RuntimeError 的结果：ImportError： numpy.core.umath 无法导入 ImportError： numpy.core.umath 无法导入...
ret = rknn_init(&context_2, model_net_2, model_len, RKNN_FLAG_PRIOR_MEDIUM); if(ret < 0) { printf("rknn_init fail! ret=%d\n", ret); source_release(context_2, model_net_2); return -1; } ctx.push_back(context_2); printf("[debug] ctx[1]:%ld\n",context_2); fclose(fp...
E RKNNAPI: rknn_init, attr[0].fmt = 1, expect RKNN_TENSOR_NCHW(0)! rknn_init fail! ret=-6 load_model error!!! Segmentation fault (core dumped) 复制代码作者: zhangzj 时间: 2019-8-23 11:44engin 发表于 2019-8-21 18:43 使用1.1生成tensorflosw模型,pc上使用python运行正常,在rk3399...
ret = r.init_runtime(core_mask=RKNNLite.NPU_CORE_AUTO) Any suggestions?! Guemann-uicommentedNov 8, 2023 @byte-6174 Try to follow the instructions below: Clone the rknpu2 repository: git clonehttps://github.com/rockchip-linux/rknpu2.git ...
if host_name == 'RK3588': ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_0) if you can share a piece of code where you get the issue, that would be helpful to check it bot-lin commented Apr 10, 2024 same issue here, I am using RK3588s onepiece8971 commented Jun...
(rknn_tensor_attr)); input_attrs.index = 0; ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs), sizeof(rknn_tensor_attr)); if (ret < 0) { printf("rknn_init error! ret=%d\n", ret); return -1; } input_attrs.type = RKNN_TENSOR_FLOAT32; input_attrs.size = input...
ret = rknn_init(&ctx, model_data, model_data_size, 0, NULL); if (ret < 0) { printf("rknn_init error ret=%d\n", ret); return -1; } #if 0 // RK3568 no support set core mask // set rknn core mask rknn_core_mask core_mask; ...
rknn = RKNN(verbose=True) rknn.config(mean_values=[], std_values=[], target_platform=[]) ret = rknn.load_onnx(model=ONNX_MODEL) # outputs=['495', '497', '499'] ret = rknn.build(do_quantization=True, dataset=DATASET) ret = rknn.init_runtime() ret = rknn.export_rknn('./'...
RKNN.NPU_CORE_0_1:表示同时运行在 NPU0,NPU1 核心上. RKNN.NPU_CORE_0_1_2:表示同时运行在 NPU0,NPU1,NPU2 核心上. 默认值为 RKNN.NPU_CORE_AUTO. 31 返回值 0:初始化运行时环境成功. -1:初始化运行时环境失败. 举例如下: # 初始化运行时环境 ret = rknn.init_runtime(target='rk3566') if...
ret = rknn_lite.init_runtime() # Inference print('--> Running model') outputs = rknn_lite.inference(inputs=[img_convert]) #print(outputs) #根据这个结果,可以自己做后续处理 print('done') rknn_lite.release() 可以看到,rknn其实就提供了一个深度学习的最基础框架,能够基于转换后的模型进程推理预...