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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.2064e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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question: ['Is the building fenced in?'], responses:['yes']
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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torch.Size([13, 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: 1860
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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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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tensor([1.0000e+00, 3.7573e-10, 1.0024e-10, 2.3582e-10, 2.1471e-10, 9.6787e-09,
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9.9881e-09, 2.5458e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.7573e-10, 1.0024e-10, 2.3582e-10, 2.1471e-10, 9.6787e-09,
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9.9881e-09, 2.5458e-10], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.0848e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 7.1761e-09, 3.8507e-09, 1.1555e-08, 5.1582e-11, 4.5991e-10,
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1.6632e-11, 4.1137e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.1761e-09, 3.8507e-09, 1.1555e-08, 5.1582e-11, 4.5991e-10,
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1.6632e-11, 4.1137e-09], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.8507e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.8507e-09, device='cuda:3', grad_fn=<DivBackward0>)}
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[2024-10-24 10:48:40,678] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.35 | optimizer_step: 0.33
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[2024-10-24 10:48:40,678] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5110.62 | backward_microstep: 8850.39 | backward_inner_microstep: 4805.17 | backward_allreduce_microstep: 4045.06 | step_microstep: 7.60
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[2024-10-24 10:48:40,678] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5110.64 | backward: 8850.38 | backward_inner: 4805.28 | backward_allreduce: 4045.05 | step: 7.61
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100%|ββββββββββ| 4840/4844 [20:07:24<00:57, 14.29s/it]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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ANSWER0=VQA(image=RIGHT,question='Is there at least one human hand in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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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 sneakers are visible 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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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=RIGHT,question='Is there a star shape near lettering above a square opening in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([1, 3, 448, 448])
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torch.Size([1, 3, 448, 448])
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ANSWER0=VQA(image=LEFT,question='Is there an item on top of the cabinet?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['How many sneakers are visible in the image?'], responses:['11']
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question: ['Is there a star shape near lettering above a square opening in the image?'], responses:['yes']
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[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)]
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[['11', '10', '12', '9', '8', '13', '7', '14']]
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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([1, 3, 448, 448]) knan debug pixel values shape
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 332
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 335
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 332
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 333
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 332
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 332
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 333
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 333
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tensor([1.0000e+00, 7.3082e-09, 8.4588e-10, 6.7018e-09, 1.1320e-10, 5.5358e-11,
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1.1974e-10, 1.1241e-08], 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, 7.3082e-09, 8.4588e-10, 6.7018e-09, 1.1320e-10, 5.5358e-11,
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1.1974e-10, 1.1241e-08], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(8.4588e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-8.4588e-10, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many ducks 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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tensor([8.3751e-01, 3.5728e-04, 1.2795e-01, 2.0333e-05, 1.5342e-08, 9.2819e-03,
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7.5197e-08, 2.4881e-02], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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11 *************
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['11', '10', '12', '9', '8', '13', '7', '14'] tensor([8.3751e-01, 3.5728e-04, 1.2795e-01, 2.0333e-05, 1.5342e-08, 9.2819e-03,
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7.5197e-08, 2.4881e-02], device='cuda:3', grad_fn=<SelectBackward0>)
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question: ['Is there an item on top of the cabinet?'], responses:['yes']
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torch.Size([13, 3, 448, 448])
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many bottles are visible 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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[('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([13, 3, 448, 448])
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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question: ['Is there at least one human hand 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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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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question: ['How many ducks are in the image?'], responses:['7']
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