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本帖最后由 wali 于 2020-4-15 23:28 编辑
- D [print_tensor:136]out(0): id[ 195] vtl[1] const[0] shape[ 32, 32, 256, 1 ] fmt[u8 ] qnt[ASM zp=114, scale=0.014985]
- D [setup_node:368]Setup node id[58] uid[156] op[RELU]
- D [print_tensor:136]in(0) : id[ 193] vtl[1] const[0] shape[ 32, 32, 256, 1 ] fmt[u8 ] qnt[ASM zp= 0, scale=0.007946]
- D [print_tensor:136]out(0): id[ 196] vtl[1] const[0] shape[ 32, 32, 256, 1 ] fmt[u8 ] qnt[ASM zp= 0, scale=0.007946]
- D [setup_node:368]Setup node id[59] uid[108] op[DECONVOLUTION]
- D [print_tensor:136]in(0) : id[ 194] vtl[1] const[0] shape[ 64, 64, 64, 1 ] fmt[u8 ] qnt[ASM zp= 0, scale=0.007091]
- D [print_tensor:136]in(1) : id[ 50] vtl[0] const[1] shape[ 5, 5, 64, 64 ] fmt[u8 ] qnt[ASM zp=111, scale=0.009331]
- D [print_tensor:136]in(2) : id[ 51] vtl[0] const[1] shape[ 64 ] fmt[i32] qnt[ASM zp= 0, scale=0.000066]
- D [print_tensor:136]out(0): id[ 197] vtl[1] const[0] shape[ 131, 131, 64, 1 ] fmt[u8 ] qnt[ASM zp= 62, scale=0.055957]
- D [setup_node:368]Setup node id[60] uid[145] op[CONV2D]
- D [print_tensor:136]in(0) : id[ 196] vtl[1] const[0] shape[ 32, 32, 256, 1 ] fmt[u8 ] qnt[ASM zp= 0, scale=0.007946]
- D [print_tensor:136]in(1) : id[ 52] vtl[0] const[1] shape[ 3, 3, 256, 256 ] fmt[u8 ] qnt[ASM zp=151, scale=0.002047]
- D [print_tensor:136]in(2) : id[ 53] vtl[0] const[1] shape[ 256 ] fmt[i32] qnt[ASM zp= 0, scale=0.000016]
- D [print_tensor:136]out(0): id[ 198] vtl[1] const[0] shape[ 32, 32, 256, 1 ] fmt[u8 ] qnt[ASM zp=143, scale=0.019981]
- D [setup_node:368]Setup node id[61] uid[109] op[SLICE]
- D [print_tensor:136]in(0) : id[ 197] vtl[1] const[0] shape[ 131, 131, 64, 1 ] fmt[u8 ] qnt[ASM zp= 62, scale=0.055957]
- D [print_tensor:136]out(0): id[ 199] vtl[1] const[0] shape[ 64, 128, 128, 4 ] fmt[u8 ] qnt[ASM zp= 62, scale=0.055957]
- D [setup_node:368]Setup node id[62] uid[120] op[ADD]
- D [print_tensor:136]in(0) : id[ 198] vtl[1] const[0] shape[ 32, 32, 256, 1 ] fmt[u8 ] qnt[ASM zp=143, scale=0.019981]
- D [print_tensor:136]in(1) : id[ 195] vtl[1] const[0] shape[ 32, 32, 256, 1 ] fmt[u8 ] qnt[ASM zp=114, scale=0.014985]
- D [print_tensor:136]out(0): id[ 200] vtl[1] const[0] shape[ 32, 32, 256, 1 ] fmt[u8 ] qnt[ASM zp=144, scale=0.024428]
- D [setup_node:368]Setup node id[63] uid[97] op[ADD]
- E [op_check:103]Invalid broadcast for inputs[0] size[64]
- E [setup_node:383]Check node[63] ADD fail
- Segmentation fault (core dumped)
API Version:1.3.0Driver Version : 1.3.1
在PC上运行遇到了相同的问题,在混合量化自己模型的时候 !不混合量化是没问题的!
在RKPro上报以下错误:
- I NPUTransfer: Starting NPU Transfer Client, Transfe
- r version 1.9.8 (cab3961@2019-12-12T09:54:26)
- D NPUTransfer: Transfer spec = local:transfer_proxy
- D NPUTransfer: Transfer interface successfully opene
- d, fd = 9
- E RKNNAPI: rknn_init, msg_load_ack fail, ack = 1(ACK_FAIL), expect 0(ACK_SUCC)!
- E RKNNAPI: ==============================================
- E RKNNAPI: RKNN VERSION:
- E RKNNAPI: API: 1.3.0 (c5654ea build: 2019-12-25 12:40:55)
- E RKNNAPI: DRV: 1.3.1 (6ebb4d7 build: 2020-01-02 09:37:58)
- E RKNNAPI: ==============================================
- D NPUTransfer: Transfer client closed, fd = 9
- E Catch exception when init runtime!
- E Traceback (most recent call last):
- E File "rknn/api/rknn_base.py", line 988, in rknn.api.rknn_base.RKNNBase.init_runtime
- E File "rknn/api/rknn_runtime.py", line 320, in rknn.api.rknn_runtime.RKNNRuntime.build_graph
- E Exception: RKNN init failed. error code: RKNN_ERR_MODEL_INVALID
- Init runtime environment failed!
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