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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9919e-01, 8.0407e-04, 1.3698e-06, 1.6071e-08, 1.2347e-07, 6.1029e-09, |
3.4632e-07, 9.3024e-09], device='cuda:3', 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(2.0266e-06, device='cuda:1', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.8371e-08, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.8371e-08, device='cuda:0', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is there rice in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: torch.Size([1, 3, 448, 448]) |
{True: tensor(0.9992, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0008, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many people are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 4') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
question: ['Is there rice in the image?'], 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([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
tensor([1.0000e+00, 9.8070e-09, 3.5817e-10, 9.9474e-09, 1.4244e-10, 3.9338e-10, |
1.4674e-11, 5.7439e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 9.8070e-09, 3.5817e-10, 9.9474e-09, 1.4244e-10, 3.9338e-10, |
1.4674e-11, 5.7439e-09], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(3.5817e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.5817e-10, device='cuda:0', grad_fn=<DivBackward0>)} |
question: ['How many people are in the image?'], responses:['ε'] |
[('geese', 0.12791273653846358), ('cushion', 0.12632164867635856), ('biking', 0.12559214056053666), ('bulldog', 0.12532071672327474), ('striped', 0.12486304389654934), ('goose', 0.12402122964730407), ('vegetable', 0.12318440383239601), ('dodgers', 0.12278408012511692)] |
[['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
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([13, 3, 448, 448]) knan debug pixel values shape |
tensor([9.9194e-01, 8.0616e-03, 1.5741e-07, 2.8952e-07, 9.1622e-09, 1.3834e-09, |
7.8066e-09, 6.2972e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9194e-01, 8.0616e-03, 1.5741e-07, 2.8952e-07, 9.1622e-09, 1.3834e-09, |
7.8066e-09, 6.2972e-09], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.8952e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([7.5064e-03, 6.6341e-02, 5.1389e-05, 2.7788e-01, 2.2151e-01, 4.8255e-02, |
3.7686e-01, 1.5932e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
vegetable ************* |
['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers'] tensor([7.5064e-03, 6.6341e-02, 5.1389e-05, 2.7788e-01, 2.2151e-01, 4.8255e-02, |
3.7686e-01, 1.5932e-03], 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([1.0000e+00, 3.7979e-10, 6.3967e-11, 1.9246e-10, 1.9665e-10, 5.2621e-09, |
1.0145e-08, 5.6605e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.7979e-10, 6.3967e-11, 1.9246e-10, 1.9665e-10, 5.2621e-09, |
1.0145e-08, 5.6605e-11], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.6297e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
[2024-10-24 09:32:13,016] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.35 | optimizer_step: 0.33 |
[2024-10-24 09:32:13,017] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5147.53 | backward_microstep: 12786.15 | backward_inner_microstep: 4956.35 | backward_allreduce_microstep: 7829.72 | step_microstep: 7.84 |
[2024-10-24 09:32:13,017] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5147.53 | backward: 12786.14 | backward_inner: 4956.36 | backward_allreduce: 7829.71 | step: 7.85 |
94%|ββββββββββ| 4533/4844 [18:50:56<1:20:07, 15.46s/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 |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='How many humans are holding cell phones in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='Is the door section displayed at an angle?') |
ANSWER1=EVAL(expr='not {ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Is there a dog sitting upright inside?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([5, 3, 448, 448]) |
ANSWER0=VQA(image=LEFT,question='How many geese with orange beaks are floating on water?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([11, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Is the door section displayed at an angle?'], 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([5, 3, 448, 448]) knan debug pixel values shape |
question: ['How many humans are holding cell phones 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']] |
question: ['How many geese with orange beaks are floating on water?'], responses:['3'] |
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