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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5394, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.4471, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0135, device='cuda:1', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9520, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0480, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many baboons are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 5') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 844 |
ANSWER0=VQA(image=RIGHT,question='Is there a skydiver in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 845 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 844 |
question: ['How many baboons are in the image?'], responses:['10'] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 844 |
[('10', 0.1277249466426885), ('11', 0.12579928416580372), ('12', 0.12560051978633632), ('8', 0.1247991444010043), ('9', 0.12459861387933152), ('26', 0.12389435171102943), ('13', 0.12388731669200545), ('6', 0.12369582272180085)] |
[['10', '11', '12', '8', '9', '26', '13', '6']] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 845 |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
question: ['How many golf balls are in the image?'], responses:['1'] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 845 |
[('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']] |
tensor([6.3371e-01, 2.1440e-02, 3.3920e-01, 1.4571e-03, 2.4089e-04, 5.5839e-04, |
6.6257e-05, 3.3296e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.3371e-01, 2.1440e-02, 3.3920e-01, 1.4571e-03, 2.4089e-04, 5.5839e-04, |
6.6257e-05, 3.3296e-03], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6337, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3392, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0271, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Does the dog on the right have a blue collar?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([7, 3, 448, 448]) |
question: ['Is there a skydiver in the image?'], responses:['yes'] |
torch.Size([11, 3, 448, 448]) knan debug pixel values shape |
[('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([7, 3, 448, 448]) knan debug pixel values shape |
tensor([0.1975, 0.1188, 0.1317, 0.1693, 0.1449, 0.0202, 0.0835, 0.1341], |
device='cuda:1', grad_fn=<SoftmaxBackward0>) |
10 ************* |
['10', '11', '12', '8', '9', '26', '13', '6'] tensor([0.1975, 0.1188, 0.1317, 0.1693, 0.1449, 0.0202, 0.0835, 0.1341], |
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(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
question: ['Does the dog on the right have a blue collar?'], 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([7, 3, 448, 448]) knan debug pixel values shape |
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 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
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([5.5138e-01, 2.6274e-02, 4.1977e-01, 1.1462e-03, 1.6250e-04, 4.6935e-04, |
1.4949e-04, 6.4585e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.5138e-01, 2.6274e-02, 4.1977e-01, 1.1462e-03, 1.6250e-04, 4.6935e-04, |
1.4949e-04, 6.4585e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5514, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.4198, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0288, device='cuda:2', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
tensor([8.6631e-01, 1.3285e-01, 2.0463e-05, 7.1090e-05, 5.5761e-05, 3.8264e-04, |
2.5524e-04, 5.0168e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.6631e-01, 1.3285e-01, 2.0463e-05, 7.1090e-05, 5.5761e-05, 3.8264e-04, |
2.5524e-04, 5.0168e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1329, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.8663, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0008, device='cuda:0', grad_fn=<SubBackward0>)} |
tensor([8.8355e-01, 2.1429e-02, 8.6574e-03, 2.8070e-03, 3.8424e-03, 2.3164e-03, |
7.7170e-02, 2.2620e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.8355e-01, 2.1429e-02, 8.6574e-03, 2.8070e-03, 3.8424e-03, 2.3164e-03, |
7.7170e-02, 2.2620e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1164, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.8836, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is the dog in the image lying down?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
question: ['Is the dog in the image lying down?'], 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([7, 3, 448, 448]) knan debug pixel values shape |
tensor([7.7262e-01, 2.9144e-02, 1.9535e-01, 1.1115e-03, 1.6082e-04, 5.2737e-04, |
8.5753e-05, 9.9776e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.7262e-01, 2.9144e-02, 1.9535e-01, 1.1115e-03, 1.6082e-04, 5.2737e-04, |
8.5753e-05, 9.9776e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7726, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1954, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0320, device='cuda:3', grad_fn=<DivBackward0>)} |
[2024-10-23 14:45:24,447] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.27 | optimizer_step: 0.31 |
[2024-10-23 14:45:24,447] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3791.20 | backward_microstep: 8898.63 | backward_inner_microstep: 3560.51 | backward_allreduce_microstep: 5338.04 | step_microstep: 7.61 |
[2024-10-23 14:45:24,447] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3791.22 | backward: 8898.62 | backward_inner: 3560.54 | backward_allreduce: 5338.02 | step: 7.62 |
0%| | 16/4844 [04:08<18:30:17, 13.80s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
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