Python object_detection.utils.config_util.get_configs_from_multiple_files() Examples
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Example #1
| Source File: offline_eval_map_corloc.py From models with Apache License 2.0 | 6 votes |
|
def main(argv):
del argv
required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
configs = config_util.get_configs_from_multiple_files(
eval_input_config_path=FLAGS.input_config_path,
eval_config_path=FLAGS.eval_config_path)
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
metrics = read_data_and_evaluate(input_config, eval_config)
# Save metrics
write_metrics(metrics, FLAGS.eval_dir)
Example #2
| Source File: offline_eval_map_corloc.py From MAX-Object-Detector with Apache License 2.0 | 6 votes |
|
def main(argv):
del argv
required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
configs = config_util.get_configs_from_multiple_files(
eval_input_config_path=FLAGS.input_config_path,
eval_config_path=FLAGS.eval_config_path)
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
metrics = read_data_and_evaluate(input_config, eval_config)
# Save metrics
write_metrics(metrics, FLAGS.eval_dir)
Example #3
| Source File: offline_eval_map_corloc.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 6 votes |
|
def main(argv):
del argv
required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
configs = config_util.get_configs_from_multiple_files(
eval_input_config_path=FLAGS.input_config_path,
eval_config_path=FLAGS.eval_config_path)
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
metrics = read_data_and_evaluate(input_config, eval_config)
# Save metrics
write_metrics(metrics, FLAGS.eval_dir)
Example #4
| Source File: offline_eval_map_corloc.py From ros_tensorflow with Apache License 2.0 | 6 votes |
|
def main(argv):
del argv
required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
configs = config_util.get_configs_from_multiple_files(
eval_input_config_path=FLAGS.input_config_path,
eval_config_path=FLAGS.eval_config_path)
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
metrics = read_data_and_evaluate(input_config, eval_config)
# Save metrics
write_metrics(metrics, FLAGS.eval_dir)
Example #5
| Source File: offline_eval_map_corloc.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
def main(argv):
del argv
required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
configs = config_util.get_configs_from_multiple_files(
eval_input_config_path=FLAGS.input_config_path,
eval_config_path=FLAGS.eval_config_path)
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
metrics = read_data_and_evaluate(input_config, eval_config)
# Save metrics
write_metrics(metrics, FLAGS.eval_dir)
Example #6
| Source File: offline_eval_map_corloc.py From Gun-Detector with Apache License 2.0 | 6 votes |
|
def main(argv):
del argv
required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
configs = config_util.get_configs_from_multiple_files(
eval_input_config_path=FLAGS.input_config_path,
eval_config_path=FLAGS.eval_config_path)
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
metrics = read_data_and_evaluate(input_config, eval_config)
# Save metrics
write_metrics(metrics, FLAGS.eval_dir)
Example #7
| Source File: offline_eval_map_corloc.py From vehicle_counting_tensorflow with MIT License | 6 votes |
|
def main(argv):
del argv
required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
configs = config_util.get_configs_from_multiple_files(
eval_input_config_path=FLAGS.input_config_path,
eval_config_path=FLAGS.eval_config_path)
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
metrics = read_data_and_evaluate(input_config, eval_config)
# Save metrics
write_metrics(metrics, FLAGS.eval_dir)
Example #8
| Source File: offline_eval_map_corloc.py From Traffic-Rule-Violation-Detection-System with MIT License | 6 votes |
|
def main(argv):
del argv
required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
configs = config_util.get_configs_from_multiple_files(
eval_input_config_path=FLAGS.input_config_path,
eval_config_path=FLAGS.eval_config_path)
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
metrics = read_data_and_evaluate(input_config, eval_config)
# Save metrics
write_metrics(metrics, FLAGS.eval_dir)
Example #9
| Source File: offline_eval_map_corloc.py From yolo_v2 with Apache License 2.0 | 6 votes |
|
def main(argv):
del argv
required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
configs = config_util.get_configs_from_multiple_files(
eval_input_config_path=FLAGS.input_config_path,
eval_config_path=FLAGS.eval_config_path)
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
metrics = read_data_and_evaluate(input_config, eval_config)
# Save metrics
write_metrics(metrics, FLAGS.eval_dir)
Example #10
| Source File: offline_eval_map_corloc.py From Elphas with Apache License 2.0 | 6 votes |
|
def main(argv):
del argv
required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
configs = config_util.get_configs_from_multiple_files(
eval_input_config_path=FLAGS.input_config_path,
eval_config_path=FLAGS.eval_config_path)
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
metrics = read_data_and_evaluate(input_config, eval_config)
# Save metrics
write_metrics(metrics, FLAGS.eval_dir)
Example #11
| Source File: offline_eval_map_corloc.py From Person-Detection-and-Tracking with MIT License | 6 votes |
|
def main(argv):
del argv
required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
configs = config_util.get_configs_from_multiple_files(
eval_input_config_path=FLAGS.input_config_path,
eval_config_path=FLAGS.eval_config_path)
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
metrics = read_data_and_evaluate(input_config, eval_config)
# Save metrics
write_metrics(metrics, FLAGS.eval_dir)
Example #12
| Source File: offline_eval_map_corloc.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
def main(argv):
del argv
required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
configs = config_util.get_configs_from_multiple_files(
eval_input_config_path=FLAGS.input_config_path,
eval_config_path=FLAGS.eval_config_path)
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
metrics = read_data_and_evaluate(input_config, eval_config)
# Save metrics
write_metrics(metrics, FLAGS.eval_dir)
Example #13
| Source File: offline_eval_map_corloc.py From object_detection_with_tensorflow with MIT License | 6 votes |
|
def main(argv):
del argv
required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
configs = config_util.get_configs_from_multiple_files(
eval_input_config_path=FLAGS.input_config_path,
eval_config_path=FLAGS.eval_config_path)
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
metrics = read_data_and_evaluate(input_config, eval_config)
# Save metrics
write_metrics(metrics, FLAGS.eval_dir)
Example #14
| Source File: offline_eval_map_corloc.py From ros_people_object_detection_tensorflow with Apache License 2.0 | 6 votes |
|
def main(argv):
del argv
required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
configs = config_util.get_configs_from_multiple_files(
eval_input_config_path=FLAGS.input_config_path,
eval_config_path=FLAGS.eval_config_path)
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
metrics = read_data_and_evaluate(input_config, eval_config)
# Save metrics
write_metrics(metrics, FLAGS.eval_dir)
Example #15
| Source File: config_util.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def get_configs_from_multiple_files(model_config_path="",
train_config_path="",
train_input_config_path="",
eval_config_path="",
eval_input_config_path="",
lstm_config_path=""):
"""Reads training configuration from multiple config files.
Args:
model_config_path: Path to model_pb2.DetectionModel.
train_config_path: Path to train_pb2.TrainConfig.
train_input_config_path: Path to input_reader_pb2.InputReader.
eval_config_path: Path to eval_pb2.EvalConfig.
eval_input_config_path: Path to input_reader_pb2.InputReader.
lstm_config_path: Path to pipeline_pb2.LstmModel.
Returns:
Dictionary of configuration objects. Keys are `model`, `train_config`,
`train_input_config`, `eval_config`, `eval_input_config`, `lstm_model`.
Key/Values are returned only for valid (non-empty) strings.
"""
configs = config_util.get_configs_from_multiple_files(
model_config_path=model_config_path,
train_config_path=train_config_path,
train_input_config_path=train_input_config_path,
eval_config_path=eval_config_path,
eval_input_config_path=eval_input_config_path)
if lstm_config_path:
lstm_config = internal_pipeline_pb2.LstmModel()
with tf.gfile.GFile(lstm_config_path, "r") as f:
text_format.Merge(f.read(), lstm_config)
configs["lstm_model"] = lstm_config
return configs
Example #16
| Source File: config_util.py From models with Apache License 2.0 | 5 votes |
|
def get_configs_from_multiple_files(model_config_path="",
train_config_path="",
train_input_config_path="",
eval_config_path="",
eval_input_config_path="",
lstm_config_path=""):
"""Reads training configuration from multiple config files.
Args:
model_config_path: Path to model_pb2.DetectionModel.
train_config_path: Path to train_pb2.TrainConfig.
train_input_config_path: Path to input_reader_pb2.InputReader.
eval_config_path: Path to eval_pb2.EvalConfig.
eval_input_config_path: Path to input_reader_pb2.InputReader.
lstm_config_path: Path to pipeline_pb2.LstmModel.
Returns:
Dictionary of configuration objects. Keys are `model`, `train_config`,
`train_input_config`, `eval_config`, `eval_input_config`, `lstm_model`.
Key/Values are returned only for valid (non-empty) strings.
"""
configs = config_util.get_configs_from_multiple_files(
model_config_path=model_config_path,
train_config_path=train_config_path,
train_input_config_path=train_input_config_path,
eval_config_path=eval_config_path,
eval_input_config_path=eval_input_config_path)
if lstm_config_path:
lstm_config = internal_pipeline_pb2.LstmModel()
with tf.gfile.GFile(lstm_config_path, "r") as f:
text_format.Merge(f.read(), lstm_config)
configs["lstm_model"] = lstm_config
return configs
Example #17
| Source File: config_util.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def get_configs_from_multiple_files(model_config_path="",
train_config_path="",
train_input_config_path="",
eval_config_path="",
eval_input_config_path="",
lstm_config_path=""):
"""Reads training configuration from multiple config files.
Args:
model_config_path: Path to model_pb2.DetectionModel.
train_config_path: Path to train_pb2.TrainConfig.
train_input_config_path: Path to input_reader_pb2.InputReader.
eval_config_path: Path to eval_pb2.EvalConfig.
eval_input_config_path: Path to input_reader_pb2.InputReader.
lstm_config_path: Path to pipeline_pb2.LstmModel.
Returns:
Dictionary of configuration objects. Keys are `model`, `train_config`,
`train_input_config`, `eval_config`, `eval_input_config`, `lstm_model`.
Key/Values are returned only for valid (non-empty) strings.
"""
configs = config_util.get_configs_from_multiple_files(
model_config_path=model_config_path,
train_config_path=train_config_path,
train_input_config_path=train_input_config_path,
eval_config_path=eval_config_path,
eval_input_config_path=eval_input_config_path)
if lstm_config_path:
lstm_config = internal_pipeline_pb2.LstmModel()
with tf.gfile.GFile(lstm_config_path, "r") as f:
text_format.Merge(f.read(), lstm_config)
configs["lstm_model"] = lstm_config
return configs
Example #18
| Source File: config_util_test.py From object_detection_with_tensorflow with MIT License | 4 votes |
|
def test_get_configs_from_multiple_files(self):
"""Tests that proto configs can be read from multiple files."""
temp_dir = self.get_temp_dir()
# Write model config file.
model_config_path = os.path.join(temp_dir, "model.config")
model = model_pb2.DetectionModel()
model.faster_rcnn.num_classes = 10
_write_config(model, model_config_path)
# Write train config file.
train_config_path = os.path.join(temp_dir, "train.config")
train_config = train_config = train_pb2.TrainConfig()
train_config.batch_size = 32
_write_config(train_config, train_config_path)
# Write train input config file.
train_input_config_path = os.path.join(temp_dir, "train_input.config")
train_input_config = input_reader_pb2.InputReader()
train_input_config.label_map_path = "path/to/label_map"
_write_config(train_input_config, train_input_config_path)
# Write eval config file.
eval_config_path = os.path.join(temp_dir, "eval.config")
eval_config = eval_pb2.EvalConfig()
eval_config.num_examples = 20
_write_config(eval_config, eval_config_path)
# Write eval input config file.
eval_input_config_path = os.path.join(temp_dir, "eval_input.config")
eval_input_config = input_reader_pb2.InputReader()
eval_input_config.label_map_path = "path/to/another/label_map"
_write_config(eval_input_config, eval_input_config_path)
configs = config_util.get_configs_from_multiple_files(
model_config_path=model_config_path,
train_config_path=train_config_path,
train_input_config_path=train_input_config_path,
eval_config_path=eval_config_path,
eval_input_config_path=eval_input_config_path)
self.assertProtoEquals(model, configs["model"])
self.assertProtoEquals(train_config, configs["train_config"])
self.assertProtoEquals(train_input_config,
configs["train_input_config"])
self.assertProtoEquals(eval_config, configs["eval_config"])
self.assertProtoEquals(eval_input_config,
configs["eval_input_config"])
Example #19
| Source File: eval.py From object_detection_with_tensorflow with MIT License | 4 votes |
|
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']
model_fn = functools.partial(
model_builder.build,
model_config=model_config,
is_training=False)
create_input_dict_fn = functools.partial(
input_reader_builder.build,
input_config)
label_map = label_map_util.load_labelmap(input_config.label_map_path)
max_num_classes = max([item.id for item in label_map.item])
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes)
if FLAGS.run_once:
eval_config.max_evals = 1
evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
FLAGS.checkpoint_dir, FLAGS.eval_dir)
Example #20
| Source File: eval.py From object_detection_with_tensorflow with MIT License | 4 votes |
|
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']
model_fn = functools.partial(
model_builder.build,
model_config=model_config,
is_training=False)
create_input_dict_fn = functools.partial(
input_reader_builder.build,
input_config)
label_map = label_map_util.load_labelmap(input_config.label_map_path)
max_num_classes = max([item.id for item in label_map.item])
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes)
if FLAGS.run_once:
eval_config.max_evals = 1
evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
FLAGS.checkpoint_dir, FLAGS.eval_dir)
Example #21
| Source File: config_util_test.py From AniSeg with Apache License 2.0 | 4 votes |
|
def test_get_configs_from_multiple_files(self):
"""Tests that proto configs can be read from multiple files."""
temp_dir = self.get_temp_dir()
# Write model config file.
model_config_path = os.path.join(temp_dir, "model.config")
model = model_pb2.DetectionModel()
model.faster_rcnn.num_classes = 10
_write_config(model, model_config_path)
# Write train config file.
train_config_path = os.path.join(temp_dir, "train.config")
train_config = train_config = train_pb2.TrainConfig()
train_config.batch_size = 32
_write_config(train_config, train_config_path)
# Write train input config file.
train_input_config_path = os.path.join(temp_dir, "train_input.config")
train_input_config = input_reader_pb2.InputReader()
train_input_config.label_map_path = "path/to/label_map"
_write_config(train_input_config, train_input_config_path)
# Write eval config file.
eval_config_path = os.path.join(temp_dir, "eval.config")
eval_config = eval_pb2.EvalConfig()
eval_config.num_examples = 20
_write_config(eval_config, eval_config_path)
# Write eval input config file.
eval_input_config_path = os.path.join(temp_dir, "eval_input.config")
eval_input_config = input_reader_pb2.InputReader()
eval_input_config.label_map_path = "path/to/another/label_map"
_write_config(eval_input_config, eval_input_config_path)
configs = config_util.get_configs_from_multiple_files(
model_config_path=model_config_path,
train_config_path=train_config_path,
train_input_config_path=train_input_config_path,
eval_config_path=eval_config_path,
eval_input_config_path=eval_input_config_path)
self.assertProtoEquals(model, configs["model"])
self.assertProtoEquals(train_config, configs["train_config"])
self.assertProtoEquals(train_input_config,
configs["train_input_config"])
self.assertProtoEquals(eval_config, configs["eval_config"])
self.assertProtoEquals(eval_input_config,
configs["eval_input_config"])
Example #22
| Source File: config_util_test.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 4 votes |
|
def test_get_configs_from_multiple_files(self):
"""Tests that proto configs can be read from multiple files."""
temp_dir = self.get_temp_dir()
# Write model config file.
model_config_path = os.path.join(temp_dir, "model.config")
model = model_pb2.DetectionModel()
model.faster_rcnn.num_classes = 10
_write_config(model, model_config_path)
# Write train config file.
train_config_path = os.path.join(temp_dir, "train.config")
train_config = train_config = train_pb2.TrainConfig()
train_config.batch_size = 32
_write_config(train_config, train_config_path)
# Write train input config file.
train_input_config_path = os.path.join(temp_dir, "train_input.config")
train_input_config = input_reader_pb2.InputReader()
train_input_config.label_map_path = "path/to/label_map"
_write_config(train_input_config, train_input_config_path)
# Write eval config file.
eval_config_path = os.path.join(temp_dir, "eval.config")
eval_config = eval_pb2.EvalConfig()
eval_config.num_examples = 20
_write_config(eval_config, eval_config_path)
# Write eval input config file.
eval_input_config_path = os.path.join(temp_dir, "eval_input.config")
eval_input_config = input_reader_pb2.InputReader()
eval_input_config.label_map_path = "path/to/another/label_map"
_write_config(eval_input_config, eval_input_config_path)
configs = config_util.get_configs_from_multiple_files(
model_config_path=model_config_path,
train_config_path=train_config_path,
train_input_config_path=train_input_config_path,
eval_config_path=eval_config_path,
eval_input_config_path=eval_input_config_path)
self.assertProtoEquals(model, configs["model"])
self.assertProtoEquals(train_config, configs["train_config"])
self.assertProtoEquals(train_input_config,
configs["train_input_config"])
self.assertProtoEquals(eval_config, configs["eval_config"])
self.assertProtoEquals(eval_input_config, configs["eval_input_configs"][0])
Example #23
| Source File: eval.py From MAX-Object-Detector with Apache License 2.0 | 4 votes |
|
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)
Example #24
| Source File: config_util_test.py From MAX-Object-Detector with Apache License 2.0 | 4 votes |
|
def test_get_configs_from_multiple_files(self):
"""Tests that proto configs can be read from multiple files."""
temp_dir = self.get_temp_dir()
# Write model config file.
model_config_path = os.path.join(temp_dir, "model.config")
model = model_pb2.DetectionModel()
model.faster_rcnn.num_classes = 10
_write_config(model, model_config_path)
# Write train config file.
train_config_path = os.path.join(temp_dir, "train.config")
train_config = train_config = train_pb2.TrainConfig()
train_config.batch_size = 32
_write_config(train_config, train_config_path)
# Write train input config file.
train_input_config_path = os.path.join(temp_dir, "train_input.config")
train_input_config = input_reader_pb2.InputReader()
train_input_config.label_map_path = "path/to/label_map"
_write_config(train_input_config, train_input_config_path)
# Write eval config file.
eval_config_path = os.path.join(temp_dir, "eval.config")
eval_config = eval_pb2.EvalConfig()
eval_config.num_examples = 20
_write_config(eval_config, eval_config_path)
# Write eval input config file.
eval_input_config_path = os.path.join(temp_dir, "eval_input.config")
eval_input_config = input_reader_pb2.InputReader()
eval_input_config.label_map_path = "path/to/another/label_map"
_write_config(eval_input_config, eval_input_config_path)
configs = config_util.get_configs_from_multiple_files(
model_config_path=model_config_path,
train_config_path=train_config_path,
train_input_config_path=train_input_config_path,
eval_config_path=eval_config_path,
eval_input_config_path=eval_input_config_path)
self.assertProtoEquals(model, configs["model"])
self.assertProtoEquals(train_config, configs["train_config"])
self.assertProtoEquals(train_input_config,
configs["train_input_config"])
self.assertProtoEquals(eval_config, configs["eval_config"])
self.assertProtoEquals(eval_input_config, configs["eval_input_configs"][0])
Example #25
| Source File: eval.py From g-tensorflow-models with Apache License 2.0 | 4 votes |
|
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)
Example #26
| Source File: config_util_test.py From g-tensorflow-models with Apache License 2.0 | 4 votes |
|
def test_get_configs_from_multiple_files(self):
"""Tests that proto configs can be read from multiple files."""
temp_dir = self.get_temp_dir()
# Write model config file.
model_config_path = os.path.join(temp_dir, "model.config")
model = model_pb2.DetectionModel()
model.faster_rcnn.num_classes = 10
_write_config(model, model_config_path)
# Write train config file.
train_config_path = os.path.join(temp_dir, "train.config")
train_config = train_config = train_pb2.TrainConfig()
train_config.batch_size = 32
_write_config(train_config, train_config_path)
# Write train input config file.
train_input_config_path = os.path.join(temp_dir, "train_input.config")
train_input_config = input_reader_pb2.InputReader()
train_input_config.label_map_path = "path/to/label_map"
_write_config(train_input_config, train_input_config_path)
# Write eval config file.
eval_config_path = os.path.join(temp_dir, "eval.config")
eval_config = eval_pb2.EvalConfig()
eval_config.num_examples = 20
_write_config(eval_config, eval_config_path)
# Write eval input config file.
eval_input_config_path = os.path.join(temp_dir, "eval_input.config")
eval_input_config = input_reader_pb2.InputReader()
eval_input_config.label_map_path = "path/to/another/label_map"
_write_config(eval_input_config, eval_input_config_path)
configs = config_util.get_configs_from_multiple_files(
model_config_path=model_config_path,
train_config_path=train_config_path,
train_input_config_path=train_input_config_path,
eval_config_path=eval_config_path,
eval_input_config_path=eval_input_config_path)
self.assertProtoEquals(model, configs["model"])
self.assertProtoEquals(train_config, configs["train_config"])
self.assertProtoEquals(train_input_config,
configs["train_input_config"])
self.assertProtoEquals(eval_config, configs["eval_config"])
self.assertProtoEquals(eval_input_config, configs["eval_input_configs"][0])
Example #27
| Source File: eval.py From models with Apache License 2.0 | 4 votes |
|
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)
Example #28
| Source File: config_util_test.py From models with Apache License 2.0 | 4 votes |
|
def test_get_configs_from_multiple_files(self):
"""Tests that proto configs can be read from multiple files."""
temp_dir = self.get_temp_dir()
# Write model config file.
model_config_path = os.path.join(temp_dir, "model.config")
model = model_pb2.DetectionModel()
model.faster_rcnn.num_classes = 10
_write_config(model, model_config_path)
# Write train config file.
train_config_path = os.path.join(temp_dir, "train.config")
train_config = train_config = train_pb2.TrainConfig()
train_config.batch_size = 32
_write_config(train_config, train_config_path)
# Write train input config file.
train_input_config_path = os.path.join(temp_dir, "train_input.config")
train_input_config = input_reader_pb2.InputReader()
train_input_config.label_map_path = "path/to/label_map"
_write_config(train_input_config, train_input_config_path)
# Write eval config file.
eval_config_path = os.path.join(temp_dir, "eval.config")
eval_config = eval_pb2.EvalConfig()
eval_config.num_examples = 20
_write_config(eval_config, eval_config_path)
# Write eval input config file.
eval_input_config_path = os.path.join(temp_dir, "eval_input.config")
eval_input_config = input_reader_pb2.InputReader()
eval_input_config.label_map_path = "path/to/another/label_map"
_write_config(eval_input_config, eval_input_config_path)
configs = config_util.get_configs_from_multiple_files(
model_config_path=model_config_path,
train_config_path=train_config_path,
train_input_config_path=train_input_config_path,
eval_config_path=eval_config_path,
eval_input_config_path=eval_input_config_path)
self.assertProtoEquals(model, configs["model"])
self.assertProtoEquals(train_config, configs["train_config"])
self.assertProtoEquals(train_input_config,
configs["train_input_config"])
self.assertProtoEquals(eval_config, configs["eval_config"])
self.assertProtoEquals(eval_input_config, configs["eval_input_configs"][0])
Example #29
| Source File: eval.py From multilabel-image-classification-tensorflow with MIT License | 4 votes |
|
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)
Example #30
| Source File: config_util_test.py From multilabel-image-classification-tensorflow with MIT License | 4 votes |
|
def test_get_configs_from_multiple_files(self):
"""Tests that proto configs can be read from multiple files."""
temp_dir = self.get_temp_dir()
# Write model config file.
model_config_path = os.path.join(temp_dir, "model.config")
model = model_pb2.DetectionModel()
model.faster_rcnn.num_classes = 10
_write_config(model, model_config_path)
# Write train config file.
train_config_path = os.path.join(temp_dir, "train.config")
train_config = train_config = train_pb2.TrainConfig()
train_config.batch_size = 32
_write_config(train_config, train_config_path)
# Write train input config file.
train_input_config_path = os.path.join(temp_dir, "train_input.config")
train_input_config = input_reader_pb2.InputReader()
train_input_config.label_map_path = "path/to/label_map"
_write_config(train_input_config, train_input_config_path)
# Write eval config file.
eval_config_path = os.path.join(temp_dir, "eval.config")
eval_config = eval_pb2.EvalConfig()
eval_config.num_examples = 20
_write_config(eval_config, eval_config_path)
# Write eval input config file.
eval_input_config_path = os.path.join(temp_dir, "eval_input.config")
eval_input_config = input_reader_pb2.InputReader()
eval_input_config.label_map_path = "path/to/another/label_map"
_write_config(eval_input_config, eval_input_config_path)
configs = config_util.get_configs_from_multiple_files(
model_config_path=model_config_path,
train_config_path=train_config_path,
train_input_config_path=train_input_config_path,
eval_config_path=eval_config_path,
eval_input_config_path=eval_input_config_path)
self.assertProtoEquals(model, configs["model"])
self.assertProtoEquals(train_config, configs["train_config"])
self.assertProtoEquals(train_input_config,
configs["train_input_config"])
self.assertProtoEquals(eval_config, configs["eval_config"])
self.assertProtoEquals(eval_input_config, configs["eval_input_configs"][0])
