def target(param_names, param_values, model_nr): # model_nr = pickle.load(open(nr_steps_path, "rb")) + 1 # pickle.dump(model_nr, open(nr_steps_path, "wb")) s = Settings() param_values = list(param_values) for i in range(len(param_names)): eval('s.{}'.format(param_names[i])) if isinstance(param_values[i], str): param_values[i] = '\'' + param_values[i] + '\'' expr = 's.{} = {}'.format(param_names[i], param_values[i]) exec(expr) # print('s.DROPOUT == {}'.format(s.DROPOUT)) # print('s.LEARNING_RATE == {}'.format(s.LEARNING_RATE)) # print('s.LOSS_FUNCTION == {}'.format(s.LOSS_FUNCTION)) s.MODEL_NAME = MAIN_FOLDER + str(model_nr) s.VALTEST_MODEL_NAMES = [s.MODEL_NAME] h = Helper(s) # with suppress_stdout(): # print('here1') not_model_nrs = [] if model_nr not in not_model_nrs: # print('here2') t = Train(s, h) t.train() del t metric_means, metric_sds = Test(s, h).test() else: # print('here3') s.CALC_PROBS = False metric_means, metric_sds = Test(s, h).test() s.CALC_PROBS = True return metric_means[s.MODEL_NAME]['Dice'], metric_sds[s.MODEL_NAME]['Dice']
s.VALTEST_MODEL_NAMES = [ # 'sf_set52_vanilla_valafter25steps', # 'sf_set52_vanilla_valafter25steps_run2', # 'sf_set52_vanilla_valafter25steps_run3', # 'sf_set52_vanilla_valafter25steps_run4', # 'sf_set52_lainput_valafter25steps', # 'sf_set52_lainput_valafter25steps_run2', # 'sf_set52_lainput_valafter25steps_run3', # 'sf_set52_lainput_valafter25steps_run4', # 'sf_set52_auxloss_valafter25steps', # 'sf_set52_auxloss_valafter25steps_run2', # 'sf_set52_auxloss_valafter25steps_run3', # 'sf_set52_auxloss_valafter25steps_run4', 'la_transfer', # 'la_2018_challenge_convpl_depth_2/2' ] # s.GROUND_TRUTH = 'scar_fibrosis' s.GROUND_TRUTH = 'left_atrium' # s.USE_LA_AUX_LOSS = True s.USE_LA_INPUT = False s.PREDICT_AUX_OUTPUT = False # s.VALTEST_SET = [45, 48] s.CALC_PROBS = True s.VALTEST_SET = s.TESTING_SET s.USE_POST_PROCESSING = True s.CALC_PROB_THRESH = True h = Helper(s) t = Test(s, h) t.test()