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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.2626e-08, 9.5477e-08, 8.0377e-12, 1.4530e-12, 2.4490e-09,
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1.6636e-10, 3.5680e-07], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.2626e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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tensor([9.8308e-01, 1.6917e-02, 2.0031e-07, 5.2779e-07, 7.0864e-08, 1.8022e-09,
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4.4699e-08, 7.6918e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.8308e-01, 1.6917e-02, 2.0031e-07, 5.2779e-07, 7.0864e-08, 1.8022e-09,
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4.4699e-08, 7.6918e-09], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.2537e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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tensor([1.0000e+00, 4.5633e-10, 1.0754e-10, 2.2410e-10, 1.5642e-10, 3.6975e-08,
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7.4807e-09, 5.5613e-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, 4.5633e-10, 1.0754e-10, 2.2410e-10, 1.5642e-10, 3.6975e-08,
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7.4807e-09, 5.5613e-10], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(7.4807e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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[2024-10-24 10:47:02,767] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.50 | optimizer_gradients: 0.26 | optimizer_step: 0.31
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[2024-10-24 10:47:02,767] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7079.90 | backward_microstep: 6780.90 | backward_inner_microstep: 6775.33 | backward_allreduce_microstep: 5.50 | step_microstep: 9.07
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[2024-10-24 10:47:02,767] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7079.92 | backward: 6780.89 | backward_inner: 6775.34 | backward_allreduce: 5.39 | step: 9.09
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100%|ββββββββββ| 4833/4844 [20:05:46<02:38, 14.41s/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 animals are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} <= 7')
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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='Which direction is the dog facing?')
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ANSWER1=EVAL(expr='{ANSWER0} == "left"')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Does the image show broccoli in a white bowl?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Does the dispenser have a round mounting bracket?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([5, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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torch.Size([11, 3, 448, 448])
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question: ['Does the dispenser have a round mounting bracket?'], 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([5, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
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question: ['Which direction is the dog facing?'], responses:['right']
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[('right', 0.12743553739412528), ('right 1', 0.12490968573275477), ('straight', 0.12485251094891832), ('floating', 0.12468075392646753), ('flip', 0.12467791878738273), ('backwards', 0.12452118816110067), ('serious', 0.12447626064603681), ('working', 0.12444614440321403)]
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[['right', 'right 1', 'straight', 'floating', 'flip', 'backwards', 'serious', 'working']]
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
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question: ['How many animals are in the image?'], responses:['7']
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
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[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)]
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[['7', '8', '11', '5', '9', '10', '6', '12']]
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question: ['Does the image show broccoli in a white bowl?'], responses:['no']
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
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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([11, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
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tensor([1.0000e+00, 5.5558e-09, 3.1609e-10, 5.7607e-09, 3.2721e-11, 3.8127e-10,
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3.3719e-11, 3.2391e-09], 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, 5.5558e-09, 3.1609e-10, 5.7607e-09, 3.2721e-11, 3.8127e-10,
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3.3719e-11, 3.2391e-09], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(3.1609e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.1609e-10, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many dogs 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([7, 3, 448, 448])
|
tensor([9.9731e-01, 1.6709e-06, 8.8509e-05, 8.3256e-07, 1.7201e-07, 2.4861e-03,
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1.0941e-04, 1.5618e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
right *************
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['right', 'right 1', 'straight', 'floating', 'flip', 'backwards', 'serious', 'working'] tensor([9.9731e-01, 1.6709e-06, 8.8509e-05, 8.3256e-07, 1.7201e-07, 2.4861e-03,
|
1.0941e-04, 1.5618e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is there a black horse in the image?')
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ANSWER1=RESULT(var=ANSWER0)
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torch.Size([7, 3, 448, 448])
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question: ['How many dogs are in the image?'], responses:['2']
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[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
|
[['2', '3', '4', '1', '5', '8', '7', '29']]
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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