def main():
args = parse_args()
cfg = parse_config_file(args.config_file)
# Replace cfg parameters with the command line values
if args.max_number_of_steps != None:
cfg.NUM_TRAIN_ITERATIONS = args.max_number_of_steps
if args.learning_rate_decay_type != None:
cfg.LEARNING_RATE_DECAY_TYPE = args.learning_rate_decay_type
if args.learning_rate != None:
cfg.INITIAL_LEARNING_RATE = args.learning_rate
if args.batch_size != None:
cfg.BATCH_SIZE = args.batch_size
if args.model_name != None:
cfg.MODEL_NAME = args.model_name
train(
tfrecords=args.tfrecords,
logdir=args.logdir,
cfg=cfg,
pretrained_model_path=args.pretrained_model,
trainable_scopes = args.trainable_scopes,
checkpoint_exclude_scopes = args.checkpoint_exclude_scopes,
restore_variables_with_moving_averages=args.restore_variables_with_moving_averages,
restore_moving_averages=args.restore_moving_averages,
read_images=args.read_images
)
def main():
args = parse_args()
cfg = parse_config_file(args.config_file)
visualize_train_inputs(
tfrecords=args.tfrecords,
cfg=cfg,
show_text_labels=args.show_text_labels,
read_images=args.read_images
)
def main():
args = parse_args()
cfg = parse_config_file(args.config_file)
if args.batch_size != None:
cfg.BATCH_SIZE = args.batch_size
if args.model_name != None:
cfg.MODEL_NAME = args.model_name
classify(tfrecords=args.tfrecords,
checkpoint_path=args.checkpoint_path,
save_path=args.save_path,
max_iterations=args.batches,
save_logits=args.save_logits,
cfg=cfg)
def main():
args = parse_args()
cfg = parse_config_file(args.config_file)
if args.batch_size != None:
cfg.BATCH_SIZE = args.batch_size
if args.model_name != None:
cfg.MODEL_NAME = args.model_name
extract_and_save(tfrecords=args.tfrecords,
checkpoint_path=args.checkpoint_path,
save_path=args.save_path,
num_iterations=args.batches,
feature_keys=args.features,
cfg=cfg)
def main():
args = parse_args()
cfg = parse_config_file(args.config_file)
if args.batch_size != None:
cfg.BATCH_SIZE = args.batch_size
if args.model_name != None:
cfg.MODEL_NAME = args.model_name
test(tfrecords=args.tfrecords,
checkpoint_path=args.checkpoint_path,
save_dir=args.savedir,
max_iterations=args.batches,
eval_interval_secs=args.eval_interval_secs,
cfg=cfg)
parser.add_argument(
'--serving',
dest='serving',
help=
'Export for TensorFlow Serving usage. Otherwise, a constant graph will be generated.',
action='store_true',
default=False)
parser.add_argument(
'--do_preprocess',
dest='do_preprocess',
help='Add the image decoding and preprocessing nodes to the graph.',
action='store_true',
default=False)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
cfg = parse_config_file(args.config_file)
export(args.checkpoint_path,
args.export_dir,
args.export_version,
args.serving,
args.do_preprocess,
cfg=cfg)