// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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/*-------------------------------------------
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Includes
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-------------------------------------------*/
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#include "rknn_api.h"
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#include <float.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <sys/time.h>
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#define STB_IMAGE_IMPLEMENTATION
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#include "stb/stb_image.h"
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#define STB_IMAGE_RESIZE_IMPLEMENTATION
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#include <stb/stb_image_resize.h>
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/*-------------------------------------------
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Functions
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-------------------------------------------*/
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static inline int64_t getCurrentTimeUs()
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{
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struct timeval tv;
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gettimeofday(&tv, NULL);
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return tv.tv_sec * 1000000 + tv.tv_usec;
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}
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static int rknn_GetTopN(float* pfProb, float* pfMaxProb, uint32_t* pMaxClass, uint32_t outputCount, uint32_t topNum)
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{
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uint32_t i, j;
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uint32_t top_count = outputCount > topNum ? topNum : outputCount;
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for (i = 0; i < topNum; ++i) {
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pfMaxProb[i] = -FLT_MAX;
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pMaxClass[i] = -1;
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}
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for (j = 0; j < top_count; j++) {
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for (i = 0; i < outputCount; i++) {
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if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) ||
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(i == *(pMaxClass + 4))) {
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continue;
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}
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if (pfProb[i] > *(pfMaxProb + j)) {
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*(pfMaxProb + j) = pfProb[i];
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*(pMaxClass + j) = i;
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}
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}
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}
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return 1;
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}
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static void dump_tensor_attr(rknn_tensor_attr* attr)
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{
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printf(" index=%d, name=%s, n_dims=%d, dims=[%d, %d, %d, %d], n_elems=%d, size=%d, fmt=%s, type=%s, qnt_type=%s, "
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"zp=%d, scale=%f\n",
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attr->index, attr->name, attr->n_dims, attr->dims[0], attr->dims[1], attr->dims[2], attr->dims[3],
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attr->n_elems, attr->size, get_format_string(attr->fmt), get_type_string(attr->type),
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get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale);
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}
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static unsigned char* load_image(const char* image_path, rknn_tensor_attr* input_attr)
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{
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int req_height = 0;
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int req_width = 0;
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int req_channel = 0;
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switch (input_attr->fmt) {
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case RKNN_TENSOR_NHWC:
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req_height = input_attr->dims[1];
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req_width = input_attr->dims[2];
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req_channel = input_attr->dims[3];
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break;
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case RKNN_TENSOR_NCHW:
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req_height = input_attr->dims[2];
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req_width = input_attr->dims[3];
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req_channel = input_attr->dims[1];
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break;
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default:
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printf("meet unsupported layout\n");
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return NULL;
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}
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int height = 0;
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int width = 0;
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int channel = 0;
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unsigned char* image_data = stbi_load(image_path, &width, &height, &channel, req_channel);
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if (image_data == NULL) {
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printf("load image failed!\n");
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return NULL;
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}
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if (width != req_width || height != req_height) {
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unsigned char* image_resized = (unsigned char*)STBI_MALLOC(req_width * req_height * req_channel);
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if (!image_resized) {
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printf("malloc image failed!\n");
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STBI_FREE(image_data);
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return NULL;
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}
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if (stbir_resize_uint8(image_data, width, height, 0, image_resized, req_width, req_height, 0, channel) != 1) {
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printf("resize image failed!\n");
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STBI_FREE(image_data);
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return NULL;
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}
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STBI_FREE(image_data);
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image_data = image_resized;
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}
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return image_data;
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}
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static unsigned char* load_model(const char* filename, int* model_size)
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{
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FILE* fp = fopen(filename, "rb");
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if (fp == nullptr) {
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printf("fopen %s fail!\n", filename);
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return NULL;
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}
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fseek(fp, 0, SEEK_END);
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int model_len = ftell(fp);
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unsigned char* model = (unsigned char*)malloc(model_len);
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fseek(fp, 0, SEEK_SET);
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if (model_len != fread(model, 1, model_len, fp)) {
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printf("fread %s fail!\n", filename);
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free(model);
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return NULL;
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}
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*model_size = model_len;
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if (fp) {
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fclose(fp);
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}
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return model;
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}
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/*-------------------------------------------
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Main Functions
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-------------------------------------------*/
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int main(int argc, char* argv[])
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{
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if (argc < 3) {
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printf("Usage:%s model_path input_path [loop_count]\n", argv[0]);
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return -1;
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}
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char* model_path = argv[1];
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char* input_path = argv[2];
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int loop_count = 1;
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if (argc > 3) {
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loop_count = atoi(argv[3]);
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}
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rknn_context ctx = 0;
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// Load RKNN Model
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int model_len = 0;
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unsigned char* model = load_model(model_path, &model_len);
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int ret = rknn_init(&ctx, model, model_len, 0, NULL);
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if (ret < 0) {
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printf("rknn_init fail! ret=%d\n", ret);
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return -1;
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}
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// Get sdk and driver version
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rknn_sdk_version sdk_ver;
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ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver));
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if (ret != RKNN_SUCC) {
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printf("rknn_query fail! ret=%d\n", ret);
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return -1;
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}
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printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version);
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// Get Model Input Output Info
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rknn_input_output_num io_num;
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ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num));
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if (ret != RKNN_SUCC) {
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printf("rknn_query fail! ret=%d\n", ret);
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return -1;
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}
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printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output);
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printf("input tensors:\n");
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rknn_tensor_attr input_attrs[io_num.n_input];
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memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr));
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for (uint32_t i = 0; i < io_num.n_input; i++) {
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input_attrs[i].index = i;
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// query info
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ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr));
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if (ret < 0) {
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printf("rknn_init error! ret=%d\n", ret);
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return -1;
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}
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dump_tensor_attr(&input_attrs[i]);
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}
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printf("output tensors:\n");
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rknn_tensor_attr output_attrs[io_num.n_output];
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memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr));
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for (uint32_t i = 0; i < io_num.n_output; i++) {
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output_attrs[i].index = i;
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// query info
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ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr));
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if (ret != RKNN_SUCC) {
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printf("rknn_query fail! ret=%d\n", ret);
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return -1;
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}
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dump_tensor_attr(&output_attrs[i]);
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}
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// Get custom string
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rknn_custom_string custom_string;
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ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string));
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if (ret != RKNN_SUCC) {
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printf("rknn_query fail! ret=%d\n", ret);
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return -1;
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}
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printf("custom string: %s\n", custom_string.string);
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unsigned char* input_data = NULL;
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rknn_tensor_type input_type = RKNN_TENSOR_UINT8;
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rknn_tensor_format input_layout = RKNN_TENSOR_NHWC;
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// Load image
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input_data = load_image(input_path, &input_attrs[0]);
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if (!input_data) {
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return -1;
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}
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// Create input tensor memory
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rknn_tensor_mem* input_mems[1];
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// default input type is int8 (normalize and quantize need compute in outside)
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// if set uint8, will fuse normalize and quantize to npu
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input_attrs[0].type = input_type;
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// default fmt is NHWC, npu only support NHWC in zero copy mode
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input_attrs[0].fmt = input_layout;
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input_mems[0] = rknn_create_mem(ctx, input_attrs[0].size_with_stride);
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// Copy input data to input tensor memory
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int width = input_attrs[0].dims[2];
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int stride = input_attrs[0].w_stride;
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if (width == stride) {
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memcpy(input_mems[0]->virt_addr, input_data, width * input_attrs[0].dims[1] * input_attrs[0].dims[3]);
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} else {
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int height = input_attrs[0].dims[1];
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int channel = input_attrs[0].dims[3];
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// copy from src to dst with stride
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uint8_t* src_ptr = input_data;
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uint8_t* dst_ptr = (uint8_t*)input_mems[0]->virt_addr;
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// width-channel elements
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int src_wc_elems = width * channel;
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int dst_wc_elems = stride * channel;
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for (int h = 0; h < height; ++h) {
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memcpy(dst_ptr, src_ptr, src_wc_elems);
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src_ptr += src_wc_elems;
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dst_ptr += dst_wc_elems;
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}
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}
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// Create output tensor memory
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rknn_tensor_mem* output_mems[io_num.n_output];
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for (uint32_t i = 0; i < io_num.n_output; ++i) {
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// default output type is depend on model, this require float32 to compute top5
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// allocate float32 output tensor
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int output_size = output_attrs[i].n_elems * sizeof(float);
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output_mems[i] = rknn_create_mem(ctx, output_size);
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}
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// Set input tensor memory
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ret = rknn_set_io_mem(ctx, input_mems[0], &input_attrs[0]);
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if (ret < 0) {
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printf("rknn_set_io_mem fail! ret=%d\n", ret);
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return -1;
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}
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// Set output tensor memory
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for (uint32_t i = 0; i < io_num.n_output; ++i) {
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// default output type is depend on model, this require float32 to compute top5
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output_attrs[i].type = RKNN_TENSOR_FLOAT32;
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// set output memory and attribute
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ret = rknn_set_io_mem(ctx, output_mems[i], &output_attrs[i]);
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if (ret < 0) {
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printf("rknn_set_io_mem fail! ret=%d\n", ret);
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return -1;
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}
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}
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// Run
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printf("Begin perf ...\n");
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for (int i = 0; i < loop_count; ++i) {
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int64_t start_us = getCurrentTimeUs();
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ret = rknn_run(ctx, NULL);
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int64_t elapse_us = getCurrentTimeUs() - start_us;
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if (ret < 0) {
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printf("rknn run error %d\n", ret);
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return -1;
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}
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printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us);
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}
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// Get top 5
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uint32_t topNum = 5;
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for (uint32_t i = 0; i < io_num.n_output; i++) {
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uint32_t MaxClass[topNum];
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float fMaxProb[topNum];
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float* buffer = (float*)output_mems[i]->virt_addr;
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uint32_t sz = output_attrs[i].n_elems;
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int top_count = sz > topNum ? topNum : sz;
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rknn_GetTopN(buffer, fMaxProb, MaxClass, sz, topNum);
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printf("---- Top%d ----\n", top_count);
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for (int j = 0; j < top_count; j++) {
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printf("%8.6f - %d\n", fMaxProb[j], MaxClass[j]);
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}
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}
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// Destroy rknn memory
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rknn_destroy_mem(ctx, input_mems[0]);
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for (uint32_t i = 0; i < io_num.n_output; ++i) {
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rknn_destroy_mem(ctx, output_mems[i]);
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}
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// destroy
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rknn_destroy(ctx);
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if (input_data != nullptr) {
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free(input_data);
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}
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if (model != nullptr) {
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free(model);
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}
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return 0;
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}
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