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Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Is there a man in tan pants standing near a building?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Are there electronic objects stacked on top of each other in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['How many bottles are in the image?'], responses:['三'] |
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)] |
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
tensor([3.6399e-06, 3.6628e-04, 2.6406e-02, 5.2215e-01, 2.3925e-01, 2.0352e-01, |
5.8927e-03, 2.4089e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
bulldog ************* |
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([3.6399e-06, 3.6628e-04, 2.6406e-02, 5.2215e-01, 2.3925e-01, 2.0352e-01, |
5.8927e-03, 2.4089e-03], device='cuda:3', grad_fn=<SelectBackward0>) |
最后的概率分布为: {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>)} |
ANSWER0=VQA(image=RIGHT,question='How many gorillas are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
question: ['Are there electronic objects stacked on top of each other in the image?'], responses:['no'] |
question: ['Is there a man in tan pants standing near a building?'], responses:['yes'] |
[('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']] |
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)] |
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867 |
question: ['Is the dog situated in the grass?'], responses:['yes'] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)] |
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']] |
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']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
tensor([1.0000e+00, 9.2374e-09, 4.7920e-07, 2.9801e-12, 2.1366e-12, 1.3266e-09, |
2.2813e-10, 6.5282e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 9.2374e-09, 4.7920e-07, 2.9801e-12, 2.1366e-12, 1.3266e-09, |
2.2813e-10, 6.5282e-07], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 4.4224e-09, 3.6954e-10, 9.9842e-09, 3.5917e-11, 4.5537e-11, |
2.7132e-11, 1.4431e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.4224e-09, 3.6954e-10, 9.9842e-09, 3.5917e-11, 4.5537e-11, |
2.7132e-11, 1.4431e-09], device='cuda:0', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(9.2374e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-06, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is the dog wearing a leash?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
最后的概率分布为: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(3.6954e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.6954e-10, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many folded towels are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([5, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['How many folded towels are in the image?'], responses:['7'] |
[('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']] |
tensor([1.0000e+00, 1.8739e-06, 1.0907e-07, 2.9023e-06, 4.0355e-09, 9.0750e-10, |
5.1014e-09, 1.3301e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.8739e-06, 1.0907e-07, 2.9023e-06, 4.0355e-09, 9.0750e-10, |
5.1014e-09, 1.3301e-10], device='cuda:3', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.9931e-06, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349 |
question: ['Is the dog wearing a leash?'], responses:['yes'] |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349 |
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)] |
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']] |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349 |
tensor([1.0000e+00, 4.7992e-09, 1.1033e-09, 9.8674e-09, 4.8469e-11, 2.5398e-10, |
2.0114e-11, 1.2722e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.6346e-01, 1.6251e-04, 3.6982e-03, 1.5573e-02, 9.3512e-04, 5.6722e-04, |
1.5570e-02, 3.8597e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
7 ************* |
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([1.0000e+00, 4.7992e-09, 1.1033e-09, 9.8674e-09, 4.8469e-11, 2.5398e-10, |
2.0114e-11, 1.2722e-08], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([9.6346e-01, 1.6251e-04, 3.6982e-03, 1.5573e-02, 9.3512e-04, 5.6722e-04, |
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