You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
94 lines
3.4 KiB
Python
94 lines
3.4 KiB
Python
5 years ago
|
from __future__ import absolute_import, division
|
||
|
|
from .auto import tqdm as tqdm_auto
|
||
|
|
from copy import deepcopy
|
||
|
|
try:
|
||
|
|
import keras
|
||
|
|
except ImportError as e:
|
||
|
|
try:
|
||
|
|
from tensorflow import keras
|
||
|
|
except ImportError:
|
||
|
|
raise e
|
||
|
|
__author__ = {"github.com/": ["casperdcl"]}
|
||
|
|
__all__ = ['TqdmCallback']
|
||
|
|
|
||
|
|
|
||
|
|
class TqdmCallback(keras.callbacks.Callback):
|
||
|
|
"""`keras` callback for epoch and batch progress"""
|
||
|
|
@staticmethod
|
||
|
|
def bar2callback(bar, pop=None, delta=(lambda logs: 1)):
|
||
|
|
def callback(_, logs=None):
|
||
|
|
n = delta(logs)
|
||
|
|
if logs:
|
||
|
|
if pop:
|
||
|
|
logs = deepcopy(logs)
|
||
|
|
[logs.pop(i, 0) for i in pop]
|
||
|
|
bar.set_postfix(logs, refresh=False)
|
||
|
|
bar.update(n)
|
||
|
|
|
||
|
|
return callback
|
||
|
|
|
||
|
|
def __init__(self, epochs=None, data_size=None, batch_size=None, verbose=1,
|
||
|
|
tqdm_class=tqdm_auto):
|
||
|
|
"""
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
epochs : int, optional
|
||
|
|
data_size : int, optional
|
||
|
|
Number of training pairs.
|
||
|
|
batch_size : int, optional
|
||
|
|
Number of training pairs per batch.
|
||
|
|
verbose : int
|
||
|
|
0: epoch, 1: batch (transient), 2: batch. [default: 1].
|
||
|
|
Will be set to `0` unless both `data_size` and `batch_size`
|
||
|
|
are given.
|
||
|
|
tqdm_class : optional
|
||
|
|
`tqdm` class to use for bars [default: `tqdm.auto.tqdm`].
|
||
|
|
"""
|
||
|
|
self.tqdm_class = tqdm_class
|
||
|
|
self.epoch_bar = tqdm_class(total=epochs, unit='epoch')
|
||
|
|
self.on_epoch_end = self.bar2callback(self.epoch_bar)
|
||
|
|
if data_size and batch_size:
|
||
|
|
self.batches = batches = (data_size + batch_size - 1) // batch_size
|
||
|
|
else:
|
||
|
|
self.batches = batches = None
|
||
|
|
self.verbose = verbose
|
||
|
|
if verbose == 1:
|
||
|
|
self.batch_bar = tqdm_class(total=batches, unit='batch',
|
||
|
|
leave=False)
|
||
|
|
self.on_batch_end = self.bar2callback(
|
||
|
|
self.batch_bar,
|
||
|
|
pop=['batch', 'size'],
|
||
|
|
delta=lambda logs: logs.get('size', 1))
|
||
|
|
|
||
|
|
def on_train_begin(self, *_, **__):
|
||
|
|
params = self.params.get
|
||
|
|
auto_total = params('epochs', params('nb_epoch', None))
|
||
|
|
if auto_total is not None:
|
||
|
|
self.epoch_bar.reset(total=auto_total)
|
||
|
|
|
||
|
|
def on_epoch_begin(self, *_, **__):
|
||
|
|
if self.verbose:
|
||
|
|
params = self.params.get
|
||
|
|
total = params('samples', params(
|
||
|
|
'nb_sample', params('steps', None))) or self.batches
|
||
|
|
if self.verbose == 2:
|
||
|
|
if hasattr(self, 'batch_bar'):
|
||
|
|
self.batch_bar.close()
|
||
|
|
self.batch_bar = self.tqdm_class(
|
||
|
|
total=total, unit='batch', leave=True,
|
||
|
|
unit_scale=1 / (params('batch_size', 1) or 1))
|
||
|
|
self.on_batch_end = self.bar2callback(
|
||
|
|
self.batch_bar,
|
||
|
|
pop=['batch', 'size'],
|
||
|
|
delta=lambda logs: logs.get('size', 1))
|
||
|
|
elif self.verbose == 1:
|
||
|
|
self.batch_bar.unit_scale = 1 / (params('batch_size', 1) or 1)
|
||
|
|
self.batch_bar.reset(total=total)
|
||
|
|
else:
|
||
|
|
raise KeyError('Unknown verbosity')
|
||
|
|
|
||
|
|
def on_train_end(self, *_, **__):
|
||
|
|
if self.verbose:
|
||
|
|
self.batch_bar.close()
|
||
|
|
self.epoch_bar.close()
|