rv1126移植并部署自写手写数字识别模型
首先搭建好rknntoolkit以及rknpu环境
-->
MNIST->https://github.com/warren-wzw/MNIST-pytorch.git
大致流程
生成rknn文件-----------------------------------------------------------------------------------------
1：进入docker
docker run -t -i --privileged -v /dev/bus/usb:/de/bus/usb \
-v /home/wzw/rk_npu_sdk/rknn-toolkit-master-v1.7.3:/rknn_toolkit rknn-toolkit:1.7.3 /bin/bash
2：进入/rknn_toolkit/examples/onnx，复制一个resnet50v2并命名为MNIST
3：准备以下文件
4：代码
import os
import urllib
import traceback
import time
import sys
import numpy as np
import cv2
from rknn.api import RKNN
ONNX_MODEL = 'model.onnx'
RKNN_MODEL = 'model.rknn'
if __name__ == '__main__':
# Create RKNN object
rknn = RKNN()
# pre-process config
print('--> Config model')
rknn.config(target_platform=["rv1126"])
print('done')
# Load ONNX model
print('--> Loading model')
ret = rknn.load_onnx(model=ONNX_MODEL,
inputs=['input.1'],
input_size_list=[[1,28,28]],
outputs=['25'])
if ret != 0:
print('Load model failed!')
exit(ret)
print('done')
# Build model
print('--> Building model')
ret = rknn.build(do_quantization=False)
if ret != 0:
print('Build model failed!')
exit(ret)
print('done')
# Export RKNN model
print('--> Export RKNN model')
ret = rknn.export_rknn(RKNN_MODEL)
if ret != 0:
print('Export resnet50v2.rknn failed!')
exit(ret)
print('done')
# Set inputs
with open("./data/MNIST/raw/train-images-idx3-ubyte","rb") as f:
file=f.read()
num=100
i = 16+784*num
image1 = [int(str(item).encode('ascii'),16) for item in file[i:i+784]]
input_data = np.array(image1,dtype=np.float32).reshape(1,1,28,28)
#save the image
image1_np = np.array(image1,dtype=np.uint8).reshape(28,28,1)
file_name = "test.jpg"
cv2.imwrite(file_name,image1_np)
# init runtime environment
print('--> Init runtime environment')
ret = rknn.init_runtime()
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('done')
# Inference
print('--> Running model')
outputs = rknn.inference(inputs=input_data)
x = outputs[0]
output = np.exp(x)/np.sum(np.exp(x))
outputs = np.argmax([output])
print("----------outputs----------",outputs)
print('done')
rknn.release()
5：python test.py
生成rknn文件
rknpu------------------------------------------------------------------------------------------------
复制一个mobilenet并命名为MNIST目录结构为：
将toolkit生成的rknn模型文件拷贝至model
build.sh
#!/bin/bash
set -e
# for rk1808 aarch64
# GCC_COMPILER=${RK1808_TOOL_CHAIN}/bin/aarch64-linux-gnu
# for rk1806 armhf
# GCC_COMPILER=~/opts/gcc-linaro-6.3.1-2017.05-x86_64_arm-linux-gnueabihf/bin/arm-linux-gnueabihf
# for rv1109/rv1126 armhf
GCC_COMPILER=/opt/atk-dlrv1126-toolchain/bin/arm-linux-gnueabihf
ROOT_PWD=$( cd "$( dirname $0 )" && cd -P "$( dirname "$SOURCE" )" && pwd )
# build rockx
BUILD_DIR=${ROOT_PWD}/build
if [[ ! -d "${BUILD_DIR}" ]]; then
mkdir -p ${BUILD_DIR}
fi
cd ${BUILD_DIR}
cmake .. \
-DCMAKE_C_COMPILER=${GCC_COMPILER}-gcc \
-DCMAKE_CXX_COMPILER=${GCC_COMPILER}-g++
make -j4
make installmain.cc
/*-------------------------------------------
Includes
-------------------------------------------*/
#include
#include
#include
#include
#include
#include
#define STB_IMAGE_IMPLEMENTATION
#include "stb/stb_image.h"
#define STB_IMAGE_RESIZE_IMPLEMENTATION
#include
#include "rknn_api.h"
using namespace std;
const int MODEL_IN_WIDTH = 28;
const int MODEL_IN_HEIGHT = 28;
const int MODEL_CHANNEL = 1;
int ret =0;
int loop_count = 1000;
/*-------------------------------------------
Functions
-------------------------------------------*/
static inline int64_t getCurrentTimeUs()
{
struct timeval tv;
gettimeofday(&tv, NULL);
return tv.tv_sec * 1000000 + tv.tv_usec;
}
static void printRKNNTensor(rknn_tensor_attr *attr)
{
printf("index=%d name=%s n_dims=%d dims=[%d %d %d %d] n_elems=%d size=%d fmt=%d type=%d qnt_type=%d fl=%d zp=%d scale=%f\n",
attr->index, attr->name, attr->n_dims, attr->dims[3], attr->dims[2], attr->dims[1], attr->dims[0],
attr->n_elems, attr->size, 0, attr->type, attr->qnt_type, attr->fl, attr->zp, attr->scale);
}
static unsigned char *load_model(const char *filename, int *model_size)
{
FILE *fp = fopen(filename, "rb");
if (fp == nullptr)
{
printf("fopen %s fail!\n", filename);
return NULL;
}
fseek(fp, 0, SEEK_END);
int model_len = ftell(fp);
unsigned char *model = (unsigned char *)malloc(model_len);
fseek(fp, 0, SEEK_SET);
if (model_len != fread(model, 1, model_len, fp))
{
printf("fread %s fail!\n", filename);
free(model);
return NULL;
}
*model_size = model_len;
if (fp)
{
fclose(fp);
}
return model;
}
void Load_data(int num,unsigned char * input_image)
{
int j=16+784*num;
FILE *file = fopen("./model/data/MNIST/raw/train-images-idx3-ubyte", "rb");
if (file == NULL) {
printf("can't open the file!\n");
}
fseek(file,j,SEEK_SET);
fread(input_image,sizeof(char),784,file);
/* for(int i=0;ibuffer[j+1]){
temp=buffer[j];
buffer[j]=buffer[j+1];
buffer[j+1]=temp;
}
}
}
}
void get_tensor_message(rknn_context ctx,rknn_tensor_attr *attrs,uint32_t num,int io)
{
for (int i = 0; i < num; i++) {
attrs[i].index = i;
if(io==1){
ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(attrs[i]), sizeof(rknn_tensor_attr));
}
else{
ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(attrs[i]), sizeof(rknn_tensor_attr));
}
if (ret != RKNN_SUCC) {
printf("rknn_query fail! ret=%d\n", ret);
}
printRKNNTensor(&(attrs[i]));
}
}
void print_Array(int num,float *buffer)
{
for(int i =0;i= 0)
{
rknn_destroy(ctx);
}
if (model)
{
free(model);
}
return 0;
} 打印结果
fp16
uint8
