# Copyright 2021 The KubeEdge Authors.
#
# 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 os
import mindspore.context as context
from sedna.backend.base import BackendBase
from sedna.common.file_ops import FileOps
[docs]class MSBackend(BackendBase):
def __init__(self, estimator, fine_tune=True, **kwargs):
super(MSBackend, self).__init__(estimator=estimator,
fine_tune=fine_tune,
**kwargs)
self.framework = "mindspore"
if self.use_npu:
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend")
elif self.use_cuda:
context.set_context(mode=context.GRAPH_MODE,
device_target="GPU")
else:
context.set_context(mode=context.GRAPH_MODE,
device_target="CPU")
if callable(self.estimator):
self.estimator = self.estimator()
[docs] def train(self, train_data, valid_data=None, **kwargs):
if callable(self.estimator):
self.estimator = self.estimator()
if self.fine_tune and FileOps.exists(self.model_save_path):
self.finetune()
self.has_load = True
varkw = self.parse_kwargs(self.estimator.train, **kwargs)
return self.estimator.train(train_data=train_data,
valid_data=valid_data,
**varkw)
[docs] def predict(self, data, **kwargs):
if not self.has_load:
self.load()
varkw = self.parse_kwargs(self.estimator.predict, **kwargs)
return self.estimator.predict(data=data, **varkw)
[docs] def evaluate(self, data, **kwargs):
if not self.has_load:
self.load()
varkw = self.parse_kwargs(self.estimator.evaluate, **kwargs)
return self.estimator.evaluate(data, **varkw)
[docs] def finetune(self):
"""todo: no support yet"""
[docs] def load_weights(self):
model_path = FileOps.join_path(self.model_save_path, self.model_name)
if os.path.exists(model_path):
self.estimator.load_weights(model_path)
[docs] def get_weights(self):
"""todo: no support yet"""
[docs] def set_weights(self, weights):
"""todo: no support yet"""