text stringlengths 0 1.16k |
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.6499, 0.2247, 0.0173, 0.0471, 0.0028, 0.0471, 0.0099, 0.0013], |
device='cuda:3', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.0471, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9529, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
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: 1861 |
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: 1861 |
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: 1861 |
question: ['Is the dog on the left sitting on the grass?'], 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: 7, images per sample: 7.0, dynamic token length: 1861 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
tensor([8.2555e-01, 4.3700e-02, 6.9142e-03, 1.1879e-01, 3.1601e-03, 8.2465e-04, |
9.6442e-04, 9.5103e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([8.2555e-01, 4.3700e-02, 6.9142e-03, 1.1879e-01, 3.1601e-03, 8.2465e-04, |
9.6442e-04, 9.5103e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.0437, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9563, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 1.17 GiB. GPU 2 has a total capacty of 44.34 GiB of which 102.94 MiB is free. Including non-PyTorch memory, this process has 44.22 GiB memory in use. Of the allocated memory 40.98 GiB is allocated by PyTorch, and 2.62 GiB is reserved by PyTorch but unalloc... |
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' |
最后的概率分布为: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998} |
[2024-10-22 17:30:39,893] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.35 | optimizer_step: 0.33 |
[2024-10-22 17:30:39,894] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 10176.71 | backward_microstep: 10669.99 | backward_inner_microstep: 9656.84 | backward_allreduce_microstep: 1012.98 | step_microstep: 7.90 |
[2024-10-22 17:30:39,894] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 10176.73 | backward: 10669.98 | backward_inner: 9656.92 | backward_allreduce: 1012.97 | step: 7.91 |
1%|▏ | 31/2424 [12:12<13:57:08, 20.99s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='Can the sky be seen behind the dog?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Is the sting ray facing towards the left?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
ANSWER0=VQA(image=LEFT,question='Are the bags displayed in the same position in both images?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Are the nipples hanging down on an adult primate?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
question: ['Are the bags displayed in the same position in both images?'], 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([1, 3, 448, 448]) knan debug pixel values shape |
tensor([5.0768e-01, 4.9157e-01, 4.7377e-05, 1.3683e-04, 9.6224e-05, 1.0837e-04, |
3.3453e-04, 1.9256e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.0768e-01, 4.9157e-01, 4.7377e-05, 1.3683e-04, 9.6224e-05, 1.0837e-04, |
3.3453e-04, 1.9256e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.4916, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.5077, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0007, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many vending machines are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
question: ['Can the sky be seen behind the dog?'], responses:['yes'] |
question: ['Is the sting ray facing towards the left?'], responses:['yes'] |
question: ['Are the nipples hanging down on an adult primate?'], responses:['no'] |
[('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']] |
[('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']] |
[('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 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
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: 1865 |
question: ['How many vending machines are in the image?'], responses:['3'] |
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)] |
[['3', '4', '1', '5', '8', '2', '6', '12']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
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: 1865 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
tensor([6.4795e-01, 8.6692e-03, 3.4225e-01, 6.1654e-04, 6.7809e-05, 1.5788e-04, |
4.6566e-05, 2.3541e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.4795e-01, 8.6692e-03, 3.4225e-01, 6.1654e-04, 6.7809e-05, 1.5788e-04, |
4.6566e-05, 2.3541e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([5.6138e-01, 4.3720e-01, 8.9261e-05, 1.4403e-04, 3.6972e-04, 4.1917e-04, |
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