This is common knowledge, so common that someone that hasn't coded anything besides some basic linear regression model like me knows about it. It's like commenting on why you'd put parenthesis in some formula, it's just gonna say "parenthesis here because this operation takes priority", similarly in a pytorch model, if it was done by those standards the code would be filled with "operation not done in place because it would break the network graph". You're more likely to encounter the opposite comment, "doing this operation in-place because it'll be discarded later" or something along those lines.
One of the first things you're taught when learning pytorch is that you're not coding in python, but actually creating a network graph that is loaded and executed on a GPU. Other common sense things is knowing that you shouldn't use stuff that is in the stdlib or in numpy and use torch.* variants instead, not doing so will incur either undefined behavior, cause massive memory copies between the CPU and GPU or most likely, error out at runtime.
Note that this is a repo that is forked from the official repo, it's a community repo focused on inference and thus doesn't care about training so it has completely different considerations than the original code.
Comments
This is common knowledge, so common that someone that hasn't coded anything besides some basic linear regression model like me knows about it. It's like commenting on why you'd put parenthesis in some formula, it's just gonna say "parenthesis here because this operation takes priority", similarly in a pytorch model, if it was done by those standards the code would be filled with "operation not done in place because it would break the network graph". You're more likely to encounter the opposite comment, "doing this operation in-place because it'll be discarded later" or something along those lines.
One of the first things you're taught when learning pytorch is that you're not coding in python, but actually creating a network graph that is loaded and executed on a GPU. Other common sense things is knowing that you shouldn't use stuff that is in the stdlib or in numpy and use torch.* variants instead, not doing so will incur either undefined behavior, cause massive memory copies between the CPU and GPU or most likely, error out at runtime.
Note that this is a repo that is forked from the official repo, it's a community repo focused on inference and thus doesn't care about training so it has completely different considerations than the original code.