net_v7, trainer_v7, operators_v7 = dataprocessor_v7.get_model(type_of_data)
y_pred_0, images_idsv7 = dataprocessor_v7._internal_validate_predict_best_param(
"v1", trainer_v7, operators_v7, enable_tqdm=False)
dataprocessor_v12 = DataProcessor(plugin_config,
base_model_name="v1",
model_name="v2",
image_dir="v2")
dataprocessor_v12.execute()
net_v12, trainer_v12, operators_v12 = dataprocessor_v12.get_model(type_of_data)
y_pred_1, images_idsv12 = dataprocessor_v12._internal_validate_predict_best_param(
"v2", trainer_v12, operators_v12, enable_tqdm=False)
dataprocessor_v16 = DataProcessor(plugin_config,
base_model_name="v1",
model_name="v3",
image_dir="v3",
is_final=True)
dataprocessor_v16.execute()
net_v16, trainer_v16, operators_v16 = dataprocessor_v16.get_model(type_of_data)
y_pred_2, images_idsv16 = dataprocessor_v16._internal_validate_predict_best_param(
"v3", trainer_v16, operators_v16, enable_tqdm=False)
dataprocessor_v17 = DataProcessor(plugin_config,
base_model_name="v1",
model_name="vfinal",
image_dir="vfinal",
is_final=False)
dataprocessor_v17.execute()
dataprocessor_v17.evalfscore_v17(y_pred_0, y_pred_1, y_pred_2)
