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ANSWER0=VQA(image=RIGHT,question='How many dispensers are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
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torch.Size([5, 3, 448, 448])
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tensor([8.3473e-01, 1.6437e-01, 1.2268e-04, 9.9947e-05, 3.3103e-04, 9.3387e-05,
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7.4286e-05, 1.8692e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.3473e-01, 1.6437e-01, 1.2268e-04, 9.9947e-05, 3.3103e-04, 9.3387e-05,
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7.4286e-05, 1.8692e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1644, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.8347, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0009, device='cuda:2', grad_fn=<SubBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 3')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
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question: ['How many dispensers are in the image?'], responses:['1']
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[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
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[['1', '3', '4', '8', '6', '12', '2', '47']]
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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
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question: ['Is there at least one person in the image?'], responses:['no']
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[('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)]
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[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
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question: ['How many dogs are in the image?'], responses:['3']
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[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
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[['3', '4', '1', '5', '8', '2', '6', '12']]
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
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tensor([8.4892e-01, 3.0894e-02, 9.7214e-03, 2.4495e-03, 4.4480e-03, 2.1151e-03,
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1.0131e-01, 1.4384e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.4892e-01, 3.0894e-02, 9.7214e-03, 2.4495e-03, 4.4480e-03, 2.1151e-03,
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1.0131e-01, 1.4384e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1511, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8489, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
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tensor([8.9108e-01, 3.3488e-02, 2.3745e-02, 4.2575e-03, 6.9474e-04, 4.4358e-02,
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1.6673e-03, 7.1415e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([8.9108e-01, 3.3488e-02, 2.3745e-02, 4.2575e-03, 6.9474e-04, 4.4358e-02,
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1.6673e-03, 7.1415e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8911, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1089, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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tensor([5.7683e-01, 4.2201e-01, 7.8545e-05, 1.2072e-04, 2.9446e-04, 2.3797e-04,
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3.7804e-04, 4.8282e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.7683e-01, 4.2201e-01, 7.8545e-05, 1.2072e-04, 2.9446e-04, 2.3797e-04,
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3.7804e-04, 4.8282e-05], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4220, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5768, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0012, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is there water in the image?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([7, 3, 448, 448])
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question: ['Is there water in the image?'], responses:['yes']
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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tensor([9.1888e-01, 8.0292e-02, 6.6375e-05, 1.4110e-04, 2.6683e-04, 6.0549e-05,
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2.2461e-04, 6.6962e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.1888e-01, 8.0292e-02, 6.6375e-05, 1.4110e-04, 2.6683e-04, 6.0549e-05,
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2.2461e-04, 6.6962e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0803, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9189, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0008, device='cuda:3', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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tensor([8.8586e-01, 1.2797e-02, 9.9391e-02, 1.2003e-03, 8.4502e-05, 2.6172e-04,
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1.8017e-05, 3.8585e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.8586e-01, 1.2797e-02, 9.9391e-02, 1.2003e-03, 8.4502e-05, 2.6172e-04,
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1.8017e-05, 3.8585e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8859, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.0994, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0147, device='cuda:0', grad_fn=<SubBackward0>)}
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[2024-10-22 17:13:25,502] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.45 | optimizer_gradients: 0.24 | optimizer_step: 0.31
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[2024-10-22 17:13:25,503] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7074.12 | backward_microstep: 6742.49 | backward_inner_microstep: 6737.04 | backward_allreduce_microstep: 5.37 | step_microstep: 7.52
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[2024-10-22 17:13:25,503] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7074.13 | backward: 6742.48 | backward_inner: 6737.05 | backward_allreduce: 5.35 | step: 7.53
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0%| | 4/4844 [01:08<21:44:49, 16.18s/it]Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=LEFT,question='How many chimneys are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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Registering VQA_lavis step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=LEFT,question='Is a person holding up the crab?')
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