text
stringlengths 0
1.16k
|
|---|
Registering RESULT step
|
Registering VQA_lavis step
|
Registering EVAL step
|
Registering RESULT step
|
ANSWER0=VQA(image=LEFT,question='How many white pillows are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([1, 3, 448, 448])
|
ANSWER0=VQA(image=RIGHT,question='How many blue parrots are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
ANSWER0=VQA(image=RIGHT,question='Do the pelicans have smaller dark birds with them?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([3, 3, 448, 448])
|
torch.Size([5, 3, 448, 448])
|
torch.Size([13, 3, 448, 448])
|
question: ['How many balloons 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([1, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
|
question: ['Do the pelicans have smaller dark birds with them?'], responses:['yes']
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
|
[('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: 1, images per sample: 1.0, dynamic token length: 325
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
|
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
|
question: ['How many white pillows are in the image?'], responses:['2']
|
tensor([9.8891e-01, 9.8634e-05, 1.0988e-02, 3.6154e-09, 1.5420e-08, 1.4095e-07,
|
8.0368e-09, 2.6673e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
4 *************
|
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.8891e-01, 9.8634e-05, 1.0988e-02, 3.6154e-09, 1.5420e-08, 1.4095e-07,
|
8.0368e-09, 2.6673e-07], device='cuda:0', grad_fn=<SelectBackward0>)
|
[('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']]
|
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.9890, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0110, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=LEFT,question='How many cheetahs are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([7, 3, 448, 448])
|
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
|
tensor([1.0000e+00, 6.7396e-08, 7.4965e-08, 2.8032e-07, 3.6070e-10, 4.6449e-09,
|
1.9602e-09, 5.5546e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.7396e-08, 7.4965e-08, 2.8032e-07, 3.6070e-10, 4.6449e-09,
|
1.9602e-09, 5.5546e-08], device='cuda:3', grad_fn=<SelectBackward0>)
|
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(7.4965e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.0187e-07, device='cuda:3', grad_fn=<DivBackward0>)}
|
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 blue parrots are in the image?'], responses:['1']
|
torch.Size([7, 3, 448, 448])
|
[('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 cheetahs 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
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
|
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
|
tensor([9.9995e-01, 1.7232e-05, 3.0243e-05, 2.0378e-07, 8.6960e-08, 8.1693e-08,
|
3.3597e-07, 4.1634e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
2 *************
|
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9995e-01, 1.7232e-05, 3.0243e-05, 2.0378e-07, 8.6960e-08, 8.1693e-08,
|
3.3597e-07, 4.1634e-09], device='cuda:1', grad_fn=<SelectBackward0>)
|
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(4.8187e-05, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7881e-07, device='cuda:1', grad_fn=<DivBackward0>)}
|
question: ['How many dogs are in the image?'], responses:['1']
|
ANSWER0=VQA(image=LEFT,question='Is the dog in the image lying down?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
|
[('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([13, 3, 448, 448])
|
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
|
tensor([1.0000e+00, 2.3307e-10, 5.0800e-11, 1.1447e-10, 7.6232e-11, 2.6708e-08,
|
2.9295e-09, 2.6626e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
1 *************
|
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.3307e-10, 5.0800e-11, 1.1447e-10, 7.6232e-11, 2.6708e-08,
|
2.9295e-09, 2.6626e-10], device='cuda:0', grad_fn=<SelectBackward0>)
|
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(2.9295e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
|
question: ['Is the dog in the image lying down?'], 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([13, 3, 448, 448]) knan debug pixel values shape
|
tensor([1.0000e+00, 1.7185e-10, 1.3780e-11, 2.2071e-11, 1.3725e-11, 1.4602e-09,
|
1.6374e-07, 4.9044e-12], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
1 *************
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.