train

Extensions to Learner that easily implement Callback

Additional training functions

train provides a number of extension methods that are added to Learner (see below for a list and details), along with three simple callbacks:

• ShowGraph
• GradientClipping
• BnFreeze
• AccumulateScheduler

Learner extension methods

These methods are automatically added to all Learner objects created after importing this module. They provide convenient access to a number of callbacks, without requiring them to be manually created.

fit_one_cycle[source][test]

fit_one_cycle(learn:Learner, cyc_len:int, max_lr:Union[float, Collection[float], slice]=slice(None, 0.003, None), moms:Point=(0.95, 0.85), div_factor:float=25.0, pct_start:float=0.3, final_div:float=None, wd:float=None, callbacks:Optional[Collection[Callback]]=None, tot_epochs:int=None, start_epoch:int=None) Tests found for fit_one_cycle:

• pytest -sv tests/test_train.py::test_fit_one_cycle [source]

Some other tests where fit_one_cycle is used:

• pytest -sv tests/test_tabular_train.py::test_empty_cont [source]
• pytest -sv tests/test_text_train.py::test_qrnn_works_if_split_fn_provided [source]
• pytest -sv tests/test_text_train.py::test_qrnn_works_with_no_split [source]

To run tests please refer to this guide.

Fit a model following the 1cycle policy.

one_cycle_scheduler[source][test]

one_cycle_scheduler(lr_max:float, **kwargs:Any) → OneCycleScheduler No tests found for one_cycle_scheduler. To contribute a test please refer to this guide and this discussion.

Instantiate a OneCycleScheduler with lr_max.

See OneCycleScheduler for details.

lr_find[source][test]

lr_find(learn:Learner, start_lr:Floats=1e-07, end_lr:Floats=10, num_it:int=100, stop_div:bool=True, wd:float=None) Tests found for lr_find:

• pytest -sv tests/test_train.py::test_lr_find [source]
• pytest -sv tests/test_vision_train.py::test_lrfind [source]

To run tests please refer to this guide.

Explore lr from start_lr to end_lr over num_it iterations in learn. If stop_div, stops when loss diverges.

See LRFinder for details.

to_fp16[source][test]

to_fp16(learn:Learner, loss_scale:float=None, max_noskip:int=1000, dynamic:bool=True, clip:float=None, flat_master:bool=False, max_scale:float=16777216, loss_fp32:bool=True) → Learner No tests found for to_fp16. To contribute a test please refer to this guide and this discussion.

Put learn in FP16 precision mode.

See MixedPrecision for details.

to_fp32[source][test]

to_fp32(learn:Learner) No tests found for to_fp32. To contribute a test please refer to this guide and this discussion.

Put learn back to FP32 precision mode.

mixup[source][test]

mixup(learn:Learner, alpha:float=0.4, stack_x:bool=False, stack_y:bool=True) → Learner No tests found for mixup. To contribute a test please refer to this guide and this discussion.

Add mixup https://arxiv.org/abs/1710.09412 to learn.

See MixUpCallback for more details.

class Interpretation[source][test]

Interpretation(learn:Learner, preds:Tensor, y_true:Tensor, losses:Tensor, ds_type:DatasetType=<DatasetType.Valid: 2>) No tests found for Interpretation. To contribute a test please refer to this guide and this discussion.

Interpretation base class, can be inherited for task specific Interpretation classes

from_learner[source][test]

from_learner(learn:Learner, ds_type:DatasetType=<DatasetType.Valid: 2>, activ:Module=None) Tests found for from_learner:

Some other tests where from_learner is used:

• pytest -sv tests/test_tabular_train.py::test_confusion_tabular [source]
• pytest -sv tests/test_vision_train.py::test_ClassificationInterpretation [source]
• pytest -sv tests/test_vision_train.py::test_interp [source]

To run tests please refer to this guide.

Gets preds, y_true, losses to construct base class from a learner

top_losses[source][test]

top_losses(k:int=None, largest=True) Tests found for top_losses:

Some other tests where top_losses is used:

• pytest -sv tests/test_vision_train.py::test_interp [source]
• pytest -sv tests/test_vision_train.py::test_interp_shortcut [source]

To run tests please refer to this guide.

k largest(/smallest) losses and indexes, defaulting to all losses (sorted by largest).

For example in ClassificationInterpretation is implemented using argmax on preds to set self.pred_class whereas an optional sigmoid is used for MultilabelClassificationInterpretation

class ClassificationInterpretation[source][test]

ClassificationInterpretation(learn:Learner, preds:Tensor, y_true:Tensor, losses:Tensor, ds_type:DatasetType=<DatasetType.Valid: 2>) :: Interpretation Tests found for ClassificationInterpretation:

• pytest -sv tests/test_vision_train.py::test_ClassificationInterpretation [source]

Some other tests where ClassificationInterpretation is used:

• pytest -sv tests/test_tabular_train.py::test_confusion_tabular [source]
• pytest -sv tests/test_vision_train.py::test_interp [source]

To run tests please refer to this guide.

Interpretation methods for classification models.

path = untar_data(URLs.MNIST_SAMPLE)
data = ImageDataBunch.from_folder(path)
learn = cnn_learner(data, models.resnet18)
learn.fit(1)
preds,y,losses = learn.get_preds(with_loss=True)
interp = ClassificationInterpretation(learn, preds, y, losses)

top_losses[source][test]

top_losses(k:int=None, largest=True) Tests found for top_losses:

Some other tests where top_losses is used:

• pytest -sv tests/test_vision_train.py::test_interp [source]
• pytest -sv tests/test_vision_train.py::test_interp_shortcut [source]

To run tests please refer to this guide.

k largest(/smallest) losses and indexes, defaulting to all losses (sorted by largest).

Returns tuple of (losses,indices).

torch.return_types.topk(
values=tensor([14.2152, 10.3850,  9.1650,  8.7286,  5.8163,  5.6689,  4.9013,  4.5471,
         4.2432]),
indices=tensor([1059,  299,  960, 1831, 1775, 1467,  750, 1892,  634]))

plot_confusion_matrix[source][test]

plot_confusion_matrix(normalize:bool=False, title:str='Confusion matrix', cmap:Any='Blues', slice_size:int=1, norm_dec:int=2, plot_txt:bool=True, return_fig:bool=None, **kwargs) → Optional[Figure] No tests found for plot_confusion_matrix. To contribute a test please refer to this guide and this discussion.

Plot the confusion matrix, with title and using cmap.

If normalize, plots the percentages with norm_dec digits. slice_size can be used to avoid out of memory error if your set is too big. kwargs are passed to plt.figure.

interp.plot_confusion_matrix()

confusion_matrix[source][test]

confusion_matrix(slice_size:int=1) Tests found for confusion_matrix:

• pytest -sv tests/test_tabular_train.py::test_confusion_tabular [source]

Some other tests where confusion_matrix is used:

• pytest -sv tests/test_vision_train.py::test_ClassificationInterpretation [source]

To run tests please refer to this guide.

Confusion matrix as an np.ndarray.

interp.confusion_matrix()

array([[989,  21],
       [ 40, 988]])

most_confused[source][test]

most_confused(min_val:int=1, slice_size:int=1) → Collection[Tuple[str, str, int]] Tests found for most_confused:

Some other tests where most_confused is used:

• pytest -sv tests/test_vision_train.py::test_ClassificationInterpretation [source]

To run tests please refer to this guide.

Sorted descending list of largest non-diagonal entries of confusion matrix, presented as actual, predicted, number of occurrences.

class MultiLabelClassificationInterpretation[source][test]

MultiLabelClassificationInterpretation(learn:Learner, preds:Tensor, y_true:Tensor, losses:Tensor, ds_type:DatasetType=<DatasetType.Valid: 2>, sigmoid:bool=True, thresh:float=0.3) :: Interpretation No tests found for MultiLabelClassificationInterpretation. To contribute a test please refer to this guide and this discussion.

Interpretation methods for classification models.

Warning: MultiLabelClassificationInterpretation is not implemented yet. Feel free to implement it :)

Working with large datasets

When working with large datasets, memory problems can arise when computing the confusion matrix. For example, an error can look like this:

RuntimeError: $ Torch: not enough memory: you tried to allocate 64GB. Buy new RAM!

In this case it is possible to force ClassificationInterpretation to compute the confusion matrix for data slices and then aggregate the result by specifying slice_size parameter.

interp.confusion_matrix(slice_size=10)

array([[989,  21],
       [ 40, 988]])

interp.plot_confusion_matrix(slice_size=10)

interp.most_confused(slice_size=10)

[('7', '3', 40), ('3', '7', 21)]

Additional callbacks

We’ll show examples below using our MNIST sample. As usual the on_something methods are directly called by the fastai library, no need to call them yourself.

path = untar_data(URLs.MNIST_SAMPLE)
data = ImageDataBunch.from_folder(path)

class ShowGraph[source][test]

ShowGraph(learn) :: LearnerCallback No tests found for ShowGraph. To contribute a test please refer to this guide and this discussion.

Update a graph of learner stats and metrics after each epoch.

learn = cnn_learner(data, models.resnet18, metrics=accuracy, callback_fns=ShowGraph)
learn.fit(3)

on_epoch_end[source][test]

on_epoch_end(n_epochs:int, last_metrics:MetricsList, **kwargs) → bool No tests found for on_epoch_end. To contribute a test please refer to this guide and this discussion.

If we have last_metrics plot them in our pbar graph

class GradientClipping[source][test]

GradientClipping(learn:Learner, clip:float=0.0) :: LearnerCallback No tests found for GradientClipping. To contribute a test please refer to this guide and this discussion.

Gradient clipping during training.

learn = cnn_learner(data, models.resnet18, metrics=accuracy,
    callback_fns=partial(GradientClipping, clip=0.1))
learn.fit(1)

on_backward_end[source][test]

on_backward_end(**kwargs) No tests found for on_backward_end. To contribute a test please refer to this guide and this discussion.

Clip the gradient before the optimizer step.

class BnFreeze[source][test]

BnFreeze(learn) :: LearnerCallback No tests found for BnFreeze. To contribute a test please refer to this guide and this discussion.

Freeze moving average statistics in all non-trainable batchnorm layers.

For batchnorm layers where requires_grad==False, you generally don’t want to update their moving average statistics, in order to avoid the model’s statistics getting out of sync with its pre-trained weights. You can add this callback to automate this freezing of statistics (internally, it calls eval on these layers).

learn = cnn_learner(data, models.resnet18, metrics=accuracy, callback_fns=BnFreeze)
learn.fit(1)

on_epoch_begin[source][test]

on_epoch_begin(**kwargs:Any) No tests found for on_epoch_begin. To contribute a test please refer to this guide and this discussion.

Put bn layers in eval mode just after model.train().

class AccumulateScheduler[source][test]

AccumulateScheduler(learn:Learner, n_step:int=1, drop_last:bool=False) :: LearnerCallback No tests found for AccumulateScheduler. To contribute a test please refer to this guide and this discussion.

Does accumlated step every nth step by accumulating gradients

Let’s force batch_size=2 to mimic a scenario where we can’t fit enough batch samples to our memory. We can then set n_step as desired to have an effective batch_size of effective_batch_size=batch_size*n_step.

It is also important to use loss func with reduce='sum' in order to calculate exact average accumulated gradients.

Another important note for users is that batchnorm is not yet adapted to accumulated gradients. So you should use this callback at your own risk until a hero fixes it :)

Here we demonstrate this callback with a model without batchnorm layers, alternatively you can use nn.InstanceNorm or nn.GroupNorm.

from torchvision.models import vgg11
data = ImageDataBunch.from_folder(path, bs=2)
learn = cnn_learner(data, resnet18, metrics=accuracy, loss_func=CrossEntropyFlat(reduction='sum'),
                    callback_fns=partial(AccumulateScheduler, n_step=16))
learn.fit(1)

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