text stringlengths 0 1.16k |
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1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.1766e-01, 1.4837e-02, 7.2286e-03, 2.7272e-03, 3.3046e-03, 2.2901e-03, |
5.1774e-02, 1.8380e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.9177, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0823, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
[2024-10-23 14:50:10,430] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.33 | optimizer_step: 0.32 |
[2024-10-23 14:50:10,431] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5156.81 | backward_microstep: 8724.33 | backward_inner_microstep: 4851.05 | backward_allreduce_microstep: 3873.16 | step_microstep: 7.76 |
[2024-10-23 14:50:10,431] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5156.83 | backward: 8724.32 | backward_inner: 4851.07 | backward_allreduce: 3873.14 | step: 7.77 |
1%| | 35/4844 [08:54<21:19:35, 15.96s/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=RIGHT,question='How many laptop computers are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 4') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many dogs are sitting on the wooden structure?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Is there any animal standing on the roof?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='Is there a ladder leaning against the bookcase?') |
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]) |
torch.Size([13, 3, 448, 448]) |
question: ['How many dogs are sitting on the wooden structure?'], 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([7, 3, 448, 448]) knan debug pixel values shape |
question: ['Is there any animal standing on the roof?'], responses:['yes'] |
question: ['How many laptop computers are in the image?'], responses:['4'] |
question: ['Is there a ladder leaning against the bookcase?'], responses:['no'] |
[('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']] |
[('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']] |
[('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([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: 3397 |
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: 3397 |
tensor([0.3556, 0.1891, 0.1229, 0.0193, 0.0441, 0.0101, 0.2584, 0.0005], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([0.3556, 0.1891, 0.1229, 0.0193, 0.0441, 0.0101, 0.2584, 0.0005], |
device='cuda:2', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.2584, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.7416, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many binders are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
question: ['How many binders 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']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
tensor([0.4122, 0.2832, 0.0340, 0.1338, 0.0137, 0.0716, 0.0434, 0.0081], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.4122, 0.2832, 0.0340, 0.1338, 0.0137, 0.0716, 0.0434, 0.0081], |
device='cuda:2', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.5178, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.4822, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
tensor([7.8792e-01, 2.1122e-01, 4.3322e-05, 9.7205e-05, 1.9129e-04, 1.7091e-04, |
2.8482e-04, 7.0857e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.8792e-01, 2.1122e-01, 4.3322e-05, 9.7205e-05, 1.9129e-04, 1.7091e-04, |
2.8482e-04, 7.0857e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([6.4625e-01, 2.2035e-02, 3.2929e-01, 8.6112e-04, 1.5858e-04, 5.3394e-04, |
1.4326e-04, 7.2852e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.4625e-01, 2.2035e-02, 3.2929e-01, 8.6112e-04, 1.5858e-04, 5.3394e-04, |
1.4326e-04, 7.2852e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([0.5494, 0.3130, 0.0398, 0.0054, 0.0699, 0.0074, 0.0142, 0.0009], |
device='cuda:3', grad_fn=<SoftmaxBackward0>) |
4 ************* |
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([0.5494, 0.3130, 0.0398, 0.0054, 0.0699, 0.0074, 0.0142, 0.0009], |
device='cuda:3', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.6462, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3293, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0245, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Are there any human beings in the image?') |
ANSWER1=EVAL(expr='not {ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.2112, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.7879, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0009, device='cuda:1', grad_fn=<DivBackward0>)} |
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