text
stringlengths 0
1.16k
|
|---|
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.4617e-10, 3.9680e-07, 2.4323e-12, 2.8411e-11, 3.2295e-09,
|
1.8029e-10, 7.7788e-07], device='cuda:1', grad_fn=<SelectBackward0>)
|
ANSWER0=VQA(image=RIGHT,question='How many zebras are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 1')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([5, 3, 448, 448])
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.4617e-10, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.1921e-06, device='cuda:1', grad_fn=<SubBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='How many ferrets are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([3, 3, 448, 448])
|
question: ['How many ferrets are in the image?'], responses:['ε']
|
[('geese', 0.12791273653846358), ('cushion', 0.12632164867635856), ('biking', 0.12559214056053666), ('bulldog', 0.12532071672327474), ('striped', 0.12486304389654934), ('goose', 0.12402122964730407), ('vegetable', 0.12318440383239601), ('dodgers', 0.12278408012511692)]
|
[['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers']]
|
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
|
question: ['How many zebras 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([5, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
|
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
|
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
|
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
|
tensor([1.0000e+00, 7.6929e-09, 9.0561e-11, 1.0041e-07, 9.4318e-10, 1.5803e-09,
|
9.7426e-11, 1.7680e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
|
yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.6929e-09, 9.0561e-11, 1.0041e-07, 9.4318e-10, 1.5803e-09,
|
9.7426e-11, 1.7680e-08], device='cuda:2', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(9.0561e-11, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1912e-07, device='cuda:2', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=LEFT,question='Does the left image contain at least one chandelier?')
|
FINAL_ANSWER=RESULT(var=ANSWER0)
|
torch.Size([1, 3, 448, 448])
|
tensor([1.0000e+00, 8.2219e-08, 1.3583e-07, 4.2239e-07, 4.6098e-09, 1.5382e-07,
|
4.6909e-08, 2.0023e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.5915e-03, 1.1856e-02, 3.0797e-06, 2.0350e-01, 1.2628e-01, 1.9588e-03,
|
6.5351e-01, 1.2983e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
vegetable *************
|
['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers'] tensor([1.0000e+00, 8.2219e-08, 1.3583e-07, 4.2239e-07, 4.6098e-09, 1.5382e-07,
|
4.6909e-08, 2.0023e-07], device='cuda:3', grad_fn=<SelectBackward0>)
|
tensor([1.5915e-03, 1.1856e-02, 3.0797e-06, 2.0350e-01, 1.2628e-01, 1.9588e-03,
|
6.5351e-01, 1.2983e-03], device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)}
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.3583e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.3706e-07, device='cuda:3', grad_fn=<DivBackward0>)}
|
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
|
ANSWER0=VQA(image=LEFT,question='How many people are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 4')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([3, 3, 448, 448])
|
question: ['Does the left image contain at least one chandelier?'], 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: 5, images per sample: 5.0, dynamic token length: 1350
|
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
|
question: ['How many people are in the image?'], responses:['δΈ']
|
[('monday', 0.12552664945614717), ('awake', 0.12538722310550102), ('leopard', 0.12525924776083958), ('kia', 0.12492343076783531), ('human', 0.1247803436217969), ('gone', 0.12471007192930383), ('thumb', 0.12470975620047144), ('halloween', 0.12470327715810482)]
|
[['monday', 'awake', 'leopard', 'kia', 'human', 'gone', 'thumb', 'halloween']]
|
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
|
tensor([1.0000e+00, 3.1775e-09, 5.6231e-11, 1.5482e-08, 2.4976e-10, 5.9857e-11,
|
7.5265e-11, 1.5626e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
|
yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.1775e-09, 5.6231e-11, 1.5482e-08, 2.4976e-10, 5.9857e-11,
|
7.5265e-11, 1.5626e-08], device='cuda:2', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(5.6231e-11, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-5.6231e-11, device='cuda:2', grad_fn=<SubBackward0>)}
|
tensor([1.0000e+00, 2.0170e-10, 6.5228e-11, 1.5106e-10, 6.8882e-11, 6.8584e-09,
|
2.0936e-09, 8.1601e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
1 *************
|
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.0170e-10, 6.5228e-11, 1.5106e-10, 6.8882e-11, 6.8584e-09,
|
2.0936e-09, 8.1601e-11], device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(9.5205e-09, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
|
tensor([1.4059e-01, 2.5074e-01, 1.1131e-02, 3.8044e-03, 2.2178e-01, 3.6271e-01,
|
5.4351e-05, 9.1862e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
gone *************
|
['monday', 'awake', 'leopard', 'kia', 'human', 'gone', 'thumb', 'halloween'] tensor([1.4059e-01, 2.5074e-01, 1.1131e-02, 3.8044e-03, 2.2178e-01, 3.6271e-01,
|
5.4351e-05, 9.1862e-03], 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>)}
|
[2024-10-24 10:44:59,504] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.22 | optimizer_step: 0.30
|
[2024-10-24 10:44:59,504] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3197.87 | backward_microstep: 4167.62 | backward_inner_microstep: 2916.64 | backward_allreduce_microstep: 1250.88 | step_microstep: 8.12
|
[2024-10-24 10:44:59,504] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3197.88 | backward: 4167.61 | backward_inner: 2916.65 | backward_allreduce: 1250.87 | step: 8.13
|
100%|ββββββββββ| 4825/4844 [20:03:43<03:58, 12.55s/it]Registering VQA_lavis step
|
Registering VQA_lavis step
|
Registering EVAL step
|
Registering RESULT step
|
Registering EVAL step
|
Registering RESULT step
|
Registering VQA_lavis step
|
Registering EVAL step
|
Registering RESULT step
|
Registering VQA_lavis step
|
Registering EVAL step
|
Registering RESULT step
|
ANSWER0=VQA(image=RIGHT,question='Is there at least one person outside with the dogs?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
ANSWER0=VQA(image=LEFT,question='How many jellyfish are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} > 1')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.