text stringlengths 0 1.16k |
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['30', '29', '40', '28', '31', '32', '26', '35'] tensor([0.2605, 0.1167, 0.1550, 0.1172, 0.0443, 0.0803, 0.1000, 0.1259], |
device='cuda:1', grad_fn=<SelectBackward0>) |
question: ['Does the train have any round windows?'], responses:['yes'] |
question: ['How many pandas are in the image?'], responses:['3'] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
[('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']] |
ANSWER0=VQA(image=LEFT,question='Is wire fence visible behind the wolf?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)] |
[['3', '4', '1', '5', '8', '2', '6', '12']] |
torch.Size([13, 3, 448, 448]) |
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: 13, images per sample: 13.0, dynamic token length: 3396 |
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: 3396 |
tensor([7.8803e-01, 1.0415e-02, 1.9924e-01, 1.2242e-03, 6.6487e-05, 2.4431e-04, |
1.6523e-05, 7.6695e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.8803e-01, 1.0415e-02, 1.9924e-01, 1.2242e-03, 6.6487e-05, 2.4431e-04, |
1.6523e-05, 7.6695e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7880, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1992, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0127, device='cuda:2', grad_fn=<DivBackward0>)} |
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([3, 3, 448, 448]) |
question: ['Is wire fence visible behind the wolf?'], responses:['yes'] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
[('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 sting rays are in the image?'], responses:['4'] |
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)] |
[['4', '5', '3', '8', '6', '1', '2', '11']] |
torch.Size([3, 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: 3396 |
tensor([8.0898e-01, 5.8524e-02, 8.4422e-03, 4.8039e-03, 5.9273e-04, 1.1655e-01, |
1.7129e-03, 3.9404e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([8.0898e-01, 5.8524e-02, 8.4422e-03, 4.8039e-03, 5.9273e-04, 1.1655e-01, |
1.7129e-03, 3.9404e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0084, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9916, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many shoes are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([5, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([0.3558, 0.3233, 0.1352, 0.0214, 0.1090, 0.0183, 0.0301, 0.0069], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
4 ************* |
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([0.3558, 0.3233, 0.1352, 0.0214, 0.1090, 0.0183, 0.0301, 0.0069], |
device='cuda:2', grad_fn=<SelectBackward0>) |
question: ['How many shoes are in the image?'], responses:['1'] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8627, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1373, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many flutes are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
[('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([5, 3, 448, 448]) |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
question: ['How many flutes 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']] |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
tensor([8.8630e-01, 1.1611e-02, 9.9440e-02, 1.4203e-03, 6.3290e-05, 2.9009e-04, |
4.0309e-05, 8.3922e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.8630e-01, 1.1611e-02, 9.9440e-02, 1.4203e-03, 6.3290e-05, 2.9009e-04, |
4.0309e-05, 8.3922e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0994, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8863, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0143, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
tensor([9.8685e-01, 3.2396e-03, 1.5788e-03, 6.9864e-04, 9.2634e-04, 7.8050e-04, |
5.8679e-03, 5.7456e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.8685e-01, 3.2396e-03, 1.5788e-03, 6.9864e-04, 9.2634e-04, 7.8050e-04, |
5.8679e-03, 5.7456e-05], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9869, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0131, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many guinea pigs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
tensor([8.3253e-01, 2.3617e-02, 1.0156e-02, 1.2748e-01, 3.1911e-03, 1.8780e-03, |
1.0360e-03, 1.1690e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([8.3253e-01, 2.3617e-02, 1.0156e-02, 1.2748e-01, 3.1911e-03, 1.8780e-03, |
1.0360e-03, 1.1690e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8325, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1675, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many wolves are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
question: ['How many dogs are in the image?'], responses:['3'] |
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