text stringlengths 0 1.16k |
|---|
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([3, 3, 448, 448]) |
question: ['How many animals are in the image?'], responses:['1'] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866 |
[('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: ['Does the image contain a paper towel stand?'], 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']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866 |
tensor([0.9396, 0.0083, 0.0068, 0.0072, 0.0120, 0.0210, 0.0015, 0.0035], |
device='cuda:3', grad_fn=<SoftmaxBackward0>) |
orange ************* |
['orange', 'purple', 'vanilla', 'cinnamon', 'maroon', 'lemon', 'lime', 'black'] tensor([0.9396, 0.0083, 0.0068, 0.0072, 0.0120, 0.0210, 0.0015, 0.0035], |
device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is the dog looking left?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867 |
tensor([7.3971e-01, 1.5606e-02, 2.4167e-01, 1.2526e-03, 1.5198e-04, 4.4036e-04, |
1.3666e-04, 1.0305e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.3971e-01, 1.5606e-02, 2.4167e-01, 1.2526e-03, 1.5198e-04, 4.4036e-04, |
1.3666e-04, 1.0305e-03], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7397, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(0.2417, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0186, device='cuda:1', grad_fn=<SubBackward0>)} |
tensor([9.1726e-01, 1.4410e-02, 6.6446e-02, 5.1993e-04, 4.0067e-05, 1.4060e-04, |
2.3340e-05, 1.1644e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.1726e-01, 1.4410e-02, 6.6446e-02, 5.1993e-04, 4.0067e-05, 1.4060e-04, |
2.3340e-05, 1.1644e-03], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9173, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.0664, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0163, device='cuda:0', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many humans are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
question: ['How many humans 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([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
tensor([9.8318e-01, 1.7830e-03, 4.2349e-04, 1.8777e-04, 2.2662e-04, 1.5733e-04, |
1.4024e-02, 1.3327e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.8318e-01, 1.7830e-03, 4.2349e-04, 1.8777e-04, 2.2662e-04, 1.5733e-04, |
1.4024e-02, 1.3327e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0168, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9832, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many zebras are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
question: ['Is the dog looking left?'], 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']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
question: ['How many zebras are in the image?'], responses:['2'] |
tensor([9.9643e-01, 7.0762e-04, 3.0431e-04, 1.3898e-04, 2.0908e-04, 1.4996e-04, |
2.0476e-03, 1.3571e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9643e-01, 7.0762e-04, 3.0431e-04, 1.3898e-04, 2.0908e-04, 1.4996e-04, |
2.0476e-03, 1.3571e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0036, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9964, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
[('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']] |
ANSWER0=VQA(image=RIGHT,question='Does the dog in the image have its mouth open?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([11, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
question: ['Does the dog in the image have its mouth open?'], 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']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
torch.Size([11, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
tensor([5.1520e-01, 4.8399e-01, 1.4859e-05, 9.4817e-05, 3.9492e-05, 4.4121e-04, |
2.0584e-04, 1.2461e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.1520e-01, 4.8399e-01, 1.4859e-05, 9.4817e-05, 3.9492e-05, 4.4121e-04, |
2.0584e-04, 1.2461e-05], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4840, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.5152, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0008, device='cuda:3', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
ANSWER0=VQA(image=RIGHT,question='How many humans are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.