def run_experiment(config):
if not os.path.exists(config["dataset_path"]):
raise FileNotFoundError("Dataset not found at {}".format(
config["dataset_path"]))
if checkpoint_dir_exists(config):
should_overwrite = ask_yes_no_question(
"[PROMPT] Overwrite existing checkpoint? {}".format(
config['checkpoint_path']))
if should_overwrite:
make_or_get_checkpoint_dir(config)
else:
print("Skipping experiment...")
return
print("===================================")
print("Starting experiment for model {}".format(config["model"]))
print("===================================")
# store visuals and files
model, hist = train(config)
history_dict = hist.history
plot_history(config, history_dict)
# save_history(config, history_dict)
#save_config(config)
# test data and write results
test(model, config, store_output=True, evaluate_splits=True)
def run_train_validation_test_final(config):
# store visuals and files
print("Startzeit:")
starttime = datetime.datetime.now()
print(str(starttime))
model, hist, score = trainfforvalidationandtest(config)
history_dict = hist.history
history_dict['validation_score'] = score
plot_history(config, history_dict)
save_history(config, history_dict)
save_config(config)
print("Meine final score für Validation set: \n")
print(score)
# test data and write results
test(model, config, store_output=True, evaluate_splits=True)
print("Endzeit:")
endtime = datetime.datetime.now()
print(str(endtime))
totaltime = endtime - starttime
print("Zeitinsgesamt:")
print(str(totaltime))
def test_checkpoint(checkpoint_path, evaluate_splits=True, store_output=False):
config = load_config(checkpoint_path)
metrics.BATCH_SIZE = config["batch_size"] # set before the metric is compiled
model = load_model(os.path.join(checkpoint_path, "weights.hdf5"), custom_objects={'motion_metric': motion_metric})
test(model, config, evaluate_splits=evaluate_splits, store_output=store_output)