!945 [ADD & BUGFIX] catch exception raise from tensorflow in MindConverter module.

From: @moran3
Reviewed-by: 
Signed-off-by:

mindinsight/mindconverter/common/exceptions.py

@@ -15,6 +15,7 @@
 """Define custom exception."""
 import sys
 from enum import unique
+from importlib import import_module
 
 from lib2to3.pgen2 import parse
 from treelib.exceptions import DuplicatedNodeIdError, MultipleRootError, NodeIDAbsentError
@@ -212,7 +213,8 @@ class GraphInitFail(MindConverterException):
                          ModuleNotFoundError,
                          ModelNotSupport,
                          TypeError,
-                         ZeroDivisionError)
+                         ZeroDivisionError,
+                         RuntimeError)
         return except_source
 
     @classmethod
@@ -294,7 +296,7 @@ class ModelNotSupport(MindConverterException):
         return except_source
 
     @classmethod
-    def check_except(cls, msg):
+    def check_except_pytorch(cls, msg):
         """Check except."""
 
         def decorator(func):
@@ -310,6 +312,29 @@ class ModelNotSupport(MindConverterException):
             return _f
         return decorator
 
+    @classmethod
+    def check_except_tf(cls, msg):
+        """Check except."""
+        tf_error_module = import_module('tensorflow.python.framework.errors_impl')
+        tf_error = getattr(tf_error_module, 'OpError')
+
+        cls._error = cls.raise_from() + (tf_error,)
+
+        def decorator(func):
+            def _f(arch, model_path, **kwargs):
+                try:
+                    output = func(arch, model_path=model_path, **kwargs)
+                except cls._error as e:
+                    error = cls(msg=msg)
+                    log.error(msg)
+                    log.exception(e)
+                    raise error from e
+                return output
+
+            return _f
+
+        return decorator
+
 
 class NodeInputMissing(MindConverterException):
     """The node input missing error."""

mindinsight/mindconverter/graph_based_converter/framework.py

@@ -119,10 +119,10 @@ def _extract_model_name(model_path):
     return model_name
 
 
+@torch_installation_validation
 @GraphInitFail.check_except_pytorch("Error occurred when init graph object.")
 @TreeCreateFail.check_except_pytorch("Error occurred when create hierarchical tree.")
 @SourceFilesSaveFail.check_except_pytorch("Error occurred when save source files.")
-@torch_installation_validation
 def graph_based_converter_pytorch_to_ms(graph_path: str, sample_shape: tuple,
                                         output_folder: str, report_folder: str = None):
     """
@@ -153,10 +153,10 @@ def graph_based_converter_pytorch_to_ms(graph_path: str, sample_shape: tuple,
                                         report_folder=report_folder)
 
 
+@tf_installation_validation
 @GraphInitFail.check_except_tf("Error occurred when init graph object.")
 @TreeCreateFail.check_except_tf("Error occurred when create hierarchical tree.")
 @SourceFilesSaveFail.check_except_tf("Error occurred when save source files.")
-@tf_installation_validation
 def graph_based_converter_tf_to_ms(graph_path: str, sample_shape: tuple,
                                    input_nodes: str, output_nodes: str,
                                    output_folder: str, report_folder: str = None):

mindinsight/mindconverter/graph_based_converter/third_party_graph/__init__.py

@@ -14,13 +14,10 @@
 # ==============================================================================
 """Graph associated definition module."""
 
-__all__ = ["GraphFactory", "PyTorchGraphNode"]
+__all__ = ["GraphFactory"]
+from importlib import import_module
 
 from .base import Graph
-from .pytorch_graph import PyTorchGraph
-from .pytorch_graph_node import PyTorchGraphNode
-from .onnx_graph import OnnxGraph
-from .onnx_graph_node import OnnxGraphNode
 
 
 class GraphFactory:
@@ -43,7 +40,13 @@ class GraphFactory:
             Graph, graph instance.
         """
         if all([input_nodes, output_nodes]):
-            return OnnxGraph.load(model_path=graph_path, input_nodes=input_nodes,
-                                  output_nodes=output_nodes, sample_shape=sample_shape)
-
-        return PyTorchGraph.load(model_path=graph_path, sample_shape=sample_shape)
+            onnx_graph_module = import_module(
+                'mindinsight.mindconverter.graph_based_converter.third_party_graph.onnx_graph')
+            onnx_graph = getattr(onnx_graph_module, 'OnnxGraph')
+            return onnx_graph.load(model_path=graph_path, input_nodes=input_nodes,
+                                   output_nodes=output_nodes, sample_shape=sample_shape)
+
+        pytorch_graph_module = import_module(
+            'mindinsight.mindconverter.graph_based_converter.third_party_graph.pytorch_graph')
+        pytorch_graph = getattr(pytorch_graph_module, 'PyTorchGraph')
+        return pytorch_graph.load(model_path=graph_path, sample_shape=sample_shape)

mindinsight/mindconverter/graph_based_converter/third_party_graph/onnx_graph.py

@@ -19,7 +19,7 @@ from mindinsight.mindconverter.common.log import logger as log
 from .base import Graph
 from .input_node import InputNode
 from .onnx_graph_node import OnnxGraphNode
-from .graph_parser import TFGraphParser
+from .tf_graph_parser import TFGraphParser
 from .onnx_utils import OnnxDataLoader
 
 NONE_SCOPE_OP = {

mindinsight/mindconverter/graph_based_converter/third_party_graph/pytorch_graph.py

@@ -20,9 +20,9 @@ from mindinsight.mindconverter.common.log import logger as log
 from .base import Graph
 from .input_node import InputNode
 from .pytorch_graph_node import PyTorchGraphNode
-from .graph_parser import PyTorchGraphParser
+from .pytorch_graph_parser import PyTorchGraphParser
 
-from ..constant import SEPARATOR_IN_SCOPE, LINK_IN_SCOPE
+from ..constant import SEPARATOR_IN_SCOPE, LINK_IN_SCOPE, SEPARATOR_BTW_NAME_AND_ID
 from ..constant import LEFT_BUCKET, RIGHT_BUCKET
 
 NONE_SCOPE_OP = {
@@ -46,7 +46,7 @@ def normalize_scope_name(node):
     """
     global NONE_SCOPE_OP
 
-    name = node.scopeName().split(SEPARATOR_IN_SCOPE)
+    name = node.scopeName().replace(SEPARATOR_BTW_NAME_AND_ID, '').split(SEPARATOR_IN_SCOPE)
     scopes = []
     for segment in name:
         segment = segment.split(LINK_IN_SCOPE)[0]

mindinsight/mindconverter/graph_based_converter/third_party_graph/pytorch_graph_parser.py

@@ -0,0 +1,60 @@
+# Copyright 2020 Huawei Technologies Co., Ltd.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.
+# ==============================================================================
+"""Third party graph parser."""
+import os
+from importlib import import_module
+
+from mindinsight.mindconverter.common.log import logger as log
+from .base import GraphParser
+from ...common.exceptions import ModelNotSupport
+
+
+class PyTorchGraphParser(GraphParser):
+    """Define pytorch graph parser."""
+
+    @classmethod
+    @ModelNotSupport.check_except_pytorch("Error occurs in loading model, make sure model.pth correct.")
+    def parse(cls, model_path: str, **kwargs):
+        """
+        Parser pytorch graph.
+
+        Args:
+            model_path (str): Model file path.
+
+        Returns:
+            object, torch model.
+        """
+        torch = import_module("torch")
+
+        if not os.path.exists(model_path):
+            error = FileNotFoundError("`model_path` must be assigned with "
+                                      "an existed file path.")
+            log.error(str(error))
+            raise error
+
+        try:
+            if torch.cuda.is_available():
+                model = torch.load(f=model_path)
+            else:
+                model = torch.load(f=model_path, map_location="cpu")
+        except ModuleNotFoundError:
+            error_msg = \
+                "Cannot find model scripts in system path, " \
+                "set `--project_path` to the path of model scripts folder correctly."
+            error = ModuleNotFoundError(error_msg)
+            log.error(str(error))
+            raise error from None
+
+        return model

mindinsight/mindconverter/graph_based_converter/third_party_graph/graph_parser.py → mindinsight/mindconverter/graph_based_converter/third_party_graph/tf_graph_parser.py

@@ -14,55 +14,18 @@
 # ==============================================================================
 """Third party graph parser."""
 import os
+from importlib import import_module
+
 from mindinsight.mindconverter.common.log import logger as log
 from .base import GraphParser
 from ...common.exceptions import ModelNotSupport
 
 
-class PyTorchGraphParser(GraphParser):
-    """Define pytorch graph parser."""
-
-    @classmethod
-    @ModelNotSupport.check_except("Error occurs in loading model, make sure model.pth correct.")
-    def parse(cls, model_path: str, **kwargs):
-        """
-        Parser pytorch graph.
-
-        Args:
-            model_path (str): Model file path.
-
-        Returns:
-            object, torch model.
-        """
-        import torch
-
-        if not os.path.exists(model_path):
-            error = FileNotFoundError("`model_path` must be assigned with "
-                                      "an existed file path.")
-            log.error(str(error))
-            raise error
-
-        try:
-            if torch.cuda.is_available():
-                model = torch.load(f=model_path)
-            else:
-                model = torch.load(f=model_path, map_location="cpu")
-        except ModuleNotFoundError:
-            error_msg = \
-                "Cannot find model scripts in system path, " \
-                "set `--project_path` to the path of model scripts folder correctly."
-            error = ModuleNotFoundError(error_msg)
-            log.error(str(error))
-            raise error from None
-
-        return model
-
-
 class TFGraphParser(GraphParser):
     """Define TF graph parser."""
 
     @classmethod
-    @ModelNotSupport.check_except("Error occurs in loading model, make sure model.pb correct.")
+    @ModelNotSupport.check_except_tf("Error occurs in loading model, make sure model.pb correct.")
     def parse(cls, model_path: str, **kwargs):
         """
         Parse TF Computational Graph File (.pb)
@@ -74,8 +37,9 @@ class TFGraphParser(GraphParser):
             object, ONNX model.
         """
 
-        from .onnx_utils import convert_tf_graph_to_onnx
-
+        onnx_utils = import_module(
+            "mindinsight.mindconverter.graph_based_converter.third_party_graph.onnx_utils")
+        convert_tf_graph_to_onnx = getattr(onnx_utils, "convert_tf_graph_to_onnx")
         tf_input_nodes = kwargs.get('input_nodes')
         tf_output_nodes = kwargs.get('output_nodes')
         if not os.path.exists(model_path):