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 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.faster_rcnn.first_stage_localization_loss_weight = ( original_localization_weight) pipeline_config.model.faster_rcnn.second_stage_classification_loss_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"].faster_rcnn self.assertAlmostEqual(1.0, loss.first_stage_localization_loss_weight) self.assertAlmostEqual(new_weight_ratio, loss.second_stage_classification_loss_weight) #def testNewFocalLossParameters(self): """Tests that the loss weight ratio is updated appropriately.
def test_export_model_with_detection_only_nodes(self): tmp_dir = self.get_temp_dir() trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt') self._save_checkpoint_from_mock_model(trained_checkpoint_prefix, use_moving_averages=True) output_directory = os.path.join(tmp_dir, 'output') inference_graph_path = os.path.join(output_directory, 'frozen_inference_graph.pb') with mock.patch.object( model_builder, 'build', autospec=True) as mock_builder: mock_builder.return_value = FakeModel(add_detection_masks=False) pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() exporter.export_inference_graph( input_type='image_tensor', pipeline_config=pipeline_config, trained_checkpoint_prefix=trained_checkpoint_prefix, output_directory=output_directory) inference_graph = self._load_inference_graph(inference_graph_path) with self.test_session(graph=inference_graph): inference_graph.get_tensor_by_name('image_tensor:0') inference_graph.get_tensor_by_name('detection_boxes:0') inference_graph.get_tensor_by_name('detection_scores:0') inference_graph.get_tensor_by_name('detection_classes:0') inference_graph.get_tensor_by_name('num_detections:0') with self.assertRaises(KeyError): inference_graph.get_tensor_by_name('detection_masks:0')
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 test_export_graph_with_fixed_size_image_tensor_input(self): input_shape = [1, 320, 320, 3] tmp_dir = self.get_temp_dir() trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt') self._save_checkpoint_from_mock_model( trained_checkpoint_prefix, use_moving_averages=False) with mock.patch.object( model_builder, 'build', autospec=True) as mock_builder: mock_builder.return_value = FakeModel() output_directory = os.path.join(tmp_dir, 'output') pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() pipeline_config.eval_config.use_moving_averages = False exporter.export_inference_graph( input_type='image_tensor', pipeline_config=pipeline_config, trained_checkpoint_prefix=trained_checkpoint_prefix, output_directory=output_directory, input_shape=input_shape) saved_model_path = os.path.join(output_directory, 'saved_model') self.assertTrue( os.path.exists(os.path.join(saved_model_path, 'saved_model.pb'))) with tf.Graph().as_default() as od_graph: with self.test_session(graph=od_graph) as sess: meta_graph = tf.saved_model.loader.load( sess, [tf.saved_model.tag_constants.SERVING], saved_model_path) signature = meta_graph.signature_def['serving_default'] input_tensor_name = signature.inputs['inputs'].name image_tensor = od_graph.get_tensor_by_name(input_tensor_name) self.assertSequenceEqual(image_tensor.get_shape().as_list(), input_shape)
def get_configs_from_pipeline_file(pipeline_config_path): """Reads configuration from a 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) configs = {} configs["model"] = pipeline_config.model configs["train_config"] = pipeline_config.train_config configs["train_input_config"] = pipeline_config.train_input_reader configs["eval_config"] = pipeline_config.eval_config configs["eval_input_config"] = pipeline_config.eval_input_reader return configs
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 main(_): pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() with tf.io.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f: text_format.Merge(f.read(), pipeline_config) text_format.Merge(FLAGS.config_override, pipeline_config) exporter_lib_v2.export_inference_graph( FLAGS.input_type, pipeline_config, FLAGS.trained_checkpoint_dir, FLAGS.output_directory, FLAGS.use_side_inputs, FLAGS.side_input_shapes, FLAGS.side_input_types, FLAGS.side_input_names)
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 get_configs_from_exp_dir(): pipeline_config_path = os.path.join(FLAGS.exp_dir, 'config/trainval.prototxt') pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() with tf.gfile.GFile(pipeline_config_path, 'r') as f: text_format.Merge(f.read(), pipeline_config) model_config = pipeline_config.model train_config = pipeline_config.train_config input_config = pipeline_config.train_input_reader return model_config, train_config, input_config
def test_export_saved_model_and_run_inference(self): tmp_dir = self.get_temp_dir() trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt') self._save_checkpoint_from_mock_model(trained_checkpoint_prefix, use_moving_averages=False) output_directory = os.path.join(tmp_dir, 'output') saved_model_path = os.path.join(output_directory, 'saved_model') with mock.patch.object( model_builder, 'build', autospec=True) as mock_builder: mock_builder.return_value = FakeModel(add_detection_masks=True) pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() pipeline_config.eval_config.use_moving_averages = False exporter.export_inference_graph( input_type='tf_example', pipeline_config=pipeline_config, trained_checkpoint_prefix=trained_checkpoint_prefix, output_directory=output_directory) tf_example_np = np.hstack([self._create_tf_example( np.ones((4, 4, 3)).astype(np.uint8))] * 2) with tf.Graph().as_default() as od_graph: with self.test_session(graph=od_graph) as sess: meta_graph = tf.saved_model.loader.load( sess, [tf.saved_model.tag_constants.SERVING], saved_model_path) signature = meta_graph.signature_def['serving_default'] input_tensor_name = signature.inputs['inputs'].name tf_example = od_graph.get_tensor_by_name(input_tensor_name) boxes = od_graph.get_tensor_by_name( signature.outputs['detection_boxes'].name) scores = od_graph.get_tensor_by_name( signature.outputs['detection_scores'].name) classes = od_graph.get_tensor_by_name( signature.outputs['detection_classes'].name) masks = od_graph.get_tensor_by_name( signature.outputs['detection_masks'].name) num_detections = od_graph.get_tensor_by_name( signature.outputs['num_detections'].name) (boxes_np, scores_np, classes_np, masks_np, num_detections_np) = sess.run( [boxes, scores, classes, masks, num_detections], feed_dict={tf_example: tf_example_np}) self.assertAllClose(boxes_np, [[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]], [[0.5, 0.5, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0]]]) self.assertAllClose(scores_np, [[0.7, 0.6], [0.9, 0.0]]) self.assertAllClose(classes_np, [[1, 2], [2, 1]]) self.assertAllClose(masks_np, np.arange(64).reshape([2, 2, 4, 4])) self.assertAllClose(num_detections_np, [2, 1])
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 main(_): assert FLAGS.pipeline_config_path, '`pipeline_config_path` is missing' assert FLAGS.trained_checkpoint_prefix, ( '`trained_checkpoint_prefix` is missing') assert FLAGS.output_directory, '`output_directory` is missing' pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f: text_format.Merge(f.read(), pipeline_config) exporter.export_inference_graph(FLAGS.input_type, pipeline_config, FLAGS.trained_checkpoint_prefix, FLAGS.output_directory)
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) 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)
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 test_export_graph_saves_pipeline_file(self): tmp_dir = self.get_temp_dir() trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt') self._save_checkpoint_from_mock_model(trained_checkpoint_prefix, use_moving_averages=True) output_directory = os.path.join(tmp_dir, 'output') with mock.patch.object( model_builder, 'build', autospec=True) as mock_builder: mock_builder.return_value = FakeModel() pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() exporter.export_inference_graph( input_type='image_tensor', pipeline_config=pipeline_config, trained_checkpoint_prefix=trained_checkpoint_prefix, output_directory=output_directory) expected_pipeline_path = os.path.join( output_directory, 'pipeline.config') self.assertTrue(os.path.exists(expected_pipeline_path)) written_pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() with tf.gfile.GFile(expected_pipeline_path, 'r') as f: proto_str = f.read() text_format.Merge(proto_str, written_pipeline_config) self.assertProtoEquals(pipeline_config, written_pipeline_config)
def test_export_and_run_inference_with_encoded_image_string_tensor(self): tmp_dir = self.get_temp_dir() trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt') self._save_checkpoint_from_mock_model(trained_checkpoint_prefix, use_moving_averages=True) output_directory = os.path.join(tmp_dir, 'output') inference_graph_path = os.path.join(output_directory, 'frozen_inference_graph.pb') with mock.patch.object( model_builder, 'build', autospec=True) as mock_builder: mock_builder.return_value = FakeModel(add_detection_masks=True) pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() pipeline_config.eval_config.use_moving_averages = False exporter.export_inference_graph( input_type='encoded_image_string_tensor', pipeline_config=pipeline_config, trained_checkpoint_prefix=trained_checkpoint_prefix, output_directory=output_directory) inference_graph = self._load_inference_graph(inference_graph_path) jpg_image_str = self._create_encoded_image_string( np.ones((4, 4, 3)).astype(np.uint8), 'jpg') png_image_str = self._create_encoded_image_string( np.ones((4, 4, 3)).astype(np.uint8), 'png') with self.test_session(graph=inference_graph) as sess: image_str_tensor = inference_graph.get_tensor_by_name( 'encoded_image_string_tensor:0') boxes = inference_graph.get_tensor_by_name('detection_boxes:0') scores = inference_graph.get_tensor_by_name('detection_scores:0') classes = inference_graph.get_tensor_by_name('detection_classes:0') masks = inference_graph.get_tensor_by_name('detection_masks:0') num_detections = inference_graph.get_tensor_by_name('num_detections:0') for image_str in [jpg_image_str, png_image_str]: image_str_batch_np = np.hstack([image_str]* 2) (boxes_np, scores_np, classes_np, masks_np, num_detections_np) = sess.run( [boxes, scores, classes, masks, num_detections], feed_dict={image_str_tensor: image_str_batch_np}) self.assertAllClose(boxes_np, [[[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.8, 0.8]], [[0.5, 0.5, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0]]]) self.assertAllClose(scores_np, [[0.7, 0.6], [0.9, 0.0]]) self.assertAllClose(classes_np, [[1, 2], [2, 1]]) self.assertAllClose(masks_np, np.arange(64).reshape([2, 2, 4, 4])) self.assertAllClose(num_detections_np, [2, 1])
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 test_export_graph_with_moving_averages(self): tmp_dir = self.get_temp_dir() trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt') self._save_checkpoint_from_mock_model(trained_checkpoint_prefix, use_moving_averages=True) output_directory = os.path.join(tmp_dir, 'output') with mock.patch.object(model_builder, 'build', autospec=True) as mock_builder: mock_builder.return_value = FakeModel() pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() pipeline_config.eval_config.use_moving_averages = True exporter.export_inference_graph( input_type='image_tensor', pipeline_config=pipeline_config, trained_checkpoint_prefix=trained_checkpoint_prefix, output_directory=output_directory)
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 get_configs_from_pipeline_file(): """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig. Reads training config from file specified by pipeline_config_path flag. Returns: model_config: model_pb2.DetectionModel train_config: train_pb2.TrainConfig input_config: input_reader_pb2.InputReader """ pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f: text_format.Merge(f.read(), pipeline_config) model_config = pipeline_config.model train_config = pipeline_config.train_config input_config = pipeline_config.train_input_reader return model_config, train_config, input_config
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 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 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 test_export_graph_with_tf_example_input(self): tmp_dir = self.get_temp_dir() trained_checkpoint_prefix = os.path.join(tmp_dir, 'model.ckpt') self._save_checkpoint_from_mock_model(trained_checkpoint_prefix, use_moving_averages=False) with mock.patch.object( model_builder, 'build', autospec=True) as mock_builder: mock_builder.return_value = FakeModel() output_directory = os.path.join(tmp_dir, 'output') pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() pipeline_config.eval_config.use_moving_averages = False exporter.export_inference_graph( input_type='tf_example', pipeline_config=pipeline_config, trained_checkpoint_prefix=trained_checkpoint_prefix, output_directory=output_directory) self.assertTrue(os.path.exists(os.path.join( output_directory, 'saved_model', 'saved_model.pb')))
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 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 create_pipeline_proto_from_configs(configs): """Creates a pipeline_pb2.TrainEvalPipelineConfig from configs dictionary. This function nearly performs the inverse operation of get_configs_from_pipeline_file(). Instead of returning a file path, it returns a `TrainEvalPipelineConfig` object. 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"]) 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)