MindSporeLite之converter_lite模型转换工具和benchmark模型推理工具

一、参考资料

二、准备工作

0. 系统环境

Environment
Operating System + Version: Ubuntu + 16.04
CPU Type: AMD Ryzen 5 5600X 6-Core Processor@3.7GHz
RAM：32GB 
Python Version (if applicable): 3.9.5
PyTorch Version (if applicable): 2.0.1+cu11
torchvision：0.10.0+cu11
onnx: 1.16.0

1. 模型准备

模型准备

MindSpore Lite提供的模型convertor工具可以支持主流的模型格式到MindIR的格式转换，用户需要导出对应的模型文件，推荐导出为ONNX格式。

1.1 PyTorch转ONNX

Exporting to ONNX from PyTorch
第三章：PyTorch 转 ONNX 详解

import torch
import torchvision


def export_onnx():
    model = torchvision.models.mobilenet_v2()
    model_name = "mobilenet_v2"

    model.eval()  # 若存在batchnorm、dropout层则一定要eval()!!!!再export

    BATCH_SIZE = 1
    dummy_input = torch.randn(BATCH_SIZE, 3, 224, 224)

    traced_script_module = torch.jit.trace(model, dummy_input)
    # 保存PyTorch模型
    traced_script_module.save("{}.pt".format(model_name))

    model_onnx = torch.jit.load("{}.pt".format(model_name))
    model_onnx.eval()
	
    # 保存onnx模型
    torch.onnx.export(model_onnx,
                      dummy_input,
                      "{}.onnx".format(model_name),
                      opset_version=13,
                      do_constant_folding=True,
                      input_names=["input_0"],
                      output_names=["output_0"],
                      dynamic_axes={"input": {0: "batch_size"},
                                    "output": {0: "batch_size"}}
                      )

1.2 TensorFlow转ONNX

Exporting to ONNX from TensorFlow

2. 参数准备

MSLite涉及到编译优化的过程，不支持完全动态的权重模式，需要在转换时确定对应的inputShape，用于模型的格式的编译与转换，可以在 netron官网进行查看，或者对于模型结构中的输入进行shape的打印，并明确输入的batch。

一般来说，推理时指定的inputShape是和用户的业务及推理场景是紧密相关的，可以通过原始模型推理脚本或者网络模型进行判断。

在这里插入图片描述

如果netron中没有显示inputShape，可能由于使用了动态shape模型导致，请确保使用的是静态shape模型，静态shape模型文件导出方法请参考上章节的【模型准备】。

3. 下载MinSporeLite

下载MindSpore Lite

4. （可选）编译MindSporeLite

如果下载的MindSporeLite软件包无法满足开发者，开发者可以自行编译MindSporeLite软件包。

编译MindSporeLite的详细步骤，请参考：编译MindSpore Lite

# 编译x86_64架构版本
bash build.sh -I x86_64 -j12

# 编译debug版本
bash build.sh -I x86_64 -j12 -d

# 增量编译
bash build.sh -I x86_64 -j12 -i

输出结果

...
...
...
[ 99%] Built target benchmark
[100%] Built target cropper
Run CPack packaging tool...
CPack: Create package using TGZ
CPack: Install projects
CPack: - Run preinstall target for: Lite
CPack: - Install project: Lite []
CPack: -   Install component: linux-x64
CPack: Create package
CPack: - package: /home/yoyo/MyDocuments/C++Projects/mindspore/output/tmp/mindspore-lite-1.8.2-linux-x64.tar.gz generated.
CPack: - checksum file: /home/yoyo/MyDocuments/C++Projects/mindspore/output/tmp/mindspore-lite-1.8.2-linux-x64.tar.gz.sha256 generated.
Python3 not found, so Python API will not be compiled. 
JAVA_HOME is not set, so jni and jar packages will not be compiled 
If you want to compile the JAR package, please set . For example: export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-amd64 
---------------- mindspore lite: build success ----------------
---------------- MindSpore: build end   ----------------

编译成功后，解压MindSporeLite软件包的目录如下：

mindspore-lite-{version}-linux-x64
└── tools
    └── converter
        ├── include
        │   └── registry             # 自定义算子、模型解析、节点解析、转换优化注册头文件
        ├── converter                # 模型转换工具
        │   └── converter_lite       # 可执行程序
        └── lib                      # 转换工具依赖的动态库
            ├── libmindspore_glog.so.0         # Glog的动态库
            ├── libmslite_converter_plugin.so  # 注册插件的动态库
            ├── libopencv_core.so.4.5          # OpenCV的动态库
            ├── libopencv_imgcodecs.so.4.5     # OpenCV的动态库
            └── libopencv_imgproc.so.4.5       # OpenCV的动态库

编译PYTORCH版本

MindSpore r2.0以上版本，支持PyTorch模型的转换。但是，由于支持转换PyTorch模型的编译选项默认关闭，因此下载的安装包不支持转换PyTorch模型，需要打开指定编译选项进行本地编译。

下载 CPU版本libtorch ，解压到/home/user/libtorch 目录下。

设置环境变量：

export MSLITE_ENABLE_CONVERT_PYTORCH_MODEL=on
export LD_LIBRARY_PATH="/home/user/libtorch/lib:${LD_LIBRARY_PATH}"
export LIB_TORCH_PATH="/home/user/libtorch"

编译
```
# 编译
bash build.sh -I x86_64 -j12
```

在这里插入图片描述
从上图可以看出，编译支持PyTorch模型的MindSporeLite软件包高达800多MB，而官方提供的Release软件包（不支持PyTorch模型）仅60多MB，这是一个很有意思的问题，博主觉得有空可以研究一下。

5. 设置日志打印级别

日志级别：0代表DEBUG，1代表INFO，2代表WARNING，3代表ERROR。

日志级别	GLOG_v
DEBUG	0
INFO	1
WARNING	2
ERROR	3

# 设置日志打印级别为INFO
set GLOG_v=1

6. MindSpore Lite API

MindSpore Lite API
基于MindSpore Lite的模型转换

MindSpore Lite提供了JAVA/C++/Python API，进行推理业务的适配，并且在构建模型时，通过上下文的参数来确定运行时的具体配置，例如运行后端的配置等。

三、`converter_lite` 模型转换

# 设置环境变量
export LD_LIBRARY_PATH=${PACKAGE_ROOT_PATH}/tools/converter/lib:${LD_LIBRARY_PATH}

# 进入转换工具所在目录
cd ${PACKAGE_ROOT_PATH}/tools/converter/converter

# ONNX模型转换
./converter_lite --fmk=ONNX --modelFile=mobilenetv2-12.onnx --outputFile=mobilenetv2-12

(mslite) yoyo@yoyo:~/MyDocuments/C++Projects/mindspore/output/mindspore-lite-1.9.0-linux-x64/tools/converter/converter$ ./converter_lite --fmk=ONNX --modelFile=
mobilenetv2-12.onnx --outputFile=mobilenetv2-12
[WARNING] LITE(24326,7fdabd607f40,converter_lite):2024-04-02-20:19:26.855.514 [mindspore/lite/tools/optimizer/graph/infershape_pass.cc:302] InferProcess] node infer shape failed, node is Conv_0
[WARNING] LITE(24326,7fdabd607f40,converter_lite):2024-04-02-20:19:26.855.529 [mindspore/lite/tools/optimizer/graph/infershape_pass.cc:184] Run] infer shape failed.
CONVERT RESULT SUCCESS:0

1. 设置输入数据格式

--inputDataFormat=<INPUTDATAFORMAT>

设置输入数据格式为：NCHW

(mslite) yoyo@yoyo:~/MyDocuments/C++Projects/mindspore/output/mindspore-lite-1.9.0-linux-x64/tools/converter/converter$ ./converter_lite --fmk=ONNX --modelFile=
mobilenetv2-12.onnx --outputFile=mobilenetv2-12NCHW --inputDataFormat=NCHW
[WARNING] LITE(24786,7feadf39df40,converter_lite):2024-04-02-20:42:39.913.710 [mindspore/lite/tools/optimizer/graph/infershape_pass.cc:302] InferProcess] node infer shape failed, node is Conv_0
[WARNING] LITE(24786,7feadf39df40,converter_lite):2024-04-02-20:42:39.913.726 [mindspore/lite/tools/optimizer/graph/infershape_pass.cc:184] Run] infer shape failed.
CONVERT RESULT SUCCESS:0

设置输入数据格式为：NHWC

(mslite) yoyo@yoyo:~/MyDocuments/C++Projects/mindspore/output/mindspore-lite-1.9.0-linux-x64/tools/converter/converter$ ./converter_lite --fmk=ONNX --modelFile=mobilenetv2-12.onnx --outputFile=mobilenetv2-12NHWC --inputDataFormat=NHWC
[WARNING] LITE(25344,7fa070c77f40,converter_lite):2024-04-02-21:10:43.436.991 [mindspore/lite/tools/optimizer/graph/infershape_pass.cc:302] InferProcess] node infer shape failed, node is Conv_0
[WARNING] LITE(25344,7fa070c77f40,converter_lite):2024-04-02-21:10:43.437.007 [mindspore/lite/tools/optimizer/graph/infershape_pass.cc:184] Run] infer shape failed.
CONVERT RESULT SUCCESS:0

2. 设置输入维度

--inputShape=<INPUTSHAPE>

设定模型输入的维度，输入维度的顺序和原始模型保持一致。对某些特定的模型可以进一步优化模型结构，但是转化后的模型将可能失去动态shape的特性。

(mslite) yoyo@yoyo:~/MyDocuments/C++Projects/mindspore/output/mindspore-lite-1.9.0-linux-x64/tools/converter/converter$ ./converter_lite --fmk=ONNX --modelFile=mobilenetv2-12.onnx --outputFile=mobilenetv2-12NCHW --inputDataFormat=NCHW --inputShape="input:1,3,224,224"
CONVERT RESULT SUCCESS:0

3. 转换PyTorch模型

# 设置libtorch环境变量
export LD_LIBRARY_PATH="/home/user/libtorch/lib:${LD_LIBRARY_PATH}"
export LIB_TORCH_PATH="/home/user/libtorch"

./converter_lite --fmk=PYTORCH --modelFile=model.pt --outputFile=model

四、`benchmark` 模型推理

# 设置环境变量
export LD_LIBRARY_PATH=${PACKAGE_ROOT_PATH}/runtime/lib:${LD_LIBRARY_PATH}

./benchmark --modelFile=/path/to/model.ms

1. 设置输入维度

--inputShapes=<INPUTSHAPES>

benchmark推理时的输入数据格式，必须与converter_lite模型转换时的一致，否则benchmark推理失败。

设置输入数据格式为：NHWC

(mslite) yoyo@yoyo:~/MyDocuments/C++Projects/mindspore/output/mindspore-lite-1.9.0-linux-x64/tools/benchmark$ ./benchmark --modelFile=mobilenetv2-12.ms --inputShapes="1,224,224,3"
ModelPath = mobilenetv2-12.ms
ModelType = MindIR
InDataPath = 
ConfigFilePath = 
InDataType = bin
LoopCount = 10
DeviceType = CPU
AccuracyThreshold = 0.5
CosineDistanceThreshold = -1.1
WarmUpLoopCount = 3
NumThreads = 2
InterOpParallelNum = 1
Fp16Priority = 0
EnableParallel = 0
calibDataPath = 
EnableGLTexture = 0
cpuBindMode = HIGHER_CPU
CalibDataType = FLOAT
Resize Dims: 1 224 224 3 
start unified benchmark run
PrepareTime = 18.894 ms
Running warm up loops...
Running benchmark loops...
Model = mobilenetv2-12.ms, NumThreads = 2, MinRunTime = 3.344000 ms, MaxRuntime = 4.421000 ms, AvgRunTime = 3.532000 ms
Run Benchmark mobilenetv2-12.ms Success.

输入数据格式为：NCHW

(mslite) yoyo@yoyo:~/MyDocuments/C++Projects/mindspore/output/mindspore-lite-1.9.0-linux-x64/tools/benchmark$ ./benchmark --modelFile=mobilenetv2-12NCHW.ms --inputShapes="1,3,224,224"
ModelPath = mobilenetv2-12NCHW.ms
ModelType = MindIR
InDataPath = 
ConfigFilePath = 
InDataType = bin
LoopCount = 10
DeviceType = CPU
AccuracyThreshold = 0.5
CosineDistanceThreshold = -1.1
WarmUpLoopCount = 3
NumThreads = 2
InterOpParallelNum = 1
Fp16Priority = 0
EnableParallel = 0
calibDataPath = 
EnableGLTexture = 0
cpuBindMode = HIGHER_CPU
CalibDataType = FLOAT
Resize Dims: 1 3 224 224 
start unified benchmark run
PrepareTime = 16.325 ms
Running warm up loops...
Running benchmark loops...
Model = mobilenetv2-12NCHW.ms, NumThreads = 2, MinRunTime = 3.459000 ms, MaxRuntime = 4.462000 ms, AvgRunTime = 3.779000 ms
Run Benchmark mobilenetv2-12NCHW.ms Success.

2. 设置推理运行次数

--loopCount=<LOOPCOUNT>

(mslite) yoyo@yoyo:~/MyDocuments/C++Projects/mindspore/output/mindspore-lite-1.9.0-linux-x64/tools/benchmark$ ./benchmark --modelFile=mobilenetv2-12NCHW.ms --inputShapes="1,3,224,224" --loopCount=1000
ModelPath = mobilenetv2-12NCHW.ms
ModelType = MindIR
InDataPath = 
ConfigFilePath = 
InDataType = bin
LoopCount = 1000
DeviceType = CPU
AccuracyThreshold = 0.5
CosineDistanceThreshold = -1.1
WarmUpLoopCount = 3
NumThreads = 2
InterOpParallelNum = 1
Fp16Priority = 0
EnableParallel = 0
calibDataPath = 
EnableGLTexture = 0
cpuBindMode = HIGHER_CPU
CalibDataType = FLOAT
Resize Dims: 1 3 224 224 
start unified benchmark run
PrepareTime = 19.141 ms
Running warm up loops...
Running benchmark loops...
Model = mobilenetv2-12NCHW.ms, NumThreads = 2, MinRunTime = 3.409000 ms, MaxRuntime = 5.176000 ms, AvgRunTime = 3.501000 ms
Run Benchmark mobilenetv2-12NCHW.ms Success.

3. 设置线程数

--numThreads=<NUMTHREADS>

(mslite) yoyo@yoyo:~/MyDocuments/C++Projects/mindspore/output/mindspore-lite-1.9.0-linux-x64/tools/benchmark$ ./benchmark --modelFile=mobilenetv2-12NCHW.ms --inputShapes="1,3,224,224" --loopCount=1000 --numThreads=12
ModelPath = mobilenetv2-12NCHW.ms
ModelType = MindIR
InDataPath = 
ConfigFilePath = 
InDataType = bin
LoopCount = 1000
DeviceType = CPU
AccuracyThreshold = 0.5
CosineDistanceThreshold = -1.1
WarmUpLoopCount = 3
NumThreads = 12
InterOpParallelNum = 1
Fp16Priority = 0
EnableParallel = 0
calibDataPath = 
EnableGLTexture = 0
cpuBindMode = HIGHER_CPU
CalibDataType = FLOAT
Resize Dims: 1 3 224 224 
start unified benchmark run
PrepareTime = 20.17 ms
Running warm up loops...
Running benchmark loops...
Model = mobilenetv2-12NCHW.ms, NumThreads = 12, MinRunTime = 1.675000 ms, MaxRuntime = 45.550999 ms, AvgRunTime = 2.028000 ms
Run Benchmark mobilenetv2-12NCHW.ms Success.

4. 计算每个算子耗时

--timeProfiling=<TIMEPROFILING>

(mslite) yoyo@yoyo:~/MyDocuments/C++Projects/mindspore/output/mindspore-lite-1.9.0-linux-x64/tools/benchmark$ ./benchmark --modelFile=mobilenetv2-12NCHW.ms --inputShapes="1,3,224,224" --timeProfiling=true
ModelPath = mobilenetv2-12NCHW.ms
ModelType = MindIR
InDataPath = 
ConfigFilePath = 
InDataType = bin
LoopCount = 10
DeviceType = CPU
AccuracyThreshold = 0.5
CosineDistanceThreshold = -1.1
WarmUpLoopCount = 3
NumThreads = 2
InterOpParallelNum = 1
Fp16Priority = 0
EnableParallel = 0
calibDataPath = 
EnableGLTexture = 0
cpuBindMode = HIGHER_CPU
CalibDataType = FLOAT
Resize Dims: 1 3 224 224 
start unified benchmark run
PrepareTime = 17.445 ms
Running warm up loops...
Running benchmark loops...
-------------------------------------------------------------------------
opName                       	avg(ms)     	percent     	calledTimes	opTotalTime 	
Add_15                       	0.013500    	0.003393    	10         	0.135000    	
Add_26                       	0.004700    	0.001181    	10         	0.047000    	
Add_32                       	0.004200    	0.001056    	10         	0.042000    	
Add_43                       	0.003700    	0.000930    	10         	0.037000    	
Add_49                       	0.003600    	0.000905    	10         	0.036000    	
Add_55                       	0.003600    	0.000905    	10         	0.036000    	
Add_66                       	0.004200    	0.001056    	10         	0.042000    	
Add_72                       	0.004300    	0.001081    	10         	0.043000    	
Add_83                       	0.003200    	0.000804    	10         	0.032000    	
Add_89                       	0.003000    	0.000754    	10         	0.030000    	
Clip_96_post                 	0.017200    	0.004324    	10         	0.172000    	
Concat_102                   	0.001800    	0.000452    	10         	0.018000    	
Conv_0                       	0.113000    	0.028405    	10         	1.130000    	
Conv_10                      	0.146100    	0.036725    	10         	1.461000    	
Conv_12                      	0.170600    	0.042884    	10         	1.706000    	
Conv_14                      	0.094200    	0.023679    	10         	0.942000    	
Conv_16                      	0.144800    	0.036398    	10         	1.448000    	
Conv_18                      	0.050000    	0.012569    	10         	0.500000    	
Conv_2                       	0.130800    	0.032879    	10         	1.308000    	
Conv_20                      	0.032200    	0.008094    	10         	0.322000    	
Conv_21                      	0.047300    	0.011890    	10         	0.473000    	
Conv_23                      	0.054200    	0.013624    	10         	0.542000    	
Conv_25                      	0.041600    	0.010457    	10         	0.416000    	
Conv_27                      	0.044900    	0.011287    	10         	0.449000    	
Conv_29                      	0.052000    	0.013071    	10         	0.520000    	
Conv_31                      	0.039500    	0.009929    	10         	0.395000    	
Conv_33                      	0.045300    	0.011387    	10         	0.453000    	
Conv_35                      	0.015400    	0.003871    	10         	0.154000    	
Conv_37                      	0.023200    	0.005832    	10         	0.232000    	
Conv_38                      	0.041000    	0.010306    	10         	0.410000    	
Conv_4                       	0.059700    	0.015007    	10         	0.597000    	
Conv_40                      	0.027000    	0.006787    	10         	0.270000    	
Conv_42                      	0.042600    	0.010708    	10         	0.426000    	
Conv_44                      	0.040900    	0.010281    	10         	0.409000    	
Conv_46                      	0.025400    	0.006385    	10         	0.254000    	
Conv_48                      	0.042700    	0.010734    	10         	0.427000    	
Conv_5                       	0.280900    	0.070610    	10         	2.809000    	
Conv_50                      	0.040900    	0.010281    	10         	0.409000    	
Conv_52                      	0.026200    	0.006586    	10         	0.262000    	
Conv_54                      	0.042700    	0.010734    	10         	0.427000    	
Conv_56                      	0.040900    	0.010281    	10         	0.409000    	
Conv_58                      	0.026700    	0.006712    	10         	0.267000    	
Conv_60                      	0.079700    	0.020034    	10         	0.797000    	
Conv_61                      	0.088100    	0.022146    	10         	0.881000    	
Conv_63                      	0.040800    	0.010256    	10         	0.408000    	
Conv_65                      	0.116800    	0.029360    	10         	1.168000    	
Conv_67                      	0.087300    	0.021945    	10         	0.873000    	
Conv_69                      	0.037600    	0.009452    	10         	0.376000    	
Conv_7                       	0.141100    	0.035468    	10         	1.411000    	
Conv_71                      	0.116200    	0.029209    	10         	1.162000    	
Conv_73                      	0.088200    	0.022171    	10         	0.882000    	
Conv_75                      	0.014100    	0.003544    	10         	0.141000    	
Conv_77                      	0.053500    	0.013448    	10         	0.535000    	
Conv_78                      	0.072400    	0.018199    	10         	0.724000    	
Conv_80                      	0.020800    	0.005228    	10         	0.208000    	
Conv_82                      	0.088000    	0.022121    	10         	0.880000    	
Conv_84                      	0.072800    	0.018300    	10         	0.728000    	
Conv_86                      	0.019600    	0.004927    	10         	0.196000    	
Conv_88                      	0.087400    	0.021970    	10         	0.874000    	
Conv_9                       	0.061100    	0.015359    	10         	0.611000    	
Conv_90                      	0.073400    	0.018451    	10         	0.734000    	
Conv_92                      	0.021300    	0.005354    	10         	0.213000    	
Conv_94                      	0.145300    	0.036524    	10         	1.453000    	
Conv_95                      	0.187200    	0.047056    	10         	1.872000    	
Gather_100                   	0.002000    	0.000503    	10         	0.020000    	
Gemm_104                     	0.122400    	0.030768    	10         	1.224000    	
GlobalAveragePool_97         	0.009100    	0.002287    	10         	0.091000    	
GlobalAveragePool_97_post    	0.001400    	0.000352    	10         	0.014000    	
Reshape_103                  	0.001100    	0.000277    	10         	0.011000    	
Shape_98                     	0.001600    	0.000402    	10         	0.016000    	
Unsqueeze_101                	0.001300    	0.000327    	10         	0.013000    	
input_nc2nh                  	0.076900    	0.019330    	10         	0.769000    	
-------------------------------------------------------------------------
opType       	avg(ms)     	percent     	calledTimes	opTotalTime 	
AddFusion    	0.048000    	0.012066    	100        	0.480000    	
AvgPoolFusion	0.009100    	0.002287    	10         	0.091000    	
Concat       	0.001800    	0.000452    	10         	0.018000    	
Conv2DFusion 	3.695398    	0.928913    	520        	36.953976   	
Gather       	0.002000    	0.000503    	10         	0.020000    	
MatMulFusion 	0.122400    	0.030768    	10         	1.224000    	
Reshape      	0.001100    	0.000277    	10         	0.011000    	
Shape        	0.001600    	0.000402    	10         	0.016000    	
Transpose    	0.095500    	0.024006    	30         	0.955000    	
Unsqueeze    	0.001300    	0.000327    	10         	0.013000    	
Model = mobilenetv2-12NCHW.ms, NumThreads = 2, MinRunTime = 3.677000 ms, MaxRuntime = 5.417000 ms, AvgRunTime = 4.200000 ms
Run Benchmark mobilenetv2-12NCHW.ms Success.

五、示例：GHost-DepthNet

1. `converter_lite` 模型转换

1.1 转换ONNX

yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/converter$ ./converter_lite --fmk=ONNX --modelFile=GHost-DepthNet.onnx --outputFile=GHost-DepthNetNCHW --inputDataFormat=NCHW --inputShape="input:1,3,224,224"
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.599 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /Resize
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.626 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /Resize_1
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.645 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /Resize_2
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.672 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ghostV2block/ghost1/Resize
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.678 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ghostV2block/ghost1/Resize
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.713 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ppma/Resize
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.719 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ppma/Resize
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.733 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ppma/Resize_1
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.739 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ppma/Resize_1
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.753 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ppma/Resize_2
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.759 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ppma/Resize_2
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.773 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ppma/Resize_3
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.779 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ppma/Resize_3
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.796 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /Resize_3
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.808 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /Resize_4
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.822 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /Resize_5
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.263.835 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /Resize_6
[WARNING] LITE(17444,7ffa346e1b40,converter_lite):2024-04-05-23:27:59.265.896 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3518] ConvertGraphInputs] Can not find name in map. name is input_0
CONVERT RESULT SUCCESS:0

1.2 转换onnx-sim

# 安装onnx-simplifier
pip install onnx-simplifier

# 执行转换
python -m onnxsim GHost-Depth.onnx GHost-Depth_sim.onnx

/tools/converter/converter$ ./converter_lite --fmk=ONNX --modelFile=GHost-DepthN
et_sim.onnx --outputFile=GHost-DepthNet_simNCHW --inputDataFormat=NCHW --inputShape="input:1,3,224,224"
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.388 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /Resize
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.411 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /Resize_1
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.425 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /Resize_2
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.446 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ghostV2block/ghost1/Resize
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.452 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ghostV2block/ghost1/Resize
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.468 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ppma/Resize
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.474 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ppma/Resize
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.483 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ppma/Resize_1
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.489 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ppma/Resize_1
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.498 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ppma/Resize_2
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.503 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ppma/Resize_2
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.512 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ppma/Resize_3
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.518 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /ppma/Resize_3
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.532 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /Resize_3
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.544 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /Resize_4
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.555 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /Resize_5
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.119.567 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3768] CheckOnnxModel] Can not find node input:  of /Resize_6
[WARNING] LITE(17870,7f9732ea5b40,converter_lite):2024-04-05-23:36:37.121.865 [mindspore/lite/build/tools/converter/parser/onnx/onnx_op_parser.cc:3518] ConvertGraphInputs] Can not find name in map. name is input_0
CONVERT RESULT SUCCESS:0

1.3 转换PyTorch

(mslite) yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/converter$ ./converter_lite --fmk=PYTORCH --modelFile=GHost-DepthNet.pt --outputFile=GHost-DepthNetNCHW --inputDataFormat=NCHW --inputShape="input:1,3,224,224"
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.434.036 [mindspore/lite/tools/converter/registry/model_parser_registry.cc:47] GetModelParser] ILLEGAL FMK: fmk must be in FmkType.1
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.434.064 [mindspore/lite/tools/converter/registry/model_parser_registry.cc:49] GetModelParser] ILLEGAL FMK: fmk must be in FmkType.2
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.434.070 [mindspore/lite/tools/converter/registry/model_parser_registry.cc:51] GetModelParser] ILLEGAL FMK: fmk must be in FmkType.3
[WARNING] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.575.566 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:730] ConvertGraphInputs] The input shape is empty.
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.635.700 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type hardtanh
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.635.911 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:950] BuildOpInputs] could not find input node: 77
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.635.922 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1055] ConvertNodes] BuildOpInputs failed.
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.635.952 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:692] ConvertTorchGraph] convert nodes failed.
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.635.960 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:626] Parse] convert pytorch graph failed.
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.636.954 [mindspore/lite/tools/converter/converter_funcgraph.cc:92] Load3rdModelToFuncgraph] Get funcGraph failed for fmk: 5
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.636.975 [mindspore/lite/tools/converter/converter_funcgraph.cc:162] Build] Load model file failed
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.636.982 [mindspore/lite/tools/converter/converter.cc:979] HandleGraphCommon] Build func graph failed
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.637.014 [mindspore/lite/tools/converter/converter.cc:947] Convert] Handle graph failed: -1 Common error code.
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.637.022 [mindspore/lite/tools/converter/converter.cc:1108] RunConverter] Convert model failed
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.637.028 [mindspore/lite/tools/converter/converter_context.h:60] PrintOps] ===========================================
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.637.033 [mindspore/lite/tools/converter/converter_context.h:61] PrintOps] UNSUPPORTED OP LIST:
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.637.041 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: hardtanh
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.637.047 [mindspore/lite/tools/converter/converter_context.h:65] PrintOps] ===========================================
[ERROR] LITE(17915,7fd1d75c5b40,converter_lite):2024-04-05-23:38:27.637.058 [mindspore/lite/tools/converter/cxx_api/converter.cc:314] Convert] Convert model failed, ret=Common error code.
ERROR [mindspore/lite/tools/converter/converter_lite/main.cc:102] main] Convert failed. Ret: Common error code.
Convert failed. Ret: Common error code.

2. benchmark模型推理

2.1 GHost-DepthNet模型

自编译版本的MindSporeLite的性能，远不及官方提供的Release版本，强烈推荐使用Release版本的软件包。

性能差距大的具体原因暂不清楚，有空再去研究一下这个问题。

2.1.1 自编译版本

(mslite) yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/benchmark$ ./benchmark --modelFile=GHost-DepthNetNCHW.ms --inputShapes="1,3,224,224" --timeProfiling=true
ModelPath = GHost-DepthNetNCHW.ms
ModelType = MindIR
InDataPath = 
ConfigFilePath = 
InDataType = bin
LoopCount = 10
DeviceType = CPU
AccuracyThreshold = 0.5
CosineDistanceThreshold = -1.1
WarmUpLoopCount = 3
NumThreads = 2
InterOpParallelNum = 1
Fp16Priority = 0
EnableParallel = 0
calibDataPath = 
EnableGLTexture = 0
cpuBindMode = HIGHER_CPU
CalibDataType = FLOAT
Resize Dims: 1 3 224 224 
start unified benchmark run
PrepareTime = 36.034 ms
Running warm up loops...
Running benchmark loops...
-------------------------------------------------------------------------
opName                                         	avg(ms)     	percent     	calledTimes	opTotalTime 	
/Add                                           	0.051300    	0.000092    	10         	0.513000    	
/Add_1                                         	0.138300    	0.000247    	10         	1.383000    	
/Add_2                                         	0.256200    	0.000458    	10         	2.562000    	
/Add_3                                         	0.502200    	0.000898    	10         	5.022000    	
/Resize                                        	0.926700    	0.001656    	10         	9.266999    	
/Resize_1                                      	0.464000    	0.000829    	10         	4.640000    	
/Resize_2                                      	0.243000    	0.000434    	10         	2.430000    	
/Resize_3                                      	0.485800    	0.000868    	10         	4.858000    	
/Resize_4                                      	0.932300    	0.001666    	10         	9.323000    	
/Resize_5                                      	1.871400    	0.003345    	10         	18.714001   	
/Resize_6                                      	3.578200    	0.006396    	10         	35.782001   	
/conv1/0/Conv                                  	17.991699   	0.032158    	10         	179.916992  	
/conv1/0/Conv_pre_0                            	0.211200    	0.000377    	10         	2.112000    	
/decoder1/0/Conv                               	1.526100    	0.002728    	10         	15.261002   	
/decoder1/3/Conv                               	35.245499   	0.062997    	10         	352.454987  	
/decoder2/0/Conv                               	3.163500    	0.005654    	10         	31.634998   	
/decoder2/3/Conv                               	35.615898   	0.063659    	10         	356.158997  	
/decoder3/0/Conv                               	6.894100    	0.012322    	10         	68.941002   	
/decoder3/3/Conv                               	38.015301   	0.067948    	10         	380.153015  	
/decoder4/0/Conv                               	12.572200   	0.022471    	10         	125.722000  	
/decoder4/3/Conv                               	4.220300    	0.007543    	10         	42.202999   	
/decoder4/5/Clip_post                          	0.015800    	0.000028    	10         	0.158000    	
/dsc_block1/0/Conv                             	3.105700    	0.005551    	10         	31.057001   	
/dsc_block1/3/Conv                             	43.002701   	0.076862    	10         	430.027008  	
/dsc_block12/0/Conv                            	0.384500    	0.000687    	10         	3.845000    	
/dsc_block12/3/Conv                            	17.429699   	0.031153    	10         	174.296997  	
/dsc_block3/0/Conv                             	3.038500    	0.005431    	10         	30.385000   	
/dsc_block3/3/Conv                             	71.039200   	0.126974    	10         	710.391968  	
/dsc_block5/0/Conv                             	1.470400    	0.002628    	10         	14.704000   	
/dsc_block5/3/Conv                             	69.926102   	0.124984    	10         	699.260986  	
/dsc_block6/0/Conv                             	0.381800    	0.000682    	10         	3.818000    	
/dsc_block6/3/Conv                             	17.436499   	0.031166    	10         	174.364990  	
/ghostV2block/Add                              	0.049100    	0.000088    	10         	0.491000    	
/ghostV2block/ghost1/AveragePool               	0.154500    	0.000276    	10         	1.545000    	
/ghostV2block/ghost1/Concat                    	0.051300    	0.000092    	10         	0.513000    	
/ghostV2block/ghost1/Mul                       	0.145700    	0.000260    	10         	1.457000    	
/ghostV2block/ghost1/Resize                    	0.034700    	0.000062    	10         	0.347000    	
/ghostV2block/ghost1/Slice                     	0.021400    	0.000038    	10         	0.214000    	
/ghostV2block/ghost1/cheap_operation/0/Conv    	0.704400    	0.001259    	10         	7.044000    	
/ghostV2block/ghost1/gate_fn/Sigmoid           	0.362100    	0.000647    	10         	3.621000    	
/ghostV2block/ghost1/primary_conv/0/Conv       	34.603901   	0.061850    	10         	346.039001  	
/ghostV2block/ghost1/short_conv/0/Conv         	17.199600   	0.030742    	10         	171.996002  	
/ghostV2block/ghost1/short_conv/2/Conv         	0.172700    	0.000309    	10         	1.727000    	
/ghostV2block/ghost1/short_conv/4/Conv         	0.173600    	0.000310    	10         	1.736000    	
/ghostV2block/ghost2/Concat                    	0.029700    	0.000053    	10         	0.297000    	
/ghostV2block/ghost2/Slice                     	0.019900    	0.000036    	10         	0.199000    	
/ghostV2block/ghost2/cheap_operation/0/Conv    	0.153300    	0.000274    	10         	1.533000    	
/ghostV2block/ghost2/primary_conv/0/Conv       	38.044197   	0.067999    	10         	380.441986  	
/ppma/Concat_8                                 	0.034400    	0.000061    	10         	0.344000    	
/ppma/Mul                                      	0.052000    	0.000093    	10         	0.520000    	
/ppma/Resize                                   	0.039800    	0.000071    	10         	0.398000    	
/ppma/Resize_1                                 	0.043300    	0.000077    	10         	0.433000    	
/ppma/Resize_2                                 	0.043600    	0.000078    	10         	0.436000    	
/ppma/Resize_3                                 	0.049500    	0.000088    	10         	0.495000    	
/ppma/stages.0/0/AveragePool                   	0.148300    	0.000265    	10         	1.483000    	
/ppma/stages.0/1/Conv                          	0.046800    	0.000084    	10         	0.468000    	
/ppma/stages.1/0/AveragePool                   	0.159100    	0.000284    	10         	1.591000    	
/ppma/stages.1/1/Conv                          	0.112100    	0.000200    	10         	1.121000    	
/ppma/stages.2/0/AveragePool                   	0.241200    	0.000431    	10         	2.412000    	
/ppma/stages.2/1/Conv                          	0.222800    	0.000398    	10         	2.228000    	
/ppma/stages.3/0/AveragePool                   	0.299600    	0.000535    	10         	2.996000    	
/ppma/stages.3/1/Conv                          	0.843400    	0.001507    	10         	8.434000    	
/pwise_block2/0/Conv                           	37.366402   	0.066788    	10         	373.664001  	
/pwise_block4/0/Conv                           	35.722004   	0.063849    	10         	357.220032  	
-------------------------------------------------------------------------
opType        	avg(ms)     	percent     	calledTimes	opTotalTime 	
Activation    	0.362100    	0.000647    	10         	3.621000    	
AddFusion     	0.997100    	0.001782    	50         	9.970998    	
AvgPoolFusion 	1.002700    	0.001792    	50         	10.027001   	
Concat        	0.115400    	0.000206    	30         	1.154000    	
Conv2DFusion  	547.825073  	0.979167    	320        	5478.250488 	
MulFusion     	0.197700    	0.000353    	20         	1.977000    	
Resize        	8.712299    	0.015572    	120        	87.122993   	
StridedSlice  	0.041300    	0.000074    	20         	0.413000    	
Transpose     	0.227000    	0.000406    	20         	2.270000    	
Model = GHost-DepthNetNCHW.ms, NumThreads = 2, MinRunTime = 559.104004 ms, MaxRuntime = 562.068970 ms, AvgRunTime = 560.484009 ms
Run Benchmark GHost-DepthNetNCHW.ms Success.

2.1.2 Release版本

yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.0-linux-x64/tools/benchmark$ ./benchmark --modelFile=GHost-DepthNetNCHW.ms --inputShapes="1,3,224,224" --timeProfiling=true
ModelPath = GHost-DepthNetNCHW.ms
ModelType = MindIR
InDataPath = 
ConfigFilePath = 
InDataType = bin
LoopCount = 10
DeviceType = CPU
AccuracyThreshold = 0.5
CosineDistanceThreshold = -1.1
WarmUpLoopCount = 3
NumThreads = 2
InterOpParallelNum = 1
Fp16Priority = 0
EnableParallel = 0
calibDataPath = 
EnableGLTexture = 0
cpuBindMode = HIGHER_CPU
CalibDataType = FLOAT
Resize Dims: 1 3 224 224 
start unified benchmark run
PrepareTime = 23.123 ms
Running warm up loops...
Running benchmark loops...
-------------------------------------------------------------------------
opName                                         	avg(ms)     	percent     	calledTimes	opTotalTime 	
/Add                                           	0.005700    	0.001144    	10         	0.057000    	
/Add_1                                         	0.031500    	0.006321    	10         	0.315000    	
/Add_2                                         	0.062900    	0.012623    	10         	0.629000    	
/Add_3                                         	0.164100    	0.032932    	10         	1.641000    	
/Resize                                        	0.060900    	0.012222    	10         	0.609000    	
/Resize_1                                      	0.032200    	0.006462    	10         	0.322000    	
/Resize_2                                      	0.017400    	0.003492    	10         	0.174000    	
/Resize_3                                      	0.022000    	0.004415    	10         	0.220000    	
/Resize_4                                      	0.052000    	0.010435    	10         	0.520000    	
/Resize_5                                      	0.125300    	0.025146    	10         	1.253000    	
/Resize_6                                      	0.233300    	0.046819    	10         	2.333000    	
/conv1/0/Conv                                  	0.095700    	0.019205    	10         	0.957000    	
/conv1/0/Conv_pre_0                            	0.064000    	0.012844    	10         	0.640000    	
/decoder1/0/Conv                               	0.042600    	0.008549    	10         	0.426000    	
/decoder1/3/Conv                               	0.198800    	0.039896    	10         	1.988000    	
/decoder2/0/Conv                               	0.071800    	0.014409    	10         	0.718000    	
/decoder2/3/Conv                               	0.200200    	0.040177    	10         	2.002000    	
/decoder3/0/Conv                               	0.163200    	0.032751    	10         	1.632000    	
/decoder3/3/Conv                               	0.196300    	0.039394    	10         	1.963000    	
/decoder4/0/Conv                               	0.294000    	0.059001    	10         	2.940000    	
/decoder4/3/Conv                               	0.118700    	0.023821    	10         	1.187000    	
/decoder4/5/Clip_post                          	0.001500    	0.000301    	10         	0.015000    	
/dsc_block1/0/Conv                             	0.069100    	0.013867    	10         	0.691000    	
/dsc_block1/3/Conv                             	0.225500    	0.045254    	10         	2.255000    	
/dsc_block12/0/Conv                            	0.013400    	0.002689    	10         	0.134000    	
/dsc_block12/3/Conv                            	0.101500    	0.020369    	10         	1.015000    	
/dsc_block3/0/Conv                             	0.076600    	0.015372    	10         	0.766000    	
/dsc_block3/3/Conv                             	0.420100    	0.084307    	10         	4.201000    	
/dsc_block5/0/Conv                             	0.059100    	0.011860    	10         	0.591000    	
/dsc_block5/3/Conv                             	0.418400    	0.083966    	10         	4.184000    	
/dsc_block6/0/Conv                             	0.014200    	0.002850    	10         	0.142000    	
/dsc_block6/3/Conv                             	0.105800    	0.021232    	10         	1.058000    	
/ghostV2block/Add                              	0.006200    	0.001244    	10         	0.062000    	
/ghostV2block/ghost1/AveragePool               	0.006300    	0.001264    	10         	0.063000    	
/ghostV2block/ghost1/Concat                    	0.026100    	0.005238    	10         	0.261000    	
/ghostV2block/ghost1/Mul                       	0.039800    	0.007987    	10         	0.398000    	
/ghostV2block/ghost1/Resize                    	0.016200    	0.003251    	10         	0.162000    	
/ghostV2block/ghost1/Slice                     	0.001900    	0.000381    	10         	0.019000    	
/ghostV2block/ghost1/cheap_operation/0/Conv    	0.027900    	0.005599    	10         	0.279000    	
/ghostV2block/ghost1/gate_fn/Sigmoid           	0.010200    	0.002047    	10         	0.102000    	
/ghostV2block/ghost1/primary_conv/0/Conv       	0.202800    	0.040698    	10         	2.028000    	
/ghostV2block/ghost1/short_conv/0/Conv         	0.123900    	0.024865    	10         	1.239000    	
/ghostV2block/ghost1/short_conv/2/Conv         	0.016500    	0.003311    	10         	0.165000    	
/ghostV2block/ghost1/short_conv/4/Conv         	0.020000    	0.004014    	10         	0.200000    	
/ghostV2block/ghost2/Concat                    	0.004100    	0.000823    	10         	0.041000    	
/ghostV2block/ghost2/Slice                     	0.001300    	0.000261    	10         	0.013000    	
/ghostV2block/ghost2/cheap_operation/0/Conv    	0.007900    	0.001585    	10         	0.079000    	
/ghostV2block/ghost2/primary_conv/0/Conv       	0.204500    	0.041040    	10         	2.045000    	
/ppma/Concat_8                                 	0.005900    	0.001184    	10         	0.059000    	
/ppma/Mul                                      	0.006500    	0.001304    	10         	0.065000    	
/ppma/Resize                                   	0.002500    	0.000502    	10         	0.025000    	
/ppma/Resize_1                                 	0.003500    	0.000702    	10         	0.035000    	
/ppma/Resize_2                                 	0.002900    	0.000582    	10         	0.029000    	
/ppma/Resize_3                                 	0.003600    	0.000722    	10         	0.036000    	
/ppma/stages.0/0/AveragePool                   	0.007400    	0.001485    	10         	0.074000    	
/ppma/stages.0/1/Conv                          	0.003600    	0.000722    	10         	0.036000    	
/ppma/stages.1/0/AveragePool                   	0.007700    	0.001545    	10         	0.077000    	
/ppma/stages.1/1/Conv                          	0.004100    	0.000823    	10         	0.041000    	
/ppma/stages.2/0/AveragePool                   	0.008700    	0.001746    	10         	0.087000    	
/ppma/stages.2/1/Conv                          	0.004400    	0.000883    	10         	0.044000    	
/ppma/stages.3/0/AveragePool                   	0.009800    	0.001967    	10         	0.098000    	
/ppma/stages.3/1/Conv                          	0.008600    	0.001726    	10         	0.086000    	
/pwise_block2/0/Conv                           	0.216100    	0.043367    	10         	2.161000    	
/pwise_block4/0/Conv                           	0.214300    	0.043006    	10         	2.143000    	
-------------------------------------------------------------------------
opType        	avg(ms)     	percent     	calledTimes	opTotalTime 	
Activation    	0.010200    	0.002047    	10         	0.102000    	
AddFusion     	0.270400    	0.054265    	50         	2.704000    	
AvgPoolFusion 	0.039900    	0.008007    	50         	0.399000    	
Concat        	0.036100    	0.007245    	30         	0.361000    	
Conv2DFusion  	3.939600    	0.790609    	320        	39.396004   	
MulFusion     	0.046300    	0.009292    	20         	0.463000    	
Resize        	0.571800    	0.114750    	120        	5.717998    	
StridedSlice  	0.003200    	0.000642    	20         	0.032000    	
Transpose     	0.065500    	0.013145    	20         	0.655000    	
Model = GHost-DepthNetNCHW.ms, NumThreads = 2, MinRunTime = 4.795000 ms, MaxRuntime = 6.984000 ms, AvgRunTime = 5.114000 ms
Run Benchmark GHost-DepthNetNCHW.ms Success.

2.2 mobilenet_v2模型

2.2.1 自编译版本

yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/benchmark$ ./benchmark --modelFile=mobilenet_v2NCHW.ms --inputShapes="1,3,224,224" --timeProfiling=true
ModelPath = mobilenet_v2NCHW.ms
ModelType = MindIR
InDataPath = 
ConfigFilePath = 
InDataType = bin
LoopCount = 10
DeviceType = CPU
AccuracyThreshold = 0.5
CosineDistanceThreshold = -1.1
WarmUpLoopCount = 3
NumThreads = 2
InterOpParallelNum = 1
Fp16Priority = 0
EnableParallel = 0
calibDataPath = 
EnableGLTexture = 0
cpuBindMode = HIGHER_CPU
CalibDataType = FLOAT
Resize Dims: 1 3 224 224 
start unified benchmark run
PrepareTime = 38.092 ms
Running warm up loops...
Running benchmark loops...
-------------------------------------------------------------------------
opName                     	avg(ms)     	percent     	calledTimes	opTotalTime 	
/Flatten                   	0.011700    	0.000028    	10         	0.117000    	
/GlobalAveragePool         	0.197900    	0.000466    	10         	1.979000    	
/GlobalAveragePool_post    	0.014500    	0.000034    	10         	0.145000    	
/classifier/1/Gemm         	1.268400    	0.002987    	10         	12.684000   	
/features/0/0/Conv         	18.322699   	0.043146    	10         	183.226990  	
/features/0/0/Conv_pre_0   	0.206400    	0.000486    	10         	2.064000    	
/features/0/2/Clip         	0.618800    	0.001457    	10         	6.188000    	
/features/1/conv/0/0/Conv  	2.496400    	0.005878    	10         	24.963999   	
/features/1/conv/0/2/Clip  	0.766300    	0.001804    	10         	7.663000    	
/features/1/conv/1/Conv    	9.646601    	0.022716    	10         	96.466003   	
/features/10/Add           	0.030000    	0.000071    	10         	0.300000    	
/features/10/conv/0/0/Conv 	6.759100    	0.015916    	10         	67.591003   	
/features/10/conv/0/2/Clip 	0.187600    	0.000442    	10         	1.876000    	
/features/10/conv/1/0/Conv 	0.404200    	0.000952    	10         	4.042000    	
/features/10/conv/1/2/Clip 	0.204300    	0.000481    	10         	2.043000    	
/features/10/conv/2/Conv   	7.108600    	0.016739    	10         	71.085999   	
/features/11/conv/0/0/Conv 	6.847900    	0.016125    	10         	68.479004   	
/features/11/conv/0/2/Clip 	0.182800    	0.000430    	10         	1.828000    	
/features/11/conv/1/0/Conv 	0.403000    	0.000949    	10         	4.030000    	
/features/11/conv/1/2/Clip 	0.201100    	0.000474    	10         	2.011000    	
/features/11/conv/2/Conv   	10.545300   	0.024832    	10         	105.452995  	
/features/12/Add           	0.033600    	0.000079    	10         	0.336000    	
/features/12/conv/0/0/Conv 	14.952499   	0.035210    	10         	149.524994  	
/features/12/conv/0/2/Clip 	0.292900    	0.000690    	10         	2.929000    	
/features/12/conv/1/0/Conv 	0.630500    	0.001485    	10         	6.305000    	
/features/12/conv/1/2/Clip 	0.300700    	0.000708    	10         	3.007000    	
/features/12/conv/2/Conv   	15.833600   	0.037285    	10         	158.335999  	
/features/13/Add           	0.032700    	0.000077    	10         	0.327000    	
/features/13/conv/0/0/Conv 	14.743700   	0.034718    	10         	147.436996  	
/features/13/conv/0/2/Clip 	0.295100    	0.000695    	10         	2.951000    	
/features/13/conv/1/0/Conv 	0.595700    	0.001403    	10         	5.957000    	
/features/13/conv/1/2/Clip 	0.303700    	0.000715    	10         	3.037000    	
/features/13/conv/2/Conv   	15.780400   	0.037159    	10         	157.804001  	
/features/14/conv/0/0/Conv 	14.601801   	0.034384    	10         	146.018005  	
/features/14/conv/0/2/Clip 	0.283900    	0.000669    	10         	2.839000    	
/features/14/conv/1/0/Conv 	0.184300    	0.000434    	10         	1.843000    	
/features/14/conv/1/2/Clip 	0.088500    	0.000208    	10         	0.885000    	
/features/14/conv/2/Conv   	6.054800    	0.014258    	10         	60.548000   	
/features/15/Add           	0.025900    	0.000061    	10         	0.259000    	
/features/15/conv/0/0/Conv 	9.959600    	0.023453    	10         	99.596001   	
/features/15/conv/0/2/Clip 	0.143800    	0.000339    	10         	1.438000    	
/features/15/conv/1/0/Conv 	0.269000    	0.000633    	10         	2.690000    	
/features/15/conv/1/2/Clip 	0.138900    	0.000327    	10         	1.389000    	
/features/15/conv/2/Conv   	10.102400   	0.023789    	10         	101.023994  	
/features/16/Add           	0.026000    	0.000061    	10         	0.260000    	
/features/16/conv/0/0/Conv 	9.984099    	0.023510    	10         	99.840996   	
/features/16/conv/0/2/Clip 	0.144400    	0.000340    	10         	1.444000    	
/features/16/conv/1/0/Conv 	0.268500    	0.000632    	10         	2.685000    	
/features/16/conv/1/2/Clip 	0.145100    	0.000342    	10         	1.451000    	
/features/16/conv/2/Conv   	10.009699   	0.023571    	10         	100.096985  	
/features/17/conv/0/0/Conv 	9.952100    	0.023435    	10         	99.520996   	
/features/17/conv/0/2/Clip 	0.141700    	0.000334    	10         	1.417000    	
/features/17/conv/1/0/Conv 	0.268300    	0.000632    	10         	2.683000    	
/features/17/conv/1/2/Clip 	0.142400    	0.000335    	10         	1.424000    	
/features/17/conv/2/Conv   	19.981201   	0.047051    	10         	199.812012  	
/features/18/0/Conv        	26.795401   	0.063097    	10         	267.954010  	
/features/18/2/Clip        	0.189100    	0.000445    	10         	1.891000    	
/features/2/conv/0/0/Conv  	30.828897   	0.072595    	10         	308.288971  	
/features/2/conv/0/2/Clip  	1.743800    	0.004106    	10         	17.438002   	
/features/2/conv/1/0/Conv  	1.833000    	0.004316    	10         	18.330000   	
/features/2/conv/1/2/Clip  	0.708300    	0.001668    	10         	7.083000    	
/features/2/conv/2/Conv    	10.147400   	0.023895    	10         	101.473999  	
/features/3/Add            	0.067800    	0.000160    	10         	0.678000    	
/features/3/conv/0/0/Conv  	16.270899   	0.038314    	10         	162.708984  	
/features/3/conv/0/2/Clip  	0.799900    	0.001884    	10         	7.999000    	
/features/3/conv/1/0/Conv  	2.530600    	0.005959    	10         	25.306002   	
/features/3/conv/1/2/Clip  	1.034900    	0.002437    	10         	10.349000   	
/features/3/conv/2/Conv    	14.719400   	0.034661    	10         	147.194000  	
/features/4/conv/0/0/Conv  	16.253298   	0.038273    	10         	162.532974  	
/features/4/conv/0/2/Clip  	0.810700    	0.001909    	10         	8.107000    	
/features/4/conv/1/0/Conv  	0.661400    	0.001557    	10         	6.613999    	
/features/4/conv/1/2/Clip  	0.289400    	0.000681    	10         	2.894000    	
/features/4/conv/2/Conv    	4.927901    	0.011604    	10         	49.279007   	
/features/5/Add            	0.036600    	0.000086    	10         	0.366000    	
/features/5/conv/0/0/Conv  	7.075700    	0.016662    	10         	70.756996   	
/features/5/conv/0/2/Clip  	0.286500    	0.000675    	10         	2.865000    	
/features/5/conv/1/0/Conv  	0.870400    	0.002050    	10         	8.704000    	
/features/5/conv/1/2/Clip  	0.377600    	0.000889    	10         	3.776000    	
/features/5/conv/2/Conv    	6.583500    	0.015503    	10         	65.834999   	
/features/6/Add            	0.038200    	0.000090    	10         	0.382000    	
/features/6/conv/0/0/Conv  	7.074800    	0.016660    	10         	70.748001   	
/features/6/conv/0/2/Clip  	0.292400    	0.000689    	10         	2.924000    	
/features/6/conv/1/0/Conv  	0.834100    	0.001964    	10         	8.341001    	
/features/6/conv/1/2/Clip  	0.357800    	0.000843    	10         	3.578000    	
/features/6/conv/2/Conv    	6.619501    	0.015587    	10         	66.195007   	
/features/7/conv/0/0/Conv  	7.052300    	0.016607    	10         	70.523003   	
/features/7/conv/0/2/Clip  	0.303600    	0.000715    	10         	3.036000    	
/features/7/conv/1/0/Conv  	0.231900    	0.000546    	10         	2.319000    	
/features/7/conv/1/2/Clip  	0.112900    	0.000266    	10         	1.129000    	
/features/7/conv/2/Conv    	3.556400    	0.008375    	10         	35.563999   	
/features/8/Add            	0.030100    	0.000071    	10         	0.301000    	
/features/8/conv/0/0/Conv  	6.689300    	0.015752    	10         	66.892998   	
/features/8/conv/0/2/Clip  	0.190400    	0.000448    	10         	1.904000    	
/features/8/conv/1/0/Conv  	0.423200    	0.000997    	10         	4.232000    	
/features/8/conv/1/2/Clip  	0.204400    	0.000481    	10         	2.044000    	
/features/8/conv/2/Conv    	7.074500    	0.016659    	10         	70.745003   	
/features/9/Add            	0.028800    	0.000068    	10         	0.288000    	
/features/9/conv/0/0/Conv  	6.731101    	0.015850    	10         	67.311005   	
/features/9/conv/0/2/Clip  	0.182200    	0.000429    	10         	1.822000    	
/features/9/conv/1/0/Conv  	0.404200    	0.000952    	10         	4.042000    	
/features/9/conv/1/2/Clip  	0.202300    	0.000476    	10         	2.023000    	
/features/9/conv/2/Conv    	7.055600    	0.016614    	10         	70.556000   	
-------------------------------------------------------------------------
opType        	avg(ms)      	percent     	calledTimes	opTotalTime   	
Activation    	12.668195    	0.029831    	350        	126.681946    	
AddFusion     	0.349700     	0.000823    	100        	3.497001      	
AvgPoolFusion 	0.197900     	0.000466    	10         	1.979000      	
Conv2DFusion  	409.950836   	0.965345    	520        	4099.508301   	
Flatten       	0.011700     	0.000028    	10         	0.117000      	
MatMulFusion  	1.268400     	0.002987    	10         	12.684000     	
Transpose     	0.220900     	0.000520    	20         	2.209000      	
Model = mobilenet_v2NCHW.ms, NumThreads = 2, MinRunTime = 424.196014 ms, MaxRuntime = 429.618988 ms, AvgRunTime = 426.023987 ms
Run Benchmark mobilenet_v2NCHW.ms Success.

2.2.2 Release版本

yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.0-linux-x64/tools/benchmark$ ./benchmark --modelFile=mobilenet_v2NCHW.ms --inputShapes="1,3,224,224" --timeProfiling=true
ModelPath = mobilenet_v2NCHW.ms
ModelType = MindIR
InDataPath = 
ConfigFilePath = 
InDataType = bin
LoopCount = 10
DeviceType = CPU
AccuracyThreshold = 0.5
CosineDistanceThreshold = -1.1
WarmUpLoopCount = 3
NumThreads = 2
InterOpParallelNum = 1
Fp16Priority = 0
EnableParallel = 0
calibDataPath = 
EnableGLTexture = 0
cpuBindMode = HIGHER_CPU
CalibDataType = FLOAT
Resize Dims: 1 3 224 224 
start unified benchmark run
PrepareTime = 28.97 ms
Running warm up loops...
Running benchmark loops...
-------------------------------------------------------------------------
opName                     	avg(ms)     	percent     	calledTimes	opTotalTime 	
/Flatten                   	0.001500    	0.000299    	10         	0.015000    	
/GlobalAveragePool         	0.010500    	0.002092    	10         	0.105000    	
/GlobalAveragePool_post    	0.001500    	0.000299    	10         	0.015000    	
/classifier/1/Gemm         	0.100600    	0.020041    	10         	1.006000    	
/features/0/0/Conv         	0.158500    	0.031575    	10         	1.585000    	
/features/0/0/Conv_pre_0   	0.068700    	0.013686    	10         	0.687000    	
/features/1/conv/0/0/Conv  	0.108900    	0.021694    	10         	1.089000    	
/features/1/conv/1/Conv    	0.088800    	0.017690    	10         	0.888000    	
/features/10/Add           	0.003700    	0.000737    	10         	0.037000    	
/features/10/conv/0/0/Conv 	0.063400    	0.012630    	10         	0.634000    	
/features/10/conv/1/0/Conv 	0.026800    	0.005339    	10         	0.268000    	
/features/10/conv/2/Conv   	0.063100    	0.012570    	10         	0.631000    	
/features/11/conv/0/0/Conv 	0.063500    	0.012650    	10         	0.635000    	
/features/11/conv/1/0/Conv 	0.026300    	0.005239    	10         	0.263000    	
/features/11/conv/2/Conv   	0.111100    	0.022132    	10         	1.111000    	
/features/12/Add           	0.004300    	0.000857    	10         	0.043000    	
/features/12/conv/0/0/Conv 	0.139500    	0.027790    	10         	1.395000    	
/features/12/conv/1/0/Conv 	0.040400    	0.008048    	10         	0.404000    	
/features/12/conv/2/Conv   	0.165500    	0.032969    	10         	1.655000    	
/features/13/Add           	0.004800    	0.000956    	10         	0.048000    	
/features/13/conv/0/0/Conv 	0.138500    	0.027591    	10         	1.385000    	
/features/13/conv/1/0/Conv 	0.039200    	0.007809    	10         	0.392000    	
/features/13/conv/2/Conv   	0.164000    	0.032671    	10         	1.640000    	
/features/14/conv/0/0/Conv 	0.138600    	0.027611    	10         	1.386000    	
/features/14/conv/1/0/Conv 	0.014800    	0.002948    	10         	0.148000    	
/features/14/conv/2/Conv   	0.068800    	0.013706    	10         	0.688000    	
/features/15/Add           	0.003000    	0.000598    	10         	0.030000    	
/features/15/conv/0/0/Conv 	0.103800    	0.020678    	10         	1.038000    	
/features/15/conv/1/0/Conv 	0.022400    	0.004462    	10         	0.224000    	
/features/15/conv/2/Conv   	0.112800    	0.022471    	10         	1.128000    	
/features/16/Add           	0.003200    	0.000637    	10         	0.032000    	
/features/16/conv/0/0/Conv 	0.102700    	0.020459    	10         	1.027000    	
/features/16/conv/1/0/Conv 	0.021800    	0.004343    	10         	0.218000    	
/features/16/conv/2/Conv   	0.112700    	0.022451    	10         	1.127000    	
/features/17/conv/0/0/Conv 	0.103700    	0.020658    	10         	1.037000    	
/features/17/conv/1/0/Conv 	0.021500    	0.004283    	10         	0.215000    	
/features/17/conv/2/Conv   	0.200500    	0.039942    	10         	2.005000    	
/features/18/0/Conv        	0.272800    	0.054345    	10         	2.728000    	
/features/2/conv/0/0/Conv  	0.374300    	0.074565    	10         	3.743000    	
/features/2/conv/1/0/Conv  	0.120600    	0.024025    	10         	1.206000    	
/features/2/conv/2/Conv    	0.091600    	0.018248    	10         	0.916000    	
/features/3/Add            	0.013900    	0.002769    	10         	0.139000    	
/features/3/conv/0/0/Conv  	0.188400    	0.037531    	10         	1.884000    	
/features/3/conv/1/0/Conv  	0.128600    	0.025619    	10         	1.286000    	
/features/3/conv/2/Conv    	0.137500    	0.027392    	10         	1.375000    	
/features/4/conv/0/0/Conv  	0.186800    	0.037213    	10         	1.868000    	
/features/4/conv/1/0/Conv  	0.043400    	0.008646    	10         	0.434000    	
/features/4/conv/2/Conv    	0.046700    	0.009303    	10         	0.467000    	
/features/5/Add            	0.005000    	0.000996    	10         	0.050000    	
/features/5/conv/0/0/Conv  	0.066600    	0.013267    	10         	0.666000    	
/features/5/conv/1/0/Conv  	0.047800    	0.009522    	10         	0.478000    	
/features/5/conv/2/Conv    	0.061600    	0.012271    	10         	0.616000    	
/features/6/Add            	0.004900    	0.000976    	10         	0.049000    	
/features/6/conv/0/0/Conv  	0.066800    	0.013307    	10         	0.668000    	
/features/6/conv/1/0/Conv  	0.049400    	0.009841    	10         	0.494000    	
/features/6/conv/2/Conv    	0.062500    	0.012451    	10         	0.625000    	
/features/7/conv/0/0/Conv  	0.066400    	0.013228    	10         	0.664000    	
/features/7/conv/1/0/Conv  	0.016800    	0.003347    	10         	0.168000    	
/features/7/conv/2/Conv    	0.033100    	0.006594    	10         	0.331000    	
/features/8/Add            	0.003100    	0.000618    	10         	0.031000    	
/features/8/conv/0/0/Conv  	0.063100    	0.012570    	10         	0.631000    	
/features/8/conv/1/0/Conv  	0.026500    	0.005279    	10         	0.265000    	
/features/8/conv/2/Conv    	0.063300    	0.012610    	10         	0.633000    	
/features/9/Add            	0.003200    	0.000637    	10         	0.032000    	
/features/9/conv/0/0/Conv  	0.063200    	0.012590    	10         	0.632000    	
/features/9/conv/1/0/Conv  	0.025900    	0.005160    	10         	0.259000    	
/features/9/conv/2/Conv    	0.062600    	0.012471    	10         	0.626000    	
-------------------------------------------------------------------------
opType       	avg(ms)     	percent     	calledTimes	opTotalTime 	
AddFusion    	0.049100    	0.009781    	100        	0.491000    	
AvgPoolFusion	0.010500    	0.002092    	10         	0.105000    	
Conv2DFusion 	4.787901    	0.953803    	520        	47.879009   	
Flatten      	0.001500    	0.000299    	10         	0.015000    	
MatMulFusion 	0.100600    	0.020041    	10         	1.006000    	
Transpose    	0.070200    	0.013985    	20         	0.702000    	
Model = mobilenet_v2NCHW.ms, NumThreads = 2, MinRunTime = 3.337000 ms, MaxRuntime = 6.158000 ms, AvgRunTime = 5.150000 ms
Run Benchmark mobilenet_v2NCHW.ms Success.

五、FAQ

问题定位指南

Q：`virtual memory exhausted: 无法分配内存`

问题解决：c++: internal compiler error: 已杀死 (program cc1plus)

virtual memory exhausted: 无法分配内存
virtual memory exhausted: 无法分配内存
virtual memory exhausted: 无法分配内存
make[2]: *** [src/litert/kernel/cpu/CMakeFiles/train_cpu_kernel_mid.dir/fp32_grad/adam_weight_decay.cc.o] Error 1
make[2]: *** [tools/converter/micro/coder/CMakeFiles/coder_mid.dir/session.cc.o] Error 1
make[2]: *** 正在等待未完成的任务....
make[1]: *** [src/litert/kernel/cpu/CMakeFiles/train_cpu_kernel_mid.dir/all] Error 2
make[1]: *** 正在等待未完成的任务....
make[2]: *** [tools/converter/config_parser/CMakeFiles/config_parser_mid.dir/preprocess_parser.cc.o] Error 1
make[2]: *** 正在等待未完成的任务....
make[1]: *** [tools/converter/micro/coder/CMakeFiles/coder_mid.dir/all] Error 2
make[1]: *** [tools/converter/config_parser/CMakeFiles/config_parser_mid.dir/all] Error 2
virtual memory exhausted: 无法分配内存
virtual memory exhausted: 无法分配内存
make[2]: *** [minddata/CMakeFiles/minddata-lite-obj.dir/home/yoyo/MyDocuments/mindspore/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/epoch_ctrl_node.cc.o] Error 1
make[2]: *** [tools/converter/CMakeFiles/converter_src_mid.dir/__/optimizer/fusion/affine_fusion.cc.o] Error 1
make[2]: *** 正在等待未完成的任务....
make[1]: *** [minddata/CMakeFiles/minddata-lite-obj.dir/all] Error 2
make[1]: *** [tools/converter/CMakeFiles/converter_src_mid.dir/all] Error 2
make: *** [all] Error 2
^Cmake[2]: *** [tools/converter/parser/tf/CMakeFiles/tf_parser_mid.dir/tf_op_parser.cc.o] 中断
make[1]: *** [tools/converter/parser/tf/CMakeFiles/tf_parser_mid.dir/all] 中断
make: *** [all] 中断

# 错误原因
编译时内存不足

# 解决办法
增加swap交换空间

#count的大小就是增加的swap空间的大小，64M是块大小，所以空间大小是bs*count=2048MB
sudo dd if=/dev/zero of=/swapfile bs=64M count=32

#把刚才空间格式化成swap格式
sudo mkswap /swapfile

#使用刚才创建的swap空间
chmod 0600 /swapfile  
sudo swapon /swapfile

# 关闭swap空间
sudo swapoff /swapfile
# 关闭所有的swap空间
sudo swapoff -a
sudo rm /swapfile

# 查看swap空间是否生效
free -m

Q：`./converter_lite: error while loading shared libraries: libtorch_cpu.so: cannot open shared object file: No such file or directory`

/tools/converter/converter$ ./converter_lite --fmk=PYTORCH --modelFile=model.pt 
--outputFile=model
./converter_lite: error while loading shared libraries: libtorch_cpu.so: cannot open shared object file: No such file or directory

# 错误原因
未设置libtorch的环境变量

# 解决办法
设置libtorch的环境变量
export LD_LIBRARY_PATH="/home/user/libtorch/lib:${LD_LIBRARY_PATH}"
export LIB_TORCH_PATH="/home/user/libtorch"

Q：`Unsupported to converter models with fmk: 5`

convert_lite转pth报错，Unsupported to converter models with fmk: 5

# 错误原因
编译MindSporeLite之前未设置libtorch的环境变量

# 解决办法
设置libtorch的环境变量
export LD_LIBRARY_PATH="/home/user/libtorch/lib:${LD_LIBRARY_PATH}"
export LIB_TORCH_PATH="/home/user/libtorch"

Q：`PytorchStreamReader failed locating file constants.pkl: file not found Exception raised from valid at`

无法加载yolov5的pt文件，比如yolov5s.pt文件就不可以加载成功，yolov5s.torchscript.pt文件可以加载#2627

PytorchStreamReader failed locating file constants.pkl: file not found #1509

PytorchStreamReader failed locating file constants.pkl: file not found

Unable to load a pt model, [enforce fail at inline_container.cc:222] . file not found: archive/constants.pkl #1193

(mslite) yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/converter$ ./converter_lite --fmk=PYTORCH --modelFile=mobilenet.pth --outputFile=mobilenet
terminate called after throwing an instance of 'c10::Error'
  what():  PytorchStreamReader failed locating file constants.pkl: file not found
Exception raised from valid at ../caffe2/serialize/inline_container.cc:178 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f80042b9d77 in /home/yoyo/libtorch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f8004283abb in /home/yoyo/libtorch/lib/libc10.so)
frame #2: caffe2::serialize::PyTorchStreamReader::valid(char const*, char const*) + 0x8e (0x7f7fe71cf1de in /home/yoyo/libtorch/lib/libtorch_cpu.so)
frame #3: caffe2::serialize::PyTorchStreamReader::getRecordID(std::string const&) + 0x46 (0x7f7fe71cf3a6 in /home/yoyo/libtorch/lib/libtorch_cpu.so)
frame #4: caffe2::serialize::PyTorchStreamReader::getRecord(std::string const&) + 0x5c (0x7f7fe71cf46c in /home/yoyo/libtorch/lib/libtorch_cpu.so)
frame #5: torch::jit::readArchiveAndTensors(std::string const&, std::string const&, std::string const&, c10::optional<std::function<c10::StrongTypePtr (c10::QualifiedName const&)> >, c10::optional<std::function<c10::intrusive_ptr<c10::ivalue::Object, c10::detail::intrusive_target_default_null_type<c10::ivalue::Object> > (c10::StrongTypePtr, c10::IValue)> >, c10::optional<c10::Device>, caffe2::serialize::PyTorchStreamReader&, c10::Type::SingletonOrSharedTypePtr<c10::Type> (*)(std::string const&), std::shared_ptr<torch::jit::DeserializationStorageContext>) + 0xa5 (0x7f7fe8322325 in /home/yoyo/libtorch/lib/libtorch_cpu.so)
frame #6: <unknown function> + 0x4fd0fe7 (0x7f7fe8306fe7 in /home/yoyo/libtorch/lib/libtorch_cpu.so)
frame #7: <unknown function> + 0x4fd4043 (0x7f7fe830a043 in /home/yoyo/libtorch/lib/libtorch_cpu.so)
frame #8: torch::jit::import_ir_module(std::shared_ptr<torch::jit::CompilationUnit>, std::string const&, c10::optional<c10::Device>, std::unordered_map<std::string, std::string, std::hash<std::string>, std::equal_to<std::string>, std::allocator<std::pair<std::string const, std::string> > >&, bool, bool) + 0x3d6 (0x7f7fe830ded6 in /home/yoyo/libtorch/lib/libtorch_cpu.so)
frame #9: torch::jit::import_ir_module(std::shared_ptr<torch::jit::CompilationUnit>, std::string const&, c10::optional<c10::Device>, bool) + 0x7f (0x7f7fe830e13f in /home/yoyo/libtorch/lib/libtorch_cpu.so)
frame #10: torch::jit::load(std::string const&, c10::optional<c10::Device>, bool) + 0xac (0x7f7fe830e21c in /home/yoyo/libtorch/lib/libtorch_cpu.so)
frame #11: mindspore::lite::PytorchModelParser::InitOriginModel(std::string const&) + 0x4bb (0x7f8001f3fb83 in /home/yoyo/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/lib/libmindspore_converter.so)
frame #12: mindspore::lite::PytorchModelParser::Parse(mindspore::converter::ConverterParameters const&) + 0x2f5 (0x7f8001f3ec7d in /home/yoyo/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/lib/libmindspore_converter.so)
frame #13: mindspore::lite::ConverterFuncGraph::Load3rdModelToFuncgraph(std::shared_ptr<mindspore::ConverterPara> const&) + 0x329 (0x7f8000d3be37 in /home/yoyo/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/lib/libmindspore_converter.so)
frame #14: mindspore::lite::ConverterFuncGraph::Build(std::shared_ptr<mindspore::ConverterPara> const&) + 0xc9 (0x7f8000d3cd31 in /home/yoyo/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/lib/libmindspore_converter.so)
frame #15: mindspore::lite::ConverterImpl::HandleGraphCommon(std::shared_ptr<mindspore::ConverterPara> const&, void**, unsigned long*, bool, bool) + 0x68 (0x7f8000d0b298 in /home/yoyo/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/lib/libmindspore_converter.so)
frame #16: mindspore::lite::ConverterImpl::Convert(std::shared_ptr<mindspore::ConverterPara> const&, void**, unsigned long*, bool) + 0x6ed (0x7f8000d0aabf in /home/yoyo/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/lib/libmindspore_converter.so)
frame #17: mindspore::lite::RunConverter(std::shared_ptr<mindspore::ConverterPara> const&, void**, unsigned long*, bool) + 0x92 (0x7f8000d0cd7e in /home/yoyo/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/lib/libmindspore_converter.so)
frame #18: mindspore::Converter::Convert() + 0x85 (0x7f8000da11df in /home/yoyo/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/lib/libmindspore_converter.so)
frame #19: <unknown function> + 0x7b841 (0x5641c1e10841 in ./converter_lite)
frame #20: __libc_start_main + 0xf0 (0x7f7fe2773840 in /lib/x86_64-linux-gnu/libc.so.6)
frame #21: <unknown function> + 0x7ae99 (0x5641c1e0fe99 in ./converter_lite)

已放弃 (核心已转储)

# 错误原因
PyTorch model格式不对，不支持Torch Script以外的其他模型格式

# 解决办法
将PyTorch model模型格式转换成Torch Script格式

Only torchscript model is supported. You must trace your model first.

See: https://docs.djl.ai/docs/pytorch/how_to_convert_your_model_to_torchscript.html#how-to-convert-your-pytorch-model-to-torchscript

You need use jit.trace() to export your model. See: https://docs.djl.ai/docs/pytorch/how_to_convert_your_model_to_torchscript.html

How did you save the model. DJL use PyTorch C++ API, it can only load traced jit model. If you use torch.save(), it saved as python pickle format, you won’t be able to load it.

See: https://docs.djl.ai/docs/pytorch/how_to_convert_your_model_to_torchscript.html

Q：不支持PyTorch模型

alexnet

(mslite) yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/converter$ ./converter_lite --fmk=PYTORCH --modelFile=traced_alexnet_model.pt --outputFile=traced_alexnet_model
[WARNING] LITE(8941,7fe3e236cb40,converter_lite):2024-04-04-14:13:16.559.930 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:730] ConvertGraphInputs] The input shape is empty.
[ERROR] LITE(8941,7fe3e236cb40,converter_lite):2024-04-04-14:13:17.490.156 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1293] SetAttrsForPool] Unsupported adaptive average pool with output kernels: [const vector][6, 6]
[ERROR] LITE(8941,7fe3e236cb40,converter_lite):2024-04-04-14:13:17.490.178 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1338] Parse] Set attributes for pooling failed.
[ERROR] LITE(8941,7fe3e236cb40,converter_lite):2024-04-04-14:13:17.490.199 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1041] ConvertNodes] parse node adaptive_avg_pool2d failed.
[ERROR] LITE(8941,7fe3e236cb40,converter_lite):2024-04-04-14:13:17.490.229 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:950] BuildOpInputs] could not find input node: x.1
[ERROR] LITE(8941,7fe3e236cb40,converter_lite):2024-04-04-14:13:17.490.237 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1055] ConvertNodes] BuildOpInputs failed.
[ERROR] LITE(8941,7fe3e236cb40,converter_lite):2024-04-04-14:13:17.490.246 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:692] ConvertTorchGraph] convert nodes failed.
[ERROR] LITE(8941,7fe3e236cb40,converter_lite):2024-04-04-14:13:17.490.252 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:626] Parse] convert pytorch graph failed.
[ERROR] LITE(8941,7fe3e236cb40,converter_lite):2024-04-04-14:13:17.495.600 [mindspore/lite/tools/converter/converter_funcgraph.cc:92] Load3rdModelToFuncgraph] Get funcGraph failed for fmk: 5
[ERROR] LITE(8941,7fe3e236cb40,converter_lite):2024-04-04-14:13:17.495.614 [mindspore/lite/tools/converter/converter_funcgraph.cc:162] Build] Load model file failed
[ERROR] LITE(8941,7fe3e236cb40,converter_lite):2024-04-04-14:13:17.495.620 [mindspore/lite/tools/converter/converter.cc:979] HandleGraphCommon] Build func graph failed
[ERROR] LITE(8941,7fe3e236cb40,converter_lite):2024-04-04-14:13:17.495.650 [mindspore/lite/tools/converter/converter.cc:947] Convert] Handle graph failed: -1 Common error code.
[ERROR] LITE(8941,7fe3e236cb40,converter_lite):2024-04-04-14:13:17.495.657 [mindspore/lite/tools/converter/converter.cc:1108] RunConverter] Convert model failed
[ERROR] LITE(8941,7fe3e236cb40,converter_lite):2024-04-04-14:13:17.495.670 [mindspore/lite/tools/converter/cxx_api/converter.cc:314] Convert] Convert model failed, ret=Common error code.
ERROR [mindspore/lite/tools/converter/converter_lite/main.cc:102] main] Convert failed. Ret: Common error code.
Convert failed. Ret: Common error code.

densenet121

(mslite) yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/converter$ ./converter_lite --fmk=PYTORCH --modelFile=traced_densenet121_model.pt --outputFile=traced_densenet121_model
[WARNING] LITE(9078,7f70baf16b40,converter_lite):2024-04-04-14:15:27.443.369 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:730] ConvertGraphInputs] The input shape is empty.
[ERROR] LITE(9078,7f70baf16b40,converter_lite):2024-04-04-14:15:27.826.552 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type cat
[ERROR] LITE(9078,7f70baf16b40,converter_lite):2024-04-04-14:15:27.826.678 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:950] BuildOpInputs] could not find input node: input.14
[ERROR] LITE(9078,7f70baf16b40,converter_lite):2024-04-04-14:15:27.826.689 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1055] ConvertNodes] BuildOpInputs failed.
[ERROR] LITE(9078,7f70baf16b40,converter_lite):2024-04-04-14:15:27.826.712 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:692] ConvertTorchGraph] convert nodes failed.
[ERROR] LITE(9078,7f70baf16b40,converter_lite):2024-04-04-14:15:27.826.720 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:626] Parse] convert pytorch graph failed.
[ERROR] LITE(9078,7f70baf16b40,converter_lite):2024-04-04-14:15:27.831.448 [mindspore/lite/tools/converter/converter_funcgraph.cc:92] Load3rdModelToFuncgraph] Get funcGraph failed for fmk: 5
[ERROR] LITE(9078,7f70baf16b40,converter_lite):2024-04-04-14:15:27.831.467 [mindspore/lite/tools/converter/converter_funcgraph.cc:162] Build] Load model file failed
[ERROR] LITE(9078,7f70baf16b40,converter_lite):2024-04-04-14:15:27.831.473 [mindspore/lite/tools/converter/converter.cc:979] HandleGraphCommon] Build func graph failed
[ERROR] LITE(9078,7f70baf16b40,converter_lite):2024-04-04-14:15:27.831.504 [mindspore/lite/tools/converter/converter.cc:947] Convert] Handle graph failed: -1 Common error code.
[ERROR] LITE(9078,7f70baf16b40,converter_lite):2024-04-04-14:15:27.831.512 [mindspore/lite/tools/converter/converter.cc:1108] RunConverter] Convert model failed
[ERROR] LITE(9078,7f70baf16b40,converter_lite):2024-04-04-14:15:27.831.518 [mindspore/lite/tools/converter/converter_context.h:60] PrintOps] ===========================================
[ERROR] LITE(9078,7f70baf16b40,converter_lite):2024-04-04-14:15:27.831.523 [mindspore/lite/tools/converter/converter_context.h:61] PrintOps] UNSUPPORTED OP LIST:
[ERROR] LITE(9078,7f70baf16b40,converter_lite):2024-04-04-14:15:27.831.530 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: cat
[ERROR] LITE(9078,7f70baf16b40,converter_lite):2024-04-04-14:15:27.831.536 [mindspore/lite/tools/converter/converter_context.h:65] PrintOps] ===========================================
[ERROR] LITE(9078,7f70baf16b40,converter_lite):2024-04-04-14:15:27.831.548 [mindspore/lite/tools/converter/cxx_api/converter.cc:314] Convert] Convert model failed, ret=Common error code.
ERROR [mindspore/lite/tools/converter/converter_lite/main.cc:102] main] Convert failed. Ret: Common error code.
Convert failed. Ret: Common error code.

googlenet

(mslite) yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/converter$ ./converter_lite --fmk=PYTORCH --modelFile=traced_googlenet_model.pt --outputFile=traced_googlenet_model
[WARNING] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.336.200 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:730] ConvertGraphInputs] The input shape is empty.
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.582.654 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type slice
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.582.685 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type select
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.582.697 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type unsqueeze
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.582.912 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:950] BuildOpInputs] could not find input node: 34
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.582.925 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1055] ConvertNodes] BuildOpInputs failed.
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.582.946 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:692] ConvertTorchGraph] convert nodes failed.
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.582.956 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:626] Parse] convert pytorch graph failed.
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.585.486 [mindspore/lite/tools/converter/converter_funcgraph.cc:92] Load3rdModelToFuncgraph] Get funcGraph failed for fmk: 5
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.585.503 [mindspore/lite/tools/converter/converter_funcgraph.cc:162] Build] Load model file failed
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.585.512 [mindspore/lite/tools/converter/converter.cc:979] HandleGraphCommon] Build func graph failed
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.585.547 [mindspore/lite/tools/converter/converter.cc:947] Convert] Handle graph failed: -1 Common error code.
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.585.555 [mindspore/lite/tools/converter/converter.cc:1108] RunConverter] Convert model failed
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.585.562 [mindspore/lite/tools/converter/converter_context.h:60] PrintOps] ===========================================
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.585.568 [mindspore/lite/tools/converter/converter_context.h:61] PrintOps] UNSUPPORTED OP LIST:
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.585.575 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: select
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.585.580 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: slice
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.585.587 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: unsqueeze
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.585.594 [mindspore/lite/tools/converter/converter_context.h:65] PrintOps] ===========================================
[ERROR] LITE(8385,7f97728d6b40,converter_lite):2024-04-04-12:55:20.585.607 [mindspore/lite/tools/converter/cxx_api/converter.cc:314] Convert] Convert model failed, ret=Common error code.
ERROR [mindspore/lite/tools/converter/converter_lite/main.cc:102] main] Convert failed. Ret: Common error code.
Convert failed. Ret: Common error code.

inception_v3

(mslite) yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/converter$ ./converter_lite --fmk=PYTORCH --modelFile=traced_inception_v3_model.pt --outputFile=traced_inception_v3_model
[WARNING] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:23.755.698 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:730] ConvertGraphInputs] The input shape is empty.
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.675.543 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type slice
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.675.572 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type select
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.675.584 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type unsqueeze
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.675.810 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:950] BuildOpInputs] could not find input node: 36
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.675.820 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1055] ConvertNodes] BuildOpInputs failed.
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.675.838 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:692] ConvertTorchGraph] convert nodes failed.
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.675.845 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:626] Parse] convert pytorch graph failed.
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.680.816 [mindspore/lite/tools/converter/converter_funcgraph.cc:92] Load3rdModelToFuncgraph] Get funcGraph failed for fmk: 5
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.680.833 [mindspore/lite/tools/converter/converter_funcgraph.cc:162] Build] Load model file failed
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.680.839 [mindspore/lite/tools/converter/converter.cc:979] HandleGraphCommon] Build func graph failed
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.680.872 [mindspore/lite/tools/converter/converter.cc:947] Convert] Handle graph failed: -1 Common error code.
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.680.880 [mindspore/lite/tools/converter/converter.cc:1108] RunConverter] Convert model failed
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.680.886 [mindspore/lite/tools/converter/converter_context.h:60] PrintOps] ===========================================
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.680.892 [mindspore/lite/tools/converter/converter_context.h:61] PrintOps] UNSUPPORTED OP LIST:
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.680.899 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: select
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.680.904 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: slice
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.680.909 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: unsqueeze
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.680.915 [mindspore/lite/tools/converter/converter_context.h:65] PrintOps] ===========================================
[ERROR] LITE(9220,7fdb71a55b40,converter_lite):2024-04-04-14:19:24.680.927 [mindspore/lite/tools/converter/cxx_api/converter.cc:314] Convert] Convert model failed, ret=Common error code.
ERROR [mindspore/lite/tools/converter/converter_lite/main.cc:102] main] Convert failed. Ret: Common error code.
Convert failed. Ret: Common error code.

mnasnet1_0

(mslite) yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/converter$ ./converter_lite --fmk=PYTORCH --modelFile=traced_mnasnet1_0_model.pt --outputFile=traced_mnasnet1_0_model
[WARNING] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.507.820 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:730] ConvertGraphInputs] The input shape is empty.
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.741.850 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type mean
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.741.876 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type dropout
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.741.886 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type linear
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.741.898 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:692] ConvertTorchGraph] convert nodes failed.
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.741.908 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:626] Parse] convert pytorch graph failed.
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.744.751 [mindspore/lite/tools/converter/converter_funcgraph.cc:92] Load3rdModelToFuncgraph] Get funcGraph failed for fmk: 5
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.744.772 [mindspore/lite/tools/converter/converter_funcgraph.cc:162] Build] Load model file failed
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.744.783 [mindspore/lite/tools/converter/converter.cc:979] HandleGraphCommon] Build func graph failed
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.744.820 [mindspore/lite/tools/converter/converter.cc:947] Convert] Handle graph failed: -1 Common error code.
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.744.830 [mindspore/lite/tools/converter/converter.cc:1108] RunConverter] Convert model failed
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.744.840 [mindspore/lite/tools/converter/converter_context.h:60] PrintOps] ===========================================
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.744.848 [mindspore/lite/tools/converter/converter_context.h:61] PrintOps] UNSUPPORTED OP LIST:
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.744.860 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: dropout
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.744.868 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: linear
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.744.878 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: mean
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.744.887 [mindspore/lite/tools/converter/converter_context.h:65] PrintOps] ===========================================
[ERROR] LITE(9347,7fa0c1b56b40,converter_lite):2024-04-04-14:21:25.744.903 [mindspore/lite/tools/converter/cxx_api/converter.cc:314] Convert] Convert model failed, ret=Common error code.
ERROR [mindspore/lite/tools/converter/converter_lite/main.cc:102] main] Convert failed. Ret: Common error code.
Convert failed. Ret: Common error code.

mobilenet_v2

model = torchvision.models.mobilenet_v2(pretrained=True)

(mslite) yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/converter$ ./converter_lite --fmk=PYTORCH --modelFile=traced_mobilenet_v2_model.pt --outputFile=traced_mobilenet_v2_model
[WARNING] LITE(7258,7fd3039b6b40,converter_lite):2024-04-04-12:37:12.761.719 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:730] ConvertGraphInputs] The input shape is empty.
[ERROR] LITE(7258,7fd3039b6b40,converter_lite):2024-04-04-12:37:12.925.570 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type hardtanh
[ERROR] LITE(7258,7fd3039b6b40,converter_lite):2024-04-04-12:37:12.925.798 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:950] BuildOpInputs] could not find input node: 61
[ERROR] LITE(7258,7fd3039b6b40,converter_lite):2024-04-04-12:37:12.925.809 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1055] ConvertNodes] BuildOpInputs failed.
[ERROR] LITE(7258,7fd3039b6b40,converter_lite):2024-04-04-12:37:12.925.839 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:692] ConvertTorchGraph] convert nodes failed.
[ERROR] LITE(7258,7fd3039b6b40,converter_lite):2024-04-04-12:37:12.925.847 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:626] Parse] convert pytorch graph failed.
[ERROR] LITE(7258,7fd3039b6b40,converter_lite):2024-04-04-12:37:12.927.755 [mindspore/lite/tools/converter/converter_funcgraph.cc:92] Load3rdModelToFuncgraph] Get funcGraph failed for fmk: 5
[ERROR] LITE(7258,7fd3039b6b40,converter_lite):2024-04-04-12:37:12.927.774 [mindspore/lite/tools/converter/converter_funcgraph.cc:162] Build] Load model file failed
[ERROR] LITE(7258,7fd3039b6b40,converter_lite):2024-04-04-12:37:12.927.784 [mindspore/lite/tools/converter/converter.cc:979] HandleGraphCommon] Build func graph failed
[ERROR] LITE(7258,7fd3039b6b40,converter_lite):2024-04-04-12:37:12.927.819 [mindspore/lite/tools/converter/converter.cc:947] Convert] Handle graph failed: -1 Common error code.
[ERROR] LITE(7258,7fd3039b6b40,converter_lite):2024-04-04-12:37:12.927.827 [mindspore/lite/tools/converter/converter.cc:1108] RunConverter] Convert model failed
[ERROR] LITE(7258,7fd3039b6b40,converter_lite):2024-04-04-12:37:12.927.833 [mindspore/lite/tools/converter/converter_context.h:60] PrintOps] ===========================================
[ERROR] LITE(7258,7fd3039b6b40,converter_lite):2024-04-04-12:37:12.927.838 [mindspore/lite/tools/converter/converter_context.h:61] PrintOps] UNSUPPORTED OP LIST:
[ERROR] LITE(7258,7fd3039b6b40,converter_lite):2024-04-04-12:37:12.927.845 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: hardtanh
[ERROR] LITE(7258,7fd3039b6b40,converter_lite):2024-04-04-12:37:12.927.851 [mindspore/lite/tools/converter/converter_context.h:65] PrintOps] ===========================================
[ERROR] LITE(7258,7fd3039b6b40,converter_lite):2024-04-04-12:37:12.927.862 [mindspore/lite/tools/converter/cxx_api/converter.cc:314] Convert] Convert model failed, ret=Common error code.
ERROR [mindspore/lite/tools/converter/converter_lite/main.cc:102] main] Convert failed. Ret: Common error code.
Convert failed. Ret: Common error code.

mobilenet_v3_small

(mslite) yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/converter$ ./converter_lite --fmk=PYTORCH --modelFile=traced_mobilenet_v3_small_model.pt --outputFile=traced_mobilenet_v3_small_model
[WARNING] LITE(9487,7f6beb70db40,converter_lite):2024-04-04-14:24:33.030.059 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:730] ConvertGraphInputs] The input shape is empty.
[ERROR] LITE(9487,7f6beb70db40,converter_lite):2024-04-04-14:24:33.123.861 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type hardswish
[ERROR] LITE(9487,7f6beb70db40,converter_lite):2024-04-04-14:24:33.124.062 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:950] BuildOpInputs] could not find input node: 55
[ERROR] LITE(9487,7f6beb70db40,converter_lite):2024-04-04-14:24:33.124.073 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1055] ConvertNodes] BuildOpInputs failed.
[ERROR] LITE(9487,7f6beb70db40,converter_lite):2024-04-04-14:24:33.124.104 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:692] ConvertTorchGraph] convert nodes failed.
[ERROR] LITE(9487,7f6beb70db40,converter_lite):2024-04-04-14:24:33.124.111 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:626] Parse] convert pytorch graph failed.
[ERROR] LITE(9487,7f6beb70db40,converter_lite):2024-04-04-14:24:33.125.767 [mindspore/lite/tools/converter/converter_funcgraph.cc:92] Load3rdModelToFuncgraph] Get funcGraph failed for fmk: 5
[ERROR] LITE(9487,7f6beb70db40,converter_lite):2024-04-04-14:24:33.125.789 [mindspore/lite/tools/converter/converter_funcgraph.cc:162] Build] Load model file failed
[ERROR] LITE(9487,7f6beb70db40,converter_lite):2024-04-04-14:24:33.125.803 [mindspore/lite/tools/converter/converter.cc:979] HandleGraphCommon] Build func graph failed
[ERROR] LITE(9487,7f6beb70db40,converter_lite):2024-04-04-14:24:33.125.834 [mindspore/lite/tools/converter/converter.cc:947] Convert] Handle graph failed: -1 Common error code.
[ERROR] LITE(9487,7f6beb70db40,converter_lite):2024-04-04-14:24:33.125.842 [mindspore/lite/tools/converter/converter.cc:1108] RunConverter] Convert model failed
[ERROR] LITE(9487,7f6beb70db40,converter_lite):2024-04-04-14:24:33.125.848 [mindspore/lite/tools/converter/converter_context.h:60] PrintOps] ===========================================
[ERROR] LITE(9487,7f6beb70db40,converter_lite):2024-04-04-14:24:33.125.853 [mindspore/lite/tools/converter/converter_context.h:61] PrintOps] UNSUPPORTED OP LIST:
[ERROR] LITE(9487,7f6beb70db40,converter_lite):2024-04-04-14:24:33.125.861 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: hardswish
[ERROR] LITE(9487,7f6beb70db40,converter_lite):2024-04-04-14:24:33.125.866 [mindspore/lite/tools/converter/converter_context.h:65] PrintOps] ===========================================
[ERROR] LITE(9487,7f6beb70db40,converter_lite):2024-04-04-14:24:33.125.878 [mindspore/lite/tools/converter/cxx_api/converter.cc:314] Convert] Convert model failed, ret=Common error code.
ERROR [mindspore/lite/tools/converter/converter_lite/main.cc:102] main] Convert failed. Ret: Common error code.
Convert failed. Ret: Common error code.

resnet18

model = torchvision.models.resnet18(pretrained=True)

(mslite) yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/converter$ ./converter_lite --fmk=PYTORCH --modelFile=traced_resnet_model.pt --outputFile=traced_resnet_model
[WARNING] LITE(6873,7f2723682b40,converter_lite):2024-04-04-12:32:55.272.634 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:730] ConvertGraphInputs] The input shape is empty.
[ERROR] LITE(6873,7f2723682b40,converter_lite):2024-04-04-12:32:55.584.596 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type linear
[ERROR] LITE(6873,7f2723682b40,converter_lite):2024-04-04-12:32:55.584.619 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:692] ConvertTorchGraph] convert nodes failed.
[ERROR] LITE(6873,7f2723682b40,converter_lite):2024-04-04-12:32:55.584.625 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:626] Parse] convert pytorch graph failed.
[ERROR] LITE(6873,7f2723682b40,converter_lite):2024-04-04-12:32:55.586.726 [mindspore/lite/tools/converter/converter_funcgraph.cc:92] Load3rdModelToFuncgraph] Get funcGraph failed for fmk: 5
[ERROR] LITE(6873,7f2723682b40,converter_lite):2024-04-04-12:32:55.586.743 [mindspore/lite/tools/converter/converter_funcgraph.cc:162] Build] Load model file failed
[ERROR] LITE(6873,7f2723682b40,converter_lite):2024-04-04-12:32:55.586.750 [mindspore/lite/tools/converter/converter.cc:979] HandleGraphCommon] Build func graph failed
[ERROR] LITE(6873,7f2723682b40,converter_lite):2024-04-04-12:32:55.586.779 [mindspore/lite/tools/converter/converter.cc:947] Convert] Handle graph failed: -1 Common error code.
[ERROR] LITE(6873,7f2723682b40,converter_lite):2024-04-04-12:32:55.586.787 [mindspore/lite/tools/converter/converter.cc:1108] RunConverter] Convert model failed
[ERROR] LITE(6873,7f2723682b40,converter_lite):2024-04-04-12:32:55.586.793 [mindspore/lite/tools/converter/converter_context.h:60] PrintOps] ===========================================
[ERROR] LITE(6873,7f2723682b40,converter_lite):2024-04-04-12:32:55.586.798 [mindspore/lite/tools/converter/converter_context.h:61] PrintOps] UNSUPPORTED OP LIST:
[ERROR] LITE(6873,7f2723682b40,converter_lite):2024-04-04-12:32:55.586.806 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: linear
[ERROR] LITE(6873,7f2723682b40,converter_lite):2024-04-04-12:32:55.586.812 [mindspore/lite/tools/converter/converter_context.h:65] PrintOps] ===========================================
[ERROR] LITE(6873,7f2723682b40,converter_lite):2024-04-04-12:32:55.586.823 [mindspore/lite/tools/converter/cxx_api/converter.cc:314] Convert] Convert model failed, ret=Common error code.
ERROR [mindspore/lite/tools/converter/converter_lite/main.cc:102] main] Convert failed. Ret: Common error code.
Convert failed. Ret: Common error code.

resnext50_32x4d

(mslite) yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/converter$ ./converter_lite --fmk=PYTORCH --modelFile=traced_resnext50_32x4d_model.pt --outputFile=traced_resnext50_32x4d_model
[WARNING] LITE(9664,7fa1d04fbb40,converter_lite):2024-04-04-14:26:47.816.592 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:730] ConvertGraphInputs] The input shape is empty.
[ERROR] LITE(9664,7fa1d04fbb40,converter_lite):2024-04-04-14:26:49.267.135 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type linear
[ERROR] LITE(9664,7fa1d04fbb40,converter_lite):2024-04-04-14:26:49.267.159 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:692] ConvertTorchGraph] convert nodes failed.
[ERROR] LITE(9664,7fa1d04fbb40,converter_lite):2024-04-04-14:26:49.267.167 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:626] Parse] convert pytorch graph failed.
[ERROR] LITE(9664,7fa1d04fbb40,converter_lite):2024-04-04-14:26:49.272.242 [mindspore/lite/tools/converter/converter_funcgraph.cc:92] Load3rdModelToFuncgraph] Get funcGraph failed for fmk: 5
[ERROR] LITE(9664,7fa1d04fbb40,converter_lite):2024-04-04-14:26:49.272.269 [mindspore/lite/tools/converter/converter_funcgraph.cc:162] Build] Load model file failed
[ERROR] LITE(9664,7fa1d04fbb40,converter_lite):2024-04-04-14:26:49.272.277 [mindspore/lite/tools/converter/converter.cc:979] HandleGraphCommon] Build func graph failed
[ERROR] LITE(9664,7fa1d04fbb40,converter_lite):2024-04-04-14:26:49.272.309 [mindspore/lite/tools/converter/converter.cc:947] Convert] Handle graph failed: -1 Common error code.
[ERROR] LITE(9664,7fa1d04fbb40,converter_lite):2024-04-04-14:26:49.272.317 [mindspore/lite/tools/converter/converter.cc:1108] RunConverter] Convert model failed
[ERROR] LITE(9664,7fa1d04fbb40,converter_lite):2024-04-04-14:26:49.272.323 [mindspore/lite/tools/converter/converter_context.h:60] PrintOps] ===========================================
[ERROR] LITE(9664,7fa1d04fbb40,converter_lite):2024-04-04-14:26:49.272.329 [mindspore/lite/tools/converter/converter_context.h:61] PrintOps] UNSUPPORTED OP LIST:
[ERROR] LITE(9664,7fa1d04fbb40,converter_lite):2024-04-04-14:26:49.272.336 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: linear
[ERROR] LITE(9664,7fa1d04fbb40,converter_lite):2024-04-04-14:26:49.272.342 [mindspore/lite/tools/converter/converter_context.h:65] PrintOps] ===========================================
[ERROR] LITE(9664,7fa1d04fbb40,converter_lite):2024-04-04-14:26:49.272.353 [mindspore/lite/tools/converter/cxx_api/converter.cc:314] Convert] Convert model failed, ret=Common error code.
ERROR [mindspore/lite/tools/converter/converter_lite/main.cc:102] main] Convert failed. Ret: Common error code.
Convert failed. Ret: Common error code.

shufflenet_v2_x1_0

(mslite) yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/converter$ ./converter_lite --fmk=PYTORCH --modelFile=traced_shufflenet_v2_x1_0_model.pt --outputFile=traced_shufflenet_v2_x1_0_model
[WARNING] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:00.913.390 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:730] ConvertGraphInputs] The input shape is empty.
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.025.182 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type cat
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.025.216 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type size
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.025.226 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type size
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.025.237 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type NumToTensor
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.025.247 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type size
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.025.257 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type size
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.025.266 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type floor_divide
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.025.277 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type Int
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.025.353 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:950] BuildOpInputs] could not find input node: 143
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.025.363 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1055] ConvertNodes] BuildOpInputs failed.
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.025.384 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:692] ConvertTorchGraph] convert nodes failed.
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.025.392 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:626] Parse] convert pytorch graph failed.
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.028.074 [mindspore/lite/tools/converter/converter_funcgraph.cc:92] Load3rdModelToFuncgraph] Get funcGraph failed for fmk: 5
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.028.092 [mindspore/lite/tools/converter/converter_funcgraph.cc:162] Build] Load model file failed
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.028.098 [mindspore/lite/tools/converter/converter.cc:979] HandleGraphCommon] Build func graph failed
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.028.132 [mindspore/lite/tools/converter/converter.cc:947] Convert] Handle graph failed: -1 Common error code.
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.028.142 [mindspore/lite/tools/converter/converter.cc:1108] RunConverter] Convert model failed
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.028.149 [mindspore/lite/tools/converter/converter_context.h:60] PrintOps] ===========================================
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.028.156 [mindspore/lite/tools/converter/converter_context.h:61] PrintOps] UNSUPPORTED OP LIST:
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.028.164 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: Int
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.028.171 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: NumToTensor
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.028.178 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: cat
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.028.185 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: floor_divide
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.028.191 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: size
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.028.198 [mindspore/lite/tools/converter/converter_context.h:65] PrintOps] ===========================================
[ERROR] LITE(9791,7fa735bf6b40,converter_lite):2024-04-04-14:29:01.028.211 [mindspore/lite/tools/converter/cxx_api/converter.cc:314] Convert] Convert model failed, ret=Common error code.
ERROR [mindspore/lite/tools/converter/converter_lite/main.cc:102] main] Convert failed. Ret: Common error code.
Convert failed. Ret: Common error code.

squeezenet1_0

(mslite) yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/converter$ ./converter_lite --fmk=PYTORCH --modelFile=traced_squeezenet1_0_model.pt --outputFile=traced_squeezenet1_0_model
[WARNING] LITE(9876,7ffbd1c4bb40,converter_lite):2024-04-04-14:31:07.519.588 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:730] ConvertGraphInputs] The input shape is empty.
[ERROR] LITE(9876,7ffbd1c4bb40,converter_lite):2024-04-04-14:31:07.583.975 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1035] ConvertNodes] not support pytorch op type cat
[ERROR] LITE(9876,7ffbd1c4bb40,converter_lite):2024-04-04-14:31:07.584.195 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:950] BuildOpInputs] could not find input node: 72
[ERROR] LITE(9876,7ffbd1c4bb40,converter_lite):2024-04-04-14:31:07.584.208 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1055] ConvertNodes] BuildOpInputs failed.
[ERROR] LITE(9876,7ffbd1c4bb40,converter_lite):2024-04-04-14:31:07.584.235 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:692] ConvertTorchGraph] convert nodes failed.
[ERROR] LITE(9876,7ffbd1c4bb40,converter_lite):2024-04-04-14:31:07.584.243 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:626] Parse] convert pytorch graph failed.
[ERROR] LITE(9876,7ffbd1c4bb40,converter_lite):2024-04-04-14:31:07.584.978 [mindspore/lite/tools/converter/converter_funcgraph.cc:92] Load3rdModelToFuncgraph] Get funcGraph failed for fmk: 5
[ERROR] LITE(9876,7ffbd1c4bb40,converter_lite):2024-04-04-14:31:07.584.999 [mindspore/lite/tools/converter/converter_funcgraph.cc:162] Build] Load model file failed
[ERROR] LITE(9876,7ffbd1c4bb40,converter_lite):2024-04-04-14:31:07.585.007 [mindspore/lite/tools/converter/converter.cc:979] HandleGraphCommon] Build func graph failed
[ERROR] LITE(9876,7ffbd1c4bb40,converter_lite):2024-04-04-14:31:07.585.046 [mindspore/lite/tools/converter/converter.cc:947] Convert] Handle graph failed: -1 Common error code.
[ERROR] LITE(9876,7ffbd1c4bb40,converter_lite):2024-04-04-14:31:07.585.056 [mindspore/lite/tools/converter/converter.cc:1108] RunConverter] Convert model failed
[ERROR] LITE(9876,7ffbd1c4bb40,converter_lite):2024-04-04-14:31:07.585.063 [mindspore/lite/tools/converter/converter_context.h:60] PrintOps] ===========================================
[ERROR] LITE(9876,7ffbd1c4bb40,converter_lite):2024-04-04-14:31:07.585.070 [mindspore/lite/tools/converter/converter_context.h:61] PrintOps] UNSUPPORTED OP LIST:
[ERROR] LITE(9876,7ffbd1c4bb40,converter_lite):2024-04-04-14:31:07.585.079 [mindspore/lite/tools/converter/converter_context.h:63] PrintOps] FMKTYPE: PYTORCH, OP TYPE: cat
[ERROR] LITE(9876,7ffbd1c4bb40,converter_lite):2024-04-04-14:31:07.585.086 [mindspore/lite/tools/converter/converter_context.h:65] PrintOps] ===========================================
[ERROR] LITE(9876,7ffbd1c4bb40,converter_lite):2024-04-04-14:31:07.585.099 [mindspore/lite/tools/converter/cxx_api/converter.cc:314] Convert] Convert model failed, ret=Common error code.
ERROR [mindspore/lite/tools/converter/converter_lite/main.cc:102] main] Convert failed. Ret: Common error code.
Convert failed. Ret: Common error code.

vgg11

model = torchvision.models.vgg11(pretrained=True)

(mslite) yoyo@yoyo:~/MyDocuments/mindspore/output/mindspore-lite-2.0.1-linux-x64/tools/converter/converter$ ./converter_lite --fmk=PYTORCH --modelFile=traced_vgg11_model.pt --outputFile=traced_vgg11_model
[WARNING] LITE(7595,7fa120fcfb40,converter_lite):2024-04-04-12:40:35.620.855 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:730] ConvertGraphInputs] The input shape is empty.
[ERROR] LITE(7595,7fa120fcfb40,converter_lite):2024-04-04-12:40:37.690.670 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1293] SetAttrsForPool] Unsupported adaptive average pool with output kernels: [const vector][7, 7]
[ERROR] LITE(7595,7fa120fcfb40,converter_lite):2024-04-04-12:40:37.690.693 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1338] Parse] Set attributes for pooling failed.
[ERROR] LITE(7595,7fa120fcfb40,converter_lite):2024-04-04-12:40:37.690.714 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1041] ConvertNodes] parse node adaptive_avg_pool2d failed.
[ERROR] LITE(7595,7fa120fcfb40,converter_lite):2024-04-04-12:40:37.690.745 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:950] BuildOpInputs] could not find input node: x.1
[ERROR] LITE(7595,7fa120fcfb40,converter_lite):2024-04-04-12:40:37.690.752 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:1055] ConvertNodes] BuildOpInputs failed.
[ERROR] LITE(7595,7fa120fcfb40,converter_lite):2024-04-04-12:40:37.690.762 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:692] ConvertTorchGraph] convert nodes failed.
[ERROR] LITE(7595,7fa120fcfb40,converter_lite):2024-04-04-12:40:37.690.768 [mindspore/lite/build/tools/converter/parser/pytorch/pytorch_op_parser.cc:626] Parse] convert pytorch graph failed.
[ERROR] LITE(7595,7fa120fcfb40,converter_lite):2024-04-04-12:40:37.702.005 [mindspore/lite/tools/converter/converter_funcgraph.cc:92] Load3rdModelToFuncgraph] Get funcGraph failed for fmk: 5
[ERROR] LITE(7595,7fa120fcfb40,converter_lite):2024-04-04-12:40:37.702.022 [mindspore/lite/tools/converter/converter_funcgraph.cc:162] Build] Load model file failed
[ERROR] LITE(7595,7fa120fcfb40,converter_lite):2024-04-04-12:40:37.702.028 [mindspore/lite/tools/converter/converter.cc:979] HandleGraphCommon] Build func graph failed
[ERROR] LITE(7595,7fa120fcfb40,converter_lite):2024-04-04-12:40:37.702.060 [mindspore/lite/tools/converter/converter.cc:947] Convert] Handle graph failed: -1 Common error code.
[ERROR] LITE(7595,7fa120fcfb40,converter_lite):2024-04-04-12:40:37.702.068 [mindspore/lite/tools/converter/converter.cc:1108] RunConverter] Convert model failed
[ERROR] LITE(7595,7fa120fcfb40,converter_lite):2024-04-04-12:40:37.702.081 [mindspore/lite/tools/converter/cxx_api/converter.cc:314] Convert] Convert model failed, ret=Common error code.
ERROR [mindspore/lite/tools/converter/converter_lite/main.cc:102] main] Convert failed. Ret: Common error code.
Convert failed. Ret: Common error code.