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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([13, 3, 448, 448])
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
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torch.Size([13, 3, 448, 448])
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question: ['Are the boats in the water?'], responses:['yes']
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
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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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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
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tensor([8.7901e-01, 2.5727e-02, 1.1416e-02, 3.2686e-03, 5.5651e-03, 2.6145e-03,
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7.2153e-02, 2.4298e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.7901e-01, 2.5727e-02, 1.1416e-02, 3.2686e-03, 5.5651e-03, 2.6145e-03,
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7.2153e-02, 2.4298e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0722, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9278, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([7.7529e-01, 2.5956e-02, 1.9602e-01, 1.1826e-03, 1.9389e-04, 6.1867e-04,
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7.6510e-05, 6.6143e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.7529e-01, 2.5956e-02, 1.9602e-01, 1.1826e-03, 1.9389e-04, 6.1867e-04,
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7.6510e-05, 6.6143e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ANSWER0=VQA(image=RIGHT,question='Is baby gorilla visible in the image?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7753, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1960, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0287, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many animals 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])
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torch.Size([7, 3, 448, 448])
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question: ['How many locks are in the image?'], responses:['3']
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question: ['Is baby gorilla visible in the image?'], responses:['yes']
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question: ['How many animals are in the image?'], responses:['1']
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question: ['How many dogs are visible on grassy ground?'], responses:['2']
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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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[('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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[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
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[['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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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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: 7, images per sample: 7.0, dynamic token length: 1864
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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: 1862
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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: 1862
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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tensor([8.4902e-01, 1.8751e-02, 1.3020e-01, 1.1417e-03, 6.1667e-05, 2.6612e-04,
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7.9212e-05, 4.8124e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.4902e-01, 1.8751e-02, 1.3020e-01, 1.1417e-03, 6.1667e-05, 2.6612e-04,
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7.9212e-05, 4.8124e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8490, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.1302, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0208, device='cuda:0', grad_fn=<SubBackward0>)}
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tensor([9.6987e-01, 6.1389e-03, 2.2583e-03, 7.5595e-04, 1.2866e-03, 7.2887e-04,
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1.8909e-02, 5.6321e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.6987e-01, 6.1389e-03, 2.2583e-03, 7.5595e-04, 1.2866e-03, 7.2887e-04,
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1.8909e-02, 5.6321e-05], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0189, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9811, 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='Is there a body of water 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([7, 3, 448, 448])
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question: ['Is there a body of water in the image?'], 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([7, 3, 448, 448]) knan debug pixel values shape
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tensor([0.8333, 0.0500, 0.0533, 0.0112, 0.0026, 0.0402, 0.0064, 0.0032],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.8333, 0.0500, 0.0533, 0.0112, 0.0026, 0.0402, 0.0064, 0.0032],
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device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0935, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9065, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=LEFT,question='How many mountain goats 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([6.7556e-01, 6.2318e-02, 8.2383e-03, 2.4852e-01, 3.5429e-03, 7.8930e-04,
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9.5323e-04, 7.9695e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([6.7556e-01, 6.2318e-02, 8.2383e-03, 2.4852e-01, 3.5429e-03, 7.8930e-04,
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9.5323e-04, 7.9695e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6756, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.3244, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', 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} == 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 mountain goats 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)]
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[['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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