How to build on Windows10?
WhiteEyeYan opened this issue · 8 comments
I success used Cmake to make a .sln file.
And I'm trying to compile it by Visual Studio 2019.
But it has too much error, after I fix one more error come out.
Could anyone help me build it on windwos?
Please make a fork and try hard... I am really interested in integrating such code to allow the Win10 build to succeed. Thanks!
Sorry, I'm not good at programming but I'll keep trying.
Now, I have another problem.
I build on ubuntu 18.04 with AMD RX480.
I set yolov4-tiny.cfg like this:
[net]
#Testing
#batch=1
#subdivisions=1
#Training
batch=1
subdivisions=1
width=32
height=32
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
flip=0
learning_rate=0.00261
burn_in=1000
max_batches = 8000
policy=steps
steps=6400,7200
scales=.1,.1[convolutional]
batch_normalize=1
filters=32
size=3
stride=2
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky[route]
layers=-1
groups=2
group_id=1[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky[route]
layers = -1,-2[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky[route]
layers = -6,-1[maxpool]
size=2
stride=2[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky[route]
layers=-1
groups=2
group_id=1[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky[route]
layers = -1,-2[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky[route]
layers = -6,-1[maxpool]
size=2
stride=2[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky[route]
layers=-1
groups=2
group_id=1[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky[route]
layers = -1,-2[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky[route]
layers = -6,-1[maxpool]
size=2
stride=2[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky##################################
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky[convolutional]
size=1
stride=1
pad=1
filters=27
activation=linear[yolo4]
mask = 3,4,5
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
classes=4
num=6
jitter=.3
scale_x_y = 1.05
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
ignore_thresh = .7
truth_thresh = 1
random=0
resize=1.5
nms_kind=greedynms
beta_nms=0.6[route]
layers = -4[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky[upsample]
stride=2[route]
layers = -1, 23[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky[convolutional]
size=1
stride=1
pad=1
filters=27
activation=linear[yolo4]
mask = 1,2,3
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
classes=4
num=6
jitter=.3
scale_x_y = 1.05
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
ignore_thresh = .7
truth_thresh = 1
random=0
resize=1.5
nms_kind=greedynms
beta_nms=0.6
It will have error "Segmentation fault (core dumped)".
If I set subdivisions > batch like this:
[net]
#Testing
#batch=1
#subdivisions=1
#Training
batch=1
subdivisions=2
width=32
height=32
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
flip=0
learning_rate=0.00261
burn_in=1000
max_batches = 8000
policy=steps
steps=6400,7200
scales=.1,.1[convolutional]
batch_normalize=1
filters=32
size=3
stride=2
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky[route]
layers=-1
groups=2
group_id=1[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky[route]
layers = -1,-2[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky[route]
layers = -6,-1[maxpool]
size=2
stride=2[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky[route]
layers=-1
groups=2
group_id=1[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky[route]
layers = -1,-2[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky[route]
layers = -6,-1[maxpool]
size=2
stride=2[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky[route]
layers=-1
groups=2
group_id=1[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky[route]
layers = -1,-2[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky[route]
layers = -6,-1[maxpool]
size=2
stride=2[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky##################################
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky[convolutional]
size=1
stride=1
pad=1
filters=27
activation=linear[yolo4]
mask = 3,4,5
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
classes=4
num=6
jitter=.3
scale_x_y = 1.05
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
ignore_thresh = .7
truth_thresh = 1
random=0
resize=1.5
nms_kind=greedynms
beta_nms=0.6[route]
layers = -4[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky[upsample]
stride=2[route]
layers = -1, 23[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky[convolutional]
size=1
stride=1
pad=1
filters=27
activation=linear[yolo4]
mask = 1,2,3
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
classes=4
num=6
jitter=.3
scale_x_y = 1.05
cls_normalizer=1.0
iou_normalizer=0.07
iou_loss=ciou
ignore_thresh = .7
truth_thresh = 1
random=0
resize=1.5
nms_kind=greedynms
beta_nms=0.6
error become
"FATAL ERROR: CL_INVALID_BUFFER_SIZE
could create buffer on device. error: CL_INVALID_BUFFER_SIZE"
I have check my label.txt without 0.0, every number is larger than 0.
I also try
export CPU_MAX_ALLOC_PERCENT=100
export GPU_SINGLE_ALLOC_PERCENT=100
export GPU_MAX_ALLOC_PERCENT=100
export GPU_MAX_HEAP_SIZE=100
Do you have any suggestions to slove this?
Hi, I am interested in building it for windows too.
Are there any libs that you are using that are specific to unix systems?
@sowson I'm interested too, currently trying to resolve the cmake issues. it might be beneficial to try and write up a simple build instructions that lists dependencies.
currently, I struggle to configure clblas. but I also see the usage of non portable includes in the src files. pthreads is one example.
Hello, I am working on build this repo on Windows and AMD GPU... at https://github.com/sowson/darknet/tree/winX64-thanks-to-jihuacao on this branch... can you help to test it? I have to make sure macOS, GNU/Linux and Windows 10 x64 are build find before integrate, but maybe you have some experience and can improve this branch? Thanks!
@elad8a @achateigner @WhiteEyeYan please look on README I have just integrated the "early" version of the build for Windows 10 x64 that is hard once I keep macOS and GNU/Linux working fine too. Can you please try it? On my machine, there is an issue all builds fine when I execute something it shows GPU info, then materializes the network but does not compute high probably because of OpenCV, but I am checking this issue now. Happy WinBuild! :D.
Pls, check https://iblog.isowa.io/2021/11/20/darknet-on-opencl-on-windows-11-x64 I only repeat steps by step guide and updated photo. It works now not sure why! ;-).