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 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.add().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_configs"])
def testUpdateMaskTypeForAllInputConfigs(self):
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_config = pipeline_config.train_input_reader
train_config.mask_type = original_mask_type
eval_1 = pipeline_config.eval_input_reader.add()
eval_1.mask_type = original_mask_type
eval_1.name = "eval_1"
eval_2 = pipeline_config.eval_input_reader.add()
eval_2.mask_type = original_mask_type
eval_2.name = "eval_2"
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
override_dict = {"mask_type": new_mask_type}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(configs["train_input_config"].mask_type,
new_mask_type)
for eval_input_config in configs["eval_input_configs"]:
self.assertEqual(eval_input_config.mask_type, new_mask_type)
def testCheckAndParseInputConfigKey(self):
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_input_reader.add().name = "eval_1"
pipeline_config.eval_input_reader.add().name = "eval_2"
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
specific_shuffle_update_key = "eval_input_configs:eval_2:shuffle"
is_valid_input_config_key, key_name, input_name, field_name = (
config_util.check_and_parse_input_config_key(
configs, specific_shuffle_update_key))
self.assertTrue(is_valid_input_config_key)
self.assertEqual(key_name, "eval_input_configs")
self.assertEqual(input_name, "eval_2")
self.assertEqual(field_name, "shuffle")
legacy_shuffle_update_key = "eval_shuffle"
is_valid_input_config_key, key_name, input_name, field_name = (
config_util.check_and_parse_input_config_key(
configs, legacy_shuffle_update_key))
self.assertTrue(is_valid_input_config_key)
self.assertEqual(key_name, "eval_input_configs")
self.assertEqual(input_name, None)
self.assertEqual(field_name, "shuffle")
non_input_config_update_key = "label_map_path"
is_valid_input_config_key, key_name, input_name, field_name = (
config_util.check_and_parse_input_config_key(
configs, non_input_config_update_key))
self.assertFalse(is_valid_input_config_key)
self.assertEqual(key_name, None)
self.assertEqual(input_name, None)
self.assertEqual(field_name, "label_map_path")
with self.assertRaisesRegexp(
ValueError, "Invalid key format when overriding configs."):
config_util.check_and_parse_input_config_key(
configs, "train_input_config:shuffle")
with self.assertRaisesRegexp(
ValueError, "Invalid key_name when overriding input config."):
config_util.check_and_parse_input_config_key(
configs, "invalid_key_name:train_name:shuffle")
with self.assertRaisesRegexp(
ValueError,
"Invalid input_name when overriding input config."):
config_util.check_and_parse_input_config_key(
configs, "eval_input_configs:unknown_eval_name:shuffle")
with self.assertRaisesRegexp(
ValueError,
"Invalid field_name when overriding input config."):
config_util.check_and_parse_input_config_key(
configs, "eval_input_configs:eval_2:unknown_field_name")
def main(unused_argv):
assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
assert FLAGS.eval_dir, '`eval_dir` is missing.'
tf.gfile.MakeDirs(FLAGS.eval_dir)
if FLAGS.pipeline_config_path:
configs = config_util.get_configs_from_pipeline_file(
FLAGS.pipeline_config_path)
tf.gfile.Copy(FLAGS.pipeline_config_path,
os.path.join(FLAGS.eval_dir, 'pipeline.config'),
overwrite=True)
else:
configs = config_util.get_configs_from_multiple_files(
model_config_path=FLAGS.model_config_path,
eval_config_path=FLAGS.eval_config_path,
eval_input_config_path=FLAGS.input_config_path)
for name, config in [('model.config', FLAGS.model_config_path),
('eval.config', FLAGS.eval_config_path),
('input.config', FLAGS.input_config_path)]:
tf.gfile.Copy(config,
os.path.join(FLAGS.eval_dir, name),
overwrite=True)
model_config = configs['model']
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
if FLAGS.eval_training_data:
input_config = configs['train_input_config']
model_fn = functools.partial(model_builder.build,
model_config=model_config,
is_training=False)
def get_next(config):
return dataset_builder.make_initializable_iterator(
dataset_builder.build(config)).get_next()
create_input_dict_fn = functools.partial(get_next, input_config)
categories = label_map_util.create_categories_from_labelmap(
input_config.label_map_path)
if FLAGS.run_once:
eval_config.max_evals = 1
graph_rewriter_fn = None
if 'graph_rewriter_config' in configs:
graph_rewriter_fn = graph_rewriter_builder.build(
configs['graph_rewriter_config'], is_training=False)
evaluator.evaluate(create_input_dict_fn,
model_fn,
eval_config,
categories,
FLAGS.checkpoint_dir,
FLAGS.eval_dir,
graph_hook_fn=graph_rewriter_fn)
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 _get_configs_for_model(model_name):
"""Returns configurations for model."""
filename = get_pipeline_config_path(model_name)
data_path = _get_data_path()
label_map_path = _get_labelmap_path()
configs = config_util.get_configs_from_pipeline_file(filename)
override_dict = {
'train_input_path': data_path,
'eval_input_path': data_path,
'label_map_path': label_map_path
}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
return configs
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)
override_dict = {"eval_with_moving_averages": True}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(True, configs["eval_config"].use_moving_averages)
def test_create_estimator_with_default_train_eval_steps(self):
"""Tests that number of train/eval defaults to config values."""
run_config = tf.estimator.RunConfig()
hparams = model_hparams.create_hparams(
hparams_overrides='load_pretrained=false')
pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
config_train_steps = configs['train_config'].num_steps
train_and_eval_dict = model_lib.create_estimator_and_inputs(
run_config, hparams, pipeline_config_path)
estimator = train_and_eval_dict['estimator']
train_steps = train_and_eval_dict['train_steps']
self.assertIsInstance(estimator, tf.estimator.Estimator)
self.assertEqual(config_train_steps, train_steps)
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.add().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 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_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.add().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 testMergingKeywordArguments(self):
"""Tests that keyword arguments get merged as do hyperparameters."""
original_num_train_steps = 100
desired_num_train_steps = 10
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
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
override_dict = {"train_steps": desired_num_train_steps}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
train_steps = configs["train_config"].num_steps
self.assertEqual(desired_num_train_steps, train_steps)
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)
override_dict = {"train_shuffle": desired_shuffle}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
train_shuffle = configs["train_input_config"].shuffle
self.assertEqual(desired_shuffle, train_shuffle)
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 testOverwriteAllEvalNumEpochs(self):
original_num_epochs = 10
new_num_epochs = 1
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_input_reader.add(
).num_epochs = original_num_epochs
pipeline_config.eval_input_reader.add(
).num_epochs = original_num_epochs
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
override_dict = {"eval_num_epochs": new_num_epochs}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
for eval_input_config in configs["eval_input_configs"]:
self.assertEqual(new_num_epochs, eval_input_config.num_epochs)
def testErrorOverwritingMultipleInputConfig(self):
original_shuffle = False
new_shuffle = True
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
eval_1 = pipeline_config.eval_input_reader.add()
eval_1.shuffle = original_shuffle
eval_1.name = "eval_1"
eval_2 = pipeline_config.eval_input_reader.add()
eval_2.shuffle = original_shuffle
eval_2.name = "eval_2"
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
override_dict = {"eval_shuffle": new_shuffle}
with self.assertRaises(ValueError):
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
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)
override_dict = {"train_input_path": new_train_path}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
reader_config = configs["train_input_config"].tf_record_input_reader
final_path = reader_config.input_path
self.assertEqual(new_train_path, final_path)
def testOverwriteAllEvalSampling(self):
original_num_eval_examples = 1
new_num_eval_examples = 10
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_input_reader.add().sample_1_of_n_examples = (
original_num_eval_examples)
pipeline_config.eval_input_reader.add().sample_1_of_n_examples = (
original_num_eval_examples)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
override_dict = {"sample_1_of_n_eval_examples": new_num_eval_examples}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
for eval_input_config in configs["eval_input_configs"]:
self.assertEqual(new_num_eval_examples,
eval_input_config.sample_1_of_n_examples)
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)
override_dict = {
"retain_original_images_in_eval": desired_retain_original_images
}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
retain_original_images = configs["eval_config"].retain_original_images
self.assertEqual(desired_retain_original_images,
retain_original_images)
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.add()
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)
override_dict = {"mask_type": new_mask_type}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(new_mask_type,
configs["train_input_config"].mask_type)
self.assertEqual(new_mask_type,
configs["eval_input_configs"][0].mask_type)
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.add()
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)
override_dict = {"label_map_path": new_label_map_path}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(original_label_map_path,
configs["train_input_config"].label_map_path)
self.assertEqual(original_label_map_path,
configs["eval_input_configs"][0].label_map_path)
def testGenericConfigOverride(self):
"""Tests generic config overrides for all top-level configs."""
# Set one parameter for each of the top-level pipeline configs:
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.ssd.num_classes = 1
pipeline_config.train_config.batch_size = 1
pipeline_config.eval_config.num_visualizations = 1
pipeline_config.train_input_reader.label_map_path = "/some/path"
pipeline_config.eval_input_reader.add().label_map_path = "/some/path"
pipeline_config.graph_rewriter.quantization.weight_bits = 1
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
_write_config(pipeline_config, pipeline_config_path)
# Override each of the parameters:
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
hparams = tf.contrib.training.HParams(
**{
"model.ssd.num_classes": 2,
"train_config.batch_size": 2,
"train_input_config.label_map_path": "/some/other/path",
"eval_config.num_visualizations": 2,
"graph_rewriter_config.quantization.weight_bits": 2
})
configs = config_util.merge_external_params_with_configs(
configs, hparams)
# Ensure that the parameters have the overridden values:
self.assertEqual(2, configs["model"].ssd.num_classes)
self.assertEqual(2, configs["train_config"].batch_size)
self.assertEqual("/some/other/path",
configs["train_input_config"].label_map_path)
self.assertEqual(2, configs["eval_config"].num_visualizations)
self.assertEqual(
2, configs["graph_rewriter_config"].quantization.weight_bits)
def testUpdateInputReaderConfigErrors(self):
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_input_reader.add().name = "same_eval_name"
pipeline_config.eval_input_reader.add().name = "same_eval_name"
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(
pipeline_config_path)
with self.assertRaisesRegexp(
ValueError, "Duplicate input name found when overriding."):
config_util.update_input_reader_config(
configs,
key_name="eval_input_configs",
input_name="same_eval_name",
field_name="shuffle",
value=False)
with self.assertRaisesRegexp(
ValueError,
"Input name name_not_exist not found when overriding."):
config_util.update_input_reader_config(
configs,
key_name="eval_input_configs",
input_name="name_not_exist",
field_name="shuffle",
value=False)
with self.assertRaisesRegexp(ValueError,
"Unknown input config overriding."):
config_util.update_input_reader_config(
configs,
key_name="eval_input_configs",
input_name=None,
field_name="shuffle",
value=False)
def testNewLabelMapPath(self):
"""Tests that label map path can be overwritten in input readers."""
original_label_map_path = "path/to/original/label_map"
new_label_map_path = "path//to/new/label_map"
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.add()
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)
override_dict = {"label_map_path": new_label_map_path}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(new_label_map_path,
configs["train_input_config"].label_map_path)
for eval_input_config in configs["eval_input_configs"]:
self.assertEqual(new_label_map_path,
eval_input_config.label_map_path)
def testUpdateInputReaderConfigSuccess(self):
original_shuffle = False
new_shuffle = True
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)
config_util.update_input_reader_config(configs,
key_name="train_input_config",
input_name=None,
field_name="shuffle",
value=new_shuffle)
self.assertEqual(configs["train_input_config"].shuffle, new_shuffle)
config_util.update_input_reader_config(configs,
key_name="train_input_config",
input_name=None,
field_name="shuffle",
value=new_shuffle)
self.assertEqual(configs["train_input_config"].shuffle, new_shuffle)
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 _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)
def _create_and_load_test_configs(self, pipeline_config):
pipeline_config_path = os.path.join(self.get_temp_dir(),
"pipeline.config")
_write_config(pipeline_config, pipeline_config_path)
return config_util.get_configs_from_pipeline_file(pipeline_config_path)
