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torch.Size([13, 3, 448, 448])
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tensor([0.2904, 0.0972, 0.1149, 0.1029, 0.1856, 0.0383, 0.0803, 0.0905],
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device='cuda:2', grad_fn=<SoftmaxBackward0>)
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20 *************
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['20', '21', '22', '26', '30', '48', '27', '28'] tensor([0.2904, 0.0972, 0.1149, 0.1029, 0.1856, 0.0383, 0.0803, 0.0905],
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device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many window screens 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 golf balls 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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question: ['How many perfume bottles are in the image?'], responses:['100']
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tensor([0.5841, 0.2435, 0.1222, 0.0025, 0.0257, 0.0078, 0.0133, 0.0009],
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device='cuda:3', grad_fn=<SoftmaxBackward0>)
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4 *************
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['4', '5', '3', '8', '6', '1', '2', '11'] tensor([0.5841, 0.2435, 0.1222, 0.0025, 0.0257, 0.0078, 0.0133, 0.0009],
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device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0211, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9789, 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 seals 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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[('100', 0.1277092174007614), ('120', 0.12519936731884676), ('88', 0.12483671971182599), ('80', 0.12474858811112934), ('60', 0.12457749608485191), ('99', 0.1243465850330014), ('90', 0.12430147627057883), ('101', 0.12428055006900451)]
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[['100', '120', '88', '80', '60', '99', '90', '101']]
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question: ['How many window screens 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([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: 3398
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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: 3398
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question: ['How many seals 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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tensor([7.3566e-01, 2.6796e-02, 8.1714e-03, 1.3829e-03, 2.4278e-03, 1.1389e-03,
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2.2436e-01, 6.0190e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([7.3566e-01, 2.6796e-02, 8.1714e-03, 1.3829e-03, 2.4278e-03, 1.1389e-03,
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2.2436e-01, 6.0190e-05], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0268, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9732, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many cheetahs 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([7, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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question: ['How many cheetahs 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: 13, images per sample: 13.0, dynamic token length: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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tensor([7.8974e-01, 1.0026e-01, 2.7023e-02, 6.8971e-02, 8.7601e-03, 2.5890e-03,
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2.5133e-03, 1.3845e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([7.8974e-01, 1.0026e-01, 2.7023e-02, 6.8971e-02, 8.7601e-03, 2.5890e-03,
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2.5133e-03, 1.3845e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7897, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.2103, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
|
tensor([0.5050, 0.0795, 0.0182, 0.1024, 0.1201, 0.0412, 0.0968, 0.0367],
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device='cuda:0', grad_fn=<SoftmaxBackward0>)
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100 *************
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['100', '120', '88', '80', '60', '99', '90', '101'] tensor([0.5050, 0.0795, 0.0182, 0.1024, 0.1201, 0.0412, 0.0968, 0.0367],
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device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is the dog in the image on the left lying down?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([7, 3, 448, 448])
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tensor([0.4081, 0.1245, 0.0855, 0.0519, 0.0122, 0.2689, 0.0412, 0.0076],
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device='cuda:2', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.4081, 0.1245, 0.0855, 0.0519, 0.0122, 0.2689, 0.0412, 0.0076],
|
device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4081, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.5919, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is there a red canoe in the image?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([7, 3, 448, 448])
|
tensor([5.5303e-01, 9.1704e-02, 2.3187e-02, 3.4458e-03, 6.6428e-03, 1.7887e-03,
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3.2008e-01, 1.1956e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([5.5303e-01, 9.1704e-02, 2.3187e-02, 3.4458e-03, 6.6428e-03, 1.7887e-03,
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3.2008e-01, 1.1956e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5530, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.4470, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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question: ['Is the dog in the image on the left lying down?'], 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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question: ['Is there a red canoe in the image?'], responses:['yes']
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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