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[('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']] |
question: ['Does the dog in the right image have its mouth open?'], responses:['yes'] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866 |
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]torch.Size([11, 3, 448, 448]) |
knan debug pixel values shape |
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866 |
question: ['How many dogs are in the image?'], responses:['0'] |
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)] |
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866 |
tensor([1.0000e+00, 4.9437e-09, 5.1098e-10, 2.9143e-09, 1.4025e-09, 5.7228e-08, |
5.2682e-07, 1.3179e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.9437e-09, 5.1098e-10, 2.9143e-09, 1.4025e-09, 5.7228e-08, |
5.2682e-07, 1.3179e-09], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(5.2682e-07, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is there at least one plant in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
tensor([1.0000e+00, 4.8145e-08, 1.5416e-10, 3.7338e-07, 1.1854e-08, 1.4746e-08, |
1.7825e-09, 2.3659e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.8145e-08, 1.5416e-10, 3.7338e-07, 1.1854e-08, 1.4746e-08, |
1.7825e-09, 2.3659e-08], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.5416e-10, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7668e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
question: ['Is there at least one plant in the image?'], responses:['yes'] |
ANSWER0=VQA(image=RIGHT,question='How many rodents are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 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']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
tensor([1.0000e+00, 1.4484e-08, 2.5005e-10, 2.9746e-08, 6.1587e-11, 8.7275e-10, |
1.2113e-10, 1.6350e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.4484e-08, 2.5005e-10, 2.9746e-08, 6.1587e-11, 8.7275e-10, |
1.2113e-10, 1.6350e-08], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.5005e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1896e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
question: ['How many rodents are in the image?'], responses:['2'] |
ANSWER0=VQA(image=RIGHT,question='How many kinds of animals are in the photo?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
[('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([7, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
tensor([9.9997e-01, 5.8740e-06, 5.5476e-07, 5.6333e-09, 1.7277e-05, 3.4014e-07, |
4.3152e-06, 2.1319e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9997e-01, 5.8740e-06, 5.5476e-07, 5.6333e-09, 1.7277e-05, 3.4014e-07, |
4.3152e-06, 2.1319e-06], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.0518e-05, device='cuda:1', grad_fn=<DivBackward0>)} |
question: ['How many kinds of animals are in the photo?'], 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']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
tensor([1.0000e+00, 3.1250e-08, 1.1056e-08, 3.3265e-08, 3.1857e-10, 1.9904e-09, |
1.0692e-09, 3.6026e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 3.1250e-08, 1.1056e-08, 3.3265e-08, 3.1857e-10, 1.9904e-09, |
1.0692e-09, 3.6026e-10], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.3265e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
tensor([9.9722e-01, 5.7900e-07, 1.5705e-07, 3.3124e-05, 4.0823e-06, 1.3232e-03, |
7.0511e-06, 1.4083e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9722e-01, 5.7900e-07, 1.5705e-07, 3.3124e-05, 4.0823e-06, 1.3232e-03, |
7.0511e-06, 1.4083e-03], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(7.0511e-06, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 3.8560e-09, 5.6230e-11, 2.1236e-08, 1.2744e-10, 1.6145e-10, |
3.3024e-11, 1.3989e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.8560e-09, 5.6230e-11, 2.1236e-08, 1.2744e-10, 1.6145e-10, |
3.3024e-11, 1.3989e-08], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(5.6230e-11, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-5.6230e-11, device='cuda:0', grad_fn=<DivBackward0>)} |
[2024-10-24 09:47:29,759] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.59 | optimizer_gradients: 0.21 | optimizer_step: 0.30 |
[2024-10-24 09:47:29,759] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7218.85 | backward_microstep: 6795.30 | backward_inner_microstep: 6790.30 | backward_allreduce_microstep: 4.91 | step_microstep: 7.74 |
[2024-10-24 09:47:29,759] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7218.87 | backward: 6795.29 | backward_inner: 6790.32 | backward_allreduce: 4.89 | step: 7.75 |
95%|ββββββββββ| 4594/4844 [19:06:13<1:07:37, 16.23s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering VQA_lavis step |
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