to_device: Handle nested lists/tuples recursively #658
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The previous implementation of `to_device` would break when a user decided to return a list of tensors in `forward`. This patch applies `to_device` recursively and adds support for lists in addition to tuples.
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Thanks for the PR.
Just a heads up, there is also #657 working on to_device. Does it make sense to also adjust to_numpy with the same logic?
Also, it seems that when passing a list, to_device will return a tuple. Do you think it should be a list instead?
Co-authored-by: Benjamin Bossan <BenjaminBossan@users.noreply.github.com>
I think it makes sense for
Fixed, thanks. |
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@BenjaminBossan please review again. |
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@BenjaminBossan I think there's an issue with this when calling Edit: I checked and this is not a regression. So everything is fine and this can be merged. |
Is it correct that this raises a |
This is a question of scope.
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That sounds very reasonable, I think |
skorch/tests/test_utils.py
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| {'a': 1}, | ||
| ]) | ||
| def test_invalid_inputs(self, to_numpy, x_invalid): | ||
| " Inputs that are invalid for the scope of to_numpy. " |
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I've never seen this style, neither docstring nor comment. I think a comment would be sufficient here?
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It is still a doc-string but according to PEP257 this style is disregarded. I converted it to a comment.
Example:
>>> def foo():
... "bla"
... return 1
...
>>> foo.__doc__
'bla'There was a problem hiding this comment.
It is still a doc-string but according to PEP257 this style is disregarded
Interesting, I didn't know, though in hindsight it makes sense.
This release of skorch contains a few minor improvements and some nice additions. As always, we fixed a few bugs and improved the documentation. Our [learning rate scheduler](https://skorch.readthedocs.io/en/latest/callbacks.html#skorch.callbacks.LRScheduler) now optionally logs learning rate changes to the history; moreover, it now allows the user to choose whether an update step should be made after each batch or each epoch. If you always longed for a metric that would just use whatever is defined by your criterion, look no further than [`loss_scoring`](https://skorch.readthedocs.io/en/latest/scoring.html#skorch.scoring.loss_scoring). Also, skorch now allows you to easily change the kind of nonlinearity to apply to the module's output when `predict` and `predict_proba` are called, by passing the `predict_nonlinearity` argument. Besides these changes, we improved the customization potential of skorch. First of all, the `criterion` is now set to `train` or `valid`, depending on the phase -- this is useful if the criterion should act differently during training and validation. Next we made it easier to add custom modules, optimizers, and criteria to your neural net; this should facilitate implementing architectures like GANs. Consult the [docs](https://skorch.readthedocs.io/en/latest/user/neuralnet.html#subclassing-neuralnet) for more on this. Conveniently, [`net.save_params`](https://skorch.readthedocs.io/en/latest/net.html#skorch.net.NeuralNet.save_params) can now persist arbitrary attributes, including those custom modules. As always, these improvements wouldn't have been possible without the community. Please keep asking questions, raising issues, and proposing new features. We are especially grateful to those community members, old and new, who contributed via PRs: ``` Aaron Berk guybuk kqf Michał Słapek Scott Sievert Yann Dubois Zhao Meng ``` Here is the full list of all changes: ### Added - Added the `event_name` argument for `LRScheduler` for optional recording of LR changes inside `net.history`. NOTE: Supported only in Pytorch>=1.4 - Make it easier to add custom modules or optimizers to a neural net class by automatically registering them where necessary and by making them available to set_params - Added the `step_every` argument for `LRScheduler` to set whether the scheduler step should be taken on every epoch or on every batch. - Added the `scoring` module with `loss_scoring` function, which computes the net's loss (using `get_loss`) on provided input data. - Added a parameter `predict_nonlinearity` to `NeuralNet` which allows users to control the nonlinearity to be applied to the module output when calling `predict` and `predict_proba` (#637, #661) - Added the possibility to save the criterion with `save_params` and with checkpoint callbacks - Added the possibility to save custom modules with `save_params` and with checkpoint callbacks ### Changed - Removed support for schedulers with a `batch_step()` method in `LRScheduler`. - Raise `FutureWarning` in `CVSplit` when `random_state` is not used. Will raise an exception in a future (#620) - The behavior of method `net.get_params` changed to make it more consistent with sklearn: it will no longer return "learned" attributes like `module_`; therefore, functions like `sklearn.base.clone`, when called with a fitted net, will no longer return a fitted net but instead an uninitialized net; if you want a copy of a fitted net, use `copy.deepcopy` instead;`net.get_params` is used under the hood by many sklearn functions and classes, such as `GridSearchCV`, whose behavior may thus be affected by the change. (#521, #527) - Raise `FutureWarning` when using `CyclicLR` scheduler, because the default behavior has changed from taking a step every batch to taking a step every epoch. (#626) - Set train/validation on criterion if it's a PyTorch module (#621) - Don't pass `y=None` to `NeuralNet.train_split` to enable the direct use of split functions without positional `y` in their signatures. This is useful when working with unsupervised data (#605). - `to_numpy` is now able to unpack dicts and lists/tuples (#657, #658) - When using `CrossEntropyLoss`, softmax is now automatically applied to the output when calling `predict` or `predict_proba` ### Fixed - Fixed a bug where `CyclicLR` scheduler would update during both training and validation rather than just during training. - Fixed a bug introduced by moving the `optimizer.zero_grad()` call outside of the train step function, making it incompatible with LBFGS and other optimizers that call the train step several times per batch (#636) - Fixed pickling of the `ProgressBar` callback (#656)
The previous implementation of
to_devicewould break when auser decided to return a list of tensors in
forward.This patch applies
to_devicerecursively and adds supportfor lists in addition to tuples.