text stringlengths 0 1.16k |
|---|
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.9315, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0685, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
[2024-10-23 14:46:40,662] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.27 | optimizer_step: 0.32 |
[2024-10-23 14:46:40,662] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5129.85 | backward_microstep: 8846.86 | backward_inner_microstep: 4841.90 | backward_allreduce_microstep: 4004.88 | step_microstep: 7.46 |
[2024-10-23 14:46:40,662] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5129.86 | backward: 8846.84 | backward_inner: 4841.92 | backward_allreduce: 4004.87 | step: 7.47 |
0%| | 21/4844 [05:24<19:50:58, 14.82s/it]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 |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='Are all of the bottles the same size?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='How many men are working on the roof of the house?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many power poles are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 6') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Are all of the bottles the same size?'], 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([5.9432e-01, 2.5465e-02, 3.7703e-01, 1.2484e-03, 2.0208e-04, 5.0023e-04, |
3.5760e-05, 1.1916e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.9432e-01, 2.5465e-02, 3.7703e-01, 1.2484e-03, 2.0208e-04, 5.0023e-04, |
3.5760e-05, 1.1916e-03], device='cuda:3', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.5943, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.3770, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0286, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many elephants are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
question: ['How many power poles 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([7, 3, 448, 448]) knan debug pixel values shape |
question: ['How many animals are in the image?'], responses:['2'] |
question: ['How many men are working on the roof of the house?'], responses:['1'] |
[('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']] |
[('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']] |
question: ['How many elephants 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([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: 13, images per sample: 13.0, dynamic token length: 3400 |
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: 3400 |
tensor([0.3305, 0.1786, 0.1216, 0.0292, 0.0511, 0.0145, 0.2740, 0.0006], |
device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([0.3305, 0.1786, 0.1216, 0.0292, 0.0511, 0.0145, 0.2740, 0.0006], |
device='cuda:1', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.0953, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9047, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many cheetahs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
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: 3400 |
tensor([9.4194e-01, 1.0463e-02, 4.3601e-03, 1.3669e-03, 1.8156e-03, 1.1237e-03, |
3.8878e-02, 5.3549e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.4194e-01, 1.0463e-02, 4.3601e-03, 1.3669e-03, 1.8156e-03, 1.1237e-03, |
3.8878e-02, 5.3549e-05], device='cuda:3', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.0105, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9895, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
question: ['How many cheetahs 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']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
torch.Size([7, 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: 3400 |
tensor([9.1421e-01, 2.9387e-02, 1.0810e-02, 4.0164e-02, 2.9096e-03, 1.3730e-03, |
1.0695e-03, 7.7042e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.1421e-01, 2.9387e-02, 1.0810e-02, 4.0164e-02, 2.9096e-03, 1.3730e-03, |
1.0695e-03, 7.7042e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.9142, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0858, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([8.2122e-01, 2.3295e-02, 6.6738e-03, 2.0332e-03, 2.6127e-03, 1.3947e-03, |
1.4271e-01, 6.2584e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.