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标题: eval_perf BUG? (different FPS reported with perf_debug=True and False) [打印本页]

作者: chuyee 时间: 2019-3-26 03:53
标题: eval_perf BUG? (different FPS reported with perf_debug=True and False)
Hi,

I found an interesting problem for rknn.eval_perf() while evaluating rknn with deeplabv3 (See [1] and [2] for more info on that topic).

After rknn.inference(), I use below code to print the performance evaluation result.

perf_results = rknn.eval_perf(inputs=[img])

However I got 3x difference (3.8 vs 13.08) with the result if I turn on/off perf_debug in rknn.init_runtime(). Below is the result, nothing else changed except the init_runtime() call.

ret = rknn.init_runtime(perf_debug=False, eval_mem=False)

========================================================================
Performance
========================================================================
Total Time(us): 263127
FPS: 3.80
========================================================================

ret = rknn.init_runtime(perf_debug=True, eval_mem=False)

========================================================================
                           Performance
      #### The performance result is just for debugging, ####
      #### may worse than actual performance!          ####
========================================================================
Layer ID Name                                        Operator          Time(us)
0          ConvolutionReluPoolingLayer2_0             RESHUFFLE          4153
1          ConvolutionReluPoolingLayer2_2             CONVOLUTION       780
2          ConvolutionReluPoolingLayer2_2             CONVOLUTION       568
3          ConvolutionReluPoolingLayer2_2             CONVOLUTION       1683
4          ConvolutionReluPoolingLayer2_0             RESHUFFLE          3944
5          ConvolutionReluPoolingLayer2_2             CONVOLUTION       269
6          ConvolutionReluPoolingLayer2_2             CONVOLUTION       314
7          ConvolutionReluPoolingLayer2_2             CONVOLUTION       1310
8          ConvolutionReluPoolingLayer2_2             CONVOLUTION       1070
9          ConvolutionReluPoolingLayer2_2             CONVOLUTION       232
10       ConvolutionReluPoolingLayer2_2             CONVOLUTION       320
11       ConvolutionReluPoolingLayer2_0             RESHUFFLE          1463
12       ConvolutionReluPoolingLayer2_2             CONVOLUTION       164
13       ConvolutionReluPoolingLayer2_2             CONVOLUTION       202
14       ConvolutionReluPoolingLayer2_2             CONVOLUTION       341
15       ConvolutionReluPoolingLayer2_2             CONVOLUTION       181
16       ConvolutionReluPoolingLayer2_2             CONVOLUTION       119
17       ConvolutionReluPoolingLayer2_2             CONVOLUTION       232
18       ConvolutionReluPoolingLayer2_2             CONVOLUTION       323
19       ConvolutionReluPoolingLayer2_2             CONVOLUTION       179
20       ConvolutionReluPoolingLayer2_2             CONVOLUTION       126
21       ConvolutionReluPoolingLayer2_2             CONVOLUTION       216
22       ConvolutionReluPoolingLayer2_2             CONVOLUTION       322
23       ConvolutionReluPoolingLayer2_2             CONVOLUTION       188
24       ConvolutionReluPoolingLayer2_2             CONVOLUTION       351
25       ConvolutionReluPoolingLayer2_2             CONVOLUTION       1144
26       ConvolutionReluPoolingLayer2_2             CONVOLUTION       278
27       ConvolutionReluPoolingLayer2_2             CONVOLUTION       158
28       ConvolutionReluPoolingLayer2_2             CONVOLUTION       351
29       ConvolutionReluPoolingLayer2_2             CONVOLUTION       1138
30       ConvolutionReluPoolingLayer2_2             CONVOLUTION       277
31       ConvolutionReluPoolingLayer2_2             CONVOLUTION       158
32       ConvolutionReluPoolingLayer2_2             CONVOLUTION       350
33       ConvolutionReluPoolingLayer2_2             CONVOLUTION       1118
34       ConvolutionReluPoolingLayer2_2             CONVOLUTION       272
35       ConvolutionReluPoolingLayer2_2             CONVOLUTION       158
36       ConvolutionReluPoolingLayer2_2             CONVOLUTION       347
37       ConvolutionReluPoolingLayer2_2             CONVOLUTION       1112
38       ConvolutionReluPoolingLayer2_2             CONVOLUTION       294
39       ConvolutionReluPoolingLayer2_2             CONVOLUTION       513
40       ConvolutionReluPoolingLayer2_2             CONVOLUTION       2144
41       ConvolutionReluPoolingLayer2_2             CONVOLUTION       773
42       ConvolutionReluPoolingLayer2_2             CONVOLUTION       278
43       ConvolutionReluPoolingLayer2_2             CONVOLUTION       511
44       ConvolutionReluPoolingLayer2_2             CONVOLUTION       2153
45       ConvolutionReluPoolingLayer2_2             CONVOLUTION       782
46       ConvolutionReluPoolingLayer2_2             CONVOLUTION       210
47       ConvolutionReluPoolingLayer2_2             CONVOLUTION       512
48       ConvolutionReluPoolingLayer2_2             CONVOLUTION       2154
49       ConvolutionReluPoolingLayer2_2             CONVOLUTION       775
50       ConvolutionReluPoolingLayer2_2             CONVOLUTION       804
51       ConvolutionReluPoolingLayer2_2             CONVOLUTION       9144
52       ConvolutionReluPoolingLayer2_2             CONVOLUTION       1231
53       ConvolutionReluPoolingLayer2_2             CONVOLUTION       372
54       ConvolutionReluPoolingLayer2_2             CONVOLUTION       804
55       ConvolutionReluPoolingLayer2_2             CONVOLUTION       9148
56       ConvolutionReluPoolingLayer2_2             CONVOLUTION       1204
57       ConvolutionReluPoolingLayer2_2             CONVOLUTION       324
58       ConvolutionReluPoolingLayer2_2             CONVOLUTION       1673
59       ConvolutionReluPoolingLayer2_2             CONVOLUTION       9531
60       ConvolutionReluPoolingLayer2_2             CONVOLUTION       834
61       PoolingLayer2_1                            POOLING          413
62       ConvolutionReluPoolingLayer2_2             CONVOLUTION       1195
63       FullyConnectedReluLayer_0                   FULLYCONNECTED    106
65       ConvolutionReluPoolingLayer2_2             CONVOLUTION       2314
66       ConvolutionReluPoolingLayer2_2             CONVOLUTION       328
Total Time(us): 76435
FPS: 13.08
========================================================================

Time measurement with Python time.time()

The real time rknn.inference() takes is about 0.3s. So perf_debug=False seems to give more closer result. But that's a black box to me. RK people please take a look at your internal implementation what might cause the problem. Thanks!

Reference:

1. http://t.rock-chips.com/forum.ph ... &extra=#pid1329
2. http://t.rock-chips.com/forum.ph ... =deeplab&page=2

作者: chuyee 时间: 2019-3-28 13:48
Looks like this is caused by ResizeBilinear function. When perf_debug=True, the layer time is not counted. However when perf_debug=False, the time spent is counted in total time...

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