):144    def _replicated_step(loss, accuracy, inputs):145      """Replicated evaluation step."""146      inputs, targets = inputs147      outputs = self.model(inputs)148      loss.update_state(tf.keras.losses.MSE(targets, outputs))149      accuracy.update_state(targets, outputs)150    self.strategy.run(151        lambda inputs: _replicated_step(self.loss, self.accuracy, inputs),152        args=(next(iterator["dataset"]),))153    self.strategy.run(154        lambda inputs: _replicated_step(self.loss2, self.accuracy2, inputs),155        args=(next(iterator["dataset2"]),))156  def eval_end(self):157    return {158        "dataset": {159            "loss": self.loss.result(),160            "accuracy": self.accuracy.result()161        },162        "dataset2": {163            "loss": self.loss2.result(),164            "accuracy": self.accuracy2.result()165        },166    }167class TestTrainerWithSummaries(standard_runner.StandardTrainer):168  """A Trainer model with summaries for testing purposes."""169  def __init__(self):170    self.strategy = tf.distribute.get_strategy()171    self.model = create_model()172    self.optimizer = tf.keras.optimizers.RMSprop(learning_rate=0.1)173    self.global_step = self.optimizer.iterations174    self.train_loss = tf.keras.metrics.Mean("train_loss", dtype=tf.float32)175    train_dataset = self.strategy.distribute_datasets_from_function(dataset_fn)176    standard_runner.StandardTrainer.__init__(177        self,178        train_dataset,179        options=standard_runner.StandardTrainerOptions(180            use_tpu_summary_optimization=True))181  def build_train_dataset(self):182    return self.strategy.distribute_datasets_from_function(dataset_fn)183  def train_step(self, iterator):184    def _replicated_step(inputs):185      """Replicated training step."""186      inputs, targets = inputs187      with tf.GradientTape() as tape:188        outputs = self.model(inputs)189        loss = tf.reduce_mean(tf.keras.losses.MSE(targets, outputs))190      tf.summary.scalar("loss", loss)191      grads = tape.gradient(loss, self.model.variables)192      self.optimizer.apply_gradients(zip(grads, self.model.variables))193      self.train_loss.update_state(loss)194    self.strategy.run(_replicated_step, args=(next(iterator),))195class ControllerTest(tf.test.TestCase, parameterized.TestCase):196  def setUp(self):197    super().setUp()198    self.model_dir = self.get_temp_dir()199  def test_no_checkpoint(self):200    test_runner = TestRunner()201    # No checkpoint manager and no strategy.202    test_controller = controller.Controller(203        trainer=test_runner,204        evaluator=test_runner,205        global_step=test_runner.global_step,206        steps_per_loop=2,207        summary_dir=os.path.join(self.model_dir, "summaries/train"),208        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"))209    test_controller.train_and_evaluate(210        train_steps=10, eval_steps=2, eval_interval=6)211    self.assertEqual(test_runner.global_step, 10)212    # Loss and accuracy values should be written into summaries.213    self.assertNotEmpty(214        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/train")))215    self.assertNotEmpty(216        summaries_with_matching_keyword(217            "loss", os.path.join(self.model_dir, "summaries/train")))218    self.assertNotEmpty(219        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/eval")))220    self.assertNotEmpty(221        summaries_with_matching_keyword(222            "eval_loss", os.path.join(self.model_dir, "summaries/eval")))223    # No checkpoint, so global step starts from 0.224    test_runner.global_step.assign(0)225    test_controller.train_and_evaluate(226        train_steps=10, eval_steps=2, eval_interval=6)227    self.assertEqual(test_runner.global_step, 10)228  def test_no_checkpoint_and_summaries(self):229    test_runner = TestRunner()230    # No checkpoint + summary directories.231    test_controller = controller.Controller(232        trainer=test_runner,233        evaluator=test_runner,234        global_step=test_runner.global_step,235        steps_per_loop=2)236    test_controller.train_and_evaluate(237        train_steps=10, eval_steps=2, eval_interval=6)238    self.assertEqual(test_runner.global_step, 10)239  def test_has_checkpoint_no_summaries(self):240    test_runner = TestRunner()241    # Has checkpoint, but no summary directories.242    checkpoint = tf.train.Checkpoint(model=test_runner.model)243    checkpoint_manager = tf.train.CheckpointManager(244        checkpoint,245        self.model_dir,246        max_to_keep=None,247        step_counter=test_runner.global_step)248    test_controller = controller.Controller(249        trainer=test_runner,250        evaluator=test_runner,251        global_step=test_runner.global_step,252        checkpoint_manager=checkpoint_manager,253        steps_per_loop=2)254    test_controller.train_and_evaluate(255        train_steps=10, eval_steps=2, eval_interval=6)256    self.assertEqual(test_runner.global_step, 10)257    # No summaries are saved.258    self.assertEmpty(tf.io.gfile.glob(259        os.path.join(checkpoint_manager.directory, "events.*")))260  def test_has_checkpoint_eval_summary_only(self):261    test_runner = TestRunner()262    # Has checkpoint, but no summary directories.263    checkpoint = tf.train.Checkpoint(model=test_runner.model)264    checkpoint_manager = tf.train.CheckpointManager(265        checkpoint,266        self.model_dir,267        max_to_keep=None,268        step_counter=test_runner.global_step)269    test_controller = controller.Controller(270        trainer=test_runner,271        evaluator=test_runner,272        global_step=test_runner.global_step,273        checkpoint_manager=checkpoint_manager,274        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"),275        steps_per_loop=2)276    test_controller.train_and_evaluate(277        train_steps=10, eval_steps=2, eval_interval=6)278    self.assertEqual(test_runner.global_step, 10)279    # Training summaries are not saved.280    self.assertEmpty(tf.io.gfile.glob(281        os.path.join(checkpoint_manager.directory, "events.*")))282    # Evaluation summaries are saved.283    self.assertNotEmpty(tf.io.gfile.glob(284        os.path.join(self.model_dir, "summaries/eval/events.*")))285  def test_restore_from_most_recent_checkpoint(self):286    test_runner = TestRunner()287    checkpoint = tf.train.Checkpoint(model=test_runner.model)288    checkpoint_manager = tf.train.CheckpointManager(289        checkpoint,290        self.model_dir,291        max_to_keep=None,292        step_counter=test_runner.global_step,293        checkpoint_interval=5)294    test_controller = controller.Controller(295        trainer=test_runner,296        global_step=test_runner.global_step,297        checkpoint_manager=checkpoint_manager,298        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"),299        steps_per_loop=5)300    test_controller.train(20)301    self.assertLen(checkpoint_manager.checkpoints, 4)302    restored_path = test_controller.restore_checkpoint()303    self.assertEqual(restored_path, checkpoint_manager.checkpoints[-1])304  @parameterized.named_parameters(("return_numpy", True),305                                  ("return_tensor", False))306  def test_train_and_evaluate(self, return_numpy):307    test_runner = TestRunner(return_numpy=return_numpy)308    checkpoint = tf.train.Checkpoint(309        model=test_runner.model, optimizer=test_runner.optimizer)310    checkpoint_manager = tf.train.CheckpointManager(311        checkpoint,312        self.model_dir,313        max_to_keep=None,314        step_counter=test_runner.global_step,315        checkpoint_interval=10)316    test_controller = controller.Controller(317        trainer=test_runner,318        evaluator=test_runner,319        global_step=test_runner.global_step,320        steps_per_loop=2,321        summary_dir=os.path.join(self.model_dir, "summaries/train"),322        checkpoint_manager=checkpoint_manager,323        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"))324    test_controller.train_and_evaluate(325        train_steps=10, eval_steps=2, eval_interval=6)326    # Checkpoints are saved.327    self.assertNotEmpty(tf.io.gfile.glob(os.path.join(self.model_dir, "ckpt*")))328    # Loss and accuracy values should be written into summaries.329    self.assertNotEmpty(330        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/train")))331    self.assertNotEmpty(332        summaries_with_matching_keyword(333            "loss", os.path.join(self.model_dir, "summaries/train")))334    self.assertNotEmpty(335        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/eval")))336    self.assertNotEmpty(337        summaries_with_matching_keyword(338            "eval_loss", os.path.join(self.model_dir, "summaries/eval")))339  def test_train_only(self):340    test_runner = TestRunner()341    checkpoint = tf.train.Checkpoint(342        model=test_runner.model, optimizer=test_runner.optimizer)343    checkpoint_manager = tf.train.CheckpointManager(344        checkpoint,345        self.model_dir,346        max_to_keep=None,347        step_counter=test_runner.global_step,348        checkpoint_interval=10)349    test_controller = controller.Controller(350        trainer=test_runner,351        global_step=test_runner.global_step,352        steps_per_loop=2,353        summary_dir=os.path.join(self.model_dir, "summaries/train"),354        checkpoint_manager=checkpoint_manager,355        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"),356    )357    test_controller.train(steps=10)358    # Checkpoints are saved.359    self.assertNotEmpty(tf.io.gfile.glob(os.path.join(self.model_dir, "ckpt*")))360    # Only train summaries are written.361    self.assertNotEmpty(362        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/train")))363    self.assertNotEmpty(364        summaries_with_matching_keyword(365            "loss", os.path.join(self.model_dir, "summaries/train")))366    self.assertFalse(367        tf.io.gfile.exists(os.path.join(self.model_dir, "summaries/eval")))368  def test_evaluate_only(self):369    test_runner = TestRunner()370    checkpoint = tf.train.Checkpoint(model=test_runner.model)371    checkpoint.save(os.path.join(self.model_dir, "ckpt"))372    checkpoint_manager = tf.train.CheckpointManager(373        checkpoint,374        self.model_dir,375        max_to_keep=None,376        step_counter=test_runner.global_step)377    test_controller = controller.Controller(378        evaluator=test_runner,379        global_step=test_runner.global_step,380        checkpoint_manager=checkpoint_manager,381        summary_dir=os.path.join(self.model_dir, "summaries/train"),382        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"))383    eval_results = test_controller.evaluate(steps=2)384    # Only eval summaries are written385    self.assertFalse(386        tf.io.gfile.exists(os.path.join(self.model_dir, "summaries/train")))387    self.assertNotEmpty(388        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/eval")))389    self.assertNotEmpty(390        summaries_with_matching_keyword(391            "eval_loss", os.path.join(self.model_dir, "summaries/eval")))392    self.assertIn("eval_loss", eval_results)393    # Tests continuous eval with timeout and timeout_fn.394    done_file = os.path.join(self.model_dir, "summaries/eval/Done")395    def timeout_fn():396      with tf.io.gfile.GFile(done_file, "w") as f:397        f.write("DONE")398        return True399    test_controller = controller.Controller(400        evaluator=test_runner,401        global_step=test_runner.global_step,402        checkpoint_manager=checkpoint_manager,403        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"))404    test_controller.evaluate_continuously(405        timeout=1, timeout_fn=timeout_fn, steps=2)406    self.assertNotEmpty(tf.io.gfile.glob(done_file))407  def test_no_eval_steps(self):408    test_runner = TestRunner()409    checkpoint = tf.train.Checkpoint(model=test_runner.model)410    checkpoint.save(os.path.join(self.model_dir, "ckpt"))411    checkpoint_manager = tf.train.CheckpointManager(412        checkpoint,413        self.model_dir,414        max_to_keep=None,415        step_counter=test_runner.global_step)416    test_controller = controller.Controller(417        evaluator=test_runner,418        global_step=test_runner.global_step,419        checkpoint_manager=checkpoint_manager)420    test_controller.evaluate()421  def test_already_trained_model(self):422    test_runner = TestRunner()423    test_runner.global_step.assign(10)424    checkpoint = tf.train.Checkpoint(425        model=test_runner.model, optimizer=test_runner.optimizer)426    checkpoint_manager = tf.train.CheckpointManager(427        checkpoint,428        self.model_dir,429        max_to_keep=None,430        step_counter=test_runner.global_step,431        checkpoint_interval=10)432    test_controller = controller.Controller(433        trainer=test_runner,434        global_step=test_runner.global_step,435        steps_per_loop=2,436        checkpoint_manager=checkpoint_manager)437    # `global_step` is already `train_steps`.438    test_controller.train(steps=10)439  def test_summaries_inside_train_fn(self):440    test_runner = TestTrainerWithSummaries()441    checkpoint = tf.train.Checkpoint(442        model=test_runner.model, optimizer=test_runner.optimizer)443    checkpoint_manager = tf.train.CheckpointManager(444        checkpoint,445        self.model_dir,446        max_to_keep=None,447        step_counter=test_runner.global_step)448    test_controller = controller.Controller(449        trainer=test_runner,450        global_step=test_runner.global_step,451        steps_per_loop=2,452        summary_dir=os.path.join(self.model_dir, "summaries/train"),453        summary_interval=2,454        checkpoint_manager=checkpoint_manager,455    )456    test_controller.train(steps=10)457    # Checkpoints are saved.458    self.assertEmpty(tf.io.gfile.glob(os.path.join(self.model_dir, "ckpt*")))459    # Only train summaries are written.460    self.assertNotEmpty(461        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/train")))462    self.assertNotEmpty(463        summaries_with_matching_keyword(464            "loss", os.path.join(self.model_dir, "summaries/train")))465    self.assertFalse(466        tf.io.gfile.exists(os.path.join(self.model_dir, "summaries/eval")))467  def test_train_and_evaluate_with_same_summary_dir(self):468    test_runner = TestRunner()469    checkpoint = tf.train.Checkpoint(470        model=test_runner.model, optimizer=test_runner.optimizer)471    checkpoint_manager = tf.train.CheckpointManager(472        checkpoint,473        self.model_dir,474        max_to_keep=None,475        step_counter=test_runner.global_step)476    test_controller = controller.Controller(477        trainer=test_runner,478        evaluator=test_runner,479        global_step=test_runner.global_step,480        steps_per_loop=2,481        summary_dir=os.path.join(self.model_dir, "summaries"),482        checkpoint_manager=checkpoint_manager,483        eval_summary_dir=os.path.join(self.model_dir, "summaries"))484    test_controller.train_and_evaluate(485        train_steps=10, eval_steps=2, eval_interval=6)486    # Loss and accuracy values should be written into summaries.487    self.assertNotEmpty(488        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries")))489    self.assertNotEmpty(490        summaries_with_matching_keyword(491            "loss", os.path.join(self.model_dir, "summaries")))492    self.assertNotEmpty(493        summaries_with_matching_keyword(494            "eval_loss", os.path.join(self.model_dir, "summaries")))495  def test_early_stop_on_eval_loss(self):496    test_runner = TestRunner()497    class EarlyStopController(controller.Controller):498      """A subclass of Controller that supports early stopping."""499      def train_and_evaluate(self,500                             train_steps: int = None,501                             eval_steps: int = None,502                             eval_interval: int = None):503        while self.global_step.numpy() < train_steps:504          interval = min(train_steps - self.global_step.numpy(), eval_interval)505          num_steps = self.global_step.numpy() + interval506          self.train(steps=num_steps, checkpoint_at_completion=False)507          self.evaluate(steps=eval_steps)508          # Early stop condition.509          if test_runner.eval_loss.result() < 0.1:510            logging.info(511                "Training early stopped as eval_loss %s is less than 0.1",512                test_runner.eval_loss.result())513            return514    checkpoint = tf.train.Checkpoint(515        model=test_runner.model, optimizer=test_runner.optimizer)516    checkpoint_manager = tf.train.CheckpointManager(517        checkpoint,518        self.model_dir,519        max_to_keep=None,520        step_counter=test_runner.global_step,521        checkpoint_interval=10)522    test_controller = EarlyStopController(523        trainer=test_runner,524        evaluator=test_runner,525        global_step=test_runner.global_step,526        steps_per_loop=2,527        checkpoint_manager=checkpoint_manager)528    test_controller.train_and_evaluate(529        train_steps=10, eval_steps=6, eval_interval=2)530    self.assertLess(test_runner.global_step, 10)531  def test_evaluate_with_loss_output(self):532    test_evaluator = TestEvaluator()533    checkpoint = tf.train.Checkpoint(model=test_evaluator.model)534    checkpoint.save(os.path.join(self.model_dir, "ckpt"))535    checkpoint_manager = tf.train.CheckpointManager(536        checkpoint, self.model_dir, max_to_keep=None)537    test_controller = controller.Controller(538        evaluator=test_evaluator,539        global_step=tf.Variable(0, dtype=tf.int64),540        checkpoint_manager=checkpoint_manager,541        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"))542    test_controller.evaluate(steps=5)543    # Only eval summaries are written544    self.assertNotEmpty(545        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/eval")))546    self.assertNotEmpty(547        summaries_with_matching_keyword(548            "eval_loss", os.path.join(self.model_dir, "summaries/eval")))549  def test_evaluate_with_no_output(self):550    test_controller = controller.Controller(551        evaluator=TestEvaluatorNoOutput(),552        global_step=tf.Variable(0, dtype=tf.int64),553        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"))554    self.assertEqual(test_controller.evaluate(steps=5), {})555  def test_train_and_evaluate_reset_datasets(self):556    test_runner = TestRunner()557    test_controller = controller.Controller(558        trainer=test_runner,559        evaluator=test_runner,560        global_step=test_runner.global_step,561        steps_per_loop=2)562    test_controller.train_and_evaluate(563        train_steps=10, eval_steps=2, eval_interval=6)564    train_dataset = (565        test_runner.strategy.distribute_datasets_from_function(dataset_fn))566    eval_dataset = (567        test_runner.strategy.distribute_datasets_from_function(dataset_fn))568    test_runner.train_dataset = train_dataset569    test_runner.eval_dataset = eval_dataset570    test_controller.train_and_evaluate(571        train_steps=10, eval_steps=2, eval_interval=6)572  def test_eval_and_checkpoint_interval(self):573    test_runner = TestRunner()574    checkpoint = tf.train.Checkpoint(575        model=test_runner.model, optimizer=test_runner.optimizer)576    checkpoint_manager = tf.train.CheckpointManager(577        checkpoint,578        self.model_dir,579        max_to_keep=None,580        step_counter=test_runner.global_step,581        checkpoint_interval=5)582    test_controller = controller.Controller(583        trainer=test_runner,584        evaluator=test_runner,585        global_step=test_runner.global_step,586        steps_per_loop=10,587        checkpoint_manager=checkpoint_manager,588        summary_dir=self.model_dir)589    test_controller.train_and_evaluate(590        train_steps=10, eval_steps=2, eval_interval=5)591    # Expect 3 checkpoints to be saved at step: 5, 10.592    self.assertLen(593        tf.io.gfile.glob(os.path.join(self.model_dir, "ckpt-*.data*")), 2)594    # Expect evaluation is performed 2 times at step: 5, 10.595    self.assertLen(596        summaries_with_matching_keyword("eval_loss", self.model_dir), 2)597  def test_evaluate_with_nested_summaries(self):598    test_evaluator = TestEvaluatorWithNestedSummary()599    test_controller = controller.Controller(600        evaluator=test_evaluator,601        global_step=tf.Variable(0, dtype=tf.int64),602        eval_summary_dir=self.model_dir)603    test_controller.evaluate(steps=5)604    self.assertNotEmpty(605        tf.io.gfile.listdir(os.path.join(self.model_dir, "dataset")))606    self.assertNotEmpty(607        summaries_with_matching_keyword(608            "loss", os.path.join(self.model_dir, "dataset")))609    self.assertNotEmpty(610        summaries_with_matching_keyword(611            "accuracy", os.path.join(self.model_dir, "dataset")))612    self.assertNotEmpty(613        tf.io.gfile.listdir(os.path.join(self.model_dir, "dataset2")))614    self.assertNotEmpty(615        summaries_with_matching_keyword(616            "loss", os.path.join(self.model_dir, "dataset2")))617    self.assertNotEmpty(618        summaries_with_matching_keyword(619            "accuracy", os.path.join(self.model_dir, "dataset2")))620  def test_actions(self):621    test_runner = TestRunner()622    checkpoint = tf.train.Checkpoint(623        model=test_runner.model, optimizer=test_runner.optimizer)624    checkpoint_manager = tf.train.CheckpointManager(625        checkpoint,626        self.model_dir,627        max_to_keep=None,628        step_counter=test_runner.global_step,629        checkpoint_interval=10)630    class OutputRecorderAction:631      """Simple `Action` that just saves the outputs passed to `__call__`."""632      def __init__(self):633        self.outputs = []634      def __call__(self, output):635        self.outputs.append(output)636    train_output_recorder = OutputRecorderAction()637    eval_output_recorder = OutputRecorderAction()638    test_controller = controller.Controller(639        trainer=test_runner,640        evaluator=test_runner,641        train_actions=[train_output_recorder],642        eval_actions=[eval_output_recorder],643        global_step=test_runner.global_step,644        steps_per_loop=2,645        summary_dir=os.path.join(self.model_dir, "summaries/train"),646        checkpoint_manager=checkpoint_manager,647        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"))648    test_controller.train_and_evaluate(649        train_steps=10, eval_steps=2, eval_interval=6)650    self.assertLen(train_output_recorder.outputs, 5)651    for output in train_output_recorder.outputs:652      self.assertIn("loss", output)653      self.assertGreaterEqual(output["loss"], 0)654    self.assertLen(eval_output_recorder.outputs, 2)655    for output in eval_output_recorder.outputs:656      self.assertIn("eval_loss", output)657      self.assertGreaterEqual(output["eval_loss"], 0)658if __name__ == "__main__":...
Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.