text stringlengths 0 1.16k |
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3.2745e-01, 1.1636e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([5.4855e-01, 8.9549e-02, 2.2641e-02, 3.3115e-03, 6.5861e-03, 1.7998e-03, |
3.2745e-01, 1.1636e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.5485, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.4515, 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: 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([9.0797e-01, 1.8263e-02, 1.0082e-02, 5.0573e-03, 6.1062e-03, 4.0299e-03, |
4.8098e-02, 3.9089e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.0797e-01, 1.8263e-02, 1.0082e-02, 5.0573e-03, 6.1062e-03, 4.0299e-03, |
4.8098e-02, 3.9089e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.0183, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9817, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)} |
[2024-10-23 14:46:12,666] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.45 | optimizer_gradients: 0.21 | optimizer_step: 0.30 |
[2024-10-23 14:46:12,666] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9019.26 | backward_microstep: 8725.79 | backward_inner_microstep: 8720.58 | backward_allreduce_microstep: 5.12 | step_microstep: 7.51 |
[2024-10-23 14:46:12,666] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9019.27 | backward: 8725.78 | backward_inner: 8720.60 | backward_allreduce: 5.08 | step: 7.52 |
0%| | 19/4844 [04:56<21:00:01, 15.67s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='Is there at least one dog standing on all fours in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
ANSWER0=VQA(image=LEFT,question='How many perfume bottles are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 4') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='How many window screens are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
ANSWER0=VQA(image=LEFT,question='How many paper towel rolls are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['How many paper towel rolls 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([1, 3, 448, 448]) knan debug pixel values shape |
tensor([5.6730e-01, 6.8714e-02, 2.5739e-02, 5.9166e-03, 9.7599e-03, 3.8145e-03, |
3.1861e-01, 1.4689e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([5.6730e-01, 6.8714e-02, 2.5739e-02, 5.9166e-03, 9.7599e-03, 3.8145e-03, |
3.1861e-01, 1.4689e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.5673, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.4327, 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 pandas are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
question: ['Is there at least one dog standing on all fours in the image?'], responses:['no'] |
torch.Size([7, 3, 448, 448]) |
[('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([7, 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: 1867 |
question: ['How many window screens are in the image?'], responses:['3'] |
question: ['How many perfume bottles are in the image?'], responses:['100'] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1868 |
[('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']] |
[('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']] |
question: ['How many pandas are in the image?'], 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']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
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: 7, images per sample: 7.0, dynamic token length: 1867 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1868 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1868 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1868 |
tensor([7.1764e-01, 2.8104e-01, 4.2943e-05, 1.0513e-04, 1.1764e-04, 7.8407e-04, |
2.3605e-04, 3.0414e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.1764e-01, 2.8104e-01, 4.2943e-05, 1.0513e-04, 1.1764e-04, 7.8407e-04, |
2.3605e-04, 3.0414e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.2810, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.7176, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0013, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many arches are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 4') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
tensor([0.5856, 0.2441, 0.1213, 0.0025, 0.0242, 0.0076, 0.0137, 0.0009], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
4 ************* |
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([0.5856, 0.2441, 0.1213, 0.0025, 0.0242, 0.0076, 0.0137, 0.0009], |
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