What is the fail_percentage? Why supervised's fail_percentage less than unsupervised?
Agchai52 opened this issue · 3 comments
Hi,
Thank you so much for your code!
I successfully generate the synthetic datasets and test both supervised and unsupervised homography network by homography_CNN_synthetic.py. One of its outputs is fail_percentage. I checked where it is from. In the train(), it should be "apply_grad_opt". But I don't really understand what it is. Can you explain it to me?
Another related question is why supervised's fail_percentage less than unsupervised?
Both l1_loss and h_loss are very closed for two networks. But big difference shows in fail_percentage.
Thanks!
Hi,
As you can see in homography_model.py, a failure is considered when the h_loss of the estimated homography > h_loss of an identity matrix (meaning the estimate is too bad).
Being said that, fail_percentage tells the percentage of estimates worst then just returning an identity matrix.
The fail_percentage can be different but should not much. Can you show your values here?
Thank you for your reply! I find the def of num_fail in homography_model.py line 278-279.
You mention that the difference should not much. I think the difference between your results and mine could be the different synthetic dataset, since it is generated randomly. I'll generate new synthetic dataset to see if the fail_percentage shows the same difference.
Here are my test results for supervised and unsupervised network on synthetic data:
Supervised
python homography_CNN_synthetic.py --mode test --lr 5e-4 --loss_type h_loss
<==================== Loading data ===================>
===> There are totally 5000 test files
===> Test: There are totally 5000 Test files
The gt_file is :
/Users/Nora/Desktop/ImageProcessing/HomographyGAN/unsupervisedDeepHomographyRAL2018-master/syntheticdata/synthetic/45/test_gt.txt
--Shape of A_mat: [64, 8, 8]
--shape of b: [64, 8, 1]
--shape of H_8el Tensor("MatrixSolve:0", shape=(64, 8, 1), dtype=float32, device=/device:GPU:0)
('--Inter- scale_h:', True)
--Shape of A_mat: [64, 8, 8]
--shape of b: [64, 8, 1]
--shape of H_8el Tensor("MatrixSolve_1:0", shape=(64, 8, 1), dtype=float32, device=/device:GPU:1)
('--Inter- scale_h:', True)
2018-05-02 16:22:51.967937: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2018-05-02 16:22:51.967958: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2018-05-02 16:22:51.967966: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2018-05-02 16:22:51.967972: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
../models/synthetic_models/h_loss_normalize
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===> This iteration num Fail: 8
('Wrote file %s', '../results/synthetic/report/128_h_loss_loss_11.815462.jpg')s241ms | Tot: 0ms | Test, h_loss 13.018, rec_loss 1.043, ssim_loss 0.406, l1_loss 0.678, fail_percent 0.0625
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===> This iteration num Fail: 8
('Wrote file %s', '../results/synthetic/report/1408_h_loss_loss_10.353984.jpg')s | Tot: 4m2s | Test, h_loss 12.364, rec_loss 1.043, ssim_loss 0.422, l1_loss 0.691, fail_percent 0.0597
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===> This iteration num Fail: 9
('Wrote file %s', '../results/synthetic/report/2688_h_loss_loss_12.811972.jpg')s | Tot: 8m5s | Test, h_loss 12.141, rec_loss 1.040, ssim_loss 0.424, l1_loss 0.688, fail_percent 0.0577
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===> This iteration num Fail: 6
('Wrote file %s', '../results/synthetic/report/3968_h_loss_loss_10.991777.jpg')3s | Tot: 11m58s | Test, h_loss 12.247, rec_loss 1.040, ssim_loss 0.424, l1_loss 0.688, fail_percent 0.0617
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===> This iteration num Fail: 9
('Wrote file %s', '../results/synthetic/report/5248_h_loss_loss_12.481041.jpg')9s | Tot: 15m57s | Test, h_loss 12.199, rec_loss 1.043, ssim_loss 0.424, l1_loss 0.690, fail_percent 0.0617
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===> This iteration num Fail: 8
('Wrote file %s', '../results/synthetic/report/6528_h_loss_loss_11.90332.jpg')56s | Tot: 19m54s | Test, h_loss 12.240, rec_loss 1.045, ssim_loss 0.424, l1_loss 0.692, fail_percent 0.0616
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===> This iteration num Fail: 11
('Wrote file %s', '../results/synthetic/report/7808_h_loss_loss_12.233584.jpg')54ms | Tot: 23m54s | Test, h_loss 12.195, rec_loss 1.045, ssim_loss 0.424, l1_loss 0.692, fail_percent 0.0619
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===> This iteration num Fail: 14
('Wrote file %s', '../results/synthetic/report/9088_h_loss_loss_11.753185.jpg')2s | Tot: 28m17s | Test, h_loss 12.169, rec_loss 1.041, ssim_loss 0.423, l1_loss 0.688, fail_percent 0.0619
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===> This iteration num Fail: 5
('Wrote file %s', '../results/synthetic/report/10368_h_loss_loss_11.462982.jpg')s | Tot: 33m2s | Test, h_loss 12.168, rec_loss 1.042, ssim_loss 0.424, l1_loss 0.689, fail_percent 0.0621
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===> This iteration num Fail: 11
('Wrote file %s', '../results/synthetic/report/11648_h_loss_loss_12.536741.jpg')s | Tot: 37m12s | Test, h_loss 12.199, rec_loss 1.044, ssim_loss 0.424, l1_loss 0.691, fail_percent 0.0607
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===> This iteration num Fail: 0
('Wrote file %s', '../results/synthetic/report/12928_h_loss_loss_12.287821.jpg') | Tot: 41m20s | Test, h_loss 12.170, rec_loss 1.042, ssim_loss 0.424, l1_loss 0.689, fail_percent 0.0599
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===> This iteration num Fail: 6
('Wrote file %s', '../results/synthetic/report/14208_h_loss_loss_11.624669.jpg')s | Tot: 45m37s | Test, h_loss 12.138, rec_loss 1.039, ssim_loss 0.424, l1_loss 0.687, fail_percent 0.0598
('Wrote file %s', '../results/synthetic/report/14336_h_loss_loss_11.554046.jpg')
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[============================ 120/120 ===========================>] Step: 3m29s | Tot: 49m6s | Test, h_loss 12.112, rec_loss 1.038, ssim_loss 0.424, l1_loss 0.685, fail_percent 0.0592
====> Result for RHO: 45 loss h_loss noise 0.5
|Steps | h_loss | l1_loss | Fail percent |
119 12.112012569109599 0.6851609165469805 5.924479166666667
Top 0 - 30 %
Top 30 - 60 %
Top 60 - 100 %
===> Percentile Values: (20, 50, 80, 100):
[[11.380283 0.3065826 ]
[12.018547 0.19068556]
[12.730908 0.3788473 ]]
======> End! ====================================
Unsupervised
python homography_CNN_synthetic.py --mode test --lr 1e-4 --loss_type l1_loss
<==================== Loading data ===================>
===> There are totally 5000 test files
===> Test: There are totally 5000 Test files
The gt_file is :
/Users/Nora/Desktop/ImageProcessing/HomographyGAN/unsupervisedDeepHomographyRAL2018-master/syntheticdata/synthetic/45/test_gt.txt
--Shape of A_mat: [64, 8, 8]
--shape of b: [64, 8, 1]
--shape of H_8el Tensor("MatrixSolve:0", shape=(64, 8, 1), dtype=float32, device=/device:GPU:0)
('--Inter- scale_h:', True)
--Shape of A_mat: [64, 8, 8]
--shape of b: [64, 8, 1]
--shape of H_8el Tensor("MatrixSolve_1:0", shape=(64, 8, 1), dtype=float32, device=/device:GPU:1)
('--Inter- scale_h:', True)
../models/synthetic_models/l1_loss_normalize
('Wrote file %s', '../results/synthetic/report/0_l1_loss_loss_13.686736.jpg')
===> This iteration num Fail: 26
[>................................................................] Step: 56s553ms | Tot: 0ms | Test, h_loss 13.687, rec_loss 0.999, ssim_loss 0.394, l1_loss 0.615, fail_percent 0.2031 ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 1/120
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===> This iteration num Fail: 24
[=====>...........................................................] Step: 4m28s | Tot: 4m28s | Test, h_loss 14.114, rec_loss 0.989, ssim_loss 0.405, l1_loss 0.626, fail_percent 0.2010 ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 11/120
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===> This iteration num Fail: 26
[==========>......................................................] Step: 4m19s | Tot: 8m47s | Test, h_loss 14.144, rec_loss 0.987, ssim_loss 0.406, l1_loss 0.627, fail_percent 0.2046 ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 21/120
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===> This iteration num Fail: 29
[================>................................................] Step: 4m13s | Tot: 13m1s | Test, h_loss 14.034, rec_loss 0.982, ssim_loss 0.405, l1_loss 0.622, fail_percent 0.2014 ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 31/120
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===> This iteration num Fail: 35
[=====================>...........................................] Step: 3m52s | Tot: 16m53s | Test, h_loss 14.013, rec_loss 0.976, ssim_loss 0.403, l1_loss 0.617, fail_percent 0.2008 ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 41/120
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===> This iteration num Fail: 31
[===========================>.....................................] Step: 3m55s | Tot: 20m49s | Test, h_loss 14.007, rec_loss 0.976, ssim_loss 0.402, l1_loss 0.615, fail_percent 0.2005 ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 51/120
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===> This iteration num Fail: 26
[================================>................................] Step: 3m57s | Tot: 24m47s | Test, h_loss 13.990, rec_loss 0.975, ssim_loss 0.403, l1_loss 0.615, fail_percent 0.1989 ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 61/120
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===> This iteration num Fail: 32
[=====================================>...........................] Step: 3m57s | Tot: 28m44s | Test, h_loss 13.998, rec_loss 0.978, ssim_loss 0.403, l1_loss 0.616, fail_percent 0.1987 ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 71/120
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===> This iteration num Fail: 18
[===========================================>.....................] Step: 4m5s | Tot: 32m50s | Test, h_loss 13.987, rec_loss 0.976, ssim_loss 0.403, l1_loss 0.615, fail_percent 0.1992 ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 81/120
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===> This iteration num Fail: 28
[================================================>................] Step: 4m6s | Tot: 36m56s | Test, h_loss 14.004, rec_loss 0.978, ssim_loss 0.403, l1_loss 0.616, fail_percent 0.1994 ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 91/120
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===> This iteration num Fail: 21
[======================================================>..........] Step: 4m5s | Tot: 41m2s | Test, h_loss 13.971, rec_loss 0.974, ssim_loss 0.403, l1_loss 0.613, fail_percent 0.1968 ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 101/120
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===> This iteration num Fail: 29
[===========================================================>.....] Step: 3m55s | Tot: 44m58s | Test, h_loss 13.956, rec_loss 0.976, ssim_loss 0.403, l1_loss 0.614, fail_percent 0.1966 ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 111/120
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('Wrote file %s', '../results/synthetic/report/14976_l1_loss_loss_14.758549.jpg')
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('Wrote file %s', '../results/synthetic/report/15232_l1_loss_loss_14.478268.jpg')
[================================================================>] Step: 3m30s | Tot: 48m28s | Test, h_loss 13.967, rec_loss 0.974, ssim_loss 0.403, l1_loss 0.613, fail_percent 0.1965 ����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 120/120
====> Result for RHO: 45 loss l1_loss noise 0.5
|Steps | h_loss | l1_loss | Fail percent |
119 13.966667119661967 0.6131097227334976 19.654947916666668
Top 0 - 30 %
Top 30 - 60 %
Top 60 - 100 %
===> Percentile Values: (20, 50, 80, 100):
[[13.17573 0.3507613 ]
[13.843848 0.15365075]
[14.651982 0.39429116]]
======> End! ====================================
I also met this problem， and supervised method‘s performance is better than unsupervised method’s， did i miss something important？