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question: ['What color are the girl'], responses:['b']
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[('b', 0.1481217199537866), ('e', 0.12602255820943076), ('g', 0.12601628916448182), ('k', 0.1220280012774652), ('f', 0.12073193162045133), ('v', 0.11959582364650344), ('c', 0.11887450331522846), ('bib', 0.11860917281265244)]
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[['b', 'e', 'g', 'k', 'f', 'v', 'c', 'bib']]
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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: 3393
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tensor([7.1767e-01, 2.8104e-01, 7.8584e-05, 2.4447e-04, 5.8678e-04, 1.9528e-04,
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1.3535e-04, 4.5702e-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([7.1767e-01, 2.8104e-01, 7.8584e-05, 2.4447e-04, 5.8678e-04, 1.9528e-04,
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1.3535e-04, 4.5702e-05], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2810, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.7177, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0013, device='cuda:1', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393
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ANSWER0=VQA(image=RIGHT,question='How many Afghan Hounds are outside 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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tensor([4.9939e-01, 4.9924e-01, 1.0395e-04, 2.7021e-04, 4.2150e-05, 1.4639e-04,
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6.8892e-04, 1.1312e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([4.9939e-01, 4.9924e-01, 1.0395e-04, 2.7021e-04, 4.2150e-05, 1.4639e-04,
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6.8892e-04, 1.1312e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4992, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.4994, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0014, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is the banana flower purple?')
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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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torch.Size([13, 3, 448, 448])
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tensor([0.3242, 0.0334, 0.2857, 0.1439, 0.1164, 0.0679, 0.0079, 0.0207],
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device='cuda:2', grad_fn=<SoftmaxBackward0>)
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5 *************
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['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.3242, 0.0334, 0.2857, 0.1439, 0.1164, 0.0679, 0.0079, 0.0207],
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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., 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 water bottles 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: 3393
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393
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question: ['How many water bottles 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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question: ['How many Afghan Hounds are outside 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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question: ['Is the banana flower purple?'], 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393
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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: 3393
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393
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tensor([0.0454, 0.1557, 0.1316, 0.1143, 0.3298, 0.0363, 0.0993, 0.0875],
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device='cuda:0', grad_fn=<SoftmaxBackward0>)
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f *************
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['b', 'e', 'g', 'k', 'f', 'v', 'c', 'bib'] tensor([0.0454, 0.1557, 0.1316, 0.1143, 0.3298, 0.0363, 0.0993, 0.0875],
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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(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Are all of the mittens in the image red?')
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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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tensor([8.7540e-01, 2.8132e-02, 1.4153e-02, 7.3179e-03, 8.8312e-03, 5.9843e-03,
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5.9649e-02, 5.3474e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.7540e-01, 2.8132e-02, 1.4153e-02, 7.3179e-03, 8.8312e-03, 5.9843e-03,
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5.9649e-02, 5.3474e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8754, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1246, 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='Do the doors in the image open to a grassy area?')
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ANSWER1=RESULT(var=ANSWER0)
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torch.Size([13, 3, 448, 448])
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question: ['Are all of the mittens in the image red?'], responses:['no']
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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: 7, images per sample: 7.0, dynamic token length: 1863
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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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: 1863
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question: ['Do the doors in the image open to a grassy area?'], 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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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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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tensor([7.8528e-01, 3.6637e-02, 1.4379e-02, 5.4636e-03, 7.6955e-03, 4.7995e-03,
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1.4527e-01, 4.7640e-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([7.8528e-01, 3.6637e-02, 1.4379e-02, 5.4636e-03, 7.6955e-03, 4.7995e-03,
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1.4527e-01, 4.7640e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1453, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8547, 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=LEFT,question='How many kids are holding pillows 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([1, 3, 448, 448])
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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tensor([7.7914e-01, 1.9911e-02, 1.9700e-01, 1.7878e-03, 1.0029e-04, 5.1458e-04,
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3.7752e-05, 1.5059e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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