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tm_unet.cpp
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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* License); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* AS IS BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
/*
* Copyright (c) 2020, OPEN AI LAB
* Author: [email protected]
* Author: [email protected]
*
* original model: https://github.com/milesial/Pytorch-UNet
*/
#include <stdlib.h>
#include <stdio.h>
#include <fstream>
#include <limits>
#include <opencv2/opencv.hpp>
#include "common.h"
#include "tengine/c_api.h"
#include "tengine_operations.h"
#define DEFAULT_IMG_H 512
#define DEFAULT_IMG_W 512
#define DEFAULT_SCALE1 (1.f / 255.f)
#define DEFAULT_SCALE2 (1.f / 255.f)
#define DEFAULT_SCALE3 (1.f / 255.f)
#define DEFAULT_MEAN1 0
#define DEFAULT_MEAN2 0
#define DEFAULT_MEAN3 0
#define DEFAULT_LOOP_COUNT 1
#define DEFAULT_THREAD_COUNT 1
#define DEFAULT_CPU_AFFINITY 255
#define DEFAULT_CONF_THRESHOLD 0.5f
/**
* unet model are tested based on https://github.com/milesial/Pytorch-UNet
* pretrained model can be downloaded from https://github.com/milesial/Pytorch-UNet/releases/tag/v1.0
* tmfile can be converted using the pretrained model
* because of the onnx->tmfile convertion problem, keep the network input size dividable by 16 (256,512)
*/
int draw_segmentation(const int32_t* data, int h, int w)
{
static std::map<int32_t, cv::Vec3b> color_table = {
{0, cv::Vec3b(0, 0, 0)},
{1, cv::Vec3b(20, 59, 255)},
{2, cv::Vec3b(120, 59, 200)},
{3, cv::Vec3b(80, 29, 129)},
{4, cv::Vec3b(210, 99, 12)}, // add more color if needed
{-1, cv::Vec3b(255, 255, 255)} // other type
};
cv::Mat img = cv::Mat::zeros(h, w, CV_8UC3);
for (int i = 0; i < h; ++i)
{
for (int j = 0; j < w; ++j)
{
cv::Vec3b color;
int32_t value = data[i * w + j];
if (color_table.count(value) > 0)
{
color = color_table.at(value);
}
else
{
color = color_table.at(-1);
}
img.at<cv::Vec3b>(i, j) = color;
}
}
cv::imwrite("unet_out.png", img);
return 0;
}
int tengine_segment(const char* model_file, const char* image_file, int img_h, int img_w, const float* mean,
const float* scale, int loop_count, int num_thread, int affinity, float conf_thresh)
{
/* set runtime options */
struct options opt;
opt.num_thread = num_thread;
opt.cluster = TENGINE_CLUSTER_ALL;
opt.precision = TENGINE_MODE_FP32;
opt.affinity = affinity;
/* inital tengine */
if (init_tengine() != 0)
{
fprintf(stderr, "Initial tengine failed.\n");
return -1;
}
fprintf(stderr, "tengine-lite library version: %s\n", get_tengine_version());
/* create graph, load tengine model xxx.tmfile */
graph_t graph = create_graph(NULL, "tengine", model_file);
if (NULL == graph)
{
fprintf(stderr, "Create graph failed.\n");
return -1;
}
/* set the shape, data buffer of input_tensor of the graph */
int img_size = img_h * img_w * 3;
int dims[] = {1, 3, img_h, img_w}; // nchw
float* input_data = (float*)malloc(img_size * sizeof(float));
tensor_t input_tensor = get_graph_input_tensor(graph, 0, 0);
if (input_tensor == NULL)
{
fprintf(stderr, "Get input tensor failed\n");
return -1;
}
if (set_tensor_shape(input_tensor, dims, 4) < 0)
{
fprintf(stderr, "Set input tensor shape failed\n");
return -1;
}
if (set_tensor_buffer(input_tensor, input_data, img_size * sizeof(float)) < 0)
{
fprintf(stderr, "Set input tensor buffer failed\n");
return -1;
}
/* prerun graph, set work options(num_thread, cluster, precision) */
if (prerun_graph_multithread(graph, opt) < 0)
{
fprintf(stderr, "Prerun multithread graph failed.\n");
return -1;
}
/* prepare process input data, set the data mem to input tensor */
get_input_data(image_file, input_data, img_h, img_w, mean, scale);
/* run graph */
double min_time = DBL_MAX;
double max_time = DBL_MIN;
double total_time = 0.;
for (int i = 0; i < loop_count; i++)
{
double start = get_current_time();
if (run_graph(graph, 1) < 0)
{
fprintf(stderr, "Run graph failed\n");
return -1;
}
double end = get_current_time();
double cur = end - start;
total_time += cur;
if (min_time > cur)
min_time = cur;
if (max_time < cur)
max_time = cur;
}
fprintf(stderr, "\nmodel file : %s\n", model_file);
fprintf(stderr, "image file : %s\n", image_file);
fprintf(stderr, "img_h, img_w, scale[3], mean[3] : %d %d , %.3f %.3f %.3f, %.1f %.1f %.1f\n", img_h, img_w,
scale[0], scale[1], scale[2], mean[0], mean[1], mean[2]);
fprintf(stderr, "Repeat %d times, thread %d, avg time %.2f ms, max_time %.2f ms, min_time %.2f ms\n", loop_count,
num_thread, total_time / loop_count, max_time, min_time);
fprintf(stderr, "--------------------------------------\n");
/* get the result of classification */
tensor_t output_tensor = get_graph_output_tensor(graph, 0, 0);
float* output_data = (float*)get_tensor_buffer(output_tensor);
int output_size = get_tensor_buffer_size(output_tensor) / sizeof(float);
int channel = output_size / img_h / img_w;
int res = output_size % (img_h * img_w);
if (res != 0)
{
fprintf(stderr, "output shape is not supported.\n");
}
else
{
int* label_data = new int[img_h * img_w];
/* single class segmentation */
if (channel == 1)
{
for (int i = 0; i < img_h; ++i)
{
for (int j = 0; j < img_w; ++j)
{
float conf = 1 / (1 + std::exp(-output_data[i * img_w + j]));
label_data[i * img_w + j] = conf > conf_thresh ? 1 : 0;
}
}
}
/* multi-class segmentation */
else
{
for (int i = 0; i < img_h; ++i)
{
for (int j = 0; j < img_w; ++j)
{
int argmax_id = -1;
float max_conf = std::numeric_limits<float>::min();
for (int k = 0; k < channel; ++k)
{
float out_value = output_data[k * img_w * img_h + i * img_w + j];
if (out_value > max_conf)
{
argmax_id = k;
max_conf = out_value;
}
}
label_data[i * img_w + j] = argmax_id;
}
}
}
/* visualization */
draw_segmentation(label_data, img_h, img_w);
fprintf(stderr, "segmentatation result is save as unet_out.png\n");
delete[] label_data;
}
/* release tengine */
free(input_data);
postrun_graph(graph);
destroy_graph(graph);
release_tengine();
return 0;
}
void show_usage()
{
fprintf(
stderr,
"[Usage]: [-h]\n [-m model_file] [-i image_file] [-r repeat_count] [-t thread_count] [-a cpu_affinity] \n");
}
int main(int argc, char* argv[])
{
int loop_count = DEFAULT_LOOP_COUNT;
int num_thread = DEFAULT_THREAD_COUNT;
int cpu_affinity = DEFAULT_CPU_AFFINITY;
float conf_thresh = DEFAULT_CONF_THRESHOLD;
char* model_file = NULL;
char* image_file = NULL;
float img_hw[2] = {0.f};
int img_h = 0;
int img_w = 0;
float mean[3] = {0.f, 0.f, 0.f};
float scale[3] = {0.f, 0.f, 0.f};
int res;
while ((res = getopt(argc, argv, "m:i:l:g:s:w:r:t:a:c:h")) != -1)
{
switch (res)
{
case 'm':
model_file = optarg;
break;
case 'i':
image_file = optarg;
break;
case 'g':
split(img_hw, optarg, ",");
img_h = (int)img_hw[0];
img_w = (int)img_hw[1];
break;
case 's':
split(scale, optarg, ",");
break;
case 'w':
split(mean, optarg, ",");
break;
case 'r':
loop_count = atoi(optarg);
break;
case 't':
num_thread = atoi(optarg);
break;
case 'a':
cpu_affinity = atoi(optarg);
break;
case 'c':
conf_thresh = atof(optarg);
break;
case 'h':
show_usage();
return 0;
default:
break;
}
}
/* check files */
if (model_file == NULL)
{
fprintf(stderr, "Error: Tengine model file not specified!\n");
show_usage();
return -1;
}
if (image_file == NULL)
{
fprintf(stderr, "Error: Image file not specified!\n");
show_usage();
return -1;
}
if (!check_file_exist(model_file) || !check_file_exist(image_file))
return -1;
if (img_h == 0)
{
img_h = DEFAULT_IMG_H;
fprintf(stderr, "Image height not specified, use default %d\n", img_h);
}
if (img_w == 0)
{
img_w = DEFAULT_IMG_W;
fprintf(stderr, "Image width not specified, use default %d\n", img_w);
}
if (scale[0] == 0.f || scale[1] == 0.f || scale[2] == 0.f)
{
scale[0] = DEFAULT_SCALE1;
scale[1] = DEFAULT_SCALE2;
scale[2] = DEFAULT_SCALE3;
fprintf(stderr, "Scale value not specified, use default %.5f, %.5f, %.5f\n", scale[0], scale[1], scale[2]);
}
if (mean[0] == -1.0 || mean[1] == -1.0 || mean[2] == -1.0)
{
mean[0] = DEFAULT_MEAN1;
mean[1] = DEFAULT_MEAN2;
mean[2] = DEFAULT_MEAN3;
fprintf(stderr, "Mean value not specified, use default %.5f, %.5f, %.5f\n", mean[0], mean[1], mean[2]);
}
if (tengine_segment(model_file, image_file, img_h, img_w, mean, scale, loop_count, num_thread, cpu_affinity, conf_thresh) < 0)
return -1;
return 0;
}