text
stringlengths 0
1.16k
|
|---|
question: ['Is a person pushing the dispenser?'], 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']]
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
|
torch.Size([1, 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: 3403
|
tensor([5.7673e-01, 4.2193e-01, 1.2655e-04, 1.5261e-04, 3.7884e-04, 2.6645e-05,
|
4.3771e-04, 2.1606e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
no *************
|
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.7673e-01, 4.2193e-01, 1.2655e-04, 1.5261e-04, 3.7884e-04, 2.6645e-05,
|
4.3771e-04, 2.1606e-04], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4219, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.5767, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0013, device='cuda:3', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='Does the right image show a large group of animals on a road?')
|
FINAL_ANSWER=RESULT(var=ANSWER0)
|
torch.Size([7, 3, 448, 448])
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
|
question: ['Does the right image show a large group of animals on a road?'], 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: 13, images per sample: 13.0, dynamic token length: 3404
|
tensor([9.0196e-01, 4.2198e-02, 8.8451e-03, 4.2197e-02, 2.7831e-03, 9.9157e-04,
|
9.6155e-04, 5.8832e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
|
2 *************
|
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.0196e-01, 4.2198e-02, 8.8451e-03, 4.2197e-02, 2.7831e-03, 9.9157e-04,
|
9.6155e-04, 5.8832e-05], device='cuda:2', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9020, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0980, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
|
ANSWER0=VQA(image=LEFT,question='Is there a human inside a store in the image?')
|
ANSWER1=EVAL(expr='not {ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([13, 3, 448, 448])
|
tensor([6.0775e-01, 3.8990e-01, 1.6928e-04, 3.2672e-04, 1.7813e-04, 1.9632e-04,
|
1.0432e-03, 4.3248e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
no *************
|
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.0775e-01, 3.8990e-01, 1.6928e-04, 3.2672e-04, 1.7813e-04, 1.9632e-04,
|
1.0432e-03, 4.3248e-04], device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3899, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.6078, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0023, device='cuda:0', grad_fn=<SubBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='Is the dog on a grassy surface?')
|
FINAL_ANSWER=RESULT(var=ANSWER0)
|
torch.Size([1, 3, 448, 448])
|
question: ['Is the dog on a grassy surface?'], 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([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: 328
|
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: 326
|
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: 326
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326
|
tensor([8.3370e-01, 1.9346e-02, 1.4462e-01, 1.1461e-03, 1.3051e-04, 4.3264e-04,
|
2.8246e-05, 5.8947e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.3370e-01, 1.9346e-02, 1.4462e-01, 1.1461e-03, 1.3051e-04, 4.3264e-04,
|
2.8246e-05, 5.8947e-04], device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8337, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.1446, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0217, device='cuda:0', grad_fn=<SubBackward0>)}
|
ANSWER0=VQA(image=LEFT,question='How many laptops are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([7, 3, 448, 448])
|
tensor([6.0770e-01, 2.5346e-02, 3.6269e-01, 1.6255e-03, 2.0186e-04, 1.0333e-03,
|
1.4988e-04, 1.2610e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.0770e-01, 2.5346e-02, 3.6269e-01, 1.6255e-03, 2.0186e-04, 1.0333e-03,
|
1.4988e-04, 1.2610e-03], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6077, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(0.3627, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0296, device='cuda:3', grad_fn=<SubBackward0>)}
|
tensor([7.6398e-01, 2.4200e-02, 2.0655e-01, 3.1991e-03, 1.3349e-04, 5.1938e-04,
|
1.7372e-04, 1.2475e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] ANSWER0=VQA(image=LEFT,question='How many elephants are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
tensor([7.6398e-01, 2.4200e-02, 2.0655e-01, 3.1991e-03, 1.3349e-04, 5.1938e-04,
|
1.7372e-04, 1.2475e-03], device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7640, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2065, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0295, device='cuda:1', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=LEFT,question='Is the laptop facing right?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([13, 3, 448, 448])
|
torch.Size([13, 3, 448, 448])
|
question: ['Is there a human inside a store in the image?'], 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
|
question: ['How many laptops 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([7, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
question: ['How many elephants are in the image?'], responses:['1']
|
question: ['Is the laptop facing right?'], responses:['no']
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.