Progress and logging

Callback and helper function to track progress of training or log results

from fastai.test_utils import *

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ProgressCallback

def ProgressCallback(
    after_create:NoneType=None, before_fit:NoneType=None, before_epoch:NoneType=None, before_train:NoneType=None,
    before_batch:NoneType=None, after_pred:NoneType=None, after_loss:NoneType=None, before_backward:NoneType=None,
    after_cancel_backward:NoneType=None, after_backward:NoneType=None, before_step:NoneType=None,
    after_cancel_step:NoneType=None, after_step:NoneType=None, after_cancel_batch:NoneType=None,
    after_batch:NoneType=None, after_cancel_train:NoneType=None, after_train:NoneType=None,
    before_validate:NoneType=None, after_cancel_validate:NoneType=None, after_validate:NoneType=None,
    after_cancel_epoch:NoneType=None, after_epoch:NoneType=None, after_cancel_fit:NoneType=None,
    after_fit:NoneType=None
):

A Callback to handle the display of progress bars

learn = synth_learner()
learn.fit(5)

epoch	train_loss	valid_loss	time
0	14.523648	10.988108	00:00
1	12.395808	7.306935	00:00
2	10.121231	4.370981	00:00
3	8.065226	2.487984	00:00
4	6.374166	1.368232	00:00

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no_bar

def no_bar(
    
):

Context manager that deactivates the use of progress bars

learn = synth_learner()
with learn.no_bar(): learn.fit(5)

[0, 15.748106002807617, 12.352150917053223, '00:00']
[1, 13.818815231323242, 8.879858016967773, '00:00']
[2, 11.650713920593262, 5.857329845428467, '00:00']
[3, 9.595088005065918, 3.7397098541259766, '00:00']
[4, 7.814438343048096, 2.327916145324707, '00:00']

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ProgressCallback.before_fit

def before_fit(
    
):

Setup the master bar over the epochs

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ProgressCallback.before_epoch

def before_epoch(
    
):

Update the master bar

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ProgressCallback.before_train

def before_train(
    
):

Launch a progress bar over the training dataloader

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ProgressCallback.before_validate

def before_validate(
    
):

Launch a progress bar over the validation dataloader

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ProgressCallback.after_batch

def after_batch(
    
):

Update the current progress bar

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ProgressCallback.after_train

def after_train(
    
):

Close the progress bar over the training dataloader

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ProgressCallback.after_validate

def after_validate(
    
):

Close the progress bar over the validation dataloader

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ProgressCallback.after_fit

def after_fit(
    
):

Close the master bar

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ShowGraphCallback

def ShowGraphCallback(
    after_create:NoneType=None, before_fit:NoneType=None, before_epoch:NoneType=None, before_train:NoneType=None,
    before_batch:NoneType=None, after_pred:NoneType=None, after_loss:NoneType=None, before_backward:NoneType=None,
    after_cancel_backward:NoneType=None, after_backward:NoneType=None, before_step:NoneType=None,
    after_cancel_step:NoneType=None, after_step:NoneType=None, after_cancel_batch:NoneType=None,
    after_batch:NoneType=None, after_cancel_train:NoneType=None, after_train:NoneType=None,
    before_validate:NoneType=None, after_cancel_validate:NoneType=None, after_validate:NoneType=None,
    after_cancel_epoch:NoneType=None, after_epoch:NoneType=None, after_cancel_fit:NoneType=None,
    after_fit:NoneType=None
):

Update a graph of training and validation loss

learn = synth_learner(cbs=ShowGraphCallback())
learn.fit(5)

epoch	train_loss	valid_loss	time
0	17.683565	10.431150	00:00
1	15.232769	7.056944	00:00
2	12.470916	4.382421	00:00
3	10.000675	2.574951	00:00
4	7.943449	1.464153	00:00

learn.predict(torch.tensor([[0.1]]))

(tensor([1.8955]), tensor([1.8955]), tensor([1.8955]))

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CSVLogger

def CSVLogger(
    fname:str='history.csv', append:bool=False
):

Log the results displayed in learn.path/fname

The results are appended to an existing file if append, or they overwrite it otherwise.

learn = synth_learner(cbs=CSVLogger())
learn.fit(5)

epoch	train_loss	valid_loss	time
0	15.606769	14.485189	00:00
1	13.840394	10.834929	00:00
2	11.842106	7.582738	00:00
3	9.937692	5.158300	00:00
4	8.244681	3.432087	00:00

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CSVLogger.read_log

def read_log(
    
):

Convenience method to quickly access the log.

df = learn.csv_logger.read_log()
test_eq(df.columns.values, learn.recorder.metric_names)
for i,v in enumerate(learn.recorder.values):
    test_close(df.iloc[i][:3], [i] + v)
os.remove(learn.path/learn.csv_logger.fname)

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CSVLogger.before_fit

def before_fit(
    
):

Prepare file with metric names.

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CSVLogger.after_fit

def after_fit(
    
):

Close the file and clean up.