text stringlengths 0 1.16k |
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.5110e-08, 1.2825e-08, 5.9177e-09, 1.5166e-10, 1.8746e-09, |
5.3338e-10, 6.8763e-10], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(4.7100e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
tensor([7.0338e-01, 1.4303e-01, 6.1112e-02, 4.3293e-04, 2.4678e-05, 5.2493e-02, |
2.2982e-03, 3.7223e-02], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
11 ************* |
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([7.0338e-01, 1.4303e-01, 6.1112e-02, 4.3293e-04, 2.4678e-05, 5.2493e-02, |
2.2982e-03, 3.7223e-02], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
question: ['How many dogs are in the image?'], responses:['1'] |
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)] |
[['1', '3', '4', '8', '6', '12', '2', '47']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0000e+00, 2.2067e-10, 7.1639e-11, 2.1894e-10, 1.1627e-10, 1.6719e-08, |
2.2549e-09, 2.4217e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.2067e-10, 7.1639e-11, 2.1894e-10, 1.1627e-10, 1.6719e-08, |
2.2549e-09, 2.4217e-10], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.7589e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
[2024-10-24 10:30:28,336] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.34 | optimizer_step: 0.33 |
[2024-10-24 10:30:28,337] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3135.10 | backward_microstep: 14618.32 | backward_inner_microstep: 3008.24 | backward_allreduce_microstep: 11609.90 | step_microstep: 7.71 |
[2024-10-24 10:30:28,337] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3135.11 | backward: 14618.31 | backward_inner: 3008.28 | backward_allreduce: 11609.88 | step: 7.72 |
98%|ββββββββββ| 4766/4844 [19:49:12<19:57, 15.35s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Does at least one shoe in the image have pink laces?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering EVAL step |
Registering RESULT step |
torch.Size([1, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='How many folded items are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 6') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='Are the dough products arranged neatly on a baking sheet?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many birds are on the beach in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
question: ['Does at least one shoe in the image have pink laces?'], 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']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0000e+00, 6.5823e-09, 2.0730e-10, 2.5311e-09, 1.9419e-10, 5.8137e-10, |
1.2894e-11, 2.8248e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.5823e-09, 2.0730e-10, 2.5311e-09, 1.9419e-10, 5.8137e-10, |
1.2894e-11, 2.8248e-09], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.0730e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.0730e-10, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
question: ['How many folded items are in the image?'], responses:['4'] |
question: ['Are the dough products arranged neatly on a baking sheet?'], responses:['yes'] |
question: ['How many birds are on the beach in the image?'], responses:['100'] |
[('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']] |
[('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']] |
[('100', 0.1277092174007614), ('120', 0.12519936731884676), ('88', 0.12483671971182599), ('80', 0.12474858811112934), ('60', 0.12457749608485191), ('99', 0.1243465850330014), ('90', 0.12430147627057883), ('101', 0.12428055006900451)] |
[['100', '120', '88', '80', '60', '99', '90', '101']] |
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: 1864 |
question: ['How many dogs are in the image?'], responses:['δΈ'] |
[('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']] |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
tensor([2.0610e-05, 1.2072e-03, 4.3653e-02, 7.7509e-01, 1.2939e-01, 2.8051e-03, |
4.6818e-03, 4.3153e-02], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
bulldog ************* |
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([2.0610e-05, 1.2072e-03, 4.3653e-02, 7.7509e-01, 1.2939e-01, 2.8051e-03, |
4.6818e-03, 4.3153e-02], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
tensor([4.1248e-01, 5.7443e-01, 2.8345e-03, 1.2205e-05, 8.8781e-03, 5.7744e-05, |
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