# 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.
# ============================================================================
"""History Callback class."""
from __future__ import absolute_import
import numpy as np
from mindspore.common.tensor import Tensor
from mindspore.train.callback._callback import Callback
[docs]class History(Callback):
"""
Records the network outputs and metrics information into a `History` object.
The network outputs information will be the loss value if not custimizing the train network or eval network;
if the custimized network returns a `Tensor` or `numpy.ndarray`, the mean value of network output
will be recorded, if the custimized network returns a `tuple` or `list`, the first element of network
outputs will be recorded.
Note:
Normally used in `mindspore.Model.train` or `mindspore.Model.fit`.
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)
>>> history_cb = ms.History()
>>> model = ms.Model(network=net, optimizer=opt, loss_fn=crit, metrics={"recall"})
>>> model.train(2, train_dataset, callbacks=[history_cb])
>>> print(history_cb.epoch)
>>> print(history_cb.history)
{'epoch': [1, 2]}
{'net_output': [1.607877, 1.6033841]}
"""
def __init__(self):
super(History, self).__init__()
self.history = {}
self.epoch = None
[docs] def begin(self, run_context):
"""
Initialize the `epoch` property at the begin of training.
Args:
run_context (RunContext): Context of the `mindspore.Model.{train | eval}`. For more details,
please refer to :class:`mindspore.RunContext`.
"""
self.epoch = {"epoch": []}
[docs] def epoch_end(self, run_context):
"""
Records the first element of network outputs and metrics information at the end of epoch.
Args:
run_context (RunContext): Context of the `mindspore.Model.{train | eval}`. For more details,
please refer to :class:`mindspore.RunContext`.
"""
cb_params = run_context.original_args()
epoch = cb_params.get("cur_epoch_num", 1)
self.epoch.get("epoch").append(epoch)
net_output = cb_params.net_outputs
if isinstance(net_output, (tuple, list)):
if isinstance(net_output[0], Tensor) and isinstance(net_output[0].asnumpy(), np.ndarray):
net_output = net_output[0]
if isinstance(net_output, Tensor) and isinstance(net_output.asnumpy(), np.ndarray):
net_output = np.mean(net_output.asnumpy())
metrics = cb_params.get("metrics")
cur_history = {"net_output": net_output}
if metrics:
cur_history.update(metrics)
for k, v in cur_history.items():
self.history.setdefault(k, []).append(v)