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ANSWER0=VQA(image=LEFT,question='Are there tinted lips 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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torch.Size([1, 3, 448, 448])
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tensor([7.3673e-01, 2.0317e-02, 2.4070e-01, 9.5940e-04, 6.1934e-05, 4.0706e-04,
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1.2233e-04, 7.0457e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.3673e-01, 2.0317e-02, 2.4070e-01, 9.5940e-04, 6.1934e-05, 4.0706e-04,
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1.2233e-04, 7.0457e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7367, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.2407, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0226, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is the dog in the image outside?')
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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: ['Are there tinted lips 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([1, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326
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tensor([5.7155e-01, 2.8645e-02, 3.9281e-01, 3.3491e-03, 2.4029e-04, 6.9731e-04,
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6.0356e-04, 2.1013e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.7155e-01, 2.8645e-02, 3.9281e-01, 3.3491e-03, 2.4029e-04, 6.9731e-04,
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6.0356e-04, 2.1013e-03], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3928, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5715, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0356, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Does the image contain food?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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question: ['Is the dog in the image outside?'], 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([4.9955e-01, 4.9955e-01, 5.3423e-05, 1.9138e-04, 7.8813e-05, 2.3149e-04,
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3.0194e-04, 5.2385e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([4.9955e-01, 4.9955e-01, 5.3423e-05, 1.9138e-04, 7.8813e-05, 2.3149e-04,
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3.0194e-04, 5.2385e-05], device='cuda:2', grad_fn=<SelectBackward0>)
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question: ['Does the image contain food?'], responses:['yes']
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4995, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.4995, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0009, device='cuda:2', grad_fn=<DivBackward0>)}
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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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tensor([5.5352e-01, 1.1455e-01, 4.7754e-02, 8.5619e-03, 1.2856e-02, 4.3508e-03,
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2.5821e-01, 1.9074e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([5.5352e-01, 1.1455e-01, 4.7754e-02, 8.5619e-03, 1.2856e-02, 4.3508e-03,
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2.5821e-01, 1.9074e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5535, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.4465, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Are a pair of lips visible 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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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: 3394
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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tensor([8.8565e-01, 1.8636e-02, 9.3417e-02, 1.1217e-03, 9.3882e-05, 4.1850e-04,
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3.0798e-05, 6.3591e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.8565e-01, 1.8636e-02, 9.3417e-02, 1.1217e-03, 9.3882e-05, 4.1850e-04,
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3.0798e-05, 6.3591e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8856, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0934, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0209, 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} == 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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question: ['Are a pair of lips visible in the image?'], responses:['no']
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torch.Size([7, 3, 448, 448])
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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([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: 3394
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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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)]
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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: 3394
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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tensor([5.3694e-01, 4.6151e-01, 5.6552e-05, 1.8264e-04, 3.8983e-04, 3.6403e-04,
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5.2966e-04, 2.1783e-05], 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([5.3694e-01, 4.6151e-01, 5.6552e-05, 1.8264e-04, 3.8983e-04, 3.6403e-04,
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5.2966e-04, 2.1783e-05], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4615, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.5369, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0015, device='cuda:1', 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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tensor([8.4372e-01, 2.3046e-02, 1.2939e-01, 1.7345e-03, 9.6294e-05, 2.9518e-04,
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4.7145e-05, 1.6727e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.4372e-01, 2.3046e-02, 1.2939e-01, 1.7345e-03, 9.6294e-05, 2.9518e-04,
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