text stringlengths 0 1.16k |
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.7571e-01, 2.6455e-02, 1.3710e-02, 5.2020e-03, 7.1169e-03, 3.9104e-03, |
6.7488e-02, 4.1040e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1243, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8757, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:0', 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([7, 3, 448, 448]) knan debug pixel values shape |
question: ['How many animals 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']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
tensor([5.8422e-01, 6.1523e-02, 2.2246e-02, 5.7110e-03, 8.9841e-03, 4.4429e-03, |
3.1271e-01, 1.6098e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([5.8422e-01, 6.1523e-02, 2.2246e-02, 5.7110e-03, 8.9841e-03, 4.4429e-03, |
3.1271e-01, 1.6098e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5842, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.4158, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is the mouth of the dog open?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
tensor([9.5106e-01, 8.7585e-03, 4.5430e-03, 2.0760e-03, 2.8414e-03, 1.8605e-03, |
2.8717e-02, 1.4100e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.5106e-01, 8.7585e-03, 4.5430e-03, 2.0760e-03, 2.8414e-03, 1.8605e-03, |
2.8717e-02, 1.4100e-04], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9511, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0489, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
question: ['Is the mouth of the dog open?'], 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 |
tensor([9.0296e-01, 4.4950e-02, 1.0030e-02, 3.7268e-02, 2.8739e-03, 9.0378e-04, |
9.6214e-04, 5.1742e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.0296e-01, 4.4950e-02, 1.0030e-02, 3.7268e-02, 2.8739e-03, 9.0378e-04, |
9.6214e-04, 5.1742e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9030, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0970, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([9.2068e-01, 1.0721e-02, 6.6694e-02, 8.2452e-04, 4.4325e-05, 2.6251e-04, |
5.4778e-05, 7.1510e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.2068e-01, 1.0721e-02, 6.6694e-02, 8.2452e-04, 4.4325e-05, 2.6251e-04, |
5.4778e-05, 7.1510e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9207, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0667, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0126, device='cuda:3', grad_fn=<DivBackward0>)} |
[2024-10-23 14:43:28,139] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.33 | optimizer_step: 0.32 |
[2024-10-23 14:43:28,140] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3145.11 | backward_microstep: 14719.57 | backward_inner_microstep: 3024.23 | backward_allreduce_microstep: 11695.22 | step_microstep: 7.77 |
[2024-10-23 14:43:28,140] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3145.12 | backward: 14719.57 | backward_inner: 3024.28 | backward_allreduce: 11695.20 | step: 7.79 |
0%| | 8/4844 [02:11<22:04:49, 16.44s/it]Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='How many jellyfish are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} > 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Is there a plant in one of the vases?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
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='How many dogs are standing on grass in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
torch.Size([1, 3, 448, 448]) |
ANSWER0=VQA(image=LEFT,question='How many children are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['How many jellyfish are in the image?'], responses:['1'] |
question: ['Is there a plant in one of the vases?'], 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']] |
[('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 |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 330 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328 |
tensor([0.6181, 0.0948, 0.0395, 0.0218, 0.0263, 0.0106, 0.1880, 0.0009], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([0.6181, 0.0948, 0.0395, 0.0218, 0.0263, 0.0106, 0.1880, 0.0009], |
device='cuda:2', grad_fn=<SelectBackward0>) |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328 |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3819, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.6181, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many sled dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 4') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
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