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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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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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[('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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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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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: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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tensor([1.0000e+00, 2.1481e-09, 7.0488e-11, 4.7107e-09, 1.6354e-10, 1.1621e-10,
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1.1033e-11, 5.6529e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.1481e-09, 7.0488e-11, 4.7107e-09, 1.6354e-10, 1.1621e-10,
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1.1033e-11, 5.6529e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 6.4857e-10, 5.6204e-07, 4.4656e-11, 1.3931e-10, 3.7547e-08,
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3.2376e-09, 5.4391e-07], 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([1.0000e+00, 6.4857e-10, 5.6204e-07, 4.4656e-11, 1.3931e-10, 3.7547e-08,
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3.2376e-09, 5.4391e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(7.0488e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-7.0488e-11, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Are there cut pizzas in the image?')
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ANSWER1=EVAL(expr='not {ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.4857e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-06, 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} >= 4')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['How many dogs 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([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: 1860
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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question: ['Are there cut pizzas in the image?'], responses:['no']
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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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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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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tensor([9.7770e-01, 3.3124e-03, 3.7535e-03, 1.5647e-03, 2.0968e-05, 3.5262e-03,
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9.5851e-03, 5.4075e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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11 *************
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['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.7770e-01, 3.3124e-03, 3.7535e-03, 1.5647e-03, 2.0968e-05, 3.5262e-03,
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9.5851e-03, 5.4075e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many instruments are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 1')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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tensor([1.0000e+00, 2.8780e-10, 2.2385e-07, 3.5803e-12, 4.9595e-12, 1.0194e-08,
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1.5576e-09, 5.5250e-07], 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, 2.8780e-10, 2.2385e-07, 3.5803e-12, 4.9595e-12, 1.0194e-08,
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1.5576e-09, 5.5250e-07], device='cuda:3', grad_fn=<SelectBackward0>)
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torch.Size([13, 3, 448, 448])
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.8780e-10, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(8.3447e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many keys 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([1, 3, 448, 448])
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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question: ['How many keys 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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torch.Size([1, 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
|
tensor([1.0000e+00, 1.6919e-10, 4.6254e-11, 1.5285e-10, 8.3732e-11, 1.3022e-08,
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1.6305e-09, 2.0717e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.6919e-10, 4.6254e-11, 1.5285e-10, 8.3732e-11, 1.3022e-08,
|
1.6305e-09, 2.0717e-10], device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.3512e-08, 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>)}
|
tensor([9.9840e-01, 2.0551e-06, 3.3890e-06, 8.6662e-09, 1.8741e-09, 1.5990e-03,
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1.7819e-07, 8.8939e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
3 *************
|
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9840e-01, 2.0551e-06, 3.3890e-06, 8.6662e-09, 1.8741e-09, 1.5990e-03,
|
1.7819e-07, 8.8939e-08], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.3890e-06, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
|
question: ['How many instruments are in the image?'], responses:['1']
|
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
|
[['1', '3', '4', '8', '6', '12', '2', '47']]
|
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
|
tensor([1.0000e+00, 2.3824e-07, 8.8559e-08, 6.5228e-11, 9.0676e-11, 1.7520e-09,
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4.3523e-10, 1.2554e-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, 2.3824e-07, 8.8559e-08, 6.5228e-11, 9.0676e-11, 1.7520e-09,
|
4.3523e-10, 1.2554e-07], device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.3824e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3842e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 4.9148e-10, 1.2771e-10, 5.5367e-10, 2.7462e-10, 2.2152e-08,
|
9.0942e-09, 4.3861e-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, 4.9148e-10, 1.2771e-10, 5.5367e-10, 2.7462e-10, 2.2152e-08,
|
9.0942e-09, 4.3861e-10], device='cuda:2', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(3.3133e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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