The code is latest. The problem deion and exception infomation are same as t

Here is the stack trace. <div class="snippet-clipboard-content notranslate positio

Interesting. Two questions: Did you test on OTB/Man sequence?<

Two answers: Yes. I tested model on the OTB/Man sequence.

Here is the related code piece in train.py : <div

Error when executing test.py which is similar to the error #17 about pygoturn HOT 9 CLOSED

fzh0917 commented on August 21, 2024

Error when executing test.py which is similar to the error #17

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Comments (9)

amoudgl commented on August 21, 2024

I guess your training checkpoint may be regressing image coordinates which are totally out of image bounds. In the next frame, GOTURN crops the image at the previous predicted box, which could be causing this issue.

I tested the PIL crop method for some cases when your box had coordinates out of image but it seemed to work fine and padded the out of box area with zeros.

Can you check bounding box values? Just print self.prev_rect in the test method of test.py.

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amoudgl commented on August 21, 2024

I'll look into this issue in detail and get back. Thanks!

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fzh0917 commented on August 21, 2024

Here is the stack trace.

$ python test.py -w ../saved_checkpoints/exp3/final_model.pth 
Namespace(data_directory='../data/OTB/Man', model_weights='../saved_checkpoints/exp3/final_model.pth')
[69. 48. 95. 87.]
frame: 1 [69.91484090260097, 44.97729134559631, 94.00789887564522, 79.06419345310756]
[69.91484090260097, 44.97729134559631, 94.00789887564522, 79.06419345310756]
frame: 2 [70.72335313629264, 42.45427948015288, 93.20678849602302, 72.32945901253103]
[70.72335313629264, 42.45427948015288, 93.20678849602302, 72.32945901253103]
frame: 3 [71.44596422895413, 40.18087777316183, 92.52283250902349, 66.50209050907307]
[71.44596422895413, 40.18087777316183, 92.52283250902349, 66.50209050907307]
frame: 4 [72.0819581102108, 38.194360139916625, 91.982948297391, 61.4136520977397]
[72.0819581102108, 38.194360139916625, 91.982948297391, 61.4136520977397]
frame: 5 [72.70461372655708, 36.44987292098039, 91.53922051077595, 57.00444798650577]
[72.70461372655708, 36.44987292098039, 91.53922051077595, 57.00444798650577]
frame: 6 [73.25235647347078, 34.8670774288637, 91.09012486983683, 53.06942336212048]
[73.25235647347078, 34.8670774288637, 91.09012486983683, 53.06942336212048]
frame: 7 [73.7997159742809, 33.39063802152363, 90.64984497727374, 49.52346064112334]
[73.7997159742809, 33.39063802152363, 90.64984497727374, 49.52346064112334]
frame: 8 [74.33964891548749, 32.07947825142874, 90.19446878277232, 46.32401496608712]
[74.33964891548749, 32.07947825142874, 90.19446878277232, 46.32401496608712]
frame: 9 [74.80704545543891, 30.894863398402784, 89.74317647908586, 43.44246254765723]
[74.80704545543891, 30.894863398402784, 89.74317647908586, 43.44246254765723]
frame: 10 [75.23365582047252, 29.825739531553893, 89.33571490345336, 40.90703973116146]
[75.23365582047252, 29.825739531553893, 89.33571490345336, 40.90703973116146]
frame: 11 [75.63508291919808, 28.880748411465042, 88.93379260174657, 38.66743267108161]
[75.63508291919808, 28.880748411465042, 88.93379260174657, 38.66743267108161]
frame: 12 [76.02298592783066, 28.04869420987427, 88.55650949052102, 36.688339074899595]
[76.02298592783066, 28.04869420987427, 88.55650949052102, 36.688339074899595]
frame: 13 [76.39282059174934, 27.309084686451303, 88.18895100018156, 34.930228446314416]
[76.39282059174934, 27.309084686451303, 88.18895100018156, 34.930228446314416]
frame: 14 [76.74394839996826, 26.663149104813172, 87.85063656222403, 33.38392899976636]
[76.74394839996826, 26.663149104813172, 87.85063656222403, 33.38392899976636]
frame: 15 [77.07216133135084, 26.096040907341003, 87.53302146845527, 32.0215889038125]
[77.07216133135084, 26.096040907341003, 87.53302146845527, 32.0215889038125]
frame: 16 [77.386230541801, 25.592030039023115, 87.23948018134384, 30.823323999482568]
[77.386230541801, 25.592030039023115, 87.23948018134384, 30.823323999482568]
frame: 17 [77.68317988020497, 25.147977970279985, 86.96296215161996, 29.765176110268104]
[77.68317988020497, 25.147977970279985, 86.96296215161996, 29.765176110268104]
frame: 18 [77.96767286954294, 24.760622054184363, 86.70353015439692, 28.83135059953726]
[77.96767286954294, 24.760622054184363, 86.70353015439692, 28.83135059953726]
frame: 19 [78.2374775743712, 24.416588343537338, 86.45592561789685, 28.007621194959516]
[78.2374775743712, 24.416588343537338, 86.45592561789685, 28.007621194959516]
frame: 20 [78.48802745934606, 24.114571811008506, 86.22119925553922, 27.28143483651797]
[78.48802745934606, 24.114571811008506, 86.22119925553922, 27.28143483651797]
frame: 21 [78.72421819885221, 23.851223058567243, 86.00152334882918, 26.64284184874425]
[78.72421819885221, 23.851223058567243, 86.00152334882918, 26.64284184874425]
frame: 22 [78.94970312784183, 23.616645286118416, 85.79674246102009, 26.07970291158498]
[78.94970312784183, 23.616645286118416, 85.79674246102009, 26.07970291158498]
frame: 23 [79.15962786768203, 23.409738777281277, 85.60566052336529, 25.583585537900294]
[79.15962786768203, 23.409738777281277, 85.60566052336529, 25.583585537900294]
frame: 24 [79.35937892253877, 23.22762385592658, 85.42669871139493, 25.146010751028438]
[79.35937892253877, 23.22762385592658, 85.42669871139493, 25.146010751028438]
frame: 25 [79.54899423843463, 23.065746749931, 85.25849664262529, 24.758920644553815]
[79.54899423843463, 23.065746749931, 85.25849664262529, 24.758920644553815]
frame: 26 [79.7236723581475, 22.923202948105345, 85.1003472269544, 24.416502399705664]
[79.7236723581475, 22.923202948105345, 85.1003472269544, 24.416502399705664]
frame: 27 [79.89399317493114, 22.797559131379458, 84.94873224929889, 24.113369222208924]
[79.89399317493114, 22.797559131379458, 84.94873224929889, 24.113369222208924]
frame: 28 [80.06665600775062, 22.687128189394073, 84.81498058002798, 23.848191262701434]
[80.06665600775062, 22.687128189394073, 84.81498058002798, 23.848191262701434]
frame: 29 [80.23123729815418, 22.590855797908404, 84.68723050483973, 23.61456645982426]
[80.23123729815418, 22.590855797908404, 84.68723050483973, 23.61456645982426]
frame: 30 [80.38573255664596, 22.506789818911205, 84.57033950654784, 23.410034145371007]
[80.38573255664596, 22.506789818911205, 84.57033950654784, 23.410034145371007]
frame: 31 [80.5218327411521, 22.434465890459414, 84.4600807250996, 23.23291744222767]
[80.5218327411521, 22.434465890459414, 84.4600807250996, 23.23291744222767]
frame: 32 [80.64565831005498, 22.369563293879104, 84.35613235680012, 23.07597268367757]
[80.64565831005498, 22.369563293879104, 84.35613235680012, 23.07597268367757]
frame: 33 [80.75870367742401, 22.311938800636874, 84.25630334716838, 22.93665367210166]
[80.75870367742401, 22.311938800636874, 84.25630334716838, 22.93665367210166]
frame: 34 [80.8659812389454, 22.262185776192485, 84.15678325158495, 22.813818156579764]
[80.8659812389454, 22.262185776192485, 84.15678325158495, 22.813818156579764]
frame: 35 [80.95911590992418, 22.218244484276283, 84.05661732407158, 22.70510316150855]
[80.95911590992418, 22.218244484276283, 84.05661732407158, 22.70510316150855]
frame: 36 [81.05086601272814, 22.17861290148088, 83.97064220051529, 22.60939950009209]
[81.05086601272814, 22.17861290148088, 83.97064220051529, 22.60939950009209]
frame: 37 [81.13862519184306, 22.143507996368758, 83.89177994640234, 22.524893682217925]
[81.13862519184306, 22.143507996368758, 83.89177994640234, 22.524893682217925]
frame: 38 [81.21971696736883, 22.112401465900792, 83.81743946579138, 22.450173329689903]
[81.21971696736883, 22.112401465900792, 83.81743946579138, 22.450173329689903]
frame: 39 [81.29651923607176, 22.084809133792607, 83.74765092877834, 22.38400236137473]
[81.29651923607176, 22.084809133792607, 83.74765092877834, 22.38400236137473]
frame: 40 [81.36936904024294, 22.060016487057496, 83.68271358699286, 22.324960707705614]
[81.36936904024294, 22.060016487057496, 83.68271358699286, 22.324960707705614]
frame: 41 [81.44074029348299, 22.038069789251153, 83.62340303218613, 22.272738047157365]
[81.44074029348299, 22.038069789251153, 83.62340303218613, 22.272738047157365]
frame: 42 [81.50821557565716, 22.018658692662488, 83.56703705347559, 22.226560267051987]
[81.50821557565716, 22.018658692662488, 83.56703705347559, 22.226560267051987]
frame: 43 [81.57038371919289, 22.001486975539407, 83.5132089237723, 22.185696625997284]
[81.57038371919289, 22.001486975539407, 83.5132089237723, 22.185696625997284]
frame: 44 [81.6285503023113, 21.98621364654747, 83.46244238136993, 22.14940888847398]
[81.6285503023113, 21.98621364654747, 83.46244238136993, 22.14940888847398]
frame: 45 [81.68421155751, 21.97273800827213, 83.41474840309357, 22.11729447270698]
[81.68421155751, 21.97273800827213, 83.41474840309357, 22.11729447270698]
frame: 46 [81.73556295155772, 21.960748081612895, 83.36917465460701, 22.08882916410996]
[81.73556295155772, 21.960748081612895, 83.36917465460701, 22.08882916410996]
frame: 47 [81.78457982454557, 21.950148231665906, 83.32665152675034, 22.06361473307373]
[81.78457982454557, 21.950148231665906, 83.32665152675034, 22.06361473307373]
frame: 48 [81.83296773319475, 21.94068234164813, 83.28685553566382, 22.04111915426526]
[81.83296773319475, 21.94068234164813, 83.28685553566382, 22.04111915426526]
frame: 49 [81.87906151982254, 21.93231684342518, 83.2502234669888, 22.021251375213243]
[81.87906151982254, 21.93231684342518, 83.2502234669888, 22.021251375213243]
frame: 50 [81.92280018100665, 21.92490965608685, 83.2152249910795, 22.003589902347866]
[81.92280018100665, 21.92490965608685, 83.2152249910795, 22.003589902347866]
frame: 51 [81.96434503062042, 21.918329896427714, 83.18234014229698, 21.987950695836513]
[81.96434503062042, 21.918329896427714, 83.18234014229698, 21.987950695836513]
frame: 52 [82.00338853170223, 21.91253297287023, 83.15160232773344, 21.974120641239207]
[82.00338853170223, 21.91253297287023, 83.15160232773344, 21.974120641239207]
frame: 53 [82.0400352030239, 21.90740114404165, 83.12223352464179, 21.96188342626112]
[82.0400352030239, 21.90740114404165, 83.12223352464179, 21.96188342626112]
Traceback (most recent call last):
  File "test.py", line 128, in <module>
    tester.test()
  File "test.py", line 114, in test
    sample = self[i]
  File "test.py", line 78, in __getitem__
    sample = self._get_sample(idx)
  File "test.py", line 87, in _get_sample
    prev_img = self.transform_prev({'image': prev, 'bb': prevbb})['image']
  File ....../tensorflow/lib/python3.6/site-packages/torchvision/transforms/transforms.py", line 49, in __call__
    img = t(img)
  File "....../src/helper.py", line 27, in __call__
    h, w = image.shape[:2]
ValueError: not enough values to unpack (expected 2, got 0)

For some reasons, I replaced my truth path with some dots in the stack trace.

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fzh0917 commented on August 21, 2024

From the information in stack trace, you can see that the bounding box became smaller and smaller instead of out of the ranges.

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amoudgl commented on August 21, 2024

Interesting. Two questions:

Did you test on OTB/Man sequence?
After how many iterations of training did you test the model?

I'll try to reproduce the error.

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fzh0917 commented on August 21, 2024

Two answers:

Yes. I tested model on the OTB/Man sequence.
The num_batches argument is 50.

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fzh0917 commented on August 21, 2024

Here is the related code piece in train.py:

parser.add_argument('-n', '--num-batches', default=50, type=int,
                    help='number of total batches to run')

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amoudgl commented on August 21, 2024

Thanks! I feel crop operations in src/helper.py need updates to handle such exceptions (just like original GOTURN). I didn't encounter this issue because I tested the final model. I'll work on it and get back as soon as I have some reasonable results.

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fzh0917 commented on August 21, 2024

OK. Thank you for your timely and detailed responses.

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Error when executing test.py which is similar to the error #17 about pygoturn HOT 9 CLOSED

Comments (9)

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