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Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Does the instrument have blue bands on it?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
ANSWER0=VQA(image=RIGHT,question='Is there a wild boar lying on the ground in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
ANSWER0=VQA(image=LEFT,question='How many sting rays are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 5') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Does the instrument have blue bands on it?'], responses:['no'] |
question: ['Is there a wild boar lying on the ground in the image?'], 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: 1866 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1869 |
question: ['How many animals are in the image?'], responses:['ๅ'] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866 |
question: ['How many sting rays are in the image?'], responses:['1'] |
[('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']] |
[('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']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867 |
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: 7, images per sample: 7.0, dynamic token length: 1866 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867 |
tensor([1.0000e+00, 5.5909e-10, 3.4924e-07, 1.1459e-10, 2.5623e-10, 5.8783e-08, |
9.2101e-09, 9.4335e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 5.5909e-10, 3.4924e-07, 1.1459e-10, 2.5623e-10, 5.8783e-08, |
9.2101e-09, 9.4335e-07], device='cuda:1', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(5.5909e-10, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.3113e-06, device='cuda:1', grad_fn=<SubBackward0>)} |
tensor([1.0000e+00, 8.5666e-09, 1.8113e-11, 1.5542e-08, 1.1061e-10, 3.4177e-10, |
3.6961e-11, 1.5134e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
tensor([1.0000e+00, 8.5666e-09, 1.8113e-11, 1.5542e-08, 1.1061e-10, 3.4177e-10, |
3.6961e-11, 1.5134e-08], device='cuda:0', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.8113e-11, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.8113e-11, device='cuda:0', grad_fn=<DivBackward0>)} |
torch.Size([7, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
question: ['How many dogs are in the image?'], responses:['1'] |
[('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']] |
question: ['How many dogs are in the image?'], responses:['1'] |
[('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 |
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 |
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([1.0022e-02, 1.1698e-02, 6.0636e-06, 1.7095e-01, 9.6593e-02, 7.9184e-03, |
7.0203e-01, 7.8625e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
vegetable ************* |
['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers'] tensor([1.0022e-02, 1.1698e-02, 6.0636e-06, 1.7095e-01, 9.6593e-02, 7.9184e-03, |
7.0203e-01, 7.8625e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many beetles are touching the dungball?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
torch.Size([7, 3, 448, 448]) |
tensor([1.0000e+00, 8.7275e-10, 1.5495e-10, 2.8112e-10, 3.1118e-10, 6.3478e-09, |
1.2238e-08, 7.5495e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 8.7275e-10, 1.5495e-10, 2.8112e-10, 3.1118e-10, 6.3478e-09, |
1.2238e-08, 7.5495e-11], device='cuda:3', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(7.0156e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Does a dog have something in its mouth?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
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