text stringlengths 0 1.16k |
|---|
question: ['Is the animal facing the camera?'], responses:['no'] |
question: ['How many green glass bottles 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']] |
[('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([3, 3, 448, 448]) knan debug pixel values shape |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
tensor([3.0766e-14, 9.4703e-01, 5.8905e-06, 5.2948e-02, 4.1538e-06, 4.7567e-06, |
4.1087e-06, 1.3196e-06], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
4 ************* |
['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag'] tensor([3.0766e-14, 9.4703e-01, 5.8905e-06, 5.2948e-02, 4.1538e-06, 4.7567e-06, |
4.1087e-06, 1.3196e-06], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.6034e-05, device='cuda:2', 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([1.0000e+00, 4.5990e-10, 1.2496e-07, 1.8982e-11, 9.0685e-12, 3.9463e-09, |
4.5577e-10, 1.6532e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 4.5990e-10, 1.2496e-07, 1.8982e-11, 9.0685e-12, 3.9463e-09, |
4.5577e-10, 1.6532e-07], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 1.4989e-10, 7.0932e-12, 7.5507e-12, 1.2744e-11, 1.3095e-09, |
2.7265e-06, 2.3796e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.4989e-10, 7.0932e-12, 7.5507e-12, 1.2744e-11, 1.3095e-09, |
2.7265e-06, 2.3796e-11], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.5990e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3842e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.7265e-06, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Does the left image include a diver wearing goggles?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
question: ['How many framed images are in the image?'], responses:['4'] |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
[('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([13, 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 dogs are in the image?'], responses:['7'] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
[('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([13, 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 dogs are in the image?'], responses:['1'] |
question: ['Does the left image include a diver wearing goggles?'], responses:['no'] |
[('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']] |
[('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: 3397 |
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: 3397 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
tensor([8.8188e-01, 9.8373e-02, 7.0570e-04, 5.0079e-04, 2.8786e-03, 1.1382e-08, |
3.6503e-08, 1.5660e-02], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
4 ************* |
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([8.8188e-01, 9.8373e-02, 7.0570e-04, 5.0079e-04, 2.8786e-03, 1.1382e-08, |
3.6503e-08, 1.5660e-02], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9993, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0007, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Does the left image show a staircase that ascends rightward before turning?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
tensor([9.9374e-01, 2.6219e-03, 7.9970e-04, 1.9516e-06, 1.1636e-03, 1.7294e-04, |
1.4940e-03, 1.5929e-06], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
7 ************* |
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.9374e-01, 2.6219e-03, 7.9970e-04, 1.9516e-06, 1.1636e-03, 1.7294e-04, |
1.4940e-03, 1.5929e-06], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
question: ['Does the left image show a staircase that ascends rightward before turning?'], responses:['yes'] |
[('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 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1868 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1871 |
tensor([1.0000e+00, 3.6524e-10, 1.0924e-10, 2.4235e-10, 1.8869e-10, 2.0474e-08, |
3.8208e-09, 4.4151e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.6524e-10, 1.0924e-10, 2.4235e-10, 1.8869e-10, 2.0474e-08, |
3.8208e-09, 4.4151e-10], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.5641e-08, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 3.2868e-10, 3.4464e-07, 5.9053e-10, 1.9637e-09, 5.1673e-08, |
3.5034e-09, 1.7293e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.2868e-10, 3.4464e-07, 5.9053e-10, 1.9637e-09, 5.1673e-08, |
3.5034e-09, 1.7293e-07], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.2868e-10, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(5.9605e-07, device='cuda:3', grad_fn=<SubBackward0>)} |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1868 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1869 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1868 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1868 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1869 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.