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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835
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tensor([9.9999e-01, 9.2192e-06, 8.8245e-08, 1.0638e-07, 4.7814e-09, 5.4036e-09,
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6.8470e-09, 7.5670e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9999e-01, 9.2192e-06, 8.8245e-08, 1.0638e-07, 4.7814e-09, 5.4036e-09,
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6.8470e-09, 7.5670e-09], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Are the fangs of the monkey shown in the image?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([13, 3, 448, 448])
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question: ['Is the laptop facing right?'], 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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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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question: ['Are the fangs of the monkey shown in the image?'], responses:['yes']
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tensor([1.0000e+00, 1.3623e-09, 4.0732e-07, 4.3289e-10, 9.1786e-09, 5.6329e-07,
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6.0833e-09, 3.1885e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.3623e-09, 4.0732e-07, 4.3289e-10, 9.1786e-09, 5.6329e-07,
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6.0833e-09, 3.1885e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 7.7020e-10, 1.2771e-10, 2.2575e-10, 2.0015e-10, 5.2598e-09,
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1.0145e-08, 1.5017e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 7.7020e-10, 1.2771e-10, 2.2575e-10, 2.0015e-10, 5.2598e-09,
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1.0145e-08, 1.5017e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.6879e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Does the image contain a white wooden cabinet?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.3623e-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>)}
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ANSWER0=VQA(image=LEFT,question='How many syringes are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 6')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([5, 3, 448, 448])
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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([5, 3, 448, 448])
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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: 3401
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question: ['Does the image contain a white wooden cabinet?'], responses:['yes']
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question: ['How many syringes are in the image?'], responses:['4']
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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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[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
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[['4', '5', '3', '8', '6', '1', '2', '11']]
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tensor([1.0000e+00, 4.4509e-07, 5.7549e-08, 7.6696e-12, 2.5588e-12, 2.7833e-10,
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1.0781e-10, 3.0908e-08], 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([1.0000e+00, 4.4509e-07, 5.7549e-08, 7.6696e-12, 2.5588e-12, 2.7833e-10,
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1.0781e-10, 3.0908e-08], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.4509e-07, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
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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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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: 3401
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
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tensor([1.0000e+00, 7.8020e-09, 7.2657e-08, 6.8943e-09, 2.8268e-11, 1.1979e-07,
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1.0473e-10, 6.1673e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.8020e-09, 7.2657e-08, 6.8943e-09, 2.8268e-11, 1.1979e-07,
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1.0473e-10, 6.1673e-09], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(7.2657e-08, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.6576e-07, device='cuda:2', grad_fn=<SubBackward0>)}
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tensor([9.9914e-01, 8.5078e-04, 7.3737e-06, 4.6816e-10, 2.0315e-08, 1.6451e-07,
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1.6102e-08, 2.0404e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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4 *************
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['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9914e-01, 8.5078e-04, 7.3737e-06, 4.6816e-10, 2.0315e-08, 1.6451e-07,
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1.6102e-08, 2.0404e-08], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.1187e-08, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
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tensor([1.0000e+00, 3.2295e-08, 6.6815e-07, 2.5787e-07, 1.2426e-08, 7.0422e-08,
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8.0697e-08, 3.3174e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.2295e-08, 6.6815e-07, 2.5787e-07, 1.2426e-08, 7.0422e-08,
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8.0697e-08, 3.3174e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(6.6815e-07, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(7.6236e-07, device='cuda:0', grad_fn=<SubBackward0>)}
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[2024-10-24 10:39:21,707] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.48 | optimizer_gradients: 0.27 | optimizer_step: 0.32
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[2024-10-24 10:39:21,708] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5734.91 | backward_microstep: 5467.41 | backward_inner_microstep: 5461.82 | backward_allreduce_microstep: 5.51 | step_microstep: 7.66
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[2024-10-24 10:39:21,708] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5734.91 | backward: 5467.40 | backward_inner: 5461.84 | backward_allreduce: 5.49 | step: 7.67
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99%|ββββββββββ| 4802/4844 [19:58:05<08:44, 12.49s/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 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='How many people's reflections are in the mirror?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 5')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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Registering VQA_lavis step
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Registering EVAL step
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