Machine_Learning_Final Project.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"Machine_Learning_Final Project.ipynb","provenance":[{"file_id":"1aw65Zv2OtCBsMAjaqgAK4LOjCXwi33TC","timestamp":1607326708689},{"file_id":"1_GdoqCJWXsChrOiY8sZMr_zbr_fH-0Fg","timestamp":1606989624899}],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"tUIcf3KENcfr"},"source":["## Clone Darknet"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"68eMertgIxaB","executionInfo":{"status":"ok","timestamp":1607385068590,"user_tz":360,"elapsed":2872,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}},"outputId":"86530ac8-daea-417b-b734-9d6452118981"},"source":["\n","!git clone https://github.com/AlexeyAB/darknet\n"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Cloning into 'darknet'...\n","remote: Enumerating objects: 21, done.\u001b[K\n","remote: Counting objects: 100% (21/21), done.\u001b[K\n","remote: Compressing objects: 100% (16/16), done.\u001b[K\n","remote: Total 14583 (delta 6), reused 9 (delta 4), pack-reused 14562\u001b[K\n","Receiving objects: 100% (14583/14583), 13.25 MiB | 25.12 MiB/s, done.\n","Resolving deltas: 100% (9916/9916), done.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xym8_m8CIyXK","executionInfo":{"status":"ok","timestamp":1607385070542,"user_tz":360,"elapsed":759,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}},"outputId":"2af0afad-03f9-4239-bca1-4b3787bff48a"},"source":["%cd darknet\n","!sed -i 's/OPENCV=0/OPENCV=1/' Makefile\n","!sed -i 's/GPU=0/GPU=1/' Makefile\n","!sed -i 's/CUDNN=0/CUDNN=1/' Makefile\n","!sed -i 's/CUDNN_HALF=0/CUDNN_HALF=1/' Makefile"],"execution_count":2,"outputs":[{"output_type":"stream","text":["/content/darknet\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5uloUwmUKF05","executionInfo":{"status":"ok","timestamp":1607385078474,"user_tz":360,"elapsed":458,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}},"outputId":"8ace516a-d6a1-4bff-f7ba-9b76979e026e"},"source":["\n","!/usr/local/cuda/bin/nvcc --version"],"execution_count":3,"outputs":[{"output_type":"stream","text":["nvcc: NVIDIA (R) Cuda compiler driver\n","Copyright (c) 2005-2019 NVIDIA Corporation\n","Built on Sun_Jul_28_19:07:16_PDT_2019\n","Cuda compilation tools, release 10.1, V10.1.243\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q2Jjv0yRKLPe","executionInfo":{"status":"ok","timestamp":1607385154385,"user_tz":360,"elapsed":71320,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}},"outputId":"c39f597c-467d-4a92-f842-37254a1e9ead"},"source":["# make darknet  \n","!make"],"execution_count":4,"outputs":[{"output_type":"stream","text":["mkdir -p ./obj/\n","mkdir -p backup\n","chmod +x *.sh\n","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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/image_opencv.cpp -o obj/image_opencv.o\n","\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_detections_cv_v3(void**, detection*, int, float, char**, image**, int, int)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/image_opencv.cpp:926:23:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Krgb\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n","                 float \u001b[01;35m\u001b[Krgb\u001b[m\u001b[K[3];\n","                       \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_train_loss(char*, void**, int, float, float, int, int, float, int, char*, float, int, int, double)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/image_opencv.cpp:1127:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n","             \u001b[01;35m\u001b[Kif\u001b[m\u001b[K (iteration_old == 0)\n","             \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/image_opencv.cpp:1130:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n","          \u001b[01;36m\u001b[Kif\u001b[m\u001b[K (iteration_old != 0){\n","          \u001b[01;36m\u001b[K^~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid cv_draw_object(image, float*, int, int, int*, float*, int*, int, char**)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/image_opencv.cpp:1424:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbuff\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","         char \u001b[01;35m\u001b[Kbuff\u001b[m\u001b[K[100];\n","              \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/image_opencv.cpp:1400:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kit_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     int \u001b[01;35m\u001b[Kit_tb_res\u001b[m\u001b[K = cv::createTrackbar(it_trackbar_name, window_name, &it_trackbar_value, 1000);\n","         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/image_opencv.cpp:1404:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Klr_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     int \u001b[01;35m\u001b[Klr_tb_res\u001b[m\u001b[K = cv::createTrackbar(lr_trackbar_name, window_name, &lr_trackbar_value, 20);\n","         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/image_opencv.cpp:1408:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcl_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     int \u001b[01;35m\u001b[Kcl_tb_res\u001b[m\u001b[K = cv::createTrackbar(cl_trackbar_name, window_name, &cl_trackbar_value, classes-1);\n","         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/image_opencv.cpp:1411:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbo_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     int \u001b[01;35m\u001b[Kbo_tb_res\u001b[m\u001b[K = cv::createTrackbar(bo_trackbar_name, window_name, boxonly, 1);\n","         \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n","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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/http_stream.cpp -o obj/http_stream.o\n","In file included from \u001b[01m\u001b[K./src/http_stream.cpp:580:0\u001b[m\u001b[K:\n","\u001b[01m\u001b[K./src/httplib.h:129:0:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"INVALID_SOCKET\" redefined\n"," #define INVALID_SOCKET (-1)\n"," \n","\u001b[01m\u001b[K./src/http_stream.cpp:73:0:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kthis is the location of the previous definition\n"," #define INVALID_SOCKET -1\n"," \n","\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool JSON_sender::write(const char*)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/http_stream.cpp:249:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","                 int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = _write(client, outputbuf, outlen);\n","                     \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool MJPG_sender::write(const cv::Mat&)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/http_stream.cpp:507:113:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%zu\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","                 sprintf(head, \"--mjpegstream\\r\\nContent-Type: image/jpeg\\r\\nContent-Length: %zu\\r\\n\\r\\n\", outlen\u001b[01;35m\u001b[K)\u001b[m\u001b[K;\n","                                                                                                                 \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid set_track_id(detection*, int, float, float, float, int, int, int)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/http_stream.cpp:863:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n","         for (int i = 0; \u001b[01;35m\u001b[Ki < v.size()\u001b[m\u001b[K; ++i) {\n","                         \u001b[01;35m\u001b[K~~^~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/http_stream.cpp:871:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n","     for (int old_id = 0; \u001b[01;35m\u001b[Kold_id < old_dets.size()\u001b[m\u001b[K; ++old_id) {\n","                          \u001b[01;35m\u001b[K~~~~~~~^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/http_stream.cpp:890:31:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n","     for (int index = 0; \u001b[01;35m\u001b[Kindex < new_dets_num*old_dets.size()\u001b[m\u001b[K; ++index) {\n","                         \u001b[01;35m\u001b[K~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/http_stream.cpp:926:28:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n","     if (\u001b[01;35m\u001b[Kold_dets_dq.size() > deque_size\u001b[m\u001b[K) old_dets_dq.pop_front();\n","         \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/gemm.c -o obj/gemm.o\n","\u001b[01m\u001b[K./src/gemm.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kconvolution_2d\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/gemm.c:2038:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     const int \u001b[01;35m\u001b[Kout_w\u001b[m\u001b[K = (w + 2 * pad - ksize) / stride + 1;    // output_width=input_width for stride=1 and pad=1\n","               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/gemm.c:2037:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     const int \u001b[01;35m\u001b[Kout_h\u001b[m\u001b[K = (h + 2 * pad - ksize) / stride + 1;    // output_height=input_height for stride=1 and pad=1\n","               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/utils.c -o obj/utils.o\n","\u001b[01m\u001b[K./src/utils.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcustom_hash\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/utils.c:1045:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around assignment used as truth value [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n","     while (\u001b[01;35m\u001b[Kc\u001b[m\u001b[K = *str++)\n","            \u001b[01;35m\u001b[K^\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/dark_cuda.c -o obj/dark_cuda.o\n","\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcudnn_check_error_extended\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/dark_cuda.c:224:20:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between ‘\u001b[01m\u001b[KcudaError_t {aka enum cudaError}\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kenum <anonymous>\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wenum-compare\u001b[m\u001b[K]\n","         if (status \u001b[01;35m\u001b[K!=\u001b[m\u001b[K CUDNN_STATUS_SUCCESS)\n","                    \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpre_allocate_pinned_memory\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/dark_cuda.c:276:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","         printf(\"pre_allocate: size = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K MB, num_of_blocks = %Iu, block_size = %Iu MB \\n\",\n","                                      \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n","                                      \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n","             \u001b[32m\u001b[Ksize / (1024*1024)\u001b[m\u001b[K, num_of_blocks, pinned_block_size / (1024 * 1024));\n","             \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K          \n","\u001b[01m\u001b[K./src/dark_cuda.c:276:64:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","         printf(\"pre_allocate: size = %Iu MB, num_of_blocks = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K, block_size = %Iu MB \\n\",\n","                                                              \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n","                                                              \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/dark_cuda.c:276:82:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","         printf(\"pre_allocate: size = %Iu MB, num_of_blocks = %Iu, block_size = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K MB \\n\",\n","                                                                                \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n","                                                                                \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/dark_cuda.c:286:37:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","                 printf(\" Allocated \u001b[01;35m\u001b[K%d\u001b[m\u001b[K pinned block \\n\", pinned_block_size);\n","                                    \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n","                                    \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcuda_make_array_pinned_preallocated\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/dark_cuda.c:307:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","             printf(\"\\n Pinned block_id = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, filled = %f %% \\n\", pinned_block_id, filled);\n","                                          \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n","                                          \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/dark_cuda.c:322:64:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","             printf(\"Try to allocate new pinned memory, size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K MB \\n\", \u001b[32m\u001b[Ksize / (1024 * 1024)\u001b[m\u001b[K);\n","                                                               \u001b[01;35m\u001b[K~^\u001b[m\u001b[K         \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n","                                                               \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/dark_cuda.c:328:63:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","             printf(\"Try to allocate new pinned BLOCK, size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K MB \\n\", \u001b[32m\u001b[Ksize / (1024 * 1024)\u001b[m\u001b[K);\n","                                                              \u001b[01;35m\u001b[K~^\u001b[m\u001b[K         \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n","                                                              \u001b[32m\u001b[K%ld\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/convolutional_layer.c -o obj/convolutional_layer.o\n","\u001b[01m\u001b[K./src/convolutional_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_convolutional_layer\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/convolutional_layer.c:1341:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kt_intput_size\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","                         size_t \u001b[01;35m\u001b[Kt_intput_size\u001b[m\u001b[K = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align);\n","                                \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/list.c -o obj/list.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/image.c -o obj/image.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/activations.c -o obj/activations.o\n","\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kactivate\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KRELU6\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n","     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n","     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n","\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n","\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KHARD_MISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n","\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n","\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n","\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX_MAXVAL\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n","\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgradient\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n","     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n","     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n","\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KHARD_MISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/im2col.c -o obj/im2col.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/col2im.c -o obj/col2im.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/blas.c -o obj/blas.o\n","\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbackward_shortcut_multilayer_cpu\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/blas.c:207:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_index\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","                 int \u001b[01;35m\u001b[Kout_index\u001b[m\u001b[K = id;\n","                     \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfind_sim\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/blas.c:597:59:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","         printf(\" Error: find_sim(): sim isn't found: i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, j = %d, z = %d \\n\", i, j, z);\n","                                                          \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n","                                                          \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/blas.c:597:67:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","         printf(\" Error: find_sim(): sim isn't found: i = %d, j = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, z = %d \\n\", i, j, z);\n","                                                                  \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n","                                                                  \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/blas.c:597:75:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","         printf(\" Error: find_sim(): sim isn't found: i = %d, j = %d, z = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, j, z);\n","                                                                          \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n","                                                                          \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfind_P_constrastive\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/blas.c:611:68:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","         printf(\" Error: find_P_constrastive(): P isn't found: i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, j = %d, z = %d \\n\", i, j, z);\n","                                                                   \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n","                                                                   \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/blas.c:611:76:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","         printf(\" Error: find_P_constrastive(): P isn't found: i = %d, j = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, z = %d \\n\", i, j, z);\n","                                                                           \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n","                                                                           \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/blas.c:611:84:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","         printf(\" Error: find_P_constrastive(): P isn't found: i = %d, j = %d, z = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, j, z);\n","                                                                                   \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n","                                                                                   \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KP_constrastive_f\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/blas.c:651:79:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","         fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, l = %d \\n\", i, l);\n","                                                                              \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n","                                                                              \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/blas.c:651:87:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","         fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = %d, l = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, l);\n","                                                                                      \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n","                                                                                      \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KP_constrastive\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/blas.c:785:79:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","         fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, l = %d \\n\", i, l);\n","                                                                              \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n","                                                                              \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/blas.c:785:87:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","         fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = %d, l = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, l);\n","                                                                                      \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n","                                                                                      \u001b[32m\u001b[K%ld\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/crop_layer.c -o obj/crop_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/dropout_layer.c -o obj/dropout_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/maxpool_layer.c -o obj/maxpool_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/softmax_layer.c -o obj/softmax_layer.o\n","\u001b[01m\u001b[K./src/softmax_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_contrastive_layer\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/softmax_layer.c:203:101:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 9 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","     fprintf(stderr, \"contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d  classes = %4d, step = \u001b[01;35m\u001b[K%4d\u001b[m\u001b[K \\n\", w, h, l.n, l.embedding_size, batch, l.classes, step);\n","                                                                                                   \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n","                                                                                                   \u001b[32m\u001b[K%4ld\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/softmax_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_contrastive_layer\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/softmax_layer.c:244:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kmax_truth\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n","                     float \u001b[01;35m\u001b[Kmax_truth\u001b[m\u001b[K = 0;\n","                           \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/softmax_layer.c:423:71:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n","             printf(\" Error: too large number of bboxes: contr_size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K > max_contr_size  = %d \\n\", contr_size, max_contr_size);\n","                                                                      \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n","                                                                      \u001b[32m\u001b[K%ld\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/data.c -o obj/data.o\n","\u001b[01m\u001b[K./src/data.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kload_data_detection\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/data.c:1297:24:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kx\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","                 int k, \u001b[01;35m\u001b[Kx\u001b[m\u001b[K, y;\n","                        \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/data.c:1090:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kr_scale\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n","     float r1 = 0, r2 = 0, r3 = 0, r4 = 0, \u001b[01;35m\u001b[Kr_scale\u001b[m\u001b[K = 0;\n","                                           \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/matrix.c -o obj/matrix.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/network.c -o obj/network.o\n","\u001b[01m\u001b[K./src/network.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_network\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/network.c:659:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n","         if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Knet->input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped))\n","                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/network.c:1\u001b[m\u001b[K:\n","\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n"," extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n","                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/connected_layer.c -o obj/connected_layer.o\n","\u001b[01m\u001b[K./src/connected_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_connected_layer_gpu\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/connected_layer.c:346:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kone\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     float \u001b[01;35m\u001b[Kone\u001b[m\u001b[K = 1;    // alpha[0], beta[0]\n","           \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/connected_layer.c:344:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kc\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     float * \u001b[01;35m\u001b[Kc\u001b[m\u001b[K = l.output_gpu;\n","             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/connected_layer.c:343:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kb\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     float * \u001b[01;35m\u001b[Kb\u001b[m\u001b[K = l.weights_gpu;\n","             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/connected_layer.c:342:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ka\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     float * \u001b[01;35m\u001b[Ka\u001b[m\u001b[K = state.input;\n","             \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/connected_layer.c:341:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = l.outputs;\n","         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/connected_layer.c:340:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kk\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     int \u001b[01;35m\u001b[Kk\u001b[m\u001b[K = l.inputs;\n","         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/connected_layer.c:339:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Km\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     int \u001b[01;35m\u001b[Km\u001b[m\u001b[K = l.batch;\n","         \u001b[01;35m\u001b[K^\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/cost_layer.c -o obj/cost_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/parser.c -o obj/parser.o\n","\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kparse_network_cfg_custom\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/parser.c:1680:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n","         if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Knet.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1;\n","                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/activation_layer.h:4\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/parser.c:6\u001b[m\u001b[K:\n","\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n"," extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n","                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kget_classes_multipliers\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/parser.c:428:29:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kargument 1 range [18446744071562067968, 18446744073709551615] exceeds maximum object size 9223372036854775807 [\u001b[01;35m\u001b[K-Walloc-size-larger-than=\u001b[m\u001b[K]\n","         \u001b[01;35m\u001b[Kclasses_multipliers = (float *)calloc(classes_counters, sizeof(float))\u001b[m\u001b[K;\n","         \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n","In file included from \u001b[01m\u001b[K./src/parser.c:3:0\u001b[m\u001b[K:\n","\u001b[01m\u001b[K/usr/include/stdlib.h:541:14:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin a call to allocation function ‘\u001b[01m\u001b[Kcalloc\u001b[m\u001b[K’ declared here\n"," extern void *\u001b[01;36m\u001b[Kcalloc\u001b[m\u001b[K (size_t __nmemb, size_t __size)\n","              \u001b[01;36m\u001b[K^~~~~~\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/option_list.c -o obj/option_list.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/darknet.c -o obj/darknet.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/detection_layer.c -o obj/detection_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/captcha.c -o obj/captcha.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/route_layer.c -o obj/route_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/writing.c -o obj/writing.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/box.c -o obj/box.o\n","\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbox_iou_kind\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/box.c:154:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMSE\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n","     \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(iou_kind) {\n","     \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdiounms_sort\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/box.c:898:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbeta_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","                     float \u001b[01;35m\u001b[Kbeta_prob\u001b[m\u001b[K = pow(dets[j].prob[k], 2) / sum_prob;\n","                           \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/box.c:897:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kalpha_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","                     float \u001b[01;35m\u001b[Kalpha_prob\u001b[m\u001b[K = pow(dets[i].prob[k], 2) / sum_prob;\n","                           \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/nightmare.c -o obj/nightmare.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/normalization_layer.c -o obj/normalization_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/avgpool_layer.c -o obj/avgpool_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/coco.c -o obj/coco.o\n","\u001b[01m\u001b[K./src/coco.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_coco_recall\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/coco.c:248:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbase\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     char *\u001b[01;35m\u001b[Kbase\u001b[m\u001b[K = \"results/comp4_det_test_\";\n","           \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/dice.c -o obj/dice.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/yolo.c -o obj/yolo.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/detector.c -o obj/detector.o\n","\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kprint_cocos\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/detector.c:484:29:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat not a string literal and no format arguments [\u001b[01;35m\u001b[K-Wformat-security\u001b[m\u001b[K]\n","                 fprintf(fp, \u001b[01;35m\u001b[Kbuff\u001b[m\u001b[K);\n","                             \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Keliminate_bdd\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/detector.c:577:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kstatement with no effect [\u001b[01;35m\u001b[K-Wunused-value\u001b[m\u001b[K]\n","                     \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K (k; buf[k + n] != '\\0'; k++)\n","                     \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/detector.c:698:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd2\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","         int \u001b[01;35m\u001b[Kmkd2\u001b[m\u001b[K = make_directory(buff2, 0777);\n","             \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/detector.c:696:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","         int \u001b[01;35m\u001b[Kmkd\u001b[m\u001b[K = make_directory(buff, 0777);\n","             \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector_map\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/detector.c:1330:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_recall\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","         float \u001b[01;35m\u001b[Kclass_recall\u001b[m\u001b[K = (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]));\n","               \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/detector.c:1329:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_precision\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","         float \u001b[01;35m\u001b[Kclass_precision\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]);\n","               \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdraw_object\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/detector.c:1865:19:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kinv_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","             float \u001b[01;35m\u001b[Kinv_loss\u001b[m\u001b[K = 1.0 / max_val_cmp(0.01, avg_loss);\n","                   \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/layer.c -o obj/layer.o\n","\u001b[01m\u001b[K./src/layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfree_layer_custom\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/layer.c:208:68:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around ‘\u001b[01m\u001b[K&&\u001b[m\u001b[K’ within ‘\u001b[01m\u001b[K||\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n","     if (l.delta_gpu && (l.optimized_memory < 1 || \u001b[01;35m\u001b[Kl.keep_delta_gpu && l.optimized_memory < 3\u001b[m\u001b[K)) cuda_free(l.delta_gpu), l.delta_gpu = NULL;\n","                                                   \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/compare.c -o obj/compare.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/classifier.c -o obj/classifier.o\n","\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_classifier\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/classifier.c:146:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcount\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     int \u001b[01;35m\u001b[Kcount\u001b[m\u001b[K = 0;\n","         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpredict_classifier\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/classifier.c:855:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktime\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     clock_t \u001b[01;35m\u001b[Ktime\u001b[m\u001b[K;\n","             \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdemo_classifier\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/classifier.c:1287:49:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktval_result\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","         struct timeval tval_before, tval_after, \u001b[01;35m\u001b[Ktval_result\u001b[m\u001b[K;\n","                                                 \u001b[01;35m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/classifier.c:1287:37:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktval_after\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","         struct timeval tval_before, \u001b[01;35m\u001b[Ktval_after\u001b[m\u001b[K, tval_result;\n","                                     \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/local_layer.c -o obj/local_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/swag.c -o obj/swag.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/shortcut_layer.c -o obj/shortcut_layer.o\n","\u001b[01m\u001b[K./src/shortcut_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_shortcut_layer\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/shortcut_layer.c:55:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kscale\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","         float \u001b[01;35m\u001b[Kscale\u001b[m\u001b[K = sqrt(2. / l.nweights);\n","               \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/activation_layer.c -o obj/activation_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/rnn_layer.c -o obj/rnn_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/gru_layer.c -o obj/gru_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/rnn.c -o obj/rnn.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/rnn_vid.c -o obj/rnn_vid.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/crnn_layer.c -o obj/crnn_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/demo.c -o obj/demo.o\n","\u001b[01m\u001b[K./src/demo.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdetect_in_thread\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/demo.c:100:16:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kprediction\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","         float *\u001b[01;35m\u001b[Kprediction\u001b[m\u001b[K = network_predict(net, X);\n","                \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/demo.c:98:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kl\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","         layer \u001b[01;35m\u001b[Kl\u001b[m\u001b[K = net.layers[net.n - 1];\n","               \u001b[01;35m\u001b[K^\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/tag.c -o obj/tag.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/cifar.c -o obj/cifar.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/go.c -o obj/go.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/batchnorm_layer.c -o obj/batchnorm_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/art.c -o obj/art.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/region_layer.c -o obj/region_layer.o\n","\u001b[01m\u001b[K./src/region_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_region_layer\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/region_layer.c:59:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     int \u001b[01;35m\u001b[Kold_h\u001b[m\u001b[K = l->h;\n","         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/region_layer.c:58:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     int \u001b[01;35m\u001b[Kold_w\u001b[m\u001b[K = l->w;\n","         \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/reorg_layer.c -o obj/reorg_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/reorg_old_layer.c -o obj/reorg_old_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/super.c -o obj/super.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/voxel.c -o obj/voxel.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/tree.c -o obj/tree.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/yolo_layer.c -o obj/yolo_layer.o\n","\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_yolo_layer\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/yolo_layer.c:68:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n","     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.output, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;\n","                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n","\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n"," extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n","                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:75:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n","     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.delta, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;\n","                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n","\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n"," extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n","                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_yolo_layer\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/yolo_layer.c:106:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n","         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n","                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n","\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n"," extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n","                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:115:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n","         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n","                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n","\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n"," extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n","                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kprocess_batch\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/yolo_layer.c:424:25:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kbest_match_t\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n","                     int \u001b[01;35m\u001b[Kbest_match_t\u001b[m\u001b[K = 0;\n","                         \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_yolo_layer\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/yolo_layer.c:892:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_iou_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","         float \u001b[01;35m\u001b[Kavg_iou_loss\u001b[m\u001b[K = 0;\n","               \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:705:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_anyobj\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     float \u001b[01;35m\u001b[Kavg_anyobj\u001b[m\u001b[K = 0;\n","           \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:704:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_obj\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     float \u001b[01;35m\u001b[Kavg_obj\u001b[m\u001b[K = 0;\n","           \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:703:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kavg_cat\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     float \u001b[01;35m\u001b[Kavg_cat\u001b[m\u001b[K = 0;\n","           \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:702:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Krecall75\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     float \u001b[01;35m\u001b[Krecall75\u001b[m\u001b[K = 0;\n","           \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:701:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Krecall\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     float \u001b[01;35m\u001b[Krecall\u001b[m\u001b[K = 0;\n","           \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:700:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_ciou_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     float \u001b[01;35m\u001b[Ktot_ciou_loss\u001b[m\u001b[K = 0;\n","           \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:699:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_diou_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     float \u001b[01;35m\u001b[Ktot_diou_loss\u001b[m\u001b[K = 0;\n","           \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:698:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_giou_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     float \u001b[01;35m\u001b[Ktot_giou_loss\u001b[m\u001b[K = 0;\n","           \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:697:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_iou_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     float \u001b[01;35m\u001b[Ktot_iou_loss\u001b[m\u001b[K = 0;\n","           \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:696:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_ciou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     float \u001b[01;35m\u001b[Ktot_ciou\u001b[m\u001b[K = 0;\n","           \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:695:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_diou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     float \u001b[01;35m\u001b[Ktot_diou\u001b[m\u001b[K = 0;\n","           \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:694:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktot_giou\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     float \u001b[01;35m\u001b[Ktot_giou\u001b[m\u001b[K = 0;\n","           \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/yolo_layer.c:666:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     int b, \u001b[01;35m\u001b[Kn\u001b[m\u001b[K;\n","            \u001b[01;35m\u001b[K^\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/gaussian_yolo_layer.c -o obj/gaussian_yolo_layer.o\n","\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_gaussian_yolo_layer\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:71:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n","     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;\n","                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n","\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n"," extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n","                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:78:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n","     if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.delta, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;\n","                                      \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n","\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n"," extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n","                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_gaussian_yolo_layer\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:110:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n","         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n","                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n","\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n"," extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n","                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:119:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n","         if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n","                                          \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n","                 from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n","\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n"," extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n","                                       \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/upsample_layer.c -o obj/upsample_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/lstm_layer.c -o obj/lstm_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/conv_lstm_layer.c -o obj/conv_lstm_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/scale_channels_layer.c -o obj/scale_channels_layer.o\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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/sam_layer.c -o obj/sam_layer.o\n","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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/convolutional_kernels.cu -o obj/convolutional_kernels.o\n","\u001b[01m\u001b[K./src/convolutional_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid backward_convolutional_layer_gpu(convolutional_layer, network_state)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/convolutional_kernels.cu:853:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n","     \u001b[01;35m\u001b[K            if (*state.net.max_output16_size < l.\u001b[m\u001b[Knweights) {\n","                 \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~\u001b[m\u001b[K\n","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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/activation_kernels.cu -o obj/activation_kernels.o\n","./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n","\n","./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n","\n","./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n","\n","./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n","\n","./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n","\n","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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/im2col_kernels.cu -o obj/im2col_kernels.o\n","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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/col2im_kernels.cu -o obj/col2im_kernels.o\n","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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/blas_kernels.cu -o obj/blas_kernels.o\n","./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n","\n","./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n","\n","./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n","\n","./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n","\n","./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n","\n","./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n","\n","\u001b[01m\u001b[K./src/blas_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid backward_shortcut_multilayer_gpu(int, int, int, int*, float**, float*, float*, float*, float*, int, float*, float**, WEIGHTS_NORMALIZATION_T)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/blas_kernels.cu:1130:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kstep\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n","     \u001b[01;35m\u001b[Kint \u001b[m\u001b[Kstep = 0;\n","     \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n","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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/crop_layer_kernels.cu -o obj/crop_layer_kernels.o\n","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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/dropout_layer_kernels.cu -o obj/dropout_layer_kernels.o\n","./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n","\n","./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n","\n","./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n","\n","./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n","\n","./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n","\n","./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n","\n","./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n","\n","./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n","\n","./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n","\n","./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n","\n","./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n","\n","./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n","\n","./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n","\n","./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n","\n","./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n","\n","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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/maxpool_layer_kernels.cu -o obj/maxpool_layer_kernels.o\n","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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/network_kernels.cu -o obj/network_kernels.o\n","./src/network_kernels.cu(364): warning: variable \"l\" was declared but never referenced\n","\n","./src/network_kernels.cu(364): warning: variable \"l\" was declared but never referenced\n","\n","./src/network_kernels.cu(364): warning: variable \"l\" was declared but never referenced\n","\n","./src/network_kernels.cu(364): warning: variable \"l\" was declared but never referenced\n","\n","./src/network_kernels.cu(364): warning: variable \"l\" was declared but never referenced\n","\n","\u001b[01m\u001b[K./src/network_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfloat train_network_datum_gpu(network, float*, float*)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/network_kernels.cu:364:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kl\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n","       \u001b[01;35m\u001b[K \u001b[m\u001b[K layer l = net.layers[net.n - 1];\n","       \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n","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] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/avgpool_layer_kernels.cu -o obj/avgpool_layer_kernels.o\n","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 -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF 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/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++\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"lfVJ2iyAOaSj"},"source":["# Helper Function"]},{"cell_type":"code","metadata":{"id":"G9Fv0wjCMPYY","executionInfo":{"status":"ok","timestamp":1607385160184,"user_tz":360,"elapsed":521,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}}},"source":["\n","def imShow(path):\n","  import cv2\n","  import matplotlib.pyplot as plt\n","  %matplotlib inline\n","\n","  image = cv2.imread(path)\n","  height, width = image.shape[:2]\n","  resized_image = cv2.resize(image,(3*width, 3*height), interpolation = cv2.INTER_CUBIC)\n","\n","  fig = plt.gcf()\n","  fig.set_size_inches(18, 10)\n","  plt.axis(\"off\")\n","  plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))\n","  plt.show()\n","\n","#  to upload files\n","def upload():\n","  from google.colab import files\n","  uploaded = files.upload() \n","  for name, data in uploaded.items():\n","    with open(name, 'wb') as f:\n","      f.write(data)\n","      print ('saved file', name)\n","\n","#  to download a file  \n","def download(path):\n","  from google.colab import files\n","  files.download(path)"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uMLpWp-DZSZY","executionInfo":{"status":"ok","timestamp":1607385228499,"user_tz":360,"elapsed":61285,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}},"outputId":"b53a42bb-2c1a-4ab3-fd9c-069b5475da9b"},"source":["%cd ..\n","from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":6,"outputs":[{"output_type":"stream","text":["/content\n","Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bBFiu7rFZdmU","executionInfo":{"status":"ok","timestamp":1607385233164,"user_tz":360,"elapsed":589,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}},"outputId":"7aa3f753-810b-452b-f63f-e650656dd336"},"source":["# creating link to my drive\n","!ln -s /content/gdrive/My\\ Drive/ /mydrive\n","!ls /mydrive"],"execution_count":7,"outputs":[{"output_type":"stream","text":["'all final.pdf'\n"," Classroom\n","'Colab Notebooks'\n","'Converted-Data-Analyst-ProfileHarris (1).pdf'\n"," creditcard.csv.zip\n"," cv\n"," CV.gdoc\n"," CV-Muhammad_Affan.pdf\n"," CytoscapeInput-edges-dose.txt\n","'CytoscapeInput-edges-time (1).txt'\n"," CytoscapeInput-edges-time.txt\n"," dat_ensemble_with_gene_names.csv\n"," dat_ensemble_with_gene_names.gsheet\n","'Document from harris.pdf'\n"," ehtsham.json\n"," f\n","'FINALPRESENTATION FYP.pptx'\n","'Handout 2 -Conditional JUMP Instructions.pdf'\n","'Harrishashmi resume  final.gdoc'\n","'Harrishashmi resume .gdoc'\n","'Harris Profile.docx'\n","'How to get started with Drive.pdf'\n"," image2.JPG\n"," images\n","'IM Lab PDF 1.pdf'\n","'Lecture - 13b.pdf'\n","'MPI After Mid.pdf'\n","'MPI Saad Nasim.pdf'\n","'Muhammad Harris Hashmi (1).gdoc'\n","'Muhammad Harris Hashmi (2).gdoc'\n","'Muhammad Harris Hashmi.docx'\n","'Muhammad Harris Hashmi.gdoc'\n","'My Drive'\n","'New Doc 2018-01-11.pdf'\n"," OIDv4_ToolKit\n"," OneDrive-2016-04-01.zip\n","'outline1 harris FINAL.docx'\n","'outline1 harris FINAL.docx.gdoc'\n","'protein_pair_preferred_names (1).csv'\n"," protein_pair_preferred_names.csv\n"," protein_pair_preferred_namess\n"," Q14.pdf\n"," Q14.pdf.gdoc\n"," Ramiz.gdoc\n","'report template.docx'\n","'Resume HAMZA ARSHAD.pdf'\n","'Resume HARRIS HASHMI (2).pdf'\n","'S&S Extra Notes ...-1.pdf'\n","'S&S Notes (which is Written in the guidance of MAB)-1.pdf'\n","'telecom_churn (1).csv.gsheet'\n"," telecom_churn.csv.gsheet\n","'UAB Transcript.pdf'\n","'Untitled spreadsheet (1).gsheet'\n","'Untitled spreadsheet (2).gsheet'\n","'Untitled spreadsheet.gsheet'\n"," what-is-islamic-culture.pdf\n"," yolov4\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JafIRIU0Z6UQ","executionInfo":{"status":"ok","timestamp":1607385239406,"user_tz":360,"elapsed":363,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}},"outputId":"0e11a83a-94c1-468c-84c7-c53ccc3aff2c"},"source":["# cd back into the darknet folder \n","%cd /content/darknet"],"execution_count":8,"outputs":[{"output_type":"stream","text":["/content/darknet\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YL--CsG0Vevs","executionInfo":{"status":"ok","timestamp":1607385241887,"user_tz":360,"elapsed":509,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}},"outputId":"3b9048b7-96ea-420d-df2b-41515dc3f1b2"},"source":["# Verifying the dataset presence inside the drive\n","!ls /mydrive/yolov4"],"execution_count":9,"outputs":[{"output_type":"stream","text":[" 00140.xml\t\t\t\t       Untitled0.ipynb\n","'Advanced Matplotlib Concepts.ipynb'\t       Untitled10.ipynb\n"," backup\t\t\t\t\t       Untitled11.ipynb\n","'Copy of YOLOv4_Training_Tutorial.ipynb'       Untitled12.ipynb\n"," cs685-python-tutorial.ipynb\t\t       Untitled13.ipynb\n","'Final Project.ipynb'\t\t\t       Untitled14.ipynb\n"," generate_test.py\t\t\t       Untitled15.ipynb\n"," generate_train.py\t\t\t       Untitled16.ipynb\n"," kangaroo\t\t\t\t       Untitled1.ipynb\n","'linear regression.ipynb'\t\t       Untitled2.ipynb\n","'Matplotlib Concepts Lecture.ipynb'\t       Untitled3.ipynb\n","'Matplotlib Exercises .ipynb'\t\t       Untitled4.ipynb\n","'Matplotlib Exercises - Solutions (1).ipynb'   Untitled5.ipynb\n","'Matplotlib Exercises - Solutions.ipynb'       Untitled6.ipynb\n"," obj.data\t\t\t\t       Untitled7.ipynb\n"," obj.names\t\t\t\t       Untitled8.ipynb\n"," obj.zip\t\t\t\t       Untitled9.ipynb\n","'PYTHON COURSE.ipynb'\t\t\t      'Untitled Folder'\n","'Python-Data Science.ipynb'\t\t       Untitled.ipynb\n","'PYTHON PRACTICE NOTEBOOK.ipynb'\t       validation.zip\n"," test.zip\t\t\t\t       yolov4-obj.cfg\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"zqLQF0fNWJro","executionInfo":{"status":"ok","timestamp":1607385254533,"user_tz":360,"elapsed":6982,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}}},"source":["# copy over both datasets into the root directory of the Colab VM \n","!cp /mydrive/yolov4/obj.zip ../\n","!cp /mydrive/yolov4/test.zip ../"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A9mYUoKOWWlR","executionInfo":{"status":"ok","timestamp":1607385264453,"user_tz":360,"elapsed":5067,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}},"outputId":"fad143f3-1514-414c-82be-8c781593b032"},"source":["# unzipping the datasets \n","!unzip ../obj.zip -d data/\n","!unzip ../test.zip -d data/"],"execution_count":11,"outputs":[{"output_type":"stream","text":["Archive:  ../obj.zip\n","   creating: data/obj/\n","  inflating: data/obj/1f38f89b963c48bb.jpg  \n","  inflating: data/obj/90e2a8c82d013e69.jpg  \n","  inflating: data/obj/0b3b1186b7067a0d.jpg  \n","  inflating: data/obj/6ff0477e5f1d66aa.jpg  \n","  inflating: data/obj/8a491ded3ad70dc4.jpg  \n","  inflating: data/obj/ba70926f885d2d07.jpg  \n","  inflating: data/obj/22a74841a6af3806.jpg  \n","  inflating: data/obj/9e0056a0058748db.jpg  \n","  inflating: data/obj/031dae4a52cf8aa1.jpg  \n","  inflating: data/obj/a86fb7f56133da84.jpg  \n","  inflating: data/obj/49a30edbcde7b152.jpg  \n","  inflating: data/obj/06cd561c61187ef6.jpg  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./data"],"execution_count":12,"outputs":[]},{"cell_type":"code","metadata":{"id":"4mgiK3C2ZF0k","executionInfo":{"status":"ok","timestamp":1607385272257,"user_tz":360,"elapsed":835,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}}},"source":["# uploading the generate_train.py and generate_test.py \n","!cp /mydrive/yolov4/generate_train.py ./\n","!cp /mydrive/yolov4/generate_test.py ./"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"id":"DAKjJSJwJb5R","executionInfo":{"status":"ok","timestamp":1607385275086,"user_tz":360,"elapsed":516,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}}},"source":["!python generate_train.py\n","!python generate_test.py"],"execution_count":14,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KcUKbtO-byOE","executionInfo":{"status":"ok","timestamp":1607385287053,"user_tz":360,"elapsed":2414,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}},"outputId":"3c008279-7844-4d0a-cea0-63a3719d454a"},"source":["!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137"],"execution_count":15,"outputs":[{"output_type":"stream","text":["--2020-12-07 23:54:44--  https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137\n","Resolving github.com (github.com)... 140.82.114.4\n","Connecting to github.com (github.com)|140.82.114.4|:443... connected.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://github-production-release-asset-2e65be.s3.amazonaws.com/75388965/48bfe500-889d-11ea-819e-c4d182fcf0db?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20201207%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201207T235444Z&X-Amz-Expires=300&X-Amz-Signature=cc22affa99db9e8b5eab5abc51ee5eaa6fce1befc1e9c2e058b7b852371de44f&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4.conv.137&response-content-type=application%2Foctet-stream [following]\n","--2020-12-07 23:54:44--  https://github-production-release-asset-2e65be.s3.amazonaws.com/75388965/48bfe500-889d-11ea-819e-c4d182fcf0db?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20201207%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20201207T235444Z&X-Amz-Expires=300&X-Amz-Signature=cc22affa99db9e8b5eab5abc51ee5eaa6fce1befc1e9c2e058b7b852371de44f&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=75388965&response-content-disposition=attachment%3B%20filename%3Dyolov4.conv.137&response-content-type=application%2Foctet-stream\n","Resolving github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)... 52.216.228.104\n","Connecting to github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)|52.216.228.104|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 170038676 (162M) [application/octet-stream]\n","Saving to: ‘yolov4.conv.137’\n","\n","yolov4.conv.137     100%[===================>] 162.16M  91.1MB/s    in 1.8s    \n","\n","2020-12-07 23:54:46 (91.1 MB/s) - ‘yolov4.conv.137’ saved [170038676/170038676]\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"810ERKeVPPyk"},"source":["# Training"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"imc0NP19hLuq","executionInfo":{"status":"ok","timestamp":1607385377265,"user_tz":360,"elapsed":26911,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}},"outputId":"eb217bee-3410-471e-f388-be84daeee246"},"source":["# training custom detector! \n","!./darknet detector train data/obj.data cfg/yolov4-obj.cfg yolov4.conv.137 -dont_show -map"],"execution_count":17,"outputs":[{"output_type":"stream","text":[" CUDA-version: 10010 (10010), cuDNN: 7.6.5, CUDNN_HALF=1, GPU count: 1  \n"," CUDNN_HALF=1 \n"," OpenCV version: 3.2.0\n"," Prepare additional network for mAP calculation...\n"," 0 : compute_capability = 750, cudnn_half = 1, GPU: Tesla T4 \n","net.optimized_memory = 0 \n","mini_batch = 1, batch = 16, time_steps = 1, train = 0 \n","   layer   filters  size/strd(dil)      input                output\n","   0 conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF\n","   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF\n","   2 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n","   3 route  1 \t\t                           ->  208 x 208 x  64 \n","   4 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n","   5 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF\n","   6 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF\n","   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF\n","   8 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n","   9 route  8 2 \t                           ->  208 x 208 x 128 \n","  10 conv     64       1 x 1/ 1    208 x 208 x 128 ->  208 x 208 x  64 0.709 BF\n","  11 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF\n","  12 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF\n","  13 route  11 \t\t                           ->  104 x 104 x 128 \n","  14 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF\n","  15 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n","  16 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n","  17 Shortcut Layer: 14,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF\n","  18 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n","  19 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n","  20 Shortcut Layer: 17,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF\n","  21 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n","  22 route  21 12 \t                           ->  104 x 104 x 128 \n","  23 conv    128       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x 128 0.354 BF\n","  24 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF\n","  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n","  26 route  24 \t\t                           ->   52 x  52 x 256 \n","  27 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n","  28 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  29 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  31 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  32 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  34 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  35 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  37 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  38 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  39 Shortcut Layer: 36,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  40 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  41 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  42 Shortcut Layer: 39,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  43 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  44 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  45 Shortcut Layer: 42,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  46 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  47 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  48 Shortcut Layer: 45,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  49 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  50 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  51 Shortcut Layer: 48,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  52 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  53 route  52 25 \t                           ->   52 x  52 x 256 \n","  54 conv    256       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 256 0.354 BF\n","  55 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF\n","  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n","  57 route  55 \t\t                           ->   26 x  26 x 512 \n","  58 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n","  59 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  60 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  62 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  63 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  64 Shortcut Layer: 61,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  65 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  66 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  67 Shortcut Layer: 64,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  68 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  69 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  70 Shortcut Layer: 67,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  71 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  72 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  73 Shortcut Layer: 70,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  74 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  75 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  76 Shortcut Layer: 73,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  77 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  78 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  79 Shortcut Layer: 76,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  80 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  81 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  82 Shortcut Layer: 79,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  83 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  84 route  83 56 \t                           ->   26 x  26 x 512 \n","  85 conv    512       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 512 0.354 BF\n","  86 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF\n","  87 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n","  88 route  86 \t\t                           ->   13 x  13 x1024 \n","  89 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n","  90 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n","  91 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n","  92 Shortcut Layer: 89,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n","  93 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n","  94 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n","  95 Shortcut Layer: 92,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n","  96 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n","  97 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n","  98 Shortcut Layer: 95,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n","  99 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n"," 100 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n"," 101 Shortcut Layer: 98,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n"," 102 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n"," 103 route  102 87 \t                           ->   13 x  13 x1024 \n"," 104 conv   1024       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x1024 0.354 BF\n"," 105 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 106 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 107 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 108 max                5x 5/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.002 BF\n"," 109 route  107 \t\t                           ->   13 x  13 x 512 \n"," 110 max                9x 9/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.007 BF\n"," 111 route  107 \t\t                           ->   13 x  13 x 512 \n"," 112 max               13x13/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.015 BF\n"," 113 route  112 110 108 107 \t                   ->   13 x  13 x2048 \n"," 114 conv    512       1 x 1/ 1     13 x  13 x2048 ->   13 x  13 x 512 0.354 BF\n"," 115 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 116 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 117 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF\n"," 118 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256\n"," 119 route  85 \t\t                           ->   26 x  26 x 512 \n"," 120 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 121 route  120 118 \t                           ->   26 x  26 x 512 \n"," 122 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 123 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 124 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 125 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 126 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 127 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF\n"," 128 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128\n"," 129 route  54 \t\t                           ->   52 x  52 x 256 \n"," 130 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n"," 131 route  130 128 \t                           ->   52 x  52 x 256 \n"," 132 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n"," 133 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n"," 134 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n"," 135 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n"," 136 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n"," 137 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n"," 138 conv     18       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x  18 0.025 BF\n"," 139 yolo\n","[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20\n","nms_kind: greedynms (1), beta = 0.600000 \n"," 140 route  136 \t\t                           ->   52 x  52 x 128 \n"," 141 conv    256       3 x 3/ 2     52 x  52 x 128 ->   26 x  26 x 256 0.399 BF\n"," 142 route  141 126 \t                           ->   26 x  26 x 512 \n"," 143 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 144 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 145 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 146 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 147 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 148 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 149 conv     18       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x  18 0.012 BF\n"," 150 yolo\n","[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10\n","nms_kind: greedynms (1), beta = 0.600000 \n"," 151 route  147 \t\t                           ->   26 x  26 x 256 \n"," 152 conv    512       3 x 3/ 2     26 x  26 x 256 ->   13 x  13 x 512 0.399 BF\n"," 153 route  152 116 \t                           ->   13 x  13 x1024 \n"," 154 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 155 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 156 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 157 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 158 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 159 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 160 conv     18       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x  18 0.006 BF\n"," 161 yolo\n","[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05\n","nms_kind: greedynms (1), beta = 0.600000 \n","Total BFLOPS 59.563 \n","avg_outputs = 489778 \n"," Allocate additional workspace_size = 52.43 MB \n","yolov4-obj\n"," 0 : compute_capability = 750, cudnn_half = 1, GPU: Tesla T4 \n","net.optimized_memory = 0 \n","mini_batch = 4, batch = 64, time_steps = 1, train = 1 \n","   layer   filters  size/strd(dil)      input                output\n","   0 conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF\n","   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF\n","   2 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n","   3 route  1 \t\t                           ->  208 x 208 x  64 \n","   4 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n","   5 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF\n","   6 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF\n","   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF\n","   8 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n","   9 route  8 2 \t                           ->  208 x 208 x 128 \n","  10 conv     64       1 x 1/ 1    208 x 208 x 128 ->  208 x 208 x  64 0.709 BF\n","  11 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF\n","  12 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF\n","  13 route  11 \t\t                           ->  104 x 104 x 128 \n","  14 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF\n","  15 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n","  16 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n","  17 Shortcut Layer: 14,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF\n","  18 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n","  19 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n","  20 Shortcut Layer: 17,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF\n","  21 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n","  22 route  21 12 \t                           ->  104 x 104 x 128 \n","  23 conv    128       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x 128 0.354 BF\n","  24 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF\n","  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n","  26 route  24 \t\t                           ->   52 x  52 x 256 \n","  27 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n","  28 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  29 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  31 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  32 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  34 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  35 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  37 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  38 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  39 Shortcut Layer: 36,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  40 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  41 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  42 Shortcut Layer: 39,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  43 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  44 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  45 Shortcut Layer: 42,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  46 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  47 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  48 Shortcut Layer: 45,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  49 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  50 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  51 Shortcut Layer: 48,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  52 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  53 route  52 25 \t                           ->   52 x  52 x 256 \n","  54 conv    256       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 256 0.354 BF\n","  55 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF\n","  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n","  57 route  55 \t\t                           ->   26 x  26 x 512 \n","  58 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n","  59 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  60 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  62 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  63 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  64 Shortcut Layer: 61,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  65 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  66 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  67 Shortcut Layer: 64,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  68 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  69 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  70 Shortcut Layer: 67,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  71 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  72 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  73 Shortcut Layer: 70,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  74 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  75 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  76 Shortcut Layer: 73,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  77 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  78 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  79 Shortcut Layer: 76,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  80 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  81 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  82 Shortcut Layer: 79,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  83 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  84 route  83 56 \t                           ->   26 x  26 x 512 \n","  85 conv    512       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 512 0.354 BF\n","  86 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF\n","  87 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n","  88 route  86 \t\t                           ->   13 x  13 x1024 \n","  89 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n","  90 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n","  91 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n","  92 Shortcut Layer: 89,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n","  93 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n","  94 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n","  95 Shortcut Layer: 92,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n","  96 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n","  97 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n","  98 Shortcut Layer: 95,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n","  99 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n"," 100 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n"," 101 Shortcut Layer: 98,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n"," 102 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n"," 103 route  102 87 \t                           ->   13 x  13 x1024 \n"," 104 conv   1024       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x1024 0.354 BF\n"," 105 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 106 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 107 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 108 max                5x 5/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.002 BF\n"," 109 route  107 \t\t                           ->   13 x  13 x 512 \n"," 110 max                9x 9/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.007 BF\n"," 111 route  107 \t\t                           ->   13 x  13 x 512 \n"," 112 max               13x13/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.015 BF\n"," 113 route  112 110 108 107 \t                   ->   13 x  13 x2048 \n"," 114 conv    512       1 x 1/ 1     13 x  13 x2048 ->   13 x  13 x 512 0.354 BF\n"," 115 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 116 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 117 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF\n"," 118 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256\n"," 119 route  85 \t\t                           ->   26 x  26 x 512 \n"," 120 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 121 route  120 118 \t                           ->   26 x  26 x 512 \n"," 122 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 123 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 124 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 125 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 126 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 127 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF\n"," 128 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128\n"," 129 route  54 \t\t                           ->   52 x  52 x 256 \n"," 130 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n"," 131 route  130 128 \t                           ->   52 x  52 x 256 \n"," 132 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n"," 133 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n"," 134 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n"," 135 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n"," 136 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n"," 137 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n"," 138 conv     18       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x  18 0.025 BF\n"," 139 yolo\n","[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20\n","nms_kind: greedynms (1), beta = 0.600000 \n"," 140 route  136 \t\t                           ->   52 x  52 x 128 \n"," 141 conv    256       3 x 3/ 2     52 x  52 x 128 ->   26 x  26 x 256 0.399 BF\n"," 142 route  141 126 \t                           ->   26 x  26 x 512 \n"," 143 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 144 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 145 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 146 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 147 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 148 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 149 conv     18       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x  18 0.012 BF\n"," 150 yolo\n","[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10\n","nms_kind: greedynms (1), beta = 0.600000 \n"," 151 route  147 \t\t                           ->   26 x  26 x 256 \n"," 152 conv    512       3 x 3/ 2     26 x  26 x 256 ->   13 x  13 x 512 0.399 BF\n"," 153 route  152 116 \t                           ->   13 x  13 x1024 \n"," 154 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 155 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 156 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 157 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 158 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 159 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 160 conv     18       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x  18 0.006 BF\n"," 161 yolo\n","[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05\n","nms_kind: greedynms (1), beta = 0.600000 \n","Total BFLOPS 59.563 \n","avg_outputs = 489778 \n"," Allocate additional workspace_size = 111.05 MB \n","Loading weights from yolov4.conv.137...\n"," seen 64, trained: 0 K-images (0 Kilo-batches_64) \n","Done! Loaded 137 layers from weights-file \n","Learning Rate: 0.001, Momentum: 0.949, Decay: 0.0005\n"," Detection layer: 139 - type = 28 \n"," Detection layer: 150 - type = 28 \n"," Detection layer: 161 - type = 28 \n","Resizing, random_coef = 1.40 \n","\n"," 608 x 608 \n"," Create 6 permanent cpu-threads \n"," try to allocate additional workspace_size = 237.17 MB \n"," CUDA allocate done! \n","Loaded: 0.000040 seconds\n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.347686), count: 6, total_loss = 4138.271484 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.382985), count: 15, total_loss = 1353.287231 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.395135), count: 8, total_loss = 296.384460 \n"," total_bbox = 29, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.275259), count: 15, total_loss = 4121.453613 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.353435), count: 9, total_loss = 1356.981567 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.000000), count: 1, total_loss = 294.140137 \n"," total_bbox = 53, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.355178), count: 8, total_loss = 4164.805176 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.313052), count: 5, total_loss = 1352.718384 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.397136), count: 4, total_loss = 297.025116 \n"," total_bbox = 70, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.160533), count: 1, total_loss = 4125.370605 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.411126), count: 4, total_loss = 1367.231812 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.374041), count: 4, total_loss = 294.582855 \n"," total_bbox = 79, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.296269), count: 4, total_loss = 4156.051270 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.291117), count: 4, total_loss = 1358.094727 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.381976), count: 3, total_loss = 296.280518 \n"," total_bbox = 90, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.318971), count: 7, total_loss = 4121.234863 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.321918), count: 7, total_loss = 1383.802368 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.000000), count: 1, total_loss = 295.410095 \n"," total_bbox = 104, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.251158), count: 4, total_loss = 4120.296875 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.406329), count: 8, total_loss = 1371.541748 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.286434), count: 4, total_loss = 296.357544 \n"," total_bbox = 120, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.275829), count: 3, total_loss = 4154.977539 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.403431), count: 6, total_loss = 1367.845093 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.496182), count: 3, total_loss = 296.578949 \n"," total_bbox = 132, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.314641), count: 11, total_loss = 4146.268555 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.354555), count: 13, total_loss = 1355.078735 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.341033), count: 6, total_loss = 295.746796 \n"," total_bbox = 162, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.289625), count: 16, total_loss = 4162.688965 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.409441), count: 4, total_loss = 1354.067505 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.449941), count: 6, total_loss = 295.303497 \n"," total_bbox = 188, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.385514), count: 16, total_loss = 4130.518555 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.408176), count: 15, total_loss = 1358.527466 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.269202), count: 3, total_loss = 296.605957 \n"," total_bbox = 222, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.292113), count: 11, total_loss = 4139.591797 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.442424), count: 6, total_loss = 1366.455811 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.279583), count: 2, total_loss = 295.554871 \n"," total_bbox = 241, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.189562), count: 1, total_loss = 4111.578125 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.455946), count: 6, total_loss = 1370.575928 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.264829), count: 6, total_loss = 297.200684 \n"," total_bbox = 254, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.365062), count: 7, total_loss = 4127.151855 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.396886), count: 7, total_loss = 1377.559570 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.227350), count: 1, total_loss = 296.035583 \n"," total_bbox = 269, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.316314), count: 6, total_loss = 4139.595703 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.401255), count: 1, total_loss = 1368.135254 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.479471), count: 3, total_loss = 296.399902 \n"," total_bbox = 279, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.288404), count: 3, total_loss = 4137.527344 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.361436), count: 9, total_loss = 1362.804321 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.449444), count: 5, total_loss = 297.152283 \n"," total_bbox = 296, rewritten_bbox = 0.000000 % \n","\n","\n"," Tensor Cores are disabled until the first 3000 iterations are reached.\n"," (next mAP calculation at 1000 iterations) \n"," 1: 1932.475952, 1932.475952 avg loss, 0.000000 rate, 5.591477 seconds, 64 images, -1.000000 hours left\n","Loaded: 0.000066 seconds\n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.273647), count: 13, total_loss = 4117.778809 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.392323), count: 5, total_loss = 1380.057129 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.476372), count: 3, total_loss = 296.181580 \n"," total_bbox = 317, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.395677), count: 3, total_loss = 4118.653320 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.304890), count: 2, total_loss = 1365.610962 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.423186), count: 3, total_loss = 295.170746 \n"," total_bbox = 325, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.314551), count: 8, total_loss = 4143.304688 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.441219), count: 4, total_loss = 1366.543213 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.433222), count: 4, total_loss = 295.308990 \n"," total_bbox = 341, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.343852), count: 21, total_loss = 4147.796387 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.329083), count: 14, total_loss = 1352.662231 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.290069), count: 4, total_loss = 294.619965 \n"," total_bbox = 380, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.277047), count: 13, total_loss = 4155.776855 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.358691), count: 5, total_loss = 1347.321533 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.000000), count: 1, total_loss = 294.888672 \n"," total_bbox = 398, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.419206), count: 8, total_loss = 4140.275391 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.294857), count: 11, total_loss = 1367.358765 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.321932), count: 7, total_loss = 295.174042 \n"," total_bbox = 424, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.324440), count: 10, total_loss = 4121.815918 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.439099), count: 4, total_loss = 1357.674438 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.640876), count: 2, total_loss = 295.128937 \n"," total_bbox = 440, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.204294), count: 8, total_loss = 4128.031250 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.479384), count: 3, total_loss = 1373.043091 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.550453), count: 3, total_loss = 295.535400 \n"," total_bbox = 454, rewritten_bbox = 0.000000 % \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 139 Avg (IOU: 0.333685), count: 15, total_loss = 4142.528809 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 150 Avg (IOU: 0.483376), count: 4, total_loss = 1376.955688 \n","v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 161 Avg (IOU: 0.113859), count: 1, total_loss = 297.324066 \n"," total_bbox = 474, rewritten_bbox = 0.000000 % \n","^C\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2UoSBmTFlNep","executionInfo":{"status":"ok","timestamp":1607385400303,"user_tz":360,"elapsed":8621,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}},"outputId":"e8fc3389-9bac-435f-c8d6-317ddf007aca"},"source":["!./darknet detector map data/obj.data cfg/yolov4-obj.cfg /mydrive/yolov4/backup/yolov4-obj_2000.weights"],"execution_count":18,"outputs":[{"output_type":"stream","text":[" CUDA-version: 10010 (10010), cuDNN: 7.6.5, CUDNN_HALF=1, GPU count: 1  \n"," CUDNN_HALF=1 \n"," OpenCV version: 3.2.0\n"," 0 : compute_capability = 750, cudnn_half = 1, GPU: Tesla T4 \n","net.optimized_memory = 0 \n","mini_batch = 1, batch = 16, time_steps = 1, train = 0 \n","   layer   filters  size/strd(dil)      input                output\n","   0 conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF\n","   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF\n","   2 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n","   3 route  1 \t\t                           ->  208 x 208 x  64 \n","   4 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n","   5 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF\n","   6 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF\n","   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF\n","   8 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n","   9 route  8 2 \t                           ->  208 x 208 x 128 \n","  10 conv     64       1 x 1/ 1    208 x 208 x 128 ->  208 x 208 x  64 0.709 BF\n","  11 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF\n","  12 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF\n","  13 route  11 \t\t                           ->  104 x 104 x 128 \n","  14 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF\n","  15 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n","  16 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n","  17 Shortcut Layer: 14,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF\n","  18 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n","  19 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n","  20 Shortcut Layer: 17,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF\n","  21 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n","  22 route  21 12 \t                           ->  104 x 104 x 128 \n","  23 conv    128       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x 128 0.354 BF\n","  24 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF\n","  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n","  26 route  24 \t\t                           ->   52 x  52 x 256 \n","  27 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n","  28 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  29 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  31 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  32 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  34 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  35 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  37 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  38 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  39 Shortcut Layer: 36,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  40 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  41 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  42 Shortcut Layer: 39,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  43 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  44 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  45 Shortcut Layer: 42,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  46 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  47 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  48 Shortcut Layer: 45,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  49 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  50 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  51 Shortcut Layer: 48,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  52 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  53 route  52 25 \t                           ->   52 x  52 x 256 \n","  54 conv    256       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 256 0.354 BF\n","  55 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF\n","  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n","  57 route  55 \t\t                           ->   26 x  26 x 512 \n","  58 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n","  59 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  60 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  62 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  63 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  64 Shortcut Layer: 61,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  65 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  66 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  67 Shortcut Layer: 64,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  68 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  69 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  70 Shortcut Layer: 67,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  71 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  72 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  73 Shortcut Layer: 70,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  74 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  75 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  76 Shortcut Layer: 73,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  77 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  78 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  79 Shortcut Layer: 76,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  80 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  81 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  82 Shortcut Layer: 79,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  83 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  84 route  83 56 \t                           ->   26 x  26 x 512 \n","  85 conv    512       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 512 0.354 BF\n","  86 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF\n","  87 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n","  88 route  86 \t\t                           ->   13 x  13 x1024 \n","  89 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n","  90 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n","  91 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n","  92 Shortcut Layer: 89,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n","  93 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n","  94 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n","  95 Shortcut Layer: 92,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n","  96 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n","  97 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n","  98 Shortcut Layer: 95,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n","  99 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n"," 100 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n"," 101 Shortcut Layer: 98,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n"," 102 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n"," 103 route  102 87 \t                           ->   13 x  13 x1024 \n"," 104 conv   1024       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x1024 0.354 BF\n"," 105 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 106 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 107 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 108 max                5x 5/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.002 BF\n"," 109 route  107 \t\t                           ->   13 x  13 x 512 \n"," 110 max                9x 9/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.007 BF\n"," 111 route  107 \t\t                           ->   13 x  13 x 512 \n"," 112 max               13x13/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.015 BF\n"," 113 route  112 110 108 107 \t                   ->   13 x  13 x2048 \n"," 114 conv    512       1 x 1/ 1     13 x  13 x2048 ->   13 x  13 x 512 0.354 BF\n"," 115 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 116 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 117 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF\n"," 118 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256\n"," 119 route  85 \t\t                           ->   26 x  26 x 512 \n"," 120 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 121 route  120 118 \t                           ->   26 x  26 x 512 \n"," 122 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 123 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 124 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 125 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 126 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 127 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF\n"," 128 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128\n"," 129 route  54 \t\t                           ->   52 x  52 x 256 \n"," 130 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n"," 131 route  130 128 \t                           ->   52 x  52 x 256 \n"," 132 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n"," 133 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n"," 134 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n"," 135 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n"," 136 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n"," 137 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n"," 138 conv     18       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x  18 0.025 BF\n"," 139 yolo\n","[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20\n","nms_kind: greedynms (1), beta = 0.600000 \n"," 140 route  136 \t\t                           ->   52 x  52 x 128 \n"," 141 conv    256       3 x 3/ 2     52 x  52 x 128 ->   26 x  26 x 256 0.399 BF\n"," 142 route  141 126 \t                           ->   26 x  26 x 512 \n"," 143 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 144 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 145 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 146 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 147 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 148 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 149 conv     18       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x  18 0.012 BF\n"," 150 yolo\n","[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10\n","nms_kind: greedynms (1), beta = 0.600000 \n"," 151 route  147 \t\t                           ->   26 x  26 x 256 \n"," 152 conv    512       3 x 3/ 2     26 x  26 x 256 ->   13 x  13 x 512 0.399 BF\n"," 153 route  152 116 \t                           ->   13 x  13 x1024 \n"," 154 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 155 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 156 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 157 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 158 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 159 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 160 conv     18       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x  18 0.006 BF\n"," 161 yolo\n","[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05\n","nms_kind: greedynms (1), beta = 0.600000 \n","Total BFLOPS 59.563 \n","avg_outputs = 489778 \n"," Allocate additional workspace_size = 52.43 MB \n","Loading weights from /mydrive/yolov4/backup/yolov4-obj_2000.weights...\n"," seen 64, trained: 128 K-images (2 Kilo-batches_64) \n","Done! Loaded 162 layers from weights-file \n","\n"," calculation mAP (mean average precision)...\n"," Detection layer: 139 - type = 28 \n"," Detection layer: 150 - type = 28 \n"," Detection layer: 161 - type = 28 \n","72^C\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"o7IIzEal90dA","executionInfo":{"status":"ok","timestamp":1607385420496,"user_tz":360,"elapsed":8493,"user":{"displayName":"harris hashmi","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWee60yVqrjJOaUBe8RkemA290TK_pE0JTNc7fcA=s64","userId":"04249132552366085646"}},"outputId":"14c81bdc-0aa3-48ca-c452-547b9a22cde9"},"source":["# running custom detector \n","!./darknet detector test data/obj.data cfg/yolov4-obj.cfg /mydrive/yolov4/backup/yolov4-obj_2000.weights /mydrive/images/car3.jpg -thresh 0.3\n","imShow('predictions.jpg')"],"execution_count":19,"outputs":[{"output_type":"stream","text":[" CUDA-version: 10010 (10010), cuDNN: 7.6.5, CUDNN_HALF=1, GPU count: 1  \n"," CUDNN_HALF=1 \n"," OpenCV version: 3.2.0\n"," 0 : compute_capability = 750, cudnn_half = 1, GPU: Tesla T4 \n","net.optimized_memory = 0 \n","mini_batch = 1, batch = 16, time_steps = 1, train = 0 \n","   layer   filters  size/strd(dil)      input                output\n","   0 conv     32       3 x 3/ 1    416 x 416 x   3 ->  416 x 416 x  32 0.299 BF\n","   1 conv     64       3 x 3/ 2    416 x 416 x  32 ->  208 x 208 x  64 1.595 BF\n","   2 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n","   3 route  1 \t\t                           ->  208 x 208 x  64 \n","   4 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n","   5 conv     32       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  32 0.177 BF\n","   6 conv     64       3 x 3/ 1    208 x 208 x  32 ->  208 x 208 x  64 1.595 BF\n","   7 Shortcut Layer: 4,  wt = 0, wn = 0, outputs: 208 x 208 x  64 0.003 BF\n","   8 conv     64       1 x 1/ 1    208 x 208 x  64 ->  208 x 208 x  64 0.354 BF\n","   9 route  8 2 \t                           ->  208 x 208 x 128 \n","  10 conv     64       1 x 1/ 1    208 x 208 x 128 ->  208 x 208 x  64 0.709 BF\n","  11 conv    128       3 x 3/ 2    208 x 208 x  64 ->  104 x 104 x 128 1.595 BF\n","  12 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF\n","  13 route  11 \t\t                           ->  104 x 104 x 128 \n","  14 conv     64       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x  64 0.177 BF\n","  15 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n","  16 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n","  17 Shortcut Layer: 14,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF\n","  18 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n","  19 conv     64       3 x 3/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.797 BF\n","  20 Shortcut Layer: 17,  wt = 0, wn = 0, outputs: 104 x 104 x  64 0.001 BF\n","  21 conv     64       1 x 1/ 1    104 x 104 x  64 ->  104 x 104 x  64 0.089 BF\n","  22 route  21 12 \t                           ->  104 x 104 x 128 \n","  23 conv    128       1 x 1/ 1    104 x 104 x 128 ->  104 x 104 x 128 0.354 BF\n","  24 conv    256       3 x 3/ 2    104 x 104 x 128 ->   52 x  52 x 256 1.595 BF\n","  25 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n","  26 route  24 \t\t                           ->   52 x  52 x 256 \n","  27 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n","  28 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  29 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  30 Shortcut Layer: 27,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  31 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  32 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  33 Shortcut Layer: 30,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  34 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  35 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  36 Shortcut Layer: 33,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  37 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  38 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  39 Shortcut Layer: 36,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  40 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  41 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  42 Shortcut Layer: 39,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  43 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  44 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  45 Shortcut Layer: 42,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  46 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  47 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  48 Shortcut Layer: 45,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  49 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  50 conv    128       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.797 BF\n","  51 Shortcut Layer: 48,  wt = 0, wn = 0, outputs:  52 x  52 x 128 0.000 BF\n","  52 conv    128       1 x 1/ 1     52 x  52 x 128 ->   52 x  52 x 128 0.089 BF\n","  53 route  52 25 \t                           ->   52 x  52 x 256 \n","  54 conv    256       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 256 0.354 BF\n","  55 conv    512       3 x 3/ 2     52 x  52 x 256 ->   26 x  26 x 512 1.595 BF\n","  56 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n","  57 route  55 \t\t                           ->   26 x  26 x 512 \n","  58 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n","  59 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  60 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  61 Shortcut Layer: 58,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  62 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  63 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  64 Shortcut Layer: 61,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  65 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  66 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  67 Shortcut Layer: 64,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  68 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  69 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  70 Shortcut Layer: 67,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  71 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  72 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  73 Shortcut Layer: 70,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  74 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  75 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  76 Shortcut Layer: 73,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  77 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  78 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  79 Shortcut Layer: 76,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  80 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  81 conv    256       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.797 BF\n","  82 Shortcut Layer: 79,  wt = 0, wn = 0, outputs:  26 x  26 x 256 0.000 BF\n","  83 conv    256       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 256 0.089 BF\n","  84 route  83 56 \t                           ->   26 x  26 x 512 \n","  85 conv    512       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 512 0.354 BF\n","  86 conv   1024       3 x 3/ 2     26 x  26 x 512 ->   13 x  13 x1024 1.595 BF\n","  87 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n","  88 route  86 \t\t                           ->   13 x  13 x1024 \n","  89 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n","  90 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n","  91 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n","  92 Shortcut Layer: 89,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n","  93 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n","  94 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n","  95 Shortcut Layer: 92,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n","  96 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n","  97 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n","  98 Shortcut Layer: 95,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n","  99 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n"," 100 conv    512       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.797 BF\n"," 101 Shortcut Layer: 98,  wt = 0, wn = 0, outputs:  13 x  13 x 512 0.000 BF\n"," 102 conv    512       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.089 BF\n"," 103 route  102 87 \t                           ->   13 x  13 x1024 \n"," 104 conv   1024       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x1024 0.354 BF\n"," 105 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 106 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 107 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 108 max                5x 5/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.002 BF\n"," 109 route  107 \t\t                           ->   13 x  13 x 512 \n"," 110 max                9x 9/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.007 BF\n"," 111 route  107 \t\t                           ->   13 x  13 x 512 \n"," 112 max               13x13/ 1     13 x  13 x 512 ->   13 x  13 x 512 0.015 BF\n"," 113 route  112 110 108 107 \t                   ->   13 x  13 x2048 \n"," 114 conv    512       1 x 1/ 1     13 x  13 x2048 ->   13 x  13 x 512 0.354 BF\n"," 115 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 116 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 117 conv    256       1 x 1/ 1     13 x  13 x 512 ->   13 x  13 x 256 0.044 BF\n"," 118 upsample                 2x    13 x  13 x 256 ->   26 x  26 x 256\n"," 119 route  85 \t\t                           ->   26 x  26 x 512 \n"," 120 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 121 route  120 118 \t                           ->   26 x  26 x 512 \n"," 122 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 123 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 124 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 125 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 126 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 127 conv    128       1 x 1/ 1     26 x  26 x 256 ->   26 x  26 x 128 0.044 BF\n"," 128 upsample                 2x    26 x  26 x 128 ->   52 x  52 x 128\n"," 129 route  54 \t\t                           ->   52 x  52 x 256 \n"," 130 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n"," 131 route  130 128 \t                           ->   52 x  52 x 256 \n"," 132 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n"," 133 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n"," 134 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n"," 135 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n"," 136 conv    128       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x 128 0.177 BF\n"," 137 conv    256       3 x 3/ 1     52 x  52 x 128 ->   52 x  52 x 256 1.595 BF\n"," 138 conv     18       1 x 1/ 1     52 x  52 x 256 ->   52 x  52 x  18 0.025 BF\n"," 139 yolo\n","[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20\n","nms_kind: greedynms (1), beta = 0.600000 \n"," 140 route  136 \t\t                           ->   52 x  52 x 128 \n"," 141 conv    256       3 x 3/ 2     52 x  52 x 128 ->   26 x  26 x 256 0.399 BF\n"," 142 route  141 126 \t                           ->   26 x  26 x 512 \n"," 143 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 144 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 145 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 146 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 147 conv    256       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x 256 0.177 BF\n"," 148 conv    512       3 x 3/ 1     26 x  26 x 256 ->   26 x  26 x 512 1.595 BF\n"," 149 conv     18       1 x 1/ 1     26 x  26 x 512 ->   26 x  26 x  18 0.012 BF\n"," 150 yolo\n","[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10\n","nms_kind: greedynms (1), beta = 0.600000 \n"," 151 route  147 \t\t                           ->   26 x  26 x 256 \n"," 152 conv    512       3 x 3/ 2     26 x  26 x 256 ->   13 x  13 x 512 0.399 BF\n"," 153 route  152 116 \t                           ->   13 x  13 x1024 \n"," 154 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 155 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 156 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 157 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 158 conv    512       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x 512 0.177 BF\n"," 159 conv   1024       3 x 3/ 1     13 x  13 x 512 ->   13 x  13 x1024 1.595 BF\n"," 160 conv     18       1 x 1/ 1     13 x  13 x1024 ->   13 x  13 x  18 0.006 BF\n"," 161 yolo\n","[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05\n","nms_kind: greedynms (1), beta = 0.600000 \n","Total BFLOPS 59.563 \n","avg_outputs = 489778 \n"," Allocate additional workspace_size = 52.43 MB \n","Loading weights from /mydrive/yolov4/backup/yolov4-obj_2000.weights...\n"," seen 64, trained: 128 K-images (2 Kilo-batches_64) \n","Done! Loaded 162 layers from weights-file \n"," Detection layer: 139 - type = 28 \n"," Detection layer: 150 - type = 28 \n"," Detection layer: 161 - type = 28 \n","/mydrive/images/car3.jpg: Predicted in 33.161000 milli-seconds.\n","license_plate: 118%\n","Unable to init server: Could not connect: Connection refused\n","\n","(predictions:1827): Gtk-\u001b[1;33mWARNING\u001b[0m **: \u001b[34m23:56:57.518\u001b[0m: cannot open display: \n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1296x720 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]}]}