i < io_num.n_output; i++){outputs.index = i;outputs.want_float = 1;}ret = rknn_run(ct...
= 0: print('加载模型失败') exit(ret) # 初始化RKNN ret = rknn.init_runtime() if ret != 0: print('RKNN初始化失败,错误代码:', ret) exit(ret) # 其他操作... 根据rknn_err_fail的具体含义和上下文,尝试可能的解决方案: 根据你在官方文档或错误代码对照表中找到的信息,尝试相应的解决方案。
ret = rknn.load_tensorflow( tf_pb='./model.pb', inputs=['input28x28_input'], # 注意,这里的input名字来自于模型转换时候打印出来的mode.input.op.name outputs=['output/Softmax'], # 注意,这里的output名字来自于模型转换时候打印出来的mode.output.op.name input_size_list=[[28, 28]]) ...
rknn_init error ret=-1 出现报错信息,重新在宿主机平台上设置平台信息; 将rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]]) 修改为rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform='rk3588') 并重新执行 python test.py 生成yol...
E RKNNAPI: rknn_init, driver open fail! ret = -4(ERROR_NO_DEVICE)! E Catch exception when init runtime! *** all device(s) with ntb mode: TS018082190800645 *** E ['TS018082190800645'] E Traceback (most recent call last): File...
exit(ret)print('done')# Set inputsorig_img = cv2.imread('./1.jpg') img = cv2.cvtColor(orig_img, cv2.COLOR_BGR2RGB) img = cv2.resize(img, (INPUT_SIZE, INPUT_SIZE), interpolation=cv2.INTER_CUBIC)# init runtime environmentprint('--> Init runtime environment') ...
And added target platform to the init_runtime: ret = rknn.init_runtime(target='rv1126', device_id='179e8563de15b86a') I get the following error when I run the test.py: E Catch exception when init runtime! E Traceback (most recent call last): E File "rknn/api/rknn_base.py", ...
ret =RK_MPI_SYS_RegisterOutCb(&cfg.session_cfg[i].stVenChn, video_packet_cb);if(ret) {printf("ERROR: register output callback for VENC[0] error! ret=%d\n", ret);return0; }srs_rtmp_destroy(rtmp_init); 4,代码编译: 对于rkmedia_vi_rknn_venc_rtmp_test.c文件编写修改完毕后,在/sdk...
rknn_init error ret=-1 出现报错信息,重新在宿主机平台上设置平台信息; 将rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]]) 修改为rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform='rk3588') ...
# Directory of images to run detection on IMAGE_DIR = os.path.join(ROOT_DIR, "images") class InferenceConfig(coco.CocoConfig): # Set batch size to 1 since we'll be running inference on # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU ...