mirror of https://github.com/morpheus65535/bazarr
125 lines
4.3 KiB
Python
125 lines
4.3 KiB
Python
from __future__ import absolute_import, division
|
|
|
|
from copy import copy
|
|
from functools import partial
|
|
|
|
from .auto import tqdm as tqdm_auto
|
|
|
|
try:
|
|
import keras
|
|
except (ImportError, AttributeError) 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 = copy(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, **tqdm_kwargs):
|
|
"""
|
|
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`].
|
|
tqdm_kwargs : optional
|
|
Any other arguments used for all bars.
|
|
"""
|
|
if tqdm_kwargs:
|
|
tqdm_class = partial(tqdm_class, **tqdm_kwargs)
|
|
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 and auto_total != self.epoch_bar.total:
|
|
self.epoch_bar.reset(total=auto_total)
|
|
|
|
def on_epoch_begin(self, epoch, *_, **__):
|
|
if self.epoch_bar.n < epoch:
|
|
ebar = self.epoch_bar
|
|
ebar.n = ebar.last_print_n = ebar.initial = epoch
|
|
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()
|
|
|
|
def display(self):
|
|
"""Displays in the current cell in Notebooks."""
|
|
container = getattr(self.epoch_bar, 'container', None)
|
|
if container is None:
|
|
return
|
|
from .notebook import display
|
|
display(container)
|
|
batch_bar = getattr(self, 'batch_bar', None)
|
|
if batch_bar is not None:
|
|
display(batch_bar.container)
|
|
|
|
@staticmethod
|
|
def _implements_train_batch_hooks():
|
|
return True
|
|
|
|
@staticmethod
|
|
def _implements_test_batch_hooks():
|
|
return True
|
|
|
|
@staticmethod
|
|
def _implements_predict_batch_hooks():
|
|
return True
|