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Problem in adding dilated convolution #37

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mrhosseini opened this issue Jun 14, 2020 · 3 comments
Closed

Problem in adding dilated convolution #37

mrhosseini opened this issue Jun 14, 2020 · 3 comments

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@mrhosseini
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mrhosseini commented Jun 14, 2020 •
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Hi
The AlexeyAB/darknet repository, supports dilated convolution. I tried to add the dilation support to tkDNN but it results in error.

Changes can be seen in my fork here.
This is the output of a test program (test_yolo3_tiny_dilation) created based on test_yolo3tiny, using this cfg file:

Not supported field: batch=128
Not supported field: subdivisions=16
Not supported field: momentum=0.9
Not supported field: decay=0.0005
Not supported field: angle=0
Not supported field: saturation = 1.5
Not supported field: exposure = 1.15
Not supported field: hue=0
Not supported field: learning_rate=0.001
Not supported field: burn_in=1000
Not supported field: max_batches = 15000
Not supported field: policy=steps
Not supported field: steps=13000,14000
Not supported field: scales=.1,.1
New NETWORK (tkDNN v0.5, CUDNN v8)
Reading weights: I=1 O=16 KERNEL=3x3x1
Reading weights: I=16 O=32 KERNEL=3x3x1
Reading weights: I=32 O=16 KERNEL=1x1x1
Reading weights: I=16 O=32 KERNEL=3x3x1
Reading weights: I=32 O=64 KERNEL=3x3x1
Reading weights: I=64 O=32 KERNEL=1x1x1
Reading weights: I=32 O=64 KERNEL=3x3x1
Reading weights: I=64 O=32 KERNEL=1x1x1
Reading weights: I=32 O=64 KERNEL=3x3x1
Reading weights: I=64 O=32 KERNEL=1x1x1
Reading weights: I=32 O=64 KERNEL=3x3x1
Reading weights: I=64 O=32 KERNEL=1x1x1
Reading weights: I=64 O=32 KERNEL=1x1x1
Reading weights: I=32 O=64 KERNEL=3x3x1
Reading weights: I=64 O=32 KERNEL=1x1x1
Reading weights: I=64 O=32 KERNEL=1x1x1
Reading weights: I=64 O=32 KERNEL=1x1x1
Reading weights: I=64 O=128 KERNEL=3x3x1
Reading weights: I=128 O=64 KERNEL=1x1x1
Reading weights: I=64 O=128 KERNEL=3x3x1
Reading weights: I=128 O=64 KERNEL=1x1x1
Reading weights: I=64 O=128 KERNEL=3x3x1
Reading weights: I=64 O=16 KERNEL=1x1x1
Reading weights: I=192 O=128 KERNEL=1x1x1
Reading weights: I=128 O=256 KERNEL=3x3x1
Reading weights: I=256 O=18 KERNEL=1x1x1
Not supported field: anchors = 25, 21,  36, 23,  37, 31,  49, 35,  63, 48,  98, 61
Not supported field: jitter=.3
Not supported field: ignore_thresh = .7
Not supported field: truth_thresh = 1
Not supported field: random=1
Reading weights: I=32 O=64 KERNEL=3x3x1
Reading weights: I=64 O=128 KERNEL=3x3x1
Reading weights: I=128 O=256 KERNEL=3x3x1
Reading weights: I=256 O=512 KERNEL=3x3x1
Reading weights: I=512 O=18 KERNEL=1x1x1
Not supported field: anchors = 25, 21,  36, 23,  37, 31,  49, 35,  63, 48,  98, 61
Not supported field: jitter=.3
Not supported field: ignore_thresh = .7
Not supported field: truth_thresh = 1
Not supported field: random=1

====================== NETWORK MODEL ======================
N.  Layer type       input (H*W,CH)        output (H*W,CH) 
  0 Conv2d           416 x  416,    1  ->  416 x  416,   16
  1 ActivationLeaky  416 x  416,   16  ->  416 x  416,   16
  2 Conv2d           416 x  416,   16  ->  208 x  208,   32
  3 ActivationLeaky  208 x  208,   32  ->  208 x  208,   32
  4 Conv2d           208 x  208,   32  ->  208 x  208,   16
  5 ActivationLeaky  208 x  208,   16  ->  208 x  208,   16
  6 Conv2d           208 x  208,   16  ->  208 x  208,   32
  7 ActivationLeaky  208 x  208,   32  ->  208 x  208,   32
  8 Conv2d           208 x  208,   32  ->  104 x  104,   64
  9 ActivationLeaky  104 x  104,   64  ->  104 x  104,   64
 10 Conv2d           104 x  104,   64  ->  104 x  104,   32
 11 ActivationLeaky  104 x  104,   32  ->  104 x  104,   32
 12 Conv2d           104 x  104,   32  ->  104 x  104,   64
 13 ActivationLeaky  104 x  104,   64  ->  104 x  104,   64
 14 Conv2d           104 x  104,   64  ->  104 x  104,   32
 15 ActivationLeaky  104 x  104,   32  ->  104 x  104,   32
 16 Conv2d           104 x  104,   32  ->  104 x  104,   64
 17 ActivationLeaky  104 x  104,   64  ->  104 x  104,   64
 18 Conv2d           104 x  104,   64  ->  104 x  104,   32
 19 ActivationLeaky  104 x  104,   32  ->  104 x  104,   32
 20 Conv2d           104 x  104,   32  ->  104 x  104,   64
 21 ActivationLeaky  104 x  104,   64  ->  104 x  104,   64
 22 Conv2d           104 x  104,   64  ->  104 x  104,   32
 23 ActivationLeaky  104 x  104,   32  ->  104 x  104,   32
 24 Route            104 x  104,   64  ->  104 x  104,   64
 25 Conv2d           104 x  104,   64  ->  104 x  104,   32
 26 ActivationLeaky  104 x  104,   32  ->  104 x  104,   32
 27 Conv2d           104 x  104,   32  ->  104 x  104,   64
 28 ActivationLeaky  104 x  104,   64  ->  104 x  104,   64
 29 Conv2d           104 x  104,   64  ->  104 x  104,   32
 30 ActivationLeaky  104 x  104,   32  ->  104 x  104,   32
 31 Route            104 x  104,   64  ->  104 x  104,   64
 32 Conv2d           104 x  104,   64  ->  104 x  104,   32
 33 ActivationLeaky  104 x  104,   32  ->  104 x  104,   32
 34 Route            104 x  104,   64  ->  104 x  104,   64
 35 Conv2d           104 x  104,   64  ->  104 x  104,   32
 36 ActivationLeaky  104 x  104,   32  ->  104 x  104,   32
 37 Route            104 x  104,   64  ->  104 x  104,   64
 38 Conv2d           104 x  104,   64  ->   52 x   52,  128
 39 ActivationLeaky   52 x   52,  128  ->   52 x   52,  128
 40 Conv2d            52 x   52,  128  ->   52 x   52,   64
 41 ActivationLeaky   52 x   52,   64  ->   52 x   52,   64
 42 Conv2d            52 x   52,   64  ->   52 x   52,  128
 43 ActivationLeaky   52 x   52,  128  ->   52 x   52,  128
 44 Conv2d            52 x   52,  128  ->   52 x   52,   64
 45 ActivationLeaky   52 x   52,   64  ->   52 x   52,   64
 46 Conv2d            52 x   52,   64  ->   52 x   52,  128
 47 ActivationLeaky   52 x   52,  128  ->   52 x   52,  128
 48 Route            104 x  104,   64  ->  104 x  104,   64
 49 Conv2d           104 x  104,   64  ->  104 x  104,   16
 50 ActivationLeaky  104 x  104,   16  ->  104 x  104,   16
 51 Reorg            104 x  104,   16  ->   52 x   52,   64
 52 Route             52 x   52,  192  ->   52 x   52,  192
 53 Conv2d            52 x   52,  192  ->   52 x   52,  128
 54 ActivationLeaky   52 x   52,  128  ->   52 x   52,  128
 55 Conv2d            52 x   52,  128  ->   52 x   52,  256
 56 ActivationLeaky   52 x   52,  256  ->   52 x   52,  256
 57 Conv2d            52 x   52,  256  ->   52 x   52,   18
 58 Yolo              52 x   52,   18  ->   52 x   52,   18
 59 Route            208 x  208,   32  ->  208 x  208,   32
 60 Pooling          208 x  208,   32  ->  104 x  104,   32
 61 Conv2d           104 x  104,   32  ->  104 x  104,   64
 62 ActivationLeaky  104 x  104,   64  ->  104 x  104,   64
 63 Pooling          104 x  104,   64  ->   52 x   52,   64
 64 Conv2d            52 x   52,   64  ->   52 x   52,  128
 65 ActivationLeaky   52 x   52,  128  ->   52 x   52,  128
 66 Pooling           52 x   52,  128  ->   26 x   26,  128
 67 Conv2d            26 x   26,  128  ->   26 x   26,  256
 68 ActivationLeaky   26 x   26,  256  ->   26 x   26,  256
 69 Pooling           26 x   26,  256  ->   13 x   13,  256
 70 Conv2d            13 x   13,  256  ->   13 x   13,  512
 71 ActivationLeaky   13 x   13,  512  ->   13 x   13,  512
 72 Pooling           13 x   13,  512  ->   13 x   13,  512
 73 Conv2d            13 x   13,  512  ->   13 x   13,   18
 74 Yolo              13 x   13,   18  ->   13 x   13,   18
===========================================================

GPU free memory: 324.542 mb.
New NetworkRT (TensorRT v7.1)
Float16 support: 1
Int8 support: 0
DLAs: 0
Selected maxBatchSize: 1
GPU free memory: 79.02 mb.
Building tensorRT cuda engine...
cloud not build cuda engine
.../tkDNN/src/NetworkRT.cpp:145
Aborting...

Any help?
@ceccocats
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ceccocats commented Jun 14, 2020 •
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Have you tried without tensorrt? Does it work?
Enable tensorrt debug to get more info on the reason why tensorrt stopped:
cmake .. -DDEBUG=True

In Conv2d.cpp you multiply padding*dilation, maybe you should do the same in NetworkRT

@mrhosseini
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Thank you Francesco.

Have you tried without tensorrt? Does it work?

No, I only tested with tensorrt.

In Conv2d.cpp you multiply padding*dilation, maybe you should do the same in NetworkRT

Yes. You are probably right.

I will check the above with DEBUG flag and report the results.

@mrhosseini
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In Conv2d.cpp you multiply padding*dilation, maybe you should do the same in NetworkRT

This solved the problem. Thanks for your great help.

@mrhosseini mrhosseini mentioned this issue Jun 18, 2020
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@mrhosseini @ceccocats