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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([9.9995e-01, 5.1436e-05, 9.6246e-08, 2.6992e-07, 1.4308e-08, 2.1098e-09, |
1.0673e-08, 2.6669e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9995e-01, 5.1436e-05, 9.6246e-08, 2.6992e-07, 1.4308e-08, 2.1098e-09, |
1.0673e-08, 2.6669e-09], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9999, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(5.1832e-05, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
[2024-10-24 10:03:34,396] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.59 | optimizer_gradients: 0.21 | optimizer_step: 0.30 |
[2024-10-24 10:03:34,397] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 8996.58 | backward_microstep: 8733.49 | backward_inner_microstep: 8728.31 | backward_allreduce_microstep: 5.10 | step_microstep: 7.66 |
[2024-10-24 10:03:34,397] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 8996.59 | backward: 8733.48 | backward_inner: 8728.33 | backward_allreduce: 5.08 | step: 7.67 |
96%|ββββββββββ| 4657/4844 [19:22:18<48:39, 15.61s/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 |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='Is there a stack of three books on the front-most corner of the shelf under the couch?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Is the animal looking toward the camera?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Are the televisions in the image turned on?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
question: ['Are the televisions in the image turned on?'], 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([3, 3, 448, 448]) knan debug pixel values shape |
question: ['Is the animal looking toward the camera?'], responses:['yes'] |
question: ['How many animals are in the image?'], responses:['2'] |
question: ['Is there a stack of three books on the front-most corner of the shelf under the couch?'], 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']] |
[('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 |
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: 1872 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1875 |
tensor([1.0000e+00, 8.4108e-09, 3.9690e-07, 1.2237e-08, 4.2874e-10, 4.2128e-08, |
9.0474e-08, 5.2503e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 8.4108e-09, 3.9690e-07, 1.2237e-08, 4.2874e-10, 4.2128e-08, |
9.0474e-08, 5.2503e-07], device='cuda:2', grad_fn=<SelectBackward0>) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1872 |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(8.4108e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.0729e-06, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Are the graduates wearing blue gowns?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1873 |
question: ['Are the graduates wearing blue gowns?'], responses:['yes'] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1872 |
[('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([3, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1872 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1873 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1873 |
tensor([1.0000e+00, 3.9198e-08, 1.6823e-09, 6.7919e-08, 6.6289e-10, 6.5503e-09, |
1.6538e-10, 9.4927e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.9198e-08, 1.6823e-09, 6.7919e-08, 6.6289e-10, 6.5503e-09, |
1.6538e-10, 9.4927e-09], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.6823e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1753e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
tensor([9.9937e-01, 4.6023e-09, 6.2633e-04, 5.4809e-09, 3.7480e-11, 2.6753e-11, |
4.2001e-10, 2.5605e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9937e-01, 4.6023e-09, 6.2633e-04, 5.4809e-09, 3.7480e-11, 2.6753e-11, |
4.2001e-10, 2.5605e-08], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 8.7642e-08, 5.1014e-09, 6.2148e-08, 3.5678e-10, 5.4190e-10, |
8.7955e-10, 5.5781e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 8.7642e-08, 5.1014e-09, 6.2148e-08, 3.5678e-10, 5.4190e-10, |
8.7955e-10, 5.5781e-11], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.5673e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
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