import matplotlib.pyplot as plt from DataProcessor import DataProcessor plt.rcParams['image.cmap'] = 'gist_earth' plugin_config = "./config/config.json" type_of_data = "multi" dataprocessor_v7 = DataProcessor(plugin_config, base_model_name="v1", model_name="v1", image_dir="v1") dataprocessor_v7.execute() 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",
import os import matplotlib.pyplot as plt from DataProcessor import DataProcessor plt.rcParams['image.cmap'] = 'gist_earth' plugin_config = "./config/config.json" type_of_data = "multi" os.environ['CUDA_VISIBLE_DEVICES'] = '-1' dataprocessor_v3 = DataProcessor(plugin_config, base_model_name="v1", model_name="v3", image_dir="v3", is_final=True) dataprocessor_v3.execute() net_v3, trainer_v3, operators_v3 = dataprocessor_v3.get_model(type_of_data) dataprocessor_v3.validate(trainer_v3, operators_v3, display_step=2, restore=True) number_of_models = dataprocessor_v3.get_total_numberof_model_count(trainer_v3) dataprocessor_v3.evalfscore_v16(trainer_v3, operators_v3, number_of_models)
import matplotlib.pyplot as plt from DataProcessor import DataProcessor plt.rcParams['image.cmap'] = 'gist_earth' # os.environ['CUDA_VISIBLE_DEVICES'] = '-1' plugin_config = "./config/config.json" type_of_data = "multi" dataprocessor_v1 = DataProcessor(plugin_config, base_model_name="v1", model_name="v1", image_dir="v1") dataprocessor_v1.execute() net_v1, trainer_v1, operators_v1 = dataprocessor_v1.get_model(type_of_data) dataprocessor_v1.validate(trainer_v1, operators_v1, display_step=2, restore=True) number_of_models = dataprocessor_v1.get_total_numberof_model_count(trainer_v1) dataprocessor_v1.evalfscore(trainer_v1, operators_v1, number_of_models)
import matplotlib.pyplot as plt from DataProcessor import DataProcessor plt.rcParams['image.cmap'] = 'gist_earth' plugin_config = "./config/config.json" type_of_data = "multi" # os.environ['CUDA_VISIBLE_DEVICES'] = '-1' dataprocessor_v2 = DataProcessor(plugin_config, base_model_name="v1", model_name="v2", image_dir="v2") dataprocessor_v2.execute() net_v2, trainer_v2, operators_v2 = dataprocessor_v2.get_model(type_of_data) dataprocessor_v2.validate(trainer_v2, operators_v2, display_step=2, restore=True) number_of_models = dataprocessor_v2.get_total_numberof_model_count(trainer_v2) dataprocessor_v2.evalfscore_v12(trainer_v2, operators_v2, number_of_models)