根据v853官方NPU模型转换的文档，将pytorch模型进行模型转换，出现了以下问题，请问大家知道如何将pytorch模型（.pth）进行模型转换吗
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carpediem 发布的最佳帖子
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v853 NPU模型转换，pytorch模型转换的问题发布在 V Series
carpediem 发布的最新帖子
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V853 使用OpenixCard烧写SD卡，插到板子上后发现是read-only file system发布在 编译和烧写问题专区
在GitHub上下载了OpenixCard之后，用其dump v853的img镜像。
xinzhe@xinzhe-G3-3590:~/Downloads$ OpenixCard -d v853.img _____ _ _____ _ | |___ ___ ___|_|_ _| |___ ___ _| | | | | . | -_| | |_'_| --| .'| _| . | |_____| _|___|_|_|_|_,_|_____|__,|_| |___| |_| Version: d0a8a7e Commit: 244 Copyright (c) 2022, YuzukiTsuru <GloomyGhost@GloomyGhost.com> [OpenixCard INFO] Input file: v853.img Now converting... [OpenixIMG INFO] Now Decrypt IMG header... [OpenixIMG INFO] The IMG version is: 0x300 [OpenixIMG INFO] Now Decrypt IMG file contents... [OpenixIMG INFO] Writing the IMG config data... [OpenixCard INFO] Convert Done! Prasing the partition tables... [OpenixCard INFO] Prase Done! Generating target image... [GenIMG INFO]: cmd: "rm -rf "/tmp/tmp.WBcefwfipH"/*" [GenIMG INFO]: cmd: "mkdir -p "/tmp/tmp.WBcefwfipH"" [GenIMG INFO]: cmd: "cp -a "/tmp/tmp.9hjbyNWcso" "/tmp/tmp.WBcefwfipH/root"" [GenIMG INFO]: cmd: "find '/tmp/tmp.WBcefwfipH/root' -depth -type d -printf '%P\0' | xargs -0 -I {} touch -r '/tmp/tmp.9hjbyNWcso/{}' '/tmp/tmp.WBcefwfipH/root/{}'" [GenIMG INFO]: cmd: "mkdir -p "/home/xinzhe/Downloads/v853.img.dump.out"" [GenIMG INFO]: hdimage(v853.img): adding partition 'boot0' from 'boot0_sdcard.fex' ... [GenIMG INFO]: hdimage(v853.img): adding partition 'boot-packages' from 'boot_package.fex' ... [GenIMG INFO]: hdimage(v853.img): adding partition 'boot-resource' (in MBR) from 'boot-resource.fex' ... [GenIMG INFO]: hdimage(v853.img): adding partition 'env' (in MBR) from 'env.fex' ... [GenIMG INFO]: hdimage(v853.img): adding partition 'boot' (in MBR) from 'boot.fex' ... [GenIMG INFO]: hdimage(v853.img): adding partition 'rootfs' (in MBR) from 'rootfs.fex' ... [GenIMG INFO]: hdimage(v853.img): adding partition 'rootfs_data' (in MBR) from 'blank.fex' ... [GenIMG INFO]: hdimage(v853.img): adding partition 'recovery' (in MBR) from 'blank.fex' ... [GenIMG INFO]: hdimage(v853.img): adding partition '[MBR]' ... [GenIMG INFO]: hdimage(v853.img): adding partition '[GPT header]' ... [GenIMG INFO]: hdimage(v853.img): adding partition '[GPT array]' ... [GenIMG INFO]: hdimage(v853.img): adding partition '[GPT backup]' ... [GenIMG INFO]: hdimage(v853.img): writing GPT [GenIMG INFO]: hdimage(v853.img): writing hybrid MBR [GenIMG INFO]: cmd: "rm -rf "/tmp/tmp.WBcefwfipH"/*" [OpenixCard INFO] Generate Done! Your image file at v853.img.dump.out Cleaning up... xinzhe@xinzhe-G3-3590:~/Downloads$然后将Dump后的img烧录到SD卡，插到板子上，使用adb查看，发现是只读文件系统。
xinzhe@xinzhe-G3-3590:~/Downloads$ adb push 0000.bin /root adb: error: failed to copy '0000.bin' to '/root/0000.bin': remote Read-only file system 0000.bin: 0 files pushed. 9.6 MB/s (327640 bytes in 0.033s) xinzhe@xinzhe-G3-3590:~/Downloads$请问这个问题如何解决
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v853NPU gaitset模型部署失败发布在 V Series
把训练好的gaitset.onnx转化为network_binary.nb模型后，在开发板上运行时，出现错误，如下图所示。
root@TinaLinux:~# vpm_run sample.txt vpm_run sample.txt loop_run_count device_id sample.txt: to include one ore more network binary graph (NBG) data file resource. See sample.txt for details. loop_run_count: the number of loop run network. device_id: specify this NBG runs device. example: ./vpm_run sample.txt 1 1, specify the NBG runs on device 1. ./vpm_run sample.txt 1000, run this network 1000 times. test started. init vip lite, driver version=0x00010800... [0xb6fbd560]vip_init[104], The version of Viplite is: 1.8.0-0-AW-2022-04-21 vip lite init OK. cid=0xee, device_count=1 device[0] core_count=1 init test resources, batch_count: 1 ... create/prepare networks ... batch i=0, binary name: ./network_binary.nb input 0 dim 44 64 100 1, data_format=2, quant_format=2, name=input[0], scale=0.003922, zero_point=0 ouput 0 dim 256 62 1 0, data_format=1, name=uid_1_out_0, none-quant nbg name=./network_binary.nb create network 1: 24090 us. memory pool size=11265280byte read golden file ./outputg.tensor input 0 name: ./input.tensor prepare network 0: 395732 us. batch: 0, loop count: 1 start to run network=./network_binary.nb [0xb6fbd560]gcvip_os_call_kernel[344], fail to ioctl vipcore, command[4]:CMD_WAIT, status=-1 [0xb6fbd560]gcvip_user_wait[484], failed to check status=-1 [0xb6fbd560]gcvip_capture_init[1240], catpure file name .//viplite_hang_capture_617de3_460_b6fbd560.log后来观察转换过程中的模型结构，发现存在问题。
上面这个是简化onnx模型后的gaitset-sim.onnx的结构图
这个是导出nb模型之后保存的gaitset-simprj_fused.json的结构图。其中pooling之后的数据的结构为【44，32，0，32】，这个是不是有问题？
请问大家有解决这个问题的办法吗？
附上模型转换过程中的代码：python -m onnxsim gaitset.onnx gaitset-sim.onnx --input-shape 1,100,64,44pegasus import onnx --model gaitset-sim.onnx --output-data gaitset-sim.data --output-model gaitset-sim.json --outputs "feature"pegasus generate inputmeta --model gaitset-sim.json --input-meta-output gaitset-sim_inputmeta.ymlpegasus generate postprocess-file --model gaitset-sim.json --postprocess-file-output gaitset-sim_postprocessmeta.ymlpegasus quantize --model gaitset-sim.json --model-data gaitset-sim.data --batch-size 1 --device CPU --with-input-meta gaitset-sim_inputmeta.yml --rebuild-all --model-quantize gaitset-sim.quantize --quantizer asymmetric_affine --qtype uint8pegasus inference --model gaitset-sim.json --model-data gaitset-sim.data --batch-size 1 --dtype quantized --model-quantize gaitset-sim.quantize --device CPU --with-input-meta gaitset-sim_inputmeta.yml --postprocess-file gaitset-sim_postprocessmeta.ymlpegasus export ovxlib --model gaitset-sim.json --model-data gaitset-sim.data --dtype quantized --model-quantize gaitset-sim.quantize --batch-size 1 --save-fused-graph --target-ide-project 'linux64' --with-input-meta gaitset-sim_inputmeta.yml --output-path ovxilb/gaitset-sim/gaitset-simprj --pack-nbg-unify --postprocess-file gaitset-sim_postprocessmeta.yml --optimize "VIP9000PICO_PID0XEE" --viv-sdk ${VIV_SDK} -
V853 NPU模型量化过程中，部分网络节点未量化的问题发布在 V Series
准备在V853上移植gaitset模型，但在量化过程中遇到了部分网络节点未量化的问题，导致板子上的NPU运行失败。
下面是模型量化的代码pegasus quantize --model gaitset-sim.json --model-data gaitset-sim.data --batch-size 1 --device CPU --with-input-meta gaitset-sim_inputmeta.yml --rebuild --model-quantize gaitset-sim.quantize --quantizer asymmetric_affine --qtype uint8量化之后，进行推理，发现输出的log中，部分网络节点没有量化为float32（fake asymmetric_affine），而是float32。由于NPU只能处理uint8，int8，int16这些类型的数据，不能直接处理float32数据，因此我猜测是量化未完全导致了板子上模型运行失败。
下面的是推理过程输出的log，可以看到部分节点未被量化D Acuity output shape(add): (1 8 128) D Tensor @Add_Add_306_13:out0 type: float32(fake asymmetric_affine) D Process Add_Add_320_15 ... D Acuity output shape(add): (1 16 128) D Tensor @Add_Add_320_15:out0 type: float32(fake asymmetric_affine) D Process Concat_Concat_321_5 ... D Acuity output shape(concat): (1 62 128) D Tensor @Concat_Concat_321_5:out0 type: float32(fake asymmetric_affine) D Process Transpose_Transpose_322_3 ... D Acuity output shape(permute): (62 1 128) D Tensor @Transpose_Transpose_322_3:out0 type: float32(fake asymmetric_affine) D Process MatMul_MatMul_323_2 ... D Acuity output shape(matmul): (62 1 256) D Tensor @MatMul_MatMul_323_2:out0 type: float32 D Process Transpose_Transpose_324_1 ... D Acuity output shape(permute): (1 62 256) D Tensor @Transpose_Transpose_324_1:out0 type: float32 D Process attach_Transpose_Transpose_324/out0_0 ... D Acuity output shape(output): (1 62 256) D Tensor @attach_Transpose_Transpose_324/out0_0:out0 type: float32下面的是在板子上运行NPU模型时出错的log
root@TinaLinux:~# gaitset network_binary.nb 0000.bin Usage: nbg_name input_data1 input_data2... [0xb6ffa560]vip_init[104], The version of Viplite is: 1.8.0-0-AW-2022-04-21 Create Neural Network: 24.63ms or 24631.92us As input, scale=0.003922, zeroPoint=0 data_format=2, num of dimension=4 size=44,64,100,1 data_format=1 , num of dimension=3 size=256,62,1,0 Input size match for 0000.bin, file data size:281600, expected:281600 Start run graph [1] times... [0xb6ffa560]gcvip_os_call_kernel[344], fail to ioctl vipcore, command[4]:CMD_WAIT, status=-1 [0xb6ffa560]gcvip_user_wait[484], failed to check status=-1 [0xb6ffa560]gcvip_capture_init[1240], catpure file name .//viplite_hang_capture_3cf88a_457_b6ffa560.log [0xb6ffa560]gcvip_wait_network_segment[801], wait network=gaitset-simprj_NCHW timeout, cmd size=0x9d38, phy=0x48d6e000 [0xb6ffa560]gcvip_wait_network[2975], failed to wait network finish in gcvip wait network [0xb6ffa560]gcvip_run_network[3021], failed to wait network finish in run network status=-1 Error: main.c: vnn_RunNeuralNetwork at 161 Error: main.c: main at 231 root@TinaLinux:~#综上所述，请问大家有没有解决方法？
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回复: v853 NPU模型转换，pytorch模型转换的问题发布在 V Series
@whycanservice 转换成.pt，既包含模型结构又包含权重之后，好像还是同样的错误。不知道是不是我的模型出了问题
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v853 NPU模型转换，pytorch模型转换的问题发布在 V Series
根据v853官方NPU模型转换的文档，将pytorch模型进行模型转换，出现了以下问题，请问大家知道如何将pytorch模型（.pth）进行模型转换吗
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v853 npu 模型转换后输出的模板代码，将其编译缺少相关库发布在 V Series
在官方文档中，对于NPU 转换部署 YOLO V5 模型，在导出模板代码与模型后。
输出的模板代码通过arm-openwrt-linux-gcc编译，发现缺少ovxlib相关的arm版本的库。
之前的demo 比如yolov3，都是基于viplite的，viplite官方已经提供。
但是ovxlib等有关的库好像没有找到。
请问这些库有没有获取方式？ -
回复: v853开发板遇到了Kernerl panic：rootfs不能挂载，进入crash dump的问题发布在 V Series
@whycan
试了试扩大roofs.fex大小，换了张64G的SD卡，重新烧录，好像还是不行。不过其他的demo，比如lenet，是不会出现这个问题的。只有YOLOV3 会有这个问题。
