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question: ['How many chairs are in the image?'], responses:['6']
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[('6', 0.12794147189263105), ('8', 0.12539492259598553), ('12', 0.12539359088927945), ('5', 0.12471292164321114), ('4', 0.12443617393590153), ('1', 0.12417386497855347), ('11', 0.12398049124372558), ('3', 0.12396656282071232)]
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[['6', '8', '12', '5', '4', '1', '11', '3']]
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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question: ['Does the image show a "Whataburger" cup sitting on a surface?'], 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([3, 3, 448, 448]) knan debug pixel values shape
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tensor([0.3794, 0.1690, 0.0206, 0.2299, 0.1522, 0.0079, 0.0154, 0.0257],
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device='cuda:3', grad_fn=<SoftmaxBackward0>)
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6 *************
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['6', '8', '12', '5', '4', '1', '11', '3'] tensor([0.3794, 0.1690, 0.0206, 0.2299, 0.1522, 0.0079, 0.0154, 0.0257],
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device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Does the left image show food served in a rectangular dish?')
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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: ['Does the image appear to feature an open air shop?'], responses:['yes']
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question: ['Is the dog swimming in a pool?'], 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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[('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([8.9172e-01, 2.2282e-02, 8.2943e-02, 8.8734e-04, 7.9411e-05, 2.3244e-04,
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5.1382e-05, 1.8040e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.9172e-01, 2.2282e-02, 8.2943e-02, 8.8734e-04, 7.9411e-05, 2.3244e-04,
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5.1382e-05, 1.8040e-03], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8917, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(0.0829, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0253, device='cuda:1', grad_fn=<SubBackward0>)}
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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ANSWER0=VQA(image=LEFT,question='How many hamsters 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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torch.Size([3, 3, 448, 448])
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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question: ['How many hamsters 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: 3402
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torch.Size([3, 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 left image show food served in a rectangular dish?'], 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([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: 3400
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tensor([6.6178e-01, 4.2317e-02, 1.0368e-02, 1.8586e-03, 3.5075e-03, 1.4470e-03,
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2.7864e-01, 8.5865e-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([6.6178e-01, 4.2317e-02, 1.0368e-02, 1.8586e-03, 3.5075e-03, 1.4470e-03,
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2.7864e-01, 8.5865e-05], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2786, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.7214, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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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([5.7465e-01, 2.6091e-02, 3.9494e-01, 2.1355e-03, 2.6346e-04, 8.5438e-04,
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1.4048e-04, 9.2398e-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([5.7465e-01, 2.6091e-02, 3.9494e-01, 2.1355e-03, 2.6346e-04, 8.5438e-04,
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1.4048e-04, 9.2398e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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tensor([8.6575e-01, 2.1739e-02, 1.1007e-01, 1.0485e-03, 1.2530e-04, 4.9131e-04,
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5.6078e-05, 7.2332e-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([8.6575e-01, 2.1739e-02, 1.1007e-01, 1.0485e-03, 1.2530e-04, 4.9131e-04,
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5.6078e-05, 7.2332e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8657, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1101, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0242, device='cuda:2', grad_fn=<DivBackward0>)}
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5746, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3949, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0304, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Are the dogs heading to the right?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=LEFT,question='Is there water in the image?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([13, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['Is there 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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question: ['Are the dogs heading to the right?'], responses:['yes']
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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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: 1859
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tensor([5.4580e-01, 4.5249e-01, 8.0996e-05, 1.2066e-04, 1.8190e-04, 8.4345e-04,
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4.4870e-04, 3.6694e-05], 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([5.4580e-01, 4.5249e-01, 8.0996e-05, 1.2066e-04, 1.8190e-04, 8.4345e-04,
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4.4870e-04, 3.6694e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4525, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.5458, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0017, device='cuda:3', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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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: 1859
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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: 1859
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
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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
|
tensor([8.9710e-01, 1.2218e-02, 8.8825e-02, 1.0302e-03, 1.0371e-04, 2.4893e-04,
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