Basic callbacks for Learner
from nbdev.showdoc import *

class Callback[source]

Callback() :: GetAttr

Basic class handling tweaks of the training loop by changing a Learner in various events

The training loop is defined in Learner a bit below and consists in a minimal set of instructions: looping through the data we:

  • compute the output of the model from the input
  • calculate a loss between this output and the desired target
  • compute the gradients of this loss with respect to all the model parameters
  • update the parameters accordingly
  • zero all the gradients

Any tweak of this training loop is defined in a Callback to avoid over-complicating the code of the training loop, and to make it easy to mix and match different techniques (since they'll be defined in different callbacks). A callback can implement actions on the following events:

  • begin_fit: called before doing anything, ideal for initial setup.
  • begin_epoch: called at the beginning of each epoch, useful for any behavior you need to reset at each epoch.
  • begin_train: called at the beginning of the training part of an epoch.
  • begin_batch: called at the beginning of each batch, just after drawing said batch. It can be used to do any setup necessary for the batch (like hyper-parameter scheduling) or to change the input/target before it goes in the model (change of the input with techniques like mixup for instance).
  • after_pred: called after computing the output of the model on the batch. It can be used to change that output before it's fed to the loss.
  • after_loss: called after the loss has been computed, but before the backward pass. It can be used to add any penalty to the loss (AR or TAR in RNN training for instance).
  • after_backward: called after the backward pass, but before the update of the parameters. It can be used to do any change to the gradients before said update (gradient clipping for instance).
  • after_step: called after the step and before the gradients are zeroed.
  • after_batch: called at the end of a batch, for any clean-up before the next one.
  • after_train: called at the end of the training phase of an epoch.
  • begin_validate: called at the beginning of the validation phase of an epoch, useful for any setup needed specifically for validation.
  • after_validate: called at the end of the validation part of an epoch.
  • after_epoch: called at the end of an epoch, for any clean-up before the next one.
  • after_fit: called at the end of training, for final clean-up.

Callback.__call__[source]

Callback.__call__(event_name)

Call self.{event_name} if it's defined

tst_cb = Callback()
tst_cb.call_me = lambda: print("maybe")
test_stdout(lambda: tst_cb("call_me"), "maybe")

GetAttr.__getattr__[source]

GetAttr.__getattr__(k)

This is a shortcut to avoid having to write self.learn.bla for any bla attribute we seek, and just write self.bla.

mk_class('TstLearner', 'a')

class TstCallback(Callback):
    def batch_begin(self): print(self.a)

learn,cb = TstLearner(1),TstCallback()
cb.learn = learn
test_stdout(lambda: cb('batch_begin'), "1")

Note that it only works to get the value of the attribute, if you want to change it, you have to manually access it with self.learn.bla. In the example below, self.a += 1 creates an a attribute of 2 in the callback instead of setting the a of the learner to 2. It also issues a warning that something is probably wrong:

learn.a
1
class TstCallback(Callback):
    def batch_begin(self): self.a += 1

learn,cb = TstLearner(1),TstCallback()
cb.learn = learn
cb('batch_begin')
test_eq(cb.a, 2)
test_eq(cb.learn.a, 1)
/home/sgugger/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:16: UserWarning: You are setting an attribute (a) that also exists in the learner. Please be advised that you're not setting it in the learner but in the callback. Use `self.learn.a` if you would like to change it in the learner.
  app.launch_new_instance()

A proper version needs to write self.learn.a = self.a + 1:

class TstCallback(Callback):
    def batch_begin(self): self.learn.a = self.a + 1

learn,cb = TstLearner(1),TstCallback()
cb.learn = learn
cb('batch_begin')
test_eq(cb.learn.a, 2)

Callback.name[source]

Name of the Callback, camel-cased and with 'Callback' removed

test_eq(TstCallback().name, 'tst')
class ComplicatedNameCallback(Callback): pass
test_eq(ComplicatedNameCallback().name, 'complicated_name')

class TrainEvalCallback[source]

TrainEvalCallback() :: Callback

Callback that tracks the number of iterations done and properly sets training/eval mode

This Callback is automatically added in every Learner at initialization.

TrainEvalCallback.begin_fit[source]

TrainEvalCallback.begin_fit()

Set the iter and epoch counters to 0, put the model and the right device

TrainEvalCallback.after_batch[source]

TrainEvalCallback.after_batch()

Update the iter counter (in training mode)

TrainEvalCallback.begin_train[source]

TrainEvalCallback.begin_train()

Set the model in training mode

TrainEvalCallback.begin_validate[source]

TrainEvalCallback.begin_validate()

Set the model in validation mode

class GatherPredsCallback[source]

GatherPredsCallback(with_input=False, with_loss=False, save_preds=None, save_targs=None, concat_dim=0) :: Callback

Callback that saves the predictions and targets, optionally with_loss

GatherPredsCallback.begin_validate[source]

GatherPredsCallback.begin_validate()

Initialize containers

GatherPredsCallback.after_batch[source]

GatherPredsCallback.after_batch()

Save predictions, targets and potentially losses

GatherPredsCallback.after_validate[source]

GatherPredsCallback.after_validate()

Concatenate all recorded tensors

class FetchPredsCallback[source]

FetchPredsCallback(ds_idx=1, dl=None, with_input=False, with_decoded=False, cbs=None) :: Callback

A callback to fetch predictions during the training loop

When writing a callback, the following attributes of Learner are available:

  • model: the model used for training/validation
  • data: the underlying DataLoaders
  • loss_func: the loss function used
  • opt: the optimizer used to udpate the model parameters
  • opt_func: the function used to create the optimizer
  • cbs: the list containing all Callbacks
  • dl: current DataLoader used for iteration
  • x/xb: last input drawn from self.dl (potentially modified by callbacks). xb is always a tuple (potentially with one element) and x is detuplified. You can only assign to xb.
  • y/yb: last target drawn from self.dl (potentially modified by callbacks). yb is always a tuple (potentially with one element) and y is detuplified. You can only assign to yb.
  • pred: last predictions from self.model (potentially modified by callbacks)
  • loss: last computed loss (potentially modified by callbacks)
  • n_epoch: the number of epochs in this training
  • n_iter: the number of iterations in the current self.dl
  • epoch: the current epoch index (from 0 to n_epoch-1)
  • iter: the current iteration index in self.dl (from 0 to n_iter-1)

The following attributes are added by TrainEvalCallback and should be available unless you went out of your way to remove that callback:

  • train_iter: the number of training iterations done since the beginning of this training
  • pct_train: from 0. to 1., the percentage of training iterations completed
  • training: flag to indicate if we're in training mode or not

The following attribute is added by Recorder and should be available unless you went out of your way to remove that callback:

  • smooth_loss: an exponentially-averaged version of the training loss

Callbacks control flow

It happens that we may want to skip some of the steps of the training loop: in gradient accumulation, we don't aways want to do the step/zeroing of the grads for instance. During an LR finder test, we don't want to do the validation phase of an epoch. Or if we're training with a strategy of early stopping, we want to be able to completely interrupt the training loop.

This is made possible by raising specific exceptions the training loop will look for (and properly catch).

class CancelBatchException[source]

CancelBatchException(*args, **kwargs) :: Exception

Interrupts training and go to after_fit

class CancelTrainException[source]

CancelTrainException(*args, **kwargs) :: Exception

Skip the rest of the validation part of the epoch and go to after_validate

class CancelValidException[source]

CancelValidException(*args, **kwargs) :: Exception

Skip the rest of this epoch and go to after_epoch

class CancelEpochException[source]

CancelEpochException(*args, **kwargs) :: Exception

Skip the rest of the training part of the epoch and go to after_train

class CancelFitException[source]

CancelFitException(*args, **kwargs) :: Exception

Skip the rest of this batch and go to after_batch

You can detect one of those exceptions occurred and add code that executes right after with the following events:

  • after_cancel_batch: reached imediately after a CancelBatchException before proceeding to after_batch
  • after_cancel_train: reached imediately after a CancelTrainException before proceeding to after_epoch
  • after_cancel_valid: reached imediately after a CancelValidException before proceeding to after_epoch
  • after_cancel_epoch: reached imediately after a CancelEpochException before proceeding to after_epoch
  • after_cancel_fit: reached imediately after a CancelFitException before proceeding to after_fit

class event[source]

event(*args, **kwargs)

All possible events as attributes to get tab-completion and typo-proofing

test_eq(event.after_backward, 'after_backward')

Here's the full list: begin_fit begin_epoch begin_train begin_batch after_pred after_loss after_backward after_step after_cancel_batch after_batch after_cancel_train after_train begin_validate after_cancel_validate after_validate after_cancel_epoch after_epoch after_cancel_fit after_fit.