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관리 메뉴

PROGRAMMING

YOLO(Darknet Setup - Colab / cuDNN) 본문

OpenCV/머신비전 - 이미지 디텍팅

YOLO(Darknet Setup - Colab / cuDNN)

Raccoon2125 2021. 1. 2. 23:46

In [ ]:

1. GPU Setup¶

메뉴에서

'런타임' > '런타임 유형 변경' > 노트 설정 > 하드웨어 가속기 > 'GPU' 선택

2. Mount Google Drive¶

In [1]:

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

In [ ]:

!ls

drive  sample_data

Download cuDNN file https://developer.nvidia.com/rdp/cudnn-download

Unzip the cuDNN from Google Drive to Colab /usr/local/cuda folder

In [ ]:

!/usr/local/cuda/bin/nvcc --version

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243

In [ ]:

!tar -xzvf drive/MyDrive/darknet/cuDNN/cudnn-10.1-linux-x64-v7.6.5.32.tgz -C /usr/local/
!chmod a+r /usr/local/cuda/include/cudnn.h

cuda/include/cudnn.h
cuda/NVIDIA_SLA_cuDNN_Support.txt
cuda/lib64/libcudnn.so
cuda/lib64/libcudnn.so.7
cuda/lib64/libcudnn.so.7.6.5
cuda/lib64/libcudnn_static.a

In [ ]:

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

In [ ]:

!cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2

#define CUDNN_MAJOR 7
#define CUDNN_MINOR 6
#define CUDNN_PATCHLEVEL 5
--
#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)

#include "driver_types.h"

4. Install DarkNet¶

DarkNet Install https://pjreddie.com/darknet/install/

In [ ]:

!git clone https://github.com/kriyeng/darknet/
%cd darknet

Cloning into 'darknet'...
remote: Enumerating objects: 10068, done.
remote: Total 10068 (delta 0), reused 0 (delta 0), pack-reused 10068
Receiving objects: 100% (10068/10068), 10.13 MiB | 23.30 MiB/s, done.
Resolving deltas: 100% (6742/6742), done.
/content/darknet

In [ ]:

!ls

3rdparty      CMakeLists.txt	      image_yolov3.sh	     scripts
appveyor.yml  CMakeSettings.json      include		     src
build	      DarknetConfig.cmake.in  json_mjpeg_streams.sh  video_v2.sh
build.ps1     darknet.py	      LICENSE		     video_yolov3.sh
build.sh      darknet_video.py	      Makefile
cfg	      data		      net_cam_v3.sh
cmake	      image_yolov2.sh	      README.md

In [ ]:

!git checkout feature/google-colab

Branch 'feature/google-colab' set up to track remote branch 'feature/google-colab' from 'origin'.
Switched to a new branch 'feature/google-colab'

In [ ]:

# Compile DarkNet
!make

mkdir -p obj
mkdir -p backup
mkdir -p results
chmod +x *.sh
g++ -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/image_opencv.cpp -o obj/image_opencv.o
./src/image_opencv.cpp: In function ‘void draw_detections_cv_v3(mat_cv*, detection*, int, float, char**, image**, int, int)’:
./src/image_opencv.cpp:896:23: warning: variable ‘rgb’ set but not used [-Wunused-but-set-variable]
                 float rgb[3];
                       ^~~
g++ -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/http_stream.cpp -o obj/http_stream.o
./src/http_stream.cpp: In member function ‘bool JSON_sender::write(const char*)’:
./src/http_stream.cpp:228:21: warning: unused variable ‘n’ [-Wunused-variable]
                 int n = _write(client, outputbuf, outlen);
                     ^
./src/http_stream.cpp: In member function ‘bool MJPG_sender::write(const cv::Mat&)’:
./src/http_stream.cpp:475:23: warning: comparison between signed and unsigned integer expressions [-Wsign-compare]
                 if (n < outlen)
                     ~~^~~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/gemm.c -o obj/gemm.o
./src/gemm.c: In function ‘convolution_2d’:
./src/gemm.c:2016:15: warning: unused variable ‘out_w’ [-Wunused-variable]
     const int out_w = (w + 2 * pad - ksize) / stride + 1;    // output_width=input_width for stride=1 and pad=1
               ^~~~~
./src/gemm.c:2015:15: warning: unused variable ‘out_h’ [-Wunused-variable]
     const int out_h = (h + 2 * pad - ksize) / stride + 1;    // output_height=input_height for stride=1 and pad=1
               ^~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/utils.c -o obj/utils.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/dark_cuda.c -o obj/dark_cuda.o
./src/dark_cuda.c: In function ‘get_cuda_stream’:
./src/dark_cuda.c:120:18: warning: unused variable ‘buffer’ [-Wunused-variable]
             char buffer[256];
                  ^~~~~~
./src/dark_cuda.c: In function ‘get_cuda_memcpy_stream’:
./src/dark_cuda.c:141:18: warning: unused variable ‘buffer’ [-Wunused-variable]
             char buffer[256];
                  ^~~~~~
./src/dark_cuda.c: In function ‘cudnn_handle’:
./src/dark_cuda.c:161:23: warning: unused variable ‘status’ [-Wunused-variable]
         cudnnStatus_t status = cudnnSetStream(handle[i], get_cuda_stream());
                       ^~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/convolutional_layer.c -o obj/convolutional_layer.o
./src/convolutional_layer.c: In function ‘forward_convolutional_layer’:
./src/convolutional_layer.c:951:28: warning: unused variable ‘t_intput_size’ [-Wunused-variable]
                     size_t t_intput_size = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align);
                            ^~~~~~~~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/list.c -o obj/list.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/image.c -o obj/image.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/activations.c -o obj/activations.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/im2col.c -o obj/im2col.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/col2im.c -o obj/col2im.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/blas.c -o obj/blas.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/crop_layer.c -o obj/crop_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/dropout_layer.c -o obj/dropout_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/maxpool_layer.c -o obj/maxpool_layer.o
./src/maxpool_layer.c: In function ‘cudnn_maxpool_setup’:
./src/maxpool_layer.c:26:19: warning: variable ‘maxpool_status’ set but not used [-Wunused-but-set-variable]
     cudnnStatus_t maxpool_status;
                   ^~~~~~~~~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/softmax_layer.c -o obj/softmax_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/data.c -o obj/data.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/matrix.c -o obj/matrix.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/network.c -o obj/network.o
./src/network.c: In function ‘resize_network’:
./src/network.c:511:42: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
         if (cudaSuccess == cudaHostAlloc(&net->input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped))
                                          ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from include/darknet.h:40,
                 from ./src/network.c:1:
/usr/local/cuda/include/cuda_runtime_api.h:4391:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/connected_layer.c -o obj/connected_layer.o
./src/connected_layer.c: In function ‘forward_connected_layer_gpu’:
./src/connected_layer.c:335:11: warning: unused variable ‘one’ [-Wunused-variable]
     float one = 1;    // alpha[0], beta[0]
           ^~~
./src/connected_layer.c:333:13: warning: unused variable ‘c’ [-Wunused-variable]
     float * c = l.output_gpu;
             ^
./src/connected_layer.c:332:13: warning: unused variable ‘b’ [-Wunused-variable]
     float * b = l.weights_gpu;
             ^
./src/connected_layer.c:331:13: warning: unused variable ‘a’ [-Wunused-variable]
     float * a = state.input;
             ^
./src/connected_layer.c:330:9: warning: unused variable ‘n’ [-Wunused-variable]
     int n = l.outputs;
         ^
./src/connected_layer.c:329:9: warning: unused variable ‘k’ [-Wunused-variable]
     int k = l.inputs;
         ^
./src/connected_layer.c:328:9: warning: unused variable ‘m’ [-Wunused-variable]
     int m = l.batch;
         ^
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/cost_layer.c -o obj/cost_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/parser.c -o obj/parser.o
./src/parser.c: In function ‘parse_network_cfg_custom’:
./src/parser.c:874:42: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
         if (cudaSuccess == cudaHostAlloc(&net.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1;
                                          ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from include/darknet.h:40,
                 from ./src/activations.h:3,
                 from ./src/activation_layer.h:4,
                 from ./src/parser.c:6:
/usr/local/cuda/include/cuda_runtime_api.h:4391:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/option_list.c -o obj/option_list.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/darknet.c -o obj/darknet.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/detection_layer.c -o obj/detection_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/captcha.c -o obj/captcha.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/route_layer.c -o obj/route_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/writing.c -o obj/writing.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/box.c -o obj/box.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/nightmare.c -o obj/nightmare.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/normalization_layer.c -o obj/normalization_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/avgpool_layer.c -o obj/avgpool_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/coco.c -o obj/coco.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/dice.c -o obj/dice.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/yolo.c -o obj/yolo.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/detector.c -o obj/detector.o
./src/detector.c: In function ‘validate_detector_map’:
./src/detector.c:1008:15: warning: unused variable ‘class_recall’ [-Wunused-variable]
         float class_recall = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)(truth_classes_count[i] - tp_for_thresh_per_class[i]));
               ^~~~~~~~~~~~
./src/detector.c:1007:15: warning: unused variable ‘class_precision’ [-Wunused-variable]
         float class_precision = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]);
               ^~~~~~~~~~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/layer.c -o obj/layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/compare.c -o obj/compare.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/classifier.c -o obj/classifier.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/local_layer.c -o obj/local_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/swag.c -o obj/swag.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/shortcut_layer.c -o obj/shortcut_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/activation_layer.c -o obj/activation_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/rnn_layer.c -o obj/rnn_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/gru_layer.c -o obj/gru_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/rnn.c -o obj/rnn.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/rnn_vid.c -o obj/rnn_vid.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/crnn_layer.c -o obj/crnn_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/demo.c -o obj/demo.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/tag.c -o obj/tag.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/cifar.c -o obj/cifar.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/go.c -o obj/go.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/batchnorm_layer.c -o obj/batchnorm_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/art.c -o obj/art.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/region_layer.c -o obj/region_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/reorg_layer.c -o obj/reorg_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/reorg_old_layer.c -o obj/reorg_old_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/super.c -o obj/super.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/voxel.c -o obj/voxel.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/tree.c -o obj/tree.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/yolo_layer.c -o obj/yolo_layer.o
./src/yolo_layer.c: In function ‘make_yolo_layer’:
./src/yolo_layer.c:58:38: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
     if (cudaSuccess == cudaHostAlloc(&l.output, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;
                                      ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from include/darknet.h:40,
                 from ./src/activations.h:3,
                 from ./src/layer.h:4,
                 from ./src/yolo_layer.h:5,
                 from ./src/yolo_layer.c:1:
/usr/local/cuda/include/cuda_runtime_api.h:4391:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
./src/yolo_layer.c:65:38: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
     if (cudaSuccess == cudaHostAlloc(&l.delta, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;
                                      ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from include/darknet.h:40,
                 from ./src/activations.h:3,
                 from ./src/layer.h:4,
                 from ./src/yolo_layer.h:5,
                 from ./src/yolo_layer.c:1:
/usr/local/cuda/include/cuda_runtime_api.h:4391:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
./src/yolo_layer.c: In function ‘resize_yolo_layer’:
./src/yolo_layer.c:92:42: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
         if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {
                                          ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from include/darknet.h:40,
                 from ./src/activations.h:3,
                 from ./src/layer.h:4,
                 from ./src/yolo_layer.h:5,
                 from ./src/yolo_layer.c:1:
/usr/local/cuda/include/cuda_runtime_api.h:4391:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
./src/yolo_layer.c:101:42: warning: passing argument 1 of ‘cudaHostAlloc’ from incompatible pointer type [-Wincompatible-pointer-types]
         if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {
                                          ^
In file included from /usr/local/cuda/include/cuda_runtime.h:96:0,
                 from include/darknet.h:40,
                 from ./src/activations.h:3,
                 from ./src/layer.h:4,
                 from ./src/yolo_layer.h:5,
                 from ./src/yolo_layer.c:1:
/usr/local/cuda/include/cuda_runtime_api.h:4391:39: note: expected ‘void **’ but argument is of type ‘float **’
 extern __host__ cudaError_t CUDARTAPI cudaHostAlloc(void **pHost, size_t size, unsigned int flags);
                                       ^~~~~~~~~~~~~
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/upsample_layer.c -o obj/upsample_layer.o
gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -c ./src/lstm_layer.c -o obj/lstm_layer.o
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 opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/convolutional_kernels.cu -o obj/convolutional_kernels.o
nvcc -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 opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/activation_kernels.cu -o obj/activation_kernels.o
nvcc -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 opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/im2col_kernels.cu -o obj/im2col_kernels.o
./src/im2col_kernels.cu:125:18: warning: "/*" within comment [-Wcomment]
                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
                   
./src/im2col_kernels.cu:1178:6: warning: "/*" within comment [-Wcomment]
     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights
       
./src/im2col_kernels.cu:125:18: warning: "/*" within comment [-Wcomment]
                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
                   
./src/im2col_kernels.cu:1178:6: warning: "/*" within comment [-Wcomment]
     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights
       
./src/im2col_kernels.cu:125:18: warning: "/*" within comment [-Wcomment]
                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
                   
./src/im2col_kernels.cu:1178:6: warning: "/*" within comment [-Wcomment]
     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights
       
./src/im2col_kernels.cu:125:18: warning: "/*" within comment [-Wcomment]
                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
                   
./src/im2col_kernels.cu:1178:6: warning: "/*" within comment [-Wcomment]
     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights
       
./src/im2col_kernels.cu:125:18: warning: "/*" within comment [-Wcomment]
                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
                   
./src/im2col_kernels.cu:1178:6: warning: "/*" within comment [-Wcomment]
     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights
       
./src/im2col_kernels.cu:125:18: warning: "/*" within comment [-Wcomment]
                 //*data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
                   
./src/im2col_kernels.cu:1178:6: warning: "/*" within comment [-Wcomment]
     //*((uint64_t *)(A_s + (local_i*lda + k) / 8)) = *((uint64_t *)(A + (i_cur*lda + k) / 8));    // weights
       
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 opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/col2im_kernels.cu -o obj/col2im_kernels.o
nvcc -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 opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/blas_kernels.cu -o obj/blas_kernels.o
nvcc -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 opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/crop_layer_kernels.cu -o obj/crop_layer_kernels.o
nvcc -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 opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/dropout_layer_kernels.cu -o obj/dropout_layer_kernels.o
nvcc -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 opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/maxpool_layer_kernels.cu -o obj/maxpool_layer_kernels.o
nvcc -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 opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/network_kernels.cu -o obj/network_kernels.o
nvcc -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 opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN --compiler-options "-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include" -c ./src/avgpool_layer_kernels.cu -o obj/avgpool_layer_kernels.o
g++ -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include obj/image_opencv.o obj/http_stream.o obj/gemm.o obj/utils.o obj/dark_cuda.o obj/convolutional_layer.o obj/list.o obj/image.o obj/activations.o obj/im2col.o obj/col2im.o obj/blas.o obj/crop_layer.o obj/dropout_layer.o obj/maxpool_layer.o obj/softmax_layer.o obj/data.o obj/matrix.o obj/network.o obj/connected_layer.o obj/cost_layer.o obj/parser.o obj/option_list.o obj/darknet.o obj/detection_layer.o obj/captcha.o obj/route_layer.o obj/writing.o obj/box.o obj/nightmare.o obj/normalization_layer.o obj/avgpool_layer.o obj/coco.o obj/dice.o obj/yolo.o obj/detector.o obj/layer.o obj/compare.o obj/classifier.o obj/local_layer.o obj/swag.o obj/shortcut_layer.o obj/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/upsample_layer.o obj/lstm_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 opencv` -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++

In [ ]:

import os
if not os.path.exists('/content/drive/MyDrive/darknet/bin'):
    os.makedirs('/content/drive/MyDrive/darknet/bin')

In [ ]:

!cp ./darknet /content/drive/MyDrive/darknet/bin/darknet

In [ ]:

# Use compiled darkent in your Google Drive after the first compile
# 1. Makes a darknet directory
#!mkdir darknet
#%cd darknet

# 2. Copy the Darkent compiled version to the VM
#!cp /content/drive/MyDrive/darknet/bin/darknet ./darknet

# 3. Set execution permissions
#!chmod +x ./darknet

In [ ]:

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

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

  fig = plt.gcf()
  fig.set_size_inches(18, 10)
  plt.axis("off")
  #plt.rcParams['figure.figsize'] = [10, 5]
  plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))
  plt.show()
  
  
def upload():
  from google.colab import files
  uploaded = files.upload() 
  for name, data in uploaded.items():
    with open(name, 'wb') as f:
      f.write(data)
      print ('saved file', name)
def download(path):
  from google.colab import files
  files.download(path)

In [ ]:

%ls

3rdparty/     CMakeLists.txt          image_yolov3.sh*        results/
appveyor.yml  CMakeSettings.json      include/                scripts/
backup/       darknet*                json_mjpeg_streams.sh*  src/
build/        DarknetConfig.cmake.in  LICENSE                 video_v2.sh*
build.ps1*    darknet.py              Makefile                video_yolov3.sh*
build.sh*     darknet_video.py        net_cam_v3.sh*          weights/
cfg/          data/                   obj/
cmake/        image_yolov2.sh*        README.md

In [ ]:

!cp -r "/content/drive/MyDrive/darknet/weights" ./weights

In [ ]:

%ls

3rdparty/     CMakeLists.txt          image_yolov3.sh*        results/
appveyor.yml  CMakeSettings.json      include/                scripts/
backup/       darknet*                json_mjpeg_streams.sh*  src/
build/        DarknetConfig.cmake.in  LICENSE                 video_v2.sh*
build.ps1*    darknet.py              Makefile                video_yolov3.sh*
build.sh*     darknet_video.py        net_cam_v3.sh*          weights/
cfg/          data/                   obj/
cmake/        image_yolov2.sh*        README.md

5. Test YOLO¶

In [ ]:

!./darknet detect cfg/yolov3.cfg weights/yolov3.weights data/dog.jpg
imShow('predictions.jpg')

layer     filters    size              input                output
   0 conv     32  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  32 0.299 BF
   1 conv     64  3 x 3 / 2   416 x 416 x  32   ->   208 x 208 x  64 1.595 BF
   2 conv     32  1 x 1 / 1   208 x 208 x  64   ->   208 x 208 x  32 0.177 BF
   3 conv     64  3 x 3 / 1   208 x 208 x  32   ->   208 x 208 x  64 1.595 BF
   4 Shortcut Layer: 1
   5 conv    128  3 x 3 / 2   208 x 208 x  64   ->   104 x 104 x 128 1.595 BF
   6 conv     64  1 x 1 / 1   104 x 104 x 128   ->   104 x 104 x  64 0.177 BF
   7 conv    128  3 x 3 / 1   104 x 104 x  64   ->   104 x 104 x 128 1.595 BF
   8 Shortcut Layer: 5
   9 conv     64  1 x 1 / 1   104 x 104 x 128   ->   104 x 104 x  64 0.177 BF
  10 conv    128  3 x 3 / 1   104 x 104 x  64   ->   104 x 104 x 128 1.595 BF
  11 Shortcut Layer: 8
  12 conv    256  3 x 3 / 2   104 x 104 x 128   ->    52 x  52 x 256 1.595 BF
  13 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
  14 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
  15 Shortcut Layer: 12
  16 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
  17 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
  18 Shortcut Layer: 15
  19 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
  20 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
  21 Shortcut Layer: 18
  22 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
  23 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
  24 Shortcut Layer: 21
  25 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
  26 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
  27 Shortcut Layer: 24
  28 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
  29 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
  30 Shortcut Layer: 27
  31 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
  32 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
  33 Shortcut Layer: 30
  34 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
  35 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
  36 Shortcut Layer: 33
  37 conv    512  3 x 3 / 2    52 x  52 x 256   ->    26 x  26 x 512 1.595 BF
  38 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  39 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  40 Shortcut Layer: 37
  41 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  42 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  43 Shortcut Layer: 40
  44 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  45 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  46 Shortcut Layer: 43
  47 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  48 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  49 Shortcut Layer: 46
  50 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  51 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  52 Shortcut Layer: 49
  53 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  54 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  55 Shortcut Layer: 52
  56 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  57 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  58 Shortcut Layer: 55
  59 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  60 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  61 Shortcut Layer: 58
  62 conv   1024  3 x 3 / 2    26 x  26 x 512   ->    13 x  13 x1024 1.595 BF
  63 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512 0.177 BF
  64 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF
  65 Shortcut Layer: 62
  66 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512 0.177 BF
  67 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF
  68 Shortcut Layer: 65
  69 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512 0.177 BF
  70 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF
  71 Shortcut Layer: 68
  72 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512 0.177 BF
  73 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF
  74 Shortcut Layer: 71
  75 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512 0.177 BF
  76 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF
  77 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512 0.177 BF
  78 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF
  79 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512 0.177 BF
  80 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF
  81 conv    255  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 255 0.088 BF
  82 yolo
  83 route  79
  84 conv    256  1 x 1 / 1    13 x  13 x 512   ->    13 x  13 x 256 0.044 BF
  85 upsample            2x    13 x  13 x 256   ->    26 x  26 x 256
  86 route  85 61
  87 conv    256  1 x 1 / 1    26 x  26 x 768   ->    26 x  26 x 256 0.266 BF
  88 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  89 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  90 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  91 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  92 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  93 conv    255  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 255 0.177 BF
  94 yolo
  95 route  91
  96 conv    128  1 x 1 / 1    26 x  26 x 256   ->    26 x  26 x 128 0.044 BF
  97 upsample            2x    26 x  26 x 128   ->    52 x  52 x 128
  98 route  97 36
  99 conv    128  1 x 1 / 1    52 x  52 x 384   ->    52 x  52 x 128 0.266 BF
 100 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
 101 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
 102 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
 103 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
 104 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
 105 conv    255  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 255 0.353 BF
 106 yolo
Total BFLOPS 65.864 
 Allocate additional workspace_size = 52.43 MB 
Loading weights from weights/yolov3.weights...
 seen 64 
Done!
data/dog.jpg: Predicted in 41.786000 milli-seconds.
bicycle: 99%
dog: 100%
truck: 94%
Unable to init server: Could not connect: Connection refused

(predictions:1666): Gtk-WARNING **: 02:59:48.785: cannot open display:

In [ ]:

!./darknet detect cfg/yolov3.cfg weights/yolov3.weights data/person.jpg
imShow('predictions.jpg')

layer     filters    size              input                output
   0 conv     32  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  32 0.299 BF
   1 conv     64  3 x 3 / 2   416 x 416 x  32   ->   208 x 208 x  64 1.595 BF
   2 conv     32  1 x 1 / 1   208 x 208 x  64   ->   208 x 208 x  32 0.177 BF
   3 conv     64  3 x 3 / 1   208 x 208 x  32   ->   208 x 208 x  64 1.595 BF
   4 Shortcut Layer: 1
   5 conv    128  3 x 3 / 2   208 x 208 x  64   ->   104 x 104 x 128 1.595 BF
   6 conv     64  1 x 1 / 1   104 x 104 x 128   ->   104 x 104 x  64 0.177 BF
   7 conv    128  3 x 3 / 1   104 x 104 x  64   ->   104 x 104 x 128 1.595 BF
   8 Shortcut Layer: 5
   9 conv     64  1 x 1 / 1   104 x 104 x 128   ->   104 x 104 x  64 0.177 BF
  10 conv    128  3 x 3 / 1   104 x 104 x  64   ->   104 x 104 x 128 1.595 BF
  11 Shortcut Layer: 8
  12 conv    256  3 x 3 / 2   104 x 104 x 128   ->    52 x  52 x 256 1.595 BF
  13 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
  14 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
  15 Shortcut Layer: 12
  16 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
  17 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
  18 Shortcut Layer: 15
  19 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
  20 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
  21 Shortcut Layer: 18
  22 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
  23 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
  24 Shortcut Layer: 21
  25 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
  26 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
  27 Shortcut Layer: 24
  28 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
  29 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
  30 Shortcut Layer: 27
  31 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
  32 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
  33 Shortcut Layer: 30
  34 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
  35 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
  36 Shortcut Layer: 33
  37 conv    512  3 x 3 / 2    52 x  52 x 256   ->    26 x  26 x 512 1.595 BF
  38 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  39 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  40 Shortcut Layer: 37
  41 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  42 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  43 Shortcut Layer: 40
  44 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  45 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  46 Shortcut Layer: 43
  47 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  48 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  49 Shortcut Layer: 46
  50 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  51 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  52 Shortcut Layer: 49
  53 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  54 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  55 Shortcut Layer: 52
  56 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  57 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  58 Shortcut Layer: 55
  59 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  60 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  61 Shortcut Layer: 58
  62 conv   1024  3 x 3 / 2    26 x  26 x 512   ->    13 x  13 x1024 1.595 BF
  63 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512 0.177 BF
  64 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF
  65 Shortcut Layer: 62
  66 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512 0.177 BF
  67 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF
  68 Shortcut Layer: 65
  69 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512 0.177 BF
  70 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF
  71 Shortcut Layer: 68
  72 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512 0.177 BF
  73 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF
  74 Shortcut Layer: 71
  75 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512 0.177 BF
  76 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF
  77 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512 0.177 BF
  78 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF
  79 conv    512  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 512 0.177 BF
  80 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF
  81 conv    255  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 255 0.088 BF
  82 yolo
  83 route  79
  84 conv    256  1 x 1 / 1    13 x  13 x 512   ->    13 x  13 x 256 0.044 BF
  85 upsample            2x    13 x  13 x 256   ->    26 x  26 x 256
  86 route  85 61
  87 conv    256  1 x 1 / 1    26 x  26 x 768   ->    26 x  26 x 256 0.266 BF
  88 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  89 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  90 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  91 conv    256  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 256 0.177 BF
  92 conv    512  3 x 3 / 1    26 x  26 x 256   ->    26 x  26 x 512 1.595 BF
  93 conv    255  1 x 1 / 1    26 x  26 x 512   ->    26 x  26 x 255 0.177 BF
  94 yolo
  95 route  91
  96 conv    128  1 x 1 / 1    26 x  26 x 256   ->    26 x  26 x 128 0.044 BF
  97 upsample            2x    26 x  26 x 128   ->    52 x  52 x 128
  98 route  97 36
  99 conv    128  1 x 1 / 1    52 x  52 x 384   ->    52 x  52 x 128 0.266 BF
 100 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
 101 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
 102 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
 103 conv    128  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 128 0.177 BF
 104 conv    256  3 x 3 / 1    52 x  52 x 128   ->    52 x  52 x 256 1.595 BF
 105 conv    255  1 x 1 / 1    52 x  52 x 256   ->    52 x  52 x 255 0.353 BF
 106 yolo
Total BFLOPS 65.864 
 Allocate additional workspace_size = 52.43 MB 
Loading weights from weights/yolov3.weights...
 seen 64 
Done!
data/person.jpg: Predicted in 41.907000 milli-seconds.
dog: 99%
person: 100%
horse: 100%
Unable to init server: Could not connect: Connection refused

(predictions:1684): Gtk-WARNING **: 03:00:07.060: cannot open display:

In [ ]:

저작자표시 비영리 변경금지

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훈련 데이터 카메라 영상 적용 (0)	2020.12.28
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PROGRAMMING

YOLO(Darknet Setup - Colab / cuDNN) 본문

YOLO(Darknet Setup - Colab / cuDNN)

1. GPU Setup¶

2. Mount Google Drive¶

3. Install CUDA related modules, cuDNN¶

4. Install DarkNet¶

5. Test YOLO¶

'OpenCV > 머신비전 - 이미지 디텍팅' 카테고리의 다른 글

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