yolo口罩

#测试显卡类别

 !nvidia-smi

Fri Aug  6 00:55:57 2021       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.42.01    Driver Version: 460.32.03    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   39C    P8    11W /  70W |      0MiB / 15109MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

# 下载yolov3

!git clone https://github.com/AlexeyAB/darknet

Cloning into 'darknet'...
remote: Enumerating objects: 15298, done.
remote: Counting objects: 100% (9/9), done.
remote: Compressing objects: 100% (9/9), done.
remote: Total 15298 (delta 1), reused 5 (delta 0), pack-reused 15289
Receiving objects: 100% (15298/15298), 13.70 MiB | 22.44 MiB/s, done.
Resolving deltas: 100% (10383/10383), done.

#设置gpu 显卡

%cd darknet
!sed -i 's/OPENCV=0/OPENCV=1/' Makefile
!sed -i 's/GPU=0/GPU=1/' Makefile
!sed -i 's/CUDNN=0/CUDNN=1/' Makefile

/content/darknet

# 看下CUDA
!/usr/local/cuda/bin/nvcc --version

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Wed_Jul_22_19:09:09_PDT_2020
Cuda compilation tools, release 11.0, V11.0.221
Build cuda_11.0_bu.TC445_37.28845127_0

#编译

!make

mkdir -p ./obj/
mkdir -p backup
chmod +x *.sh
g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/image_opencv.cpp -o obj/image_opencv.o
./src/image_opencv.cpp: In function ‘void draw_detections_cv_v3(void**, detection*, int, float, char**, image**, int, int)’:
./src/image_opencv.cpp:946:23: warning: variable ‘rgb’ set but not used [-Wunused-but-set-variable]
                 float rgb[3];
                       ^~~
./src/image_opencv.cpp: In function ‘void draw_train_loss(char*, void**, int, float, float, int, int, float, int, char*, float, int, int, double)’:
./src/image_opencv.cpp:1147:13: warning: this ‘if’ clause does not guard... [-Wmisleading-indentation]
             if (iteration_old == 0)
             ^~
./src/image_opencv.cpp:1150:10: note: ...this statement, but the latter is misleadingly indented as if it were guarded by the ‘if’
          if (iteration_old != 0){
          ^~
./src/image_opencv.cpp: In function ‘void cv_draw_object(image, float*, int, int, int*, float*, int*, int, char**)’:
./src/image_opencv.cpp:1444:14: warning: unused variable ‘buff’ [-Wunused-variable]
         char buff[100];
              ^~~~
./src/image_opencv.cpp:1420:9: warning: unused variable ‘it_tb_res’ [-Wunused-variable]
     int it_tb_res = cv::createTrackbar(it_trackbar_name, window_name, &it_trackbar_value, 1000);
         ^~~~~~~~~
./src/image_opencv.cpp:1424:9: warning: unused variable ‘lr_tb_res’ [-Wunused-variable]
     int lr_tb_res = cv::createTrackbar(lr_trackbar_name, window_name, &lr_trackbar_value, 20);
         ^~~~~~~~~
./src/image_opencv.cpp:1428:9: warning: unused variable ‘cl_tb_res’ [-Wunused-variable]
     int cl_tb_res = cv::createTrackbar(cl_trackbar_name, window_name, &cl_trackbar_value, classes-1);
         ^~~~~~~~~
./src/image_opencv.cpp:1431:9: warning: unused variable ‘bo_tb_res’ [-Wunused-variable]
     int bo_tb_res = cv::createTrackbar(bo_trackbar_name, window_name, boxonly, 1);
         ^~~~~~~~~
g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/http_stream.cpp -o obj/http_stream.o
./src/http_stream.cpp: In member function ‘bool JSON_sender::write(const char*)’:
./src/http_stream.cpp:253:21: warning: unused variable ‘n’ [-Wunused-variable]
                 int n = _write(client, outputbuf, outlen);
                     ^
./src/http_stream.cpp: In member function ‘bool MJPG_sender::write(const cv::Mat&)’:
./src/http_stream.cpp:511:113: warning: format ‘%zu’ expects argument of type ‘size_t’, but argument 3 has type ‘int’ [-Wformat=]
                 sprintf(head, "--mjpegstream\r\nContent-Type: image/jpeg\r\nContent-Length: %zu\r\n\r\n", outlen);
                                                                                                                 ^
./src/http_stream.cpp: In function ‘void set_track_id(detection*, int, float, float, float, int, int, int)’:
./src/http_stream.cpp:867:27: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
         for (int i = 0; i < v.size(); ++i) {
                         ~~^~~~~~~~~~
./src/http_stream.cpp:875:33: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
     for (int old_id = 0; old_id < old_dets.size(); ++old_id) {
                          ~~~~~~~^~~~~~~~~~~~~~~~~
./src/http_stream.cpp:894:31: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
     for (int index = 0; index < new_dets_num*old_dets.size(); ++index) {
                         ~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
./src/http_stream.cpp:930:28: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
     if (old_dets_dq.size() > deque_size) old_dets_dq.pop_front();
         ~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/gemm.c -o obj/gemm.o
./src/gemm.c: In function ‘convolution_2d’:
./src/gemm.c:2044:15: warning: unused variable ‘out_w’ [-Wunused-variable]
     const int out_w = (w + 2 * pad - ksize) / stride + 1;    // output_width=input_width for stride=1 and pad=1
               ^~~~~
./src/gemm.c:2043:15: warning: unused variable ‘out_h’ [-Wunused-variable]
     const int out_h = (h + 2 * pad - ksize) / stride + 1;    // output_height=input_height for stride=1 and pad=1
               ^~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/utils.c -o obj/utils.o
./src/utils.c: In function ‘custom_hash’:
./src/utils.c:1061:12: warning: suggest parentheses around assignment used as truth value [-Wparentheses]
     while (c = *str++)
            ^
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/dark_cuda.c -o obj/dark_cuda.o
./src/dark_cuda.c: In function ‘cudnn_check_error_extended’:
./src/dark_cuda.c:231:20: warning: comparison between ‘cudaError_t {aka enum cudaError}’ and ‘enum <anonymous>’ [-Wenum-compare]
         if (status != CUDNN_STATUS_SUCCESS)
                    ^~
./src/dark_cuda.c: In function ‘cublas_check_error_extended’:
./src/dark_cuda.c:265:18: warning: comparison between ‘cudaError_t {aka enum cudaError}’ and ‘enum cudaError_enum’ [-Wenum-compare]
       if (status != CUDA_SUCCESS)
                  ^~
./src/dark_cuda.c: In function ‘pre_allocate_pinned_memory’:
./src/dark_cuda.c:396:40: warning: format ‘%u’ expects argument of type ‘unsigned int’, but argument 2 has type ‘long unsigned int’ [-Wformat=]
         printf("pre_allocate: size = %Iu MB, num_of_blocks = %Iu, block_size = %Iu MB \n",
                                      ~~^
                                      %Ilu
             size / (1024*1024), num_of_blocks, pinned_block_size / (1024 * 1024));
             ~~~~~~~~~~~~~~~~~~          
./src/dark_cuda.c:396:64: warning: format ‘%u’ expects argument of type ‘unsigned int’, but argument 3 has type ‘size_t {aka const long unsigned int}’ [-Wformat=]
         printf("pre_allocate: size = %Iu MB, num_of_blocks = %Iu, block_size = %Iu MB \n",
                                                              ~~^
                                                              %Ilu
./src/dark_cuda.c:396:82: warning: format ‘%u’ expects argument of type ‘unsigned int’, but argument 4 has type ‘long unsigned int’ [-Wformat=]
         printf("pre_allocate: size = %Iu MB, num_of_blocks = %Iu, block_size = %Iu MB \n",
                                                                                ~~^
                                                                                %Ilu
./src/dark_cuda.c:406:37: warning: format ‘%d’ expects argument of type ‘int’, but argument 2 has type ‘size_t {aka const long unsigned int}’ [-Wformat=]
                 printf(" Allocated %d pinned block \n", pinned_block_size);
                                    ~^
                                    %ld
./src/dark_cuda.c: In function ‘cuda_make_array_pinned_preallocated’:
./src/dark_cuda.c:427:43: warning: format ‘%d’ expects argument of type ‘int’, but argument 2 has type ‘size_t {aka long unsigned int}’ [-Wformat=]
             printf("\n Pinned block_id = %d, filled = %f %% \n", pinned_block_id, filled);
                                          ~^
                                          %ld
./src/dark_cuda.c:442:64: warning: format ‘%d’ expects argument of type ‘int’, but argument 2 has type ‘long unsigned int’ [-Wformat=]
             printf("Try to allocate new pinned memory, size = %d MB \n", size / (1024 * 1024));
                                                               ~^         ~~~~~~~~~~~~~~~~~~~~
                                                               %ld
./src/dark_cuda.c:448:63: warning: format ‘%d’ expects argument of type ‘int’, but argument 2 has type ‘long unsigned int’ [-Wformat=]
             printf("Try to allocate new pinned BLOCK, size = %d MB \n", size / (1024 * 1024));
                                                              ~^         ~~~~~~~~~~~~~~~~~~~~
                                                              %ld
At top level:
./src/dark_cuda.c:288:23: warning: ‘switchBlasHandle’ defined but not used [-Wunused-variable]
 static cublasHandle_t switchBlasHandle[16];
                       ^~~~~~~~~~~~~~~~
./src/dark_cuda.c:287:12: warning: ‘switchBlasInit’ defined but not used [-Wunused-variable]
 static int switchBlasInit[16] = { 0 };
            ^~~~~~~~~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/convolutional_layer.c -o obj/convolutional_layer.o
./src/convolutional_layer.c: In function ‘forward_convolutional_layer’:
./src/convolutional_layer.c:1341:32: warning: unused variable ‘t_intput_size’ [-Wunused-variable]
                         size_t t_intput_size = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align);
                                ^~~~~~~~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/list.c -o obj/list.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/image.c -o obj/image.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/activations.c -o obj/activations.o
./src/activations.c: In function ‘activate’:
./src/activations.c:79:5: warning: enumeration value ‘RELU6’ not handled in switch [-Wswitch]
     switch(a){
     ^~~~~~
./src/activations.c:79:5: warning: enumeration value ‘SWISH’ not handled in switch [-Wswitch]
./src/activations.c:79:5: warning: enumeration value ‘MISH’ not handled in switch [-Wswitch]
./src/activations.c:79:5: warning: enumeration value ‘HARD_MISH’ not handled in switch [-Wswitch]
./src/activations.c:79:5: warning: enumeration value ‘NORM_CHAN’ not handled in switch [-Wswitch]
./src/activations.c:79:5: warning: enumeration value ‘NORM_CHAN_SOFTMAX’ not handled in switch [-Wswitch]
./src/activations.c:79:5: warning: enumeration value ‘NORM_CHAN_SOFTMAX_MAXVAL’ not handled in switch [-Wswitch]
./src/activations.c: In function ‘gradient’:
./src/activations.c:310:5: warning: enumeration value ‘SWISH’ not handled in switch [-Wswitch]
     switch(a){
     ^~~~~~
./src/activations.c:310:5: warning: enumeration value ‘MISH’ not handled in switch [-Wswitch]
./src/activations.c:310:5: warning: enumeration value ‘HARD_MISH’ not handled in switch [-Wswitch]
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/im2col.c -o obj/im2col.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/col2im.c -o obj/col2im.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/blas.c -o obj/blas.o
./src/blas.c: In function ‘backward_shortcut_multilayer_cpu’:
./src/blas.c:207:21: warning: unused variable ‘out_index’ [-Wunused-variable]
                 int out_index = id;
                     ^~~~~~~~~
./src/blas.c: In function ‘find_sim’:
./src/blas.c:597:59: warning: format ‘%d’ expects argument of type ‘int’, but argument 2 has type ‘size_t {aka long unsigned int}’ [-Wformat=]
         printf(" Error: find_sim(): sim isn't found: i = %d, j = %d, z = %d \n", i, j, z);
                                                          ~^
                                                          %ld
./src/blas.c:597:67: warning: format ‘%d’ expects argument of type ‘int’, but argument 3 has type ‘size_t {aka long unsigned int}’ [-Wformat=]
         printf(" Error: find_sim(): sim isn't found: i = %d, j = %d, z = %d \n", i, j, z);
                                                                  ~^
                                                                  %ld
./src/blas.c:597:75: warning: format ‘%d’ expects argument of type ‘int’, but argument 4 has type ‘size_t {aka long unsigned int}’ [-Wformat=]
         printf(" Error: find_sim(): sim isn't found: i = %d, j = %d, z = %d \n", i, j, z);
                                                                          ~^
                                                                          %ld
./src/blas.c: In function ‘find_P_constrastive’:
./src/blas.c:611:68: warning: format ‘%d’ expects argument of type ‘int’, but argument 2 has type ‘size_t {aka long unsigned int}’ [-Wformat=]
         printf(" Error: find_P_constrastive(): P isn't found: i = %d, j = %d, z = %d \n", i, j, z);
                                                                   ~^
                                                                   %ld
./src/blas.c:611:76: warning: format ‘%d’ expects argument of type ‘int’, but argument 3 has type ‘size_t {aka long unsigned int}’ [-Wformat=]
         printf(" Error: find_P_constrastive(): P isn't found: i = %d, j = %d, z = %d \n", i, j, z);
                                                                           ~^
                                                                           %ld
./src/blas.c:611:84: warning: format ‘%d’ expects argument of type ‘int’, but argument 4 has type ‘size_t {aka long unsigned int}’ [-Wformat=]
         printf(" Error: find_P_constrastive(): P isn't found: i = %d, j = %d, z = %d \n", i, j, z);
                                                                                   ~^
                                                                                   %ld
./src/blas.c: In function ‘P_constrastive_f’:
./src/blas.c:651:79: warning: format ‘%d’ expects argument of type ‘int’, but argument 3 has type ‘size_t {aka long unsigned int}’ [-Wformat=]
         fprintf(stderr, " Error: in P_constrastive must be i != l, while i = %d, l = %d \n", i, l);
                                                                              ~^
                                                                              %ld
./src/blas.c:651:87: warning: format ‘%d’ expects argument of type ‘int’, but argument 4 has type ‘size_t {aka long unsigned int}’ [-Wformat=]
         fprintf(stderr, " Error: in P_constrastive must be i != l, while i = %d, l = %d \n", i, l);
                                                                                      ~^
                                                                                      %ld
./src/blas.c: In function ‘P_constrastive’:
./src/blas.c:785:79: warning: format ‘%d’ expects argument of type ‘int’, but argument 3 has type ‘size_t {aka long unsigned int}’ [-Wformat=]
         fprintf(stderr, " Error: in P_constrastive must be i != l, while i = %d, l = %d \n", i, l);
                                                                              ~^
                                                                              %ld
./src/blas.c:785:87: warning: format ‘%d’ expects argument of type ‘int’, but argument 4 has type ‘size_t {aka long unsigned int}’ [-Wformat=]
         fprintf(stderr, " Error: in P_constrastive must be i != l, while i = %d, l = %d \n", i, l);
                                                                                      ~^
                                                                                      %ld
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/crop_layer.c -o obj/crop_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/dropout_layer.c -o obj/dropout_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/maxpool_layer.c -o obj/maxpool_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/softmax_layer.c -o obj/softmax_layer.o
./src/softmax_layer.c: In function ‘make_contrastive_layer’:
./src/softmax_layer.c:203:101: warning: format ‘%d’ expects argument of type ‘int’, but argument 9 has type ‘size_t {aka const long unsigned int}’ [-Wformat=]
     fprintf(stderr, "contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d  classes = %4d, step = %4d \n", w, h, l.n, l.embedding_size, batch, l.classes, step);
                                                                                                   ~~^
                                                                                                   %4ld
./src/softmax_layer.c: In function ‘forward_contrastive_layer’:
./src/softmax_layer.c:244:27: warning: variable ‘max_truth’ set but not used [-Wunused-but-set-variable]
                     float max_truth = 0;
                           ^~~~~~~~~
./src/softmax_layer.c:423:71: warning: format ‘%d’ expects argument of type ‘int’, but argument 2 has type ‘size_t {aka const long unsigned int}’ [-Wformat=]
             printf(" Error: too large number of bboxes: contr_size = %d > max_contr_size  = %d \n", contr_size, max_contr_size);
                                                                      ~^
                                                                      %ld
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/data.c -o obj/data.o
./src/data.c: In function ‘load_data_detection’:
./src/data.c:1297:24: warning: unused variable ‘x’ [-Wunused-variable]
                 int k, x, y;
                        ^
./src/data.c:1090:43: warning: variable ‘r_scale’ set but not used [-Wunused-but-set-variable]
     float r1 = 0, r2 = 0, r3 = 0, r4 = 0, r_scale = 0;
                                           ^~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/matrix.c -o obj/matrix.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/network.c -o obj/network.o
./src/network.c: In function ‘train_network_waitkey’:
./src/network.c:435:13: warning: unused variable ‘ema_period’ [-Wunused-variable]
         int ema_period = (net.max_batches - ema_start_point - 1000) * (1.0 - net.ema_alpha);
             ^~~~~~~~~~
./src/network.c: In function ‘resize_network’:
./src/network.c:660:42: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
         if (cudaSuccess == cudaHostAlloc(&net->input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped))
                                          ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from include/darknet.h:41,
                 from ./src/network.c:1:
/usr/local/cuda/include/cuda_runtime_api.h:4707:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/connected_layer.c -o obj/connected_layer.o
./src/connected_layer.c: In function ‘forward_connected_layer_gpu’:
./src/connected_layer.c:346:11: warning: unused variable ‘one’ [-Wunused-variable]
     float one = 1;    // alpha[0], beta[0]
           ^~~
./src/connected_layer.c:344:13: warning: unused variable ‘c’ [-Wunused-variable]
     float * c = l.output_gpu;
             ^
./src/connected_layer.c:343:13: warning: unused variable ‘b’ [-Wunused-variable]
     float * b = l.weights_gpu;
             ^
./src/connected_layer.c:342:13: warning: unused variable ‘a’ [-Wunused-variable]
     float * a = state.input;
             ^
./src/connected_layer.c:341:9: warning: unused variable ‘n’ [-Wunused-variable]
     int n = l.outputs;
         ^
./src/connected_layer.c:340:9: warning: unused variable ‘k’ [-Wunused-variable]
     int k = l.inputs;
         ^
./src/connected_layer.c:339:9: warning: unused variable ‘m’ [-Wunused-variable]
     int m = l.batch;
         ^
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/cost_layer.c -o obj/cost_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/parser.c -o obj/parser.o
./src/parser.c: In function ‘parse_network_cfg_custom’:
./src/parser.c:1777:42: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
         if (cudaSuccess == cudaHostAlloc(&net.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1;
                                          ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from include/darknet.h:41,
                 from ./src/activations.h:3,
                 from ./src/activation_layer.h:4,
                 from ./src/parser.c:6:
/usr/local/cuda/include/cuda_runtime_api.h:4707:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
./src/parser.c: In function ‘save_implicit_weights’:
./src/parser.c:1909:9: warning: unused variable ‘i’ [-Wunused-variable]
     int i;
         ^
./src/parser.c: In function ‘get_classes_multipliers’:
./src/parser.c:438:29: warning: argument 1 range [18446744071562067968, 18446744073709551615] exceeds maximum object size 9223372036854775807 [-Walloc-size-larger-than=]
         classes_multipliers = (float *)calloc(classes_counters, sizeof(float));
         ~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In file included from ./src/parser.c:3:0:
/usr/include/stdlib.h:541:14: note: in a call to allocation function ‘calloc’ declared here
 extern void *calloc (size_t __nmemb, size_t __size)
              ^~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/option_list.c -o obj/option_list.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/darknet.c -o obj/darknet.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/detection_layer.c -o obj/detection_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/captcha.c -o obj/captcha.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/route_layer.c -o obj/route_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/writing.c -o obj/writing.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/box.c -o obj/box.o
./src/box.c: In function ‘box_iou_kind’:
./src/box.c:154:5: warning: enumeration value ‘MSE’ not handled in switch [-Wswitch]
     switch(iou_kind) {
     ^~~~~~
./src/box.c: In function ‘diounms_sort’:
./src/box.c:898:27: warning: unused variable ‘beta_prob’ [-Wunused-variable]
                     float beta_prob = pow(dets[j].prob[k], 2) / sum_prob;
                           ^~~~~~~~~
./src/box.c:897:27: warning: unused variable ‘alpha_prob’ [-Wunused-variable]
                     float alpha_prob = pow(dets[i].prob[k], 2) / sum_prob;
                           ^~~~~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/nightmare.c -o obj/nightmare.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/normalization_layer.c -o obj/normalization_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/avgpool_layer.c -o obj/avgpool_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/coco.c -o obj/coco.o
./src/coco.c: In function ‘validate_coco_recall’:
./src/coco.c:248:11: warning: unused variable ‘base’ [-Wunused-variable]
     char *base = "results/comp4_det_test_";
           ^~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/dice.c -o obj/dice.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/yolo.c -o obj/yolo.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/detector.c -o obj/detector.o
./src/detector.c: In function ‘train_detector’:
./src/detector.c:386:72: warning: suggest parentheses around ‘&&’ within ‘||’ [-Wparentheses]
             (iteration >= (iter_save + 1000) || iteration % 1000 == 0) && net.max_batches < 10000)
             ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~
./src/detector.c: In function ‘print_cocos’:
./src/detector.c:486:29: warning: format not a string literal and no format arguments [-Wformat-security]
                 fprintf(fp, buff);
                             ^~~~
./src/detector.c: In function ‘eliminate_bdd’:
./src/detector.c:579:21: warning: statement with no effect [-Wunused-value]
                     for (k; buf[k + n] != '\0'; k++)
                     ^~~
./src/detector.c: In function ‘validate_detector’:
./src/detector.c:700:13: warning: unused variable ‘mkd2’ [-Wunused-variable]
         int mkd2 = make_directory(buff2, 0777);
             ^~~~
./src/detector.c:698:13: warning: unused variable ‘mkd’ [-Wunused-variable]
         int mkd = make_directory(buff, 0777);
             ^~~
./src/detector.c: In function ‘validate_detector_map’:
./src/detector.c:1315:24: warning: variable ‘cur_prob’ set but not used [-Wunused-but-set-variable]
                 double cur_prob = 0;
                        ^~~~~~~~
./src/detector.c:1336:15: warning: unused variable ‘class_recall’ [-Wunused-variable]
         float class_recall = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)(truth_classes_count[i] - tp_for_thresh_per_class[i]));
               ^~~~~~~~~~~~
./src/detector.c:1335:15: warning: unused variable ‘class_precision’ [-Wunused-variable]
         float class_precision = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]);
               ^~~~~~~~~~~~~~~
./src/detector.c: In function ‘draw_object’:
./src/detector.c:1871:19: warning: unused variable ‘inv_loss’ [-Wunused-variable]
             float inv_loss = 1.0 / max_val_cmp(0.01, avg_loss);
                   ^~~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/layer.c -o obj/layer.o
./src/layer.c: In function ‘free_layer_custom’:
./src/layer.c:208:68: warning: suggest parentheses around ‘&&’ within ‘||’ [-Wparentheses]
     if (l.delta_gpu && (l.optimized_memory < 1 || l.keep_delta_gpu && l.optimized_memory < 3)) cuda_free(l.delta_gpu), l.delta_gpu = NULL;
                                                   ~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/compare.c -o obj/compare.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/classifier.c -o obj/classifier.o
./src/classifier.c: In function ‘train_classifier’:
./src/classifier.c:146:9: warning: unused variable ‘count’ [-Wunused-variable]
     int count = 0;
         ^~~~~
./src/classifier.c: In function ‘predict_classifier’:
./src/classifier.c:855:13: warning: unused variable ‘time’ [-Wunused-variable]
     clock_t time;
             ^~~~
./src/classifier.c: In function ‘demo_classifier’:
./src/classifier.c:1287:49: warning: unused variable ‘tval_result’ [-Wunused-variable]
         struct timeval tval_before, tval_after, tval_result;
                                                 ^~~~~~~~~~~
./src/classifier.c:1287:37: warning: unused variable ‘tval_after’ [-Wunused-variable]
         struct timeval tval_before, tval_after, tval_result;
                                     ^~~~~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/local_layer.c -o obj/local_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/swag.c -o obj/swag.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/shortcut_layer.c -o obj/shortcut_layer.o
./src/shortcut_layer.c: In function ‘make_shortcut_layer’:
./src/shortcut_layer.c:55:15: warning: unused variable ‘scale’ [-Wunused-variable]
         float scale = sqrt(2. / l.nweights);
               ^~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/representation_layer.c -o obj/representation_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/activation_layer.c -o obj/activation_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/rnn_layer.c -o obj/rnn_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/gru_layer.c -o obj/gru_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/rnn.c -o obj/rnn.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/rnn_vid.c -o obj/rnn_vid.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/crnn_layer.c -o obj/crnn_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/demo.c -o obj/demo.o
./src/demo.c: In function ‘detect_in_thread’:
./src/demo.c:101:15: warning: unused variable ‘l’ [-Wunused-variable]
         layer l = net.layers[net.n - 1];
               ^
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/tag.c -o obj/tag.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/cifar.c -o obj/cifar.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/go.c -o obj/go.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/batchnorm_layer.c -o obj/batchnorm_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/art.c -o obj/art.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/region_layer.c -o obj/region_layer.o
./src/region_layer.c: In function ‘resize_region_layer’:
./src/region_layer.c:63:9: warning: unused variable ‘old_h’ [-Wunused-variable]
     int old_h = l->h;
         ^~~~~
./src/region_layer.c:62:9: warning: unused variable ‘old_w’ [-Wunused-variable]
     int old_w = l->w;
         ^~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/reorg_layer.c -o obj/reorg_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/reorg_old_layer.c -o obj/reorg_old_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/super.c -o obj/super.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/voxel.c -o obj/voxel.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/tree.c -o obj/tree.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/yolo_layer.c -o obj/yolo_layer.o
./src/yolo_layer.c: In function ‘make_yolo_layer’:
./src/yolo_layer.c:68:38: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
     if (cudaSuccess == cudaHostAlloc(&l.output, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;
                                      ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from include/darknet.h:41,
                 from ./src/activations.h:3,
                 from ./src/layer.h:4,
                 from ./src/yolo_layer.h:5,
                 from ./src/yolo_layer.c:1:
/usr/local/cuda/include/cuda_runtime_api.h:4707:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
./src/yolo_layer.c:75:38: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
     if (cudaSuccess == cudaHostAlloc(&l.delta, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;
                                      ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from include/darknet.h:41,
                 from ./src/activations.h:3,
                 from ./src/layer.h:4,
                 from ./src/yolo_layer.h:5,
                 from ./src/yolo_layer.c:1:
/usr/local/cuda/include/cuda_runtime_api.h:4707:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
./src/yolo_layer.c: In function ‘resize_yolo_layer’:
./src/yolo_layer.c:106:42: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
         if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {
                                          ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from include/darknet.h:41,
                 from ./src/activations.h:3,
                 from ./src/layer.h:4,
                 from ./src/yolo_layer.h:5,
                 from ./src/yolo_layer.c:1:
/usr/local/cuda/include/cuda_runtime_api.h:4707:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
./src/yolo_layer.c:115:42: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
         if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {
                                          ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from include/darknet.h:41,
                 from ./src/activations.h:3,
                 from ./src/layer.h:4,
                 from ./src/yolo_layer.h:5,
                 from ./src/yolo_layer.c:1:
/usr/local/cuda/include/cuda_runtime_api.h:4707:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
./src/yolo_layer.c: In function ‘process_batch’:
./src/yolo_layer.c:426:25: warning: variable ‘best_match_t’ set but not used [-Wunused-but-set-variable]
                     int best_match_t = 0;
                         ^~~~~~~~~~~~
./src/yolo_layer.c: In function ‘forward_yolo_layer’:
./src/yolo_layer.c:707:11: warning: unused variable ‘avg_anyobj’ [-Wunused-variable]
     float avg_anyobj = 0;
           ^~~~~~~~~~
./src/yolo_layer.c:706:11: warning: unused variable ‘avg_obj’ [-Wunused-variable]
     float avg_obj = 0;
           ^~~~~~~
./src/yolo_layer.c:705:11: warning: unused variable ‘avg_cat’ [-Wunused-variable]
     float avg_cat = 0;
           ^~~~~~~
./src/yolo_layer.c:704:11: warning: unused variable ‘recall75’ [-Wunused-variable]
     float recall75 = 0;
           ^~~~~~~~
./src/yolo_layer.c:703:11: warning: unused variable ‘recall’ [-Wunused-variable]
     float recall = 0;
           ^~~~~~
./src/yolo_layer.c:702:11: warning: unused variable ‘tot_ciou_loss’ [-Wunused-variable]
     float tot_ciou_loss = 0;
           ^~~~~~~~~~~~~
./src/yolo_layer.c:701:11: warning: unused variable ‘tot_diou_loss’ [-Wunused-variable]
     float tot_diou_loss = 0;
           ^~~~~~~~~~~~~
./src/yolo_layer.c:698:11: warning: unused variable ‘tot_ciou’ [-Wunused-variable]
     float tot_ciou = 0;
           ^~~~~~~~
./src/yolo_layer.c:697:11: warning: unused variable ‘tot_diou’ [-Wunused-variable]
     float tot_diou = 0;
           ^~~~~~~~
./src/yolo_layer.c:696:11: warning: unused variable ‘tot_giou’ [-Wunused-variable]
     float tot_giou = 0;
           ^~~~~~~~
./src/yolo_layer.c:668:12: warning: unused variable ‘n’ [-Wunused-variable]
     int b, n;
            ^
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/gaussian_yolo_layer.c -o obj/gaussian_yolo_layer.o
./src/gaussian_yolo_layer.c: In function ‘make_gaussian_yolo_layer’:
./src/gaussian_yolo_layer.c:72:38: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
     if (cudaSuccess == cudaHostAlloc(&l.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;
                                      ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from include/darknet.h:41,
                 from ./src/gaussian_yolo_layer.h:5,
                 from ./src/gaussian_yolo_layer.c:7:
/usr/local/cuda/include/cuda_runtime_api.h:4707:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
./src/gaussian_yolo_layer.c:79:38: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
     if (cudaSuccess == cudaHostAlloc(&l.delta, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;
                                      ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from include/darknet.h:41,
                 from ./src/gaussian_yolo_layer.h:5,
                 from ./src/gaussian_yolo_layer.c:7:
/usr/local/cuda/include/cuda_runtime_api.h:4707:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
./src/gaussian_yolo_layer.c: In function ‘resize_gaussian_yolo_layer’:
./src/gaussian_yolo_layer.c:111:42: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
         if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {
                                          ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from include/darknet.h:41,
                 from ./src/gaussian_yolo_layer.h:5,
                 from ./src/gaussian_yolo_layer.c:7:
/usr/local/cuda/include/cuda_runtime_api.h:4707:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
./src/gaussian_yolo_layer.c:120:42: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
         if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {
                                          ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from include/darknet.h:41,
                 from ./src/gaussian_yolo_layer.h:5,
                 from ./src/gaussian_yolo_layer.c:7:
/usr/local/cuda/include/cuda_runtime_api.h:4707:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/upsample_layer.c -o obj/upsample_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/lstm_layer.c -o obj/lstm_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/conv_lstm_layer.c -o obj/conv_lstm_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/scale_channels_layer.c -o obj/scale_channels_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/sam_layer.c -o obj/sam_layer.o
nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/convolutional_kernels.cu -o obj/convolutional_kernels.o
nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/activation_kernels.cu -o obj/activation_kernels.o
nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
./src/activation_kernels.cu(263): warning: variable "MISH_THRESHOLD" was declared but never referenced

./src/activation_kernels.cu(263): warning: variable "MISH_THRESHOLD" was declared but never referenced

./src/activation_kernels.cu(263): warning: variable "MISH_THRESHOLD" was declared but never referenced

./src/activation_kernels.cu(263): warning: variable "MISH_THRESHOLD" was declared but never referenced

nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/im2col_kernels.cu -o obj/im2col_kernels.o
nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/col2im_kernels.cu -o obj/col2im_kernels.o
nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/blas_kernels.cu -o obj/blas_kernels.o
nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
./src/blas_kernels.cu(1086): warning: variable "out_index" was declared but never referenced

./src/blas_kernels.cu(1130): warning: variable "step" was set but never used

./src/blas_kernels.cu(1736): warning: variable "stage_id" was declared but never referenced

./src/blas_kernels.cu(1086): warning: variable "out_index" was declared but never referenced

./src/blas_kernels.cu(1130): warning: variable "step" was set but never used

./src/blas_kernels.cu(1736): warning: variable "stage_id" was declared but never referenced

./src/blas_kernels.cu(1086): warning: variable "out_index" was declared but never referenced

./src/blas_kernels.cu(1130): warning: variable "step" was set but never used

./src/blas_kernels.cu(1736): warning: variable "stage_id" was declared but never referenced

./src/blas_kernels.cu(1086): warning: variable "out_index" was declared but never referenced

./src/blas_kernels.cu(1130): warning: variable "step" was set but never used

./src/blas_kernels.cu(1736): warning: variable "stage_id" was declared but never referenced

./src/blas_kernels.cu: In function ‘void backward_shortcut_multilayer_gpu(int, int, int, int*, float**, float*, float*, float*, float*, int, float*, float**, WEIGHTS_NORMALIZATION_T)’:
./src/blas_kernels.cu:1130:5: warning: variable ‘step’ set but not used [-Wunused-but-set-variable]
     int step = 0;
     ^~~~
nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/crop_layer_kernels.cu -o obj/crop_layer_kernels.o
nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/dropout_layer_kernels.cu -o obj/dropout_layer_kernels.o
nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/maxpool_layer_kernels.cu -o obj/maxpool_layer_kernels.o
nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/network_kernels.cu -o obj/network_kernels.o
nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
./src/network_kernels.cu(379): warning: variable "l" was declared but never referenced

./src/network_kernels.cu(379): warning: variable "l" was declared but never referenced

./src/network_kernels.cu(379): warning: variable "l" was declared but never referenced

./src/network_kernels.cu(379): warning: variable "l" was declared but never referenced

./src/network_kernels.cu: In function ‘float train_network_datum_gpu(network, float*, float*)’:
./src/network_kernels.cu:379:7: warning: variable ‘l’ set but not used [-Wunused-but-set-variable]
         layer l = net.layers[net.n - 1];
       ^
nvcc -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/avgpool_layer_kernels.cu -o obj/avgpool_layer_kernels.o
nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include obj/image_opencv.o obj/http_stream.o obj/gemm.o obj/utils.o obj/dark_cuda.o obj/convolutional_layer.o obj/list.o obj/image.o obj/activations.o obj/im2col.o obj/col2im.o obj/blas.o obj/crop_layer.o obj/dropout_layer.o obj/maxpool_layer.o obj/softmax_layer.o obj/data.o obj/matrix.o obj/network.o obj/connected_layer.o obj/cost_layer.o obj/parser.o obj/option_list.o obj/darknet.o obj/detection_layer.o obj/captcha.o obj/route_layer.o obj/writing.o obj/box.o obj/nightmare.o obj/normalization_layer.o obj/avgpool_layer.o obj/coco.o obj/dice.o obj/yolo.o obj/detector.o obj/layer.o obj/compare.o obj/classifier.o obj/local_layer.o obj/swag.o obj/shortcut_layer.o obj/representation_layer.o obj/activation_layer.o obj/rnn_layer.o obj/gru_layer.o obj/rnn.o obj/rnn_vid.o obj/crnn_layer.o obj/demo.o obj/tag.o obj/cifar.o obj/go.o obj/batchnorm_layer.o obj/art.o obj/region_layer.o obj/reorg_layer.o obj/reorg_old_layer.o obj/super.o obj/voxel.o obj/tree.o obj/yolo_layer.o obj/gaussian_yolo_layer.o obj/upsample_layer.o obj/lstm_layer.o obj/conv_lstm_layer.o obj/scale_channels_layer.o obj/sam_layer.o obj/convolutional_kernels.o obj/activation_kernels.o obj/im2col_kernels.o obj/col2im_kernels.o obj/blas_kernels.o obj/crop_layer_kernels.o obj/dropout_layer_kernels.o obj/maxpool_layer_kernels.o obj/network_kernels.o obj/avgpool_layer_kernels.o -o darknet -lm -pthread `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv` -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++

# 下载预训练的权重
!wget https://pjreddie.com/media/files/yolov3.weights

--2021-08-06 00:58:07--  https://pjreddie.com/media/files/yolov3.weights
Resolving pjreddie.com (pjreddie.com)... 128.208.4.108
Connecting to pjreddie.com (pjreddie.com)|128.208.4.108|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 248007048 (237M) [application/octet-stream]
Saving to: ‘yolov3.weights’

yolov3.weights      100%[===================>] 236.52M  80.0MB/s    in 3.0s    

2021-08-06 00:58:10 (80.0 MB/s) - ‘yolov3.weights’ saved [248007048/248007048]

# 显示
def imShow(path):
  import cv2
  import matplotlib.pyplot as plt
  %matplotlib inline

  image = cv2.imread(path)
  height, width = image.shape[:2]
  resized_image = cv2.resize(image,(3*width, 3*height), interpolation = cv2.INTER_CUBIC)

  fig = plt.gcf()
  fig.set_size_inches(18, 10)
  plt.axis("off")
  plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))
  plt.show()

# 上传文件
def upload():
  from google.colab import files
  uploaded = files.upload() 
  for name, data in uploaded.items():
    with open(name, 'wb') as f:
      f.write(data)
      print ('saved file', name)

# 下载文件 
def download(path):
  from google.colab import files
  files.download(path)

%cd /content/darknet

/content/darknet

# 检测试一下

!./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
# 上述函数写好了，显示一下
imShow('predictions.jpg')

 CUDA-version: 11000 (11020), cuDNN: 7.6.5, GPU count: 1  
 OpenCV version: 3.2.0
 0 : compute_capability = 750, cudnn_half = 0, GPU: Tesla T4 
net.optimized_memory = 0 
mini_batch = 1, batch = 1, time_steps = 1, train = 0 
   layer   filters  size/strd(dil)      input                output
   0 Create CUDA-stream - 0 
 Create cudnn-handle 0 
conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF
   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF
   2 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF
   3 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF
   4 Shortcut Layer: 1,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF
   5 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF
   6 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
   7 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
   8 Shortcut Layer: 5,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
   9 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
  10 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
  11 Shortcut Layer: 8,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
  12 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF
  13 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  14 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  15 Shortcut Layer: 12,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  16 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  17 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  18 Shortcut Layer: 15,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  19 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  20 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  21 Shortcut Layer: 18,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  22 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  23 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  24 Shortcut Layer: 21,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  26 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  27 Shortcut Layer: 24,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  28 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  29 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  31 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  32 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  34 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  35 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  37 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF
  38 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  39 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  40 Shortcut Layer: 37,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  41 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  42 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  43 Shortcut Layer: 40,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  44 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  45 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  46 Shortcut Layer: 43,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  47 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  48 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  49 Shortcut Layer: 46,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  50 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  51 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  52 Shortcut Layer: 49,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  53 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  54 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  55 Shortcut Layer: 52,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  57 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  58 Shortcut Layer: 55,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  59 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  60 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  62 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF
  63 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  64 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  65 Shortcut Layer: 62,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  66 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  67 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  68 Shortcut Layer: 65,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  69 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  70 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  71 Shortcut Layer: 68,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  72 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  73 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  74 Shortcut Layer: 71,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  75 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  76 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  77 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  78 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  79 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  80 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  81 conv    255       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 255 0.088 BF
  82 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  83 route  79 		                           ->   13 x  13 x 512 
  84 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF
  85 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256
  86 route  85 61 	                           ->   26 x  26 x 768 
  87 conv    256       1 x 1/ 1     26 x  26 x 768 ->   26 x  26 x 256 0.266 BF
  88 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  89 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  90 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  91 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  92 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  93 conv    255       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 255 0.177 BF
  94 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  95 route  91 		                           ->   26 x  26 x 256 
  96 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF
  97 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128
  98 route  97 36 	                           ->   52 x  52 x 384 
  99 conv    128       1 x 1/ 1     52 x  52 x 384 ->   52 x  52 x 128 0.266 BF
 100 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 101 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 102 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 103 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 104 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 105 conv    255       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 255 0.353 BF
 106 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 65.879 
avg_outputs = 532444 
 Allocate additional workspace_size = 52.43 MB 
Loading weights from yolov3.weights...
 seen 64, trained: 32013 K-images (500 Kilo-batches_64) 
Done! Loaded 107 layers from weights-file 
 Detection layer: 82 - type = 28 
 Detection layer: 94 - type = 28 
 Detection layer: 106 - type = 28 
data/dog.jpg: Predicted in 40.893000 milli-seconds.
bicycle: 99%
dog: 100%
truck: 94%
Unable to init server: Could not connect: Connection refused

(predictions:1183): Gtk-WARNING **: 00:58:30.073: cannot open display: 

# 为了创建自定义YOLOv3检测器，需要以下内容：

# 自己的带标签的数据集
# 配置自己的 .cfg 文件
# obj.data 和 obj.names 文件
# train.txt 文件，测试集可选

! rm -rf /content/cfg_mask
! mkdir /content/cfg_mask

! head /content/drive/MyDrive/cfg_mask/obj.data

classes= 1
train  = /content/drive/MyDrive/cfg_mask/train.txt
valid  = /content/drive/MyDrive/cfg_mask/test.txt
names  = /content/cfg_mask/obj.names
backup = /content/cfg_mask/weights/

#从谷歌云盘上传根据自己数据集修改的.cfg文件 会上传到darknet/cfg文件夹下
!cp /content/drive/MyDrive/cfg_mask/yolov3_custom2.cfg /content/cfg_mask

#从谷歌云盘上传自己数据集的obj.data obj.names
!cp /content/drive/MyDrive/cfg_mask/obj.data /content/cfg_mask
!cp /content/drive/MyDrive/cfg_mask/obj.names  /content/cfg_mask

#上传generate_train.py 以在服务器的data下生成train.txt
!cp /content/drive/MyDrive/cfg_mask/train.txt /content/cfg_mask

%cd /content/cfg_mask

/content/cfg_mask

!wget http://pjreddie.com/media/files/darknet53.conv.74

URL transformed to HTTPS due to an HSTS policy
--2021-08-06 01:01:11--  https://pjreddie.com/media/files/darknet53.conv.74
Resolving pjreddie.com (pjreddie.com)... 128.208.4.108
Connecting to pjreddie.com (pjreddie.com)|128.208.4.108|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 162482580 (155M) [application/octet-stream]
Saving to: ‘darknet53.conv.74’

darknet53.conv.74   100%[===================>] 154.96M   107MB/s    in 1.5s    

2021-08-06 01:01:13 (107 MB/s) - ‘darknet53.conv.74’ saved [162482580/162482580]

# 开始训练

# function ClickConnect(){
# console.log("Working"); 
# document.querySelector("colab-toolbar-button#connect").click() 
# }
# setInterval(ClickConnect,60000)

%cd /content/darknet

/content/darknet

imShow('/content/drive/MyDrive/yolov3/data/obj/raccoon-100.jpg')

# 运行就好，从本地或者云端
!./darknet detector test /content/cfg_mask/obj.data /content/cfg_mask/yolov3_custom2.cfg /content/cfg_mask/yolov3_custom2_final.weights /content/drive/MyDrive/yolo3/mask_dataset/02C96BZM5DKA178WY34I.jpg -thresh 0.99
imShow('predictions.jpg')

 CUDA-version: 11000 (11020), cuDNN: 7.6.5, GPU count: 1  
 OpenCV version: 3.2.0
 0 : compute_capability = 750, cudnn_half = 0, GPU: Tesla T4 
net.optimized_memory = 0 
mini_batch = 1, batch = 1, time_steps = 1, train = 0 
   layer   filters  size/strd(dil)      input                output
   0 Create CUDA-stream - 0 
 Create cudnn-handle 0 
conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF
   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF
   2 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF
   3 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF
   4 Shortcut Layer: 1,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF
   5 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF
   6 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
   7 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
   8 Shortcut Layer: 5,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
   9 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
  10 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
  11 Shortcut Layer: 8,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
  12 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF
  13 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  14 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  15 Shortcut Layer: 12,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  16 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  17 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  18 Shortcut Layer: 15,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  19 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  20 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  21 Shortcut Layer: 18,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  22 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  23 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  24 Shortcut Layer: 21,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  26 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  27 Shortcut Layer: 24,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  28 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  29 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  31 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  32 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  34 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  35 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  37 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF
  38 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  39 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  40 Shortcut Layer: 37,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  41 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  42 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  43 Shortcut Layer: 40,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  44 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  45 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  46 Shortcut Layer: 43,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  47 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  48 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  49 Shortcut Layer: 46,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  50 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  51 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  52 Shortcut Layer: 49,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  53 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  54 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  55 Shortcut Layer: 52,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  57 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  58 Shortcut Layer: 55,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  59 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  60 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  62 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF
  63 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  64 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  65 Shortcut Layer: 62,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  66 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  67 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  68 Shortcut Layer: 65,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  69 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  70 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  71 Shortcut Layer: 68,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  72 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  73 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  74 Shortcut Layer: 71,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  75 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  76 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  77 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  78 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  79 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  80 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  81 conv     18       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x  18 0.006 BF
  82 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  83 route  79 		                           ->   13 x  13 x 512 
  84 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF
  85 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256
  86 route  85 61 	                           ->   26 x  26 x 768 
  87 conv    256       1 x 1/ 1     26 x  26 x 768 ->   26 x  26 x 256 0.266 BF
  88 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  89 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  90 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  91 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  92 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  93 conv     18       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x  18 0.012 BF
  94 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  95 route  91 		                           ->   26 x  26 x 256 
  96 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF
  97 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128
  98 route  97 36 	                           ->   52 x  52 x 384 
  99 conv    128       1 x 1/ 1     52 x  52 x 384 ->   52 x  52 x 128 0.266 BF
 100 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 101 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 102 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 103 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 104 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 105 conv     18       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x  18 0.025 BF
 106 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 65.304 
avg_outputs = 516723 
 Allocate additional workspace_size = 52.43 MB 
Loading weights from /content/cfg_mask/yolov3_custom2_final.weights...
 seen 64, trained: 0 K-images (0 Kilo-batches_64) 
Done! Loaded 107 layers from weights-file 
 Detection layer: 82 - type = 28 
 Detection layer: 94 - type = 28 
 Detection layer: 106 - type = 28 
/content/drive/MyDrive/yolo3/mask_dataset/02C96BZM5DKA178WY34I.jpg: Predicted in 40.201000 milli-seconds.
kouzhao: 100%
kouzhao: 100%
kouzhao: 100%
kouzhao: 99%
kouzhao: 100%
kouzhao: 99%
kouzhao: 100%
kouzhao: 100%
kouzhao: 100%
kouzhao: 99%
kouzhao: 100%
kouzhao: 100%
kouzhao: 99%
Unable to init server: Could not connect: Connection refused

(predictions:3399): Gtk-WARNING **: 01:10:08.120: cannot open display: 

# 1000次训练结果

from PIL import Image
img = Image.open('/content/drive/MyDrive/yolo3/mask_dataset/02C96BZM5DKA178WY34I.jpg')
img

# 运行就好，从本地或者云端
%cd /content/darknet
!./darknet detector test /content/cfg_mask/obj.data /content/cfg_mask/yolov3_custom2.cfg /content/cfg_mask/yolov3_custom2_final.weights /content/drive/MyDrive/yolo3/mask_dataset/02C96BZM5DKA178WY34I.jpg
imShow('predictions.jpg')

/content/darknet
 CUDA-version: 11000 (11020), cuDNN: 7.6.5, GPU count: 1  
 OpenCV version: 3.2.0
 0 : compute_capability = 750, cudnn_half = 0, GPU: Tesla T4 
net.optimized_memory = 0 
mini_batch = 1, batch = 1, time_steps = 1, train = 0 
   layer   filters  size/strd(dil)      input                output
   0 Create CUDA-stream - 0 
 Create cudnn-handle 0 
conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF
   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF
   2 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF
   3 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF
   4 Shortcut Layer: 1,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF
   5 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF
   6 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
   7 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
   8 Shortcut Layer: 5,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
   9 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF
  10 conv    128       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x 128 1.595 BF
  11 Shortcut Layer: 8,  wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
  12 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF
  13 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  14 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  15 Shortcut Layer: 12,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  16 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  17 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  18 Shortcut Layer: 15,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  19 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  20 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  21 Shortcut Layer: 18,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  22 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  23 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  24 Shortcut Layer: 21,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  26 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  27 Shortcut Layer: 24,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  28 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  29 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  31 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  32 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  34 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
  35 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 256 0.001 BF
  37 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF
  38 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  39 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  40 Shortcut Layer: 37,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  41 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  42 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  43 Shortcut Layer: 40,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  44 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  45 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  46 Shortcut Layer: 43,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  47 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  48 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  49 Shortcut Layer: 46,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  50 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  51 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  52 Shortcut Layer: 49,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  53 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  54 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  55 Shortcut Layer: 52,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  57 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  58 Shortcut Layer: 55,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  59 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  60 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 512 0.000 BF
  62 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF
  63 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  64 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  65 Shortcut Layer: 62,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  66 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  67 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  68 Shortcut Layer: 65,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  69 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  70 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  71 Shortcut Layer: 68,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  72 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  73 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  74 Shortcut Layer: 71,  wt = 0, wn = 0, outputs:  13 x  13 x1024 0.000 BF
  75 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  76 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  77 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  78 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  79 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF
  80 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF
  81 conv     18       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x  18 0.006 BF
  82 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  83 route  79 		                           ->   13 x  13 x 512 
  84 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF
  85 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256
  86 route  85 61 	                           ->   26 x  26 x 768 
  87 conv    256       1 x 1/ 1     26 x  26 x 768 ->   26 x  26 x 256 0.266 BF
  88 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  89 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  90 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  91 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF
  92 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF
  93 conv     18       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x  18 0.012 BF
  94 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
  95 route  91 		                           ->   26 x  26 x 256 
  96 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF
  97 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128
  98 route  97 36 	                           ->   52 x  52 x 384 
  99 conv    128       1 x 1/ 1     52 x  52 x 384 ->   52 x  52 x 128 0.266 BF
 100 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 101 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 102 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 103 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF
 104 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF
 105 conv     18       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x  18 0.025 BF
 106 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 65.304 
avg_outputs = 516723 
 Allocate additional workspace_size = 52.43 MB 
Loading weights from /content/cfg_mask/yolov3_custom2_final.weights...
 seen 64, trained: 32 K-images (0 Kilo-batches_64) 
Done! Loaded 107 layers from weights-file 
 Detection layer: 82 - type = 28 
 Detection layer: 94 - type = 28 
 Detection layer: 106 - type = 28 
/content/drive/MyDrive/yolo3/mask_dataset/02C96BZM5DKA178WY34I.jpg: Predicted in 40.202000 milli-seconds.
kouzhao: 75%
kouzhao: 57%
Unable to init server: Could not connect: Connection refused

(predictions:100884): Gtk-WARNING **: 02:16:44.443: cannot open display: 

!cp /content/cfg_mask/yolov3_custom2_final.weights /content/drive/MyDrive/yolo3/mask
!cp /content/cfg_mask/obj.data /content/drive/MyDrive/yolo3/mask
!cp /content/cfg_mask/obj.names /content/drive/MyDrive/yolo3/mask
!cp /content/cfg_mask/yolov3_custom2.cfg /content/drive/MyDrive/yolo3/mask

# 使用opencv

cp: -r not specified; omitting directory '/content/cfg_mask'

import cv2
import numpy as np
net = cv2.dnn.readNetFromDarknet("/content/drive/MyDrive/yolo3/mask/yolov3_custom2.cfg","/content/drive/MyDrive/yolo3/mask/yolov3_custom2_final.weights")

layer_names = net.getLayerNames()
print(layer_names)

['conv_0', 'bn_0', 'relu_0', 'conv_1', 'bn_1', 'relu_1', 'conv_2', 'bn_2', 'relu_2', 'conv_3', 'bn_3', 'relu_3', 'shortcut_4', 'conv_5', 'bn_5', 'relu_5', 'conv_6', 'bn_6', 'relu_6', 'conv_7', 'bn_7', 'relu_7', 'shortcut_8', 'conv_9', 'bn_9', 'relu_9', 'conv_10', 'bn_10', 'relu_10', 'shortcut_11', 'conv_12', 'bn_12', 'relu_12', 'conv_13', 'bn_13', 'relu_13', 'conv_14', 'bn_14', 'relu_14', 'shortcut_15', 'conv_16', 'bn_16', 'relu_16', 'conv_17', 'bn_17', 'relu_17', 'shortcut_18', 'conv_19', 'bn_19', 'relu_19', 'conv_20', 'bn_20', 'relu_20', 'shortcut_21', 'conv_22', 'bn_22', 'relu_22', 'conv_23', 'bn_23', 'relu_23', 'shortcut_24', 'conv_25', 'bn_25', 'relu_25', 'conv_26', 'bn_26', 'relu_26', 'shortcut_27', 'conv_28', 'bn_28', 'relu_28', 'conv_29', 'bn_29', 'relu_29', 'shortcut_30', 'conv_31', 'bn_31', 'relu_31', 'conv_32', 'bn_32', 'relu_32', 'shortcut_33', 'conv_34', 'bn_34', 'relu_34', 'conv_35', 'bn_35', 'relu_35', 'shortcut_36', 'conv_37', 'bn_37', 'relu_37', 'conv_38', 'bn_38', 'relu_38', 'conv_39', 'bn_39', 'relu_39', 'shortcut_40', 'conv_41', 'bn_41', 'relu_41', 'conv_42', 'bn_42', 'relu_42', 'shortcut_43', 'conv_44', 'bn_44', 'relu_44', 'conv_45', 'bn_45', 'relu_45', 'shortcut_46', 'conv_47', 'bn_47', 'relu_47', 'conv_48', 'bn_48', 'relu_48', 'shortcut_49', 'conv_50', 'bn_50', 'relu_50', 'conv_51', 'bn_51', 'relu_51', 'shortcut_52', 'conv_53', 'bn_53', 'relu_53', 'conv_54', 'bn_54', 'relu_54', 'shortcut_55', 'conv_56', 'bn_56', 'relu_56', 'conv_57', 'bn_57', 'relu_57', 'shortcut_58', 'conv_59', 'bn_59', 'relu_59', 'conv_60', 'bn_60', 'relu_60', 'shortcut_61', 'conv_62', 'bn_62', 'relu_62', 'conv_63', 'bn_63', 'relu_63', 'conv_64', 'bn_64', 'relu_64', 'shortcut_65', 'conv_66', 'bn_66', 'relu_66', 'conv_67', 'bn_67', 'relu_67', 'shortcut_68', 'conv_69', 'bn_69', 'relu_69', 'conv_70', 'bn_70', 'relu_70', 'shortcut_71', 'conv_72', 'bn_72', 'relu_72', 'conv_73', 'bn_73', 'relu_73', 'shortcut_74', 'conv_75', 'bn_75', 'relu_75', 'conv_76', 'bn_76', 'relu_76', 'conv_77', 'bn_77', 'relu_77', 'conv_78', 'bn_78', 'relu_78', 'conv_79', 'bn_79', 'relu_79', 'conv_80', 'bn_80', 'relu_80', 'conv_81', 'permute_82', 'yolo_82', 'identity_83', 'conv_84', 'bn_84', 'relu_84', 'upsample_85', 'concat_86', 'conv_87', 'bn_87', 'relu_87', 'conv_88', 'bn_88', 'relu_88', 'conv_89', 'bn_89', 'relu_89', 'conv_90', 'bn_90', 'relu_90', 'conv_91', 'bn_91', 'relu_91', 'conv_92', 'bn_92', 'relu_92', 'conv_93', 'permute_94', 'yolo_94', 'identity_95', 'conv_96', 'bn_96', 'relu_96', 'upsample_97', 'concat_98', 'conv_99', 'bn_99', 'relu_99', 'conv_100', 'bn_100', 'relu_100', 'conv_101', 'bn_101', 'relu_101', 'conv_102', 'bn_102', 'relu_102', 'conv_103', 'bn_103', 'relu_103', 'conv_104', 'bn_104', 'relu_104', 'conv_105', 'permute_106', 'yolo_106']

classes = [line.strip() for line in open("/content/drive/MyDrive/yolo3/mask/obj.names")]
colors = [(0,0,255),(255,0,0),(0,255,0)]

from PIL import Image
Image.open('/content/drive/MyDrive/yolo3/mask/download.png')

from PIL import Image
Image.open('/content/drive/MyDrive/yolo3/mask/download.png')

img = cv2.imread("/content/drive/MyDrive/yolo3/mask/download.png")
img.shape

(675, 900, 3)

path="/content/drive/MyDrive/yolo3/mask/download.png"
img=cv2.imread(path)
img.shape

(675, 900, 3)

output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
output_layers

['yolo_82', 'yolo_94', 'yolo_106']

img = cv2.resize(img, None, fx=0.4, fy=0.4)
height, width, channels = img.shape 
blob = cv2.dnn.blobFromImage(img, 1/255.0, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)

class_ids = []
confidences = []
boxes = []
    
for out in outs:
    for detection in out:
        tx, ty, tw, th, confidence = detection[0:5]
        scores = detection[5:]
        class_id = np.argmax(scores)  
        if confidence > 0.3:   
            center_x = int(tx * width)
            center_y = int(ty * height)
            w = int(tw * width)
            h = int(th * height)
            
            # 取得箱子方框座標
            x = int(center_x - w / 2)
            y = int(center_y - h / 2)
            boxes.append([x, y, w, h])
            confidences.append(float(confidence))
            class_ids.append(class_id)

indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.3, 0.4)

%pylab inline
from matplotlib import pyplot as plt
plt.rcParams['figure.figsize'] = [15, 10]
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img_rgb)

Populating the interactive namespace from numpy and matplotlib





<matplotlib.image.AxesImage at 0x7faa4e397c90>

font = cv2.FONT_HERSHEY_PLAIN

for i in range(len(boxes)):
    if i in indexes:
        x, y, w, h = boxes[i]
        label = str(classes[class_ids[i]])
        color = colors[class_ids[i]]
        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
        cv2.putText(img, label, (x, y - 5), font, 2, color, 3)

import cv2
import numpy as np
net = net = cv2.dnn.readNetFromDarknet("/content/drive/MyDrive/yolo3/mask/yolov3_custom2.cfg","/content/drive/MyDrive/yolo3/mask/yolov3_custom2_final.weights")
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
classes = [line.strip() for line in open("/content/drive/MyDrive/yolo3/mask/obj.names")]
colors = [(0,0,255),(255,0,0),(0,255,0)]

def yolo_detect(frame):
    # forward propogation
    img = cv2.resize(frame, None, fx=0.4, fy=0.4)
    height, width, channels = img.shape 
    blob = cv2.dnn.blobFromImage(img, 1/255.0, (416, 416), (0, 0, 0), True, crop=False)
    net.setInput(blob)
    outs = net.forward(output_layers)

    # get detection boxes
    class_ids = []
    confidences = []
    boxes = []
    
    for out in outs:
        for detection in out:
            tx, ty, tw, th, confidence = detection[0:5]
            scores = detection[5:]
            class_id = np.argmax(scores)  
            if confidence > 0.3:   
                center_x = int(tx * width)
                center_y = int(ty * height)
                w = int(tw * width)
                h = int(th * height)

                # 取得箱子方框座標
                x = int(center_x - w / 2)
                y = int(center_y - h / 2)
                boxes.append([x, y, w, h])
                confidences.append(float(confidence))
                class_ids.append(class_id)
                
    # draw boxes
    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.3, 0.4)
    font = cv2.FONT_HERSHEY_PLAIN
    for i in range(len(boxes)):
        if i in indexes:
            x, y, w, h = boxes[i]
            label = str(classes[class_ids[i]])
            color = colors[class_ids[i]]
            cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
            cv2.putText(img, label, (x, y -5), font, 3, color, 3)
    return img

img = cv2.imread(path)
im = yolo_detect(img)
img_rgb = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
plt.imshow(img_rgb)

<matplotlib.image.AxesImage at 0x7faa4e33b2d0>

print("opencv版本:%s"% cv2.__version__)

opencv版本:4.1.2

from google.colab.patches import cv2_imshow 

import cv2
import imutils
import time

VIDEO_IN = cv2.VideoCapture(0)

while True:
    hasFrame, frame = VIDEO_IN.read()
    print(frame)
    #img = yolo_detect(frame)

    cv2.imshow("Frame", imutils.resize(frame, width=850))

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
        
VIDEO_IN.release()
cv2.destroyAllWindows()

None



---------------------------------------------------------------------------

AttributeError                            Traceback (most recent call last)

<ipython-input-70-79e306786c1d> in <module>()
     10     #img = yolo_detect(frame)
     11 
---> 12     cv2.imshow("Frame", imutils.resize(frame, width=850))
     13 
     14     if cv2.waitKey(1) & 0xFF == ord('q'):


/usr/local/lib/python3.7/dist-packages/imutils/convenience.py in resize(image, width, height, inter)
     67     # grab the image size
     68     dim = None
---> 69     (h, w) = image.shape[:2]
     70 
     71     # if both the width and height are None, then return the


AttributeError: 'NoneType' object has no attribute 'shape'

import cv2

#选择摄像头号，一般从 0 开始
cap = cv2.VideoCapture(0)
 
while True:
    ret, img = cap.read()
    cv2.imshow("input", img)
# 按 ESC 键退出
    key = cv2.waitKey(10)
    if key == 27:
        break
 
cv2.destroyAllWindows() 
cv2.VideoCapture(0).release()

#从谷歌云盘上传根据自己数据集修改的.cfg文件 会上传到darknet/cfg文件夹下
!cp /content/drive/MyDrive/cfg_mask/yolov3_custom2.cfg ./cfg

#从谷歌云盘上传自己数据集的obj.data obj.names
!cp /content/drive/MyDrive/cfg_mask/obj.data ./data
!cp /content/drive/MyDrive/cfg_mask/obj.names  ./data

#上传generate_train.py 以在服务器的data下生成train.txt
!cp /content/drive/MyDrive/cfg_mask/train.txt ./data

• https://github.com/ywchiu/largitdata/blob/master/code/Course_128.ipynb
• https://www.cnpython.com/qa/134907
• https://github.com/ZhangYouyi-Lab/ZhangYouyi-Lab/blob/master/YOLOv3.ipynb