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ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
question: ['Does one dog have a red collar?'], responses:['yes'] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
torch.Size([13, 3, 448, 448]) |
[('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']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
tensor([7.3873e-01, 1.8312e-02, 2.3983e-01, 1.2551e-03, 1.7852e-04, 4.8920e-04, |
5.5570e-05, 1.1486e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.3873e-01, 1.8312e-02, 2.3983e-01, 1.2551e-03, 1.7852e-04, 4.8920e-04, |
5.5570e-05, 1.1486e-03], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([8.8796e-01, 1.6363e-02, 9.3530e-02, 1.0503e-03, 8.4183e-05, 3.7006e-04, |
2.6307e-05, 6.1483e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.8796e-01, 1.6363e-02, 9.3530e-02, 1.0503e-03, 8.4183e-05, 3.7006e-04, |
2.6307e-05, 6.1483e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
question: ['How many dogs are in the image?'], responses:['2'] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7387, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2398, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0214, device='cuda:0', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8880, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0935, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0185, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Does the image contain food?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Is a picture frame visible in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
[('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]) |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
question: ['Is a picture frame visible in the image?'], responses:['no'] |
[('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([3, 3, 448, 448]) knan debug pixel values shape |
tensor([8.3497e-01, 1.6441e-01, 3.1765e-05, 9.0838e-05, 1.0028e-04, 1.3197e-04, |
2.0092e-04, 6.2057e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.3497e-01, 1.6441e-01, 3.1765e-05, 9.0838e-05, 1.0028e-04, 1.3197e-04, |
2.0092e-04, 6.2057e-05], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1644, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.8350, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0006, device='cuda:3', grad_fn=<DivBackward0>)} |
question: ['Does the image contain food?'], 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 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394 |
tensor([7.4901e-01, 1.9167e-02, 2.2973e-01, 8.7346e-04, 5.7646e-05, 3.6729e-04, |
1.1607e-04, 6.8187e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.4901e-01, 1.9167e-02, 2.2973e-01, 8.7346e-04, 5.7646e-05, 3.6729e-04, |
1.1607e-04, 6.8187e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7490, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.2297, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0213, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Are a pair of lips visible in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394 |
question: ['Are a pair of lips visible in the image?'], responses:['no'] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395 |
[('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: 13, images per sample: 13.0, dynamic token length: 3394 |
tensor([7.1352e-01, 2.7864e-02, 6.6184e-03, 2.4833e-01, 2.0825e-03, 8.1476e-04, |
7.1433e-04, 5.5044e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([7.1352e-01, 2.7864e-02, 6.6184e-03, 2.4833e-01, 2.0825e-03, 8.1476e-04, |
7.1433e-04, 5.5044e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0381, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9619, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395 |
tensor([5.4590e-01, 4.5256e-01, 5.2590e-05, 1.8570e-04, 3.9132e-04, 3.6606e-04, |
5.3051e-04, 1.8552e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.4590e-01, 4.5256e-01, 5.2590e-05, 1.8570e-04, 3.9132e-04, 3.6606e-04, |
5.3051e-04, 1.8552e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4526, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.5459, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0015, device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([8.5240e-01, 2.0984e-02, 1.2297e-01, 1.6512e-03, 9.1042e-05, 2.7623e-04, |
4.5182e-05, 1.5864e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.5240e-01, 2.0984e-02, 1.2297e-01, 1.6512e-03, 9.1042e-05, 2.7623e-04, |
4.5182e-05, 1.5864e-03], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8524, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.1230, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0246, device='cuda:0', grad_fn=<DivBackward0>)} |
[2024-10-23 14:50:38,337] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.47 | optimizer_gradients: 0.24 | optimizer_step: 0.31 |
[2024-10-23 14:50:38,337] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7093.53 | backward_microstep: 6848.35 | backward_inner_microstep: 6843.68 | backward_allreduce_microstep: 4.59 | step_microstep: 8.47 |
[2024-10-23 14:50:38,337] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7093.54 | backward: 6848.34 | backward_inner: 6843.71 | backward_allreduce: 4.56 | step: 8.48 |
1%| | 37/4844 [09:22<19:56:56, 14.94s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
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