11630ab1e51124079549a99e0c2e06c8c43ce928,evaluate.py,,main,#Any#,19

Before Change

	FLAGS.save_images = False

	logging.info("evaluate model performance")
	test_filenames = util.get_files_in_directory(FLAGS.data_dir + "/" + FLAGS.test_dataset)

	model = DCSCN.SuperResolution(FLAGS, model_name=FLAGS.model_name)
	model.build_graph()
	model.build_summary_saver()
	model.init_all_variables(load_model_name=FLAGS.load_model_name)

	total_psnr = total_mse = 0

	for filename in test_filenames:
		mse = model.do_super_resolution_for_evaluate(filename, output_folder=FLAGS.output_dir, output=FLAGS.save_results)
		total_mse += mse
		total_psnr += util.get_psnr(mse)

	logging.info("\n=== Average [%s] MSE:%f, PSNR:%f ===" % (
		FLAGS.test_dataset, total_mse / len(test_filenames), total_psnr / len(test_filenames)))


if __name__ == "__main__":

After Change

		test_list = [FLAGS.test_dataset]

	for i in range(FLAGS.tests):
		model.load_model(FLAGS.load_model_name, i, True if FLAGS.tests > 1 else False)
		for test_data in test_list:
			test(model, test_data)

Instances