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tensor([0.3117, 0.0205, 0.3298, 0.1144, 0.1617, 0.0451, 0.0049, 0.0120], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
4 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.3117, 0.0205, 0.3298, 0.1144, 0.1617, 0.0451, 0.0049, 0.0120], |
device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([5.9197e-01, 4.0685e-01, 3.2680e-05, 1.6849e-04, 1.4898e-04, 4.3118e-04, |
3.5731e-04, 3.9928e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.9197e-01, 4.0685e-01, 3.2680e-05, 1.6849e-04, 1.4898e-04, 4.3118e-04, |
3.5731e-04, 3.9928e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
ANSWER0=VQA(image=RIGHT,question='How many collies are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} > 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4069, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.5920, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0012, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is the writing in the image cursive?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
tensor([5.4593e-01, 4.5262e-01, 3.9468e-05, 1.5072e-04, 3.5105e-04, 7.4195e-04, |
1.5123e-04, 1.9187e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.4593e-01, 4.5262e-01, 3.9468e-05, 1.5072e-04, 3.5105e-04, 7.4195e-04, |
1.5123e-04, 1.9187e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([0.2714, 0.0647, 0.2250, 0.1652, 0.1003, 0.1064, 0.0197, 0.0474], |
device='cuda:3', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.2714, 0.0647, 0.2250, 0.1652, 0.1003, 0.1064, 0.0197, 0.0474], |
device='cuda:3', grad_fn=<SelectBackward0>) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4526, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5459, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0015, device='cuda:0', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Are the dogs outside?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many boxes are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Is the writing in the image cursive?'], responses:['no'] |
question: ['How many collies are in the image?'], responses:['1'] |
[('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']] |
[('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: ['Are the dogs outside?'], responses:['yes'] |
question: ['How many boxes 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']] |
[('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 |
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: 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 |
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([5.0700e-01, 4.9189e-01, 4.6755e-05, 1.0144e-04, 2.4753e-04, 3.4940e-04, |
2.7003e-04, 9.0714e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.0700e-01, 4.9189e-01, 4.6755e-05, 1.0144e-04, 2.4753e-04, 3.4940e-04, |
2.7003e-04, 9.0714e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([8.4641e-01, 3.1825e-02, 1.8110e-02, 8.5503e-03, 9.4075e-03, 6.1824e-03, |
7.8772e-02, 7.4026e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.4641e-01, 3.1825e-02, 1.8110e-02, 8.5503e-03, 9.4075e-03, 6.1824e-03, |
7.8772e-02, 7.4026e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4919, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(0.5070, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0011, device='cuda:1', grad_fn=<SubBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1536, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.8464, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([6.5259e-01, 5.6986e-02, 1.9100e-02, 9.6034e-03, 1.2332e-02, 8.7401e-03, |
2.4008e-01, 5.7094e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([6.5259e-01, 5.6986e-02, 1.9100e-02, 9.6034e-03, 1.2332e-02, 8.7401e-03, |
2.4008e-01, 5.7094e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6526, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3474, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([7.2279e-01, 2.3818e-02, 2.4979e-01, 1.2660e-03, 1.5194e-04, 6.1214e-04, |
1.5295e-04, 1.4154e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.2279e-01, 2.3818e-02, 2.4979e-01, 1.2660e-03, 1.5194e-04, 6.1214e-04, |
1.5295e-04, 1.4154e-03], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7228, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.2498, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0274, device='cuda:3', grad_fn=<DivBackward0>)} |
[2024-10-23 14:47:08,618] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.21 | optimizer_step: 0.30 |
[2024-10-23 14:47:08,618] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7153.19 | backward_microstep: 6848.51 | backward_inner_microstep: 6807.63 | backward_allreduce_microstep: 40.80 | step_microstep: 7.37 |
[2024-10-23 14:47:08,618] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7153.21 | backward: 6848.50 | backward_inner: 6807.65 | backward_allreduce: 40.79 | step: 7.38 |
0%| | 23/4844 [05:52<19:16:26, 14.39s/it]Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Is the bird facing towards the left?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
Registering VQA_lavis step |
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