text
stringlengths 0
1.16k
|
|---|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.1862e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1862e-08, device='cuda:1', grad_fn=<DivBackward0>)}
|
tensor([9.9999e-01, 4.9408e-06, 1.3039e-07, 4.0417e-10, 2.9356e-07, 1.6649e-08,
|
5.0502e-07, 1.7968e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
0 *************
|
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9999e-01, 4.9408e-06, 1.3039e-07, 4.0417e-10, 2.9356e-07, 1.6649e-08,
|
5.0502e-07, 1.7968e-07], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-06, device='cuda:3', grad_fn=<DivBackward0>)}
|
[2024-10-24 10:47:25,566] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.37 | optimizer_gradients: 0.27 | optimizer_step: 0.32
|
[2024-10-24 10:47:25,567] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3774.64 | backward_microstep: 6296.05 | backward_inner_microstep: 3510.57 | backward_allreduce_microstep: 2785.41 | step_microstep: 7.95
|
[2024-10-24 10:47:25,567] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3774.65 | backward: 6296.04 | backward_inner: 3510.59 | backward_allreduce: 2785.37 | step: 7.96
|
100%|ββββββββββ| 4835/4844 [20:06:09<01:54, 12.76s/it]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='Does the image show one model in pajamas with solid trim at the hems?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
Registering VQA_lavis step
|
Registering EVAL step
|
Registering RESULT step
|
Registering VQA_lavis step
|
ANSWER0=VQA(image=LEFT,question='Are the shelves placed in a corner?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
Registering EVAL step
|
Registering RESULT step
|
ANSWER0=VQA(image=RIGHT,question='Does the canopy bed have a two-drawer chest next to it?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([1, 3, 448, 448])
|
ANSWER0=VQA(image=LEFT,question='Is a person holding up the crab?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([7, 3, 448, 448])
|
torch.Size([7, 3, 448, 448])
|
torch.Size([13, 3, 448, 448])
|
question: ['Does the canopy bed have a two-drawer chest next to it?'], 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
|
tensor([9.9996e-01, 7.0205e-09, 3.5356e-05, 6.1658e-09, 2.5536e-12, 3.3314e-11,
|
2.2706e-11, 5.4696e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
|
yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9996e-01, 7.0205e-09, 3.5356e-05, 6.1658e-09, 2.5536e-12, 3.3314e-11,
|
2.2706e-11, 5.4696e-09], device='cuda:2', grad_fn=<SelectBackward0>)
|
question: ['Are the shelves placed in a corner?'], responses:['yes']
|
question: ['Is a person holding up the crab?'], responses:['no']
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(3.5356e-05, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.8873e-08, device='cuda:2', grad_fn=<DivBackward0>)}
|
[('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']]
|
[('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']]
|
ANSWER0=VQA(image=RIGHT,question='How many water buffalo are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([3, 3, 448, 448])
|
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
question: ['How many water buffalo are in the image?'], responses:['3']
|
[('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']]
|
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
|
question: ['Does the image show one model in pajamas with solid trim at the hems?'], 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
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3406
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3409
|
tensor([9.9743e-01, 9.5865e-05, 1.0545e-07, 1.6850e-07, 1.0811e-10, 2.4724e-03,
|
1.6519e-09, 9.6530e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>)
|
3 *************
|
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9743e-01, 9.5865e-05, 1.0545e-07, 1.6850e-07, 1.0811e-10, 2.4724e-03,
|
1.6519e-09, 9.6530e-11], device='cuda:2', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0545e-07, 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: 3406
|
tensor([1.0000e+00, 1.8294e-09, 2.2380e-07, 4.4970e-10, 1.2509e-12, 3.6229e-12,
|
6.1410e-12, 5.5139e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.8294e-09, 2.2380e-07, 4.4970e-10, 1.2509e-12, 3.6229e-12,
|
6.1410e-12, 5.5139e-10], device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.2380e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.4616e-08, device='cuda:1', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='How many mittens are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
tensor([1.0000e+00, 6.6916e-10, 2.9027e-07, 8.7586e-12, 7.6668e-11, 1.2075e-08,
|
3.6400e-10, 2.2904e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
no *************
|
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 6.6916e-10, 2.9027e-07, 8.7586e-12, 7.6668e-11, 1.2075e-08,
|
3.6400e-10, 2.2904e-07], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.6916e-10, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:3', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=LEFT,question='Are multiple tracks visible in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([13, 3, 448, 448])
|
torch.Size([7, 3, 448, 448])
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3407
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3406
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.