Merge pull request #10338 from srihari-humbarwadi:readme

PiperOrigin-RevId: 413033276

official/README.md

@@ -60,7 +60,7 @@ In the near future, we will add:
 |-------|-------------------|
 | [ALBERT (A Lite BERT)](nlp/albert) | [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942) |
 | [BERT (Bidirectional Encoder Representations from Transformers)](nlp/bert) | [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) |
-| [NHNet (News Headline generation model)](nlp/nhnet) | [Generating Representative Headlines for News Stories](https://arxiv.org/abs/2001.09386) |
+| [NHNet (News Headline generation model)](projects/nhnet) | [Generating Representative Headlines for News Stories](https://arxiv.org/abs/2001.09386) |
 | [Transformer](nlp/transformer) | [Attention Is All You Need](https://arxiv.org/abs/1706.03762) |
 | [XLNet](nlp/xlnet) | [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) |
 | [MobileBERT](nlp/projects/mobilebert) | [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) |

official/common/distribute_utils.py

@@ -141,8 +141,8 @@ def get_distribution_strategy(distribution_strategy="mirrored",
   distribution_strategy = distribution_strategy.lower()
   if distribution_strategy == "off":
     if num_gpus > 1:
-      raise ValueError("When {} GPUs are specified, distribution_strategy "
-                       "flag cannot be set to `off`.".format(num_gpus))
+      raise ValueError(f"When {num_gpus} GPUs are specified, "
+                       "distribution_strategy flag cannot be set to `off`.")
     # Return the default distribution strategy.
     return tf.distribute.get_strategy()
 

official/common/distribute_utils_test.py

@@ -12,39 +12,98 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-""" Tests for distribution util functions."""
+"""Tests for distribution util functions."""
 
+import sys
 import tensorflow as tf
 
 from official.common import distribute_utils
 
+TPU_TEST = 'test_tpu' in sys.argv[0]
 
-class GetDistributionStrategyTest(tf.test.TestCase):
-  """Tests for get_distribution_strategy."""
+
+class DistributeUtilsTest(tf.test.TestCase):
+  """Tests for distribute util functions."""
+
+  def test_invalid_args(self):
+    with self.assertRaisesRegex(ValueError, '`num_gpus` can not be negative.'):
+      _ = distribute_utils.get_distribution_strategy(num_gpus=-1)
+
+    with self.assertRaisesRegex(ValueError,
+                                '.*If you meant to pass the string .*'):
+      _ = distribute_utils.get_distribution_strategy(
+          distribution_strategy=False, num_gpus=0)
+    with self.assertRaisesRegex(ValueError, 'When 2 GPUs are specified.*'):
+      _ = distribute_utils.get_distribution_strategy(
+          distribution_strategy='off', num_gpus=2)
+    with self.assertRaisesRegex(ValueError,
+                                '`OneDeviceStrategy` can not be used.*'):
+      _ = distribute_utils.get_distribution_strategy(
+          distribution_strategy='one_device', num_gpus=2)
 
   def test_one_device_strategy_cpu(self):
-    ds = distribute_utils.get_distribution_strategy(num_gpus=0)
+    ds = distribute_utils.get_distribution_strategy('one_device', num_gpus=0)
     self.assertEquals(ds.num_replicas_in_sync, 1)
     self.assertEquals(len(ds.extended.worker_devices), 1)
     self.assertIn('CPU', ds.extended.worker_devices[0])
 
   def test_one_device_strategy_gpu(self):
-    ds = distribute_utils.get_distribution_strategy(num_gpus=1)
+    ds = distribute_utils.get_distribution_strategy('one_device', num_gpus=1)
     self.assertEquals(ds.num_replicas_in_sync, 1)
     self.assertEquals(len(ds.extended.worker_devices), 1)
     self.assertIn('GPU', ds.extended.worker_devices[0])
 
   def test_mirrored_strategy(self):
+    # CPU only.
+    _ = distribute_utils.get_distribution_strategy(num_gpus=0)
+    # 5 GPUs.
     ds = distribute_utils.get_distribution_strategy(num_gpus=5)
     self.assertEquals(ds.num_replicas_in_sync, 5)
     self.assertEquals(len(ds.extended.worker_devices), 5)
     for device in ds.extended.worker_devices:
       self.assertIn('GPU', device)
 
+    _ = distribute_utils.get_distribution_strategy(
+        distribution_strategy='mirrored',
+        num_gpus=2,
+        all_reduce_alg='nccl',
+        num_packs=2)
+    with self.assertRaisesRegex(
+        ValueError,
+        'When used with `mirrored`, valid values for all_reduce_alg are.*'):
+      _ = distribute_utils.get_distribution_strategy(
+          distribution_strategy='mirrored',
+          num_gpus=2,
+          all_reduce_alg='dummy',
+          num_packs=2)
+
+  def test_mwms(self):
+    distribute_utils.configure_cluster(worker_hosts=None, task_index=-1)
+    ds = distribute_utils.get_distribution_strategy(
+        'multi_worker_mirrored', all_reduce_alg='nccl')
+    self.assertIsInstance(
+        ds, tf.distribute.experimental.MultiWorkerMirroredStrategy)
+
+    with self.assertRaisesRegex(
+        ValueError,
+        'When used with `multi_worker_mirrored`, valid values.*'):
+      _ = distribute_utils.get_distribution_strategy(
+          'multi_worker_mirrored', all_reduce_alg='dummy')
+
   def test_no_strategy(self):
     ds = distribute_utils.get_distribution_strategy('off')
     self.assertIs(ds, tf.distribute.get_strategy())
 
+  def test_tpu_strategy(self):
+    if not TPU_TEST:
+      self.skipTest('Only Cloud TPU VM instances can have local TPUs.')
+    with self.assertRaises(ValueError):
+      _ = distribute_utils.get_distribution_strategy('tpu')
+
+    ds = distribute_utils.get_distribution_strategy('tpu', tpu_address='local')
+    self.assertIsInstance(
+        ds, tf.distribute.TPUStrategy)
+
   def test_invalid_strategy(self):
     with self.assertRaisesRegexp(
         ValueError,
@@ -54,6 +113,12 @@ class GetDistributionStrategyTest(tf.test.TestCase):
         ValueError, 'distribution_strategy must be a string but got: 1'):
       distribute_utils.get_distribution_strategy(1)
 
+  def test_get_strategy_scope(self):
+    ds = distribute_utils.get_distribution_strategy('one_device', num_gpus=0)
+    with distribute_utils.get_strategy_scope(ds):
+      self.assertIs(tf.distribute.get_strategy(), ds)
+    with distribute_utils.get_strategy_scope(None):
+      self.assertIsNot(tf.distribute.get_strategy(), ds)
 
 if __name__ == '__main__':
   tf.test.main()

official/core/actions.py

@@ -28,7 +28,7 @@ from official.core import config_definitions
 from official.modeling import optimization
 
 
-class PruningActions:
+class PruningAction:
   """Train action to updates pruning related information.
 
   This action updates pruning steps at the end of trainig loop, and log
@@ -66,7 +66,7 @@ class PruningActions:
     """Update pruning step and log pruning summaries.
 
     Args:
-      output: The train output to test.
+      output: The train output.
     """
     self.update_pruning_step.on_epoch_end(batch=None)
     self.pruning_summaries.on_epoch_begin(epoch=None)
@@ -81,8 +81,11 @@ class EMACheckpointing:
   than training.
   """
 
-  def __init__(self, export_dir: str, optimizer: tf.keras.optimizers.Optimizer,
-               checkpoint: tf.train.Checkpoint, max_to_keep: int = 1):
+  def __init__(self,
+               export_dir: str,
+               optimizer: tf.keras.optimizers.Optimizer,
+               checkpoint: tf.train.Checkpoint,
+               max_to_keep: int = 1):
     """Initializes the instance.
 
     Args:
@@ -99,8 +102,7 @@ class EMACheckpointing:
                        'EMACheckpointing action')
 
     export_dir = os.path.join(export_dir, 'ema_checkpoints')
-    tf.io.gfile.makedirs(
-        os.path.dirname(export_dir))
+    tf.io.gfile.makedirs(os.path.dirname(export_dir))
     self._optimizer = optimizer
     self._checkpoint = checkpoint
     self._checkpoint_manager = tf.train.CheckpointManager(
@@ -113,7 +115,7 @@ class EMACheckpointing:
     """Swaps model weights, and saves the checkpoint.
 
     Args:
-      output: The train or eval output to test.
+      output: The train or eval output.
     """
     self._optimizer.swap_weights()
     self._checkpoint_manager.save(checkpoint_number=self._optimizer.iterations)
@@ -173,10 +175,9 @@ class RecoveryCondition:
 
 
 @gin.configurable
-def get_eval_actions(
-    params: config_definitions.ExperimentConfig,
-    trainer: base_trainer.Trainer,
-    model_dir: str) -> List[orbit.Action]:
+def get_eval_actions(params: config_definitions.ExperimentConfig,
+                     trainer: base_trainer.Trainer,
+                     model_dir: str) -> List[orbit.Action]:
   """Gets eval actions for TFM trainer."""
   eval_actions = []
   # Adds ema checkpointing action to save the average weights under
@@ -202,7 +203,7 @@ def get_train_actions(
   # Adds pruning callback actions.
   if hasattr(params.task, 'pruning'):
     train_actions.append(
-        PruningActions(
+        PruningAction(
             export_dir=model_dir,
             model=trainer.model,
             optimizer=trainer.optimizer))

official/core/actions_test.py

@@ -27,14 +27,16 @@ from official.core import actions
 from official.modeling import optimization
 
 
-class TestModel(tf.Module):
+class TestModel(tf.keras.Model):
 
   def __init__(self):
-    self.value = tf.Variable(0)
+    super().__init__()
+    self.value = tf.Variable(0.0)
+    self.dense = tf.keras.layers.Dense(2)
+    _ = self.dense(tf.zeros((2, 2), tf.float32))
 
-  @tf.function(input_signature=[])
-  def __call__(self):
-    return self.value
+  def call(self, x, training=None):
+    return self.value + x
 
 
 class ActionsTest(tf.test.TestCase, parameterized.TestCase):
@@ -43,7 +45,7 @@ class ActionsTest(tf.test.TestCase, parameterized.TestCase):
       combinations.combine(
           distribution=[
               strategy_combinations.cloud_tpu_strategy,
-              strategy_combinations.one_device_strategy_gpu,
+              strategy_combinations.one_device_strategy,
           ],))
   def test_ema_checkpointing(self, distribution):
     with distribution.scope():
@@ -62,18 +64,25 @@ class ActionsTest(tf.test.TestCase, parameterized.TestCase):
       model.value.assign(3)
 
       # Checks model.value is 3
-      self.assertEqual(model(), 3)
+      self.assertEqual(model(0.), 3)
       ema_action = actions.EMACheckpointing(directory, optimizer, checkpoint)
 
       ema_action({})
       self.assertNotEmpty(
           tf.io.gfile.glob(os.path.join(directory, 'ema_checkpoints')))
 
-      checkpoint.read(tf.train.latest_checkpoint(
-          os.path.join(directory, 'ema_checkpoints')))
+      checkpoint.read(
+          tf.train.latest_checkpoint(
+              os.path.join(directory, 'ema_checkpoints')))
 
       # Checks model.value is 0 after swapping.
-      self.assertEqual(model(), 0)
+      self.assertEqual(model(0.), 0)
+
+      # Raises an error for a normal optimizer.
+      with self.assertRaisesRegex(ValueError,
+                                  'Optimizer has to be instance of.*'):
+        _ = actions.EMACheckpointing(directory, tf.keras.optimizers.SGD(),
+                                     checkpoint)
 
   @combinations.generate(
       combinations.combine(
@@ -102,6 +111,21 @@ class ActionsTest(tf.test.TestCase, parameterized.TestCase):
       with self.assertRaises(RuntimeError):
         recover_condition(outputs)
 
+  @combinations.generate(
+      combinations.combine(
+          distribution=[
+              strategy_combinations.one_device_strategy_gpu,
+              strategy_combinations.one_device_strategy,
+          ],))
+  def test_pruning(self, distribution):
+    with distribution.scope():
+      directory = self.get_temp_dir()
+      model = TestModel()
+      optimizer = tf.keras.optimizers.SGD()
+      pruning = actions.PruningAction(directory, model, optimizer)
+
+      pruning({})
+
 
 if __name__ == '__main__':
   tf.test.main()

official/core/base_trainer.py

@@ -75,8 +75,8 @@ class Recovery:
     self.recover_counter += 1
     if self.recover_counter > self.recovery_max_trials:
       raise RuntimeError(
-          "The loss value is NaN after training loop and it happens %d times." %
-          self.recover_counter)
+          "The loss value is NaN or out of range after training loop and "
+          f"this happens {self.recover_counter} times.")
     # Loads the previous good checkpoint.
     checkpoint_path = self.checkpoint_manager.restore_or_initialize()
     logging.warning(
@@ -247,14 +247,12 @@ class Trainer(_AsyncTrainer):
     self._validation_loss = tf.keras.metrics.Mean(
         "validation_loss", dtype=tf.float32)
     model_metrics = model.metrics if hasattr(model, "metrics") else []
-    self._train_metrics = self.task.build_metrics(
-        training=True) + model_metrics
-    self._validation_metrics = self.task.build_metrics(
-        training=False) + model_metrics
 
     self.init_async()
 
     if train:
+      self._train_metrics = self.task.build_metrics(
+          training=True) + model_metrics
       train_dataset = train_dataset or self.distribute_dataset(
           self.task.build_inputs, self.config.task.train_data)
       orbit.StandardTrainer.__init__(
@@ -266,6 +264,8 @@ class Trainer(_AsyncTrainer):
               use_tpu_summary_optimization=config.trainer.allow_tpu_summary))
 
     if evaluate:
+      self._validation_metrics = self.task.build_metrics(
+          training=False) + model_metrics
       validation_dataset = validation_dataset or self.distribute_dataset(
           self.task.build_inputs, self.config.task.validation_data)
       orbit.StandardEvaluator.__init__(
@@ -370,16 +370,6 @@ class Trainer(_AsyncTrainer):
     """Accesses the training checkpoint."""
     return self._checkpoint
 
-  # TODO(yejiayu): Remove this once all deps are fixed.
-  def add_recovery(self, params: TrainerConfig,
-                   checkpoint_manager: tf.train.CheckpointManager):
-    if params.recovery_max_trials >= 0:
-      self._recovery = Recovery(
-          loss_upper_bound=params.loss_upper_bound,
-          recovery_begin_steps=params.recovery_begin_steps,
-          recovery_max_trials=params.recovery_max_trials,
-          checkpoint_manager=checkpoint_manager)
-
   def train_loop_end(self):
     """See base class."""
     self.join()

official/core/base_trainer_test.py

@@ -150,6 +150,30 @@ class MockAsyncTrainer(trainer_lib._AsyncTrainer):
     return self.eval_global_step.numpy()
 
 
+class RecoveryTest(tf.test.TestCase):
+
+  def test_recovery_module(self):
+    ckpt = tf.train.Checkpoint(v=tf.Variable(1, dtype=tf.int32))
+    model_dir = self.get_temp_dir()
+    manager = tf.train.CheckpointManager(ckpt, model_dir, max_to_keep=1)
+    recovery_module = trainer_lib.Recovery(
+        loss_upper_bound=1.0,
+        checkpoint_manager=manager,
+        recovery_begin_steps=1,
+        recovery_max_trials=1)
+    self.assertFalse(recovery_module.should_recover(1.1, 0))
+    self.assertFalse(recovery_module.should_recover(0.1, 1))
+    self.assertTrue(recovery_module.should_recover(1.1, 2))
+
+    # First triggers the recovery once.
+    recovery_module.maybe_recover(1.1, 10)
+
+    # Second time, it raises.
+    with self.assertRaisesRegex(
+        RuntimeError, 'The loss value is NaN .*'):
+      recovery_module.maybe_recover(1.1, 10)
+
+
 class TrainerTest(tf.test.TestCase, parameterized.TestCase):
 
   def setUp(self):

official/core/export_base.py

@@ -28,13 +28,35 @@ class ExportModule(tf.Module, metaclass=abc.ABCMeta):
   def __init__(self,
                params,
                model: Union[tf.Module, tf.keras.Model],
-               inference_step: Optional[Callable[..., Any]] = None):
+               inference_step: Optional[Callable[..., Any]] = None,
+               *,
+               preprocessor: Optional[Callable[..., Any]] = None,
+               postprocessor: Optional[Callable[..., Any]] = None):
     """Instantiates an ExportModel.
 
+    Examples:
+
+    `inference_step` must be a function that has `model` as an kwarg or the
+    second positional argument.
+    ```
+    def _inference_step(inputs, model=None):
+      return model(inputs, training=False)
+
+    module = ExportModule(params, model, inference_step=_inference_step)
+    ```
+
+    `preprocessor` and `postprocessor` could be either functions or `tf.Module`.
+    The usages of preprocessor and postprocessor are managed by the
+    implementation of `serve()` method.
+
     Args:
       params: A dataclass for parameters to the module.
       model: A model instance which contains weights and forward computation.
-      inference_step: An optional callable to define how the model is called.
+      inference_step: An optional callable to forward-pass the model. If not
+        specified, it creates a parital function with `model` as an required
+        kwarg.
+      preprocessor: An optional callable to preprocess the inputs.
+      postprocessor: An optional callable to postprocess the model outputs.
     """
     super().__init__(name=None)
     self.model = model
@@ -45,6 +67,8 @@ class ExportModule(tf.Module, metaclass=abc.ABCMeta):
     else:
       self.inference_step = functools.partial(
           self.model.__call__, training=False)
+    self.preprocessor = preprocessor
+    self.postprocessor = postprocessor
 
   @abc.abstractmethod
   def serve(self) -> Mapping[Text, tf.Tensor]:

official/core/export_base_test.py

@@ -25,7 +25,11 @@ class TestModule(export_base.ExportModule):
 
   @tf.function
   def serve(self, inputs: tf.Tensor) -> Mapping[Text, tf.Tensor]:
-    return {'outputs': self.inference_step(inputs)}
+    x = inputs if self.preprocessor is None else self.preprocessor(
+        inputs=inputs)
+    x = self.inference_step(x)
+    x = self.postprocessor(x) if self.postprocessor else x
+    return {'outputs': x}
 
   def get_inference_signatures(
       self, function_keys: Dict[Text, Text]) -> Mapping[Text, Any]:
@@ -83,6 +87,40 @@ class ExportBaseTest(tf.test.TestCase):
     output = imported.signatures['foo'](inputs)
     self.assertAllClose(output['outputs'].numpy(), expected_output.numpy())
 
+  def test_processors(self):
+    model = tf.Module()
+    inputs = tf.zeros((), tf.float32)
+
+    def _inference_step(inputs, model):
+      del model
+      return inputs + 1.0
+
+    def _preprocessor(inputs):
+      print(inputs)
+      return inputs + 0.1
+
+    module = TestModule(
+        params=None,
+        model=model,
+        inference_step=_inference_step,
+        preprocessor=_preprocessor)
+    output = module.serve(inputs)
+    self.assertAllClose(output['outputs'].numpy(), 1.1)
+
+    class _PostProcessor(tf.Module):
+
+      def __call__(self, inputs):
+        return inputs + 0.01
+
+    module = TestModule(
+        params=None,
+        model=model,
+        inference_step=_inference_step,
+        preprocessor=_preprocessor,
+        postprocessor=_PostProcessor())
+    output = module.serve(inputs)
+    self.assertAllClose(output['outputs'].numpy(), 1.11)
+
 
 if __name__ == '__main__':
   tf.test.main()

official/legacy/image_classification/README.md

+# Image Classification
+
+**Warning:** the features in the `image_classification/` folder have been fully
+integrated into vision/beta. Please use the [new code base](../../vision/beta/README.md).
+
+This folder contains TF 2.0 model examples for image classification:
+
+* [MNIST](#mnist)
+* [Classifier Trainer](#classifier-trainer), a framework that uses the Keras
+compile/fit methods for image classification models, including:
+  * ResNet
+  * EfficientNet[^1]
+
+[^1]: Currently a work in progress. We cannot match "AutoAugment (AA)" in [the original version](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet).
+For more information about other types of models, please refer to this
+[README file](../../README.md).
+
+## Before you begin
+Please make sure that you have the latest version of TensorFlow
+installed and
+[add the models folder to your Python path](/official/#running-the-models).
+
+### ImageNet preparation
+
+#### Using TFDS
+`classifier_trainer.py` supports ImageNet with
+[TensorFlow Datasets (TFDS)](https://www.tensorflow.org/datasets/overview).
+
+Please see the following [example snippet](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/scripts/download_and_prepare.py)
+for more information on how to use TFDS to download and prepare datasets, and
+specifically the [TFDS ImageNet readme](https://github.com/tensorflow/datasets/blob/master/docs/catalog/imagenet2012.md)
+for manual download instructions.
+
+#### Legacy TFRecords
+Download the ImageNet dataset and convert it to TFRecord format.
+The following [script](https://github.com/tensorflow/tpu/blob/master/tools/datasets/imagenet_to_gcs.py)
+and [README](https://github.com/tensorflow/tpu/tree/master/tools/datasets#imagenet_to_gcspy)
+provide a few options.
+
+Note that the legacy ResNet runners, e.g. [resnet/resnet_ctl_imagenet_main.py](resnet/resnet_ctl_imagenet_main.py)
+require TFRecords whereas `classifier_trainer.py` can use both by setting the
+builder to 'records' or 'tfds' in the configurations.
+
+### Running on Cloud TPUs
+
+Note: These models will **not** work with TPUs on Colab.
+
+You can train image classification models on Cloud TPUs using
+[tf.distribute.TPUStrategy](https://www.tensorflow.org/api_docs/python/tf.distribute.TPUStrategy?version=nightly).
+If you are not familiar with Cloud TPUs, it is strongly recommended that you go
+through the
+[quickstart](https://cloud.google.com/tpu/docs/quickstart) to learn how to
+create a TPU and GCE VM.
+
+### Running on multiple GPU hosts
+
+You can also train these models on multiple hosts, each with GPUs, using
+[tf.distribute.experimental.MultiWorkerMirroredStrategy](https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/MultiWorkerMirroredStrategy).
+
+The easiest way to run multi-host benchmarks is to set the
+[`TF_CONFIG`](https://www.tensorflow.org/guide/distributed_training#TF_CONFIG)
+appropriately at each host.  e.g., to run using `MultiWorkerMirroredStrategy` on
+2 hosts, the `cluster` in `TF_CONFIG` should have 2 `host:port` entries, and
+host `i` should have the `task` in `TF_CONFIG` set to `{"type": "worker",
+"index": i}`.  `MultiWorkerMirroredStrategy` will automatically use all the
+available GPUs at each host.
+
+## MNIST
+
+To download the data and run the MNIST sample model locally for the first time,
+run one of the following command:
+
+```bash
+python3 mnist_main.py \
+  --model_dir=$MODEL_DIR \
+  --data_dir=$DATA_DIR \
+  --train_epochs=10 \
+  --distribution_strategy=one_device \
+  --num_gpus=$NUM_GPUS \
+  --download
+```
+
+To train the model on a Cloud TPU, run the following command:
+
+```bash
+python3 mnist_main.py \
+  --tpu=$TPU_NAME \
+  --model_dir=$MODEL_DIR \
+  --data_dir=$DATA_DIR \
+  --train_epochs=10 \
+  --distribution_strategy=tpu \
+  --download
+```
+
+Note: the `--download` flag is only required the first time you run the model.
+
+
+## Classifier Trainer
+The classifier trainer is a unified framework for running image classification
+models using Keras's compile/fit methods. Experiments should be provided in the
+form of YAML files, some examples are included within the configs/examples
+folder. Please see [configs/examples](./configs/examples) for more example
+configurations.
+
+The provided configuration files use a per replica batch size and is scaled
+by the number of devices. For instance, if `batch size` = 64, then for 1 GPU
+the global batch size would be 64 * 1 = 64. For 8 GPUs, the global batch size
+would be 64 * 8 = 512. Similarly, for a v3-8 TPU, the global batch size would
+be 64 * 8 = 512, and for a v3-32, the global batch size is 64 * 32 = 2048.
+
+### ResNet50
+
+#### On GPU:
+```bash
+python3 classifier_trainer.py \
+  --mode=train_and_eval \
+  --model_type=resnet \
+  --dataset=imagenet \
+  --model_dir=$MODEL_DIR \
+  --data_dir=$DATA_DIR \
+  --config_file=configs/examples/resnet/imagenet/gpu.yaml \
+  --params_override='runtime.num_gpus=$NUM_GPUS'
+```
+
+To train on multiple hosts, each with GPUs attached using
+[MultiWorkerMirroredStrategy](https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/MultiWorkerMirroredStrategy)
+please update `runtime` section in gpu.yaml
+(or override using `--params_override`) with:
+
+```YAML
+# gpu.yaml
+runtime:
+  distribution_strategy: 'multi_worker_mirrored'
+  worker_hosts: '$HOST1:port,$HOST2:port'
+  num_gpus: $NUM_GPUS
+  task_index: 0
+```
+By having `task_index: 0` on the first host and `task_index: 1` on the second
+and so on. `$HOST1` and `$HOST2` are the IP addresses of the hosts, and `port`
+can be chosen any free port on the hosts. Only the first host will write
+TensorBoard Summaries and save checkpoints.
+
+#### On TPU:
+```bash
+python3 classifier_trainer.py \
+  --mode=train_and_eval \
+  --model_type=resnet \
+  --dataset=imagenet \
+  --tpu=$TPU_NAME \
+  --model_dir=$MODEL_DIR \
+  --data_dir=$DATA_DIR \
+  --config_file=configs/examples/resnet/imagenet/tpu.yaml
+```
+
+### EfficientNet
+**Note: EfficientNet development is a work in progress.**
+#### On GPU:
+```bash
+python3 classifier_trainer.py \
+  --mode=train_and_eval \
+  --model_type=efficientnet \
+  --dataset=imagenet \
+  --model_dir=$MODEL_DIR \
+  --data_dir=$DATA_DIR \
+  --config_file=configs/examples/efficientnet/imagenet/efficientnet-b0-gpu.yaml \
+  --params_override='runtime.num_gpus=$NUM_GPUS'
+```
+
+
+#### On TPU:
+```bash
+python3 classifier_trainer.py \
+  --mode=train_and_eval \
+  --model_type=efficientnet \
+  --dataset=imagenet \
+  --tpu=$TPU_NAME \
+  --model_dir=$MODEL_DIR \
+  --data_dir=$DATA_DIR \
+  --config_file=configs/examples/efficientnet/imagenet/efficientnet-b0-tpu.yaml
+```
+
+Note that the number of GPU devices can be overridden in the command line using
+`--params_overrides`. The TPU does not need this override as the device is fixed
+by providing the TPU address or name with the `--tpu` flag.
+

official/legacy/image_classification/augment_test.py

+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+
+"""Tests for autoaugment."""
+
+from __future__ import absolute_import
+from __future__ import division
+# from __future__ import google_type_annotations
+from __future__ import print_function
+
+from absl.testing import parameterized
+
+import tensorflow as tf
+
+from official.legacy.image_classification import augment
+
+
+def get_dtype_test_cases():
+  return [
+      ('uint8', tf.uint8),
+      ('int32', tf.int32),
+      ('float16', tf.float16),
+      ('float32', tf.float32),
+  ]
+
+
+@parameterized.named_parameters(get_dtype_test_cases())
+class TransformsTest(parameterized.TestCase, tf.test.TestCase):
+  """Basic tests for fundamental transformations."""
+
+  def test_to_from_4d(self, dtype):
+    for shape in [(10, 10), (10, 10, 10), (10, 10, 10, 10)]:
+      original_ndims = len(shape)
+      image = tf.zeros(shape, dtype=dtype)
+      image_4d = augment.to_4d(image)
+      self.assertEqual(4, tf.rank(image_4d))
+      self.assertAllEqual(image, augment.from_4d(image_4d, original_ndims))
+
+  def test_transform(self, dtype):
+    image = tf.constant([[1, 2], [3, 4]], dtype=dtype)
+    self.assertAllEqual(
+        augment.transform(image, transforms=[1] * 8), [[4, 4], [4, 4]])
+
+  def test_translate(self, dtype):
+    image = tf.constant(
+        [[1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1]], dtype=dtype)
+    translations = [-1, -1]
+    translated = augment.translate(image=image, translations=translations)
+    expected = [[1, 0, 1, 1], [0, 1, 0, 0], [1, 0, 1, 1], [1, 0, 1, 1]]
+    self.assertAllEqual(translated, expected)
+
+  def test_translate_shapes(self, dtype):
+    translation = [0, 0]
+    for shape in [(3, 3), (5, 5), (224, 224, 3)]:
+      image = tf.zeros(shape, dtype=dtype)
+      self.assertAllEqual(image, augment.translate(image, translation))
+
+  def test_translate_invalid_translation(self, dtype):
+    image = tf.zeros((1, 1), dtype=dtype)
+    invalid_translation = [[[1, 1]]]
+    with self.assertRaisesRegex(TypeError, 'rank 1 or 2'):
+      _ = augment.translate(image, invalid_translation)
+
+  def test_rotate(self, dtype):
+    image = tf.reshape(tf.cast(tf.range(9), dtype), (3, 3))
+    rotation = 90.
+    transformed = augment.rotate(image=image, degrees=rotation)
+    expected = [[2, 5, 8], [1, 4, 7], [0, 3, 6]]
+    self.assertAllEqual(transformed, expected)
+
+  def test_rotate_shapes(self, dtype):
+    degrees = 0.
+    for shape in [(3, 3), (5, 5), (224, 224, 3)]:
+      image = tf.zeros(shape, dtype=dtype)
+      self.assertAllEqual(image, augment.rotate(image, degrees))
+
+
+class AutoaugmentTest(tf.test.TestCase):
+
+  def test_autoaugment(self):
+    """Smoke test to be sure there are no syntax errors."""
+    image = tf.zeros((224, 224, 3), dtype=tf.uint8)
+
+    augmenter = augment.AutoAugment()
+    aug_image = augmenter.distort(image)
+
+    self.assertEqual((224, 224, 3), aug_image.shape)
+
+  def test_randaug(self):
+    """Smoke test to be sure there are no syntax errors."""
+    image = tf.zeros((224, 224, 3), dtype=tf.uint8)
+
+    augmenter = augment.RandAugment()
+    aug_image = augmenter.distort(image)
+
+    self.assertEqual((224, 224, 3), aug_image.shape)
+
+  def test_all_policy_ops(self):
+    """Smoke test to be sure all augmentation functions can execute."""
+
+    prob = 1
+    magnitude = 10
+    replace_value = [128] * 3
+    cutout_const = 100
+    translate_const = 250
+
+    image = tf.ones((224, 224, 3), dtype=tf.uint8)
+
+    for op_name in augment.NAME_TO_FUNC:
+      func, _, args = augment._parse_policy_info(op_name, prob, magnitude,
+                                                 replace_value, cutout_const,
+                                                 translate_const)
+      image = func(image, *args)
+
+    self.assertEqual((224, 224, 3), image.shape)
+
+
+if __name__ == '__main__':
+  tf.test.main()

official/legacy/image_classification/callbacks.py

+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+
+# Lint as: python3
+"""Common modules for callbacks."""
+from __future__ import absolute_import
+from __future__ import division
+# from __future__ import google_type_annotations
+from __future__ import print_function
+
+import os
+from typing import Any, List, MutableMapping, Optional, Text
+
+from absl import logging
+import tensorflow as tf
+
+from official.modeling import optimization
+from official.utils.misc import keras_utils
+
+
+def get_callbacks(
+    model_checkpoint: bool = True,
+    include_tensorboard: bool = True,
+    time_history: bool = True,
+    track_lr: bool = True,
+    write_model_weights: bool = True,
+    apply_moving_average: bool = False,
+    initial_step: int = 0,
+    batch_size: int = 0,
+    log_steps: int = 0,
+    model_dir: Optional[str] = None,
+    backup_and_restore: bool = False) -> List[tf.keras.callbacks.Callback]:
+  """Get all callbacks."""
+  model_dir = model_dir or ''
+  callbacks = []
+  if model_checkpoint:
+    ckpt_full_path = os.path.join(model_dir, 'model.ckpt-{epoch:04d}')
+    callbacks.append(
+        tf.keras.callbacks.ModelCheckpoint(
+            ckpt_full_path, save_weights_only=True, verbose=1))
+  if backup_and_restore:
+    backup_dir = os.path.join(model_dir, 'tmp')
+    callbacks.append(
+        tf.keras.callbacks.experimental.BackupAndRestore(backup_dir))
+  if include_tensorboard:
+    callbacks.append(
+        CustomTensorBoard(
+            log_dir=model_dir,
+            track_lr=track_lr,
+            initial_step=initial_step,
+            write_images=write_model_weights,
+            profile_batch=0))
+  if time_history:
+    callbacks.append(
+        keras_utils.TimeHistory(
+            batch_size,
+            log_steps,
+            logdir=model_dir if include_tensorboard else None))
+  if apply_moving_average:
+    # Save moving average model to a different file so that
+    # we can resume training from a checkpoint
+    ckpt_full_path = os.path.join(model_dir, 'average',
+                                  'model.ckpt-{epoch:04d}')
+    callbacks.append(
+        AverageModelCheckpoint(
+            update_weights=False,
+            filepath=ckpt_full_path,
+            save_weights_only=True,
+            verbose=1))
+    callbacks.append(MovingAverageCallback())
+  return callbacks
+
+
+def get_scalar_from_tensor(t: tf.Tensor) -> int:
+  """Utility function to convert a Tensor to a scalar."""
+  t = tf.keras.backend.get_value(t)
+  if callable(t):
+    return t()
+  else:
+    return t
+
+
+class CustomTensorBoard(tf.keras.callbacks.TensorBoard):
+  """A customized TensorBoard callback that tracks additional datapoints.
+
+  Metrics tracked:
+  - Global learning rate
+
+  Attributes:
+    log_dir: the path of the directory where to save the log files to be parsed
+      by TensorBoard.
+    track_lr: `bool`, whether or not to track the global learning rate.
+    initial_step: the initial step, used for preemption recovery.
+    **kwargs: Additional arguments for backwards compatibility. Possible key is
+      `period`.
+  """
+
+  # TODO(b/146499062): track params, flops, log lr, l2 loss,
+  # classification loss
+
+  def __init__(self,
+               log_dir: str,
+               track_lr: bool = False,
+               initial_step: int = 0,
+               **kwargs):
+    super(CustomTensorBoard, self).__init__(log_dir=log_dir, **kwargs)
+    self.step = initial_step
+    self._track_lr = track_lr
+
+  def on_batch_begin(self,
+                     epoch: int,
+                     logs: Optional[MutableMapping[str, Any]] = None) -> None:
+    self.step += 1
+    if logs is None:
+      logs = {}
+    logs.update(self._calculate_metrics())
+    super(CustomTensorBoard, self).on_batch_begin(epoch, logs)
+
+  def on_epoch_begin(self,
+                     epoch: int,
+                     logs: Optional[MutableMapping[str, Any]] = None) -> None:
+    if logs is None:
+      logs = {}
+    metrics = self._calculate_metrics()
+    logs.update(metrics)
+    for k, v in metrics.items():
+      logging.info('Current %s: %f', k, v)
+    super(CustomTensorBoard, self).on_epoch_begin(epoch, logs)
+
+  def on_epoch_end(self,
+                   epoch: int,
+                   logs: Optional[MutableMapping[str, Any]] = None) -> None:
+    if logs is None:
+      logs = {}
+    metrics = self._calculate_metrics()
+    logs.update(metrics)
+    super(CustomTensorBoard, self).on_epoch_end(epoch, logs)
+
+  def _calculate_metrics(self) -> MutableMapping[str, Any]:
+    logs = {}
+    # TODO(b/149030439): disable LR reporting.
+    # if self._track_lr:
+    #   logs['learning_rate'] = self._calculate_lr()
+    return logs
+
+  def _calculate_lr(self) -> int:
+    """Calculates the learning rate given the current step."""
+    return get_scalar_from_tensor(
+        self._get_base_optimizer()._decayed_lr(var_dtype=tf.float32))  # pylint:disable=protected-access
+
+  def _get_base_optimizer(self) -> tf.keras.optimizers.Optimizer:
+    """Get the base optimizer used by the current model."""
+
+    optimizer = self.model.optimizer
+
+    # The optimizer might be wrapped by another class, so unwrap it
+    while hasattr(optimizer, '_optimizer'):
+      optimizer = optimizer._optimizer  # pylint:disable=protected-access
+
+    return optimizer
+
+
+class MovingAverageCallback(tf.keras.callbacks.Callback):
+  """A Callback to be used with a `ExponentialMovingAverage` optimizer.
+
+  Applies moving average weights to the model during validation time to test
+  and predict on the averaged weights rather than the current model weights.
+  Once training is complete, the model weights will be overwritten with the
+  averaged weights (by default).
+
+  Attributes:
+    overwrite_weights_on_train_end: Whether to overwrite the current model
+      weights with the averaged weights from the moving average optimizer.
+    **kwargs: Any additional callback arguments.
+  """
+
+  def __init__(self, overwrite_weights_on_train_end: bool = False, **kwargs):
+    super(MovingAverageCallback, self).__init__(**kwargs)
+    self.overwrite_weights_on_train_end = overwrite_weights_on_train_end
+
+  def set_model(self, model: tf.keras.Model):
+    super(MovingAverageCallback, self).set_model(model)
+    assert isinstance(self.model.optimizer,
+                      optimization.ExponentialMovingAverage)
+    self.model.optimizer.shadow_copy(self.model)
+
+  def on_test_begin(self, logs: Optional[MutableMapping[Text, Any]] = None):
+    self.model.optimizer.swap_weights()
+
+  def on_test_end(self, logs: Optional[MutableMapping[Text, Any]] = None):
+    self.model.optimizer.swap_weights()
+
+  def on_train_end(self, logs: Optional[MutableMapping[Text, Any]] = None):
+    if self.overwrite_weights_on_train_end:
+      self.model.optimizer.assign_average_vars(self.model.variables)
+
+
+class AverageModelCheckpoint(tf.keras.callbacks.ModelCheckpoint):
+  """Saves and, optionally, assigns the averaged weights.
+
+  Taken from tfa.callbacks.AverageModelCheckpoint.
+
+  Attributes:
+    update_weights: If True, assign the moving average weights to the model, and
+      save them. If False, keep the old non-averaged weights, but the saved
+      model uses the average weights. See `tf.keras.callbacks.ModelCheckpoint`
+      for the other args.
+  """
+
+  def __init__(self,
+               update_weights: bool,
+               filepath: str,
+               monitor: str = 'val_loss',
+               verbose: int = 0,
+               save_best_only: bool = False,
+               save_weights_only: bool = False,
+               mode: str = 'auto',
+               save_freq: str = 'epoch',
+               **kwargs):
+    self.update_weights = update_weights
+    super().__init__(filepath, monitor, verbose, save_best_only,
+                     save_weights_only, mode, save_freq, **kwargs)
+
+  def set_model(self, model):
+    if not isinstance(model.optimizer, optimization.ExponentialMovingAverage):
+      raise TypeError('AverageModelCheckpoint is only used when training'
+                      'with MovingAverage')
+    return super().set_model(model)
+
+  def _save_model(self, epoch, logs):
+    assert isinstance(self.model.optimizer,
+                      optimization.ExponentialMovingAverage)
+
+    if self.update_weights:
+      self.model.optimizer.assign_average_vars(self.model.variables)
+      return super()._save_model(epoch, logs)  # pytype: disable=attribute-error  # typed-keras
+    else:
+      # Note: `model.get_weights()` gives us the weights (non-ref)
+      # whereas `model.variables` returns references to the variables.
+      non_avg_weights = self.model.get_weights()
+      self.model.optimizer.assign_average_vars(self.model.variables)
+      # result is currently None, since `super._save_model` doesn't
+      # return anything, but this may change in the future.
+      result = super()._save_model(epoch, logs)  # pytype: disable=attribute-error  # typed-keras
+      self.model.set_weights(non_avg_weights)
+      return result

official/legacy/image_classification/classifier_trainer.py

+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+
+# Lint as: python3
+"""Runs an Image Classification model."""
+
+import os
+import pprint
+from typing import Any, Mapping, Optional, Text, Tuple
+
+from absl import app
+from absl import flags
+from absl import logging
+import tensorflow as tf
+from official.common import distribute_utils
+from official.legacy.image_classification import callbacks as custom_callbacks
+from official.legacy.image_classification import dataset_factory
+from official.legacy.image_classification import optimizer_factory
+from official.legacy.image_classification.configs import base_configs
+from official.legacy.image_classification.configs import configs
+from official.legacy.image_classification.efficientnet import efficientnet_model
+from official.legacy.image_classification.resnet import common
+from official.legacy.image_classification.resnet import resnet_model
+from official.modeling import hyperparams
+from official.modeling import performance
+from official.utils import hyperparams_flags
+from official.utils.misc import keras_utils
+
+
+def get_models() -> Mapping[str, tf.keras.Model]:
+  """Returns the mapping from model type name to Keras model."""
+  return {
+      'efficientnet': efficientnet_model.EfficientNet.from_name,
+      'resnet': resnet_model.resnet50,
+  }
+
+
+def get_dtype_map() -> Mapping[str, tf.dtypes.DType]:
+  """Returns the mapping from dtype string representations to TF dtypes."""
+  return {
+      'float32': tf.float32,
+      'bfloat16': tf.bfloat16,
+      'float16': tf.float16,
+      'fp32': tf.float32,
+      'bf16': tf.bfloat16,
+  }
+
+
+def _get_metrics(one_hot: bool) -> Mapping[Text, Any]:
+  """Get a dict of available metrics to track."""
+  if one_hot:
+    return {
+        # (name, metric_fn)
+        'acc':
+            tf.keras.metrics.CategoricalAccuracy(name='accuracy'),
+        'accuracy':
+            tf.keras.metrics.CategoricalAccuracy(name='accuracy'),
+        'top_1':
+            tf.keras.metrics.CategoricalAccuracy(name='accuracy'),
+        'top_5':
+            tf.keras.metrics.TopKCategoricalAccuracy(
+                k=5, name='top_5_accuracy'),
+    }
+  else:
+    return {
+        # (name, metric_fn)
+        'acc':
+            tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy'),
+        'accuracy':
+            tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy'),
+        'top_1':
+            tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy'),
+        'top_5':
+            tf.keras.metrics.SparseTopKCategoricalAccuracy(
+                k=5, name='top_5_accuracy'),
+    }
+
+
+def get_image_size_from_model(
+    params: base_configs.ExperimentConfig) -> Optional[int]:
+  """If the given model has a preferred image size, return it."""
+  if params.model_name == 'efficientnet':
+    efficientnet_name = params.model.model_params.model_name
+    if efficientnet_name in efficientnet_model.MODEL_CONFIGS:
+      return efficientnet_model.MODEL_CONFIGS[efficientnet_name].resolution
+  return None
+
+
+def _get_dataset_builders(params: base_configs.ExperimentConfig,
+                          strategy: tf.distribute.Strategy,
+                          one_hot: bool) -> Tuple[Any, Any]:
+  """Create and return train and validation dataset builders."""
+  if one_hot:
+    logging.warning('label_smoothing > 0, so datasets will be one hot encoded.')
+  else:
+    logging.warning('label_smoothing not applied, so datasets will not be one '
+                    'hot encoded.')
+
+  num_devices = strategy.num_replicas_in_sync if strategy else 1
+
+  image_size = get_image_size_from_model(params)
+
+  dataset_configs = [params.train_dataset, params.validation_dataset]
+  builders = []
+
+  for config in dataset_configs:
+    if config is not None and config.has_data:
+      builder = dataset_factory.DatasetBuilder(
+          config,
+          image_size=image_size or config.image_size,
+          num_devices=num_devices,
+          one_hot=one_hot)
+    else:
+      builder = None
+    builders.append(builder)
+
+  return builders
+
+
+def get_loss_scale(params: base_configs.ExperimentConfig,
+                   fp16_default: float = 128.) -> float:
+  """Returns the loss scale for initializations."""
+  loss_scale = params.runtime.loss_scale
+  if loss_scale == 'dynamic':
+    return loss_scale
+  elif loss_scale is not None:
+    return float(loss_scale)
+  elif (params.train_dataset.dtype == 'float32' or
+        params.train_dataset.dtype == 'bfloat16'):
+    return 1.
+  else:
+    assert params.train_dataset.dtype == 'float16'
+    return fp16_default
+
+
+def _get_params_from_flags(flags_obj: flags.FlagValues):
+  """Get ParamsDict from flags."""
+  model = flags_obj.model_type.lower()
+  dataset = flags_obj.dataset.lower()
+  params = configs.get_config(model=model, dataset=dataset)
+
+  flags_overrides = {
+      'model_dir': flags_obj.model_dir,
+      'mode': flags_obj.mode,
+      'model': {
+          'name': model,
+      },
+      'runtime': {
+          'run_eagerly': flags_obj.run_eagerly,
+          'tpu': flags_obj.tpu,
+      },
+      'train_dataset': {
+          'data_dir': flags_obj.data_dir,
+      },
+      'validation_dataset': {
+          'data_dir': flags_obj.data_dir,
+      },
+      'train': {
+          'time_history': {
+              'log_steps': flags_obj.log_steps,
+          },
+      },
+  }
+
+  overriding_configs = (flags_obj.config_file, flags_obj.params_override,
+                        flags_overrides)
+
+  pp = pprint.PrettyPrinter()
+
+  logging.info('Base params: %s', pp.pformat(params.as_dict()))
+
+  for param in overriding_configs:
+    logging.info('Overriding params: %s', param)
+    params = hyperparams.override_params_dict(params, param, is_strict=True)
+
+  params.validate()
+  params.lock()
+
+  logging.info('Final model parameters: %s', pp.pformat(params.as_dict()))
+  return params
+
+
+def resume_from_checkpoint(model: tf.keras.Model, model_dir: str,
+                           train_steps: int) -> int:
+  """Resumes from the latest checkpoint, if possible.
+
+  Loads the model weights and optimizer settings from a checkpoint.
+  This function should be used in case of preemption recovery.
+
+  Args:
+    model: The model whose weights should be restored.
+    model_dir: The directory where model weights were saved.
+    train_steps: The number of steps to train.
+
+  Returns:
+    The epoch of the latest checkpoint, or 0 if not restoring.
+
+  """
+  logging.info('Load from checkpoint is enabled.')
+  latest_checkpoint = tf.train.latest_checkpoint(model_dir)
+  logging.info('latest_checkpoint: %s', latest_checkpoint)
+  if not latest_checkpoint:
+    logging.info('No checkpoint detected.')
+    return 0
+
+  logging.info('Checkpoint file %s found and restoring from '
+               'checkpoint', latest_checkpoint)
+  model.load_weights(latest_checkpoint)
+  initial_epoch = model.optimizer.iterations // train_steps
+  logging.info('Completed loading from checkpoint.')
+  logging.info('Resuming from epoch %d', initial_epoch)
+  return int(initial_epoch)
+
+
+def initialize(params: base_configs.ExperimentConfig,
+               dataset_builder: dataset_factory.DatasetBuilder):
+  """Initializes backend related initializations."""
+  keras_utils.set_session_config(enable_xla=params.runtime.enable_xla)
+  performance.set_mixed_precision_policy(dataset_builder.dtype)
+  if tf.config.list_physical_devices('GPU'):
+    data_format = 'channels_first'
+  else:
+    data_format = 'channels_last'
+  tf.keras.backend.set_image_data_format(data_format)
+  if params.runtime.run_eagerly:
+    # Enable eager execution to allow step-by-step debugging
+    tf.config.experimental_run_functions_eagerly(True)
+  if tf.config.list_physical_devices('GPU'):
+    if params.runtime.gpu_thread_mode:
+      keras_utils.set_gpu_thread_mode_and_count(
+          per_gpu_thread_count=params.runtime.per_gpu_thread_count,
+          gpu_thread_mode=params.runtime.gpu_thread_mode,
+          num_gpus=params.runtime.num_gpus,
+          datasets_num_private_threads=params.runtime
+          .dataset_num_private_threads)  # pylint:disable=line-too-long
+    if params.runtime.batchnorm_spatial_persistent:
+      os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '1'
+
+
+def define_classifier_flags():
+  """Defines common flags for image classification."""
+  hyperparams_flags.initialize_common_flags()
+  flags.DEFINE_string(
+      'data_dir', default=None, help='The location of the input data.')
+  flags.DEFINE_string(
+      'mode',
+      default=None,
+      help='Mode to run: `train`, `eval`, `train_and_eval` or `export`.')
+  flags.DEFINE_bool(
+      'run_eagerly',
+      default=None,
+      help='Use eager execution and disable autograph for debugging.')
+  flags.DEFINE_string(
+      'model_type',
+      default=None,
+      help='The type of the model, e.g. EfficientNet, etc.')
+  flags.DEFINE_string(
+      'dataset',
+      default=None,
+      help='The name of the dataset, e.g. ImageNet, etc.')
+  flags.DEFINE_integer(
+      'log_steps',
+      default=100,
+      help='The interval of steps between logging of batch level stats.')
+
+
+def serialize_config(params: base_configs.ExperimentConfig, model_dir: str):
+  """Serializes and saves the experiment config."""
+  params_save_path = os.path.join(model_dir, 'params.yaml')
+  logging.info('Saving experiment configuration to %s', params_save_path)
+  tf.io.gfile.makedirs(model_dir)
+  hyperparams.save_params_dict_to_yaml(params, params_save_path)
+
+
+def train_and_eval(
+    params: base_configs.ExperimentConfig,
+    strategy_override: tf.distribute.Strategy) -> Mapping[str, Any]:
+  """Runs the train and eval path using compile/fit."""
+  logging.info('Running train and eval.')
+
+  distribute_utils.configure_cluster(params.runtime.worker_hosts,
+                                     params.runtime.task_index)
+
+  # Note: for TPUs, strategy and scope should be created before the dataset
+  strategy = strategy_override or distribute_utils.get_distribution_strategy(
+      distribution_strategy=params.runtime.distribution_strategy,
+      all_reduce_alg=params.runtime.all_reduce_alg,
+      num_gpus=params.runtime.num_gpus,
+      tpu_address=params.runtime.tpu)
+
+  strategy_scope = distribute_utils.get_strategy_scope(strategy)
+
+  logging.info('Detected %d devices.',
+               strategy.num_replicas_in_sync if strategy else 1)
+
+  label_smoothing = params.model.loss.label_smoothing
+  one_hot = label_smoothing and label_smoothing > 0
+
+  builders = _get_dataset_builders(params, strategy, one_hot)
+  datasets = [
+      builder.build(strategy) if builder else None for builder in builders
+  ]
+
+  # Unpack datasets and builders based on train/val/test splits
+  train_builder, validation_builder = builders  # pylint: disable=unbalanced-tuple-unpacking
+  train_dataset, validation_dataset = datasets
+
+  train_epochs = params.train.epochs
+  train_steps = params.train.steps or train_builder.num_steps
+  validation_steps = params.evaluation.steps or validation_builder.num_steps
+
+  initialize(params, train_builder)
+
+  logging.info('Global batch size: %d', train_builder.global_batch_size)
+
+  with strategy_scope:
+    model_params = params.model.model_params.as_dict()
+    model = get_models()[params.model.name](**model_params)
+    learning_rate = optimizer_factory.build_learning_rate(
+        params=params.model.learning_rate,
+        batch_size=train_builder.global_batch_size,
+        train_epochs=train_epochs,
+        train_steps=train_steps)
+    optimizer = optimizer_factory.build_optimizer(
+        optimizer_name=params.model.optimizer.name,
+        base_learning_rate=learning_rate,
+        params=params.model.optimizer.as_dict(),
+        model=model)
+    optimizer = performance.configure_optimizer(
+        optimizer,
+        use_float16=train_builder.dtype == 'float16',
+        loss_scale=get_loss_scale(params))
+
+    metrics_map = _get_metrics(one_hot)
+    metrics = [metrics_map[metric] for metric in params.train.metrics]
+    steps_per_loop = train_steps if params.train.set_epoch_loop else 1
+
+    if one_hot:
+      loss_obj = tf.keras.losses.CategoricalCrossentropy(
+          label_smoothing=params.model.loss.label_smoothing)
+    else:
+      loss_obj = tf.keras.losses.SparseCategoricalCrossentropy()
+    model.compile(
+        optimizer=optimizer,
+        loss=loss_obj,
+        metrics=metrics,
+        steps_per_execution=steps_per_loop)
+
+    initial_epoch = 0
+    if params.train.resume_checkpoint:
+      initial_epoch = resume_from_checkpoint(
+          model=model, model_dir=params.model_dir, train_steps=train_steps)
+
+    callbacks = custom_callbacks.get_callbacks(
+        model_checkpoint=params.train.callbacks.enable_checkpoint_and_export,
+        include_tensorboard=params.train.callbacks.enable_tensorboard,
+        time_history=params.train.callbacks.enable_time_history,
+        track_lr=params.train.tensorboard.track_lr,
+        write_model_weights=params.train.tensorboard.write_model_weights,
+        initial_step=initial_epoch * train_steps,
+        batch_size=train_builder.global_batch_size,
+        log_steps=params.train.time_history.log_steps,
+        model_dir=params.model_dir,
+        backup_and_restore=params.train.callbacks.enable_backup_and_restore)
+
+  serialize_config(params=params, model_dir=params.model_dir)
+
+  if params.evaluation.skip_eval:
+    validation_kwargs = {}
+  else:
+    validation_kwargs = {
+        'validation_data': validation_dataset,
+        'validation_steps': validation_steps,
+        'validation_freq': params.evaluation.epochs_between_evals,
+    }
+
+  history = model.fit(
+      train_dataset,
+      epochs=train_epochs,
+      steps_per_epoch=train_steps,
+      initial_epoch=initial_epoch,
+      callbacks=callbacks,
+      verbose=2,
+      **validation_kwargs)
+
+  validation_output = None
+  if not params.evaluation.skip_eval:
+    validation_output = model.evaluate(
+        validation_dataset, steps=validation_steps, verbose=2)
+
+  # TODO(dankondratyuk): eval and save final test accuracy
+  stats = common.build_stats(history, validation_output, callbacks)
+  return stats
+
+
+def export(params: base_configs.ExperimentConfig):
+  """Runs the model export functionality."""
+  logging.info('Exporting model.')
+  model_params = params.model.model_params.as_dict()
+  model = get_models()[params.model.name](**model_params)
+  checkpoint = params.export.checkpoint
+  if checkpoint is None:
+    logging.info('No export checkpoint was provided. Using the latest '
+                 'checkpoint from model_dir.')
+    checkpoint = tf.train.latest_checkpoint(params.model_dir)
+
+  model.load_weights(checkpoint)
+  model.save(params.export.destination)
+
+
+def run(flags_obj: flags.FlagValues,
+        strategy_override: tf.distribute.Strategy = None) -> Mapping[str, Any]:
+  """Runs Image Classification model using native Keras APIs.
+
+  Args:
+    flags_obj: An object containing parsed flag values.
+    strategy_override: A `tf.distribute.Strategy` object to use for model.
+
+  Returns:
+    Dictionary of training/eval stats
+  """
+  params = _get_params_from_flags(flags_obj)
+  if params.mode == 'train_and_eval':
+    return train_and_eval(params, strategy_override)
+  elif params.mode == 'export_only':
+    export(params)
+  else:
+    raise ValueError('{} is not a valid mode.'.format(params.mode))
+
+
+def main(_):
+  stats = run(flags.FLAGS)
+  if stats:
+    logging.info('Run stats:\n%s', stats)
+
+
+if __name__ == '__main__':
+  logging.set_verbosity(logging.INFO)
+  define_classifier_flags()
+  flags.mark_flag_as_required('data_dir')
+  flags.mark_flag_as_required('mode')
+  flags.mark_flag_as_required('model_type')
+  flags.mark_flag_as_required('dataset')
+
+  app.run(main)

official/legacy/image_classification/classifier_trainer_test.py

+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+
+# Lint as: python3
+"""Unit tests for the classifier trainer models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import functools
+import json
+
+import os
+import sys
+
+from typing import Any, Callable, Iterable, Mapping, MutableMapping, Optional, Tuple
+
+from absl import flags
+from absl.testing import flagsaver
+from absl.testing import parameterized
+import tensorflow as tf
+
+from tensorflow.python.distribute import combinations
+from tensorflow.python.distribute import strategy_combinations
+from official.legacy.image_classification import classifier_trainer
+from official.utils.flags import core as flags_core
+
+
+classifier_trainer.define_classifier_flags()
+
+
+def distribution_strategy_combinations() -> Iterable[Tuple[Any, ...]]:
+  """Returns the combinations of end-to-end tests to run."""
+  return combinations.combine(
+      distribution=[
+          strategy_combinations.default_strategy,
+          strategy_combinations.cloud_tpu_strategy,
+          strategy_combinations.one_device_strategy_gpu,
+          strategy_combinations.mirrored_strategy_with_two_gpus,
+      ],
+      model=[
+          'efficientnet',
+          'resnet',
+      ],
+      dataset=[
+          'imagenet',
+      ],
+  )
+
+
+def get_params_override(params_override: Mapping[str, Any]) -> str:
+  """Converts params_override dict to string command."""
+  return '--params_override=' + json.dumps(params_override)
+
+
+def basic_params_override(dtype: str = 'float32') -> MutableMapping[str, Any]:
+  """Returns a basic parameter configuration for testing."""
+  return {
+      'train_dataset': {
+          'builder': 'synthetic',
+          'use_per_replica_batch_size': True,
+          'batch_size': 1,
+          'image_size': 224,
+          'dtype': dtype,
+      },
+      'validation_dataset': {
+          'builder': 'synthetic',
+          'batch_size': 1,
+          'use_per_replica_batch_size': True,
+          'image_size': 224,
+          'dtype': dtype,
+      },
+      'train': {
+          'steps': 1,
+          'epochs': 1,
+          'callbacks': {
+              'enable_checkpoint_and_export': True,
+              'enable_tensorboard': False,
+          },
+      },
+      'evaluation': {
+          'steps': 1,
+      },
+  }
+
+
+@flagsaver.flagsaver
+def run_end_to_end(main: Callable[[Any], None],
+                   extra_flags: Optional[Iterable[str]] = None,
+                   model_dir: Optional[str] = None):
+  """Runs the classifier trainer end-to-end."""
+  extra_flags = [] if extra_flags is None else extra_flags
+  args = [sys.argv[0], '--model_dir', model_dir] + extra_flags
+  flags_core.parse_flags(argv=args)
+  main(flags.FLAGS)
+
+
+class ClassifierTest(tf.test.TestCase, parameterized.TestCase):
+  """Unit tests for Keras models."""
+  _tempdir = None
+
+  @classmethod
+  def setUpClass(cls):  # pylint: disable=invalid-name
+    super(ClassifierTest, cls).setUpClass()
+
+  def tearDown(self):
+    super(ClassifierTest, self).tearDown()
+    tf.io.gfile.rmtree(self.get_temp_dir())
+
+  @combinations.generate(distribution_strategy_combinations())
+  def test_end_to_end_train_and_eval(self, distribution, model, dataset):
+    """Test train_and_eval and export for Keras classifier models."""
+    # Some parameters are not defined as flags (e.g. cannot run
+    # classifier_train.py --batch_size=...) by design, so use
+    # "--params_override=..." instead
+    model_dir = self.create_tempdir().full_path
+    base_flags = [
+        '--data_dir=not_used',
+        '--model_type=' + model,
+        '--dataset=' + dataset,
+    ]
+    train_and_eval_flags = base_flags + [
+        get_params_override(basic_params_override()),
+        '--mode=train_and_eval',
+    ]
+
+    run = functools.partial(
+        classifier_trainer.run, strategy_override=distribution)
+    run_end_to_end(
+        main=run, extra_flags=train_and_eval_flags, model_dir=model_dir)
+
+  @combinations.generate(
+      combinations.combine(
+          distribution=[
+              strategy_combinations.one_device_strategy_gpu,
+          ],
+          model=[
+              'efficientnet',
+              'resnet',
+          ],
+          dataset='imagenet',
+          dtype='float16',
+      ))
+  def test_gpu_train(self, distribution, model, dataset, dtype):
+    """Test train_and_eval and export for Keras classifier models."""
+    # Some parameters are not defined as flags (e.g. cannot run
+    # classifier_train.py --batch_size=...) by design, so use
+    # "--params_override=..." instead
+    model_dir = self.create_tempdir().full_path
+    base_flags = [
+        '--data_dir=not_used',
+        '--model_type=' + model,
+        '--dataset=' + dataset,
+    ]
+    train_and_eval_flags = base_flags + [
+        get_params_override(basic_params_override(dtype)),
+        '--mode=train_and_eval',
+    ]
+
+    export_params = basic_params_override()
+    export_path = os.path.join(model_dir, 'export')
+    export_params['export'] = {}
+    export_params['export']['destination'] = export_path
+    export_flags = base_flags + [
+        '--mode=export_only',
+        get_params_override(export_params)
+    ]
+
+    run = functools.partial(
+        classifier_trainer.run, strategy_override=distribution)
+    run_end_to_end(
+        main=run, extra_flags=train_and_eval_flags, model_dir=model_dir)
+    run_end_to_end(main=run, extra_flags=export_flags, model_dir=model_dir)
+    self.assertTrue(os.path.exists(export_path))
+
+  @combinations.generate(
+      combinations.combine(
+          distribution=[
+              strategy_combinations.cloud_tpu_strategy,
+          ],
+          model=[
+              'efficientnet',
+              'resnet',
+          ],
+          dataset='imagenet',
+          dtype='bfloat16',
+      ))
+  def test_tpu_train(self, distribution, model, dataset, dtype):
+    """Test train_and_eval and export for Keras classifier models."""
+    # Some parameters are not defined as flags (e.g. cannot run
+    # classifier_train.py --batch_size=...) by design, so use
+    # "--params_override=..." instead
+    model_dir = self.create_tempdir().full_path
+    base_flags = [
+        '--data_dir=not_used',
+        '--model_type=' + model,
+        '--dataset=' + dataset,
+    ]
+    train_and_eval_flags = base_flags + [
+        get_params_override(basic_params_override(dtype)),
+        '--mode=train_and_eval',
+    ]
+
+    run = functools.partial(
+        classifier_trainer.run, strategy_override=distribution)
+    run_end_to_end(
+        main=run, extra_flags=train_and_eval_flags, model_dir=model_dir)
+
+  @combinations.generate(distribution_strategy_combinations())
+  def test_end_to_end_invalid_mode(self, distribution, model, dataset):
+    """Test the Keras EfficientNet model with `strategy`."""
+    model_dir = self.create_tempdir().full_path
+    extra_flags = [
+        '--data_dir=not_used',
+        '--mode=invalid_mode',
+        '--model_type=' + model,
+        '--dataset=' + dataset,
+        get_params_override(basic_params_override()),
+    ]
+
+    run = functools.partial(
+        classifier_trainer.run, strategy_override=distribution)
+    with self.assertRaises(ValueError):
+      run_end_to_end(main=run, extra_flags=extra_flags, model_dir=model_dir)
+
+
+if __name__ == '__main__':
+  tf.test.main()

official/legacy/image_classification/classifier_trainer_util_test.py

+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+
+# Lint as: python3
+"""Unit tests for the classifier trainer models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+import os
+
+from absl.testing import parameterized
+import tensorflow as tf
+
+from official.legacy.image_classification import classifier_trainer
+from official.legacy.image_classification import dataset_factory
+from official.legacy.image_classification import test_utils
+from official.legacy.image_classification.configs import base_configs
+
+
+def get_trivial_model(num_classes: int) -> tf.keras.Model:
+  """Creates and compiles trivial model for ImageNet dataset."""
+  model = test_utils.trivial_model(num_classes=num_classes)
+  lr = 0.01
+  optimizer = tf.keras.optimizers.SGD(learning_rate=lr)
+  loss_obj = tf.keras.losses.SparseCategoricalCrossentropy()
+  model.compile(optimizer=optimizer, loss=loss_obj, run_eagerly=True)
+  return model
+
+
+def get_trivial_data() -> tf.data.Dataset:
+  """Gets trivial data in the ImageNet size."""
+
+  def generate_data(_) -> tf.data.Dataset:
+    image = tf.zeros(shape=(224, 224, 3), dtype=tf.float32)
+    label = tf.zeros([1], dtype=tf.int32)
+    return image, label
+
+  dataset = tf.data.Dataset.range(1)
+  dataset = dataset.repeat()
+  dataset = dataset.map(
+      generate_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)
+  dataset = dataset.prefetch(buffer_size=1).batch(1)
+  return dataset
+
+
+class UtilTests(parameterized.TestCase, tf.test.TestCase):
+  """Tests for individual utility functions within classifier_trainer.py."""
+
+  @parameterized.named_parameters(
+      ('efficientnet-b0', 'efficientnet', 'efficientnet-b0', 224),
+      ('efficientnet-b1', 'efficientnet', 'efficientnet-b1', 240),
+      ('efficientnet-b2', 'efficientnet', 'efficientnet-b2', 260),
+      ('efficientnet-b3', 'efficientnet', 'efficientnet-b3', 300),
+      ('efficientnet-b4', 'efficientnet', 'efficientnet-b4', 380),
+      ('efficientnet-b5', 'efficientnet', 'efficientnet-b5', 456),
+      ('efficientnet-b6', 'efficientnet', 'efficientnet-b6', 528),
+      ('efficientnet-b7', 'efficientnet', 'efficientnet-b7', 600),
+      ('resnet', 'resnet', '', None),
+  )
+  def test_get_model_size(self, model, model_name, expected):
+    config = base_configs.ExperimentConfig(
+        model_name=model,
+        model=base_configs.ModelConfig(
+            model_params={
+                'model_name': model_name,
+            },))
+    size = classifier_trainer.get_image_size_from_model(config)
+    self.assertEqual(size, expected)
+
+  @parameterized.named_parameters(
+      ('dynamic', 'dynamic', None, 'dynamic'),
+      ('scalar', 128., None, 128.),
+      ('float32', None, 'float32', 1),
+      ('float16', None, 'float16', 128),
+  )
+  def test_get_loss_scale(self, loss_scale, dtype, expected):
+    config = base_configs.ExperimentConfig(
+        runtime=base_configs.RuntimeConfig(loss_scale=loss_scale),
+        train_dataset=dataset_factory.DatasetConfig(dtype=dtype))
+    ls = classifier_trainer.get_loss_scale(config, fp16_default=128)
+    self.assertEqual(ls, expected)
+
+  @parameterized.named_parameters(('float16', 'float16'),
+                                  ('bfloat16', 'bfloat16'))
+  def test_initialize(self, dtype):
+    config = base_configs.ExperimentConfig(
+        runtime=base_configs.RuntimeConfig(
+            run_eagerly=False,
+            enable_xla=False,
+            per_gpu_thread_count=1,
+            gpu_thread_mode='gpu_private',
+            num_gpus=1,
+            dataset_num_private_threads=1,
+        ),
+        train_dataset=dataset_factory.DatasetConfig(dtype=dtype),
+        model=base_configs.ModelConfig(),
+    )
+
+    class EmptyClass:
+      pass
+
+    fake_ds_builder = EmptyClass()
+    fake_ds_builder.dtype = dtype
+    fake_ds_builder.config = EmptyClass()
+    classifier_trainer.initialize(config, fake_ds_builder)
+
+  def test_resume_from_checkpoint(self):
+    """Tests functionality for resuming from checkpoint."""
+    # Set the keras policy
+    tf.keras.mixed_precision.set_global_policy('mixed_bfloat16')
+
+    # Get the model, datasets, and compile it.
+    model = get_trivial_model(10)
+
+    # Create the checkpoint
+    model_dir = self.create_tempdir().full_path
+    train_epochs = 1
+    train_steps = 10
+    ds = get_trivial_data()
+    callbacks = [
+        tf.keras.callbacks.ModelCheckpoint(
+            os.path.join(model_dir, 'model.ckpt-{epoch:04d}'),
+            save_weights_only=True)
+    ]
+    model.fit(
+        ds,
+        callbacks=callbacks,
+        epochs=train_epochs,
+        steps_per_epoch=train_steps)
+
+    # Test load from checkpoint
+    clean_model = get_trivial_model(10)
+    weights_before_load = copy.deepcopy(clean_model.get_weights())
+    initial_epoch = classifier_trainer.resume_from_checkpoint(
+        model=clean_model, model_dir=model_dir, train_steps=train_steps)
+    self.assertEqual(initial_epoch, 1)
+    self.assertNotAllClose(weights_before_load, clean_model.get_weights())
+
+    tf.io.gfile.rmtree(model_dir)
+
+  def test_serialize_config(self):
+    """Tests functionality for serializing data."""
+    config = base_configs.ExperimentConfig()
+    model_dir = self.create_tempdir().full_path
+    classifier_trainer.serialize_config(params=config, model_dir=model_dir)
+    saved_params_path = os.path.join(model_dir, 'params.yaml')
+    self.assertTrue(os.path.exists(saved_params_path))
+    tf.io.gfile.rmtree(model_dir)
+
+
+if __name__ == '__main__':
+  tf.test.main()

official/legacy/image_classification/configs/base_configs.py

+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+
+# Lint as: python3
+"""Definitions for high level configuration groups.."""
+
+import dataclasses
+from typing import Any, List, Optional
+from official.core import config_definitions
+from official.modeling import hyperparams
+
+RuntimeConfig = config_definitions.RuntimeConfig
+
+
+@dataclasses.dataclass
+class TensorBoardConfig(hyperparams.Config):
+  """Configuration for TensorBoard.
+
+  Attributes:
+    track_lr: Whether or not to track the learning rate in TensorBoard. Defaults
+      to True.
+    write_model_weights: Whether or not to write the model weights as images in
+      TensorBoard. Defaults to False.
+  """
+  track_lr: bool = True
+  write_model_weights: bool = False
+
+
+@dataclasses.dataclass
+class CallbacksConfig(hyperparams.Config):
+  """Configuration for Callbacks.
+
+  Attributes:
+    enable_checkpoint_and_export: Whether or not to enable checkpoints as a
+      Callback. Defaults to True.
+    enable_backup_and_restore: Whether or not to add BackupAndRestore
+      callback. Defaults to True.
+    enable_tensorboard: Whether or not to enable TensorBoard as a Callback.
+      Defaults to True.
+    enable_time_history: Whether or not to enable TimeHistory Callbacks.
+      Defaults to True.
+  """
+  enable_checkpoint_and_export: bool = True
+  enable_backup_and_restore: bool = False
+  enable_tensorboard: bool = True
+  enable_time_history: bool = True
+
+
+@dataclasses.dataclass
+class ExportConfig(hyperparams.Config):
+  """Configuration for exports.
+
+  Attributes:
+    checkpoint: the path to the checkpoint to export.
+    destination: the path to where the checkpoint should be exported.
+  """
+  checkpoint: str = None
+  destination: str = None
+
+
+@dataclasses.dataclass
+class MetricsConfig(hyperparams.Config):
+  """Configuration for Metrics.
+
+  Attributes:
+    accuracy: Whether or not to track accuracy as a Callback. Defaults to None.
+    top_5: Whether or not to track top_5_accuracy as a Callback. Defaults to
+      None.
+  """
+  accuracy: bool = None
+  top_5: bool = None
+
+
+@dataclasses.dataclass
+class TimeHistoryConfig(hyperparams.Config):
+  """Configuration for the TimeHistory callback.
+
+  Attributes:
+    log_steps: Interval of steps between logging of batch level stats.
+  """
+  log_steps: int = None
+
+
+@dataclasses.dataclass
+class TrainConfig(hyperparams.Config):
+  """Configuration for training.
+
+  Attributes:
+    resume_checkpoint: Whether or not to enable load checkpoint loading.
+      Defaults to None.
+    epochs: The number of training epochs to run. Defaults to None.
+    steps: The number of steps to run per epoch. If None, then this will be
+      inferred based on the number of images and batch size. Defaults to None.
+    callbacks: An instance of CallbacksConfig.
+    metrics: An instance of MetricsConfig.
+    tensorboard: An instance of TensorBoardConfig.
+    set_epoch_loop: Whether or not to set `steps_per_execution` to
+      equal the number of training steps in `model.compile`. This reduces the
+      number of callbacks run per epoch which significantly improves end-to-end
+      TPU training time.
+  """
+  resume_checkpoint: bool = None
+  epochs: int = None
+  steps: int = None
+  callbacks: CallbacksConfig = CallbacksConfig()
+  metrics: MetricsConfig = None
+  tensorboard: TensorBoardConfig = TensorBoardConfig()
+  time_history: TimeHistoryConfig = TimeHistoryConfig()
+  set_epoch_loop: bool = False
+
+
+@dataclasses.dataclass
+class EvalConfig(hyperparams.Config):
+  """Configuration for evaluation.
+
+  Attributes:
+    epochs_between_evals: The number of train epochs to run between evaluations.
+      Defaults to None.
+    steps: The number of eval steps to run during evaluation. If None, this will
+      be inferred based on the number of images and batch size. Defaults to
+      None.
+    skip_eval: Whether or not to skip evaluation.
+  """
+  epochs_between_evals: int = None
+  steps: int = None
+  skip_eval: bool = False
+
+
+@dataclasses.dataclass
+class LossConfig(hyperparams.Config):
+  """Configuration for Loss.
+
+  Attributes:
+    name: The name of the loss. Defaults to None.
+    label_smoothing: Whether or not to apply label smoothing to the loss. This
+      only applies to 'categorical_cross_entropy'.
+  """
+  name: str = None
+  label_smoothing: float = None
+
+
+@dataclasses.dataclass
+class OptimizerConfig(hyperparams.Config):
+  """Configuration for Optimizers.
+
+  Attributes:
+    name: The name of the optimizer. Defaults to None.
+    decay: Decay or rho, discounting factor for gradient. Defaults to None.
+    epsilon: Small value used to avoid 0 denominator. Defaults to None.
+    momentum: Plain momentum constant. Defaults to None.
+    nesterov: Whether or not to apply Nesterov momentum. Defaults to None.
+    moving_average_decay: The amount of decay to apply. If 0 or None, then
+      exponential moving average is not used. Defaults to None.
+    lookahead: Whether or not to apply the lookahead optimizer. Defaults to
+      None.
+    beta_1: The exponential decay rate for the 1st moment estimates. Used in the
+      Adam optimizers. Defaults to None.
+    beta_2: The exponential decay rate for the 2nd moment estimates. Used in the
+      Adam optimizers. Defaults to None.
+    epsilon: Small value used to avoid 0 denominator. Defaults to 1e-7.
+  """
+  name: str = None
+  decay: float = None
+  epsilon: float = None
+  momentum: float = None
+  nesterov: bool = None
+  moving_average_decay: Optional[float] = None
+  lookahead: Optional[bool] = None
+  beta_1: float = None
+  beta_2: float = None
+  epsilon: float = None
+
+
+@dataclasses.dataclass
+class LearningRateConfig(hyperparams.Config):
+  """Configuration for learning rates.
+
+  Attributes:
+    name: The name of the learning rate. Defaults to None.
+    initial_lr: The initial learning rate. Defaults to None.
+    decay_epochs: The number of decay epochs. Defaults to None.
+    decay_rate: The rate of decay. Defaults to None.
+    warmup_epochs: The number of warmup epochs. Defaults to None.
+    batch_lr_multiplier: The multiplier to apply to the base learning rate, if
+      necessary. Defaults to None.
+    examples_per_epoch: the number of examples in a single epoch. Defaults to
+      None.
+    boundaries: boundaries used in piecewise constant decay with warmup.
+    multipliers: multipliers used in piecewise constant decay with warmup.
+    scale_by_batch_size: Scale the learning rate by a fraction of the batch
+      size. Set to 0 for no scaling (default).
+    staircase: Apply exponential decay at discrete values instead of continuous.
+  """
+  name: str = None
+  initial_lr: float = None
+  decay_epochs: float = None
+  decay_rate: float = None
+  warmup_epochs: int = None
+  examples_per_epoch: int = None
+  boundaries: List[int] = None
+  multipliers: List[float] = None
+  scale_by_batch_size: float = 0.
+  staircase: bool = None
+
+
+@dataclasses.dataclass
+class ModelConfig(hyperparams.Config):
+  """Configuration for Models.
+
+  Attributes:
+    name: The name of the model. Defaults to None.
+    model_params: The parameters used to create the model. Defaults to None.
+    num_classes: The number of classes in the model. Defaults to None.
+    loss: A `LossConfig` instance. Defaults to None.
+    optimizer: An `OptimizerConfig` instance. Defaults to None.
+  """
+  name: str = None
+  model_params: hyperparams.Config = None
+  num_classes: int = None
+  loss: LossConfig = None
+  optimizer: OptimizerConfig = None
+
+
+@dataclasses.dataclass
+class ExperimentConfig(hyperparams.Config):
+  """Base configuration for an image classification experiment.
+
+  Attributes:
+    model_dir: The directory to use when running an experiment.
+    mode: e.g. 'train_and_eval', 'export'
+    runtime: A `RuntimeConfig` instance.
+    train: A `TrainConfig` instance.
+    evaluation: An `EvalConfig` instance.
+    model: A `ModelConfig` instance.
+    export: An `ExportConfig` instance.
+  """
+  model_dir: str = None
+  model_name: str = None
+  mode: str = None
+  runtime: RuntimeConfig = None
+  train_dataset: Any = None
+  validation_dataset: Any = None
+  train: TrainConfig = None
+  evaluation: EvalConfig = None
+  model: ModelConfig = None
+  export: ExportConfig = None

official/legacy/image_classification/configs/configs.py

+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+
+# Lint as: python3
+"""Configuration utils for image classification experiments."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import dataclasses
+
+from official.legacy.image_classification import dataset_factory
+from official.legacy.image_classification.configs import base_configs
+from official.legacy.image_classification.efficientnet import efficientnet_config
+from official.legacy.image_classification.resnet import resnet_config
+
+
+@dataclasses.dataclass
+class EfficientNetImageNetConfig(base_configs.ExperimentConfig):
+  """Base configuration to train efficientnet-b0 on ImageNet.
+
+  Attributes:
+    export: An `ExportConfig` instance
+    runtime: A `RuntimeConfig` instance.
+    dataset: A `DatasetConfig` instance.
+    train: A `TrainConfig` instance.
+    evaluation: An `EvalConfig` instance.
+    model: A `ModelConfig` instance.
+  """
+  export: base_configs.ExportConfig = base_configs.ExportConfig()
+  runtime: base_configs.RuntimeConfig = base_configs.RuntimeConfig()
+  train_dataset: dataset_factory.DatasetConfig = dataset_factory.ImageNetConfig(
+      split='train')
+  validation_dataset: dataset_factory.DatasetConfig = dataset_factory.ImageNetConfig(
+      split='validation')
+  train: base_configs.TrainConfig = base_configs.TrainConfig(
+      resume_checkpoint=True,
+      epochs=500,
+      steps=None,
+      callbacks=base_configs.CallbacksConfig(
+          enable_checkpoint_and_export=True, enable_tensorboard=True),
+      metrics=['accuracy', 'top_5'],
+      time_history=base_configs.TimeHistoryConfig(log_steps=100),
+      tensorboard=base_configs.TensorBoardConfig(
+          track_lr=True, write_model_weights=False),
+      set_epoch_loop=False)
+  evaluation: base_configs.EvalConfig = base_configs.EvalConfig(
+      epochs_between_evals=1, steps=None)
+  model: base_configs.ModelConfig = efficientnet_config.EfficientNetModelConfig(
+  )
+
+
+@dataclasses.dataclass
+class ResNetImagenetConfig(base_configs.ExperimentConfig):
+  """Base configuration to train resnet-50 on ImageNet."""
+  export: base_configs.ExportConfig = base_configs.ExportConfig()
+  runtime: base_configs.RuntimeConfig = base_configs.RuntimeConfig()
+  train_dataset: dataset_factory.DatasetConfig = \
+      dataset_factory.ImageNetConfig(split='train',
+                                     one_hot=False,
+                                     mean_subtract=True,
+                                     standardize=True)
+  validation_dataset: dataset_factory.DatasetConfig = \
+      dataset_factory.ImageNetConfig(split='validation',
+                                     one_hot=False,
+                                     mean_subtract=True,
+                                     standardize=True)
+  train: base_configs.TrainConfig = base_configs.TrainConfig(
+      resume_checkpoint=True,
+      epochs=90,
+      steps=None,
+      callbacks=base_configs.CallbacksConfig(
+          enable_checkpoint_and_export=True, enable_tensorboard=True),
+      metrics=['accuracy', 'top_5'],
+      time_history=base_configs.TimeHistoryConfig(log_steps=100),
+      tensorboard=base_configs.TensorBoardConfig(
+          track_lr=True, write_model_weights=False),
+      set_epoch_loop=False)
+  evaluation: base_configs.EvalConfig = base_configs.EvalConfig(
+      epochs_between_evals=1, steps=None)
+  model: base_configs.ModelConfig = resnet_config.ResNetModelConfig()
+
+
+def get_config(model: str, dataset: str) -> base_configs.ExperimentConfig:
+  """Given model and dataset names, return the ExperimentConfig."""
+  dataset_model_config_map = {
+      'imagenet': {
+          'efficientnet': EfficientNetImageNetConfig(),
+          'resnet': ResNetImagenetConfig(),
+      }
+  }
+  try:
+    return dataset_model_config_map[dataset][model]
+  except KeyError:
+    if dataset not in dataset_model_config_map:
+      raise KeyError('Invalid dataset received. Received: {}. Supported '
+                     'datasets include: {}'.format(
+                         dataset, ', '.join(dataset_model_config_map.keys())))
+    raise KeyError('Invalid model received. Received: {}. Supported models for'
+                   '{} include: {}'.format(
+                       model, dataset,
+                       ', '.join(dataset_model_config_map[dataset].keys())))