// 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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#include <vector>
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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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#include "postprocess.h"
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#define PERF_WITH_POST 1
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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 void dump_tensor_attr(rknn_tensor_attr *attr)
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{
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char dims[128] = {0};
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for (int i = 0; i < attr->n_dims; ++i)
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{
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int idx = strlen(dims);
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sprintf(&dims[idx], "%d%s", attr->dims[i], (i == attr->n_dims - 1) ? "" : ", ");
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}
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printf(" index=%d, name=%s, n_dims=%d, dims=[%s], 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, dims, attr->n_elems, attr->size, get_format_string(attr->fmt),
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get_type_string(attr->type), get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale);
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}
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static void *load_file(const char *file_path, size_t *file_size)
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{
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FILE *fp = fopen(file_path, "r");
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if (fp == NULL)
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{
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printf("failed to open file: %s\n", file_path);
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return NULL;
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}
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fseek(fp, 0, SEEK_END);
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size_t size = (size_t)ftell(fp);
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fseek(fp, 0, SEEK_SET);
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void *file_data = malloc(size);
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if (file_data == NULL)
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{
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fclose(fp);
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printf("failed allocate file size: %zu\n", size);
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return NULL;
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}
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if (fread(file_data, 1, size, fp) != size)
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{
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fclose(fp);
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free(file_data);
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printf("failed to read file data!\n");
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return NULL;
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}
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fclose(fp);
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*file_size = size;
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return file_data;
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}
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static unsigned char *load_image(const char *image_path, rknn_tensor_attr *input_attr, int *img_height, int *img_width)
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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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{
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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 channel = 0;
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unsigned char *image_data = stbi_load(image_path, img_width, img_height, &channel, req_channel);
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if (image_data == NULL)
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{
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printf("load image failed!\n");
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return NULL;
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}
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if (*img_width != req_width || *img_height != req_height)
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{
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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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{
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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, *img_width, *img_height, 0, image_resized, req_width, req_height, 0, channel) != 1)
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{
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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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// 量化模型的npu输出结果为int8数据类型，后处理要按照int8数据类型处理
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// 如下提供了int8排布的NC1HWC2转换成int8的nchw转换代码
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int NC1HWC2_int8_to_NCHW_int8(const int8_t *src, int8_t *dst, int *dims, int channel, int h, int w)
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{
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int batch = dims[0];
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int C1 = dims[1];
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int C2 = dims[4];
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int hw_src = dims[2] * dims[3];
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int hw_dst = h * w;
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for (int i = 0; i < batch; i++)
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{
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src = src + i * C1 * hw_src * C2;
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dst = dst + i * channel * hw_dst;
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for (int c = 0; c < channel; ++c)
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{
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int plane = c / C2;
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const int8_t *src_c = plane * hw_src * C2 + src;
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int offset = c % C2;
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for (int cur_h = 0; cur_h < h; ++cur_h)
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for (int cur_w = 0; cur_w < w; ++cur_w)
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{
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int cur_hw = cur_h * w + cur_w;
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dst[c * hw_dst + cur_h * w + cur_w] = src_c[C2 * cur_hw + offset];
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}
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}
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}
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return 0;
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}
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// 量化模型的npu输出结果为int8数据类型，后处理要按照int8数据类型处理
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// 如下提供了int8排布的NC1HWC2转换成float的nchw转换代码
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int NC1HWC2_int8_to_NCHW_float(const int8_t *src, float *dst, int *dims, int channel, int h, int w, int zp, float scale)
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{
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int batch = dims[0];
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int C1 = dims[1];
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int C2 = dims[4];
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int hw_src = dims[2] * dims[3];
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int hw_dst = h * w;
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for (int i = 0; i < batch; i++)
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{
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src = src + i * C1 * hw_src * C2;
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dst = dst + i * channel * hw_dst;
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for (int c = 0; c < channel; ++c)
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{
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int plane = c / C2;
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const int8_t *src_c = plane * hw_src * C2 + src;
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int offset = c % C2;
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for (int cur_h = 0; cur_h < h; ++cur_h)
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for (int cur_w = 0; cur_w < w; ++cur_w)
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{
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int cur_hw = cur_h * w + cur_w;
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dst[c * hw_dst + cur_h * w + cur_w] = (src_c[C2 * cur_hw + offset] - zp) * scale; // int8-->float
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}
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}
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}
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return 0;
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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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{
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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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{
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loop_count = atoi(argv[3]);
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}
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const float nms_threshold = NMS_THRESH;
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const float box_conf_threshold = BOX_THRESH;
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int img_width = 0;
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int img_height = 0;
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rknn_context ctx = 0;
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// Load RKNN Model
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#if 1
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// Init rknn from model path
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int ret = rknn_init(&ctx, model_path, 0, 0, NULL);
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#else
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// Init rknn from model data
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size_t model_size;
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void *model_data = load_file(model_path, &model_size);
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if (model_data == NULL)
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{
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return -1;
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}
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int ret = rknn_init(&ctx, model_data, model_size, 0, NULL);
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free(model_data);
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#endif
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if (ret < 0)
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{
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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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{
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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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{
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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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{
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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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{
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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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{
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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_NATIVE_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr));
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if (ret != RKNN_SUCC)
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{
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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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{
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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], &img_height, &img_width);
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if (!input_data)
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{
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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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{
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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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}
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else
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{
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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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{
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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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{
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output_mems[i] = rknn_create_mem(ctx, output_attrs[i].size_with_stride);
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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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{
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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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{
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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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{
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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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{
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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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{
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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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printf("output origin tensors:\n");
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rknn_tensor_attr orig_output_attrs[io_num.n_output];
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memset(orig_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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{
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orig_output_attrs[i].index = i;
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// query info
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ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(orig_output_attrs[i]), sizeof(rknn_tensor_attr));
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if (ret != RKNN_SUCC)
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{
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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(&orig_output_attrs[i]);
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}
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int8_t *output_mems_nchw[io_num.n_output];
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for (uint32_t i = 0; i < io_num.n_output; ++i)
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{
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int size = orig_output_attrs[i].size_with_stride;
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output_mems_nchw[i] = (int8_t *)malloc(size);
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}
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for (uint32_t i = 0; i < io_num.n_output; i++)
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{
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int channel = orig_output_attrs[i].dims[1];
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int h = orig_output_attrs[i].n_dims > 2 ? orig_output_attrs[i].dims[2] : 1;
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int w = orig_output_attrs[i].n_dims > 3 ? orig_output_attrs[i].dims[3] : 1;
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int hw = h * w;
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NC1HWC2_int8_to_NCHW_int8((int8_t *)output_mems[i]->virt_addr, (int8_t *)output_mems_nchw[i], (int *)output_attrs[i].dims,
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channel, h, w);
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}
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int model_width = 0;
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int model_height = 0;
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if (input_attrs[0].fmt == RKNN_TENSOR_NCHW)
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{
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printf("model is NCHW input fmt\n");
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model_width = input_attrs[0].dims[2];
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model_height = input_attrs[0].dims[3];
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}
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else
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{
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printf("model is NHWC input fmt\n");
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model_width = input_attrs[0].dims[1];
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model_height = input_attrs[0].dims[2];
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}
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// post process
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float scale_w = (float)model_width / img_width;
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float scale_h = (float)model_height / img_height;
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detect_result_group_t detect_result_group;
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std::vector<float> out_scales;
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std::vector<int32_t> out_zps;
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for (int i = 0; i < io_num.n_output; ++i)
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{
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out_scales.push_back(output_attrs[i].scale);
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out_zps.push_back(output_attrs[i].zp);
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}
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post_process((int8_t *)output_mems_nchw[0], (int8_t *)output_mems_nchw[1], (int8_t *)output_mems_nchw[2], 640, 640,
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box_conf_threshold, nms_threshold, scale_w, scale_h, out_zps, out_scales, &detect_result_group);
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char text[256];
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for (int i = 0; i < detect_result_group.count; i++)
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{
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detect_result_t *det_result = &(detect_result_group.results[i]);
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sprintf(text, "%s %.1f%%", det_result->name, det_result->prop * 100);
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printf("%s @ (%d %d %d %d) %f\n",
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det_result->name,
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det_result->box.left, det_result->box.top, det_result->box.right, det_result->box.bottom,
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det_result->prop);
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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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{
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rknn_destroy_mem(ctx, output_mems[i]);
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free(output_mems_nchw[i]);
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}
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// destroy
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rknn_destroy(ctx);
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free(input_data);
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return 0;
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}
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