inference not work
shartoo opened this issue · comments
The compile stage ran but inference not actually work .
pi@raspberrypi:~/workspace/darknet2ncnn $ make yolov3-tiny.net
./darknet2ncnn data/yolov3-tiny.cfg data/yolov3-tiny.weights example/zoo/yolov3-tiny.param example/zoo/yolov3-tiny.bin
layer filters size input output
0 conv 16 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 16 0.150 BFLOPs
1 max 2 x 2 / 2 416 x 416 x 16 -> 208 x 208 x 16
2 conv 32 3 x 3 / 1 208 x 208 x 16 -> 208 x 208 x 32 0.399 BFLOPs
3 max 2 x 2 / 2 208 x 208 x 32 -> 104 x 104 x 32
4 conv 64 3 x 3 / 1 104 x 104 x 32 -> 104 x 104 x 64 0.399 BFLOPs
5 max 2 x 2 / 2 104 x 104 x 64 -> 52 x 52 x 64
6 conv 128 3 x 3 / 1 52 x 52 x 64 -> 52 x 52 x 128 0.399 BFLOPs
7 max 2 x 2 / 2 52 x 52 x 128 -> 26 x 26 x 128
8 conv 256 3 x 3 / 1 26 x 26 x 128 -> 26 x 26 x 256 0.399 BFLOPs
9 max 2 x 2 / 2 26 x 26 x 256 -> 13 x 13 x 256
10 conv 512 3 x 3 / 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BFLOPs
11 max 2 x 2 / 1 13 x 13 x 512 -> 13 x 13 x 512
12 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs
13 conv 256 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 256 0.089 BFLOPs
14 conv 512 3 x 3 / 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BFLOPs
15 conv 255 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 255 0.044 BFLOPs
16 yolo
17 route 13
18 conv 128 1 x 1 / 1 13 x 13 x 256 -> 13 x 13 x 128 0.011 BFLOPs
19 upsample 2x 13 x 13 x 128 -> 26 x 26 x 128
20 route 19 8
21 conv 256 3 x 3 / 1 26 x 26 x 384 -> 26 x 26 x 256 1.196 BFLOPs
22 conv 255 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 255 0.088 BFLOPs
23 yolo
Loading weights from data/yolov3-tiny.weights...Done!
./convert_verify data/yolov3-tiny.cfg data/yolov3-tiny.weights example/zoo/yolov3-tiny.param example/zoo/yolov3-tiny.bin example/data/dog.jpg
layer filters size input output
0 conv 16 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 16 0.150 BFLOPs
1 max 2 x 2 / 2 416 x 416 x 16 -> 208 x 208 x 16
2 conv 32 3 x 3 / 1 208 x 208 x 16 -> 208 x 208 x 32 0.399 BFLOPs
3 max 2 x 2 / 2 208 x 208 x 32 -> 104 x 104 x 32
4 conv 64 3 x 3 / 1 104 x 104 x 32 -> 104 x 104 x 64 0.399 BFLOPs
5 max 2 x 2 / 2 104 x 104 x 64 -> 52 x 52 x 64
6 conv 128 3 x 3 / 1 52 x 52 x 64 -> 52 x 52 x 128 0.399 BFLOPs
7 max 2 x 2 / 2 52 x 52 x 128 -> 26 x 26 x 128
8 conv 256 3 x 3 / 1 26 x 26 x 128 -> 26 x 26 x 256 0.399 BFLOPs
9 max 2 x 2 / 2 26 x 26 x 256 -> 13 x 13 x 256
10 conv 512 3 x 3 / 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BFLOPs
11 max 2 x 2 / 1 13 x 13 x 512 -> 13 x 13 x 512
12 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs
13 conv 256 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 256 0.089 BFLOPs
14 conv 512 3 x 3 / 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BFLOPs
15 conv 255 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 255 0.044 BFLOPs
16 yolo
17 route 13
18 conv 128 1 x 1 / 1 13 x 13 x 256 -> 13 x 13 x 128 0.011 BFLOPs
19 upsample 2x 13 x 13 x 128 -> 26 x 26 x 128
20 route 19 8
21 conv 256 3 x 3 / 1 26 x 26 x 384 -> 26 x 26 x 256 1.196 BFLOPs
22 conv 255 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 255 0.088 BFLOPs
23 yolo
Loading weights from data/yolov3-tiny.weights...Done!
Start run all operation:
conv_0 : weights diff : 0.000000
conv_0_batch_norm : slope diff : 0.000000
conv_0_batch_norm : mean diff : 0.000000
conv_0_batch_norm : variance diff : 0.000000
conv_0_batch_norm : biases diff : 0.000000
Layer: 0, Blob : conv_0_activation, Total Diff nan Avg Diff: nan
Layer: 1, Blob : maxpool_1, Total Diff nan Avg Diff: nan
conv_2 : weights diff : 0.000000
conv_2_batch_norm : slope diff : 0.000000
conv_2_batch_norm : mean diff : 0.000000
conv_2_batch_norm : variance diff : 0.000000
conv_2_batch_norm : biases diff : 0.000000
Layer: 2, Blob : conv_2_activation, Total Diff nan Avg Diff: nan
Layer: 3, Blob : maxpool_3, Total Diff nan Avg Diff: nan
conv_4 : weights diff : 0.000000
conv_4_batch_norm : slope diff : 0.000000
conv_4_batch_norm : mean diff : 0.000000
conv_4_batch_norm : variance diff : 0.000000
conv_4_batch_norm : biases diff : 0.000000
Layer: 4, Blob : conv_4_activation, Total Diff nan Avg Diff: nan
Layer: 5, Blob : maxpool_5, Total Diff nan Avg Diff: nan
conv_6 : weights diff : 0.000000
conv_6_batch_norm : slope diff : 0.000000
conv_6_batch_norm : mean diff : 0.000000
conv_6_batch_norm : variance diff : 0.000000
conv_6_batch_norm : biases diff : 0.000000
Layer: 6, Blob : conv_6_activation, Total Diff nan Avg Diff: nan
Layer: 7, Blob : maxpool_7, Total Diff nan Avg Diff: nan
conv_8 : weights diff : 0.000000
conv_8_batch_norm : slope diff : 0.000000
conv_8_batch_norm : mean diff : 0.000000
conv_8_batch_norm : variance diff : 0.000000
conv_8_batch_norm : biases diff : 0.000000
Layer: 8, Blob : conv_8_activation, Total Diff nan Avg Diff: nan
Layer: 9, Blob : maxpool_9, Total Diff nan Avg Diff: nan
conv_10 : weights diff : 0.000000
conv_10_batch_norm : slope diff : 0.000000
conv_10_batch_norm : mean diff : 0.000000
conv_10_batch_norm : variance diff : 0.000000
conv_10_batch_norm : biases diff : 0.000000
Layer: 10, Blob : conv_10_activation, Total Diff nan Avg Diff: nan
Layer: 11, Blob : maxpool_11, Total Diff nan Avg Diff: nan
conv_12 : weights diff : 0.000000
conv_12_batch_norm : slope diff : 0.000000
conv_12_batch_norm : mean diff : 0.000000
conv_12_batch_norm : variance diff : 0.000000
conv_12_batch_norm : biases diff : 0.000000
Layer: 12, Blob : conv_12_activation, Total Diff nan Avg Diff: nan
conv_13 : weights diff : 0.000000
conv_13_batch_norm : slope diff : 0.000000
conv_13_batch_norm : mean diff : 0.000000
conv_13_batch_norm : variance diff : 0.000000
conv_13_batch_norm : biases diff : 0.000000
Layer: 13, Blob : conv_13_activation, Total Diff nan Avg Diff: nan
conv_14 : weights diff : 0.000000
conv_14_batch_norm : slope diff : 0.000000
conv_14_batch_norm : mean diff : 0.000000
conv_14_batch_norm : variance diff : 0.000000
conv_14_batch_norm : biases diff : 0.000000
Layer: 14, Blob : conv_14_activation, Total Diff nan Avg Diff: nan
conv_15 : weights diff : 0.000000
conv_15 : biases diff : 0.000000
Layer: 15, Blob : conv_15_activation, Total Diff nan Avg Diff: nan
Layer: 17, Blob : route_17, Total Diff nan Avg Diff: nan
conv_18 : weights diff : 0.000000
conv_18_batch_norm : slope diff : 0.000000
conv_18_batch_norm : mean diff : 0.000000
conv_18_batch_norm : variance diff : 0.000000
conv_18_batch_norm : biases diff : 0.000000
Layer: 18, Blob : conv_18_activation, Total Diff nan Avg Diff: nan
Layer: 19, Blob : upsample_19, Total Diff nan Avg Diff: nan
Layer: 20, Blob : route_20, Total Diff nan Avg Diff: nan
conv_21 : weights diff : 0.000000
conv_21_batch_norm : slope diff : 0.000000
conv_21_batch_norm : mean diff : 0.000000
conv_21_batch_norm : variance diff : 0.000000
conv_21_batch_norm : biases diff : 0.000000
Layer: 21, Blob : conv_21_activation, Total Diff nan Avg Diff: nan
conv_22 : weights diff : 0.000000
conv_22 : biases diff : 0.000000
Layer: 22, Blob : conv_22_activation, Total Diff nan Avg Diff: nan
All the difference are 0.000 .And i tried another example like cifar.cifar
pi@raspberrypi:~/workspace/darknet2ncnn/example $ make cifar.cifar
./classifier zoo/cifar.param zoo/cifar.bin data/32516_dog.png data/cifar_lable.txt
3 cat = nan
4 deer = nan
1 automobile = nan
0 airplane = nan
2 bird = nan
Sth wrong with the weights file?
Errors have occurred since the first layer，
···
Layer: 0, Blob : conv_0_activation, Total Diff nan Avg Diff: nan
Layer: 1, Blob : maxpool_1, Total Diff nan Avg Diff: nan
···
You can cancel the comment in convert_verify.cpp, from 177 to 192 lines, compile and run again to print the actual output values and differences.Of course, it is best to redirect the output to a file for easy checking.