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question: ['How many pieces of food are on the dish?'], responses:['7'] |
[('geese', 0.12791273653846358), ('cushion', 0.12632164867635856), ('biking', 0.12559214056053666), ('bulldog', 0.12532071672327474), ('striped', 0.12486304389654934), ('goose', 0.12402122964730407), ('vegetable', 0.12318440383239601), ('dodgers', 0.12278408012511692)] |
[['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers']] |
[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)] |
[['7', '8', '11', '5', '9', '10', '6', '12']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
question: ['How many corgi dogs are in the image?'], responses:['2'] |
question: ['Is there a yellow breasted bird in the image?'], responses:['no'] |
[('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']] |
[('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)] |
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
tensor([1.3132e-03, 1.2309e-02, 3.2705e-06, 2.9712e-01, 1.8432e-01, 2.4590e-03, |
4.9624e-01, 6.2356e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
vegetable ************* |
['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers'] tensor([1.3132e-03, 1.2309e-02, 3.2705e-06, 2.9712e-01, 1.8432e-01, 2.4590e-03, |
4.9624e-01, 6.2356e-03], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([7.2218e-01, 2.9966e-04, 1.5208e-02, 2.3775e-01, 1.4136e-03, 7.9480e-04, |
2.2127e-02, 2.2773e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
7 ************* |
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([7.2218e-01, 2.9966e-04, 1.5208e-02, 2.3775e-01, 1.4136e-03, 7.9480e-04, |
2.2127e-02, 2.2773e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Does the sink on the right have a rectangular shape?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([1, 3, 448, 448]) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many lipsticks are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
question: ['Does the sink on the right have a rectangular shape?'], 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)] |
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
tensor([1.0000e+00, 3.4663e-07, 2.2614e-07, 1.1579e-12, 3.0054e-13, 2.0142e-10, |
2.7768e-11, 3.3018e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.4663e-07, 2.2614e-07, 1.1579e-12, 3.0054e-13, 2.0142e-10, |
2.7768e-11, 3.3018e-08], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.4663e-07, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(2.3842e-07, device='cuda:1', grad_fn=<SubBackward0>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
question: ['How many lipsticks are in the image?'], responses:['ε'] |
[('geese', 0.12791273653846358), ('cushion', 0.12632164867635856), ('biking', 0.12559214056053666), ('bulldog', 0.12532071672327474), ('striped', 0.12486304389654934), ('goose', 0.12402122964730407), ('vegetable', 0.12318440383239601), ('dodgers', 0.12278408012511692)] |
[['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
tensor([9.9997e-01, 2.7536e-05, 1.4761e-08, 3.2241e-08, 1.6368e-09, 3.3385e-10, |
1.2022e-09, 4.8383e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9997e-01, 2.7536e-05, 1.4761e-08, 3.2241e-08, 1.6368e-09, 3.3385e-10, |
1.2022e-09, 4.8383e-10], device='cuda:3', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 7.3493e-10, 9.6204e-07, 1.6110e-11, 4.1473e-10, 3.2637e-08, |
1.0128e-09, 1.6363e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 7.3493e-10, 9.6204e-07, 1.6110e-11, 4.1473e-10, 3.2637e-08, |
1.0128e-09, 1.6363e-06], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(7.3493e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.6226e-06, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is the spoon in the cup?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([1, 3, 448, 448]) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.7554e-05, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is there a woman in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
question: ['Is the spoon in the cup?'], 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)] |
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
tensor([1.0000e+00, 8.5922e-10, 4.1142e-07, 1.3047e-11, 9.2002e-13, 3.4703e-09, |
3.3045e-10, 1.8031e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 8.5922e-10, 4.1142e-07, 1.3047e-11, 9.2002e-13, 3.4703e-09, |
3.3045e-10, 1.8031e-07], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(8.5922e-10, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(5.9605e-07, device='cuda:0', grad_fn=<SubBackward0>)} |
question: ['Is there a woman in the image?'], 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)] |
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
tensor([5.3444e-04, 1.3917e-02, 1.7208e-06, 4.1469e-02, 6.8394e-02, 1.5567e-03, |
8.7016e-01, 3.9678e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
vegetable ************* |
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