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1.16k
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 1.5942e-09, 9.4367e-10, 1.3712e-08, 5.5453e-10, 9.9463e-11,
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3.1625e-12, 2.8259e-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, 1.5942e-09, 9.4367e-10, 1.3712e-08, 5.5453e-10, 9.9463e-11,
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3.1625e-12, 2.8259e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(9.4367e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-9.4367e-10, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Does the image contain a blue china cabinet?')
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FINAL_ANSWER=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:['1']
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question: ['Is there at least one orange cap visible in the image?'], responses:['yes']
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question: ['How many chimpanzees are in the image?'], responses:['3']
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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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[('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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question: ['Does the image contain a blue china cabinet?'], responses:['yes']
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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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[('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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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: 13, images per sample: 13.0, dynamic token length: 3400
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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: 3400
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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: 3400
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tensor([1.0000e+00, 3.0573e-09, 8.4412e-11, 9.5350e-09, 3.2976e-10, 4.9238e-11,
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3.5890e-11, 1.0401e-08], 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, 3.0573e-09, 8.4412e-11, 9.5350e-09, 3.2976e-10, 4.9238e-11,
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3.5890e-11, 1.0401e-08], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(8.4412e-11, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-8.4412e-11, device='cuda:1', grad_fn=<SubBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
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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: 3401
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tensor([1.0000e+00, 3.9957e-10, 6.4217e-11, 1.4585e-10, 1.1446e-10, 1.6986e-08,
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4.1312e-09, 3.6177e-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, 3.9957e-10, 6.4217e-11, 1.4585e-10, 1.1446e-10, 1.6986e-08,
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4.1312e-09, 3.6177e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 5.2916e-09, 3.1239e-08, 3.0263e-09, 4.1564e-10, 1.3373e-10,
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1.0541e-10, 1.4674e-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, 5.2916e-09, 3.1239e-08, 3.0263e-09, 4.1564e-10, 1.3373e-10,
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1.0541e-10, 1.4674e-09], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.9957e-10, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many towels are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 5')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([1, 3, 448, 448])
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tensor([9.9993e-01, 7.3053e-05, 1.3554e-07, 1.1097e-08, 5.1449e-11, 1.4051e-07,
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1.2962e-10, 1.1606e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9993e-01, 7.3053e-05, 1.3554e-07, 1.1097e-08, 5.1449e-11, 1.4051e-07,
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1.2962e-10, 1.1606e-08], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(3.1239e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.1239e-08, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many wolves 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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torch.Size([1, 3, 448, 448])
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.4051e-07, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, 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 computers 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([13, 3, 448, 448])
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question: ['How many towels are in the image?'], responses:['5']
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question: ['How many wolves are in the image?'], responses:['1']
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[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
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[['5', '8', '4', '6', '3', '7', '11', '9']]
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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([1, 3, 448, 448]) knan debug pixel values shape
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
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tensor([9.7846e-01, 2.5748e-08, 2.1443e-02, 9.2731e-05, 7.6798e-06, 5.6057e-07,
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1.9299e-07, 2.3521e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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5 *************
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['5', '8', '4', '6', '3', '7', '11', '9'] tensor([9.7846e-01, 2.5748e-08, 2.1443e-02, 9.2731e-05, 7.6798e-06, 5.6057e-07,
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1.9299e-07, 2.3521e-07], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9785, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0215, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 2.3035e-10, 1.2475e-10, 3.9361e-10, 8.9855e-11, 6.1505e-09,
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2.0451e-09, 9.5595e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.3035e-10, 1.2475e-10, 3.9361e-10, 8.9855e-11, 6.1505e-09,
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2.0451e-09, 9.5595e-11], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(9.1297e-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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question: ['How many computers 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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