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question: ['How many white sails are in the image?'], responses:['2']
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question: ['How many people are standing in front of the vending machines and staring ahead?'], responses:['0']
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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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[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
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[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
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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: 3404
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405
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tensor([1.0000e+00, 5.2633e-09, 4.3137e-07, 1.1230e-08, 4.5632e-08, 8.4991e-07,
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1.5034e-08, 1.1732e-07], 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([1.0000e+00, 5.2633e-09, 4.3137e-07, 1.1230e-08, 4.5632e-08, 8.4991e-07,
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1.5034e-08, 1.1732e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(5.2633e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.4305e-06, device='cuda:1', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 3.6954e-10, 2.7320e-07, 8.7590e-12, 7.4646e-12, 6.6794e-09,
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4.5584e-10, 5.6505e-07], 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([1.0000e+00, 3.6954e-10, 2.7320e-07, 8.7590e-12, 7.4646e-12, 6.6794e-09,
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4.5584e-10, 5.6505e-07], device='cuda:2', grad_fn=<SelectBackward0>)
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ANSWER0=VQA(image=RIGHT,question='How many cheetas 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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ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.6954e-10, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(8.3447e-07, device='cuda:2', grad_fn=<SubBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many gorillas 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([13, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405
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question: ['How many gorillas 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405
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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: 3405
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question: ['How many cheetas 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: 3406
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 2.0700e-06, 3.6251e-08, 2.6743e-11, 1.6047e-06, 4.3167e-08,
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3.0882e-07, 5.3796e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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0 *************
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['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 2.0700e-06, 3.6251e-08, 2.6743e-11, 1.6047e-06, 4.3167e-08,
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3.0882e-07, 5.3796e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 8.9932e-07, 1.8170e-06, 5.2848e-07, 8.1522e-09, 1.8271e-07,
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1.1703e-07, 2.9253e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 8.9932e-07, 1.8170e-06, 5.2848e-07, 8.1522e-09, 1.8271e-07,
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1.1703e-07, 2.9253e-08], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.5300e-06, device='cuda:0', 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} == 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(8.9932e-07, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, 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=LEFT,question='How many ferrets 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([13, 3, 448, 448])
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tensor([1.0000e+00, 1.1079e-07, 7.6742e-08, 2.2603e-06, 2.7520e-09, 1.0132e-08,
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1.1482e-08, 9.9050e-10], 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, 1.1079e-07, 7.6742e-08, 2.2603e-06, 2.7520e-09, 1.0132e-08,
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1.1482e-08, 9.9050e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.4732e-06, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
|
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)]
|
[['1', '3', '4', '8', '6', '12', '2', '47']]
|
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
|
question: ['How many ferrets are in the image?'], responses:['2']
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
[('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']]
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
torch.Size([13, 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
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
tensor([1.0000e+00, 6.5740e-11, 3.8565e-12, 2.0847e-11, 1.5015e-11, 5.2563e-09,
|
1.2099e-06, 3.5877e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 6.5740e-11, 3.8565e-12, 2.0847e-11, 1.5015e-11, 5.2563e-09,
|
1.2099e-06, 3.5877e-11], device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.2099e-06, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
|
tensor([1.0000e+00, 2.7249e-10, 8.5074e-11, 2.5201e-10, 8.3755e-11, 1.5961e-08,
|
3.0462e-09, 3.5236e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
1 *************
|
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.7249e-10, 8.5074e-11, 2.5201e-10, 8.3755e-11, 1.5961e-08,
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3.0462e-09, 3.5236e-10], device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.7006e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
|
tensor([1.0000e+00, 6.1186e-08, 3.7911e-09, 1.0738e-07, 4.0587e-10, 1.0692e-09,
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1.3197e-09, 1.4889e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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