Paddle/python/paddle/incubate/hapi/callbacks.py

# 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.

from paddle.fluid.dygraph.parallel import ParallelEnv

from .progressbar import ProgressBar

__all__ = ['Callback', 'ProgBarLogger', 'ModelCheckpoint']


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)]

    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(object):
    def __init__(self, callbacks=None):
        # copy
        self.callbacks = [c for c in 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', 'test'], \
            'mode should be train, eval or test'

    def on_begin(self, mode, logs=None):
        self._check_mode(mode)
        name = 'on_{}_begin'.format(mode)
        self._call(name, logs)

    def on_end(self, mode, logs=None):
        self._check_mode(mode)
        name = 'on_{}_end'.format(mode)
        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 = 'on_{}_batch_begin'.format(mode)
        self._call(name, step, logs)

    def on_batch_end(self, mode, step=None, logs=None):
        self._check_mode(mode)
        name = 'on_{}_batch_end'.format(mode)
        self._call(name, step, logs)


class Callback(object):
    """
    Base class used to build new callbacks.

    Examples:

        .. code-block:: python
            
            from paddle.incubate.hapi.callbacks import Callback

            # build a simple model checkpoint callback
            class ModelCheckpoint(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 hapi.Metric.
        """
        self.params = params

    def set_model(self, model):
        """model is instance of hapi.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 hapi.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 hapi.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 hapi.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
                hapi.Model is a dict contains 'loss', metrics and 'batch_size'
                of last batch of validation dataset.
        """

    def on_test_begin(self, logs=None):
        """Called at the beginning of predict.

        Args:
            logs (dict): The logs is a dict or None.
        """

    def on_test_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
                hapi.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
                hapi.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
                hapi.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
                hapi.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
                hapi.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
                hapi.Model is a dict, contains 'loss', metrics and 'batch_size'
                of current batch.
        """

    def on_test_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_test_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
    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 per epoch. Default: 2.

    Examples:
        .. code-block:: python

            import numpy as np
            from paddle import fluid
            from paddle.incubate.hapi.metrics import Accuracy
            from paddle.incubate.hapi.loss import CrossEntropy
            from paddle.incubate.hapi.datasets import MNIST
            from paddle.incubate.hapi.vision.models import LeNet
            from paddle.incubate.hapi.callbacks import ProgBarLogger
            from paddle.incubate.hapi.model import Input, set_device

            inputs = [Input([-1, 1, 28, 28], 'float32', name='image')]
            labels = [Input([None, 1], 'int64', name='label')]

            train_dataset = MNIST(mode='train')

            model = LeNet()

            optim = fluid.optimizer.Adam(0.001)
            model.prepare(optimizer=optim, 
                        loss_function=CrossEntropy(), 
                        metrics=Accuracy(), 
                        inputs=inputs, 
                        labels=labels)

            callback = 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 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

    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)

    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]))

        progbar.update(steps, values)

    def on_train_batch_end(self, step, logs=None):
        logs = logs or {}
        self.train_step += 1

        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')

    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_progbar = ProgressBar(
            num=self.eval_steps, verbose=self.verbose)
        if self._is_print():
            print('Eval begin...')

    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

        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')

    def on_test_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_progbar = ProgressBar(
            num=self.test_steps, verbose=self.verbose)
        if self._is_print():
            print('Predict begin...')

    def on_test_batch_end(self, step, logs=None):
        logs = logs or {}
        self.test_step += 1
        samples = logs.get('batch_size', 1)
        self.tested_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')

    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_test_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
    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 numpy as np
            from paddle import fluid
            from paddle.incubate.hapi.metrics import Accuracy
            from paddle.incubate.hapi.loss import CrossEntropy
            from paddle.incubate.hapi.datasets import MNIST
            
            from paddle.incubate.hapi.vision.models import LeNet
            from paddle.incubate.hapi.callbacks import ModelCheckpoint
            from paddle.incubate.hapi.model import Input, set_device

            inputs = [Input([-1, 1, 28, 28], 'float32', name='image')]
            labels = [Input([None, 1], 'int64', name='label')]

            train_dataset = MNIST(mode='train')

            model = LeNet()

            optim = fluid.optimizer.Adam(0.001)
            model.prepare(optimizer=optim, 
                        loss_function=CrossEntropy(), 
                        metrics=Accuracy(), 
                        inputs=inputs, 
                        labels=labels)

            callback = 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 ParallelEnv().local_rank == 0

    def on_epoch_end(self, epoch, logs=None):
        if self._is_save() and self.epoch % self.save_freq == 0:
            path = '{}/{}'.format(self.save_dir, epoch)
            print('save checkpoint at {}'.format(path))
            self.model.save(path)

    def on_train_end(self, logs=None):
        if self._is_save():
            path = '{}/final'.format(self.save_dir)
            print('save checkpoint at {}'.format(path))
            self.model.save(path)