text
stringlengths 0
1.16k
|
|---|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5016, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.4712, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0271, device='cuda:2', grad_fn=<DivBackward0>)}
|
question: ['Is the mouth of the dog open?'], responses:['yes']
|
question: ['How many paper towel rolls are in the image?'], responses:['1']
|
ANSWER0=VQA(image=LEFT,question='How many chimneys are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([7, 3, 448, 448])
|
[('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']]
|
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
|
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
|
tensor([4.1900e-01, 2.8798e-01, 1.4141e-01, 7.7616e-02, 5.0112e-02, 1.0489e-02,
|
1.3072e-02, 3.1894e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
2 *************
|
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([4.1900e-01, 2.8798e-01, 1.4141e-01, 7.7616e-02, 5.0112e-02, 1.0489e-02,
|
1.3072e-02, 3.1894e-04], device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9224, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0776, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=LEFT,question='How many sled dogs are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} <= 6')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
|
question: ['How many chimneys are in the image?'], responses:['1']
|
torch.Size([13, 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']]
|
torch.Size([7, 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: 3397
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
|
question: ['How many sled dogs are in the image?'], responses:['5']
|
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
|
[['5', '8', '4', '6', '3', '7', '11', '9']]
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
|
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
|
tensor([6.1189e-01, 6.8653e-02, 2.0938e-02, 2.5798e-03, 5.4632e-03, 1.3803e-03,
|
2.8902e-01, 7.4347e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
|
1 *************
|
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([6.1189e-01, 6.8653e-02, 2.0938e-02, 2.5798e-03, 5.4632e-03, 1.3803e-03,
|
2.8902e-01, 7.4347e-05], device='cuda:2', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3881, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.6119, 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 white dogs are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([13, 3, 448, 448])
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
|
tensor([9.2040e-01, 1.0906e-02, 6.6697e-02, 8.6639e-04, 4.3972e-05, 2.6239e-04,
|
5.8785e-05, 7.6106e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.2040e-01, 1.0906e-02, 6.6697e-02, 8.6639e-04, 4.3972e-05, 2.6239e-04,
|
5.8785e-05, 7.6106e-04], device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9204, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0667, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0129, device='cuda:0', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=LEFT,question='How many white dogs are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
tensor([5.9790e-01, 5.8415e-02, 2.3579e-02, 5.9490e-03, 9.0751e-03, 4.3513e-03,
|
3.0057e-01, 1.6294e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
1 *************
|
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([5.9790e-01, 5.8415e-02, 2.3579e-02, 5.9490e-03, 9.0751e-03, 4.3513e-03,
|
3.0057e-01, 1.6294e-04], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5979, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.4021, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=LEFT,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([13, 3, 448, 448])
|
question: ['How many white dogs 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']]
|
question: ['How many white 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
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
|
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
|
question: ['How many animals 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']]
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
|
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
|
tensor([0.2839, 0.0561, 0.1956, 0.2218, 0.0841, 0.1184, 0.0094, 0.0308],
|
device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
5 *************
|
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.2839, 0.0561, 0.1956, 0.2218, 0.0841, 0.1184, 0.0094, 0.0308],
|
device='cuda:1', grad_fn=<SelectBackward0>)
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7854, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2146, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 1')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([7, 3, 448, 448])
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
|
tensor([8.7444e-01, 2.8109e-02, 1.3706e-02, 4.8859e-03, 7.1062e-03, 3.9086e-03,
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.