# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""Lambda Callback class."""
from __future__ import absolute_import
from ._callback import Callback
[docs]class LambdaCallback(Callback):
"""
Callback for creating simple, custom callbacks.
This callback is constructed with anonymous functions that will be called
at the appropriate time (during `mindspore.Model.{train | eval | fit}`). Note that
each stage of callbacks expects one positional arguments: `run_context`.
Note:
This is an experimental interface that is subject to change or deletion.
Args:
on_train_epoch_begin (Function): called at each train epoch begin.
on_train_epoch_end (Function): called at each train epoch end.
on_train_step_begin (Function): called at each train step begin.
on_train_step_end (Function): called at each train step end.
on_train_begin (Function): called at the beginning of model train.
on_train_end (Function): called at the end of model train.
on_eval_epoch_begin (Function): called at eval epoch begin.
on_eval_epoch_end (Function): called at eval epoch end.
on_eval_step_begin (Function): called at each eval step begin.
on_eval_step_end (Function): called at each eval step end.
on_eval_begin (Function): called at the beginning of model eval.
on_eval_end (Function): called at the end of model eval.
Examples:
>>> import numpy as np
>>> import mindspore as ms
>>> import mindspore.dataset as ds
>>> from mindspore import nn
>>> data = {"x": np.float32(np.random.rand(64, 10)), "y": np.random.randint(0, 5, (64,))}
>>> train_dataset = ds.NumpySlicesDataset(data=data).batch(32)
>>> net = nn.Dense(10, 5)
>>> crit = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
>>> opt = nn.Momentum(net.trainable_params(), 0.01, 0.9)
>>> lambda_callback = ms.LambdaCallback(on_train_epoch_end=
... lambda run_context: print("loss: ", run_context.original_args().net_outputs))
>>> model = ms.Model(network=net, optimizer=opt, loss_fn=crit, metrics={"recall"})
>>> model.train(2, train_dataset, callbacks=[lambda_callback])
loss: 1.6127687
loss: 1.6106578
"""
def __init__(self, on_train_epoch_begin=None, on_train_epoch_end=None, on_train_step_begin=None,
on_train_step_end=None, on_train_begin=None, on_train_end=None,
on_eval_epoch_begin=None, on_eval_epoch_end=None, on_eval_step_begin=None,
on_eval_step_end=None, on_eval_begin=None, on_eval_end=None):
super(LambdaCallback, self).__init__()
self.on_train_epoch_begin = on_train_epoch_begin if on_train_epoch_begin else lambda run_context: None
self.on_train_epoch_end = on_train_epoch_end if on_train_epoch_end else lambda run_context: None
self.on_train_step_begin = on_train_step_begin if on_train_step_begin else lambda run_context: None
self.on_train_step_end = on_train_step_end if on_train_step_end else lambda run_context: None
self.on_train_begin = on_train_begin if on_train_begin else lambda run_context: None
self.on_train_end = on_train_end if on_train_end else lambda run_context: None
self.on_eval_epoch_begin = on_eval_epoch_begin if on_eval_epoch_begin else lambda run_context: None
self.on_eval_epoch_end = on_eval_epoch_end if on_eval_epoch_end else lambda run_context: None
self.on_eval_step_begin = on_eval_step_begin if on_eval_step_begin else lambda run_context: None
self.on_eval_step_end = on_eval_step_end if on_eval_step_end else lambda run_context: None
self.on_eval_begin = on_eval_begin if on_eval_begin else lambda run_context: None
self.on_eval_end = on_eval_end if on_eval_end else lambda run_context: None