text stringlengths 0 1.16k |
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6.3814e-04, 4.7029e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.2772e-01, 2.9821e-02, 7.5411e-03, 3.1749e-02, 1.7360e-03, 7.4594e-04, |
6.3814e-04, 4.7029e-05], device='cuda:3', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.9683, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0317, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
tensor([9.8677e-01, 2.2266e-03, 9.8729e-04, 5.8914e-04, 8.1491e-04, 5.5821e-04, |
8.0199e-03, 3.3763e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.8677e-01, 2.2266e-03, 9.8729e-04, 5.8914e-04, 8.1491e-04, 5.5821e-04, |
8.0199e-03, 3.3763e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.9868, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0132, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)} |
[2024-10-23 14:53:54,309] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.39 | optimizer_gradients: 0.24 | optimizer_step: 0.31 |
[2024-10-23 14:53:54,310] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7093.70 | backward_microstep: 6793.93 | backward_inner_microstep: 6788.25 | backward_allreduce_microstep: 5.48 | step_microstep: 7.49 |
[2024-10-23 14:53:54,310] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7093.72 | backward: 6793.92 | backward_inner: 6788.32 | backward_allreduce: 5.41 | step: 7.50 |
1%| | 49/4844 [12:38<21:14:43, 15.95s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='How many boats are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 6') |
ANSWER2=EVAL(expr='{ANSWER0} >= 12') |
ANSWER3=EVAL(expr='{ANSWER1} or {ANSWER2}') |
FINAL_ANSWER=RESULT(var=ANSWER3) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Are seats available in the reading area?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Does the duck in the image have its beak on the ground?') |
ANSWER1=RESULT(var=ANSWER0) |
torch.Size([11, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='Is the dog standing with a front leg off the ground?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Does the duck in the image have its beak on the ground?'], 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([7, 3, 448, 448]) knan debug pixel values shape |
question: ['How many boats are in the image?'], responses:['2'] |
[('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']] |
question: ['Are seats available in the reading area?'], responses:['no'] |
torch.Size([11, 3, 448, 448]) knan debug pixel values shape |
[('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']] |
question: ['Is the dog standing with a front leg off the ground?'], 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([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: 3400 |
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: 3400 |
tensor([6.4339e-01, 1.4377e-02, 3.3984e-01, 1.3510e-03, 7.1322e-05, 3.1334e-04, |
6.5276e-05, 5.9740e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.4339e-01, 1.4377e-02, 3.3984e-01, 1.3510e-03, 7.1322e-05, 3.1334e-04, |
6.5276e-05, 5.9740e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.6434, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(0.3398, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0168, device='cuda:3', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
question: ['How many animals are in the image?'], responses:['3'] |
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)] |
[['3', '4', '1', '5', '8', '2', '6', '12']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401 |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
tensor([0.7305, 0.1269, 0.0221, 0.0235, 0.0017, 0.0872, 0.0072, 0.0009], |
device='cuda:3', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.7305, 0.1269, 0.0221, 0.0235, 0.0017, 0.0872, 0.0072, 0.0009], |
device='cuda:3', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.7305, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.2695, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
tensor([0.1981, 0.1933, 0.1655, 0.0766, 0.1622, 0.0963, 0.1051, 0.0029], |
device='cuda:1', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([0.1981, 0.1933, 0.1655, 0.0766, 0.1622, 0.0963, 0.1051, 0.0029], |
device='cuda:1', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.2044, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.7956, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Does an animal in the image have wheels?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401 |
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