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ANSWER1=EVAL(expr='{ANSWER0} == 4')
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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=LEFT,question='How many pairs of shoes are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 15')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='How many glass panes 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='Does the artwork include a dragon?')
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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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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['How many bags/pencil-cases are in the image?'], responses:['3']
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[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
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[['3', '4', '1', '5', '8', '2', '6', '12']]
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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question: ['How many glass panes are 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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torch.Size([3, 3, 448, 448]) knan debug pixel values shape
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tensor([9.5625e-01, 4.3742e-02, 5.0481e-06, 4.2474e-06, 1.2864e-09, 6.9309e-07,
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2.9767e-09, 9.8063e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.5625e-01, 4.3742e-02, 5.0481e-06, 4.2474e-06, 1.2864e-09, 6.9309e-07,
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2.9767e-09, 9.8063e-07], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0437, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9563, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many dogs 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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tensor([4.5852e-07, 3.0269e-02, 1.8186e-02, 4.0143e-04, 9.4718e-01, 4.0802e-04,
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1.0452e-03, 2.5094e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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feet *************
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['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([4.5852e-07, 3.0269e-02, 1.8186e-02, 4.0143e-04, 9.4718e-01, 4.0802e-04,
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1.0452e-03, 2.5094e-03], device='cuda:3', grad_fn=<SelectBackward0>)
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question: ['How many pairs of shoes are in the image?'], responses:['27']
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question: ['Does the artwork include a dragon?'], responses:['no']
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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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ANSWER0=VQA(image=LEFT,question='How many dogs 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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[('27', 0.12710882596290443), ('26', 0.12541372481545682), ('29', 0.12521998295639536), ('21', 0.12494054332455375), ('44', 0.12446807634062888), ('28', 0.12446182789848768), ('22', 0.1242571830671583), ('43', 0.12412983563441463)]
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[['27', '26', '29', '21', '44', '28', '22', '43']]
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question: ['How many dogs are in the image?'], 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([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: 3395
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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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([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: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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tensor([1.0000e+00, 5.6146e-08, 2.3358e-09, 1.9334e-08, 4.6001e-10, 2.5051e-09,
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1.8187e-09, 1.8023e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 5.6146e-08, 2.3358e-09, 1.9334e-08, 4.6001e-10, 2.5051e-09,
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1.8187e-09, 1.8023e-09], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.9334e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, 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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tensor([1.0000e+00, 2.3490e-10, 3.8950e-11, 1.1447e-10, 7.5644e-11, 1.0790e-08,
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2.1941e-09, 2.2709e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.3490e-10, 3.8950e-11, 1.1447e-10, 7.5644e-11, 1.0790e-08,
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2.1941e-09, 2.2709e-10], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.3675e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', 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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tensor([0.2476, 0.0178, 0.2084, 0.0184, 0.0099, 0.2554, 0.2278, 0.0147],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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28 *************
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['27', '26', '29', '21', '44', '28', '22', '43'] tensor([0.2476, 0.0178, 0.2084, 0.0184, 0.0099, 0.2554, 0.2278, 0.0147],
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device='cuda:1', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 5.7241e-10, 3.5948e-07, 6.1036e-11, 2.0198e-11, 3.6792e-08,
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9.0379e-10, 6.9579e-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, 5.7241e-10, 3.5948e-07, 6.1036e-11, 2.0198e-11, 3.6792e-08,
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9.0379e-10, 6.9579e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(5.7241e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.0729e-06, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many people are on the television?')
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ANSWER1=EVAL(expr='{ANSWER0} == 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='How many arches 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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torch.Size([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['How many arches are in the image?'], responses:['1']
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