update ncnn example
This commit is contained in:
jinyanhua
parent
6d08de2a42
commit
b1d7230a61
@ -186,6 +186,13 @@ TRAIN:
|
||||
```
|
||||
python3 test.py --yaml configs/coco.yaml --weight weights/coco_ap05_0.250_280epoch.pth --img data/3.jpg --torchscript
|
||||
```
|
||||
## NCNN
|
||||
* Need to compile ncnn and opencv in advance and modify the path in build.sh
|
||||
```
|
||||
cd example/ncnn/
|
||||
sh build.sh
|
||||
./FastestDet
|
||||
```
|
||||
## onnx-runtime
|
||||
* You can learn about the pre and post-processing methods of FastestDet in this Sample
|
||||
```
|
||||
|
||||
BIN
example/ncnn/1.jpg
Normal file
BIN
example/ncnn/1.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 198 KiB |
BIN
example/ncnn/FastestDet.bin
Normal file
BIN
example/ncnn/FastestDet.bin
Normal file
Binary file not shown.
224
example/ncnn/FastestDet.cpp
Normal file
224
example/ncnn/FastestDet.cpp
Normal file
@ -0,0 +1,224 @@
|
||||
#include <math.h>
|
||||
#include <algorithm>
|
||||
|
||||
#include "net.h"
|
||||
#include "benchmark.h"
|
||||
#include <opencv2/opencv.hpp>
|
||||
|
||||
float Sigmoid(float x)
|
||||
{
|
||||
return 1.0f / (1.0f + exp(-x));
|
||||
}
|
||||
|
||||
float Tanh(float x)
|
||||
{
|
||||
return 2.0f / (1.0f + exp(-2 * x)) - 1;
|
||||
}
|
||||
|
||||
class TargetBox
|
||||
{
|
||||
private:
|
||||
float GetWidth() { return (x2 - x1); };
|
||||
float GetHeight() { return (y2 - y1); };
|
||||
|
||||
public:
|
||||
int x1;
|
||||
int y1;
|
||||
int x2;
|
||||
int y2;
|
||||
|
||||
int category;
|
||||
float score;
|
||||
|
||||
float area() { return GetWidth() * GetHeight(); };
|
||||
};
|
||||
|
||||
float IntersectionArea(const TargetBox &a, const TargetBox &b)
|
||||
{
|
||||
if (a.x1 > b.x2 || a.x2 < b.x1 || a.y1 > b.y2 || a.y2 < b.y1)
|
||||
{
|
||||
// no intersection
|
||||
return 0.f;
|
||||
}
|
||||
|
||||
float inter_width = std::min(a.x2, b.x2) - std::max(a.x1, b.x1);
|
||||
float inter_height = std::min(a.y2, b.y2) - std::max(a.y1, b.y1);
|
||||
|
||||
return inter_width * inter_height;
|
||||
}
|
||||
|
||||
bool scoreSort(TargetBox a, TargetBox b)
|
||||
{
|
||||
return (a.score > b.score);
|
||||
}
|
||||
|
||||
//NMS处理
|
||||
int nmsHandle(std::vector<TargetBox> &src_boxes, std::vector<TargetBox> &dst_boxes)
|
||||
{
|
||||
std::vector<int> picked;
|
||||
|
||||
sort(src_boxes.begin(), src_boxes.end(), scoreSort);
|
||||
|
||||
for (int i = 0; i < src_boxes.size(); i++)
|
||||
{
|
||||
int keep = 1;
|
||||
for (int j = 0; j < picked.size(); j++)
|
||||
{
|
||||
//交集
|
||||
float inter_area = IntersectionArea(src_boxes[i], src_boxes[picked[j]]);
|
||||
//并集
|
||||
float union_area = src_boxes[i].area() + src_boxes[picked[j]].area() - inter_area;
|
||||
float IoU = inter_area / union_area;
|
||||
|
||||
if(IoU > 0.45 && src_boxes[i].category == src_boxes[picked[j]].category)
|
||||
{
|
||||
keep = 0;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if (keep) {
|
||||
picked.push_back(i);
|
||||
}
|
||||
}
|
||||
|
||||
for (int i = 0; i < picked.size(); i++)
|
||||
{
|
||||
dst_boxes.push_back(src_boxes[picked[i]]);
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
int main()
|
||||
{
|
||||
// 类别标签
|
||||
static const char* class_names[] = {
|
||||
"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
|
||||
"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
|
||||
"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
|
||||
"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
|
||||
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
|
||||
"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
|
||||
"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
|
||||
"microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
|
||||
"hair drier", "toothbrush"
|
||||
};
|
||||
// 类别数量
|
||||
int class_num = sizeof(class_names) / sizeof(class_names[0]);
|
||||
|
||||
// 阈值
|
||||
float thresh = 0.65;
|
||||
|
||||
// 模型输入宽高
|
||||
int input_width = 352;
|
||||
int input_height = 352;
|
||||
|
||||
// 加载模型
|
||||
ncnn::Net net;
|
||||
net.load_param("FastestDet.param");
|
||||
net.load_model("FastestDet.bin");
|
||||
printf("ncnn model load sucess...\n");
|
||||
|
||||
// 加载图片
|
||||
cv::Mat img = cv::imread("1.jpg");
|
||||
int img_width = img.cols;
|
||||
int img_height = img.rows;
|
||||
|
||||
// resize of input image data
|
||||
ncnn::Mat input = ncnn::Mat::from_pixels_resize(img.data, ncnn::Mat::PIXEL_BGR,\
|
||||
img.cols, img.rows, input_width, input_height);
|
||||
// Normalization of input image data
|
||||
const float mean_vals[3] = {0.f, 0.f, 0.f};
|
||||
const float norm_vals[3] = {1/255.f, 1/255.f, 1/255.f};
|
||||
input.substract_mean_normalize(mean_vals, norm_vals);
|
||||
|
||||
// creat extractor
|
||||
ncnn::Extractor ex = net.create_extractor();
|
||||
ex.set_num_threads(1);
|
||||
|
||||
double start = ncnn::get_current_time();
|
||||
//set input tensor
|
||||
ex.input("in0", input);
|
||||
|
||||
// get output tensor
|
||||
ncnn::Mat output;
|
||||
ex.extract("out0", output);
|
||||
printf("output: %d, %d, %d\n", output.c, output.h, output.w);
|
||||
|
||||
// handle output tensor
|
||||
std::vector<TargetBox> target_boxes;
|
||||
|
||||
for (int h = 0; h < output.h; h++)
|
||||
{
|
||||
for (int w = 0; w < output.h; w++)
|
||||
{
|
||||
// 前景概率
|
||||
int obj_score_index = (0 * output.h * output.w) + (h * output.w) + w;
|
||||
float obj_score = output[obj_score_index];
|
||||
|
||||
// 解析类别
|
||||
int category;
|
||||
float max_score = 0.0f;
|
||||
for (size_t i = 0; i < class_num; i++)
|
||||
{
|
||||
int obj_score_index = ((5 + i) * output.h * output.w) + (h * output.w) + w;
|
||||
float cls_score = output[obj_score_index];
|
||||
if (cls_score > max_score)
|
||||
{
|
||||
max_score = cls_score;
|
||||
category = i;
|
||||
}
|
||||
}
|
||||
float score = max_score * obj_score;
|
||||
|
||||
// 阈值筛选
|
||||
if(score > thresh)
|
||||
{
|
||||
// 解析坐标
|
||||
int x_offset_index = (1 * output.h * output.w) + (h * output.w) + w;
|
||||
int y_offset_index = (2 * output.h * output.w) + (h * output.w) + w;
|
||||
int box_width_index = (3 * output.h * output.w) + (h * output.w) + w;
|
||||
int box_height_index = (4 * output.h * output.w) + (h * output.w) + w;
|
||||
|
||||
float x_offset = Tanh(output[x_offset_index]);
|
||||
float y_offset = Tanh(output[y_offset_index]);
|
||||
float box_width = Sigmoid(output[box_width_index]);
|
||||
float box_height = Sigmoid(output[box_height_index]);
|
||||
|
||||
float cx = (w + x_offset) / output.w;
|
||||
float cy = (h + y_offset) / output.h;
|
||||
|
||||
int x1 = (int)((cx - box_width * 0.5) * img_width);
|
||||
int y1 = (int)((cy - box_height * 0.5) * img_height);
|
||||
int x2 = (int)((cx + box_width * 0.5) * img_width);
|
||||
int y2 = (int)((cy + box_height * 0.5) * img_height);
|
||||
|
||||
target_boxes.push_back(TargetBox{x1, y1, x2, y2, category, score});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// NMS处理
|
||||
std::vector<TargetBox> nms_boxes;
|
||||
nmsHandle(target_boxes, nms_boxes);
|
||||
|
||||
// 打印耗时
|
||||
double end = ncnn::get_current_time();
|
||||
double time = end - start;
|
||||
printf("Time:%7.2f ms\n",time);
|
||||
|
||||
// draw result
|
||||
for (size_t i = 0; i < nms_boxes.size(); i++)
|
||||
{
|
||||
TargetBox box = nms_boxes[i];
|
||||
printf("x1:%d y1:%d x2:%d y2:%d %s:%.2f%%\n", box.x1, box.y1, box.x2, box.y2, class_names[box.category], box.score * 100);
|
||||
|
||||
cv::rectangle(img, cv::Point(box.x1, box.y1), cv::Point(box.x2, box.y2), cv::Scalar(0, 0, 255), 2);
|
||||
cv::putText(img, class_names[box.category], cv::Point(box.x1, box.y1), cv::FONT_HERSHEY_SIMPLEX, 0.75, cv::Scalar(0, 255, 0), 2);
|
||||
}
|
||||
|
||||
cv::imwrite("result.jpg", img);
|
||||
|
||||
return 0;
|
||||
}
|
||||
129
example/ncnn/FastestDet.param
Normal file
129
example/ncnn/FastestDet.param
Normal file
@ -0,0 +1,129 @@
|
||||
7767517
|
||||
127 150
|
||||
Input in0 0 1 in0
|
||||
Convolution convrelu_0 1 1 in0 1 0=24 1=3 11=3 12=1 13=2 14=1 2=1 3=2 4=1 5=1 6=648 9=1
|
||||
Pooling maxpool2d_43 1 1 1 2 0=0 1=3 11=3 12=2 13=1 2=2 3=1 5=1
|
||||
Split splitncnn_0 1 2 2 3 4
|
||||
ConvolutionDepthWise convdw_95 1 1 4 5 0=24 1=3 11=3 12=1 13=2 14=1 2=1 3=2 4=1 5=1 6=216 7=24
|
||||
Convolution convrelu_1 1 1 3 6 0=24 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=576 9=1
|
||||
ConvolutionDepthWise convdw_96 1 1 6 7 0=24 1=3 11=3 12=1 13=2 14=1 2=1 3=2 4=1 5=1 6=216 7=24
|
||||
Convolution convrelu_3 1 1 5 8 0=24 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=576 9=1
|
||||
Convolution convrelu_2 1 1 7 9 0=24 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=576 9=1
|
||||
Concat cat_0 2 1 8 9 10 0=0
|
||||
ShuffleChannel shufflechannel_0 1 1 10 11 0=2 1=1
|
||||
Slice shufflechannel_0_slice 1 2 11 12 13 -23300=2,-233,-233 1=0
|
||||
Convolution convrelu_4 1 1 13 14 0=24 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=576 9=1
|
||||
ConvolutionDepthWise convdw_97 1 1 14 15 0=24 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=216 7=24
|
||||
Convolution convrelu_5 1 1 15 16 0=24 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=576 9=1
|
||||
Concat cat_1 2 1 12 16 17 0=0
|
||||
ShuffleChannel shufflechannel_1 1 1 17 18 0=2 1=1
|
||||
Slice shufflechannel_1_slice 1 2 18 19 20 -23300=2,-233,-233 1=0
|
||||
Convolution convrelu_6 1 1 20 21 0=24 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=576 9=1
|
||||
ConvolutionDepthWise convdw_98 1 1 21 22 0=24 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=216 7=24
|
||||
Convolution convrelu_7 1 1 22 23 0=24 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=576 9=1
|
||||
Concat cat_2 2 1 19 23 24 0=0
|
||||
ShuffleChannel shufflechannel_2 1 1 24 25 0=2 1=1
|
||||
Slice shufflechannel_2_slice 1 2 25 26 27 -23300=2,-233,-233 1=0
|
||||
Convolution convrelu_8 1 1 27 28 0=24 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=576 9=1
|
||||
ConvolutionDepthWise convdw_99 1 1 28 29 0=24 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=216 7=24
|
||||
Convolution convrelu_9 1 1 29 30 0=24 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=576 9=1
|
||||
Concat cat_3 2 1 26 30 31 0=0
|
||||
Split splitncnn_1 1 3 31 32 33 34
|
||||
ConvolutionDepthWise convdw_100 1 1 34 35 0=48 1=3 11=3 12=1 13=2 14=1 2=1 3=2 4=1 5=1 6=432 7=48
|
||||
Convolution convrelu_10 1 1 33 36 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
ConvolutionDepthWise convdw_101 1 1 36 37 0=48 1=3 11=3 12=1 13=2 14=1 2=1 3=2 4=1 5=1 6=432 7=48
|
||||
Convolution convrelu_12 1 1 35 38 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
Convolution convrelu_11 1 1 37 39 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
Concat cat_4 2 1 38 39 40 0=0
|
||||
ShuffleChannel shufflechannel_3 1 1 40 41 0=2 1=1
|
||||
Slice shufflechannel_3_slice 1 2 41 42 43 -23300=2,-233,-233 1=0
|
||||
Convolution convrelu_13 1 1 43 44 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
ConvolutionDepthWise convdw_102 1 1 44 45 0=48 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=432 7=48
|
||||
Convolution convrelu_14 1 1 45 46 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
Concat cat_5 2 1 42 46 47 0=0
|
||||
ShuffleChannel shufflechannel_4 1 1 47 48 0=2 1=1
|
||||
Slice shufflechannel_4_slice 1 2 48 49 50 -23300=2,-233,-233 1=0
|
||||
Convolution convrelu_15 1 1 50 51 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
ConvolutionDepthWise convdw_103 1 1 51 52 0=48 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=432 7=48
|
||||
Convolution convrelu_16 1 1 52 53 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
Concat cat_6 2 1 49 53 54 0=0
|
||||
ShuffleChannel shufflechannel_5 1 1 54 55 0=2 1=1
|
||||
Slice shufflechannel_5_slice 1 2 55 56 57 -23300=2,-233,-233 1=0
|
||||
Convolution convrelu_17 1 1 57 58 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
ConvolutionDepthWise convdw_104 1 1 58 59 0=48 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=432 7=48
|
||||
Convolution convrelu_18 1 1 59 60 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
Concat cat_7 2 1 56 60 61 0=0
|
||||
ShuffleChannel shufflechannel_6 1 1 61 62 0=2 1=1
|
||||
Slice shufflechannel_6_slice 1 2 62 63 64 -23300=2,-233,-233 1=0
|
||||
Convolution convrelu_19 1 1 64 65 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
ConvolutionDepthWise convdw_105 1 1 65 66 0=48 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=432 7=48
|
||||
Convolution convrelu_20 1 1 66 67 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
Concat cat_8 2 1 63 67 68 0=0
|
||||
ShuffleChannel shufflechannel_7 1 1 68 69 0=2 1=1
|
||||
Slice shufflechannel_7_slice 1 2 69 70 71 -23300=2,-233,-233 1=0
|
||||
Convolution convrelu_21 1 1 71 72 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
ConvolutionDepthWise convdw_106 1 1 72 73 0=48 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=432 7=48
|
||||
Convolution convrelu_22 1 1 73 74 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
Concat cat_9 2 1 70 74 75 0=0
|
||||
ShuffleChannel shufflechannel_8 1 1 75 76 0=2 1=1
|
||||
Slice shufflechannel_8_slice 1 2 76 77 78 -23300=2,-233,-233 1=0
|
||||
Convolution convrelu_23 1 1 78 79 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
ConvolutionDepthWise convdw_107 1 1 79 80 0=48 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=432 7=48
|
||||
Convolution convrelu_24 1 1 80 81 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
Concat cat_10 2 1 77 81 82 0=0
|
||||
ShuffleChannel shufflechannel_9 1 1 82 83 0=2 1=1
|
||||
Slice shufflechannel_9_slice 1 2 83 84 85 -23300=2,-233,-233 1=0
|
||||
Convolution convrelu_25 1 1 85 86 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
ConvolutionDepthWise convdw_108 1 1 86 87 0=48 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=432 7=48
|
||||
Convolution convrelu_26 1 1 87 88 0=48 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=2304 9=1
|
||||
Concat cat_11 2 1 84 88 89 0=0
|
||||
Split splitncnn_2 1 3 89 90 91 92
|
||||
ConvolutionDepthWise convdw_109 1 1 92 93 0=96 1=3 11=3 12=1 13=2 14=1 2=1 3=2 4=1 5=1 6=864 7=96
|
||||
Convolution convrelu_27 1 1 91 94 0=96 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=9216 9=1
|
||||
ConvolutionDepthWise convdw_110 1 1 94 95 0=96 1=3 11=3 12=1 13=2 14=1 2=1 3=2 4=1 5=1 6=864 7=96
|
||||
Convolution convrelu_29 1 1 93 96 0=96 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=9216 9=1
|
||||
Convolution convrelu_28 1 1 95 97 0=96 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=9216 9=1
|
||||
Concat cat_12 2 1 96 97 98 0=0
|
||||
ShuffleChannel shufflechannel_10 1 1 98 99 0=2 1=1
|
||||
Slice shufflechannel_10_slice 1 2 99 100 101 -23300=2,-233,-233 1=0
|
||||
Convolution convrelu_30 1 1 101 102 0=96 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=9216 9=1
|
||||
ConvolutionDepthWise convdw_111 1 1 102 103 0=96 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=864 7=96
|
||||
Convolution convrelu_31 1 1 103 104 0=96 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=9216 9=1
|
||||
Concat cat_13 2 1 100 104 105 0=0
|
||||
ShuffleChannel shufflechannel_11 1 1 105 106 0=2 1=1
|
||||
Slice shufflechannel_11_slice 1 2 106 107 108 -23300=2,-233,-233 1=0
|
||||
Convolution convrelu_32 1 1 108 109 0=96 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=9216 9=1
|
||||
ConvolutionDepthWise convdw_112 1 1 109 110 0=96 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=864 7=96
|
||||
Convolution convrelu_33 1 1 110 111 0=96 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=9216 9=1
|
||||
Concat cat_14 2 1 107 111 112 0=0
|
||||
ShuffleChannel shufflechannel_12 1 1 112 113 0=2 1=1
|
||||
Slice shufflechannel_12_slice 1 2 113 114 115 -23300=2,-233,-233 1=0
|
||||
Convolution convrelu_34 1 1 115 116 0=96 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=9216 9=1
|
||||
ConvolutionDepthWise convdw_113 1 1 116 117 0=96 1=3 11=3 12=1 13=1 14=1 2=1 3=1 4=1 5=1 6=864 7=96
|
||||
Convolution convrelu_35 1 1 117 118 0=96 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=9216 9=1
|
||||
Concat cat_15 2 1 114 118 119 0=0
|
||||
Pooling avgpool2d_0 1 1 32 120 0=1 1=3 11=3 12=2 13=1 2=2 3=1 5=1 6=1
|
||||
Interp upsample_94 1 1 119 121 0=1 1=2.000000e+00 2=2.000000e+00 6=0
|
||||
Concat cat_16 3 1 120 90 121 122 0=0
|
||||
Convolution convrelu_36 1 1 122 123 0=96 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=32256 9=1
|
||||
Split splitncnn_3 1 4 123 124 125 126 127
|
||||
ConvolutionDepthWise convdwrelu_5 1 1 127 128 0=96 1=5 11=5 12=1 13=1 14=2 2=1 3=1 4=2 5=1 6=2400 7=96 9=1
|
||||
ConvolutionDepthWise convdwrelu_0 1 1 126 129 0=96 1=5 11=5 12=1 13=1 14=2 2=1 3=1 4=2 5=1 6=2400 7=96 9=1
|
||||
ConvolutionDepthWise convdwrelu_4 1 1 129 130 0=96 1=5 11=5 12=1 13=1 14=2 2=1 3=1 4=2 5=1 6=2400 7=96 9=1
|
||||
ConvolutionDepthWise convdwrelu_1 1 1 125 131 0=96 1=5 11=5 12=1 13=1 14=2 2=1 3=1 4=2 5=1 6=2400 7=96 9=1
|
||||
ConvolutionDepthWise convdwrelu_2 1 1 131 132 0=96 1=5 11=5 12=1 13=1 14=2 2=1 3=1 4=2 5=1 6=2400 7=96 9=1
|
||||
ConvolutionDepthWise convdwrelu_3 1 1 132 133 0=96 1=5 11=5 12=1 13=1 14=2 2=1 3=1 4=2 5=1 6=2400 7=96 9=1
|
||||
Concat cat_17 3 1 128 130 133 134 0=0
|
||||
Convolution conv_38 1 1 134 135 0=96 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=27648
|
||||
BinaryOp add_0 2 1 124 135 136 0=0
|
||||
ReLU relu_87 1 1 136 137
|
||||
Convolution convrelu_37 1 1 137 138 0=96 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=9216 9=1
|
||||
Split splitncnn_4 1 3 138 139 140 141
|
||||
ConvolutionDepthWise convdwrelu_7 1 1 139 142 0=96 1=5 11=5 12=1 13=1 14=2 2=1 3=1 4=2 5=1 6=2400 7=96 9=1
|
||||
Convolution conv_41 1 1 142 143 0=80 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=7680
|
||||
ConvolutionDepthWise convdwrelu_8 1 1 140 144 0=96 1=5 11=5 12=1 13=1 14=2 2=1 3=1 4=2 5=1 6=2400 7=96 9=1
|
||||
Convolution conv_42 1 1 144 145 0=4 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=384
|
||||
Softmax softmax_93 1 1 143 146 0=0 1=1
|
||||
ConvolutionDepthWise convdwrelu_6 1 1 141 147 0=96 1=5 11=5 12=1 13=1 14=2 2=1 3=1 4=2 5=1 6=2400 7=96 9=1
|
||||
Convolution convsigmoid_38 1 1 147 148 0=1 1=1 11=1 12=1 13=1 14=0 2=1 3=1 4=0 5=1 6=96 9=4
|
||||
Concat cat_18 3 1 148 145 146 out0 0=0
|
||||
1
example/ncnn/build.sh
Normal file
1
example/ncnn/build.sh
Normal file
@ -0,0 +1 @@
|
||||
g++ -o FastestDet FastestDet.cpp -I include/ncnn lib/libncnn.a `pkg-config --libs --cflags opencv` -fopenmp
|
||||
BIN
example/ncnn/result.jpg
Normal file
BIN
example/ncnn/result.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 155 KiB |
Loading…
x
Reference in New Issue
Block a user