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[('metal', 0.1263430186656571), ('glass', 0.12522357438951787), ('steel', 0.12503695230689602), ('iron', 0.12479778246180684), ('rust', 0.12469877097234755), ('fur', 0.12466446134913117), ('stone', 0.12462037078361551), ('wine', 0.12461506907102783)]
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[['metal', 'glass', 'steel', 'iron', 'rust', 'fur', 'stone', 'wine']]
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question: ['How many laptop computers are in the image?'], responses:['4']
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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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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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: 3399
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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tensor([0.6116, 0.0938, 0.0686, 0.0237, 0.0034, 0.1865, 0.0105, 0.0019],
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device='cuda:1', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.6116, 0.0938, 0.0686, 0.0237, 0.0034, 0.1865, 0.0105, 0.0019],
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device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9314, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0686, 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='Are there any human beings in the image?')
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ANSWER1=EVAL(expr='not {ANSWER0}')
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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: 3399
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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question: ['Are there any human beings in the image?'], 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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tensor([0.3421, 0.1847, 0.1303, 0.0219, 0.0461, 0.0114, 0.2630, 0.0005],
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device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([0.3421, 0.1847, 0.1303, 0.0219, 0.0461, 0.0114, 0.2630, 0.0005],
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device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.2630, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.7370, 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=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([1, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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question: ['How many binders 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
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tensor([0.4040, 0.2764, 0.0363, 0.1390, 0.0156, 0.0744, 0.0451, 0.0092],
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device='cuda:2', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.4040, 0.2764, 0.0363, 0.1390, 0.0156, 0.0744, 0.0451, 0.0092],
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device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5147, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.4853, 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=RIGHT,question='Is there at least one person standing on a curb by the open door of a parked yellow bus with a non-flat front?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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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: 3399
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
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tensor([9.4268e-01, 5.6612e-02, 5.9767e-05, 9.4927e-05, 1.4720e-04, 1.6108e-04,
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1.6319e-04, 7.9462e-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([9.4268e-01, 5.6612e-02, 5.9767e-05, 9.4927e-05, 1.4720e-04, 1.6108e-04,
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1.6319e-04, 7.9462e-05], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9427, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0566, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0007, device='cuda:1', grad_fn=<DivBackward0>)}
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question: ['Is there at least one person standing on a curb by the open door of a parked yellow bus with a non-flat front?'], responses:['no']
|
[('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([0.8024, 0.0585, 0.0723, 0.0332, 0.0060, 0.0120, 0.0099, 0.0057],
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device='cuda:0', grad_fn=<SoftmaxBackward0>)
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metal *************
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['metal', 'glass', 'steel', 'iron', 'rust', 'fur', 'stone', 'wine'] tensor([0.8024, 0.0585, 0.0723, 0.0332, 0.0060, 0.0120, 0.0099, 0.0057],
|
device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)}
|
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
ANSWER0=VQA(image=LEFT,question='How many insects are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 1')
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FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([7, 3, 448, 448])
|
tensor([0.5290, 0.3209, 0.0434, 0.0061, 0.0762, 0.0079, 0.0155, 0.0011],
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device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
4 *************
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['4', '5', '3', '8', '6', '1', '2', '11'] tensor([0.5290, 0.3209, 0.0434, 0.0061, 0.0762, 0.0079, 0.0155, 0.0011],
|
device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5290, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.4710, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='How many gorillas are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([7, 3, 448, 448])
|
question: ['How many insects are in the image?'], responses:['2']
|
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
|
[['2', '3', '4', '1', '5', '8', '7', '29']]
|
question: ['How many gorillas are in the image?'], responses:['2']
|
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
|
[['2', '3', '4', '1', '5', '8', '7', '29']]
|
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
tensor([7.7528e-01, 2.2325e-01, 1.0385e-04, 1.7831e-04, 6.1305e-04, 7.0073e-05,
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3.0515e-04, 1.9387e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
|
no *************
|
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.7528e-01, 2.2325e-01, 1.0385e-04, 1.7831e-04, 6.1305e-04, 7.0073e-05,
|
3.0515e-04, 1.9387e-04], device='cuda:2', grad_fn=<SelectBackward0>)
|
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