This PR enables users to print extra information of their subclassed nn.Module.
Now I simply insert the user-defined string at the ending of module name, which should be discussed in this PR.
Before this PR, users should redefine the __repr__ and copy&paste the source code from Module.
* Add support for extra information on Module
* Rewrite the repr method of Module
* Fix flake8
* Change the __repr__ to get_extra_repr in Linear
* Fix extra new-line for empty line
* Add test for __repr__ method
* Fix bug of block string indent
* Add indent for multi-line repr test.
* Address review comments
* Update tutorial for creating nn.Module
* Fix flake8, add extra_repr of bilinear
* Refactor DropoutNd
* Change to extra_repr in some Modules
* Fix flake8
* Refactor padding modules
* Refactor pooling module
* Fix typo
* Change to extra_repr
* Fix bug for GroupNorm
* Fix bug for LayerNorm
Here's the command I used to invoke autopep8 (in parallel!):
git ls-files | grep '\.py$' | xargs -n1 -P`nproc` autopep8 -i
Several rules are ignored in setup.cfg. The goal is to let autopep8
handle everything which it can handle safely, and to disable any rules
which are tricky or controversial to address. We may want to come back
and re-enable some of these rules later, but I'm trying to make this
patch as safe as possible.
Also configures flake8 to match pep8's behavior.
Also configures TravisCI to check the whole project for lint.
The load_state_dict() function now raises an error if the argument
state_dict has extra keys or is missing keys.
Previously, load_state_dict() ignored extra and missing keys, which made
it hard to notice when you load an invalid state_dict. This could
happen, for example, if you save the state_dict for a DataParallel, but
load it into a single model.
The state_dict() function now only includes the Tensor data from the
paramters, which reduces checkpoint size by not saving gradients.
The __getstate__ and __setstate__ functions are called from copy.copy as
well as pickling. The source code inspection currently slows down the
data parallel code because it makes a copy of the object every
iteration.
modules(): returns an iterator over all modules in the network
children(): returns an iterator over immediate children
Also fix __getitem__ in Sequential
* _forward is renamed forward since users should override it
* some __call__ overrides are changed to forward
* function which return a single variable are changed to return that
variable instead of a one-element tuple
- nn.Container.modules is just a python list and used by nn.Sequential
- Every module in nn.Sequential has a name. This fixes Module.type()
- nn.Sequential constructor accepts either a list or an OrderedDict. With a
list, the modules are named "0", "1", "2", ...
