If you have enabled significant logging, look into using the -G option to increase log buffer sizes.

Thank you for your question. The statement you provided seems to be a suggestion for increasing the log buffer sizes if significant logging has been enabled. The -G option can be used to achieve this. However, without more context, it is difficult to provide a more specific answer or explanation. Can you please provide more information or context about your question?

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capturing trace... Start to Reproduce Issues. logcat: Unexpected EOF! This means that either the device shut down, logd crashed, or this instance of logcat was unable to read log messages as quickly as they were being produced. If you have enabled significant logging, look into using the -G option to increase log buffer sizes.解释一下

这段话是在执行 Android 设备故障排除时可能会遇到的提示信息。它的意思是，正在捕获设备的跟踪信息并尝试重现问题，但是在执行 logcat 命令时出现了“Unexpected EOF”的意外情况。这可能是由于设备关闭、logd 进程崩溃或 logcat 工具无法及时读取日志消息等原因导致的。如果您启用了大量的日志记录，请考虑使用 -G 选项来增加日志缓冲区的大小，以便更好地捕获并分析日志信息。

翻译：Loading the raw data and processing by Rawpy takes significant more time than the backpropagation. By default, the code will load all the groundtruth data processed by Rawpy into memory without 8-bit or 16-bit quantization. This requires at least 64 GB RAM for training the Sony model and 128 GB RAM for the Fuji model. If you need to train it on a machine with less RAM, you may need to revise the code and use the groundtruth data on the disk. We provide the 16-bit groundtruth images processed by Rawpy: Sony (12 GB) and Fuji (22 GB).

使用Rawpy加载原始数据并进行处理比反向传播所需的时间要长得多。默认情况下，该代码将加载由Rawpy处理的所有groundtruth数据到内存中，而不进行8位或16位量化。这需要至少64 GB的RAM来训练Sony模型，以及128 GB的RAM来训练Fuji模型。如果您需要在RAM较少的机器上进行训练，则可能需要修改代码并使用磁盘上的groundtruth数据。我们提供了由Rawpy处理的16位groundtruth图像：Sony（12 GB）和Fuji（22 GB）。