264 lines
9.6 KiB
Python
264 lines
9.6 KiB
Python
import numpy as np
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import os, shutil
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from pytorch_lightning.pt_overrides.override_data_parallel import LightningDistributedDataParallel
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class Callback(object):
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"""Abstract base class used to build new callbacks.
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# Properties
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params: dict. Training parameters
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(eg. verbosity, batch size, number of epochs...).
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model: instance of `keras.models.Model`.
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Reference of the model being trained.
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The `logs` dictionary that callback methods
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take as argument will contain keys for quantities relevant to
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the current batch or epoch.
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Currently, the `.fit()` method of the `Sequential` model class
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will include the following quantities in the `logs` that
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it passes to its callbacks:
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on_epoch_end: logs include `acc` and `loss`, and
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optionally include `val_loss`
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(if validation is enabled in `fit`), and `val_acc`
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(if validation and accuracy monitoring are enabled).
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on_batch_begin: logs include `size`,
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the number of samples in the current batch.
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on_batch_end: logs include `loss`, and optionally `acc`
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(if accuracy monitoring is enabled).
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"""
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def __init__(self):
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self.validation_data = None
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self.model = None
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def set_params(self, params):
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self.params = params
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def set_model(self, model):
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if type(model) is LightningDistributedDataParallel:
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model = model.module
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self.model = model
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def on_epoch_begin(self, epoch, logs=None):
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pass
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def on_epoch_end(self, epoch, logs=None):
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pass
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def on_batch_begin(self, batch, logs=None):
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pass
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def on_batch_end(self, batch, logs=None):
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pass
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def on_train_begin(self, logs=None):
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pass
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def on_train_end(self, logs=None):
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pass
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class EarlyStopping(Callback):
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"""Stop training when a monitored quantity has stopped improving.
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# Arguments
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monitor: quantity to be monitored.
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min_delta: minimum change in the monitored quantity
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to qualify as an improvement, i.e. an absolute
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change of less than min_delta, will count as no
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improvement.
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patience: number of epochs with no improvement
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after which training will be stopped.
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verbose: verbosity mode.
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mode: one of {auto, min, max}. In `min` mode,
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training will stop when the quantity
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monitored has stopped decreasing; in `max`
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mode it will stop when the quantity
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monitored has stopped increasing; in `auto`
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mode, the direction is automatically inferred
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from the name of the monitored quantity.
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"""
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def __init__(self, monitor='val_loss',
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min_delta=0.0, patience=0, verbose=0, mode='auto'):
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super(EarlyStopping, self).__init__()
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self.monitor = monitor
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self.patience = patience
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self.verbose = verbose
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self.min_delta = min_delta
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self.wait = 0
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self.stopped_epoch = 0
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if mode not in ['auto', 'min', 'max']:
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print('EarlyStopping mode %s is unknown, fallback to auto mode.' % mode)
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mode = 'auto'
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if mode == 'min':
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self.monitor_op = np.less
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elif mode == 'max':
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self.monitor_op = np.greater
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else:
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if 'acc' in self.monitor:
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self.monitor_op = np.greater
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else:
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self.monitor_op = np.less
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if self.monitor_op == np.greater:
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self.min_delta *= 1
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else:
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self.min_delta *= -1
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self.on_train_begin()
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def on_train_begin(self, logs=None):
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# Allow instances to be re-used
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self.wait = 0
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self.stopped_epoch = 0
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self.best = np.Inf if self.monitor_op == np.less else -np.Inf
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def on_epoch_end(self, epoch, logs=None):
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current = logs.get(self.monitor)
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stop_training = False
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if current is None:
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print('Early stopping conditioned on metric `%s` ''which is not available. Available metrics are: %s' %
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(self.monitor, ','.join(list(logs.keys()))), RuntimeWarning
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)
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exit(-1)
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if self.monitor_op(current - self.min_delta, self.best):
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self.best = current
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self.wait = 0
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else:
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self.wait += 1
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if self.wait >= self.patience:
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self.stopped_epoch = epoch
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stop_training = True
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self.on_train_end()
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return stop_training
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def on_train_end(self, logs=None):
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if self.stopped_epoch > 0 and self.verbose > 0:
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print('Epoch %05d: early stopping' % (self.stopped_epoch + 1))
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class ModelCheckpoint(Callback):
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"""Save the model after every epoch.
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`filepath` can contain named formatting options,
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which will be filled the value of `epoch` and
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keys in `logs` (passed in `on_epoch_end`).
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For example: if `filepath` is `weights.{epoch:02d}-{val_loss:.2f}.hdf5`,
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then the model checkpoints will be saved with the epoch number and
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the validation loss in the filename.
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# Arguments
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filepath: string, path to save the model file.
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monitor: quantity to monitor.
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verbose: verbosity mode, 0 or 1.
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save_best_only: if `save_best_only=True`,
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the latest best model according to
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the quantity monitored will not be overwritten.
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mode: one of {auto, min, max}.
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If `save_best_only=True`, the decision
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to overwrite the current save file is made
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based on either the maximization or the
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minimization of the monitored quantity. For `val_acc`,
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this should be `max`, for `val_loss` this should
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be `min`, etc. In `auto` mode, the direction is
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automatically inferred from the name of the monitored quantity.
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save_weights_only: if True, then only the model's weights will be
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saved (`model.save_weights(filepath)`), else the full model
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is saved (`model.save(filepath)`).
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period: Interval (number of epochs) between checkpoints.
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"""
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def __init__(self, filepath, monitor='val_loss', verbose=0,
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save_best_only=False, save_weights_only=False,
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mode='auto', period=1, prefix=''):
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super(ModelCheckpoint, self).__init__()
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self.monitor = monitor
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self.verbose = verbose
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self.filepath = filepath
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self.save_best_only = save_best_only
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self.save_weights_only = save_weights_only
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self.period = period
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self.epochs_since_last_save = 0
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self.prefix = prefix
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if mode not in ['auto', 'min', 'max']:
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print('ModelCheckpoint mode %s is unknown, '
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'fallback to auto mode.' % (mode),
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RuntimeWarning)
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mode = 'auto'
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if mode == 'min':
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self.monitor_op = np.less
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self.best = np.Inf
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elif mode == 'max':
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self.monitor_op = np.greater
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self.best = -np.Inf
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else:
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if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):
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self.monitor_op = np.greater
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self.best = -np.Inf
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else:
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self.monitor_op = np.less
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self.best = np.Inf
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def save_model(self, filepath, overwrite):
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dirpath = '/'.join(filepath.split('/')[:-1])
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# make paths
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os.makedirs(os.path.dirname(filepath), exist_ok=True)
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if overwrite:
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for filename in os.listdir(dirpath):
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if self.prefix in filename:
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path_to_delete = os.path.join(dirpath, filename)
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try:
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shutil.rmtree(path_to_delete)
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except OSError:
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os.remove(path_to_delete)
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# delegate the saving to the model
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self.save_function(filepath)
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def on_epoch_end(self, epoch, logs=None):
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logs = logs or {}
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self.epochs_since_last_save += 1
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if self.epochs_since_last_save >= self.period:
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self.epochs_since_last_save = 0
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filepath = '{}/{}_ckpt_epoch_{}.ckpt'.format(self.filepath, self.prefix, epoch + 1)
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if self.save_best_only:
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current = logs.get(self.monitor)
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if current is None:
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print('Can save best model only with %s available, '
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'skipping.' % (self.monitor), RuntimeWarning)
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else:
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if self.monitor_op(current, self.best):
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if self.verbose > 0:
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print('\nEpoch %05d: %s improved from %0.5f to %0.5f,'
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' saving model to %s'
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% (epoch + 1, self.monitor, self.best,
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current, filepath))
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self.best = current
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self.save_model(filepath, overwrite=True)
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else:
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if self.verbose > 0:
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print('\nEpoch %05d: %s did not improve' %
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(epoch + 1, self.monitor))
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else:
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if self.verbose > 0:
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print('\nEpoch %05d: saving model to %s' % (epoch + 1, filepath))
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self.save_model(filepath, overwrite=False)
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if __name__ == '__main__':
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c = EarlyStopping(min_delta=0.9, patience=2, verbose=True)
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losses = [10, 9, 8, 8, 6, 4.3, 5, 4.4, 2.8, 2.5]
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for i, loss in enumerate(losses):
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should_stop = c.on_epoch_end(i, logs={'val_loss': loss})
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print(loss)
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if should_stop:
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break
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