/
callbacks.py
1348 lines (1114 loc) · 46.4 KB
/
callbacks.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numbers
import os
import time
import warnings
import numpy as np
import paddle
from paddle.utils import try_import
from .progressbar import ProgressBar
__all__ = []
def config_callbacks(
callbacks=None,
model=None,
batch_size=None,
epochs=None,
steps=None,
log_freq=2,
verbose=2,
save_freq=1,
save_dir=None,
metrics=None,
mode='train',
):
cbks = callbacks or []
cbks = cbks if isinstance(cbks, (list, tuple)) else [cbks]
if not any(isinstance(k, ProgBarLogger) for k in cbks) and verbose:
cbks = [ProgBarLogger(log_freq, verbose=verbose)] + cbks
if not any(isinstance(k, ModelCheckpoint) for k in cbks):
cbks = cbks + [ModelCheckpoint(save_freq, save_dir)]
for k in cbks:
if isinstance(k, EarlyStopping):
k.save_dir = save_dir
if not any(isinstance(k, LRScheduler) for k in cbks):
cbks = cbks + [LRScheduler()]
cbk_list = CallbackList(cbks)
cbk_list.set_model(model)
metrics = metrics or [] if mode != 'test' else []
params = {
'batch_size': batch_size,
'epochs': epochs,
'steps': steps,
'verbose': verbose,
'metrics': metrics,
}
cbk_list.set_params(params)
return cbk_list
class CallbackList:
def __init__(self, callbacks=None):
# copy
self.callbacks = list(callbacks)
self.params = {}
self.model = None
def append(self, callback):
self.callbacks.append(callback)
def __iter__(self):
return iter(self.callbacks)
def set_params(self, params):
for c in self.callbacks:
c.set_params(params)
def set_model(self, model):
for c in self.callbacks:
c.set_model(model)
def _call(self, name, *args):
for c in self.callbacks:
func = getattr(c, name)
func(*args)
def _check_mode(self, mode):
assert mode in [
'train',
'eval',
'predict',
], 'mode should be train, eval or predict'
def on_begin(self, mode, logs=None):
self._check_mode(mode)
name = f'on_{mode}_begin'
self._call(name, logs)
def on_end(self, mode, logs=None):
self._check_mode(mode)
name = f'on_{mode}_end'
self._call(name, logs)
def on_epoch_begin(self, epoch=None, logs=None):
self._call('on_epoch_begin', epoch, logs)
def on_epoch_end(self, epoch=None, logs=None):
self._call('on_epoch_end', epoch, logs)
def on_batch_begin(self, mode, step=None, logs=None):
self._check_mode(mode)
name = f'on_{mode}_batch_begin'
self._call(name, step, logs)
def on_batch_end(self, mode, step=None, logs=None):
self._check_mode(mode)
name = f'on_{mode}_batch_end'
self._call(name, step, logs)
class Callback:
"""
Base class used to build new callbacks. And new callbacks could also
terminate training by setting `model.stop_training=True`.
Examples:
.. code-block:: python
>>> import paddle
>>> # build a simple model checkpoint callback
>>> class ModelCheckpoint(paddle.callbacks.Callback):
... def __init__(self, save_freq=1, save_dir=None):
... self.save_freq = save_freq
... self.save_dir = save_dir
...
... def on_epoch_end(self, epoch, logs=None):
... if self.model is not None and epoch % self.save_freq == 0:
... path = '{}/{}'.format(self.save_dir, epoch)
... print('save checkpoint at {}'.format(path))
... self.model.save(path)
"""
def __init__(self):
self.model = None
self.params = {}
def set_params(self, params):
"""
Set parameters, which is dict. The keys contain:
- 'batch_size': an integer. Number of samples per batch.
- 'epochs': an integer. Number of epochs.
- 'steps': an integer. Number of steps of one epoch.
- 'verbose': an integer. Verbose mode is 0, 1 or 2. 0 = silent, 1 = progress bar, 2 = one line per epoch.
- 'metrics': a list of str. Names of metrics, including 'loss' and the names of paddle.metric.Metric.
"""
self.params = params
def set_model(self, model):
"""model is instance of paddle.Model."""
self.model = model
def on_train_begin(self, logs=None):
"""Called at the start of training.
Args:
logs (dict): The logs is a dict or None.
"""
def on_train_end(self, logs=None):
"""Called at the end of training.
Args:
logs (dict): The logs is a dict or None. The keys of logs
passed by paddle.Model contains 'loss', metric names and
`batch_size`.
"""
def on_eval_begin(self, logs=None):
"""Called at the start of evaluation.
Args:
logs (dict): The logs is a dict or None. The keys of logs
passed by paddle.Model contains 'steps' and 'metrics',
The `steps` is number of total steps of validation dataset.
The `metrics` is a list of str including 'loss' and the names
of paddle.metric.Metric.
"""
def on_eval_end(self, logs=None):
"""Called at the end of evaluation.
Args:
logs (dict): The logs is a dict or None. The `logs` passed by
paddle.Model is a dict contains 'loss', metrics and 'batch_size'
of last batch of validation dataset.
"""
def on_predict_begin(self, logs=None):
"""Called at the beginning of predict.
Args:
logs (dict): The logs is a dict or None.
"""
def on_predict_end(self, logs=None):
"""Called at the end of predict.
Args:
logs (dict): The logs is a dict or None.
"""
def on_epoch_begin(self, epoch, logs=None):
"""Called at the beginning of each epoch.
Args:
epoch (int): The index of epoch.
logs (dict): The logs is a dict or None. The `logs` passed by
paddle.Model is None.
"""
def on_epoch_end(self, epoch, logs=None):
"""Called at the end of each epoch.
Args:
epoch (int): The index of epoch.
logs (dict): The logs is a dict or None. The `logs` passed by
paddle.Model is a dict, contains 'loss', metrics and 'batch_size'
of last batch.
"""
def on_train_batch_begin(self, step, logs=None):
"""Called at the beginning of each batch in training.
Args:
step (int): The index of step (or iteration).
logs (dict): The logs is a dict or None. The `logs` passed by
paddle.Model is empty.
"""
def on_train_batch_end(self, step, logs=None):
"""Called at the end of each batch in training.
Args:
step (int): The index of step (or iteration).
logs (dict): The logs is a dict or None. The `logs` passed by
paddle.Model is a dict, contains 'loss', metrics and 'batch_size'
of current batch.
"""
def on_eval_batch_begin(self, step, logs=None):
"""Called at the beginning of each batch in evaluation.
Args:
step (int): The index of step (or iteration).
logs (dict): The logs is a dict or None. The `logs` passed by
paddle.Model is empty.
"""
def on_eval_batch_end(self, step, logs=None):
"""Called at the end of each batch in evaluation.
Args:
step (int): The index of step (or iteration).
logs (dict): The logs is a dict or None. The `logs` passed by
paddle.Model is a dict, contains 'loss', metrics and 'batch_size'
of current batch.
"""
def on_predict_batch_begin(self, step, logs=None):
"""Called at the beginning of each batch in predict.
Args:
step (int): The index of step (or iteration).
logs (dict): The logs is a dict or None.
"""
def on_predict_batch_end(self, step, logs=None):
"""Called at the end of each batch in predict.
Args:
step (int): The index of step (or iteration).
logs (dict): The logs is a dict or None.
"""
class ProgBarLogger(Callback):
"""
Logger callback function to print loss and metrics to stdout. It supports
silent mode (not print), progress bar or one line per each printing,
see arguments for more detailed.
Args:
log_freq (int): The frequency, in number of steps,
the logs such as loss, metrics are printed. Default: 1.
verbose (int): The verbosity mode, should be 0, 1, or 2.
0 = silent, 1 = progress bar, 2 = one line each printing, 3 = 2 +
time counter, such as average reader cost, samples per second.
Default: 2.
Examples:
.. code-block:: python
>>> import paddle
>>> import paddle.vision.transforms as T
>>> from paddle.vision.datasets import MNIST
>>> from paddle.static import InputSpec
>>> inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
>>> labels = [InputSpec([None, 1], 'int64', 'label')]
>>> transform = T.Compose([
... T.Transpose(),
... T.Normalize([127.5], [127.5])
... ])
>>> train_dataset = MNIST(mode='train', transform=transform)
>>> lenet = paddle.vision.models.LeNet()
>>> model = paddle.Model(lenet,
... inputs, labels)
>>> optim = paddle.optimizer.Adam(0.001, parameters=lenet.parameters())
>>> model.prepare(optimizer=optim,
... loss=paddle.nn.CrossEntropyLoss(),
... metrics=paddle.metric.Accuracy())
>>> callback = paddle.callbacks.ProgBarLogger(log_freq=10)
>>> model.fit(train_dataset, batch_size=64, callbacks=callback)
"""
def __init__(self, log_freq=1, verbose=2):
self.epochs = None
self.steps = None
self.progbar = None
self.verbose = verbose
self.log_freq = log_freq
def _is_print(self):
return self.verbose and paddle.distributed.ParallelEnv().local_rank == 0
def on_train_begin(self, logs=None):
self.epochs = self.params['epochs']
assert self.epochs
self.train_metrics = self.params['metrics']
assert self.train_metrics
self._train_timer = {
'data_time': 0,
'batch_time': 0,
'count': 0,
'samples': 0,
}
if self._is_print():
print(
"The loss value printed in the log is the current step, and the metric is the average value of previous steps."
)
def on_epoch_begin(self, epoch=None, logs=None):
self.steps = self.params['steps']
self.epoch = epoch
self.train_step = 0
if self.epochs and self._is_print():
print('Epoch %d/%d' % (epoch + 1, self.epochs))
self.train_progbar = ProgressBar(num=self.steps, verbose=self.verbose)
self._train_timer['batch_start_time'] = time.time()
def _updates(self, logs, mode):
values = []
metrics = getattr(self, '%s_metrics' % (mode))
progbar = getattr(self, '%s_progbar' % (mode))
steps = getattr(self, '%s_step' % (mode))
for k in metrics:
if k in logs:
values.append((k, logs[k]))
if self.verbose == 3 and hasattr(self, '_%s_timer' % (mode)):
timer = getattr(self, '_%s_timer' % (mode))
cnt = timer['count'] if timer['count'] > 0 else 1.0
samples = timer['samples'] if timer['samples'] > 0 else 1.0
values.append(
('avg_reader_cost', "%.5f sec" % (timer['data_time'] / cnt))
)
values.append(
('avg_batch_cost', "%.5f sec" % (timer['batch_time'] / cnt))
)
values.append(
(
'ips',
"%.5f samples/sec"
% (samples / (timer['data_time'] + timer['batch_time'])),
)
)
timer['count'] = 0
timer['samples'] = 0
timer['data_time'] = 0.0
timer['batch_time'] = 0.0
progbar.update(steps, values)
def on_train_batch_begin(self, step, logs=None):
self._train_timer['batch_data_end_time'] = time.time()
self._train_timer['data_time'] += (
self._train_timer['batch_data_end_time']
- self._train_timer['batch_start_time']
)
def on_train_batch_end(self, step, logs=None):
logs = logs or {}
self.train_step += 1
self._train_timer['batch_time'] += (
time.time() - self._train_timer['batch_data_end_time']
)
self._train_timer['count'] += 1
samples = logs.get('batch_size', 1)
self._train_timer['samples'] += samples
if self._is_print() and self.train_step % self.log_freq == 0:
if self.steps is None or self.train_step < self.steps:
self._updates(logs, 'train')
self._train_timer['batch_start_time'] = time.time()
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
if self._is_print() and (self.steps is not None):
self._updates(logs, 'train')
def on_eval_begin(self, logs=None):
self.eval_steps = logs.get('steps', None)
self.eval_metrics = logs.get('metrics', [])
self.eval_step = 0
self.evaled_samples = 0
self._eval_timer = {
'data_time': 0,
'batch_time': 0,
'count': 0,
'samples': 0,
}
self.eval_progbar = ProgressBar(
num=self.eval_steps, verbose=self.verbose
)
if self._is_print():
print('Eval begin...')
self._eval_timer['batch_start_time'] = time.time()
def on_eval_batch_begin(self, step, logs=None):
self._eval_timer['batch_data_end_time'] = time.time()
self._eval_timer['data_time'] += (
self._eval_timer['batch_data_end_time']
- self._eval_timer['batch_start_time']
)
def on_eval_batch_end(self, step, logs=None):
logs = logs or {}
self.eval_step += 1
samples = logs.get('batch_size', 1)
self.evaled_samples += samples
self._eval_timer['batch_time'] += (
time.time() - self._eval_timer['batch_data_end_time']
)
self._eval_timer['count'] += 1
samples = logs.get('batch_size', 1)
self._eval_timer['samples'] += samples
if self._is_print() and self.eval_step % self.log_freq == 0:
if self.eval_steps is None or self.eval_step < self.eval_steps:
self._updates(logs, 'eval')
self._eval_timer['batch_start_time'] = time.time()
def on_predict_begin(self, logs=None):
self.test_steps = logs.get('steps', None)
self.test_metrics = logs.get('metrics', [])
self.test_step = 0
self.tested_samples = 0
self._test_timer = {
'data_time': 0,
'batch_time': 0,
'count': 0,
'samples': 0,
}
self.test_progbar = ProgressBar(
num=self.test_steps, verbose=self.verbose
)
if self._is_print():
print('Predict begin...')
self._test_timer['batch_start_time'] = time.time()
def on_predict_batch_begin(self, step, logs=None):
self._test_timer['batch_data_end_time'] = time.time()
self._test_timer['data_time'] += (
self._test_timer['batch_data_end_time']
- self._test_timer['batch_start_time']
)
def on_predict_batch_end(self, step, logs=None):
logs = logs or {}
self.test_step += 1
samples = logs.get('batch_size', 1)
self.tested_samples += samples
self._test_timer['batch_time'] += (
time.time() - self._test_timer['batch_data_end_time']
)
self._test_timer['count'] += 1
samples = logs.get('batch_size', 1)
self._test_timer['samples'] += samples
if self.test_step % self.log_freq == 0 and self._is_print():
if self.test_steps is None or self.test_step < self.test_steps:
self._updates(logs, 'test')
self._test_timer['batch_start_time'] = time.time()
def on_eval_end(self, logs=None):
logs = logs or {}
if self._is_print() and (self.eval_steps is not None):
self._updates(logs, 'eval')
print('Eval samples: %d' % (self.evaled_samples))
def on_predict_end(self, logs=None):
logs = logs or {}
if self._is_print():
if self.test_step % self.log_freq != 0 or self.verbose == 1:
self._updates(logs, 'test')
print('Predict samples: %d' % (self.tested_samples))
class ModelCheckpoint(Callback):
"""
Model checkpoint callback function to save model weights and optimizer
state during training in conjunction with model.fit(). Currently,
ModelCheckpoint only supports saving after a fixed number of epochs.
Args:
save_freq(int): The frequency, in number of epochs, the model checkpoint
are saved. Default: 1.
save_dir(str|None): The directory to save checkpoint during training.
If None, will not save checkpoint. Default: None.
Examples:
.. code-block:: python
>>> import paddle
>>> import paddle.vision.transforms as T
>>> from paddle.vision.datasets import MNIST
>>> from paddle.static import InputSpec
>>> inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
>>> labels = [InputSpec([None, 1], 'int64', 'label')]
>>> transform = T.Compose([
... T.Transpose(),
... T.Normalize([127.5], [127.5])
... ])
>>> train_dataset = MNIST(mode='train', transform=transform)
>>> lenet = paddle.vision.models.LeNet()
>>> model = paddle.Model(lenet,
... inputs, labels)
>>> optim = paddle.optimizer.Adam(0.001, parameters=lenet.parameters())
>>> model.prepare(optimizer=optim,
... loss=paddle.nn.CrossEntropyLoss(),
... metrics=paddle.metric.Accuracy())
>>> callback = paddle.callbacks.ModelCheckpoint(save_dir='./temp')
>>> model.fit(train_dataset, batch_size=64, callbacks=callback)
"""
def __init__(self, save_freq=1, save_dir=None):
self.save_freq = save_freq
self.save_dir = save_dir
def on_epoch_begin(self, epoch=None, logs=None):
self.epoch = epoch
def _is_save(self):
return (
self.model
and self.save_dir
and paddle.distributed.ParallelEnv().local_rank == 0
)
def on_epoch_end(self, epoch, logs=None):
if self._is_save() and self.epoch % self.save_freq == 0:
path = f'{self.save_dir}/{epoch}'
print(f'save checkpoint at {os.path.abspath(path)}')
self.model.save(path)
def on_train_end(self, logs=None):
if self._is_save():
path = f'{self.save_dir}/final'
print(f'save checkpoint at {os.path.abspath(path)}')
self.model.save(path)
class LRScheduler(Callback):
"""Lr scheduler callback function
Args:
by_step(bool, optional): whether to update learning rate scheduler
by step. Default: True.
by_epoch(bool, optional): whether to update learning rate scheduler
by epoch. Default: False.
Examples:
.. code-block:: python
>>> import paddle
>>> import paddle.vision.transforms as T
>>> from paddle.static import InputSpec
>>> inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
>>> labels = [InputSpec([None, 1], 'int64', 'label')]
>>> transform = T.Compose([
... T.Transpose(),
... T.Normalize([127.5], [127.5])
... ])
>>> train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
>>> lenet = paddle.vision.models.LeNet()
>>> model = paddle.Model(lenet,
... inputs, labels)
>>> base_lr = 1e-3
>>> boundaries = [5, 8]
>>> wamup_steps = 4
>>> def make_optimizer(parameters=None):
... momentum = 0.9
... weight_decay = 5e-4
... values = [base_lr * (0.1**i) for i in range(len(boundaries) + 1)]
... learning_rate = paddle.optimizer.lr.PiecewiseDecay(
... boundaries=boundaries, values=values)
... learning_rate = paddle.optimizer.lr.LinearWarmup(
... learning_rate=learning_rate,
... warmup_steps=wamup_steps,
... start_lr=base_lr / 5.,
... end_lr=base_lr,
... verbose=True)
... optimizer = paddle.optimizer.Momentum(
... learning_rate=learning_rate,
... weight_decay=weight_decay,
... momentum=momentum,
... parameters=parameters)
... return optimizer
>>> optim = make_optimizer(parameters=lenet.parameters())
>>> model.prepare(optimizer=optim,
... loss=paddle.nn.CrossEntropyLoss(),
... metrics=paddle.metric.Accuracy())
>>> # if LRScheduler callback not set, an instance LRScheduler update by step
>>> # will be created auto.
>>> model.fit(train_dataset, batch_size=64)
>>> # create a learning rate scheduler update by epoch
>>> callback = paddle.callbacks.LRScheduler(by_step=False, by_epoch=True)
>>> model.fit(train_dataset, batch_size=64, callbacks=callback)
"""
def __init__(self, by_step=True, by_epoch=False):
if by_step and by_epoch:
raise ValueError(
"by_step option is mutually exclusive with by_epoch"
)
self.by_step = by_step
self.by_epoch = by_epoch
def on_epoch_end(self, epoch, logs=None):
if self.by_epoch:
if (
self.model._optimizer
and hasattr(self.model._optimizer, '_learning_rate')
and isinstance(
self.model._optimizer._learning_rate,
paddle.optimizer.lr.LRScheduler,
)
):
self.model._optimizer._learning_rate.step()
def on_train_batch_end(self, step, logs=None):
if self.by_step:
if (
self.model._optimizer
and hasattr(self.model._optimizer, '_learning_rate')
and isinstance(
self.model._optimizer._learning_rate,
paddle.optimizer.lr.LRScheduler,
)
):
self.model._optimizer._learning_rate.step()
class EarlyStopping(Callback):
"""Stop training when the given monitor stopped improving during evaluation
by setting `model.stop_training=True`.
Args:
monitor(str): Quantity to be monitored. Default: 'loss'.
mode(str|None): Mode should be one of 'auto', 'min' or 'max'. In 'min'
mode, training will stop until monitored quantity stops decreasing.
In 'max' mode, training will stop until monitored quantity stops
increasing. In 'auto' mode, exact mode can be inferred by the name
of monitor. If 'acc' in monitor, the mode will be considered as
'max', otherwise the mode will be set to 'min'. Default: 'auto'.
patience(int): Number of epochs with no improvement after which
training will be stopped. Default: 0.
verbose(int): The verbosity mode, should be 0 or 1. When verbose=0,
logs will not be printed. When verbose=1, logs will be printed.
Default: 1.
min_delta(int|float): The minimum change of monitored quantity. If
the change is less than min_delta, model could be considered as no
improvement. Default: 0.
baseline(int|float|None): Baseline value for the monitored quantity.
Training will stop if the model doesn't show improvement over the
baseline. Default: None.
save_best_model(bool): Whether to save best model. Default: True.
Examples:
.. code-block:: python
>>> import paddle
>>> from paddle import Model
>>> from paddle.static import InputSpec
>>> from paddle.vision.models import LeNet
>>> from paddle.vision.datasets import MNIST
>>> from paddle.metric import Accuracy
>>> from paddle.nn import CrossEntropyLoss
>>> import paddle.vision.transforms as T
>>> device = paddle.set_device('cpu')
>>> sample_num = 200
>>> save_dir = './best_model_checkpoint'
>>> transform = T.Compose(
... [T.Transpose(), T.Normalize([127.5], [127.5])])
>>> train_dataset = MNIST(mode='train', transform=transform)
>>> val_dataset = MNIST(mode='test', transform=transform)
>>> net = LeNet()
>>> optim = paddle.optimizer.Adam(
... learning_rate=0.001, parameters=net.parameters())
>>> inputs = [InputSpec([None, 1, 28, 28], 'float32', 'x')]
>>> labels = [InputSpec([None, 1], 'int64', 'label')]
>>> model = Model(net, inputs=inputs, labels=labels)
>>> model.prepare(
... optim,
... loss=CrossEntropyLoss(reduction="sum"),
... metrics=[Accuracy()])
>>> callbacks = paddle.callbacks.EarlyStopping(
... 'loss',
... mode='min',
... patience=1,
... verbose=1,
... min_delta=0,
... baseline=None,
... save_best_model=True)
>>> model.fit(train_dataset,
... val_dataset,
... batch_size=64,
... log_freq=200,
... save_freq=10,
... save_dir=save_dir,
... epochs=20,
... callbacks=[callbacks])
"""
def __init__(
self,
monitor='loss',
mode='auto',
patience=0,
verbose=1,
min_delta=0,
baseline=None,
save_best_model=True,
):
super().__init__()
self.monitor = monitor
self.patience = patience
self.verbose = verbose
self.baseline = baseline
self.min_delta = abs(min_delta)
self.wait_epoch = 0
self.best_weights = None
self.stopped_epoch = 0
self.save_best_model = save_best_model
# The value of `save_dir` is set in function `config_callbacks`
self.save_dir = None
if mode not in ['auto', 'min', 'max']:
warnings.warn(
'EarlyStopping mode %s is unknown, '
'fallback to auto mode.' % mode
)
mode = 'auto'
if mode == 'min':
self.monitor_op = np.less
elif mode == 'max':
self.monitor_op = np.greater
# When mode == 'auto', the mode should be inferred by `self.monitor`
else:
if 'acc' in self.monitor:
self.monitor_op = np.greater
else:
self.monitor_op = np.less
if self.monitor_op == np.greater:
self.min_delta *= 1
else:
self.min_delta *= -1
def on_train_begin(self, logs=None):
self.wait_epoch = 0
if self.baseline is not None:
self.best_value = self.baseline
else:
self.best_value = np.inf if self.monitor_op == np.less else -np.inf
self.best_weights = None
def on_eval_end(self, logs=None):
if logs is None or self.monitor not in logs:
warnings.warn(
'Monitor of EarlyStopping should be loss or metric name.'
)
return
current = logs[self.monitor]
if isinstance(current, (list, tuple)):
current = current[0]
elif isinstance(current, numbers.Number):
current = current
else:
return
if self.monitor_op(current - self.min_delta, self.best_value):
self.best_value = current
self.wait_epoch = 0
if self.save_best_model and self.save_dir is not None:
path = os.path.join(self.save_dir, 'best_model')
self.model.save(path)
else:
self.wait_epoch += 1
if self.wait_epoch >= self.patience:
self.model.stop_training = True
if self.verbose > 0:
print('Epoch %d: Early stopping.' % (self.stopped_epoch + 1))
if self.save_best_model and self.save_dir is not None:
print(
'Best checkpoint has been saved at %s'
% (
os.path.abspath(
os.path.join(self.save_dir, 'best_model')
)
)
)
self.stopped_epoch += 1
class VisualDL(Callback):
"""
VisualDL callback class. After storing the loss values and evaluation metrics in a log file during the training time , the panel is launched to view the visual results.
Args:
log_dir (str): The directory to save visualdl log file.
Examples:
.. code-block:: python
>>> import paddle
>>> import paddle.vision.transforms as T
>>> from paddle.static import InputSpec
>>> inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
>>> labels = [InputSpec([None, 1], 'int64', 'label')]
>>> transform = T.Compose([
... T.Transpose(),
... T.Normalize([127.5], [127.5])
... ])
>>> train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
>>> eval_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)
>>> net = paddle.vision.models.LeNet()
>>> model = paddle.Model(net, inputs, labels)
>>> optim = paddle.optimizer.Adam(0.001, parameters=net.parameters())
>>> model.prepare(optimizer=optim,
... loss=paddle.nn.CrossEntropyLoss(),
... metrics=paddle.metric.Accuracy())
>>> ## uncomment following lines to fit model with visualdl callback function
>>> # callback = paddle.callbacks.VisualDL(log_dir='visualdl_log_dir')
>>> # model.fit(train_dataset, eval_dataset, batch_size=64, callbacks=callback)
"""
def __init__(self, log_dir):
self.log_dir = log_dir
self.epochs = None
self.steps = None
self.epoch = 0
def _is_write(self):
return paddle.distributed.ParallelEnv().local_rank == 0
def on_train_begin(self, logs=None):
self.epochs = self.params['epochs']
assert self.epochs
self.train_metrics = self.params['metrics']
assert self.train_metrics
self._is_fit = True
self.train_step = 0
def on_epoch_begin(self, epoch=None, logs=None):
self.steps = self.params['steps']
self.epoch = epoch
def _updates(self, logs, mode):
if not self._is_write():
return
if not hasattr(self, 'writer'):
visualdl = try_import('visualdl')
self.writer = visualdl.LogWriter(self.log_dir)
metrics = getattr(self, '%s_metrics' % (mode))
current_step = getattr(self, '%s_step' % (mode))
if mode == 'train':
total_step = current_step
else:
total_step = self.epoch
for k in metrics:
if k in logs:
temp_tag = mode + '/' + k
if isinstance(logs[k], (list, tuple)):
temp_value = logs[k][0]
elif isinstance(logs[k], numbers.Number):
temp_value = logs[k]
else:
continue
self.writer.add_scalar(
tag=temp_tag, step=total_step, value=temp_value
)
def on_train_batch_end(self, step, logs=None):
logs = logs or {}
self.train_step += 1
if self._is_write():
self._updates(logs, 'train')
def on_eval_begin(self, logs=None):
self.eval_steps = logs.get('steps', None)
self.eval_metrics = logs.get('metrics', [])
self.eval_step = 0
self.evaled_samples = 0
def on_train_end(self, logs=None):
if hasattr(self, 'writer'):
self.writer.close()
delattr(self, 'writer')
def on_eval_end(self, logs=None):
if self._is_write():
self._updates(logs, 'eval')
if (not hasattr(self, '_is_fit')) and hasattr(self, 'writer'):
self.writer.close()
delattr(self, 'writer')
class WandbCallback(Callback):
"""Track your training and system metrics using `Weights and Biases <https://docs.wandb.ai>`_.