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torch.Size([13, 3, 448, 448]) |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839 |
tensor([9.9527e-01, 4.7253e-03, 1.3007e-06, 5.2950e-07, 4.2127e-09, 1.2817e-07, |
2.7719e-08, 6.5745e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9527e-01, 4.7253e-03, 1.3007e-06, 5.2950e-07, 4.2127e-09, 1.2817e-07, |
2.7719e-08, 6.5745e-06], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9953, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0047, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
tensor([9.8593e-01, 2.2283e-06, 5.3540e-09, 1.2397e-10, 4.1935e-10, 9.3041e-10, |
1.4063e-02, 1.3135e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.8593e-01, 2.2283e-06, 5.3540e-09, 1.2397e-10, 4.1935e-10, 9.3041e-10, |
1.4063e-02, 1.3135e-11], device='cuda:0', grad_fn=<SelectBackward0>) |
question: ['Is the screen of the laptop slightly curved and concave?'], responses:['yes'] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.2352e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
torch.Size([13, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='Is the dog wearing something?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
[('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([3, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
question: ['Is the dog wearing something?'], 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 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 834 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 834 |
question: ['How many animals are in the image?'], responses:['δΈ'] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835 |
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)] |
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 834 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 834 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 835 |
question: ['How many dogs are in the image?'], responses:['δΈ'] |
tensor([1.0000e+00, 7.3645e-09, 4.8856e-07, 2.4719e-08, 3.9215e-09, 8.3958e-07, |
1.6828e-08, 6.2768e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 7.3645e-09, 4.8856e-07, 2.4719e-08, 3.9215e-09, 8.3958e-07, |
1.6828e-08, 6.2768e-07], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(7.3645e-09, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(2.0266e-06, device='cuda:0', grad_fn=<SubBackward0>)} |
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)] |
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
tensor([9.9998e-01, 3.2437e-09, 1.6701e-05, 5.9994e-10, 4.0392e-12, 2.2320e-12, |
7.8513e-12, 8.7354e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9998e-01, 3.2437e-09, 1.6701e-05, 5.9994e-10, 4.0392e-12, 2.2320e-12, |
7.8513e-12, 8.7354e-10], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.6701e-05, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.2122e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many hyenas are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
question: ['How many hyenas 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([3, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0238e-04, 6.2081e-02, 5.7493e-02, 5.3607e-01, 1.7208e-01, 1.6327e-01, |
5.2398e-03, 3.6688e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
bulldog ************* |
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([1.0238e-04, 6.2081e-02, 5.7493e-02, 5.3607e-01, 1.7208e-01, 1.6327e-01, |
5.2398e-03, 3.6688e-03], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)} |
tensor([9.6461e-05, 1.4266e-03, 1.2295e-02, 8.2507e-01, 5.5237e-02, 5.1530e-02, |
5.0492e-03, 4.9299e-02], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
bulldog ************* |
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([9.6461e-05, 1.4266e-03, 1.2295e-02, 8.2507e-01, 5.5237e-02, 5.1530e-02, |
5.0492e-03, 4.9299e-02], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)} |
tensor([9.9993e-01, 4.6908e-08, 3.1056e-10, 7.0312e-05, 5.8465e-11, 1.6516e-10, |
3.8188e-10, 7.1616e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9993e-01, 4.6908e-08, 3.1056e-10, 7.0312e-05, 5.8465e-11, 1.6516e-10, |
3.8188e-10, 7.1616e-11], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9999, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(7.0360e-05, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
[2024-10-24 09:40:33,296] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.36 | optimizer_step: 0.33 |
[2024-10-24 09:40:33,297] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 2556.13 | backward_microstep: 8626.64 | backward_inner_microstep: 2196.10 | backward_allreduce_microstep: 6430.45 | step_microstep: 8.02 |
[2024-10-24 09:40:33,297] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 2556.13 | backward: 8626.63 | backward_inner: 2196.12 | backward_allreduce: 6430.40 | step: 8.04 |
94%|ββββββββββ| 4566/4844 [18:59:17<1:00:32, 13.07s/it]Registering VQA_lavis step |
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 there a plant near the furniture in the room?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
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