Comments (10)
将一些开始通过.sh脚本里传入的必要的参数通过argparse形式在unimatch.py里传入。然后将rank, world_size等获取判断的条件删除,接着再将model和data有关多卡的代码调整下就可以了。如:torch.nn.parallel.DistributedDataParallel,torch.nn.SyncBatchNorm.convert_sync_batchnorm(model),torch.utils.data.distributed.DistributedSampler
from unimatch.
抱歉这样很难看出来问题,有可能是因为对模型输出的处理不当。
from unimatch.
好的,谢谢您的回复
from unimatch.
抱歉这样很难看出来问题,有可能是因为对模型输出的处理不当。
作者你好!我看论文是多卡代码,请问如果是单机单卡需要配置哪些参数?谢谢
from unimatch.
只需要在执行训练指令的时候将num_gpus
定为1即可。
from unimatch.
只需要在执行训练指令的时候将
num_gpus
定为1即可。
在本地IDE想调试代码,不使用.sh的方式运行代码,直接运行unimatch.py文件。这种情况下该如何指定呢?
目前直接运行在 rank = int(os.environ["RANK"])即会报错。
是否需要将unimatch.py里相关DDP的代码屏蔽?
谢谢作者耐心解答。
from unimatch.
由于需要传参,所以需要在command里执行,不能直接运行unimatch.py
from unimatch.
由于需要传参,所以需要在command里执行,不能直接运行unimatch.py
nproc_per_node ,master_port这两个参数确实需要命令里传入,这是DDP机制决定吧(目前了解情况)。
不过我修改了unimatch.py里DDP的代码,可以直接运行了,感谢作者。
from unimatch.
nproc_per_node ,master_port这两个参数确实需要命令里传入,这是DDP机制决定吧(目前了解情况)。
不过我修改了unimatch.py里DDP的代码,可以直接运行了,感谢作者。
您好,我也是单机单卡本地IDE想调试代码,想问一下应该如何修改unimatch.py里DDP的代码使其可以直接运行呢?非常感谢!期待您的回复!也感谢作者的代码,很棒的工作!
from unimatch.
将一些开始通过.sh脚本里传入的必要的参数通过argparse形式在unimatch.py里传入。然后将rank, world_size等获取判断的条件删除,接着再将model和data有关多卡的代码调整下就可以了。如:torch.nn.parallel.DistributedDataParallel,torch.nn.SyncBatchNorm.convert_sync_batchnorm(model),torch.utils.data.distributed.DistributedSampler
感谢您的指点!很有用!
from unimatch.
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from unimatch.