Datasets:
TMarcus
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InternVL

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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
tensor([1.0000e+00, 1.9363e-09, 2.9815e-07, 7.4121e-12, 1.4735e-10, 3.6989e-09,
1.0891e-09, 5.7610e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.9363e-09, 2.9815e-07, 7.4121e-12, 1.4735e-10, 3.6989e-09,
1.0891e-09, 5.7610e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.9363e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:0', grad_fn=<DivBackward0>)}
question: ['Is there a visible orange vegetable in the image?'], responses:['no']
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
question: ['How many folded towels are in the image?'], responses:['3']
torch.Size([7, 3, 448, 448])
[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)]
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
question: ['How many golf balls are in the image?'], responses:['3']
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
question: ['How many dogs are in the image?'], responses:['δΈ‰']
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)]
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([2.9289e-02, 2.3051e-05, 1.2470e-06, 2.8507e-07, 4.3929e-07, 9.7065e-01,
9.8814e-06, 2.2920e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([2.9289e-02, 2.3051e-05, 1.2470e-06, 2.8507e-07, 4.3929e-07, 9.7065e-01,
9.8814e-06, 2.2920e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9707, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0293, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([2.8343e-05, 1.0372e-03, 4.6417e-02, 7.1778e-01, 6.5810e-02, 3.3192e-02,
2.5261e-03, 1.3321e-01], device='cuda:0', grad_fn=<SoftmaxBackward0>)
bulldog *************
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([2.8343e-05, 1.0372e-03, 4.6417e-02, 7.1778e-01, 6.5810e-02, 3.3192e-02,
2.5261e-03, 1.3321e-01], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.5080e-09, 3.9972e-07, 4.8474e-11, 9.9438e-10, 2.6411e-08,
1.3443e-09, 8.1417e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.5080e-09, 3.9972e-07, 4.8474e-11, 9.9438e-10, 2.6411e-08,
1.3443e-09, 8.1417e-07], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([9.9946e-01, 3.3528e-04, 2.4567e-07, 2.8721e-07, 2.6868e-09, 2.0336e-04,
1.0901e-07, 1.4753e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9946e-01, 3.3528e-04, 2.4567e-07, 2.8721e-07, 2.6868e-09, 2.0336e-04,
1.0901e-07, 1.4753e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.5080e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-06, device='cuda:1', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9995, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0005, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Does the car in the image have its top up?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Does the car in the image have its top up?'], responses:['no']
[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)]
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 2.9990e-09, 1.0680e-07, 9.2514e-12, 5.6368e-12, 5.8086e-10,
5.3537e-10, 1.4046e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.9990e-09, 1.0680e-07, 9.2514e-12, 5.6368e-12, 5.8086e-10,
5.3537e-10, 1.4046e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.9990e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3842e-07, device='cuda:1', grad_fn=<DivBackward0>)}
question: ['How many dogs are in the image?'], responses:['δΈ‰']
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)]
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([3.4681e-05, 1.5564e-03, 6.1145e-02, 6.6425e-01, 1.2814e-01, 5.3142e-02,
5.9212e-03, 8.5820e-02], device='cuda:2', grad_fn=<SoftmaxBackward0>)
bulldog *************
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([3.4681e-05, 1.5564e-03, 6.1145e-02, 6.6425e-01, 1.2814e-01, 5.3142e-02,
5.9212e-03, 8.5820e-02], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 09:33:06,758] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.28 | optimizer_step: 0.31
[2024-10-24 09:33:06,758] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3774.99 | backward_microstep: 13797.45 | backward_inner_microstep: 3450.30 | backward_allreduce_microstep: 10347.08 | step_microstep: 7.70
[2024-10-24 09:33:06,759] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3774.99 | backward: 13797.44 | backward_inner: 3450.32 | backward_allreduce: 10347.07 | step: 7.71
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4536/4844 [18:51:50<1:27:26, 17.03s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step