text stringlengths 0 1.16k |
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 7.0423e-08, 3.1742e-08, 8.0594e-07, 3.8728e-10, 2.1224e-09, |
5.1304e-10, 7.9647e-10], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(9.1192e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Can you see the lamp in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
question: ['Is the dog facing right?'], 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([1.0000e+00, 5.7943e-09, 3.5326e-11, 1.9235e-08, 4.9632e-11, 1.2871e-10, |
1.0335e-10, 7.4680e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.7943e-09, 3.5326e-11, 1.9235e-08, 4.9632e-11, 1.2871e-10, |
1.0335e-10, 7.4680e-09], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(3.5326e-11, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.5326e-11, device='cuda:2', grad_fn=<DivBackward0>)} |
question: ['Are there people in front of the bookshop in the image?'], responses:['no'] |
ANSWER0=VQA(image=RIGHT,question='Is there snow on the ground in the image?') |
ANSWER1=RESULT(var=ANSWER0) |
torch.Size([3, 3, 448, 448]) |
[('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([13, 3, 448, 448]) knan debug pixel values shape |
question: ['Is there snow on the ground in the image?'], responses:['no'] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401 |
[('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 |
question: ['Can you see the lamp 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']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402 |
tensor([1.0000e+00, 6.4857e-10, 5.2867e-07, 5.1600e-11, 8.3011e-11, 1.1327e-08, |
1.1957e-09, 7.0631e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 6.4857e-10, 5.2867e-07, 5.1600e-11, 8.3011e-11, 1.1327e-08, |
1.1957e-09, 7.0631e-07], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.4857e-10, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.1921e-06, device='cuda:2', grad_fn=<SubBackward0>)} |
tensor([1.0000e+00, 3.6772e-09, 2.5798e-10, 1.8465e-08, 1.2173e-10, 1.2117e-09, |
5.4884e-11, 5.4536e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.6772e-09, 2.5798e-10, 1.8465e-08, 1.2173e-10, 1.2117e-09, |
5.4884e-11, 5.4536e-09], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.5798e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.5798e-10, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many elephants are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401 |
question: ['How many elephants 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']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402 |
tensor([1.0000e+00, 2.1941e-09, 6.0826e-07, 1.1653e-09, 3.4453e-09, 9.5586e-08, |
4.4089e-09, 3.1559e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.1941e-09, 6.0826e-07, 1.1653e-09, 3.4453e-09, 9.5586e-08, |
4.4089e-09, 3.1559e-07], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.1941e-09, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is the dog facing right?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([7, 3, 448, 448]) |
tensor([1.0000e+00, 1.2453e-09, 6.4759e-07, 1.7221e-09, 1.8028e-08, 1.2667e-07, |
1.2533e-08, 3.8468e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.2453e-09, 6.4759e-07, 1.7221e-09, 1.8028e-08, 1.2667e-07, |
1.2533e-08, 3.8468e-07], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.2453e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-06, device='cuda:1', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 6.5878e-10, 6.9809e-11, 7.2541e-11, 7.3598e-11, 2.2262e-09, |
1.3440e-08, 1.9873e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 6.5878e-10, 6.9809e-11, 7.2541e-11, 7.3598e-11, 2.2262e-09, |
1.3440e-08, 1.9873e-11], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.6561e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
question: ['Is the dog facing right?'], 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 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859 |
tensor([1.0000e+00, 2.2359e-09, 1.2752e-07, 3.0357e-09, 7.7425e-12, 5.4928e-11, |
1.8662e-11, 6.1152e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.2359e-09, 1.2752e-07, 3.0357e-09, 7.7425e-12, 5.4928e-11, |
1.8662e-11, 6.1152e-10], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.2752e-07, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-8.3096e-09, device='cuda:0', grad_fn=<SubBackward0>)} |
[2024-10-24 10:22:27,912] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.40 | optimizer_gradients: 0.24 | optimizer_step: 0.31 |
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