mode = mode_parser.parse_args()
#set experiment and log configs
experiment = Experiment(api_key="ypQZhYfs3nSyKzOfz13iuJpj2",project_name='deeplidar',log_code=True)
DeepForest_config = load_config()
#Log parameters
experiment.log_parameter("Start Time", mode.dir)
experiment.log_parameter("Training Mode", mode.mode)
experiment.log_parameters(DeepForest_config)
DeepForest_config["mode"] = mode.mode
if mode.mode == "train":
data = load_training_data(DeepForest_config)
if mode.mode == "retrain":
data = load_retraining_data(DeepForest_config)
for x in DeepForest_config["evaluation_site"]:
DeepForest_config[x]["h5"] = os.path.join(DeepForest_config[x]["h5"],"hand_annotations")
#pass an args object instead of using command line
args = [
"--batch-size", str(DeepForest_config['batch_size']),
'--score-threshold', str(DeepForest_config['score_threshold']),
'--suppression-threshold', '0.1',
'--save-path', 'snapshots/images/',
'--model', mode.saved_model,
'--convert-model'
]
def test_train():
data = load_training_data(DeepForest_config)
print("Data shape is {}".format(data.shape))
assert data.shape[0] > 0, "Data is empty"