125 lines
4.3 KiB
Python
125 lines
4.3 KiB
Python
from __future__ import absolute_import, division
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from copy import copy
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from functools import partial
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from .auto import tqdm as tqdm_auto
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try:
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import keras
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except (ImportError, AttributeError) as e:
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try:
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from tensorflow import keras
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except ImportError:
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raise e
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__author__ = {"github.com/": ["casperdcl"]}
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__all__ = ['TqdmCallback']
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class TqdmCallback(keras.callbacks.Callback):
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"""Keras callback for epoch and batch progress."""
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@staticmethod
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def bar2callback(bar, pop=None, delta=(lambda logs: 1)):
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def callback(_, logs=None):
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n = delta(logs)
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if logs:
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if pop:
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logs = copy(logs)
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[logs.pop(i, 0) for i in pop]
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bar.set_postfix(logs, refresh=False)
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bar.update(n)
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return callback
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def __init__(self, epochs=None, data_size=None, batch_size=None, verbose=1,
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tqdm_class=tqdm_auto, **tqdm_kwargs):
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"""
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Parameters
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----------
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epochs : int, optional
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data_size : int, optional
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Number of training pairs.
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batch_size : int, optional
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Number of training pairs per batch.
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verbose : int
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0: epoch, 1: batch (transient), 2: batch. [default: 1].
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Will be set to `0` unless both `data_size` and `batch_size`
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are given.
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tqdm_class : optional
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`tqdm` class to use for bars [default: `tqdm.auto.tqdm`].
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tqdm_kwargs : optional
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Any other arguments used for all bars.
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"""
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if tqdm_kwargs:
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tqdm_class = partial(tqdm_class, **tqdm_kwargs)
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self.tqdm_class = tqdm_class
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self.epoch_bar = tqdm_class(total=epochs, unit='epoch')
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self.on_epoch_end = self.bar2callback(self.epoch_bar)
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if data_size and batch_size:
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self.batches = batches = (data_size + batch_size - 1) // batch_size
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else:
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self.batches = batches = None
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self.verbose = verbose
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if verbose == 1:
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self.batch_bar = tqdm_class(total=batches, unit='batch', leave=False)
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self.on_batch_end = self.bar2callback(
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self.batch_bar, pop=['batch', 'size'],
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delta=lambda logs: logs.get('size', 1))
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def on_train_begin(self, *_, **__):
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params = self.params.get
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auto_total = params('epochs', params('nb_epoch', None))
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if auto_total is not None and auto_total != self.epoch_bar.total:
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self.epoch_bar.reset(total=auto_total)
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def on_epoch_begin(self, epoch, *_, **__):
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if self.epoch_bar.n < epoch:
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ebar = self.epoch_bar
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ebar.n = ebar.last_print_n = ebar.initial = epoch
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if self.verbose:
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params = self.params.get
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total = params('samples', params(
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'nb_sample', params('steps', None))) or self.batches
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if self.verbose == 2:
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if hasattr(self, 'batch_bar'):
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self.batch_bar.close()
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self.batch_bar = self.tqdm_class(
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total=total, unit='batch', leave=True,
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unit_scale=1 / (params('batch_size', 1) or 1))
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self.on_batch_end = self.bar2callback(
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self.batch_bar, pop=['batch', 'size'],
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delta=lambda logs: logs.get('size', 1))
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elif self.verbose == 1:
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self.batch_bar.unit_scale = 1 / (params('batch_size', 1) or 1)
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self.batch_bar.reset(total=total)
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else:
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raise KeyError('Unknown verbosity')
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def on_train_end(self, *_, **__):
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if self.verbose:
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self.batch_bar.close()
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self.epoch_bar.close()
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def display(self):
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"""Displays in the current cell in Notebooks."""
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container = getattr(self.epoch_bar, 'container', None)
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if container is None:
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return
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from .notebook import display
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display(container)
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batch_bar = getattr(self, 'batch_bar', None)
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if batch_bar is not None:
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display(batch_bar.container)
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@staticmethod
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def _implements_train_batch_hooks():
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return True
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@staticmethod
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def _implements_test_batch_hooks():
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return True
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@staticmethod
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def _implements_predict_batch_hooks():
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return True
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