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CUDA status Error: file: ./src/dark_cuda.c : () : line: 239 : build time: Apr 16 2020 - 16:43:48 CUDA Error: invalid argument: File exists darknet: ./src/utils.c:325: error: Assertion `0' failed. #2117
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i have the same issue on colab |
@SK124 I solved that issue by downloading darknet like that
|
that worked for me |
updated subdivision as 64 and resolved the issue. |
If you are using Google colab. Then check your GPU settings in colab. |
I did this but then I started getting this error
|
I tried so many things. Also, tried dos2unix(), turned to ubuntu, but getting same error. After that I tried this, which really solved my problem. !git clone https://github.com/AlexeyAB/darknet/ There is nothing wrong with files copied from windows or linux, problem exist for the the darknet version cloned from: !git clone 'https://github.com/pjreddie/darknet' changed it. |
I had the same error except it didn't have the last line ( My fix was to remove Given above sba17 mentioned the |
Hello Guys! I'm trying to train YOLOv3 on a custom dataset on Google Colab and I'm running into this error whenever I give the train command
!./darknet detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -dont_show
OUTPUT is as follows:
CUDA-version: 10010 (10010), GPU count: 1
OpenCV version: 3.2.0
yolo-obj
compute_capability = 370, cudnn_half = 0
net.optimized_memory = 0
mini_batch = 1, batch = 64, time_steps = 1, train = 1
layer filters size/strd(dil) 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, wt = 0, wn = 0, outputs: 208 x 208 x 64 0.003 BF
5 conv 128 3 x 3/ 2 208 x 208 x 64 -> 104 x 104 x 128 1.595 BF
6 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
7 conv 128 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BF
8 Shortcut Layer: 5, wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
9 conv 64 1 x 1/ 1 104 x 104 x 128 -> 104 x 104 x 64 0.177 BF
10 conv 128 3 x 3/ 1 104 x 104 x 64 -> 104 x 104 x 128 1.595 BF
11 Shortcut Layer: 8, wt = 0, wn = 0, outputs: 104 x 104 x 128 0.001 BF
12 conv 256 3 x 3/ 2 104 x 104 x 128 -> 52 x 52 x 256 1.595 BF
13 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
14 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
15 Shortcut Layer: 12, wt = 0, wn = 0, outputs: 52 x 52 x 256 0.001 BF
16 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
17 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
18 Shortcut Layer: 15, wt = 0, wn = 0, outputs: 52 x 52 x 256 0.001 BF
19 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
20 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
21 Shortcut Layer: 18, wt = 0, wn = 0, outputs: 52 x 52 x 256 0.001 BF
22 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
23 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
24 Shortcut Layer: 21, wt = 0, wn = 0, outputs: 52 x 52 x 256 0.001 BF
25 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
26 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
27 Shortcut Layer: 24, wt = 0, wn = 0, outputs: 52 x 52 x 256 0.001 BF
28 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
29 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 52 x 52 x 256 0.001 BF
31 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
32 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 52 x 52 x 256 0.001 BF
34 conv 128 1 x 1/ 1 52 x 52 x 256 -> 52 x 52 x 128 0.177 BF
35 conv 256 3 x 3/ 1 52 x 52 x 128 -> 52 x 52 x 256 1.595 BF
36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 52 x 52 x 256 0.001 BF
37 conv 512 3 x 3/ 2 52 x 52 x 256 -> 26 x 26 x 512 1.595 BF
38 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
39 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
40 Shortcut Layer: 37, wt = 0, wn = 0, outputs: 26 x 26 x 512 0.000 BF
41 conv 256 1 x 1/ 1 26 x 26 x 512 -> 26 x 26 x 256 0.177 BF
42 conv 512 3 x 3/ 1 26 x 26 x 256 -> 26 x 26 x 512 1.595 BF
Allocate additional workspace_size = 49.84 MB
Loading weights from darknet53.conv.74...
seen 64, trained: 0 K-images (0 Kilo-batches_64)
Done! Loaded 75 layers from weights-file
Learning Rate: 0.001, Momentum: 0.9, Decay: 0.0005
Resizing, random_coef = 1.40
608 x 608
try to allocate additional workspace_size = 106.46 MB
CUDA allocate done!
Loaded: 0.000055 seconds
CUDA status Error: file: ./src/dark_cuda.c : () : line: 239 : build time: Apr 16 2020 - 16:43:48
CUDA Error: invalid argument
CUDA Error: invalid argument: File exists
darknet: ./src/utils.c:325: error: Assertion `0' failed.
My Makefile
GPU=1
CUDNN=1
CUDNN_HALF=0
OPENCV=1
AVX=0
OPENMP=0
LIBSO=0
ZED_CAMERA=0 # ZED SDK 3.0 and above
ZED_CAMERA_v2_8=0 # ZED SDK 2.X
USE_CPP=0
DEBUG=0
ARCH= -gencode arch=compute_30, code=sm_30
-gencode arch=compute_37, code=sm_37
-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]\
Dataset Used: BDD100K
It seems more like a CUDA Driver Issue to me
CUDA-version: 10010 (10010) is given as output in colab cell but when I try !/usr/local/cuda/bin/nvcc --version
OUTPUT is as follows:
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
##I have attached my dark_cuda.c, utils.c along with yolo-obj.cfg ##
utils_and_darkcuda_files.zip
Any help is highly appreciated
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