def test_get_configs_from_pipeline_file(self):
"""Test that proto configs can be read from pipeline config file."""
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 10
pipeline_config.train_config.batch_size = 32
pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
pipeline_config.eval_config.num_examples = 20
pipeline_config.eval_input_reader.queue_capacity = 100
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
self.assertProtoEquals(pipeline_config.model, configs["model"])
self.assertProtoEquals(pipeline_config.train_config,
configs["train_config"])
self.assertProtoEquals(pipeline_config.train_input_reader,
configs["train_input_config"])
self.assertProtoEquals(pipeline_config.eval_config,
configs["eval_config"])
self.assertProtoEquals(pipeline_config.eval_input_reader,
configs["eval_input_config"])
def testNewFocalLossParameters(self):
"""Tests that the loss weight ratio is updated appropriately."""
original_alpha = 1.0
original_gamma = 1.0
new_alpha = 0.3
new_gamma = 2.0
hparams = tf.contrib.training.HParams(focal_loss_alpha=new_alpha,
focal_loss_gamma=new_gamma)
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
classification_loss = pipeline_config.model.ssd.loss.classification_loss
classification_loss.weighted_sigmoid_focal.alpha = original_alpha
classification_loss.weighted_sigmoid_focal.gamma = original_gamma
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
configs, hparams)
classification_loss = configs["model"].ssd.loss.classification_loss
self.assertAlmostEqual(
new_alpha, classification_loss.weighted_sigmoid_focal.alpha)
self.assertAlmostEqual(
new_gamma, classification_loss.weighted_sigmoid_focal.gamma)
def testKeyValueOverrideBadKey(self):
"""Tests that overwriting with a bad key causes an exception."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
configs = self._create_and_load_test_configs(pipeline_config)
hparams = tf.contrib.training.HParams(
**{"train_config.no_such_field": 10})
with self.assertRaises(ValueError):
config_util.merge_external_params_with_configs(configs, hparams)
def testOverwriteBatchSizeWithBadValueType(self):
"""Tests that overwriting with a bad valuye type causes an exception."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_config.batch_size = 2
configs = self._create_and_load_test_configs(pipeline_config)
# Type should be an integer, but we're passing a string "10".
hparams = tf.contrib.training.HParams(
**{"train_config.batch_size": "10"})
with self.assertRaises(TypeError):
config_util.merge_external_params_with_configs(configs, hparams)
def testOverwriteBatchSizeWithKeyValue(self):
"""Tests that batch size is overwritten based on key/value."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_config.batch_size = 2
configs = self._create_and_load_test_configs(pipeline_config)
hparams = tf.contrib.training.HParams(
**{"train_config.batch_size": 10})
configs = config_util.merge_external_params_with_configs(
configs, hparams)
new_batch_size = configs["train_config"].batch_size
self.assertEqual(10, new_batch_size)
def testGetNumberOfClasses(self):
"""Tests that number of classes can be retrieved."""
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 20
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
number_of_classes = config_util.get_number_of_classes(configs["model"])
self.assertEqual(20, number_of_classes)
def testUseMovingAverageForEval(self):
use_moving_averages_orig = False
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.use_moving_averages = use_moving_averages_orig
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
configs, eval_with_moving_averages=True)
self.assertEqual(True, configs["eval_config"].use_moving_averages)
def main(argv):
del argv # Unused.
flags.mark_flag_as_required('output_directory')
flags.mark_flag_as_required('pipeline_config_path')
flags.mark_flag_as_required('trained_checkpoint_prefix')
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:
text_format.Merge(f.read(), pipeline_config)
text_format.Merge(FLAGS.config_override, pipeline_config)
export_tflite_ssd_graph_lib.export_tflite_graph(
pipeline_config, FLAGS.trained_checkpoint_prefix, FLAGS.output_directory,
FLAGS.add_postprocessing_op, FLAGS.max_detections,
FLAGS.max_classes_per_detection)
def test_save_pipeline_config(self):
"""Tests that the pipeline config is properly saved to disk."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 10
pipeline_config.train_config.batch_size = 32
pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
pipeline_config.eval_config.num_examples = 20
pipeline_config.eval_input_reader.queue_capacity = 100
config_util.save_pipeline_config(pipeline_config, self.get_temp_dir())
configs = config_util.get_configs_from_pipeline_file(
os.path.join(self.get_temp_dir(), "pipeline.config"))
pipeline_config_reconstructed = (
config_util.create_pipeline_proto_from_configs(configs))
self.assertEqual(pipeline_config, pipeline_config_reconstructed)
def main(_):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:
text_format.Merge(f.read(), pipeline_config)
text_format.Merge(FLAGS.config_override, pipeline_config)
if FLAGS.input_shape:
input_shape = [
int(dim) if dim != '-1' else None
for dim in FLAGS.input_shape.split(',')
]
else:
input_shape = None
exporter.export_inference_graph(
FLAGS.input_type, pipeline_config, FLAGS.trained_checkpoint_prefix,
FLAGS.output_directory, input_shape=input_shape,
write_inference_graph=FLAGS.write_inference_graph)
def testNewBatchSizeWithClipping(self):
"""Tests that batch size is clipped to 1 from below."""
original_batch_size = 2
hparams = tf.contrib.training.HParams(batch_size=0.5)
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_config.batch_size = original_batch_size
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
configs, hparams)
new_batch_size = configs["train_config"].batch_size
self.assertEqual(1, new_batch_size) # Clipped to 1.0.
def testNewBatchSize(self):
"""Tests that batch size is updated appropriately."""
original_batch_size = 2
hparams = tf.contrib.training.HParams(batch_size=16)
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_config.batch_size = original_batch_size
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
configs, hparams)
new_batch_size = configs["train_config"].batch_size
self.assertEqual(16, new_batch_size)
def test_export_tflite_graph_without_moving_averages(self):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.use_moving_averages = False
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
pipeline_config.model.ssd.num_classes = 2
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale = 5.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale = 5.0
tflite_graph_file = self._export_graph(pipeline_config)
self.assertTrue(os.path.exists(tflite_graph_file))
(box_encodings_np, class_predictions_np
) = self._import_graph_and_run_inference(tflite_graph_file)
self.assertAllClose(box_encodings_np,
[[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]]])
self.assertAllClose(class_predictions_np, [[[0.7, 0.6], [0.9, 0.0]]])
def get_configs_from_pipeline_file(pipeline_config_path):
"""Reads config from a file containing pipeline_pb2.TrainEvalPipelineConfig.
Args:
pipeline_config_path: Path to pipeline_pb2.TrainEvalPipelineConfig text
proto.
Returns:
Dictionary of configuration objects. Keys are `model`, `train_config`,
`train_input_config`, `eval_config`, `eval_input_config`. Value are the
corresponding config objects.
"""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
with tf.gfile.GFile(pipeline_config_path, "r") as f:
proto_str = f.read()
text_format.Merge(proto_str, pipeline_config)
return create_configs_from_pipeline_proto(pipeline_config)
def testTrainShuffle(self):
"""Tests that `train_shuffle` keyword arguments are applied correctly."""
original_shuffle = True
desired_shuffle = False
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_input_reader.shuffle = original_shuffle
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
configs, train_shuffle=desired_shuffle)
train_shuffle = configs["train_input_config"].shuffle
self.assertEqual(desired_shuffle, train_shuffle)
def test_create_pipeline_proto_from_configs(self):
"""Tests that proto can be reconstructed from configs dictionary."""
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 10
pipeline_config.train_config.batch_size = 32
pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
pipeline_config.eval_config.num_examples = 20
pipeline_config.eval_input_reader.queue_capacity = 100
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
pipeline_config_reconstructed = (
config_util.create_pipeline_proto_from_configs(configs))
self.assertEqual(pipeline_config, pipeline_config_reconstructed)
def testNewMomentumOptimizerValue(self):
"""Tests that new momentum value is updated appropriately."""
original_momentum_value = 0.4
hparams = tf.contrib.training.HParams(momentum_optimizer_value=1.1)
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
optimizer_config = pipeline_config.train_config.optimizer.rms_prop_optimizer
optimizer_config.momentum_optimizer_value = original_momentum_value
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
configs, hparams)
optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer
new_momentum_value = optimizer_config.momentum_optimizer_value
self.assertAlmostEqual(1.0, new_momentum_value) # Clipped to 1.0.
def testNewTrainInputPathList(self):
"""Tests that train input path can be overwritten with multiple files."""
original_train_path = ["path/to/data"]
new_train_path = ["another/path/to/data", "yet/another/path/to/data"]
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
reader_config = pipeline_config.train_input_reader.tf_record_input_reader
reader_config.input_path.extend(original_train_path)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
configs, train_input_path=new_train_path)
reader_config = configs["train_input_config"].tf_record_input_reader
final_path = reader_config.input_path
self.assertEqual(new_train_path, final_path)
def testOverWriteRetainOriginalImages(self):
"""Tests that `train_shuffle` keyword arguments are applied correctly."""
original_retain_original_images = True
desired_retain_original_images = False
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.retain_original_images = (
original_retain_original_images)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
configs,
retain_original_images_in_eval=desired_retain_original_images)
retain_original_images = configs["eval_config"].retain_original_images
self.assertEqual(desired_retain_original_images,
retain_original_images)
def test_export_tflite_graph_with_softmax_score_conversion(self):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.use_moving_averages = False
pipeline_config.model.ssd.post_processing.score_converter = (
post_processing_pb2.PostProcessing.SOFTMAX)
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.height = 10
pipeline_config.model.ssd.image_resizer.fixed_shape_resizer.width = 10
pipeline_config.model.ssd.num_classes = 2
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.y_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.x_scale = 10.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.height_scale = 5.0
pipeline_config.model.ssd.box_coder.faster_rcnn_box_coder.width_scale = 5.0
tflite_graph_file = self._export_graph(pipeline_config)
self.assertTrue(os.path.exists(tflite_graph_file))
(box_encodings_np, class_predictions_np
) = self._import_graph_and_run_inference(tflite_graph_file)
self.assertAllClose(box_encodings_np,
[[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]]])
self.assertAllClose(class_predictions_np,
[[[0.524979, 0.475021], [0.710949, 0.28905]]])
def create_pipeline_proto_from_configs(configs):
"""Creates a pipeline_pb2.TrainEvalPipelineConfig from configs dictionary.
This function performs the inverse operation of
create_configs_from_pipeline_proto().
Args:
configs: Dictionary of configs. See get_configs_from_pipeline_file().
Returns:
A fully populated pipeline_pb2.TrainEvalPipelineConfig.
"""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.CopyFrom(configs["model"])
pipeline_config.train_config.CopyFrom(configs["train_config"])
pipeline_config.train_input_reader.CopyFrom(configs["train_input_config"])
pipeline_config.eval_config.CopyFrom(configs["eval_config"])
pipeline_config.eval_input_reader.CopyFrom(configs["eval_input_config"])
if "graph_rewriter_config" in configs:
pipeline_config.graph_rewriter.CopyFrom(configs["graph_rewriter_config"])
return pipeline_config
def testNewMaskType(self):
"""Tests that mask type can be overwritten in input readers."""
original_mask_type = input_reader_pb2.NUMERICAL_MASKS
new_mask_type = input_reader_pb2.PNG_MASKS
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
train_input_reader = pipeline_config.train_input_reader
train_input_reader.mask_type = original_mask_type
eval_input_reader = pipeline_config.eval_input_reader
eval_input_reader.mask_type = original_mask_type
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
configs, mask_type=new_mask_type)
self.assertEqual(new_mask_type,
configs["train_input_config"].mask_type)
self.assertEqual(new_mask_type, configs["eval_input_config"].mask_type)
def test_create_configs_from_pipeline_proto(self):
"""Tests creating configs dictionary from pipeline proto."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 10
pipeline_config.train_config.batch_size = 32
pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
pipeline_config.eval_config.num_examples = 20
pipeline_config.eval_input_reader.queue_capacity = 100
configs = config_util.create_configs_from_pipeline_proto(
pipeline_config)
self.assertProtoEquals(pipeline_config.model, configs["model"])
self.assertProtoEquals(pipeline_config.train_config,
configs["train_config"])
self.assertProtoEquals(pipeline_config.train_input_reader,
configs["train_input_config"])
self.assertProtoEquals(pipeline_config.eval_config,
configs["eval_config"])
self.assertProtoEquals(pipeline_config.eval_input_reader,
configs["eval_input_config"])
def testDontOverwriteEmptyLabelMapPath(self):
"""Tests that label map path will not by overwritten with empty string."""
original_label_map_path = "path/to/original/label_map"
new_label_map_path = ""
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
train_input_reader = pipeline_config.train_input_reader
train_input_reader.label_map_path = original_label_map_path
eval_input_reader = pipeline_config.eval_input_reader
eval_input_reader.label_map_path = original_label_map_path
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
configs, label_map_path=new_label_map_path)
self.assertEqual(original_label_map_path,
configs["train_input_config"].label_map_path)
self.assertEqual(original_label_map_path,
configs["eval_input_config"].label_map_path)
def testNewClassificationLocalizationWeightRatio(self):
"""Tests that the loss weight ratio is updated appropriately."""
original_localization_weight = 0.1
original_classification_weight = 0.2
new_weight_ratio = 5.0
hparams = tf.contrib.training.HParams(
classification_localization_weight_ratio=new_weight_ratio)
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.ssd.loss.localization_weight = (
original_localization_weight)
pipeline_config.model.ssd.loss.classification_weight = (
original_classification_weight)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
configs, hparams)
loss = configs["model"].ssd.loss
self.assertAlmostEqual(1.0, loss.localization_weight)
self.assertAlmostEqual(new_weight_ratio, loss.classification_weight)
def testMergingKeywordArguments(self):
"""Tests that keyword arguments get merged as do hyperparameters."""
original_num_train_steps = 100
original_num_eval_steps = 5
desired_num_train_steps = 10
desired_num_eval_steps = 1
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_config.num_steps = original_num_train_steps
pipeline_config.eval_config.num_examples = original_num_eval_steps
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
configs,
train_steps=desired_num_train_steps,
eval_steps=desired_num_eval_steps)
train_steps = configs["train_config"].num_steps
eval_steps = configs["eval_config"].num_examples
self.assertEqual(desired_num_train_steps, train_steps)
self.assertEqual(desired_num_eval_steps, eval_steps)
def _assertOptimizerWithNewLearningRate(self, optimizer_name):
"""Asserts successful updating of all learning rate schemes."""
original_learning_rate = 0.7
learning_rate_scaling = 0.1
warmup_learning_rate = 0.07
hparams = tf.contrib.training.HParams(learning_rate=0.15)
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
# Constant learning rate.
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
optimizer = getattr(pipeline_config.train_config.optimizer,
optimizer_name)
_update_optimizer_with_constant_learning_rate(optimizer,
original_learning_rate)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
configs, hparams)
optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
constant_lr = optimizer.learning_rate.constant_learning_rate
self.assertAlmostEqual(hparams.learning_rate,
constant_lr.learning_rate)
# Exponential decay learning rate.
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
optimizer = getattr(pipeline_config.train_config.optimizer,
optimizer_name)
_update_optimizer_with_exponential_decay_learning_rate(
optimizer, original_learning_rate)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
configs, hparams)
optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
exponential_lr = optimizer.learning_rate.exponential_decay_learning_rate
self.assertAlmostEqual(hparams.learning_rate,
exponential_lr.initial_learning_rate)
# Manual step learning rate.
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
optimizer = getattr(pipeline_config.train_config.optimizer,
optimizer_name)
_update_optimizer_with_manual_step_learning_rate(
optimizer, original_learning_rate, learning_rate_scaling)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
configs, hparams)
optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
manual_lr = optimizer.learning_rate.manual_step_learning_rate
self.assertAlmostEqual(hparams.learning_rate,
manual_lr.initial_learning_rate)
for i, schedule in enumerate(manual_lr.schedule):
self.assertAlmostEqual(
hparams.learning_rate * learning_rate_scaling**i,
schedule.learning_rate)
# Cosine decay learning rate.
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
optimizer = getattr(pipeline_config.train_config.optimizer,
optimizer_name)
_update_optimizer_with_cosine_decay_learning_rate(
optimizer, original_learning_rate, warmup_learning_rate)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
configs = config_util.merge_external_params_with_configs(
configs, hparams)
optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
cosine_lr = optimizer.learning_rate.cosine_decay_learning_rate
self.assertAlmostEqual(hparams.learning_rate,
cosine_lr.learning_rate_base)
warmup_scale_factor = warmup_learning_rate / original_learning_rate
self.assertAlmostEqual(hparams.learning_rate * warmup_scale_factor,
cosine_lr.warmup_learning_rate)
