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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 drawers are on the cabinet?')
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ANSWER1=EVAL(expr='{ANSWER0} == 4')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='How many binders 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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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 a person holding up the crab?'], responses:['no']
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question: ['How many drawers are on the cabinet?'], responses:['2']
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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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[('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([7, 3, 448, 448]) knan debug pixel values shape
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question: ['How many binders are in the image?'], responses:['5']
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question: ['How many chimneys are in the image?'], responses:['2']
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[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
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[['5', '8', '4', '6', '3', '7', '11', '9']]
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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([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: 3397
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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([6.5084e-01, 3.4837e-01, 7.2027e-05, 1.0915e-04, 1.8906e-04, 1.1665e-04,
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2.1959e-04, 8.0821e-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([6.5084e-01, 3.4837e-01, 7.2027e-05, 1.0915e-04, 1.8906e-04, 1.1665e-04,
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2.1959e-04, 8.0821e-05], device='cuda:2', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.3484, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.6508, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0008, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many pillows 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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tensor([2.8870e-01, 2.7118e-01, 2.3918e-01, 4.7113e-02, 9.9701e-02, 2.7688e-02,
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2.6226e-02, 2.0167e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([2.8870e-01, 2.7118e-01, 2.3918e-01, 4.7113e-02, 9.9701e-02, 2.7688e-02,
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2.6226e-02, 2.0167e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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torch.Size([13, 3, 448, 448])
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.2392, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.7608, 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 white 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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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 white 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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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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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question: ['How many pillows are in the image?'], responses:['5']
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[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
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[['5', '8', '4', '6', '3', '7', '11', '9']]
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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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tensor([0.2046, 0.0986, 0.1583, 0.1524, 0.1241, 0.1234, 0.0483, 0.0903],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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5 *************
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['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.2046, 0.0986, 0.1583, 0.1524, 0.1241, 0.1234, 0.0483, 0.0903],
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device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.1241, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8759, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many glass bottles are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 0')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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tensor([5.7705e-01, 1.4585e-01, 4.4483e-02, 2.1221e-01, 1.3566e-02, 3.0578e-03,
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3.6885e-03, 9.8646e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([5.7705e-01, 1.4585e-01, 4.4483e-02, 2.1221e-01, 1.3566e-02, 3.0578e-03,
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3.6885e-03, 9.8646e-05], device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.7878, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2122, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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torch.Size([7, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='Does the dispenser on the right have a black base?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([5, 3, 448, 448])
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question: ['Does the dispenser on the right have a black base?'], 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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tensor([8.8125e-01, 1.5163e-02, 5.4027e-03, 1.4092e-03, 2.4733e-03, 1.3480e-03,
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9.2823e-02, 1.2981e-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([8.8125e-01, 1.5163e-02, 5.4027e-03, 1.4092e-03, 2.4733e-03, 1.3480e-03,
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9.2823e-02, 1.2981e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.1187, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.8813, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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question: ['How many glass bottles are in the image?'], responses:['many']
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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
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[('many', 0.12680051474066337), ('few', 0.12559712123098582), ('several', 0.12545126119006317), ('blinds', 0.12452572291517987), ('moss', 0.12441899466837554), ('rainbow', 0.1244056457460399), ('kite', 0.12440323404357946), ('directions', 0.12439750546511286)]
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[['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions']]
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1355
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dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
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