text
stringlengths 0
1.16k
|
|---|
2 *************
|
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 6.1186e-08, 3.7911e-09, 1.0738e-07, 4.0587e-10, 1.0692e-09,
|
1.3197e-09, 1.4889e-09], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.7664e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
|
[2024-10-24 10:36:53,910] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.38 | optimizer_step: 0.33
|
[2024-10-24 10:36:53,910] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7055.75 | backward_microstep: 10828.67 | backward_inner_microstep: 6786.40 | backward_allreduce_microstep: 4042.06 | step_microstep: 7.67
|
[2024-10-24 10:36:53,911] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7055.77 | backward: 10828.66 | backward_inner: 6786.49 | backward_allreduce: 4042.01 | step: 7.68
|
99%|ββββββββββ| 4791/4844 [19:55:37<14:22, 16.28s/it]Registering VQA_lavis 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='How many dingos are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
Registering EVAL step
|
Registering RESULT step
|
Registering VQA_lavis step
|
Registering EVAL step
|
Registering RESULT step
|
ANSWER0=VQA(image=LEFT,question='How many graduates are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
ANSWER0=VQA(image=LEFT,question='How many acorns are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([7, 3, 448, 448])
|
torch.Size([7, 3, 448, 448])
|
torch.Size([13, 3, 448, 448])
|
ANSWER0=VQA(image=LEFT,question='Does the left image contain a woman carrying groceries?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([7, 3, 448, 448])
|
question: ['How many dingos are in the image?'], responses:['3']
|
question: ['How many acorns are in the image?'], responses:['2']
|
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
|
[['3', '4', '1', '5', '8', '2', '6', '12']]
|
question: ['Does the left image contain a woman carrying groceries?'], responses:['yes']
|
[('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']]
|
[('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: 7, images per sample: 7.0, dynamic token length: 1862
|
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
|
question: ['How many graduates are in the image?'], responses:['2']
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
|
[('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: 1862
|
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
|
tensor([9.9998e-01, 2.0677e-05, 1.0009e-08, 1.0945e-08, 5.3218e-11, 8.6297e-09,
|
2.0963e-10, 5.6636e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
3 *************
|
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9998e-01, 2.0677e-05, 1.0009e-08, 1.0945e-08, 5.3218e-11, 8.6297e-09,
|
2.0963e-10, 5.6636e-09], device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.8638e-08, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='How many parrots are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 1')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
tensor([9.9991e-01, 1.9946e-06, 1.5381e-07, 8.4812e-05, 1.1317e-08, 1.4220e-09,
|
5.3249e-09, 1.5050e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
2 *************
|
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9991e-01, 1.9946e-06, 1.5381e-07, 8.4812e-05, 1.1317e-08, 1.4220e-09,
|
5.3249e-09, 1.5050e-09], device='cuda:3', grad_fn=<SelectBackward0>)
|
tensor([1.0000e+00, 1.7352e-09, 9.0561e-11, 5.3765e-09, 2.1161e-11, 4.5537e-11,
|
3.0824e-12, 1.9501e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
|
yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.7352e-09, 9.0561e-11, 5.3765e-09, 2.1161e-11, 4.5537e-11,
|
3.0824e-12, 1.9501e-09], device='cuda:2', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9999, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(8.6980e-05, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
|
ζεηζ¦ηεεΈδΈΊ: torch.Size([13, 3, 448, 448])
|
{True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(9.0561e-11, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-9.0561e-11, device='cuda:2', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='Is there a blue couch in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
ANSWER0=VQA(image=RIGHT,question='How many whole pizzas are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 1')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([13, 3, 448, 448])
|
torch.Size([13, 3, 448, 448])
|
question: ['How many parrots 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']]
|
question: ['How many whole pizzas are in the image?'], responses:['1']
|
question: ['Is there a blue couch in the image?'], responses:['no']
|
[('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']]
|
[('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
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.