LIB: Add mindspore backend

1. Add mindspore backend
2. If both GPU and NPU environments exist in the environment, NPU environment is preferred

Signed-off-by: chou-shun <[email protected]>

lib/sedna/backend/__init__.py

@@ -27,14 +27,20 @@ def set_backend(estimator=None, config=None):
     if config is None:
         config = BaseConfig()
     use_cuda = False
+    use_ascend = False
     backend_type = os.getenv(
         'BACKEND_TYPE', config.get("backend_type", "UNKNOWN")
     )
     backend_type = str(backend_type).upper()
     device_category = os.getenv(
         'DEVICE_CATEGORY', config.get("device_category", "CPU")
     )
-    if 'CUDA_VISIBLE_DEVICES' in os.environ:
+
+    # NPU>GPU>CPU
+    if device_category == "ASCEND":
+        use_ascend = True
+        os.environ['DEVICE_CATEGORY'] = "ASCEND"
+    elif 'CUDA_VISIBLE_DEVICES' in os.environ:
         os.environ['DEVICE_CATEGORY'] = 'GPU'
         use_cuda = True
     else:
@@ -44,14 +50,18 @@ def set_backend(estimator=None, config=None):
         from sedna.backend.tensorflow import TFBackend as REGISTER
     elif backend_type == "KERAS":
         from sedna.backend.tensorflow import KerasBackend as REGISTER
+    elif backend_type == "MINDSPORE":
+        from sedna.backend.mindspore import MSBackend as REGISTER
     else:
         warnings.warn(f"{backend_type} Not Support yet, use itself")
         from sedna.backend.base import BackendBase as REGISTER
+
     model_save_url = config.get("model_url")
     base_model_save = config.get("base_model_url") or model_save_url
     model_save_name = config.get("model_name")
     return REGISTER(
         estimator=estimator, use_cuda=use_cuda,
+        use_ascend=use_ascend,
         model_save_path=base_model_save,
         model_name=model_save_name,
         model_save_url=model_save_url

lib/sedna/backend/base.py

@@ -24,6 +24,7 @@ class BackendBase:
     def __init__(self, estimator, fine_tune=True, **kwargs):
         self.framework = ""
         self.estimator = estimator
+        self.use_ascend = True if kwargs.get("use_ascend") else False
         self.use_cuda = True if kwargs.get("use_cuda") else False
         self.fine_tune = fine_tune
         self.model_save_path = kwargs.get("model_save_path") or "/tmp"
@@ -35,7 +36,7 @@ def model_name(self):
         if self.default_name:
             return self.default_name
         model_postfix = {"pytorch": ".pth",
-                         "keras": ".pb", "tensorflow": ".pb"}
+                         "keras": ".pb", "tensorflow": ".pb", "mindspore": ".ckpt"}
         continue_flag = "_finetune_" if self.fine_tune else ""
         post_fix = model_postfix.get(self.framework, ".pkl")
         return f"model{continue_flag}{self.framework}{post_fix}"

lib/sedna/backend/mindspore/__init__.py

@@ -0,0 +1,90 @@
+# 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
+
+
+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_ascend:
+            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()
+
+    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
+        )
+
+    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)
+
+    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)
+
+    def finetune(self):
+        """todo: no support yet"""
+
+    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)
+
+    def get_weights(self):
+        """todo: no support yet"""
+
+    def set_weights(self, weights):
+        """todo: no support yet"""
+
+    def model_info(self, model, relpath=None, result=None):
+        _, _type = os.path.splitext(model)
+        if relpath:
+            _url = FileOps.remove_path_prefix(model, relpath)
+        else:
+            _url = model
+        _type = _type.lstrip(".").lower()
+        results = [{
+                "format": _type,
+                "url": _url,
+                "metrics": result
+            }]
+        return results