text stringlengths 0 1.16k |
|---|
"loss_scale": 0, |
"initial_scale_power": 32, |
"loss_scale_window": 1000, |
"hysteresis": 2, |
"min_loss_scale": 1 |
}, |
"bf16": { |
"enabled": true |
}, |
"optimizer": { |
"type": "AdamW", |
"params": { |
"lr": 4e-05, |
"betas": [0.9, 0.999], |
"eps": 1e-08, |
"weight_decay": 0.01 |
} |
}, |
"gradient_accumulation_steps": 1, |
"gradient_clipping": 1.0, |
"steps_per_print": inf, |
"train_batch_size": 16, |
"train_micro_batch_size_per_gpu": 4, |
"wall_clock_breakdown": true |
} |
[INFO|trainer.py:1721] 2024-10-22 17:18:27,733 >> ***** Running training ***** |
[INFO|trainer.py:1722] 2024-10-22 17:18:27,733 >> Num examples = 9,681 |
[INFO|trainer.py:1723] 2024-10-22 17:18:27,733 >> Num Epochs = 4 |
[INFO|trainer.py:1724] 2024-10-22 17:18:27,733 >> Instantaneous batch size per device = 4 |
[INFO|trainer.py:1727] 2024-10-22 17:18:27,733 >> Total train batch size (w. parallel, distributed & accumulation) = 16 |
[INFO|trainer.py:1728] 2024-10-22 17:18:27,733 >> Gradient Accumulation steps = 1 |
[INFO|trainer.py:1729] 2024-10-22 17:18:27,733 >> Total optimization steps = 2,424 |
[INFO|trainer.py:1730] 2024-10-22 17:18:27,736 >> Number of trainable parameters = 15,728,640 |
0%| | 0/2424 [00:00<?, ?it/s][2024-10-22 17:18:29,441] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:18:29,449] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:18:29,454] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:18:29,468] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:18:32,501] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:18:32,776] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:18:32,780] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:18:32,796] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:18:35,375] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:18:35,680] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:18:35,757] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:18:35,790] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:18:38,205] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:18:38,495] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:18:38,663] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:18:38,720] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Can you see the customers in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Is there apparent damage to the bus in the image?') |
ANSWER1=EVAL(expr='not {ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='How many animal species are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='How many windows are on the left wall?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
question: ['Can you see the customers in the image?'], responses:['yes'] |
[WARNING|tokenization_utils_base.py:2697] 2024-10-22 17:18:41,401 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation. |
question: ['Is there apparent damage to the bus in the image?'], responses:['yes'] |
[WARNING|tokenization_utils_base.py:2697] 2024-10-22 17:18:41,816 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation. |
[('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 |
question: ['How many animal species are in the image?'], responses:['1'] |
[WARNING|tokenization_utils_base.py:2697] 2024-10-22 17:18:42,162 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation. |
[('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']] |
tensor([6.9835e-01, 2.3419e-02, 2.7348e-01, 2.6040e-03, 1.8967e-04, 6.1416e-04, |
1.0074e-04, 1.2463e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.9835e-01, 2.3419e-02, 2.7348e-01, 2.6040e-03, 1.8967e-04, 6.1416e-04, |
1.0074e-04, 1.2463e-03], device='cuda:2', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.6983, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.2735, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0282, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is liquid being poured into a cup?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
torch.Size([5, 3, 448, 448]) |
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