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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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torch.Size([13, 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: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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tensor([7.7207e-01, 2.9025e-02, 1.9599e-01, 1.0840e-03, 1.5834e-04, 5.3147e-04,
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8.5063e-05, 1.0540e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.7207e-01, 2.9025e-02, 1.9599e-01, 1.0840e-03, 1.5834e-04, 5.3147e-04,
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8.5063e-05, 1.0540e-03], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7721, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.1960, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0319, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Does the dog on the right have a blue collar?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([7, 3, 448, 448])
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question: ['Does the dog on the right have a blue collar?'], 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([8.8015e-01, 1.1912e-01, 5.0128e-05, 7.0085e-05, 5.1119e-05, 2.4099e-04,
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2.3392e-04, 8.8458e-05], 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([8.8015e-01, 1.1912e-01, 5.0128e-05, 7.0085e-05, 5.1119e-05, 2.4099e-04,
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2.3392e-04, 8.8458e-05], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1191, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8802, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0007, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many parrots 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]) 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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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question: ['How many parrots 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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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: 1864
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tensor([3.8352e-01, 3.5977e-01, 8.2489e-02, 1.4089e-01, 2.3730e-02, 3.8735e-03,
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5.4618e-03, 2.6171e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([3.8352e-01, 3.5977e-01, 8.2489e-02, 1.4089e-01, 2.3730e-02, 3.8735e-03,
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5.4618e-03, 2.6171e-04], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1158, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.8842, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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ANSWER0=VQA(image=RIGHT,question='How many golf balls 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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tensor([5.9158e-01, 4.0659e-01, 5.7645e-05, 2.1778e-04, 6.2694e-04, 6.0894e-04,
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2.8355e-04, 3.7943e-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'] tensor([5.9158e-01, 4.0659e-01, 5.7645e-05, 2.1778e-04, 6.2694e-04, 6.0894e-04,
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2.8355e-04, 3.7943e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4066, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.5916, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0018, device='cuda:3', grad_fn=<DivBackward0>)}
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torch.Size([11, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='Are there humans in the image?')
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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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question: ['Are there humans in the image?'], responses:['no']
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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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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tensor([8.6632e-01, 1.3285e-01, 2.0898e-05, 7.1074e-05, 5.2375e-05, 3.7477e-04,
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2.5450e-04, 5.2353e-05], 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([8.6632e-01, 1.3285e-01, 2.0898e-05, 7.1074e-05, 5.2375e-05, 3.7477e-04,
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2.5450e-04, 5.2353e-05], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1329, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.8663, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0008, device='cuda:0', grad_fn=<SubBackward0>)}
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tensor([9.6844e-01, 3.1130e-02, 5.1228e-05, 6.2972e-05, 9.9981e-05, 4.9674e-05,
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9.2891e-05, 7.6630e-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'] tensor([9.6844e-01, 3.1130e-02, 5.1228e-05, 6.2972e-05, 9.9981e-05, 4.9674e-05,
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9.2891e-05, 7.6630e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0311, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9684, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0004, device='cuda:3', grad_fn=<DivBackward0>)}
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question: ['How many golf balls 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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tensor([9.1954e-01, 2.7768e-02, 5.8203e-03, 4.3007e-02, 2.0751e-03, 8.6291e-04,
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8.6336e-04, 6.5356e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.1954e-01, 2.7768e-02, 5.8203e-03, 4.3007e-02, 2.0751e-03, 8.6291e-04,
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8.6336e-04, 6.5356e-05], device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9625, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0375, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is there a skydiver in the image?')
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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([11, 3, 448, 448]) knan debug pixel values shape
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question: ['Is there a skydiver in the image?'], 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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tensor([5.5148e-01, 2.6044e-02, 4.1985e-01, 1.1810e-03, 1.6603e-04, 4.7712e-04,
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1.5546e-04, 6.4720e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.5148e-01, 2.6044e-02, 4.1985e-01, 1.1810e-03, 1.6603e-04, 4.7712e-04,
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1.5546e-04, 6.4720e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5515, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.4198, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0287, device='cuda:1', grad_fn=<DivBackward0>)}
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