yolo_v4_test_train_Roboflow_ok.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"yolo_v4_test_train_Roboflow_ok.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"GNVU7eu9CQj3"},"source":["# Introduction\n"]},{"cell_type":"markdown","metadata":{"id":"mDTvGt2zt7cm"},"source":["# Configuring cuDNN on Colab for YOLOv4\n","\n"]},{"cell_type":"code","metadata":{"id":"u-bguKWgtxSx","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1609425275341,"user_tz":-540,"elapsed":564,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}},"outputId":"af11bd0e-f43a-4fef-ad61-bd5b99e2027d"},"source":["# CUDA: Let's check that Nvidia CUDA drivers are already pre-installed and which version is it.\n","!/usr/local/cuda/bin/nvcc --version\n","# We need to install the correct cuDNN according to this output"],"execution_count":13,"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":{"id":"KJYM7-_Had0Q","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1609425276800,"user_tz":-540,"elapsed":769,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}},"outputId":"63f8c7c1-1ea3-4bcb-e064-7f935d0d2d54"},"source":["!nvidia-smi"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Thu Dec 31 14:34:36 2020       \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 460.27.04    Driver Version: 418.67       CUDA Version: 10.1     |\n","|-------------------------------+----------------------+----------------------+\n","| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|                               |                      |               MIG M. |\n","|===============================+======================+======================|\n","|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   43C    P8     9W /  70W |      0MiB / 15079MiB |      0%      Default |\n","|                               |                      |                 ERR! |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                                  |\n","|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n","|        ID   ID                                                   Usage      |\n","|=============================================================================|\n","|  No running processes found                                                 |\n","+-----------------------------------------------------------------------------+\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"zJA-BC1U5N3e","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1609425278107,"user_tz":-540,"elapsed":588,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}},"outputId":"be84dd2c-cb70-426d-d51e-92af6d42fc49"},"source":["# Change the number depending on what GPU is listed above, under NVIDIA-SMI > Name.\n","# Tesla K80: 30\n","# Tesla P100: 60\n","# Tesla T4: 75\n","%env compute_capability=60"],"execution_count":15,"outputs":[{"output_type":"stream","text":["env: compute_capability=60\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"v3nkYzWwMuBk"},"source":["## STEP 1. Install cuDNN according to the current CUDA version\n","Colab added cuDNN as an inherent install - so you don't have to do a thing - major win\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"16pvdFMa1FEe"},"source":["# Step 2: Installing Darknet for YOLOv4 on Colab\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"K9uY-38P93oz","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608563236784,"user_tz":-540,"elapsed":1575,"user":{"displayName":"남중구","photoUrl":"","userId":"02914767085172164696"}},"outputId":"1cd0f527-b2bd-47fe-b776-82ca2b4e6b65"},"source":["%cd /content/\n","%rm -rf darknet"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"HQEktcfj9y9O","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608563239429,"user_tz":-540,"elapsed":3609,"user":{"displayName":"남중구","photoUrl":"","userId":"02914767085172164696"}},"outputId":"87e847b1-7547-4f3c-d83b-9f5826c5812e"},"source":["#we clone the fork of darknet maintained by roboflow\n","#small changes have been made to configure darknet for training\n","!git clone https://github.com/roboflow-ai/darknet.git"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Cloning into 'darknet'...\n","remote: Enumerating objects: 13289, done.\u001b[K\n","remote: Total 13289 (delta 0), reused 0 (delta 0), pack-reused 13289\u001b[K\n","Receiving objects: 100% (13289/13289), 12.13 MiB | 22.95 MiB/s, done.\n","Resolving deltas: 100% (9104/9104), done.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"9O6dTiq5ga0I","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608563239430,"user_tz":-540,"elapsed":3051,"user":{"displayName":"남중구","photoUrl":"","userId":"02914767085172164696"}},"outputId":"2e4bb8b0-d271-4f90-aeea-1490e95abdf2"},"source":["%cd /content/darknet/\n","%rm Makefile"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/darknet\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"OyTAyEhOgd__","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608563239430,"user_tz":-540,"elapsed":1790,"user":{"displayName":"남중구","photoUrl":"","userId":"02914767085172164696"}},"outputId":"4deefed7-0b72-46b7-8d53-8d4f5a9cdca3"},"source":["#colab occasionally shifts dependencies around, at the time of authorship, this Makefile works for building Darknet on Colab\n","\n","%%writefile Makefile\n","GPU=1\n","CUDNN=1\n","CUDNN_HALF=0\n","OPENCV=1\n","AVX=0\n","OPENMP=0\n","LIBSO=1\n","ZED_CAMERA=0\n","ZED_CAMERA_v2_8=0\n","\n","# set GPU=1 and CUDNN=1 to speedup on GPU\n","# set CUDNN_HALF=1 to further speedup 3 x times (Mixed-precision on Tensor Cores) GPU: Volta, Xavier, Turing and higher\n","# set AVX=1 and OPENMP=1 to speedup on CPU (if error occurs then set AVX=0)\n","# set ZED_CAMERA=1 to enable ZED SDK 3.0 and above\n","# set ZED_CAMERA_v2_8=1 to enable ZED SDK 2.X\n","\n","USE_CPP=0\n","DEBUG=0\n","\n","ARCH= -gencode arch=compute_30,code=sm_30 \\\n","      -gencode arch=compute_35,code=sm_35 \\\n","      -gencode arch=compute_50,code=[sm_50,compute_50] \\\n","      -gencode arch=compute_52,code=[sm_52,compute_52] \\\n","\t    -gencode arch=compute_61,code=[sm_61,compute_61]\n","\n","OS := $(shell uname)\n","\n","# Tesla V100\n","# ARCH= -gencode arch=compute_70,code=[sm_70,compute_70]\n","\n","# GeForce RTX 2080 Ti, RTX 2080, RTX 2070, Quadro RTX 8000, Quadro RTX 6000, Quadro RTX 5000, Tesla T4, XNOR Tensor Cores\n","# ARCH= -gencode arch=compute_75,code=[sm_75,compute_75]\n","\n","# Jetson XAVIER\n","# ARCH= -gencode arch=compute_72,code=[sm_72,compute_72]\n","\n","# GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4\n","# ARCH= -gencode arch=compute_61,code=sm_61 -gencode arch=compute_61,code=compute_61\n","\n","# GP100/Tesla P100 - DGX-1\n","# ARCH= -gencode arch=compute_60,code=sm_60\n","\n","# For Jetson TX1, Tegra X1, DRIVE CX, DRIVE PX - uncomment:\n","# ARCH= -gencode arch=compute_53,code=[sm_53,compute_53]\n","\n","# For Jetson Tx2 or Drive-PX2 uncomment:\n","# ARCH= -gencode arch=compute_62,code=[sm_62,compute_62]\n","\n","\n","VPATH=./src/\n","EXEC=darknet\n","OBJDIR=./obj/\n","\n","ifeq ($(LIBSO), 1)\n","LIBNAMESO=libdarknet.so\n","APPNAMESO=uselib\n","endif\n","\n","ifeq ($(USE_CPP), 1)\n","CC=g++\n","else\n","CC=gcc\n","endif\n","\n","CPP=g++ -std=c++11\n","NVCC=nvcc\n","OPTS=-Ofast\n","LDFLAGS= -lm -pthread\n","COMMON= -Iinclude/ -I3rdparty/stb/include\n","CFLAGS=-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC\n","\n","ifeq ($(DEBUG), 1)\n","#OPTS= -O0 -g\n","#OPTS= -Og -g\n","COMMON+= -DDEBUG\n","CFLAGS+= -DDEBUG\n","else\n","ifeq ($(AVX), 1)\n","CFLAGS+= -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a\n","endif\n","endif\n","\n","CFLAGS+=$(OPTS)\n","\n","ifneq (,$(findstring MSYS_NT,$(OS)))\n","LDFLAGS+=-lws2_32\n","endif\n","\n","ifeq ($(OPENCV), 1)\n","COMMON+= -DOPENCV\n","CFLAGS+= -DOPENCV\n","LDFLAGS+= `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv`\n","COMMON+= `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv`\n","endif\n","\n","ifeq ($(OPENMP), 1)\n","CFLAGS+= -fopenmp\n","LDFLAGS+= -lgomp\n","endif\n","\n","ifeq ($(GPU), 1)\n","COMMON+= -DGPU -I/usr/local/cuda/include/\n","CFLAGS+= -DGPU\n","ifeq ($(OS),Darwin) #MAC\n","LDFLAGS+= -L/usr/local/cuda/lib -lcuda -lcudart -lcublas -lcurand\n","else\n","LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand\n","endif\n","endif\n","\n","ifeq ($(CUDNN), 1)\n","COMMON+= -DCUDNN\n","ifeq ($(OS),Darwin) #MAC\n","CFLAGS+= -DCUDNN -I/usr/local/cuda/include\n","LDFLAGS+= -L/usr/local/cuda/lib -lcudnn\n","else\n","CFLAGS+= -DCUDNN -I/usr/local/cudnn/include\n","LDFLAGS+= -L/usr/local/cudnn/lib64 -lcudnn\n","endif\n","endif\n","\n","ifeq ($(CUDNN_HALF), 1)\n","COMMON+= -DCUDNN_HALF\n","CFLAGS+= -DCUDNN_HALF\n","ARCH+= -gencode arch=compute_70,code=[sm_70,compute_70]\n","endif\n","\n","ifeq ($(ZED_CAMERA), 1)\n","CFLAGS+= -DZED_STEREO -I/usr/local/zed/include\n","ifeq ($(ZED_CAMERA_v2_8), 1)\n","LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed\n","#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0\n","else\n","LDFLAGS+= -L/usr/local/zed/lib -lsl_zed\n","#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0\n","endif\n","endif\n","\n","OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o gaussian_yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam_layer.o\n","ifeq ($(GPU), 1)\n","LDFLAGS+= -lstdc++\n","OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o\n","endif\n","\n","OBJS = $(addprefix $(OBJDIR), $(OBJ))\n","DEPS = $(wildcard src/*.h) Makefile include/darknet.h\n","\n","all: $(OBJDIR) backup results setchmod $(EXEC) $(LIBNAMESO) $(APPNAMESO)\n","\n","ifeq ($(LIBSO), 1)\n","CFLAGS+= -fPIC\n","\n","$(LIBNAMESO): $(OBJDIR) $(OBJS) include/yolo_v2_class.hpp src/yolo_v2_class.cpp\n","\t$(CPP) -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS $(COMMON) $(CFLAGS) $(OBJS) src/yolo_v2_class.cpp -o $@ $(LDFLAGS)\n","\n","$(APPNAMESO): $(LIBNAMESO) include/yolo_v2_class.hpp src/yolo_console_dll.cpp\n","\t$(CPP) -std=c++11 $(COMMON) $(CFLAGS) -o $@ src/yolo_console_dll.cpp $(LDFLAGS) -L ./ -l:$(LIBNAMESO)\n","endif\n","\n","$(EXEC): $(OBJS)\n","\t$(CPP) -std=c++11 $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS)\n","\n","$(OBJDIR)%.o: %.c $(DEPS)\n","\t$(CC) $(COMMON) $(CFLAGS) -c $< -o $@\n","\n","$(OBJDIR)%.o: %.cpp $(DEPS)\n","\t$(CPP) -std=c++11 $(COMMON) $(CFLAGS) -c $< -o $@\n","\n","$(OBJDIR)%.o: %.cu $(DEPS)\n","\t$(NVCC) $(ARCH) $(COMMON) --compiler-options \"$(CFLAGS)\" -c $< -o $@\n","\n","$(OBJDIR):\n","\tmkdir -p $(OBJDIR)\n","backup:\n","\tmkdir -p backup\n","results:\n","\tmkdir -p results\n","setchmod:\n","\tchmod +x *.sh\n","\n",".PHONY: clean\n","\n","clean:\n","\trm -rf $(OBJS) $(EXEC) $(LIBNAMESO) $(APPNAMESO)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Writing Makefile\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"QyMBDkaL-Aep","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608563314770,"user_tz":-540,"elapsed":75557,"user":{"displayName":"남중구","photoUrl":"","userId":"02914767085172164696"}},"outputId":"b26729d3-2bba-4d54-bf7c-64f7cb922b4e"},"source":["#install environment from the Makefile\n","#note if you are on Colab Pro this works on a P100 GPU\n","#if you are on Colab free, you may need to change the Makefile for the K80 GPU\n","#this goes for any GPU, you need to change the Makefile to inform darknet which GPU you are running on.\n","#note the Makefile above should work for you, if you need to tweak, try the below\n","%cd darknet/\n","#!sed -i 's/OPENCV=0/OPENCV=1/g' Makefile\n","#!sed -i 's/GPU=0/GPU=1/g' Makefile\n","#!sed -i 's/CUDNN=0/CUDNN=1/g' Makefile\n","!#sed -i \"s/ARCH= -gencode arch=compute_60,code=sm_60/ARCH= -gencode arch=compute_${compute_capability},code=sm_${compute_capability}/g\" Makefile\n","!make"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[Errno 2] No such file or directory: 'darknet/'\n","/content/darknet\n","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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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:910:23:\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 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:1391:14:\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:1367:9:\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:1371:9:\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:1375:9:\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:1378:9:\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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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","gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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:2039:15:\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:2038:15:\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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/utils.c -o obj/utils.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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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:1204:32:\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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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:77:5:\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:77:5:\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:77:5:\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:77:5:\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:77:5:\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:77:5:\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:287:5:\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:287:5:\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","gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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","gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/softmax_layer.c -o obj/softmax_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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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:1134:24:\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:984:43:\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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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:610:42:\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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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:1573:42:\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","gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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:451:29:\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:544:21:\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:657:13:\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:655:13:\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:1276:15:\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:1275:15:\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:1792:19:\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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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:191:9:\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 (l.x_gpu)                   cuda_free(l.x_gpu);  l.x_gpu = NULL;\n","         \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n","\u001b[01m\u001b[K./src/layer.c:191:61:\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","         if (l.x_gpu)                   cuda_free(l.x_gpu);  \u001b[01;36m\u001b[Kl\u001b[m\u001b[K.x_gpu = NULL;\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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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:138:9:\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:831:13:\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:1261:49:\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:1261:37:\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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/demo.c -o obj/demo.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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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:58:9:\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:57:9:\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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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:61:38:\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: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.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:95:42:\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:104:42:\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[Kforward_yolo_layer\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/yolo_layer.c:371:25:\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","gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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","\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_gaussian_yolo_layer\u001b[m\u001b[K’:\n","\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:460:25:\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","gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/sam_layer.c -o obj/sam_layer.o\n","nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/convolutional_kernels.cu -o obj/convolutional_kernels.o\n","nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/activation_kernels.cu -o obj/activation_kernels.o\n","nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/im2col_kernels.cu -o obj/im2col_kernels.o\n","\u001b[01m\u001b[K./src/im2col_kernels.cu:125:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n","                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?\n","                   \n","\u001b[01m\u001b[K./src/im2col_kernels.cu:1178:6:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n","     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights\n","       \n","\u001b[01m\u001b[K./src/im2col_kernels.cu:125:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n","                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?\n","                   \n","\u001b[01m\u001b[K./src/im2col_kernels.cu:1178:6:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n","     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights\n","       \n","\u001b[01m\u001b[K./src/im2col_kernels.cu:125:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n","                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?\n","                   \n","\u001b[01m\u001b[K./src/im2col_kernels.cu:1178:6:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n","     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights\n","       \n","\u001b[01m\u001b[K./src/im2col_kernels.cu:125:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n","                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?\n","                   \n","\u001b[01m\u001b[K./src/im2col_kernels.cu:1178:6:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n","     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights\n","       \n","\u001b[01m\u001b[K./src/im2col_kernels.cu:125:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n","                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?\n","                   \n","\u001b[01m\u001b[K./src/im2col_kernels.cu:1178:6:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n","     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights\n","       \n","\u001b[01m\u001b[K./src/im2col_kernels.cu:125:18:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n","                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?\n","                   \n","\u001b[01m\u001b[K./src/im2col_kernels.cu:1178:6:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"/*\" within comment [\u001b[01;35m\u001b[K-Wcomment\u001b[m\u001b[K]\n","     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights\n","       \n","nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/col2im_kernels.cu -o obj/col2im_kernels.o\n","nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/blas_kernels.cu -o obj/blas_kernels.o\n","./src/blas_kernels.cu(1038): warning: variable \"out_index\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1082): warning: variable \"step\" was set but never used\n","\n","./src/blas_kernels.cu(1686): warning: variable \"stage_id\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1038): warning: variable \"out_index\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1082): warning: variable \"step\" was set but never used\n","\n","./src/blas_kernels.cu(1686): warning: variable \"stage_id\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1038): warning: variable \"out_index\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1082): warning: variable \"step\" was set but never used\n","\n","./src/blas_kernels.cu(1686): warning: variable \"stage_id\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1038): warning: variable \"out_index\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1082): warning: variable \"step\" was set but never used\n","\n","./src/blas_kernels.cu(1686): warning: variable \"stage_id\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1038): warning: variable \"out_index\" was declared but never referenced\n","\n","./src/blas_kernels.cu(1082): warning: variable \"step\" was set but never used\n","\n","./src/blas_kernels.cu(1686): 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:1082:5:\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_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/crop_layer_kernels.cu -o obj/crop_layer_kernels.o\n","nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -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_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/maxpool_layer_kernels.cu -o obj/maxpool_layer_kernels.o\n","nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -c ./src/network_kernels.cu -o obj/network_kernels.o\n","./src/network_kernels.cu(359): warning: variable \"l\" was declared but never referenced\n","\n","./src/network_kernels.cu(368): warning: variable \"im\" was set but never used\n","\n","./src/network_kernels.cu(359): warning: variable \"l\" was declared but never referenced\n","\n","./src/network_kernels.cu(368): warning: variable \"im\" was set but never used\n","\n","./src/network_kernels.cu(359): warning: variable \"l\" was declared but never referenced\n","\n","./src/network_kernels.cu(368): warning: variable \"im\" was set but never used\n","\n","./src/network_kernels.cu(359): warning: variable \"l\" was declared but never referenced\n","\n","./src/network_kernels.cu(368): warning: variable \"im\" was set but never used\n","\n","./src/network_kernels.cu(359): warning: variable \"l\" was declared but never referenced\n","\n","./src/network_kernels.cu(368): warning: variable \"im\" was set but never used\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:359:7:\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","\u001b[01m\u001b[K./src/network_kernels.cu:368:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kim\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[Kimage im;\n","       \u001b[01;35m\u001b[K^\u001b[m\u001b[K \n","nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC\" -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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC 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","g++ -std=c++11 -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC ./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 src/yolo_v2_class.cpp -o libdarknet.so -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","In file included from \u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:2:0\u001b[m\u001b[K:\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In constructor ‘\u001b[01m\u001b[Ktrack_kalman_t::track_kalman_t(int, int, float, cv::Size)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:708:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Ktrack_kalman_t::img_size\u001b[m\u001b[K’ will be initialized after [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n","     cv::Size \u001b[01;35m\u001b[Kimg_size\u001b[m\u001b[K;  // max value of x,y,w,h\n","              \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:700:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  ‘\u001b[01m\u001b[Kint track_kalman_t::track_id_counter\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n","     int \u001b[01;35m\u001b[Ktrack_id_counter\u001b[m\u001b[K;\n","         \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:853:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  when initialized here [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n","     \u001b[01;35m\u001b[Ktrack_kalman_t\u001b[m\u001b[K(int _max_objects = 1000, int _min_frames = 3, float _max_dist = 40, cv::Size _img_size = cv::Size(10000, 10000)) :\n","     \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kvoid track_kalman_t::clear_old_states()\u001b[m\u001b[K’:\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:879:50:\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 ((result_vec_pred[state_id].x > img_size.width) ||\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:880:50:\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","                     (result_vec_pred[state_id].y > img_size.height))\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Ktrack_kalman_t::tst_t track_kalman_t::get_state_id(bbox_t, std::vector<bool>&)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:900:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n","                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::predict()\u001b[m\u001b[K’:\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:990:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n","                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::correct(std::vector<bbox_t>)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:1025:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n","                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> Detector::tracking_id(std::vector<bbox_t>, bool, int, int)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:370:40:\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","         if (\u001b[01;35m\u001b[Kprev_bbox_vec_deque.size() > frames_story\u001b[m\u001b[K) prev_bbox_vec_deque.pop_back();\n","             \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:385:34:\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[Kcur_dist < max_dist\u001b[m\u001b[K && (k.track_id == 0 || dist_vec[m] > cur_dist)) {\n","                         \u001b[01;35m\u001b[K~~~~~~~~~^~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Ksrc/yolo_v2_class.cpp:409:40:\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","         if (\u001b[01;35m\u001b[Kprev_bbox_vec_deque.size() > frames_story\u001b[m\u001b[K) prev_bbox_vec_deque.pop_back();\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 -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -o uselib src/yolo_console_dll.cpp -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++ -L ./ -l:libdarknet.so\n","In file included from \u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:23:0\u001b[m\u001b[K:\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In constructor ‘\u001b[01m\u001b[Ktrack_kalman_t::track_kalman_t(int, int, float, cv::Size)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:708:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Ktrack_kalman_t::img_size\u001b[m\u001b[K’ will be initialized after [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n","     cv::Size \u001b[01;35m\u001b[Kimg_size\u001b[m\u001b[K;  // max value of x,y,w,h\n","              \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:700:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  ‘\u001b[01m\u001b[Kint track_kalman_t::track_id_counter\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n","     int \u001b[01;35m\u001b[Ktrack_id_counter\u001b[m\u001b[K;\n","         \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:853:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  when initialized here [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n","     \u001b[01;35m\u001b[Ktrack_kalman_t\u001b[m\u001b[K(int _max_objects = 1000, int _min_frames = 3, float _max_dist = 40, cv::Size _img_size = cv::Size(10000, 10000)) :\n","     \u001b[01;35m\u001b[K^~~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kvoid track_kalman_t::clear_old_states()\u001b[m\u001b[K’:\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:879:50:\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 ((result_vec_pred[state_id].x > img_size.width) ||\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:880:50:\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","                     (result_vec_pred[state_id].y > img_size.height))\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Ktrack_kalman_t::tst_t track_kalman_t::get_state_id(bbox_t, std::vector<bool>&)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:900:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n","                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::predict()\u001b[m\u001b[K’:\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:990:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n","                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kstd::vector<bbox_t> track_kalman_t::correct(std::vector<bbox_t>)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[Kinclude/yolo_v2_class.hpp:1025:30:\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 (size_t i = 0; \u001b[01;35m\u001b[Ki < max_objects\u001b[m\u001b[K; ++i)\n","                            \u001b[01;35m\u001b[K~~^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_boxes(cv::Mat, std::vector<bbox_t>, std::vector<std::__cxx11::basic_string<char> >, int, int)\u001b[m\u001b[K’:\n","\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:192:46:\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","             int max_width = (\u001b[01;35m\u001b[Ktext_size.width > i.w + 2\u001b[m\u001b[K) ? text_size.width : (i.w + 2);\n","                              \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~^~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:201:62:\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","                 int const max_width_3d = (\u001b[01;35m\u001b[Ktext_size_3d.width > i.w + 2\u001b[m\u001b[K) ? text_size_3d.width : (i.w + 2);\n","                                           \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~^~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:183:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcolors\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n","     int const \u001b[01;35m\u001b[Kcolors\u001b[m\u001b[K[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } };\n","               \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:\u001b[m\u001b[K In constructor ‘\u001b[01m\u001b[Kmain(int, char**)::detection_data_t::detection_data_t()\u001b[m\u001b[K’:\n","\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:398:26:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kmain(int, char**)::detection_data_t::exit_flag\u001b[m\u001b[K’ will be initialized after [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n","                     bool \u001b[01;35m\u001b[Kexit_flag\u001b[m\u001b[K;\n","                          \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:396:26:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  ‘\u001b[01m\u001b[Kbool main(int, char**)::detection_data_t::new_detection\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n","                     bool \u001b[01;35m\u001b[Knew_detection\u001b[m\u001b[K;\n","                          \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n","\u001b[01m\u001b[Ksrc/yolo_console_dll.cpp:401:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K  when initialized here [\u001b[01;35m\u001b[K-Wreorder\u001b[m\u001b[K]\n","                     \u001b[01;35m\u001b[Kdetection_data_t\u001b[m\u001b[K() : exit_flag(false), new_detection(false) {}\n","                     \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~~\u001b[m\u001b[K\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZUXGzkmUYOw4","executionInfo":{"status":"ok","timestamp":1609425246559,"user_tz":-540,"elapsed":101087,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}},"outputId":"51e93894-6706-40b0-f963-4fd12eadd50c"},"source":["from google.colab import drive\n","isMount=!df |grep /content/drive\n","if len(isMount)<1: drive.mount('/content/drive')\n"],"execution_count":8,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"LGPDEjfAALrQ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1609425252431,"user_tz":-540,"elapsed":12319,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}},"outputId":"f87a2f2a-f9a8-4739-8707-b3f23e37e3ec"},"source":["#download the newly released yolov4 ConvNet weights\n","%cd /content/darknet\n","\n","from os import getcwd\n","from os.path import join\n","from tensorflow.keras.utils import get_file\n","\n","# 향후 바꾸세요.\n","base_path=\"/content/drive/MyDrive/Lec_Capture/_data/yolo_v4\"\n","url = \"https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137\"\n","filename = join(base_path, \"yolov4.conv.137\")\n","get_file(filename, url,extract=True)\n","\n","# 향후 바꾸세요.\n","!cp /content/drive/MyDrive/Lec_Capture/_data/yolo_v4/yolov4.conv.137 ./"],"execution_count":9,"outputs":[{"output_type":"stream","text":["[Errno 2] No such file or directory: '/content/darknet'\n","/content\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"kWOiKj37l4wW"},"source":["# Set up Custom Dataset for YOLOv4"]},{"cell_type":"markdown","metadata":{"id":"zbniFj-eSimL"},"source":["We'll use Roboflow to convert our dataset from any format to the YOLO Darknet format. \n","\n","1. To do so, create a free [Roboflow account](https://app.roboflow.ai).\n","2. Upload your images and their annotations (in any format: VOC XML, COCO JSON, TensorFlow CSV, etc).\n","3. Apply preprocessing and augmentation steps you may like. We recommend at least `auto-orient` and a `resize` to 416x416. Generate your dataset.\n","4. Export your dataset in the **YOLO Darknet format**.\n","5. Copy your download link, and paste it below.\n","\n","See our [blog post](https://blog.roboflow.ai/training-yolov4-on-a-custom-dataset/) for greater detail.\n","\n","In this example, I used the open source [BCCD Dataset](https://public.roboflow.ai/object-detection/bccd). (You can `fork` it to your Roboflow account to follow along.)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WWBlGz3uSNDe","executionInfo":{"status":"ok","timestamp":1609425253642,"user_tz":-540,"elapsed":12348,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}},"outputId":"03c984db-77c6-4a2b-dbbb-50fd3d171cd1"},"source":["#1. !cp /content/drive/MyDrive/Lec_Capture/_data/yolo_v4/HardHatSample.v3.darknet.zip ./\n","#    !unzip HardHatSample.v3.darknet.zip\n","\n","#2.3. 복붙 !curl -L \"https://app.roboflow.com/ds/59n8Pc2AtB?key=q2XmaWvulm\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip\n","# !curl -L \"https://app.roboflow.com/ds/59n8Pc2AtB?key=q2XmaWvulm\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip\n","# !curl -L \"https://app.roboflow.com/ds/59n8Pc2AtB?key=q2XmaWvulm\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip\n","\n","# 용량  50M 안넘으면 git에 추가해도 무방\n","!curl -L \"https://app.roboflow.com/ds/s3uZB23NSu?key=jV6b59KDFH\" > roboflow.zip\n","!unzip roboflow.zip\n","!rm roboflow.zip\n"],"execution_count":10,"outputs":[{"output_type":"stream","text":["  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n","                                 Dload  Upload   Total   Spent    Left  Speed\n","100   890  100   890    0     0   1241      0 --:--:-- --:--:-- --:--:--  1239\n","100  252k  100  252k    0     0   265k      0 --:--:-- --:--:-- --:--:--  265k\n","Archive:  roboflow.zip\n"," extracting: README.roboflow.txt     \n","   creating: test/\n"," extracting: test/KakaoTalk_Photo_2020-12-21-20-36-47_jpeg.rf.1c80e25dee5e120b273f4c96a7fae68a.jpg  \n"," extracting: test/KakaoTalk_Photo_2020-12-21-20-36-47_jpeg.rf.1c80e25dee5e120b273f4c96a7fae68a.txt  \n"," extracting: test/_darknet.labels    \n","   creating: train/\n"," extracting: train/KakaoTalk_Photo_2020-12-21-20-35-57_jpeg.rf.bcbb779f4c1ea3b4013b2dbae72468cc.jpg  \n"," extracting: train/KakaoTalk_Photo_2020-12-21-20-35-57_jpeg.rf.bcbb779f4c1ea3b4013b2dbae72468cc.txt  \n"," extracting: train/KakaoTalk_Photo_2020-12-21-20-36-02_jpeg.rf.0fc4f8719326970c9514d51b5ff00292.jpg  \n"," extracting: train/KakaoTalk_Photo_2020-12-21-20-36-02_jpeg.rf.0fc4f8719326970c9514d51b5ff00292.txt  \n"," extracting: train/KakaoTalk_Photo_2020-12-21-20-36-14_jpeg.rf.5eeb40998b1e040eb823653da3f87e2f.jpg  \n"," extracting: train/KakaoTalk_Photo_2020-12-21-20-36-14_jpeg.rf.5eeb40998b1e040eb823653da3f87e2f.txt  \n"," extracting: train/KakaoTalk_Photo_2020-12-21-20-36-26_jpeg.rf.e3d85d95840392e4e06c49567b3cca0a.jpg  \n"," extracting: train/KakaoTalk_Photo_2020-12-21-20-36-26_jpeg.rf.e3d85d95840392e4e06c49567b3cca0a.txt  \n"," extracting: train/KakaoTalk_Photo_2020-12-21-20-36-35_jpeg.rf.2ee582a03f924896f4293f76996d5294.jpg  \n"," extracting: train/KakaoTalk_Photo_2020-12-21-20-36-35_jpeg.rf.2ee582a03f924896f4293f76996d5294.txt  \n"," extracting: train/KakaoTalk_Photo_2020-12-21-20-36-52_jpeg.rf.98027b8d065c3302d5af8d0a5a520ec0.jpg  \n"," extracting: train/KakaoTalk_Photo_2020-12-21-20-36-52_jpeg.rf.98027b8d065c3302d5af8d0a5a520ec0.txt  \n"," extracting: train/KakaoTalk_Photo_2020-12-21-20-36-58_jpeg.rf.f145e923661c4d975df75e364ec0b302.jpg  \n"," extracting: train/KakaoTalk_Photo_2020-12-21-20-36-58_jpeg.rf.f145e923661c4d975df75e364ec0b302.txt  \n"," extracting: train/KakaoTalk_Photo_2020-12-21-20-37-02_jpeg.rf.52847027af117c96db97bd4373e0edbd.jpg  \n"," extracting: train/KakaoTalk_Photo_2020-12-21-20-37-02_jpeg.rf.52847027af117c96db97bd4373e0edbd.txt  \n"," extracting: train/_darknet.labels   \n","   creating: valid/\n"," extracting: valid/KakaoTalk_Photo_2020-12-21-20-35-47_jpeg.rf.9a847c8561cdb410535a41fb9b733bdd.jpg  \n"," extracting: valid/KakaoTalk_Photo_2020-12-21-20-35-47_jpeg.rf.9a847c8561cdb410535a41fb9b733bdd.txt  \n"," extracting: valid/KakaoTalk_Photo_2020-12-21-20-36-40_jpeg.rf.85f78413f4cf8662a17b8b65ee737735.jpg  \n"," extracting: valid/KakaoTalk_Photo_2020-12-21-20-36-40_jpeg.rf.85f78413f4cf8662a17b8b65ee737735.txt  \n"," extracting: valid/_darknet.labels   \n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Cdj4tmT5Cmdl","executionInfo":{"status":"ok","timestamp":1609425253643,"user_tz":-540,"elapsed":12104,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}}},"source":["\n","# # #if you already have YOLO darknet format, you can skip this step\n","# %cd /content/darknet\n","# # !curl -L [YOUR LINK HERE] > roboflow.zip; unzip roboflow.zip; rm roboflow.zip\n","# !unzip /content/drive/MyDrive/Lec_Capture/_data/yolo_v4/HardHatSample.v3.darknet.zip\n","\n"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"id":"KiCILEbs1NII","colab":{"base_uri":"https://localhost:8080/","height":373},"executionInfo":{"status":"error","timestamp":1609425254699,"user_tz":-540,"elapsed":13123,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}},"outputId":"e9373302-6d36-4a5e-a0a1-f29cec7c7826"},"source":["#Set up training file directories for custom dataset\n","%cd /content/darknet/\n","%cp train/_darknet.labels data/obj.names\n","![ ! -d data/obj ]&&mkdir data/obj\n","#copy image and labels\n","%cp train/*.jpg data/obj/\n","%cp valid/*.jpg data/obj/\n","\n","%cp train/*.txt data/obj/\n","%cp valid/*.txt data/obj/\n","\n","with open('data/obj.data', 'w') as out:\n","  out.write('classes = 3\\n')\n","  out.write('train = data/train.txt\\n')\n","  out.write('valid = data/valid.txt\\n')\n","  out.write('names = data/obj.names\\n')\n","  out.write('backup = backup/')\n","\n","#write train file (just the image list)\n","import os\n","\n","with open('data/train.txt', 'w') as out:\n","  for img in [f for f in os.listdir('train') if f.endswith('jpg')]:\n","    out.write('data/obj/' + img + '\\n')\n","\n","#write the valid file (just the image list)\n","import os\n","\n","with open('data/valid.txt', 'w') as out:\n","  for img in [f for f in os.listdir('valid') if f.endswith('jpg')]:\n","    out.write('data/obj/' + img + '\\n')"],"execution_count":12,"outputs":[{"output_type":"stream","text":["[Errno 2] No such file or directory: '/content/darknet/'\n","/content\n","cp: cannot create regular file 'data/obj.names': No such file or directory\n","mkdir: cannot create directory ‘data/obj’: No such file or directory\n","cp: target 'data/obj/' is not a directory\n","cp: target 'data/obj/' is not a directory\n","cp: target 'data/obj/' is not a directory\n","cp: target 'data/obj/' is not a directory\n"],"name":"stdout"},{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-12-23c414ddd7b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cp valid/*.txt data/obj/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data/obj.data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m   \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'classes = 3\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m   \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'train = data/train.txt\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data/obj.data'"]}]},{"cell_type":"markdown","metadata":{"id":"5HtRqO3QvjkP"},"source":["# Write Custom Training Config for YOLOv4"]},{"cell_type":"code","metadata":{"id":"U_WJcqHhpeVr","executionInfo":{"status":"aborted","timestamp":1609425254687,"user_tz":-540,"elapsed":13024,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}}},"source":["#we build config dynamically based on number of classes\n","#we build iteratively from base config files. This is the same file shape as cfg/yolo-obj.cfg\n","def file_len(fname):\n","  with open(fname) as f:\n","    for i, l in enumerate(f):\n","      pass\n","  return i + 1\n","\n","num_classes = file_len('train/_darknet.labels')\n","print(\"writing config for a custom YOLOv4 detector detecting number of classes: \" + str(num_classes))\n","\n","#Instructions from the darknet repo\n","#change line max_batches to (classes*2000 but not less than number of training images, and not less than 6000), f.e. max_batches=6000 if you train for 3 classes\n","#change line steps to 80% and 90% of max_batches, f.e. steps=4800,5400\n","if os.path.exists('./cfg/custom-yolov4-detector.cfg'): os.remove('./cfg/custom-yolov4-detector.cfg')\n","\n","\n","with open('./cfg/custom-yolov4-detector.cfg', 'a') as f:\n","  f.write('[net]' + '\\n')\n","  f.write('batch=64' + '\\n')\n","  #####smaller subdivisions help the GPU run faster. 12 is optimal, but you might need to change to 24,36,64####\n","  f.write('subdivisions=24' + '\\n')\n","  f.write('width=416' + '\\n')\n","  f.write('height=416' + '\\n')\n","  f.write('channels=3' + '\\n')\n","  f.write('momentum=0.949' + '\\n')\n","  f.write('decay=0.0005' + '\\n')\n","  f.write('angle=0' + '\\n')\n","  f.write('saturation = 1.5' + '\\n')\n","  f.write('exposure = 1.5' + '\\n')\n","  f.write('hue = .1' + '\\n')\n","  f.write('\\n')\n","  f.write('learning_rate=0.001' + '\\n')\n","  f.write('burn_in=1000' + '\\n')\n","  ######you can adjust up and down to change training time#####\n","  ##Darknet does iterations with batches, not epochs####\n","  max_batches = num_classes*2000\n","  #max_batches = 2000\n","  f.write('max_batches=' + str(max_batches) + '\\n')\n","  f.write('policy=steps' + '\\n')\n","  steps1 = .8 * max_batches\n","  steps2 = .9 * max_batches\n","  f.write('steps='+str(steps1)+','+str(steps2) + '\\n')\n","\n","#Instructions from the darknet repo\n","#change line classes=80 to your number of objects in each of 3 [yolo]-layers:\n","#change [filters=255] to filters=(classes + 5)x3 in the 3 [convolutional] before each [yolo] layer, keep in mind that it only has to be the last [convolutional] before each of the [yolo] layers.\n","\n","  with open('cfg/yolov4-custom2.cfg', 'r') as f2:\n","    content = f2.readlines()\n","    for line in content:\n","      f.write(line)    \n","    num_filters = (num_classes + 5) * 3\n","    f.write('filters='+str(num_filters) + '\\n')\n","    f.write('activation=linear')\n","    f.write('\\n')\n","    f.write('\\n')\n","    f.write('[yolo]' + '\\n')\n","    f.write('mask = 0,1,2' + '\\n')\n","    f.write('anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401' + '\\n')\n","    f.write('classes=' + str(num_classes) + '\\n')\n","\n","  with open('cfg/yolov4-custom3.cfg', 'r') as f3:\n","    content = f3.readlines()\n","    for line in content:\n","      f.write(line)    \n","    num_filters = (num_classes + 5) * 3\n","    f.write('filters='+str(num_filters) + '\\n')\n","    f.write('activation=linear')\n","    f.write('\\n')\n","    f.write('\\n')\n","    f.write('[yolo]' + '\\n')\n","    f.write('mask = 3,4,5' + '\\n')\n","    f.write('anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401' + '\\n')\n","    f.write('classes=' + str(num_classes) + '\\n')\n","\n","  with open('cfg/yolov4-custom4.cfg', 'r') as f4:\n","    content = f4.readlines()\n","    for line in content:\n","      f.write(line)    \n","    num_filters = (num_classes + 5) * 3\n","    f.write('filters='+str(num_filters) + '\\n')\n","    f.write('activation=linear')\n","    f.write('\\n')\n","    f.write('\\n')\n","    f.write('[yolo]' + '\\n')\n","    f.write('mask = 6,7,8' + '\\n')\n","    f.write('anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401' + '\\n')\n","    f.write('classes=' + str(num_classes) + '\\n')\n","    \n","  with open('cfg/yolov4-custom5.cfg', 'r') as f5:\n","    content = f5.readlines()\n","    for line in content:\n","      f.write(line)\n","\n","print(\"file is written!\")    \n","\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"u2LAciMh4Cut","executionInfo":{"status":"aborted","timestamp":1609425254689,"user_tz":-540,"elapsed":12997,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}}},"source":["#here is the file that was just written. \n","#you may consider adjusting certain things\n","\n","#like the number of subdivisions 64 runs faster but Colab GPU may not be big enough\n","#if Colab GPU memory is too small, you will need to adjust subdivisions to 16\n","%cat cfg/custom-yolov4-detector.cfg"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"vWrG9EGamSpH"},"source":["# Train Custom YOLOv4 Detector"]},{"cell_type":"code","metadata":{"id":"6miYFbvExqMd","executionInfo":{"status":"aborted","timestamp":1609425254690,"user_tz":-540,"elapsed":12905,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}}},"source":["!./darknet detector train data/obj.data cfg/custom-yolov4-detector.cfg yolov4.conv.137 -dont_show -map\n","#If you get CUDA out of memory adjust subdivisions above!\n","#adjust max batches down for shorter training above"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nBnwpBV5ZXxQ"},"source":["# Infer Custom Objects with Saved YOLOv4 Weights"]},{"cell_type":"code","metadata":{"id":"FzoJQQw8Zdco","executionInfo":{"status":"aborted","timestamp":1609425254691,"user_tz":-540,"elapsed":12831,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}}},"source":["#define utility function\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.rcParams['figure.figsize'] = [10, 5]\n","  plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))\n","  plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"x-_E3O5Mf4Mf","executionInfo":{"status":"aborted","timestamp":1609425254692,"user_tz":-540,"elapsed":12805,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}}},"source":["#coco.names is hardcoded somewhere in the detector\n","!cp data/obj.names data/coco.names"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"mjp6E68kNm89","executionInfo":{"status":"aborted","timestamp":1609425254693,"user_tz":-540,"elapsed":12747,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"NjKzw2TvZrOQ","executionInfo":{"status":"aborted","timestamp":1609425254695,"user_tz":-540,"elapsed":12722,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}}},"source":["#/test has images that we can test our detector on\n","test_images = [f for f in os.listdir('test') if f.endswith('.jpg')]\n","import random\n","img_path = \"test/\" + random.choice(test_images);\n","\n","#test out our detector!\n","!./darknet detect cfg/custom-yolov4-detector.cfg backup/custom-yolov4-detector_last.weights {img_path} -dont-show\n","imShow('predictions.jpg')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"GSCKx3i7aWr9","executionInfo":{"status":"aborted","timestamp":1609425254696,"user_tz":-540,"elapsed":12699,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}}},"source":["!cp -r /content/darknet /content/drive/MyDrive/darknet"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-EiUVXHL8qYP","executionInfo":{"status":"aborted","timestamp":1609425254697,"user_tz":-540,"elapsed":12642,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}}},"source":["!pwd"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"fPeI2O-YNMhK","executionInfo":{"status":"aborted","timestamp":1609425254698,"user_tz":-540,"elapsed":12639,"user":{"displayName":"남중구","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhdjUdzM516IqW5OZZ8t6jU96Fd8d5gitHsvgLBeA=s64","userId":"04014122822541888792"}}},"source":[""],"execution_count":null,"outputs":[]}]}