위는 오리지널 darknet을 아무런 옵션없이 라즈베리에서 빌드한 결과
$ ldd darknet
linux-vdso.so.1 (0x7efe1000)
/usr/lib/arm-linux-gnueabihf/libarmmem-${PLATFORM}.so => /usr/lib/arm-linux-gnueabihf/libarmmem-v7l.so (0x76f54000)
libm.so.6 => /lib/arm-linux-gnueabihf/libm.so.6 (0x76eb6000)
libpthread.so.0 => /lib/arm-linux-gnueabihf/libpthread.so.0 (0x76e8c000)
libc.so.6 => /lib/arm-linux-gnueabihf/libc.so.6 (0x76d3e000)
/lib/ld-linux-armhf.so.3 (0x76f69000)
아래는 darknet AlexeyAB 버전을 neon과 openmp 설정해서 빌드한 결과
$ ldd darknet
linux-vdso.so.1 (0x7eefc000)
/usr/lib/arm-linux-gnueabihf/libarmmem-${PLATFORM}.so => /usr/lib/arm-linux-gnueabihf/libarmmem-v7l.so (0x76f79000)
libgomp.so.1 => /lib/arm-linux-gnueabihf/libgomp.so.1 (0x76f25000)
libstdc++.so.6 => /lib/arm-linux-gnueabihf/libstdc++.so.6 (0x76dde000)
libm.so.6 => /lib/arm-linux-gnueabihf/libm.so.6 (0x76d5c000)
libgcc_s.so.1 => /lib/arm-linux-gnueabihf/libgcc_s.so.1 (0x76d2f000)
libpthread.so.0 => /lib/arm-linux-gnueabihf/libpthread.so.0 (0x76d05000)
libc.so.6 => /lib/arm-linux-gnueabihf/libc.so.6 (0x76bb7000)
libdl.so.2 => /lib/arm-linux-gnueabihf/libdl.so.2 (0x76ba4000)
/lib/ld-linux-armhf.so.3 (0x76f8e000)
+
cpu only = 30.13sec
$ ./tiny.sh
GPU isn't used
OpenCV isn't used - data augmentation will be slow
mini_batch = 1, batch = 1, time_steps = 1, train = 0
layer filters size/strd(dil) input output
0 conv 16 3 x 3/ 1 416 x 416 x 3 -> 416 x 416 x 16 0.150 BF
1 max 2x 2/ 2 416 x 416 x 16 -> 208 x 208 x 16 0.003 BF
2 conv 32 3 x 3/ 1 208 x 208 x 16 -> 208 x 208 x 32 0.399 BF
3 max 2x 2/ 2 208 x 208 x 32 -> 104 x 104 x 32 0.001 BF
4 conv 64 3 x 3/ 1 104 x 104 x 32 -> 104 x 104 x 64 0.399 BF
5 max 2x 2/ 2 104 x 104 x 64 -> 52 x 52 x 64 0.001 BF
6 conv 128 3 x 3/ 1 52 x 52 x 64 -> 52 x 52 x 128 0.399 BF
7 max 2x 2/ 2 52 x 52 x 128 -> 26 x 26 x 128 0.000 BF
8 conv 256 3 x 3/ 1 26 x 26 x 128 -> 26 x 26 x 256 0.399 BF
9 max 2x 2/ 2 26 x 26 x 256 -> 13 x 13 x 256 0.000 BF
10 conv 512 3 x 3/ 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF
11 max 2x 2/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.000 BF
12 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
13 conv 256 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 256 0.089 BF
14 conv 512 3 x 3/ 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF
15 conv 255 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 255 0.044 BF
16 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
17 route 13 -> 13 x 13 x 256
18 conv 128 1 x 1/ 1 13 x 13 x 256 -> 13 x 13 x 128 0.011 BF
19 upsample 2x 13 x 13 x 128 -> 26 x 26 x 128
20 route 19 8 -> 26 x 26 x 384
21 conv 256 3 x 3/ 1 26 x 26 x 384 -> 26 x 26 x 256 1.196 BF
22 conv 255 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 255 0.088 BF
23 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 5.571
avg_outputs = 341534
Loading weights from ../yolov3-tiny.weights...
seen 64, trained: 32013 K-images (500 Kilo-batches_64)
Done! Loaded 24 layers from weights-file
Detection layer: 16 - type = 28
Detection layer: 23 - type = 28
data/dog.jpg: Predicted in 30133.750000 milli-seconds.
dog: 81%
bicycle: 38%
car: 71%
truck: 42%
truck: 62%
car: 40%
Not compiled with OpenCV, saving to predictions.png instead
neon = 10.718 sec
$ ./tiny.sh
GPU isn't used
OpenCV isn't used - data augmentation will be slow
mini_batch = 1, batch = 1, time_steps = 1, train = 0
layer filters size/strd(dil) input output
0 conv 16 3 x 3/ 1 416 x 416 x 3 -> 416 x 416 x 16 0.150 BF
1 max 2x 2/ 2 416 x 416 x 16 -> 208 x 208 x 16 0.003 BF
2 conv 32 3 x 3/ 1 208 x 208 x 16 -> 208 x 208 x 32 0.399 BF
3 max 2x 2/ 2 208 x 208 x 32 -> 104 x 104 x 32 0.001 BF
4 conv 64 3 x 3/ 1 104 x 104 x 32 -> 104 x 104 x 64 0.399 BF
5 max 2x 2/ 2 104 x 104 x 64 -> 52 x 52 x 64 0.001 BF
6 conv 128 3 x 3/ 1 52 x 52 x 64 -> 52 x 52 x 128 0.399 BF
7 max 2x 2/ 2 52 x 52 x 128 -> 26 x 26 x 128 0.000 BF
8 conv 256 3 x 3/ 1 26 x 26 x 128 -> 26 x 26 x 256 0.399 BF
9 max 2x 2/ 2 26 x 26 x 256 -> 13 x 13 x 256 0.000 BF
10 conv 512 3 x 3/ 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF
11 max 2x 2/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.000 BF
12 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
13 conv 256 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 256 0.089 BF
14 conv 512 3 x 3/ 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF
15 conv 255 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 255 0.044 BF
16 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
17 route 13 -> 13 x 13 x 256
18 conv 128 1 x 1/ 1 13 x 13 x 256 -> 13 x 13 x 128 0.011 BF
19 upsample 2x 13 x 13 x 128 -> 26 x 26 x 128
20 route 19 8 -> 26 x 26 x 384
21 conv 256 3 x 3/ 1 26 x 26 x 384 -> 26 x 26 x 256 1.196 BF
22 conv 255 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 255 0.088 BF
23 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 5.571
avg_outputs = 341534
Loading weights from ../yolov3-tiny.weights...
seen 64, trained: 32013 K-images (500 Kilo-batches_64)
Done! Loaded 24 layers from weights-file
Detection layer: 16 - type = 28
Detection layer: 23 - type = 28
data/dog.jpg: Predicted in 10718.416000 milli-seconds.
dog: 81%
bicycle: 38%
car: 71%
truck: 42%
truck: 62%
car: 40%
Not compiled with OpenCV, saving to predictions.png instead
openmp = 8.686 sec
$ ./tiny.sh
GPU isn't used
OpenCV isn't used - data augmentation will be slow
mini_batch = 1, batch = 1, time_steps = 1, train = 0
layer filters size/strd(dil) input output
0 conv 16 3 x 3/ 1 416 x 416 x 3 -> 416 x 416 x 16 0.150 BF
1 max 2x 2/ 2 416 x 416 x 16 -> 208 x 208 x 16 0.003 BF
2 conv 32 3 x 3/ 1 208 x 208 x 16 -> 208 x 208 x 32 0.399 BF
3 max 2x 2/ 2 208 x 208 x 32 -> 104 x 104 x 32 0.001 BF
4 conv 64 3 x 3/ 1 104 x 104 x 32 -> 104 x 104 x 64 0.399 BF
5 max 2x 2/ 2 104 x 104 x 64 -> 52 x 52 x 64 0.001 BF
6 conv 128 3 x 3/ 1 52 x 52 x 64 -> 52 x 52 x 128 0.399 BF
7 max 2x 2/ 2 52 x 52 x 128 -> 26 x 26 x 128 0.000 BF
8 conv 256 3 x 3/ 1 26 x 26 x 128 -> 26 x 26 x 256 0.399 BF
9 max 2x 2/ 2 26 x 26 x 256 -> 13 x 13 x 256 0.000 BF
10 conv 512 3 x 3/ 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF
11 max 2x 2/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.000 BF
12 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
13 conv 256 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 256 0.089 BF
14 conv 512 3 x 3/ 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF
15 conv 255 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 255 0.044 BF
16 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
17 route 13 -> 13 x 13 x 256
18 conv 128 1 x 1/ 1 13 x 13 x 256 -> 13 x 13 x 128 0.011 BF
19 upsample 2x 13 x 13 x 128 -> 26 x 26 x 128
20 route 19 8 -> 26 x 26 x 384
21 conv 256 3 x 3/ 1 26 x 26 x 384 -> 26 x 26 x 256 1.196 BF
22 conv 255 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 255 0.088 BF
23 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 5.571
avg_outputs = 341534
Loading weights from ../yolov3-tiny.weights...
seen 64, trained: 32013 K-images (500 Kilo-batches_64)
Done! Loaded 24 layers from weights-file
Detection layer: 16 - type = 28
Detection layer: 23 - type = 28
data/dog.jpg: Predicted in 8686.237000 milli-seconds.
dog: 81%
bicycle: 38%
car: 71%
truck: 42%
truck: 62%
car: 40%
Not compiled with OpenCV, saving to predictions.png instead
openmp + neon = 4.449 sec
$ ./tiny.sh
GPU isn't used
OpenCV isn't used - data augmentation will be slow
mini_batch = 1, batch = 1, time_steps = 1, train = 0
layer filters size/strd(dil) input output
0 conv 16 3 x 3/ 1 416 x 416 x 3 -> 416 x 416 x 16 0.150 BF
1 max 2x 2/ 2 416 x 416 x 16 -> 208 x 208 x 16 0.003 BF
2 conv 32 3 x 3/ 1 208 x 208 x 16 -> 208 x 208 x 32 0.399 BF
3 max 2x 2/ 2 208 x 208 x 32 -> 104 x 104 x 32 0.001 BF
4 conv 64 3 x 3/ 1 104 x 104 x 32 -> 104 x 104 x 64 0.399 BF
5 max 2x 2/ 2 104 x 104 x 64 -> 52 x 52 x 64 0.001 BF
6 conv 128 3 x 3/ 1 52 x 52 x 64 -> 52 x 52 x 128 0.399 BF
7 max 2x 2/ 2 52 x 52 x 128 -> 26 x 26 x 128 0.000 BF
8 conv 256 3 x 3/ 1 26 x 26 x 128 -> 26 x 26 x 256 0.399 BF
9 max 2x 2/ 2 26 x 26 x 256 -> 13 x 13 x 256 0.000 BF
10 conv 512 3 x 3/ 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF
11 max 2x 2/ 1 13 x 13 x 512 -> 13 x 13 x 512 0.000 BF
12 conv 1024 3 x 3/ 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BF
13 conv 256 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 256 0.089 BF
14 conv 512 3 x 3/ 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BF
15 conv 255 1 x 1/ 1 13 x 13 x 512 -> 13 x 13 x 255 0.044 BF
16 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
17 route 13 -> 13 x 13 x 256
18 conv 128 1 x 1/ 1 13 x 13 x 256 -> 13 x 13 x 128 0.011 BF
19 upsample 2x 13 x 13 x 128 -> 26 x 26 x 128
20 route 19 8 -> 26 x 26 x 384
21 conv 256 3 x 3/ 1 26 x 26 x 384 -> 26 x 26 x 256 1.196 BF
22 conv 255 1 x 1/ 1 26 x 26 x 256 -> 26 x 26 x 255 0.088 BF
23 yolo
[yolo] params: iou loss: mse (2), iou_norm: 0.75, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.00
Total BFLOPS 5.571
avg_outputs = 341534
Loading weights from ../yolov3-tiny.weights...
seen 64, trained: 32013 K-images (500 Kilo-batches_64)
Done! Loaded 24 layers from weights-file
Detection layer: 16 - type = 28
Detection layer: 23 - type = 28
data/dog.jpg: Predicted in 4449.888000 milli-seconds.
dog: 81%
bicycle: 38%
car: 71%
truck: 42%
truck: 62%
car: 40%
Not compiled with OpenCV, saving to predictions.png instead
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