Page Comparison

This document will provide a detailed description of:

1. How to convert the YOLOV5 ONNX model into a model for use on the C3V platform

2. Write sample code for object detection based on YOLOV5

3. Execute object detection program and obtain recognition results in the C3V Linux environment

The tool versions involved in the current document are as follows:

1. Model Conversation

Before the conversion, it is necessary to first set up the environment for model conversion. Please refer to the following document to prepare the environment：NN model conversion

1.1. Project Preparation

1. Create Model folder

Create a folder yolov5s in path ~/c3v/Models. Please make sure the folder name is as same as the ONNX file name.

~/c3v/Models$ mkdir yolov5s && cd yolov5s

2. Copy the ONNX file and input.jpg which resolution is 640x640 to the folder yolov5s. These two files will be used as input files during model conversion.

~/c3v/Models$ cp yolov5s.onnx yolov5s/
~/c3v/Models$ cp input.jpg yolov5s/

3. Create a dataset.txt file, the content of dataset.txt is the input.jpg file name.

./input.jpg

4. Create inputs_outputs.txt file and get the information from yolov5s.onnx via netron tool/webpage.

write --input-size-list and --outputs informations to inputs_outputs.txt:

--outputs 'onnx::Sigmoid_375 onnx::Sigmoid_419 onnx::Sigmoid_462'

After completing the above steps, there will be the following files under the yolov5s path：

Image Removed

Image Added

1.2. Implementing

Using shell script tools to convert the model from ONNX to the NB file. There are 4 steps: import quantize inference and export. Tools are in ~/c3v/Models:

• pegasus_import.sh

• pegasus_quantize.sh

• pegasus_inference.sh

• pegasus_export_ovx.sh

Import

Execute the command in the console or terminal, and wait for it to complete. It will import and translate an NN model to ACUITY formats.

./pegasus_import.sh yolov5s

Wait until the tool execution is complete and check there are no errors like this:

Image Removed

Image Added

Then we will see the following four files added under the folder ~/c3v/Models/yolov5s.

Image Removed

Image Added

Quantize

Modify the scale value(1/255=0.003921569) of the yolov5s_inputmeta.yml file, which is in ~/c3v/Models/yolov5s.

Select one quantized type for your need, such as uint8 / int16 / bf16 / pcq. In this sample we use uint8.

./pegasus_quantize.sh yolov5s uint8

Wait until the tool execution is complete and check there are no errors like this:

Then we will see the following four files added under the folder ~/c3v/Models/yolov5s.

Image Removed

Image Added

Inference

Inference the ACUITY model with the quantization data type.

./pegasus_inference.sh yolov5s uint8

Wait until the tool execution is complete and check there are no errors like this:

Export

Export the quantized application for device deployment. Please modify the pegasus_export_ovx.sh for the nb file generating, and add both 3 lines marked in the red box.

./pegasus_export_ovx.sh yolov5s uint8

Wait until the tool execution is complete and check there are no errors like this:

In the path ~/c3v/Models/yolov5s/wksp, you will find a folder named yolov5s_uint8_nbg_unify.

Image Removed

Image Added

We can get the nb file and a c file for NN graph setup information.

Image Removed

Image Added

2. Object detection program

2.1. Post processing

The post-processing of the example code automatically transferred out by the tool will print the top 5. We need to increase the parsing of the results to obtain complete results of target recognition. The relevant post-processing functions are located in the file vnn_post_process.c.

We provide an example function for post-processing, which can complete the parsing of NN processing results：

• post_proc_init

• post_proc_process

• post_proc_deinit

The function needs to be modified：vnn_PostProcessYolov5Uint8

vsi_status vnn_PostProcessYolov5sUint8(vsi_nn_graph_t *graph)
{
    vsi_status status = VSI_FAILURE;

#if DETECT_RESULT_IMPL
    /*detect result sample implement*/
    post_proc_init(graph);
    post_proc_process(graph);
    post_proc_deinit();

#else
    /* Show the top5 result */
    status = show_top5(graph, vsi_nn_GetTensor(graph, graph->output.tensors[0]));
    TEST_CHECK_STATUS(status, final);

    /* Save all output tensor data to txt file */
    save_output_data(graph);

final:
#endif

    return VSI_SUCCESS;
}

For detailed function implementation, please refer to the following file:

2.2. Linux terminal Cross Compile

When compiling NN-related applications, it is necessary to include SDK's headers and libraries.

• Example of SDK Includes Path：

INCLUDES+=-I$(VIVANTE_SDK_DIR)/include/ \
-I$(VIVANTE_SDK_DIR)/include/CL \
-I$(VIVANTE_SDK_DIR)/include/VX \
-I$(VIVANTE_SDK_DIR)/include/ovxlib \
-I$(VIVANTE_SDK_DIR)/include/jpeg

• Example of SDK Link Libraries：

LIBS+=-lOpenVX -lOpenVXU -lCLC -lVSC -lGAL -ljpeg -lovxlib

This is an example Makefile that just needs to be placed in ~/c3v/Models/yolov5s/wksp/yolov5s_uint8_nbg_unify Folder. And set the relevant VIVIANTE_ SDK_ DIR and TOOLCHAIN can complete the compilation of the app:

BIN=sampleApp

VIVANTE_SDK_DIR=Path to VIVANTE SDK directory
TOOLCHAIN=Path to toolchain directory

VIVANTE_SDK_INC=$(VIVANTE_SDK_DIR)/include
VIVANTE_SDK_LIB=$(VIVANTE_SDK_DIR)/lib

CROSS_COMPILE=$(TOOLCHAIN)/aarch64-none-linux-gnu-

CC=$(CROSS_COMPILE)gcc
CXX=$(CROSS_COMPILE)g++

CFLAGS=-Wall -O3

INCLUDE += -I$(VIVANTE_SDK_INC) -I$(VIVANTE_SDK_INC)/HAL -I$(VIVANTE_SDK_INC)/ovxlib
LIBS += -L$(VIVANTE_SDK_LIB) -L./ -L$(STD_LOG_INC)
LIBS += -lOpenVX -lOpenVXU -lOpenVX -lCLC -lVSC -lGAL -ljpeg -lovxlib -lm
LIBS += -lNNArchPerf -lArchModelSw
LIBS += -lstdc++ -ldl -lpthread -lgcc_s

CFLAGS += $(INCLUDE) -fPIC

SRCS=${wildcard *.c}
SRCS+=${wildcard *.cpp}

OBJS=$(addsuffix .o, $(basename $(SRCS)))

.SUFFIXES: .hpp .cpp .c 

.cpp.o:
	$(CXX) $(CFLAGS) -std=c++11 -c $<

.c.o:
	$(CC) $(CFLAGS) -c $<

all: $(BIN)

$(BIN): $(OBJS)
	$(CC) $(CFLAGS) $(LFLAGS) $(OBJS) -o $@ $(LIBS) 
	rm -rf *.o

clean:
	rm -rf *.o
	rm -rf $(BIN) $(LIB)
	rm -rf *~

3. Running on the C3V Linux

Insmod to kernel

insmod ./galcore.ko
[14358.019373] galcore f8140000.galcore: NPU get power success
[14358.019458] galcore f8140000.galcore: galcore irq number is 44
[14358.020542] galcore f8140000.galcore: NPU clock: 900000000
[14358.026015] Galcore version 6.4.15.9.700103

Copy the application and related libraries into C3V Linux and run:

./yolov5suint8 ./network_binary.nb ./input.jpg

The result is like this:

/mnt/yolov5s_uint8_nbg_unify# ./yolov5suint8 ./network_binary.nb input.jpg
Create Neural Network: 30ms or 30801us
Verify...
Verify Graph: 23ms or 23647us
Start run graph [1] times...
Run the 1 time: 43.14ms or 43144.08us
vxProcessGraph execution time:
Total   43.28ms or 43277.80us
Average 43.28ms or 43277.80us
obj: L: 0 P:0.88, [(302, 266) - (200, 378)]
obj: L: 0 P:0.83, [(-3, 55) - (221, 582)]
obj: L:15 P:0.39, [(1, 520) - (55, 117)]
obj: L: 0 P:0.39, [(549, 259) - (92, 372)]
obj: L: 0 P:0.31, [(348, 166) - (181, 277)]