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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
question: ['Does the dog in the image have food in its mouth?'], responses:['no'] |
[('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([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
question: ['Is there a console television 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: 3399 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
tensor([1.0000e+00, 6.0928e-10, 3.6135e-07, 3.9563e-11, 2.4867e-11, 1.5143e-08, |
6.2365e-10, 3.2730e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 6.0928e-10, 3.6135e-07, 3.9563e-11, 2.4867e-11, 1.5143e-08, |
6.2365e-10, 3.2730e-07], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.0928e-10, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:2', grad_fn=<SubBackward0>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
tensor([1.0000e+00, 2.0329e-10, 4.2115e-11, 9.1717e-11, 7.0595e-11, 3.2424e-09, |
2.6055e-09, 1.8251e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.0329e-10, 4.2115e-11, 9.1717e-11, 7.0595e-11, 3.2424e-09, |
2.6055e-09, 1.8251e-11], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([9.9753e-01, 2.4726e-03, 1.3895e-07, 2.2389e-11, 4.6777e-11, 1.7367e-09, |
1.1274e-10, 5.4280e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9753e-01, 2.4726e-03, 1.3895e-07, 2.2389e-11, 4.6777e-11, 1.7367e-09, |
1.1274e-10, 5.4280e-08], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.2739e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 5') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0025, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.9975, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.7881e-07, device='cuda:0', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many puppies are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 4') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['How many puppies 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([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
question: ['How many animals are in the image?'], responses:['4'] |
tensor([1.0000e+00, 5.5102e-09, 4.5020e-09, 1.7460e-08, 8.1123e-11, 2.1849e-11, |
7.6850e-11, 3.2426e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.5102e-09, 4.5020e-09, 1.7460e-08, 8.1123e-11, 2.1849e-11, |
7.6850e-11, 3.2426e-09], device='cuda:3', grad_fn=<SelectBackward0>) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(4.5020e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-4.5020e-09, device='cuda:3', grad_fn=<DivBackward0>)} |
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)] |
[['4', '5', '3', '8', '6', '1', '2', '11']] |
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: 1861 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
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([9.4485e-04, 1.2593e-02, 1.3511e-06, 2.5957e-01, 1.0707e-01, 4.0617e-03, |
6.1354e-01, 2.2234e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
vegetable ************* |
['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers'] tensor([9.4485e-04, 1.2593e-02, 1.3511e-06, 2.5957e-01, 1.0707e-01, 4.0617e-03, |
6.1354e-01, 2.2234e-03], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([9.9874e-01, 1.2453e-03, 1.7216e-05, 2.2390e-10, 7.7719e-09, 1.5918e-07, |
6.6448e-09, 3.0915e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
4 ************* |
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9874e-01, 1.2453e-03, 1.7216e-05, 2.2390e-10, 7.7719e-09, 1.5918e-07, |
6.6448e-09, 3.0915e-09], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0012, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9988, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
[2024-10-24 09:26:00,636] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.38 | optimizer_step: 0.33 |
[2024-10-24 09:26:00,637] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7122.23 | backward_microstep: 10661.14 | backward_inner_microstep: 6758.45 | backward_allreduce_microstep: 3902.57 | step_microstep: 8.10 |
[2024-10-24 09:26:00,637] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7122.23 | backward: 10661.13 | backward_inner: 6758.50 | backward_allreduce: 3902.55 | step: 8.11 |
93%|ββββββββββ| 4508/4844 [18:44:44<1:17:25, 13.83s/it]Registering VQA_lavis step |
Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering EVAL stepRegistering RESULT step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='How many monkeys are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} > 7') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Is someone watching TV while sitting on a couch?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Does the image show the back end of a bus?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
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