用户您好，请详细描述您所遇到的问题：
- 系统软件版本： oe2.2.3
- 问题涉及的技术领域：模型转换
- 问题描述：模型转换过程中，检查模型时，发现部分leakyrelu算子在cpu上运行
- 提供必要的问题日志：
[root@cd3ec36e4054 mapper]# sh 01_check.sh
cd $(dirname $0) || exit
model_type=“onnx”
#onnx_model=“best0526.onnx”
onnx_model=“best32.onnx”
march=“bernoulli2”
hb_mapper checker --model-type ${model_type} \
--model ${onnx_model} \
--march ${march}
2022-11-02 14:02:35,435 INFO Start hb_mapper…
2022-11-02 14:02:35,435 INFO log will be stored in /data/data/ddk/samples/ai_toolchain/horizon_model_convert_sample/04_detection/03_yolov5s/mapper/hb_mapper_checker.log
2022-11-02 14:02:35,435 INFO hbdk version 3.27.1
2022-11-02 14:02:35,436 INFO horizon_nn version 0.12.8
2022-11-02 14:02:35,436 INFO hb_mapper version 1.5.5
2022-11-02 14:02:35,436 WARNING parameter [output] is deprecated
2022-11-02 14:02:35,472 INFO Model type: onnx
2022-11-02 14:02:35,472 INFO input names
2022-11-02 14:02:35,472 INFO input shapes {}
2022-11-02 14:02:35,472 INFO Begin model checking…
2022-11-02 14:02:35,472 INFO [Wed Nov 2 14:02:35 2022] Start to Horizon NN Model Convert.
2022-11-02 14:02:35,472 INFO The input parameter is not specified, convert with default parameters.
2022-11-02 14:02:35,472 INFO The hbdk parameter is not specified, and the submodel will be compiled with the default parameter.
2022-11-02 14:02:35,473 INFO HorizonNN version: 0.12.8
2022-11-02 14:02:35,473 INFO HBDK version: 3.27.1
2022-11-02 14:02:35,473 INFO [Wed Nov 2 14:02:35 2022] Start to parse the onnx model.
2022-11-02 14:02:35,605 INFO ONNX model info:
ONNX IR version: 6
Opset version: 11
Input name: data, [1, 3, 672, 672]
2022-11-02 14:02:35,781 INFO [Wed Nov 2 14:02:35 2022] End to parse the onnx model.
2022-11-02 14:02:35,782 INFO Model input names: [‘data’]
2022-11-02 14:02:35,879 INFO Saving the original float model: ./.hb_check/original_float_model.onnx.
2022-11-02 14:02:35,881 INFO [Wed Nov 2 14:02:35 2022] Start to optimize the model.
2022-11-02 14:02:36,270 INFO [Wed Nov 2 14:02:36 2022] End to optimize the model.
2022-11-02 14:02:36,335 INFO Saving the optimized model: ./.hb_check/optimized_float_model.onnx.
2022-11-02 14:02:36,336 INFO [Wed Nov 2 14:02:36 2022] Start to calibrate the model.
2022-11-02 14:02:36,596 INFO There are 1 samples in the calibration data set.
2022-11-02 14:02:36,599 INFO Run calibration model with max method.
2022-11-02 14:02:37,243 INFO [Wed Nov 2 14:02:37 2022] End to calibrate the model.
2022-11-02 14:02:37,244 INFO [Wed Nov 2 14:02:37 2022] Start to quantize the model.
2022-11-02 14:02:38,804 INFO [Wed Nov 2 14:02:38 2022] End to quantize the model.
2022-11-02 14:02:39,150 INFO Saving the quantized model: ./.hb_check/quantized_model.onnx.
2022-11-02 14:02:39,152 INFO [Wed Nov 2 14:02:39 2022] Start to compile the model with march bernoulli2.
2022-11-02 14:02:39,152 INFO Parsing the hbdk parameter:{‘hbdk_pass_through_params’: ‘–O0’}
2022-11-02 14:02:39,483 INFO Compile submodel: torch-jit-export_subgraph_0
2022-11-02 14:02:39,804 INFO hbdk-cc parameters:[‘–O0’, ‘–input-layout’, ‘NHWC’, ‘–output-layout’, ‘NCHW’]
[==================================================] 100%
consumed time 0.711069
2022-11-02 14:02:40,575 INFO Compile submodel: torch-jit-export_subgraph_1
2022-11-02 14:02:40,632 INFO hbdk-cc parameters:[‘–O0’, ‘–input-layout’, ‘NHWC’, ‘–output-layout’, ‘NHWC’]
[==================================================] 100%
consumed time 0.0490467
2022-11-02 14:02:40,738 INFO Compile submodel: torch-jit-export_subgraph_2
2022-11-02 14:02:40,790 INFO hbdk-cc parameters:[‘–O0’, ‘–input-layout’, ‘NHWC’, ‘–output-layout’, ‘NCHW’]
[==================================================] 100%
consumed time 0.0216448
2022-11-02 14:02:40,869 INFO Compile submodel: torch-jit-export_subgraph_3
2022-11-02 14:02:40,936 INFO hbdk-cc parameters:[‘–O0’, ‘–input-layout’, ‘NHWC’, ‘–output-layout’, ‘NCHW’]
[==================================================] 100%
consumed time 0.0472112
2022-11-02 14:02:41,038 INFO Compile submodel: torch-jit-export_subgraph_4
2022-11-02 14:02:41,090 INFO hbdk-cc parameters:[‘–O0’, ‘–input-layout’, ‘NHWC’, ‘–output-layout’, ‘NHWC’]
[==================================================] 100%
consumed time 0.0578241
2022-11-02 14:02:41,208 INFO Compile submodel: torch-jit-export_subgraph_5
2022-11-02 14:02:41,256 INFO hbdk-cc parameters:[‘–O0’, ‘–input-layout’, ‘NHWC’, ‘–output-layout’, ‘NCHW’]
[==================================================] 100%
consumed time 0.0421627
2022-11-02 14:02:41,356 INFO Compile submodel: torch-jit-export_subgraph_6
2022-11-02 14:02:41,411 INFO hbdk-cc parameters:[‘–O0’, ‘–input-layout’, ‘NHWC’, ‘–output-layout’, ‘NHWC’]
[==================================================] 100%
consumed time 0.0613376
2022-11-02 14:02:41,528 INFO Compile submodel: torch-jit-export_subgraph_7
2022-11-02 14:02:41,582 INFO hbdk-cc parameters:[‘–O0’, ‘–input-layout’, ‘NHWC’, ‘–output-layout’, ‘NHWC’]
[==================================================] 100%
consumed time 0.0355284
2022-11-02 14:02:41,674 INFO Compile submodel: torch-jit-export_subgraph_8
2022-11-02 14:02:41,722 INFO hbdk-cc parameters:[‘–O0’, ‘–input-layout’, ‘NHWC’, ‘–output-layout’, ‘NCHW’]
[==================================================] 100%
consumed time 0.0213657
2022-11-02 14:02:41,799 INFO Compile submodel: torch-jit-export_subgraph_9
2022-11-02 14:02:41,863 INFO hbdk-cc parameters:[‘–O0’, ‘–input-layout’, ‘NHWC’, ‘–output-layout’, ‘NHWC’]
[==================================================] 100%
consumed time 0.0431496
2022-11-02 14:02:41,963 INFO Compile submodel: torch-jit-export_subgraph_10
2022-11-02 14:02:42,029 INFO hbdk-cc parameters:[‘–O0’, ‘–input-layout’, ‘NHWC’, ‘–output-layout’, ‘NHWC’]
[==================================================] 100%
consumed time 0.0367165
2022-11-02 14:02:42,125 INFO Compile submodel: torch-jit-export_subgraph_11
2022-11-02 14:02:42,175 INFO hbdk-cc parameters:[‘–O0’, ‘–input-layout’, ‘NHWC’, ‘–output-layout’, ‘NCHW’]
[==================================================] 100%
consumed time 0.0212378
2022-11-02 14:02:42,252 INFO Compile submodel: torch-jit-export_subgraph_12
2022-11-02 14:02:42,357 INFO hbdk-cc parameters:[‘–O0’, ‘–input-layout’, ‘NHWC’, ‘–output-layout’, ‘NHWC’]
[==================================================] 100%
consumed time 0.0455437
2022-11-02 14:02:43,041 INFO [Wed Nov 2 14:02:43 2022] End to compile the model with march bernoulli2.
2022-11-02 14:02:43,044 INFO The converted model node information:
=========================================================
Node ON Subgraph Type
---------------------------------------------------------
Conv_0 BPU id(0) HzSQuantizedConv
LeakyRelu_1 BPU id(0) HzLeakyRelu
Conv_2 BPU id(0) HzSQuantizedConv
LeakyRelu_3 BPU id(0) HzLeakyRelu
Conv_4 BPU id(0) HzSQuantizedConv
LeakyRelu_5 BPU id(0) HzLeakyRelu
Conv_6 BPU id(0) HzSQuantizedConv
LeakyRelu_7 BPU id(0) HzLeakyRelu
Conv_8 BPU id(0) HzSQuantizedConv
LeakyRelu_9 BPU id(0) HzLeakyRelu
UNIT_CONV_FOR_Add_10 BPU id(0) HzSQuantizedConv
Conv_11 BPU id(0) HzSQuantizedConv
LeakyRelu_12 BPU id(0) HzLeakyRelu
Concat_13 BPU id(0) Concat
Conv_14 BPU id(0) HzSQuantizedConv
LeakyRelu_15 BPU id(0) HzLeakyRelu
Conv_16 BPU id(0) HzSQuantizedConv
LeakyRelu_17 BPU id(0) HzLeakyRelu
Conv_18 BPU id(0) HzSQuantizedConv
LeakyRelu_19 BPU id(0) HzLeakyRelu
Conv_20 BPU id(0) HzSQuantizedConv
LeakyRelu_21 BPU id(0) HzLeakyRelu
Conv_22 BPU id(0) HzSQuantizedConv
LeakyRelu_23 BPU id(0) HzLeakyRelu
UNIT_CONV_FOR_Add_24 BPU id(0) HzSQuantizedConv
Conv_25 BPU id(0) HzSQuantizedConv
LeakyRelu_26 BPU id(0) HzLeakyRelu
Conv_27 BPU id(0) HzSQuantizedConv
LeakyRelu_28 BPU id(0) HzLeakyRelu
UNIT_CONV_FOR_Add_29 BPU id(0) HzSQuantizedConv
Conv_30 BPU id(0) HzSQuantizedConv
LeakyRelu_31 BPU id(0) HzLeakyRelu
Concat_32 BPU id(0) Concat
Conv_33 BPU id(0) HzSQuantizedConv
LeakyRelu_34 BPU id(0) HzLeakyRelu
Conv_35 BPU id(0) HzSQuantizedConv
LeakyRelu_36 BPU id(0) HzLeakyRelu
Conv_37 BPU id(0) HzSQuantizedConv
LeakyRelu_38 BPU id(0) HzLeakyRelu
Conv_39 BPU id(0) HzSQuantizedConv
LeakyRelu_40 BPU id(0) HzLeakyRelu
Conv_41 BPU id(0) HzSQuantizedConv
LeakyRelu_42 BPU id(0) HzLeakyRelu
UNIT_CONV_FOR_Add_43 BPU id(0) HzSQuantizedConv
Conv_44 BPU id(0) HzSQuantizedConv
LeakyRelu_45 BPU id(0) HzLeakyRelu
Conv_46 BPU id(0) HzSQuantizedConv
LeakyRelu_47 BPU id(0) HzLeakyRelu
UNIT_CONV_FOR_Add_48 BPU id(0) HzSQuantizedConv
Conv_49 BPU id(0) HzSQuantizedConv
LeakyRelu_50 BPU id(0) HzLeakyRelu
Conv_51 BPU id(0) HzSQuantizedConv
LeakyRelu_52 BPU id(0) HzLeakyRelu
UNIT_CONV_FOR_Add_53 BPU id(0) HzSQuantizedConv
Conv_54 BPU id(0) HzSQuantizedConv
LeakyRelu_55 BPU id(0) HzLeakyRelu
Concat_56 BPU id(0) Concat
Conv_57 BPU id(0) HzSQuantizedConv
LeakyRelu_58 BPU id(0) HzLeakyRelu
Conv_59 BPU id(0) HzSQuantizedConv
LeakyRelu_60 BPU id(0) HzLeakyRelu
Conv_61 BPU id(0) HzSQuantizedConv
LeakyRelu_62 BPU id(0) HzLeakyRelu
Conv_63 BPU id(0) HzSQuantizedConv
LeakyRelu_64 BPU id(0) HzLeakyRelu
Conv_65 BPU id(0) HzSQuantizedConv
LeakyRelu_66 BPU id(0) HzLeakyRelu
UNIT_CONV_FOR_Add_67 BPU id(0) HzSQuantizedConv
Conv_68 BPU id(0) HzSQuantizedConv
LeakyRelu_69 BPU id(0) HzLeakyRelu
Concat_70 BPU id(0) Concat
Conv_71 BPU id(0) HzSQuantizedConv
LeakyRelu_72 BPU id(0) HzLeakyRelu
Conv_73 BPU id(0) HzSQuantizedConv
LeakyRelu_74 BPU id(0) HzLeakyRelu
MaxPool_75 BPU id(0) HzQuantizedMaxPool
MaxPool_76 BPU id(0) HzQuantizedMaxPool
MaxPool_77 BPU id(0) HzQuantizedMaxPool
Concat_78 BPU id(0) Concat
Conv_79 BPU id(0) HzSQuantizedConv
LeakyRelu_80 BPU id(0) HzLeakyRelu
Conv_81 BPU id(0) HzSQuantizedConv
LeakyRelu_82 BPU id(0) HzLeakyRelu
Conv_83 BPU id(0) HzSQuantizedConv
LeakyRelu_84 CPU – LeakyRelu
Concat_85 CPU – Concat
Reshape_87 CPU – Reshape
Transpose_88 CPU – Transpose
Reshape_90 CPU – Reshape
Gather_92 CPU – Gather
Gather_94 CPU – Gather
Concat_95 CPU – Concat
Resize_97 CPU – Resize
Concat_98 CPU – Concat
Conv_99 BPU id(1) HzSQuantizedConv
LeakyRelu_100 BPU id(1) HzLeakyRelu
Conv_101 BPU id(1) HzSQuantizedConv
LeakyRelu_102 BPU id(1) HzLeakyRelu
Conv_103 BPU id(1) HzSQuantizedConv
LeakyRelu_104 CPU – LeakyRelu
Concat_105 CPU – Concat
Reshape_107 CPU – Reshape
Transpose_108 CPU – Transpose
Reshape_110 CPU – Reshape
Gather_112 CPU – Gather
Gather_114 CPU – Gather
Concat_115 CPU – Concat
Conv_116 BPU id(2) HzSQuantizedConv
Conv_117 BPU id(2) HzSQuantizedConv
Concat_118 CPU – Concat
Reshape_120 CPU – Reshape
Transpose_121 CPU – Transpose
Reshape_123 CPU – Reshape
Gather_125 CPU – Gather
Gather_127 CPU – Gather
Concat_128 CPU – Concat
Conv_129 BPU id(3) HzSQuantizedConv
Conv_131 BPU id(1) HzSQuantizedConv
LeakyRelu_132 BPU id(1) HzLeakyRelu
Concat_133 BPU id(3) Concat
Conv_134 BPU id(3) HzSQuantizedConv
LeakyRelu_135 BPU id(3) HzLeakyRelu
Conv_136 BPU id(3) HzSQuantizedConv
LeakyRelu_137 BPU id(3) HzLeakyRelu
Conv_138 BPU id(3) HzSQuantizedConv
LeakyRelu_139 CPU – LeakyRelu
Concat_140 CPU – Concat
Reshape_142 CPU – Reshape
Transpose_143 CPU – Transpose
Reshape_145 CPU – Reshape
Gather_147 CPU – Gather
Gather_149 CPU – Gather
Concat_150 CPU – Concat
Resize_152 CPU – Resize
Concat_153 CPU – Concat
Conv_154 BPU id(4) HzSQuantizedConv
LeakyRelu_155 BPU id(4) HzLeakyRelu
Conv_156 BPU id(4) HzSQuantizedConv
LeakyRelu_157 BPU id(4) HzLeakyRelu
Conv_158 BPU id(4) HzSQuantizedConv
LeakyRelu_159 CPU – LeakyRelu
Concat_160 CPU – Concat
Reshape_162 CPU – Reshape
Transpose_163 CPU – Transpose
Reshape_165 CPU – Reshape
Gather_167 CPU – Gather
Gather_169 CPU – Gather
Concat_170 CPU – Concat
Conv_171 BPU id(5) HzSQuantizedConv
Conv_172 BPU id(5) HzSQuantizedConv
Concat_173 CPU – Concat
Reshape_175 CPU – Reshape
Transpose_176 CPU – Transpose
Reshape_178 CPU – Reshape
Gather_180 CPU – Gather
Gather_182 CPU – Gather
Concat_183 CPU – Concat
Conv_184 BPU id(6) HzSQuantizedConv
Conv_186 BPU id(4) HzSQuantizedConv
LeakyRelu_187 BPU id(4) HzLeakyRelu
Concat_188 BPU id(6) Concat
Conv_189 BPU id(6) HzSQuantizedConv
LeakyRelu_190 BPU id(6) HzLeakyRelu
Conv_194 BPU id(6) HzSQuantizedConv
LeakyRelu_196 BPU id(6) HzLeakyRelu
Concat_197 CPU – Concat
Conv_198 BPU id(7) HzSQuantizedConv
LeakyRelu_199 BPU id(7) HzLeakyRelu
Conv_200 BPU id(7) HzSQuantizedConv
LeakyRelu_201 BPU id(7) HzLeakyRelu
Conv_202 BPU id(7) HzSQuantizedConv
LeakyRelu_203 CPU – LeakyRelu
Concat_204 CPU – Concat
Reshape_206 CPU – Reshape
Transpose_207 CPU – Transpose
Reshape_209 CPU – Reshape
Gather_211 CPU – Gather
Gather_213 CPU – Gather
Concat_214 CPU – Concat
Conv_215 BPU id(8) HzSQuantizedConv
Conv_216 BPU id(8) HzSQuantizedConv
Concat_217 CPU – Concat
Reshape_219 CPU – Reshape
Transpose_220 CPU – Transpose
Reshape_222 CPU – Reshape
Gather_224 CPU – Gather
Gather_226 CPU – Gather
Concat_227 CPU – Concat
Conv_228 BPU id(9) HzSQuantizedConv
Conv_230 BPU id(7) HzSQuantizedConv
LeakyRelu_231 BPU id(7) HzLeakyRelu
Concat_232 BPU id(9) Concat
Conv_233 BPU id(9) HzSQuantizedConv
LeakyRelu_234 BPU id(9) HzLeakyRelu
Conv_238 BPU id(9) HzSQuantizedConv
LeakyRelu_240 BPU id(9) HzLeakyRelu
Concat_241 CPU – Concat
Conv_242 BPU id(10) HzSQuantizedConv
LeakyRelu_243 BPU id(10) HzLeakyRelu
Conv_244 BPU id(10) HzSQuantizedConv
LeakyRelu_245 BPU id(10) HzLeakyRelu
Conv_246 BPU id(10) HzSQuantizedConv
LeakyRelu_247 CPU – LeakyRelu
Concat_248 CPU – Concat
Reshape_250 CPU – Reshape
Transpose_251 CPU – Transpose
Reshape_253 CPU – Reshape
Gather_255 CPU – Gather
Gather_257 CPU – Gather
Concat_258 CPU – Concat
Conv_259 BPU id(11) HzSQuantizedConv
Conv_260 BPU id(11) HzSQuantizedConv
Concat_261 CPU – Concat
Reshape_263 CPU – Reshape
Transpose_264 CPU – Transpose
Reshape_266 CPU – Reshape
Gather_268 CPU – Gather
Gather_270 CPU – Gather
Concat_271 CPU – Concat
Conv_272 BPU id(12) HzSQuantizedConv
Conv_274 BPU id(10) HzSQuantizedConv
LeakyRelu_275 BPU id(10) HzLeakyRelu
Concat_276 BPU id(12) Concat
Conv_277 BPU id(12) HzSQuantizedConv
LeakyRelu_278 BPU id(12) HzLeakyRelu
Conv_279 BPU id(6) HzSQuantizedConv
Conv_281 BPU id(9) HzSQuantizedConv
Conv_283 BPU id(12) HzSQuantizedConv
2022-11-02 14:02:43,045 INFO [Wed Nov 2 14:02:43 2022] End to Horizon NN Model Convert.
2022-11-02 14:02:43,053 INFO ONNX model output num : 3
2022-11-02 14:02:43,084 INFO End model checking…