f4503bb3a3be014b452f54d8e2d187bb6419f627,autokeras/classifier.py,ImageClassifier,predict,#ImageClassifier#Any#,216

Before Change

        Returns:
            An numpy.ndarray containing the results.
        
        if constant.LIMIT_MEMORY:
            config = tf.ConfigProto()
            config.gpu_options.allow_growth = True
            sess = tf.Session(config=config)
            init = tf.global_variables_initializer()
            sess.run(init)
            backend.set_session(sess)
        x_test = x_test.astype("float32") / 255
        model = self.load_searcher().load_best_model().produce_model()
        return self.y_encoder.inverse_transform(model.predict(x_test, ))

After Change

        if constant.LIMIT_MEMORY:
            // TODO: limit pytorch memory.
            pass
        test_data = self.data_transformer.transform_test(x_test)
        test_loader = DataLoader(test_data, batch_size=constant.MAX_BATCH_SIZE, shuffle=True)
        model = self.load_searcher().load_best_model().produce_model()
        model.eval()

        outputs = []
        with torch.no_grad():
            for index, inputs in enumerate(test_loader):
                outputs.append(model(inputs).numpy())
        output = reduce(lambda x, y: np.concatenate((x, y)), outputs)
        return self.y_encoder.inverse_transform(output)

Instances