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Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='How many Afghan Hounds are outside in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='Is there a glove on a hand in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many water bottles are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Is the animal in the image holding one paw off the ground?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([5, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
question: ['Is there a glove on a hand 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([5, 3, 448, 448]) knan debug pixel values shape |
question: ['How many water bottles are in the image?'], responses:['1'] |
question: ['Is the animal in the image holding one paw off the ground?'], responses:['yes'] |
[('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']] |
[('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 Afghan Hounds are outside in the image?'], responses:['1'] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866 |
[('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 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866 |
tensor([7.1765e-01, 2.8103e-01, 7.7594e-05, 2.5221e-04, 6.0537e-04, 1.9528e-04, |
1.4454e-04, 4.5412e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.1765e-01, 2.8103e-01, 7.7594e-05, 2.5221e-04, 6.0537e-04, 1.9528e-04, |
1.4454e-04, 4.5412e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.2810, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.7177, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0013, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Do the doors in the image open to a grassy area?') |
ANSWER1=RESULT(var=ANSWER0) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866 |
torch.Size([13, 3, 448, 448]) |
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([8.7662e-01, 2.7303e-02, 1.4151e-02, 7.3190e-03, 8.8278e-03, 5.6242e-03, |
5.9651e-02, 5.0364e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.7662e-01, 2.7303e-02, 1.4151e-02, 7.3190e-03, 8.8278e-03, 5.6242e-03, |
5.9651e-02, 5.0364e-04], device='cuda:1', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.8766, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.1234, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
tensor([6.0848e-01, 1.9412e-02, 3.6906e-01, 1.3711e-03, 1.6528e-04, 6.2706e-04, |
9.5743e-05, 7.8275e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.0848e-01, 1.9412e-02, 3.6906e-01, 1.3711e-03, 1.6528e-04, 6.2706e-04, |
9.5743e-05, 7.8275e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ANSWER0=VQA(image=RIGHT,question='How many zebras are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.6085, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3691, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0225, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many hyenas are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([3, 3, 448, 448]) |
question: ['How many hyenas 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([3, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
question: ['Do the doors in the image open to a grassy area?'], responses:['yes'] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
question: ['How many zebras are in the image?'], responses:['1'] |
[('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']] |
[('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: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
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: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837 |
tensor([8.3733e-01, 2.6119e-02, 1.0212e-02, 4.3917e-03, 5.4724e-03, 2.9073e-03, |
1.1333e-01, 2.3001e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.3733e-01, 2.6119e-02, 1.0212e-02, 4.3917e-03, 5.4724e-03, 2.9073e-03, |
1.1333e-01, 2.3001e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.1133, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8867, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([7.8679e-01, 3.4565e-02, 1.4407e-02, 5.4633e-03, 7.9548e-03, 4.8083e-03, |
1.4554e-01, 4.6977e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
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