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()
