本文整理匯總了Python中object_detection.protos.train_pb2.TrainConfig方法的典型用法代碼示例。如果您正苦於以下問題：Python train_pb2.TrainConfig方法的具體用法？Python train_pb2.TrainConfig怎麽用？Python train_pb2.TrainConfig使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類object_detection.protos.train_pb2的用法示例。
在下文中一共展示了train_pb2.TrainConfig方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: get_configs_from_pipeline_file
# 需要導入模塊: from object_detection.protos import train_pb2 [as 別名]
# 或者: from object_detection.protos.train_pb2 import TrainConfig [as 別名]
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 示例2: get_configs_from_pipeline_file
# 需要導入模塊: from object_detection.protos import train_pb2 [as 別名]
# 或者: from object_detection.protos.train_pb2 import TrainConfig [as 別名]
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.ssd
train_config = pipeline_config.train_config
input_config = pipeline_config.train_input_reader
return model_config, train_config, input_config 示例3: test_configure_trainer_and_train_two_steps
# 需要導入模塊: from object_detection.protos import train_pb2 [as 別名]
# 或者: from object_detection.protos.train_pb2 import TrainConfig [as 別名]
def test_configure_trainer_and_train_two_steps(self):
train_config_text_proto = """
optimizer {
adam_optimizer {
learning_rate {
constant_learning_rate {
learning_rate: 0.01
}
}
}
}
data_augmentation_options {
random_adjust_brightness {
max_delta: 0.2
}
}
data_augmentation_options {
random_adjust_contrast {
min_delta: 0.7
max_delta: 1.1
}
}
num_steps: 2
"""
train_config = train_pb2.TrainConfig()
text_format.Merge(train_config_text_proto, train_config)
train_dir = self.get_temp_dir()
trainer.train(create_tensor_dict_fn=get_input_function,
create_model_fn=FakeDetectionModel,
train_config=train_config,
master='',
task=0,
num_clones=1,
worker_replicas=1,
clone_on_cpu=True,
ps_tasks=0,
worker_job_name='worker',
is_chief=True,
train_dir=train_dir) 示例4: get_configs_from_multiple_files
# 需要導入模塊: from object_detection.protos import train_pb2 [as 別名]
# 或者: from object_detection.protos.train_pb2 import TrainConfig [as 別名]
def get_configs_from_multiple_files():
"""Reads training configuration from multiple config files.
Reads the training config from the following files:
model_config: Read from --model_config_path
train_config: Read from --train_config_path
input_config: Read from --input_config_path
Returns:
model_config: model_pb2.DetectionModel
train_config: train_pb2.TrainConfig
input_config: input_reader_pb2.InputReader
"""
train_config = train_pb2.TrainConfig()
with tf.gfile.GFile(FLAGS.train_config_path, 'r') as f:
text_format.Merge(f.read(), train_config)
model_config = model_pb2.DetectionModel()
with tf.gfile.GFile(FLAGS.model_config_path, 'r') as f:
text_format.Merge(f.read(), model_config)
input_config = input_reader_pb2.InputReader()
with tf.gfile.GFile(FLAGS.input_config_path, 'r') as f:
text_format.Merge(f.read(), input_config)
return model_config, train_config, input_config 示例5: get_optimizer_type
# 需要導入模塊: from object_detection.protos import train_pb2 [as 別名]
# 或者: from object_detection.protos.train_pb2 import TrainConfig [as 別名]
def get_optimizer_type(train_config):
"""Returns the optimizer type for training.
Args:
train_config: A train_pb2.TrainConfig.
Returns:
The type of the optimizer
"""
return train_config.optimizer.WhichOneof("optimizer")