text stringlengths 0 1.16k |
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['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers'] tensor([5.3444e-04, 1.3917e-02, 1.7208e-06, 4.1469e-02, 6.8394e-02, 1.5567e-03, |
8.7016e-01, 3.9678e-03], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([9.9997e-01, 2.4300e-05, 5.3127e-07, 5.6551e-12, 5.8313e-11, 9.7085e-10, |
1.4564e-10, 3.0239e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9997e-01, 2.4300e-05, 5.3127e-07, 5.6551e-12, 5.8313e-11, 9.7085e-10, |
1.4564e-10, 3.0239e-07], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.4300e-05, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
[2024-10-24 09:37:54,997] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.27 | optimizer_step: 0.31 |
[2024-10-24 09:37:54,998] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5114.21 | backward_microstep: 12735.37 | backward_inner_microstep: 4971.60 | backward_allreduce_microstep: 7763.70 | step_microstep: 7.55 |
[2024-10-24 09:37:54,998] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5114.22 | backward: 12735.36 | backward_inner: 4971.61 | backward_allreduce: 7763.69 | step: 7.56 |
94%|ββββββββββ| 4555/4844 [18:56:38<1:14:15, 15.42s/it]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='What color is the base of the anemone in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == "red"') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Are there animals in the blue water?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 5') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='Does the left image include things that look like glowing blue jellyfish?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Are there animals in the blue water?'], responses:['yes'] |
question: ['How many animals are in the image?'], responses:['five'] |
[('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']] |
[('7 eleven', 0.1264466744091217), ('babies', 0.124977990347662), ('sunrise', 0.12490143984830117), ('eating', 0.1247676656843781), ('feet', 0.12475702323703439), ('candle', 0.12473210928138137), ('light', 0.12472650705175181), ('floating', 0.12469059014036947)] |
[['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
question: ['What color is the base of the anemone in the image?'], responses:['green'] |
question: ['Does the left image include things that look like glowing blue jellyfish?'], responses:['yes'] |
[('green', 0.1326115459908909), ('yellow', 0.12668030247077625), ('red', 0.12551779073733718), ('wild', 0.12324669870262604), ('orange and blue', 0.12319974118412196), ('bronze', 0.1230515752050065), ('pink', 0.12286305245049417), ('red white blue', 0.12282929325874692)] |
[['green', 'yellow', 'red', 'wild', 'orange and blue', 'bronze', 'pink', 'red white blue']] |
[('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: 3402 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402 |
tensor([1.5203e-07, 6.0474e-01, 1.7432e-02, 7.6423e-04, 3.7660e-01, 1.4935e-04, |
1.9086e-04, 1.1698e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
babies ************* |
['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([1.5203e-07, 6.0474e-01, 1.7432e-02, 7.6423e-04, 3.7660e-01, 1.4935e-04, |
1.9086e-04, 1.1698e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 6.0948e-09, 2.6204e-10, 7.9454e-08, 3.6355e-10, 5.2935e-10, |
1.0238e-10, 1.2418e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.0948e-09, 2.6204e-10, 7.9454e-08, 3.6355e-10, 5.2935e-10, |
1.0238e-10, 1.2418e-08], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.6204e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1895e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many wolves are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Is a toilet visible in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403 |
question: ['Is a toilet visible in the image?'], responses:['yes'] |
question: ['How many wolves are in the image?'], responses:['ε'] |
[('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']] |
[('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']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
tensor([6.0598e-01, 5.2851e-02, 2.5332e-03, 5.1409e-04, 1.5149e-03, 2.2873e-02, |
3.1367e-01, 5.9344e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
green ************* |
['green', 'yellow', 'red', 'wild', 'orange and blue', 'bronze', 'pink', 'red white blue'] tensor([6.0598e-01, 5.2851e-02, 2.5332e-03, 5.1409e-04, 1.5149e-03, 2.2873e-02, |
3.1367e-01, 5.9344e-05], 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>)} |
ANSWER0=VQA(image=RIGHT,question='Does the dog on the right have a blue collar?') |
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