text
stringlengths 0
1.16k
|
|---|
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.3226e-01, 8.6774e-01, 4.0912e-08, 6.8709e-10, 4.5592e-09, 3.0834e-09,
|
2.8032e-09, 3.3732e-08], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8677, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1323, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=LEFT,question='Can stairs be seen in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([1, 3, 448, 448])
|
question: ['Can stairs be seen 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']]
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
|
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
|
tensor([1.0000e+00, 8.3768e-09, 3.7680e-10, 1.3465e-06, 1.1045e-09, 9.3827e-09,
|
2.9857e-10, 2.6670e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 8.3768e-09, 3.7680e-10, 1.3465e-06, 1.1045e-09, 9.3827e-09,
|
2.9857e-10, 2.6670e-08], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.7680e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.4301e-06, device='cuda:3', grad_fn=<DivBackward0>)}
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
|
tensor([1.0000e+00, 3.6810e-10, 6.4469e-11, 1.0691e-10, 1.2327e-10, 4.7176e-09,
|
4.6449e-09, 4.1755e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
1 *************
|
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.6810e-10, 6.4469e-11, 1.0691e-10, 1.2327e-10, 4.7176e-09,
|
4.6449e-09, 4.1755e-11], device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.6449e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
|
tensor([1.0000e+00, 3.0115e-07, 2.7421e-07, 2.7794e-08, 2.3910e-09, 1.2479e-08,
|
1.9362e-09, 3.1117e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
|
2 *************
|
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 3.0115e-07, 2.7421e-07, 2.7794e-08, 2.3910e-09, 1.2479e-08,
|
1.9362e-09, 3.1117e-09], device='cuda:2', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(6.2308e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
|
[2024-10-24 10:35:46,569] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.33 | optimizer_step: 0.31
|
[2024-10-24 10:35:46,569] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5792.24 | backward_microstep: 7974.14 | backward_inner_microstep: 5439.25 | backward_allreduce_microstep: 2534.78 | step_microstep: 8.00
|
[2024-10-24 10:35:46,569] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5792.25 | backward: 7974.13 | backward_inner: 5439.30 | backward_allreduce: 2534.76 | step: 8.01
|
99%|ββββββββββ| 4787/4844 [19:54:30<14:03, 14.80s/it]Registering VQA_lavis step
|
Registering EVAL step
|
Registering RESULT step
|
Registering VQA_lavis step
|
Registering EVAL step
|
Registering RESULT step
|
Registering VQA_lavis step
|
Registering VQA_lavis step
|
Registering EVAL step
|
Registering RESULT step
|
ANSWER0=VQA(image=RIGHT,question='How many vending machines are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
Registering EVAL step
|
Registering RESULT step
|
ANSWER0=VQA(image=RIGHT,question='Is the sky partially visible behind a book stall?')
|
FINAL_ANSWER=RESULT(var=ANSWER0)
|
ANSWER0=VQA(image=LEFT,question='How many wolves are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
ANSWER0=VQA(image=RIGHT,question='Does the sink have a double basin?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([13, 3, 448, 448])
|
torch.Size([13, 3, 448, 448])
|
torch.Size([13, 3, 448, 448])
|
torch.Size([13, 3, 448, 448])
|
question: ['Is the sky partially visible behind a book stall?'], responses:['no']
|
question: ['How many vending machines are in the image?'], responses:['11']
|
question: ['How many wolves are in the image?'], responses:['5']
|
[('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']]
|
question: ['Does the sink have a double basin?'], responses:['yes']
|
[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)]
|
[['11', '10', '12', '9', '8', '13', '7', '14']]
|
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
|
[['5', '8', '4', '6', '3', '7', '11', '9']]
|
[('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
|
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
|
torch.Size([13, 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: 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
|
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
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
|
tensor([1.0000e+00, 1.5230e-08, 2.7849e-07, 1.1159e-11, 2.2135e-10, 1.2875e-09,
|
1.3280e-10, 1.6982e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
no *************
|
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.5230e-08, 2.7849e-07, 1.1159e-11, 2.2135e-10, 1.2875e-09,
|
1.3280e-10, 1.6982e-07], device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.5230e-08, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:1', grad_fn=<SubBackward0>)}
|
ANSWER0=VQA(image=LEFT,question='How many chimps are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 1')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
tensor([9.1882e-01, 9.4932e-04, 3.5610e-02, 9.3953e-05, 7.8766e-07, 3.1392e-02,
|
5.0257e-05, 1.3086e-02], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
11 *************
|
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.1882e-01, 9.4932e-04, 3.5610e-02, 9.3953e-05, 7.8766e-07, 3.1392e-02,
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.