mirror of https://github.com/open-mmlab/mmyolo.git
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@ -463,3 +463,22 @@ mim run mmdet print_config \
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```
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Running the above command will save the `yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py` config file with the inheritance relationship expanded to \`\`yolov5_s-v61_syncbn_fast_1xb4-300e_balloon_whole.py`in the`./work_dirs\` folder.
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## Set the random seed
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If you want to set the random seed during training, you can use the following command.
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```shell
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python ./tools/train.py \
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${CONFIG} \ # path of the config file
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--cfg-options randomness.seed=2023 \ # set seed to 2023
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[randomness.diff_rank_seed=True] \ # set different seeds according to global rank
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[randomness.deterministic=True] # set the deterministic option for CUDNN backend
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# [] stands for optional parameters, when actually entering the command line, you do not need to enter []
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```
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`randomness` has three parameters that can be set, with the following meanings.
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- `randomness.seed=2023`, set the random seed to 2023.
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- `randomness.diff_rank_seed=True`, set different seeds according to global rank. Defaults to False.
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- `randomness.deterministic=True`, set the deterministic option for cuDNN backend, i.e., set `torch.backends.cudnn.deterministic` to True and `torch.backends.cudnn.benchmark` to False. Defaults to False. See https://pytorch.org/docs/stable/notes/randomness.html for more details.
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@ -466,3 +466,24 @@ mim run mmdet print_config \
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```
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运行以上命令,会将 `yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py` 继承关系展开后的配置文件保存到 `./work_dirs` 文件夹内的 `yolov5_s-v61_syncbn_fast_1xb4-300e_balloon_whole.py` 文件中。
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## 设置随机种子
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如果想要在训练时指定随机种子,可以使用以下命令:
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```shell
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python ./tools/train.py \
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${CONFIG} \ # 配置文件路径
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--cfg-options randomness.seed=2023 \ # 设置随机种子为 2023
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[randomness.diff_rank_seed=True] \ # 根据 rank 来设置不同的种子。
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[randomness.deterministic=True] # 把 cuDNN 后端确定性选项设置为 True
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# [] 代表可选参数,实际输入命令行时,不用输入 []
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```
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`randomness` 有三个参数可设置,具体含义如下:
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- `randomness.seed=2023` ,设置随机种子为 2023。
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- `randomness.diff_rank_seed=True`,根据 rank 来设置不同的种子,`diff_rank_seed` 默认为 False。
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- `randomness.deterministic=True`,把 cuDNN 后端确定性选项设置为 True,即把`torch.backends.cudnn.deterministic` 设为 True,把 `torch.backends.cudnn.benchmark` 设为False。`deterministic` 默认为 False。更多细节见 https://pytorch.org/docs/stable/notes/randomness.html。
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