def _train(self):
tf.disable_eager_execution()
ps_tasks = 0
worker_replicas = 1
worker_job_name = 'lonely_worker'
task = 0
is_chief = True
master = ''
graph_rewriter_fn = None
# loading and reading the config file
configs = create_configs_from_pipeline_proto(self.pipeline)
model_config = configs['model']
train_config = configs['train_config']
input_config = configs['train_input_config']
# creating the tf object detection api model (from the config parameters)
model_fn = functools.partial(model_builder.build, model_config=model_config, is_training=True)
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)
if 'graph_rewriter_config' in configs:
graph_rewriter_fn = graph_rewriter_builder.build(configs['graph_rewriter_config'], is_training=True)
# training the model with the new parameters
trainer.train(create_input_dict_fn, model_fn, train_config, master, task, 1, worker_replicas, False, ps_tasks,
worker_job_name, is_chief, str(self._out_folder), graph_hook_fn=graph_rewriter_fn)
def test_create_configs_from_pipeline_proto(self):
"""Tests creating configs dictionary from pipeline proto."""
print(
'\n=================================================================='
)
print('test_create_configs_from_pipeline_proto')
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
configs = config_util.create_configs_from_pipeline_proto(
pipeline_config)
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_create_configs_from_pipeline_proto(self):
"""Tests creating configs dictionary from pipeline proto."""
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
configs = config_util.create_configs_from_pipeline_proto(pipeline_config)
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 _set_config_paths(self):
configs = create_configs_from_pipeline_proto(self.pipeline)
update_input_reader_config(configs,
key_name="train_input_config",
input_name=None,
field_name="input_path",
value=str(self._val_record_file),
path_updater=_update_tf_record_input_path)
update_input_reader_config(configs,
key_name="eval_input_configs",
input_name=None,
field_name="input_path",
value=str(self._train_record_file),
path_updater=_update_tf_record_input_path)
update_dict = {
"label_map_path": str(self._labels_map_file),
"train_config.fine_tune_checkpoint": str(self._checkpoint_model_folder.joinpath("model.ckpt"))
}
configs = merge_external_params_with_configs(configs, kwargs_dict=update_dict)
self._pipeline = create_pipeline_proto_from_configs(configs)
def get_configs_from_pipeline_string(pipeline_config_string):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
text_format.Merge(pipeline_config_string, pipeline_config)
return config_util.create_configs_from_pipeline_proto(pipeline_config)