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ANSWER0=VQA(image=RIGHT,question='How many window shades 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([5, 3, 448, 448])
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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: 1862
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question: ['How many window shades 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: 7, images per sample: 7.0, dynamic token length: 1862
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torch.Size([5, 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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tensor([7.2611e-01, 1.9664e-02, 2.5094e-01, 1.3322e-03, 1.8464e-04, 5.5342e-04,
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5.5676e-05, 1.1622e-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([7.2611e-01, 1.9664e-02, 2.5094e-01, 1.3322e-03, 1.8464e-04, 5.5342e-04,
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5.5676e-05, 1.1622e-03], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7261, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2509, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0230, device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([0.5986, 0.1826, 0.1826, 0.0023, 0.0170, 0.0045, 0.0113, 0.0012],
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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([0.5986, 0.1826, 0.1826, 0.0023, 0.0170, 0.0045, 0.0113, 0.0012],
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device='cuda:3', grad_fn=<SelectBackward0>)
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ANSWER0=VQA(image=RIGHT,question='How many antelope 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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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9955, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0045, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Does one dog have a red collar?')
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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([6.4079e-01, 6.7521e-02, 2.4860e-02, 6.9487e-03, 1.5554e-02, 8.0511e-03,
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2.3591e-01, 3.7104e-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([6.4079e-01, 6.7521e-02, 2.4860e-02, 6.9487e-03, 1.5554e-02, 8.0511e-03,
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2.3591e-01, 3.7104e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2359, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.7641, 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='Is a picture frame 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([3, 3, 448, 448])
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question: ['Is a picture frame visible in the image?'], 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([3, 3, 448, 448]) knan debug pixel values shape
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question: ['How many antelope are in the image?'], responses:['7']
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question: ['Does one dog have a red collar?'], responses:['yes']
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[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)]
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[['7', '8', '11', '5', '9', '10', '6', '12']]
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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: 3398
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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tensor([8.2616e-01, 1.7317e-01, 3.4188e-05, 9.5642e-05, 1.1261e-04, 1.3764e-04,
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2.1590e-04, 6.9305e-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([8.2616e-01, 1.7317e-01, 3.4188e-05, 9.5642e-05, 1.1261e-04, 1.3764e-04,
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2.1590e-04, 6.9305e-05], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1732, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.8262, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0007, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is the lipstick in the image uncapped?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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tensor([6.6040e-01, 2.4046e-02, 3.1195e-01, 1.1453e-03, 2.4132e-04, 9.9086e-04,
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3.7003e-05, 1.1822e-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([6.6040e-01, 2.4046e-02, 3.1195e-01, 1.1453e-03, 2.4132e-04, 9.9086e-04,
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3.7003e-05, 1.1822e-03], device='cuda:1', grad_fn=<SelectBackward0>)
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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: 3398
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6604, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.3120, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0276, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many frames are on the wall 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([13, 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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question: ['Is the lipstick in the image uncapped?'], 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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question: ['How many frames are on the wall 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: 3398
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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: 3398
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
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tensor([0.1902, 0.1398, 0.0538, 0.1808, 0.1026, 0.0945, 0.1975, 0.0408],
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device='cuda:0', grad_fn=<SoftmaxBackward0>)
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6 *************
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['7', '8', '11', '5', '9', '10', '6', '12'] tensor([0.1902, 0.1398, 0.0538, 0.1808, 0.1026, 0.0945, 0.1975, 0.0408],
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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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