Python object_detection.utils.config_util.save_pipeline_config() Examples
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Example #1
| Source File: config_util_test.py From vehicle_counting_tensorflow with MIT License | 6 votes |
|
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)
Example #2
| Source File: config_util_test.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
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)
Example #3
| Source File: config_util_test.py From models with Apache License 2.0 | 6 votes |
|
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)
Example #4
| Source File: config_util_test.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
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)
Example #5
| Source File: config_util_test.py From MAX-Object-Detector with Apache License 2.0 | 6 votes |
|
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)
Example #6
| Source File: config_util_test.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 6 votes |
|
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)
Example #7
| Source File: config_util_test.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 6 votes |
|
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)
Example #8
| Source File: config_util_test.py From ros_tensorflow with Apache License 2.0 | 6 votes |
|
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)
Example #9
| Source File: config_util_test.py From Gun-Detector with Apache License 2.0 | 6 votes |
|
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)
Example #10
| Source File: config_util_test.py From Person-Detection-and-Tracking with MIT License | 6 votes |
|
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)
Example #11
| Source File: create_config_file.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 5 votes |
|
def create_config_file(input_path, config_params, network_type):
configs = config_util.get_configs_from_pipeline_file(input_path)
if config_params['checkpoint_path'] is not None:
prefix = ""
for ckpt_file in os.listdir(os.path.join('/checkpoints/'+ network_type, config_params['checkpoint_path'])):
if ckpt_file.endswith(".index"):
prefix = ckpt_file.split(".index")[0]
config_params['checkpoint_path'] = '/checkpoints/'+network_type+'/'+config_params['checkpoint_path']+'/'+prefix
else:
config_params['checkpoint_path'] = '/weights/'+network_type+'/model.ckpt'
new_configs = None
if network_type == "ssd_mobilenet" or network_type == "ssd_inception":
new_configs = config_ssd_mobilenet_inception(configs, config_params)
elif network_type == "ssd_resnet_50" or network_type == "ssd_fpn":
new_configs = config_ssd_mobilenet_inception(configs, config_params)
elif network_type == "frcnn_resnet_50" or network_type == "frcnn_resnet_101":
new_configs = config_frcnn_resnet_50_101(configs, config_params)
pipeline_config = config_util.create_pipeline_proto_from_configs(new_configs)
config_util.save_pipeline_config(pipeline_config, '/training_dir/model')
Example #12
| Source File: exporter.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 4 votes |
|
def export_inference_graph(input_type,
pipeline_config,
trained_checkpoint_prefix,
output_directory,
input_shape=None,
output_collection_name='inference_op',
additional_output_tensor_names=None,
write_inference_graph=False):
"""Exports inference graph for the model specified in the pipeline config.
Args:
input_type: Type of input for the graph. Can be one of ['image_tensor',
'encoded_image_string_tensor', 'tf_example'].
pipeline_config: pipeline_pb2.TrainAndEvalPipelineConfig proto.
trained_checkpoint_prefix: Path to the trained checkpoint file.
output_directory: Path to write outputs.
input_shape: Sets a fixed shape for an `image_tensor` input. If not
specified, will default to [None, None, None, 3].
output_collection_name: Name of collection to add output tensors to.
If None, does not add output tensors to a collection.
additional_output_tensor_names: list of additional output
tensors to include in the frozen graph.
write_inference_graph: If true, writes inference graph to disk.
"""
detection_model = model_builder.build(pipeline_config.model,
is_training=False)
graph_rewriter_fn = None
if pipeline_config.HasField('graph_rewriter'):
graph_rewriter_config = pipeline_config.graph_rewriter
graph_rewriter_fn = graph_rewriter_builder.build(graph_rewriter_config,
is_training=False)
_export_inference_graph(
input_type,
detection_model,
pipeline_config.eval_config.use_moving_averages,
trained_checkpoint_prefix,
output_directory,
additional_output_tensor_names,
input_shape,
output_collection_name,
graph_hook_fn=graph_rewriter_fn,
write_inference_graph=write_inference_graph)
pipeline_config.eval_config.use_moving_averages = False
config_util.save_pipeline_config(pipeline_config, output_directory)
Example #13
| Source File: exporter.py From MAX-Object-Detector with Apache License 2.0 | 4 votes |
|
def export_inference_graph(input_type,
pipeline_config,
trained_checkpoint_prefix,
output_directory,
input_shape=None,
output_collection_name='inference_op',
additional_output_tensor_names=None,
write_inference_graph=False):
"""Exports inference graph for the model specified in the pipeline config.
Args:
input_type: Type of input for the graph. Can be one of ['image_tensor',
'encoded_image_string_tensor', 'tf_example'].
pipeline_config: pipeline_pb2.TrainAndEvalPipelineConfig proto.
trained_checkpoint_prefix: Path to the trained checkpoint file.
output_directory: Path to write outputs.
input_shape: Sets a fixed shape for an `image_tensor` input. If not
specified, will default to [None, None, None, 3].
output_collection_name: Name of collection to add output tensors to.
If None, does not add output tensors to a collection.
additional_output_tensor_names: list of additional output
tensors to include in the frozen graph.
write_inference_graph: If true, writes inference graph to disk.
"""
detection_model = model_builder.build(pipeline_config.model,
is_training=False)
graph_rewriter_fn = None
if pipeline_config.HasField('graph_rewriter'):
graph_rewriter_config = pipeline_config.graph_rewriter
graph_rewriter_fn = graph_rewriter_builder.build(graph_rewriter_config,
is_training=False)
_export_inference_graph(
input_type,
detection_model,
pipeline_config.eval_config.use_moving_averages,
trained_checkpoint_prefix,
output_directory,
additional_output_tensor_names,
input_shape,
output_collection_name,
graph_hook_fn=graph_rewriter_fn,
write_inference_graph=write_inference_graph)
pipeline_config.eval_config.use_moving_averages = False
config_util.save_pipeline_config(pipeline_config, output_directory)
Example #14
| Source File: exporter.py From g-tensorflow-models with Apache License 2.0 | 4 votes |
|
def export_inference_graph(input_type,
pipeline_config,
trained_checkpoint_prefix,
output_directory,
input_shape=None,
output_collection_name='inference_op',
additional_output_tensor_names=None,
write_inference_graph=False):
"""Exports inference graph for the model specified in the pipeline config.
Args:
input_type: Type of input for the graph. Can be one of ['image_tensor',
'encoded_image_string_tensor', 'tf_example'].
pipeline_config: pipeline_pb2.TrainAndEvalPipelineConfig proto.
trained_checkpoint_prefix: Path to the trained checkpoint file.
output_directory: Path to write outputs.
input_shape: Sets a fixed shape for an `image_tensor` input. If not
specified, will default to [None, None, None, 3].
output_collection_name: Name of collection to add output tensors to.
If None, does not add output tensors to a collection.
additional_output_tensor_names: list of additional output
tensors to include in the frozen graph.
write_inference_graph: If true, writes inference graph to disk.
"""
detection_model = model_builder.build(pipeline_config.model,
is_training=False)
graph_rewriter_fn = None
if pipeline_config.HasField('graph_rewriter'):
graph_rewriter_config = pipeline_config.graph_rewriter
graph_rewriter_fn = graph_rewriter_builder.build(graph_rewriter_config,
is_training=False)
_export_inference_graph(
input_type,
detection_model,
pipeline_config.eval_config.use_moving_averages,
trained_checkpoint_prefix,
output_directory,
additional_output_tensor_names,
input_shape,
output_collection_name,
graph_hook_fn=graph_rewriter_fn,
write_inference_graph=write_inference_graph)
pipeline_config.eval_config.use_moving_averages = False
config_util.save_pipeline_config(pipeline_config, output_directory)
Example #15
| Source File: exporter.py From models with Apache License 2.0 | 4 votes |
|
def export_inference_graph(input_type,
pipeline_config,
trained_checkpoint_prefix,
output_directory,
input_shape=None,
output_collection_name='inference_op',
additional_output_tensor_names=None,
write_inference_graph=False,
use_side_inputs=False,
side_input_shapes=None,
side_input_names=None,
side_input_types=None):
"""Exports inference graph for the model specified in the pipeline config.
Args:
input_type: Type of input for the graph. Can be one of ['image_tensor',
'encoded_image_string_tensor', 'tf_example'].
pipeline_config: pipeline_pb2.TrainAndEvalPipelineConfig proto.
trained_checkpoint_prefix: Path to the trained checkpoint file.
output_directory: Path to write outputs.
input_shape: Sets a fixed shape for an `image_tensor` input. If not
specified, will default to [None, None, None, 3].
output_collection_name: Name of collection to add output tensors to.
If None, does not add output tensors to a collection.
additional_output_tensor_names: list of additional output
tensors to include in the frozen graph.
write_inference_graph: If true, writes inference graph to disk.
use_side_inputs: If True, the model requires side_inputs.
side_input_shapes: List of shapes of the side input tensors,
required if use_side_inputs is True.
side_input_names: List of names of the side input tensors,
required if use_side_inputs is True.
side_input_types: List of types of the side input tensors,
required if use_side_inputs is True.
"""
detection_model = model_builder.build(pipeline_config.model,
is_training=False)
graph_rewriter_fn = None
if pipeline_config.HasField('graph_rewriter'):
graph_rewriter_config = pipeline_config.graph_rewriter
graph_rewriter_fn = graph_rewriter_builder.build(graph_rewriter_config,
is_training=False)
_export_inference_graph(
input_type,
detection_model,
pipeline_config.eval_config.use_moving_averages,
trained_checkpoint_prefix,
output_directory,
additional_output_tensor_names,
input_shape,
output_collection_name,
graph_hook_fn=graph_rewriter_fn,
write_inference_graph=write_inference_graph,
use_side_inputs=use_side_inputs,
side_input_shapes=side_input_shapes,
side_input_names=side_input_names,
side_input_types=side_input_types)
pipeline_config.eval_config.use_moving_averages = False
config_util.save_pipeline_config(pipeline_config, output_directory)
Example #16
| Source File: exporter_lib_v2.py From models with Apache License 2.0 | 4 votes |
|
def export_inference_graph(input_type,
pipeline_config,
trained_checkpoint_dir,
output_directory):
"""Exports inference graph for the model specified in the pipeline config.
This function creates `output_directory` if it does not already exist,
which will hold a copy of the pipeline config with filename `pipeline.config`,
and two subdirectories named `checkpoint` and `saved_model`
(containing the exported checkpoint and SavedModel respectively).
Args:
input_type: Type of input for the graph. Can be one of ['image_tensor',
'encoded_image_string_tensor', 'tf_example'].
pipeline_config: pipeline_pb2.TrainAndEvalPipelineConfig proto.
trained_checkpoint_dir: Path to the trained checkpoint file.
output_directory: Path to write outputs.
Raises:
ValueError: if input_type is invalid.
"""
output_checkpoint_directory = os.path.join(output_directory, 'checkpoint')
output_saved_model_directory = os.path.join(output_directory, 'saved_model')
detection_model = model_builder.build(pipeline_config.model,
is_training=False)
ckpt = tf.train.Checkpoint(
model=detection_model)
manager = tf.train.CheckpointManager(
ckpt, trained_checkpoint_dir, max_to_keep=1)
status = ckpt.restore(manager.latest_checkpoint).expect_partial()
if input_type not in DETECTION_MODULE_MAP:
raise ValueError('Unrecognized `input_type`')
detection_module = DETECTION_MODULE_MAP[input_type](detection_model)
# Getting the concrete function traces the graph and forces variables to
# be constructed --- only after this can we save the checkpoint and
# saved model.
concrete_function = detection_module.__call__.get_concrete_function()
status.assert_existing_objects_matched()
exported_checkpoint_manager = tf.train.CheckpointManager(
ckpt, output_checkpoint_directory, max_to_keep=1)
exported_checkpoint_manager.save(checkpoint_number=0)
tf.saved_model.save(detection_module,
output_saved_model_directory,
signatures=concrete_function)
config_util.save_pipeline_config(pipeline_config, output_directory)
Example #17
| Source File: exporter.py From multilabel-image-classification-tensorflow with MIT License | 4 votes |
|
def export_inference_graph(input_type,
pipeline_config,
trained_checkpoint_prefix,
output_directory,
input_shape=None,
output_collection_name='inference_op',
additional_output_tensor_names=None,
write_inference_graph=False):
"""Exports inference graph for the model specified in the pipeline config.
Args:
input_type: Type of input for the graph. Can be one of ['image_tensor',
'encoded_image_string_tensor', 'tf_example'].
pipeline_config: pipeline_pb2.TrainAndEvalPipelineConfig proto.
trained_checkpoint_prefix: Path to the trained checkpoint file.
output_directory: Path to write outputs.
input_shape: Sets a fixed shape for an `image_tensor` input. If not
specified, will default to [None, None, None, 3].
output_collection_name: Name of collection to add output tensors to.
If None, does not add output tensors to a collection.
additional_output_tensor_names: list of additional output
tensors to include in the frozen graph.
write_inference_graph: If true, writes inference graph to disk.
"""
detection_model = model_builder.build(pipeline_config.model,
is_training=False)
graph_rewriter_fn = None
if pipeline_config.HasField('graph_rewriter'):
graph_rewriter_config = pipeline_config.graph_rewriter
graph_rewriter_fn = graph_rewriter_builder.build(graph_rewriter_config,
is_training=False)
_export_inference_graph(
input_type,
detection_model,
pipeline_config.eval_config.use_moving_averages,
trained_checkpoint_prefix,
output_directory,
additional_output_tensor_names,
input_shape,
output_collection_name,
graph_hook_fn=graph_rewriter_fn,
write_inference_graph=write_inference_graph)
pipeline_config.eval_config.use_moving_averages = False
config_util.save_pipeline_config(pipeline_config, output_directory)
Example #18
| Source File: exporter.py From vehicle_counting_tensorflow with MIT License | 4 votes |
|
def export_inference_graph(input_type,
pipeline_config,
trained_checkpoint_prefix,
output_directory,
input_shape=None,
output_collection_name='inference_op',
additional_output_tensor_names=None,
write_inference_graph=False):
"""Exports inference graph for the model specified in the pipeline config.
Args:
input_type: Type of input for the graph. Can be one of ['image_tensor',
'encoded_image_string_tensor', 'tf_example'].
pipeline_config: pipeline_pb2.TrainAndEvalPipelineConfig proto.
trained_checkpoint_prefix: Path to the trained checkpoint file.
output_directory: Path to write outputs.
input_shape: Sets a fixed shape for an `image_tensor` input. If not
specified, will default to [None, None, None, 3].
output_collection_name: Name of collection to add output tensors to.
If None, does not add output tensors to a collection.
additional_output_tensor_names: list of additional output
tensors to include in the frozen graph.
write_inference_graph: If true, writes inference graph to disk.
"""
detection_model = model_builder.build(pipeline_config.model,
is_training=False)
graph_rewriter_fn = None
if pipeline_config.HasField('graph_rewriter'):
graph_rewriter_config = pipeline_config.graph_rewriter
graph_rewriter_fn = graph_rewriter_builder.build(graph_rewriter_config,
is_training=False)
_export_inference_graph(
input_type,
detection_model,
pipeline_config.eval_config.use_moving_averages,
trained_checkpoint_prefix,
output_directory,
additional_output_tensor_names,
input_shape,
output_collection_name,
graph_hook_fn=graph_rewriter_fn,
write_inference_graph=write_inference_graph)
pipeline_config.eval_config.use_moving_averages = False
config_util.save_pipeline_config(pipeline_config, output_directory)
