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[2024-10-24 10:05:59,347] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.39 | optimizer_gradients: 0.27 | optimizer_step: 0.32 |
[2024-10-24 10:05:59,347] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7079.19 | backward_microstep: 6810.04 | backward_inner_microstep: 6803.92 | backward_allreduce_microstep: 5.97 | step_microstep: 7.65 |
[2024-10-24 10:05:59,348] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7079.19 | backward: 6810.03 | backward_inner: 6804.00 | backward_allreduce: 5.94 | step: 7.66 |
96%|ββββββββββ| 4666/4844 [19:24:43<47:32, 16.03s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='How many dog tongues are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
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='How many humans are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='How many horizontal rows of cell phones are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
ANSWER0=VQA(image=LEFT,question='How many animals are lying down in the grass?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([3, 3, 448, 448]) |
question: ['How many dog tongues are in the image?'], responses:['0'] |
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)] |
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
question: ['How many animals are lying down in the grass?'], responses:['4'] |
[('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']] |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
tensor([1.0000e+00, 1.2559e-07, 1.0063e-07, 7.2438e-11, 1.4228e-07, 1.9838e-08, |
2.4251e-07, 5.0867e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 1.2559e-07, 1.0063e-07, 7.2438e-11, 1.4228e-07, 1.9838e-08, |
2.4251e-07, 5.0867e-07], device='cuda:3', grad_fn=<SelectBackward0>) |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.0729e-06, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many parrots are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
question: ['How many humans are in the image?'], responses:['δΈ'] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)] |
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838 |
tensor([5.9588e-01, 3.9171e-01, 8.0752e-03, 5.3274e-06, 4.3239e-03, 5.5595e-08, |
6.3056e-06, 2.2465e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
4 ************* |
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([5.9588e-01, 3.9171e-01, 8.0752e-03, 5.3274e-06, 4.3239e-03, 5.5595e-08, |
6.3056e-06, 2.2465e-06], device='cuda:0', grad_fn=<SelectBackward0>) |
question: ['How many horizontal rows of cell phones are in the image?'], responses:['12'] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(6.3612e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many instruments are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
[('12', 0.1271623397239889), ('11', 0.12513993889798333), ('10', 0.1250472536472656), ('8', 0.12474319370676636), ('6', 0.12462998196332449), ('26', 0.12450301801207538), ('47', 0.1243904847365581), ('13', 0.12438378931203788)] |
[['12', '11', '10', '8', '6', '26', '47', '13']] |
question: ['How many parrots are in the image?'], responses:['2'] |
torch.Size([13, 3, 448, 448]) |
[('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']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
tensor([0.0012, 0.0023, 0.0868, 0.6898, 0.1238, 0.0806, 0.0038, 0.0116], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
bulldog ************* |
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([0.0012, 0.0023, 0.0868, 0.6898, 0.1238, 0.0806, 0.0038, 0.0116], |
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>)} |
ANSWER0=VQA(image=RIGHT,question='Is there a round canopy bed in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
question: ['How many instruments are in the image?'], responses:['4'] |
torch.Size([3, 3, 448, 448]) |
[('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']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
question: ['Is there a round canopy bed in the image?'], responses:['yes'] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
[('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 |
tensor([1.0000e+00, 3.8669e-07, 6.9358e-09, 2.0335e-08, 1.3518e-09, 1.8476e-09, |
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