import functools import tensorflow as tf from core import trainer, input_reader from core.model_builder import build_man_model from google.protobuf import text_format from object_detection.builders import input_reader_builder from object_detection.protos import input_reader_pb2 from object_detection.protos import model_pb2 from object_detection.protos import pipeline_pb2 from object_detection.protos import train_pb2 import os os.environ["CUDA_VISIBLE_DEVICES"]="1,2" tf.logging.set_verbosity(tf.logging.INFO) flags = tf.app.flags flags.DEFINE_string('train_dir', 'model/dump', 'Directory to save the checkpoints and training summaries.') flags.DEFINE_string('pipeline_config_path', 'model/ssd_mobilenet_imagenet.config', 'Path to a pipeline_pb2.TrainEvalPipelineConfig config ' 'file. If provided, other configs are ignored') flags.DEFINE_string('train_config_path', '', 'Path to a train_pb2.TrainConfig config file.') flags.DEFINE_string('input_config_path', '', 'Path to an input_reader_pb2.InputReader config file.') flags.DEFINE_string('model_config_path', '', 'Path to a model_pb2.DetectionModel config file.') flags.DEFINE_string('image_root', '/media/Linux/Research/DataSet/ILSVRC2014_DET/image/ILSVRC2014_DET_train', 'Root path to input images') FLAGS = flags.FLAGS 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 def main(_): model_config, train_config, input_config = get_configs_from_pipeline_file() model_fn = functools.partial( build_man_model, model_config=model_config, is_training=True) create_input_dict_fn = functools.partial( input_reader.read, input_config) trainer.train(model_fn, create_input_dict_fn, train_config, FLAGS.train_dir, FLAGS.image_root) if __name__ == '__main__': # update moving average tf.app.run()