model.py 文件源码

def test(self, input_path, output_path):
        if not self.load()[0]:
            raise Exception("No model is found, please train first")

        mean, std = self.sess.run([self.mean, self.std])

        images = np.empty((1, self.im_size[0], self.im_size[1], self.im_size[2], 1), dtype=np.float32)
        #labels = np.empty((1, self.im_size[0], self.im_size[1], self.im_size[2], self.nclass), dtype=np.float32)
        for f in input_path:
            images[0, ..., 0], read_info = read_testing_inputs(f, self.roi[0], self.im_size, output_path)
            probs = self.sess.run(self.probs, feed_dict = { self.images: (images - mean) / std,
                                                            self.is_training: True,
                                                            self.keep_prob: 1 })
            #print(self.roi[1] + os.path.basename(f) + ":" + str(dice))
            output_file = os.path.join(output_path, self.roi[1] + '_' + os.path.basename(f))
            f_h5 = h5py.File(output_file, 'w')
            if self.roi[0] < 0:
                f_h5['predictions'] = restore_labels(np.argmax(probs[0], 3), self.roi[0], read_info)
            else:
                f_h5['probs'] = restore_labels(probs[0, ..., 1], self.roi[0], read_info)
            f_h5.close()