text stringlengths 0 1.16k |
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[['1', '3', '4', '8', '6', '12', '2', '47']] |
[('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: 1860 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
tensor([7.0284e-01, 1.2159e-01, 1.8729e-02, 1.4732e-01, 6.4435e-03, 1.3144e-03, |
1.6316e-03, 1.2779e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([7.0284e-01, 1.2159e-01, 1.8729e-02, 1.4732e-01, 6.4435e-03, 1.3144e-03, |
1.6316e-03, 1.2779e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.7028, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2972, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is the drum on the left white?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
tensor([7.9706e-01, 2.2560e-02, 1.7785e-01, 1.1782e-03, 1.2777e-04, 5.6396e-04, |
7.8238e-05, 5.8722e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.9706e-01, 2.2560e-02, 1.7785e-01, 1.1782e-03, 1.2777e-04, 5.6396e-04, |
7.8238e-05, 5.8722e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.1778, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.7971, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0251, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is the bowl on the left image all white?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([13, 3, 448, 448]) |
question: ['Is the drum on the left white?'], 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']] |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 5.86 GiB. GPU 2 has a total capacty of 44.34 GiB of which 4.32 GiB is free. Including non-PyTorch memory, this process has 40.01 GiB memory in use. Of the allocated memory 36.91 GiB is allocated by PyTorch, and 2.46 GiB is reserved by PyTorch but unallocat... |
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([5.4619e-01, 4.5281e-01, 2.7730e-05, 1.6172e-04, 1.0275e-04, 1.0245e-04, |
5.8692e-04, 1.4959e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.4619e-01, 4.5281e-01, 2.7730e-05, 1.6172e-04, 1.0275e-04, 1.0245e-04, |
5.8692e-04, 1.4959e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.4528, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.5462, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0010, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Are there any fish in the image?') |
ANSWER1=EVAL(expr='not {ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
tensor([9.3908e-01, 1.0431e-02, 3.2814e-03, 1.0205e-03, 1.3232e-03, 9.0174e-04, |
4.3921e-02, 3.9694e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.3908e-01, 1.0431e-02, 3.2814e-03, 1.0205e-03, 1.3232e-03, 9.0174e-04, |
4.3921e-02, 3.9694e-05], device='cuda:3', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.0439, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9561, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
torch.Size([13, 3, 448, 448]) |
最后的概率分布为: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998} |
ANSWER0=VQA(image=LEFT,question='How many baboons are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
torch.Size([7, 3, 448, 448]) |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 5.85 GiB. GPU 1 has a total capacty of 44.34 GiB of which 1.87 GiB is free. Including non-PyTorch memory, this process has 42.46 GiB memory in use. Of the allocated memory 39.95 GiB is allocated by PyTorch, and 1.87 GiB is reserved by PyTorch but unallocat... |
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
question: ['How many baboons 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: 3396 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
最后的概率分布为: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
tensor([5.4618e-01, 4.5280e-01, 2.3082e-05, 1.1825e-04, 1.0979e-04, 1.8486e-04, |
5.7801e-04, 1.2334e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.4618e-01, 4.5280e-01, 2.3082e-05, 1.1825e-04, 1.0979e-04, 1.8486e-04, |
5.7801e-04, 1.2334e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.4528, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5462, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0010, device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([5.8034e-01, 2.8893e-02, 7.7772e-03, 2.3344e-03, 3.6720e-03, 2.0747e-03, |
3.7477e-01, 1.3864e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([5.8034e-01, 2.8893e-02, 7.7772e-03, 2.3344e-03, 3.6720e-03, 2.0747e-03, |
3.7477e-01, 1.3864e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.9840, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0160, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
[2024-10-22 17:25:23,538] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.37 | optimizer_gradients: 0.25 | optimizer_step: 0.32 |
[2024-10-22 17:25:23,538] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12634.14 | backward_microstep: 11762.56 | backward_inner_microstep: 11756.67 | backward_allreduce_microstep: 5.66 | step_microstep: 7.57 |
[2024-10-22 17:25:23,538] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12634.16 | backward: 11762.55 | backward_inner: 11756.82 | backward_allreduce: 5.64 | step: 7.58 |
1%| | 17/2424 [06:55<16:11:43, 24.22s/it]Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='Is the dog wearing a collar?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
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