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ANSWER0=VQA(image=RIGHT,question='Is there a person holding a knife to a bottle?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([1, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='Is the dog standing in the water?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([1, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['Is there a person holding a knife to a bottle?'], responses:['no']
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question: ['Is the dog standing in the water?'], 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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[('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([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: 327
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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: 327
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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: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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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: 328
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328
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tensor([1.0000e+00, 3.6174e-09, 1.7062e-07, 2.5292e-12, 3.7441e-13, 2.5170e-09,
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6.7478e-11, 2.4228e-07], 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([1.0000e+00, 3.6174e-09, 1.7062e-07, 2.5292e-12, 3.7441e-13, 2.5170e-09,
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6.7478e-11, 2.4228e-07], device='cuda:2', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 5.8138e-10, 5.9556e-07, 6.4973e-11, 1.6354e-09, 4.2189e-08,
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7.9474e-09, 3.6446e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 5.8138e-10, 5.9556e-07, 6.4973e-11, 1.6354e-09, 4.2189e-08,
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7.9474e-09, 3.6446e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(3.6174e-09, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(3.5763e-07, device='cuda:2', grad_fn=<SubBackward0>)}
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(5.8138e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many zebras 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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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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torch.Size([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['How many open pencil cases are in the image?'], responses:['0']
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question: ['Does the image contain a ferret sticking their head out of a dirt hole?'], responses:['yes']
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[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
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[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
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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: ['How many zebras 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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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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question: ['How many dogs 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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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: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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tensor([9.5531e-01, 4.4687e-02, 4.8652e-06, 9.5115e-08, 2.0089e-07, 4.0401e-09,
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6.1031e-08, 3.4927e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.5531e-01, 4.4687e-02, 4.8652e-06, 9.5115e-08, 2.0089e-07, 4.0401e-09,
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6.1031e-08, 3.4927e-08], device='cuda:2', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(9.5115e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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tensor([1.0000e+00, 2.0853e-07, 6.3754e-08, 2.6400e-10, 6.9027e-07, 1.3467e-08,
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9.2073e-08, 9.7238e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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0 *************
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['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 2.0853e-07, 6.3754e-08, 2.6400e-10, 6.9027e-07, 1.3467e-08,
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9.2073e-08, 9.7238e-07], device='cuda:3', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 3.1154e-09, 1.2117e-09, 1.7163e-08, 5.8331e-11, 4.8096e-11,
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6.1186e-11, 1.0509e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.1154e-09, 1.2117e-09, 1.7163e-08, 5.8331e-11, 4.8096e-11,
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6.1186e-11, 1.0509e-08], device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.0266e-06, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Are there pink jellyfish in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.2117e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.2117e-09, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is the photo of a puppy?')
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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])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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tensor([1.0000e+00, 2.2678e-10, 4.1462e-11, 8.7088e-11, 7.8665e-11, 4.3625e-09,
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3.7323e-09, 2.5521e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.2678e-10, 4.1462e-11, 8.7088e-11, 7.8665e-11, 4.3625e-09,
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3.7323e-09, 2.5521e-11], device='cuda:0', grad_fn=<SelectBackward0>)
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