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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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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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question: ['How many pillows are in the image?'], responses:['5']
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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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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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([2.9218e-01, 2.6988e-01, 2.3807e-01, 4.6899e-02, 9.9289e-02, 2.7579e-02,
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2.5903e-02, 2.0089e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([2.9218e-01, 2.6988e-01, 2.3807e-01, 4.6899e-02, 9.9289e-02, 2.7579e-02,
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2.5903e-02, 2.0089e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2381, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.7619, 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=RIGHT,question='Is the animal in the image on all fours?')
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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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tensor([0.8158, 0.0419, 0.0211, 0.0140, 0.0154, 0.0093, 0.0809, 0.0015],
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device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([0.8158, 0.0419, 0.0211, 0.0140, 0.0154, 0.0093, 0.0809, 0.0015],
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device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1842, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.8158, 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='How many glass bottles are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 0')
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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 the animal in the image on all fours?'], 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([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: 3399
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tensor([0.2050, 0.0994, 0.1589, 0.1499, 0.1280, 0.1234, 0.0466, 0.0888],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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5 *************
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['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.2050, 0.0994, 0.1589, 0.1499, 0.1280, 0.1234, 0.0466, 0.0888],
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device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1280, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8720, 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='How many white 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: ['How many glass bottles are in the image?'], responses:['many']
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torch.Size([7, 3, 448, 448])
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[('many', 0.12680051474066337), ('few', 0.12559712123098582), ('several', 0.12545126119006317), ('blinds', 0.12452572291517987), ('moss', 0.12441899466837554), ('rainbow', 0.1244056457460399), ('kite', 0.12440323404357946), ('directions', 0.12439750546511286)]
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[['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions']]
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
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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: 3399
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question: ['How many white dogs are in the image?'], responses:['1']
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tensor([0.2020, 0.0896, 0.1747, 0.1752, 0.1017, 0.1383, 0.0443, 0.0743],
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device='cuda:3', grad_fn=<SoftmaxBackward0>)
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5 *************
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['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.2020, 0.0896, 0.1747, 0.1752, 0.1017, 0.1383, 0.0443, 0.0743],
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device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5216, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.4784, 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='Does the dispenser on the right have a black base?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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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([5, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
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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: 3399
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question: ['Does the dispenser on the right have a black base?'], 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([5, 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: 3399
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tensor([0.5913, 0.1354, 0.1832, 0.0104, 0.0114, 0.0451, 0.0054, 0.0178],
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device='cuda:2', grad_fn=<SoftmaxBackward0>)
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many *************
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['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions'] tensor([0.5913, 0.1354, 0.1832, 0.0104, 0.0114, 0.0451, 0.0054, 0.0178],
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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(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
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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: 3400
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tensor([8.8028e-01, 1.6100e-02, 5.5679e-03, 1.4974e-03, 2.3946e-03, 1.3034e-03,
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9.2720e-02, 1.3803e-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([8.8028e-01, 1.6100e-02, 5.5679e-03, 1.4974e-03, 2.3946e-03, 1.3034e-03,
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9.2720e-02, 1.3803e-04], device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1197, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8803, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many chimpanzees 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([3, 3, 448, 448])
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tensor([6.6781e-01, 1.7072e-02, 3.1205e-01, 9.6504e-04, 1.5166e-04, 5.9859e-04,
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5.4166e-05, 1.2925e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.6781e-01, 1.7072e-02, 3.1205e-01, 9.6504e-04, 1.5166e-04, 5.9859e-04,
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5.4166e-05, 1.2925e-03], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6678, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(0.3121, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0201, device='cuda:3', grad_fn=<SubBackward0>)}
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tensor([7.1106e-01, 2.2035e-02, 2.6365e-01, 1.0010e-03, 2.1135e-04, 7.1537e-04,
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5.8157e-05, 1.2676e-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([7.1106e-01, 2.2035e-02, 2.6365e-01, 1.0010e-03, 2.1135e-04, 7.1537e-04,
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