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torch.Size([1, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='Do sea anemones rest in a container with holes in it?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Does the buffalo in the image have its head down in the grass?')
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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([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['Is there at least one person outside with the dogs?'], 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([1, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 4.2550e-09, 2.1054e-11, 9.7476e-08, 3.0277e-10, 1.5405e-10,
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2.1698e-10, 5.9449e-09], 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, 4.2550e-09, 2.1054e-11, 9.7476e-08, 3.0277e-10, 1.5405e-10,
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2.1698e-10, 5.9449e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.1054e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1919e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many red trucks 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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question: ['Does the buffalo in the image have its head down in the grass?'], 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([7, 3, 448, 448]) knan debug pixel values shape
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question: ['How many jellyfish are in the image?'], responses:['100']
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question: ['Do sea anemones rest in a container with holes in it?'], responses:['no']
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question: ['How many red trucks are in the image?'], responses:['1']
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[('100', 0.1277092174007614), ('120', 0.12519936731884676), ('88', 0.12483671971182599), ('80', 0.12474858811112934), ('60', 0.12457749608485191), ('99', 0.1243465850330014), ('90', 0.12430147627057883), ('101', 0.12428055006900451)]
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[['100', '120', '88', '80', '60', '99', '90', '101']]
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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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[('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([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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
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tensor([1.0000e+00, 1.6426e-08, 6.5878e-10, 2.5421e-08, 7.9019e-11, 4.8197e-10,
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1.4656e-10, 1.0738e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.6426e-08, 6.5878e-10, 2.5421e-08, 7.9019e-11, 4.8197e-10,
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1.4656e-10, 1.0738e-08], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(6.5878e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-6.5878e-10, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many lotion bottles are visible 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
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question: ['How many lotion bottles are visible in the image?'], responses:['five']
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[('7 eleven', 0.1264466744091217), ('babies', 0.124977990347662), ('sunrise', 0.12490143984830117), ('eating', 0.1247676656843781), ('feet', 0.12475702323703439), ('candle', 0.12473210928138137), ('light', 0.12472650705175181), ('floating', 0.12469059014036947)]
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[['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating']]
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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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tensor([1.0000e+00, 8.0716e-10, 9.3071e-11, 3.8024e-11, 8.3428e-11, 1.3308e-09,
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5.3158e-08, 2.0582e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 8.0716e-10, 9.3071e-11, 3.8024e-11, 8.3428e-11, 1.3308e-09,
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5.3158e-08, 2.0582e-11], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(5.5531e-08, 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: 3403
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
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tensor([1.4809e-08, 3.7381e-01, 2.5953e-01, 2.9436e-04, 3.6533e-01, 1.7979e-04,
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5.2397e-04, 3.3740e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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babies *************
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['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([1.4809e-08, 3.7381e-01, 2.5953e-01, 2.9436e-04, 3.6533e-01, 1.7979e-04,
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5.2397e-04, 3.3740e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
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tensor([9.8099e-01, 1.8447e-03, 4.3921e-06, 7.2622e-03, 6.0472e-03, 9.4987e-04,
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2.2833e-03, 6.1457e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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100 *************
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['100', '120', '88', '80', '60', '99', '90', '101'] tensor([9.8099e-01, 1.8447e-03, 4.3921e-06, 7.2622e-03, 6.0472e-03, 9.4987e-04,
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2.2833e-03, 6.1457e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 5.6028e-09, 2.0239e-07, 5.1445e-12, 5.8272e-12, 9.6853e-10,
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2.9401e-10, 2.8112e-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, 5.6028e-09, 2.0239e-07, 5.1445e-12, 5.8272e-12, 9.6853e-10,
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2.9401e-10, 2.8112e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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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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ζεηζ¦ηεεΈδΈΊ: {True: tensor(5.6028e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many cheetahs 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([13, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['How many cheetahs 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([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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