def test_wrapper(args: Namespace) -> None:
model_name = args.model
model = load_model(model_name)
if args.edit:
predictions, data_iter, metadata = test_model_masked(
model, args.dataset, args.edit)
edit_predictions(predictions, data_iter, metadata)
else:
predictions, test_iter = test_model_masked(model, args.dataset,
args.edit)
plot_predictions(predictions, test_iter)
def run_gui(folder: str, model) -> None:
predictions, data_iter, metadata = test_model_masked(
model, folder, True, dems=512
)
edit_predictions(
predictions, data_iter, metadata, dem=512
)
def test_wrapper(args: Namespace) -> None:
model_name = args.model
model = load_model(model_name)
if model_type(model) != dataset_type(args.dataset):
print("ERROR: This dataset is not compatible with your model")
return
if dataset_type(args.dataset) == ModelType.MASKED:
predictions, test_iter = test_model_masked(model, args.dataset)
plot_masked_predictions(predictions, test_iter, args.dataset)
else:
details, confusion_matrix = test_model_binary(model, args.dataset)
model_dir = os.path.dirname(path_from_model_name(model_name))
with open(os.path.join(model_dir, 'results.csv'), 'w') as f:
write_dict_to_csv(details, f)
plot_confusion_chart(confusion_matrix)
plot_predictions(details['Percent'], args.dataset)
def mkdata_wrapper(args: Namespace) -> None:
etl_wm()
setup_data(args.size)
dataset_fpath = f"syntheticTriainingData{date.isoformat(date.today())}"
dataset_dir = os.path.join('datasets', args.directory)
model_name = args.model
model = load_model(model_name)
if args.environment:
final_dataset_fpath = os.path.join(
dataset_dir, f'{args.dataset}_{args.environment}')
dataset = os.path.join(args.directory,
f'{args.dataset}_{args.environment}')
else:
final_dataset_fpath = os.path.join('datasets', args.dataset)
dataset = args.dataset
if not os.path.isdir(dataset_dir):
os.mkdir(dataset_dir)
if not os.path.isdir(final_dataset_fpath):
os.mkdir(final_dataset_fpath)
for folder in os.listdir(dataset_fpath):
for img in os.listdir(os.path.join(dataset_fpath, folder)):
os.rename(os.path.join(dataset_fpath, folder, img),
os.path.join(final_dataset_fpath, img))
shutil.rmtree(dataset_fpath)
move_imgs(final_dataset_fpath)
prepare_data(final_dataset_fpath, 0.2)
predictions, data_iter, metadata = test_model_masked(model,
dataset,
edit=True)
edit_predictions(predictions, data_iter, metadata)