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Registering EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=LEFT,question='How many primates are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} <= 6')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=LEFT,question='Is the puppy's head laying flat on a surface?')
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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='How many graduation students 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([7, 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 the puppy'], responses:['Yes']
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question: ['Is there at least one dog standing on all fours in the image?'], responses:['no']
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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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[('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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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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question: ['How many primates are in the image?'], responses:['15']
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question: ['How many graduation students are in the image?'], responses:['20']
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[('15', 0.12850265658859292), ('14', 0.12554598114685298), ('13', 0.12491622450863256), ('16', 0.12450938797787274), ('29', 0.12444750181633149), ('35', 0.12413627702798803), ('22', 0.12400388658176363), ('21', 0.12393808435196574)]
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[['15', '14', '13', '16', '29', '35', '22', '21']]
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[('20', 0.12771895156791702), ('21', 0.12586912554208884), ('22', 0.12503044546440548), ('26', 0.12459144863554222), ('30', 0.1243482131473721), ('48', 0.12418849501124658), ('27', 0.12415656019926104), ('28', 0.12409676043216668)]
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[['20', '21', '22', '26', '30', '48', '27', '28']]
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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: 3397
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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: 3397
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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tensor([0.5989, 0.0452, 0.3319, 0.0049, 0.0016, 0.0037, 0.0017, 0.0120],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([0.5989, 0.0452, 0.3319, 0.0049, 0.0016, 0.0037, 0.0017, 0.0120],
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device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.5989, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.3319, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0692, device='cuda:1', grad_fn=<DivBackward0>)}
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tensor([7.1764e-01, 2.8103e-01, 4.6215e-05, 1.0678e-04, 1.1790e-04, 7.8418e-04,
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2.4119e-04, 3.3005e-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'] ANSWER0=VQA(image=RIGHT,question='Is there a paper poking out of the dispenser?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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tensor([7.1764e-01, 2.8103e-01, 4.6215e-05, 1.0678e-04, 1.1790e-04, 7.8418e-04,
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2.4119e-04, 3.3005e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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torch.Size([5, 3, 448, 448])
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.2810, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.7176, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0013, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many paper towel rolls 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([1, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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question: ['How many paper towel rolls 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([1, 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: 3397
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question: ['Is there a paper poking out of the dispenser?'], 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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tensor([5.7023e-01, 7.6003e-02, 2.7957e-02, 6.3293e-03, 1.0432e-02, 3.9527e-03,
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3.0494e-01, 1.5659e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([5.7023e-01, 7.6003e-02, 2.7957e-02, 6.3293e-03, 1.0432e-02, 3.9527e-03,
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3.0494e-01, 1.5659e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.5702, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.4298, 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=RIGHT,question='How many pandas 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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question: ['How many pandas are in the image?'], responses:['4']
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[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
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[['4', '5', '3', '8', '6', '1', '2', '11']]
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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tensor([5.9206e-01, 4.0694e-01, 1.5724e-04, 1.7998e-04, 8.2363e-05, 1.2335e-04,
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2.4488e-04, 2.0532e-04], 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([5.9206e-01, 4.0694e-01, 1.5724e-04, 1.7998e-04, 8.2363e-05, 1.2335e-04,
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2.4488e-04, 2.0532e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.4069, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.5921, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0010, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many golf balls 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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tensor([0.2343, 0.1425, 0.1904, 0.1741, 0.0512, 0.0350, 0.0871, 0.0853],
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device='cuda:0', grad_fn=<SoftmaxBackward0>)
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15 *************
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['15', '14', '13', '16', '29', '35', '22', '21'] tensor([0.2343, 0.1425, 0.1904, 0.1741, 0.0512, 0.0350, 0.0871, 0.0853],
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device='cuda:0', grad_fn=<SelectBackward0>)
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torch.Size([7, 3, 448, 448])
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many perfume bottles are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} <= 4')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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