Solve cycle-ref.

mindinsight/mindconverter/graph_based_converter/framework.py

@@ -84,10 +84,11 @@ def graph_based_converter(graph_path: str, sample_shape: tuple,
 
     """
     from .third_party_graph import GraphFactory
+    from .hierarchical_tree import HierarchicalTreeFactory
 
     graph_obj = GraphFactory.init(graph_path, sample_shape=sample_shape,
                                   checkpoint=checkpoint_path)
-    hierarchical_tree = graph_obj.to_hierarchical_tree()
+    hierarchical_tree = HierarchicalTreeFactory.create(graph_obj)
     hierarchical_tree.save_source_files(output_folder, mapper=ONNXToMindSporeMapper,
                                         report_folder=report_folder)
 

mindinsight/mindconverter/graph_based_converter/hierarchical_tree/__init__.py

@@ -16,5 +16,42 @@
 from .hierarchical_tree import HierarchicalTree
 
 __all__ = [
-    "HierarchicalTree"
+    "HierarchicalTreeFactory"
 ]
+
+
+class HierarchicalTreeFactory:
+    """Hierarchical tree factory."""
+
+    @classmethod
+    def create(cls, graph):
+        """
+        Factory method of hierarchical tree.
+
+        Args:
+            graph: Graph obj.
+
+        Returns:
+            HierarchicalTree, tree.
+        """
+        tree = HierarchicalTree()
+        node_input = None
+        for _, node_name in enumerate(graph.nodes_in_topological_order):
+            node_inst = graph.get_node(node_name)
+            node_output = graph.get_output_shape(node_name)
+            if node_inst.in_degree == 0:
+                # If in-degree equals to zero, then it's a input node.
+                continue
+
+            # If the node is on the top, then fetch its input
+            # from input table.
+            if not node_input:
+                node_input = graph.get_input_shape(node_name)
+
+            if not node_input:
+                raise ValueError(f"This model is not supported now. "
+                                 f"Cannot find {node_name}'s input shape.")
+
+            tree.insert(node_inst, node_name, node_input, node_output)
+            node_input = node_output
+        return tree

mindinsight/mindconverter/graph_based_converter/hierarchical_tree/name_mgr.py

@@ -53,10 +53,6 @@ class ModuleNameMgr(NameMgr):
     """Module name manager."""
 
 
-class VariableNameMgrInModule(NameMgr):
-    """Variable name mgr for a module."""
-
-
 global_op_namespace = dict()
 START_IDX = 0
 

mindinsight/mindconverter/graph_based_converter/third_party_graph/base.py

@@ -15,9 +15,6 @@
 """Define graph entity."""
 import abc
 from collections import OrderedDict
-from typing import Dict, Union, Any
-
-from torch.nn import Module
 
 from ..constant import SEPARATOR_IN_ONNX_OP
 from ..mapper.base import Mapper
@@ -40,21 +37,13 @@ class BaseGraph(metaclass=abc.ABCMeta):
     def build(self, input_shape: tuple):
         """Build graph."""
 
-    @abc.abstractmethod
-    def to_ir(self, mapper):
-        """Convert graph to ir graph."""
-
-    @abc.abstractmethod
-    def to_hierarchical_tree(self):
-        """Convert to hierarchical tree."""
-
     @abc.abstractmethod
     def sub_graph_merging(self):
         """Merge split nodes into one."""
 
     @staticmethod
     @abc.abstractmethod
-    def load_checkpoint(ckpt_path: str) -> Dict:
+    def load_checkpoint(ckpt_path: str) -> dict:
         """Load checkpoint file."""
 
     @staticmethod
@@ -88,15 +77,14 @@ class Graph(BaseGraph, abc.ABC):
     Define Factory method to create Graph sub-class.
 
     Args:
-        model (Union[Module, Any]): Graph file.
+        model (Union[torch.nn.Module, Any]): Graph file.
         checkpoint (dict): Checkpoint path.
 
     """
 
     sorted = False
 
-    def __init__(self, model: Union[Module, Any],
-                 **kwargs):
+    def __init__(self, model, **kwargs):
         super(Graph, self).__init__()
         self.model = model
         self.checkpoint = kwargs.get("checkpoint", None)
@@ -108,6 +96,27 @@ class Graph(BaseGraph, abc.ABC):
         self._topological_order = []
         self._input_shape = dict()
 
+    def get_output_shape(self, name):
+        """
+        Get node output shape.
+
+        Args:
+            name (str): Node name.
+
+        Returns:
+            list, shape.
+        """
+        return self._shape_dict.get(name)
+
+    def get_input_shape(self, name):
+        """
+        Get node input shape.
+
+        Returns:
+            list, shape.
+        """
+        return self._input_shape.get(name)
+
     @property
     def nodes_in_topological_order(self):
         """
@@ -192,17 +201,11 @@ class Graph(BaseGraph, abc.ABC):
             idx += 1
         self.sorted = True
 
-    def to_ir(self, mapper):
-        raise NotImplementedError
-
-    def to_hierarchical_tree(self):
-        raise NotImplementedError
-
     def sub_graph_merging(self):
         raise NotImplementedError
 
     @staticmethod
-    def load_checkpoint(ckpt_path: str) -> Dict:
+    def load_checkpoint(ckpt_path: str) -> dict:
         raise NotImplementedError
 
     @staticmethod

mindinsight/mindconverter/graph_based_converter/third_party_graph/pytorch_graph.py

@@ -17,18 +17,11 @@ import warnings
 import re
 from typing import Dict, NoReturn
 
-import torch
-from torch.nn import Module
-from torch.onnx import OperatorExportTypes
-
 from .base import Graph
 from .input_node import InputNode
 from .pytorch_graph_node import PyTorchGraphNode
 from .graph_parser import PyTorchGraphParser
-from .torch_utils import OverloadTorchModuleTemporarily, unique_state_dict
-from .torch_utils import create_autograd_variable
-from .torch_utils import onnx_tracer
-from ..hierarchical_tree import HierarchicalTree
+
 from ..constant import SEPARATOR_IN_SCOPE, LINK_IN_SCOPE
 from ..constant import LEFT_BUCKET, RIGHT_BUCKET
 
@@ -78,8 +71,11 @@ class PyTorchGraph(Graph):
 
     """
 
-    def __init__(self, model: Module, sample_shape: tuple):
+    def __init__(self, model, sample_shape: tuple):
         super(PyTorchGraph, self).__init__(model=model)
+
+        from .torch_utils import unique_state_dict
+
         self._params_dict = unique_state_dict(model)
         self.build(sample_shape)
 
@@ -108,6 +104,12 @@ class PyTorchGraph(Graph):
             input_shape (tuple): Input shape of model.
 
         """
+        import torch
+        from torch.onnx import OperatorExportTypes
+        from .torch_utils import OverloadTorchModuleTemporarily
+        from .torch_utils import create_autograd_variable
+        from .torch_utils import onnx_tracer
+
         self._check_input_shape(input_shape)
 
         def _extract_shape(shape):
@@ -188,32 +190,6 @@ class PyTorchGraph(Graph):
         """
         raise NotImplementedError()
 
-    def to_hierarchical_tree(self):
-        """
-        Generate hierarchical tree based on graph.
-        """
-        tree = HierarchicalTree()
-        node_input = None
-        for _, node_name in enumerate(self.nodes_in_topological_order):
-            node_inst = self.get_node(node_name)
-            node_output = self._shape_dict.get(node_name)
-            if node_inst.in_degree == 0:
-                # If in-degree equals to zero, then it's a input node.
-                continue
-
-            # If the node is on the top, then fetch its input
-            # from input table.
-            if not node_input:
-                node_input = self._input_shape.get(node_name)
-
-            if not node_input:
-                raise ValueError(f"This model is not supported now. "
-                                 f"Cannot find {node_name}'s input shape.")
-
-            tree.insert(node_inst, node_name, node_input, node_output)
-            node_input = node_output
-        return tree
-
     def build_connection(self, src, tgt) -> NoReturn:
         """
         Build connection between source node and target node.

mindinsight/mindconverter/graph_based_converter/third_party_graph/pytorch_graph_node.py

@@ -14,7 +14,7 @@
 # ==============================================================================
 """Define PyTorch graph node."""
 from .base import GraphNode
-from .torch_utils import getitem_of_node
+
 from ..constant import NodeType, SEPARATOR_IN_SCOPE, SEPARATOR_BTW_NAME_AND_ID, LEFT_BUCKET, RIGHT_BUCKET, \
     SEPARATOR_IN_ONNX_OP
 from ..mapper.base import Mapper
@@ -202,6 +202,8 @@ class PyTorchGraphNode(GraphNode):
         Returns:
             dict, raw params.
         """
+        from .torch_utils import getitem_of_node
+
         raw_params = dict()
 
         if not node:

mindinsight/mindconverter/graph_based_converter/third_party_graph/torch_utils.py

@@ -16,7 +16,6 @@
 import importlib
 
 from torch.nn import Module
-from torch.jit import _unique_state_dict
 from torch.onnx.utils import _trace
 from torch.onnx.utils import _node_getitem
 
@@ -36,6 +35,8 @@ def unique_state_dict(model):
     Returns:
         dict, params.
     """
+    from torch.jit import _unique_state_dict
+
     return _unique_state_dict(model)
 
 

tests/ut/mindconverter/graph_based_converter/__init__.py

@@ -0,0 +1,14 @@
+# 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.
+# ==============================================================================

tests/ut/mindconverter/graph_based_converter/hierarchical_tree/__init__.py

@@ -0,0 +1,14 @@
+# 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.
+# ==============================================================================

tests/ut/mindconverter/graph_based_converter/hierarchical_tree/test_name_mgr.py

@@ -0,0 +1,26 @@
+# 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.
+# ==============================================================================
+"""Test name manager module."""
+from unittest import TestCase
+from mindinsight.mindconverter.graph_based_converter.hierarchical_tree.name_mgr import GlobalVarNameMgr
+
+
+class TestNameMgr(TestCase):
+    """Tester of name mgr."""
+
+    def test_global_name_mgr(self):
+        """Test global name mgr."""
+        name = GlobalVarNameMgr().get_name("onnx::Conv")
+        assert isinstance(name, str)