Add code template generation.

mindinsight/mindconverter/graph_based_converter/common/code_fragment.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -14,26 +14,10 @@
 # ==============================================================================
 """Define CodeLine object."""
 import abc
+import re
+from typing import List, Tuple
 
-
-class TrainableParams:
-    """Trainable parameters."""
-
-    def __init__(self, shape, dtype, reference):
-        self.param_name = None
-        self.shape = shape
-        self.dtype = dtype
-        self.reference = reference  # Weight name in global npy.
-
-
-class CodeSetting:
-    """Code generation settings."""
-
-    def __init__(self):
-        self.output_vars_suffix = []
-        self.operation_input_type = None  # Construct input type, tensor or list.
-        self.operation_extra_input = dict()  # `values` in original setting dict.
-        self.operation_extra_tensor = None  # For `MatMul`, `BiasAdd` op, need a tensor
+from mindinsight.mindconverter.graph_based_converter.constant import ExchangeMessageKeywords, TemplateKeywords
 
 
 class Fragment(abc.ABC):
@@ -222,3 +206,144 @@ class ModuleFragment(Fragment):
         super(ModuleFragment, self).__init__(operation=operation, actual_args=actual_args,
                                              input_shape=input_shape, output_shape=output_shape,
                                              settings=settings)
+
+
+class NewFragment:
+    """
+    Fragment definition for MindSpore code generation.
+
+    Args:
+        data_entity (dict): Required data by operations. The format of `data_entity` is as follow:
+            {
+                "var1": {
+                    "metadata": {  # ONNX Metadata
+                        "operation": "Conv2d",
+                        "source": "conv_pw_13/Conv2D",
+                        "attributes": {
+                            # Put original onnx attributes here.
+                        }
+                    },
+                    "variable_name": None,
+                    "inputs": [],
+                    "output_type": "tensor" | "array",
+                    "args": {"in_channels": 768, "out_channels": 1024},
+                    "trainable_params": {"weight": "Parameter(Tensor(GLOBAL_W[NAME]))"}
+                },
+                "var2": {
+                    "variable_name": "pad",
+                    "args": {"padding": [0, 1, 1, 0], "mode": "SAME"}
+                }
+            }
+        code_template (dict): Code template generated by mapper. The format of `code_template` is as follow:
+            {
+                "var1": {
+                    "init": [
+                        "self.{var1} = nn.Conv2d(in_channels={in_channels})",
+                        "self.{var1}.weight = {weight}"
+                    ],
+                    "construct": [
+                        "opt_{var1} = self.{var1}({inputs}[, extra])"
+                    ]
+                },
+                "var2": {
+                    "init": [
+                        "self.{var2} = nn.Pad(padding={padding}, mode={mode})"
+                    ],
+                    "construct": [
+                        "opt_{var2} = self.{var2}(opt_{var1}[, extra])"
+                    ]
+                }
+            }
+        outputs (list[str]): Outputs name slot list.
+        outputs_mapping (tuple): Outputs index mapping between ir node and MindSpore operation.
+    """
+
+    def __init__(self, data_entity: dict, code_template: dict, outputs: List[str], outputs_mapping):
+        self.exchange_msg = data_entity
+        self._code_template = code_template
+        self.inputs = []
+        self._outputs = outputs
+        self.outputs_mapping = outputs_mapping
+        self.format_args = dict()
+
+    def _get_outputs(self):
+        """
+        Get outputs of the code snippet.
+
+        Returns:
+            list[str], outputs of current code block.
+        """
+        outputs = []
+        variables = {
+            k: self.exchange_msg[k][ExchangeMessageKeywords.VariableScope.value.VARIABLE_NAME.value]
+            for k in self.exchange_msg if k != ExchangeMessageKeywords.METADATA.value
+        }
+        for o in self._outputs:
+            extractor = r".*\{(?P<var>.+)\}.*"
+            var_def = re.match(extractor, o)
+            if not var_def:
+                raise ValueError(f"Output variable name {o} is illegal.")
+            outputs.append(
+                (
+                    o.format(**variables),
+                    self.exchange_msg[var_def.group("var")][
+                        ExchangeMessageKeywords.VariableScope.value.OUTPUT_TYPE.value]
+                )
+            )
+        return outputs
+
+    def get_outputs_by_idx(self, idx, inner_idx=-1):
+        """Get outputs by idx."""
+        outputs = self._get_outputs()
+        opt, opt_type = outputs[idx]
+        if opt_type == ExchangeMessageKeywords.VariableScope.value.ARR_TYPE.value:
+            return f"{opt}[{inner_idx}]"
+        return opt
+
+    def __call__(self) -> Tuple[List[str], List[str]]:
+        """
+        Define parameter rewrite function.
+
+        Returns:
+            tuple[list[str], list[str]], init statement and construct statement.
+        """
+        init_stats, call_stats = [], []
+        precursor_node_var = [None, None]
+        for op_var, template in self._code_template.items():
+            if ExchangeMessageKeywords.VariableScope.value.INPUTS.value not in self.exchange_msg[op_var]:
+                # It's possible inputs and precursor node both exists.
+                self.exchange_msg[op_var][ExchangeMessageKeywords.VariableScope.value.ARGS.value][
+                    precursor_node_var[0]] = precursor_node_var[1]
+            for tpl in template[TemplateKeywords.INIT.value]:
+                init_stat = self._rewrite(op_var, self.exchange_msg[op_var], tpl)
+                init_stats.append(init_stat)
+            for tpl in template[TemplateKeywords.CONSTRUCT.value]:
+                call_stat = self._rewrite(op_var, self.exchange_msg[op_var], tpl)
+                call_stats.append(call_stat)
+            precursor_node_var = op_var, self.exchange_msg[op_var].get(
+                ExchangeMessageKeywords.VariableScope.value.VARIABLE_NAME.value)
+        return init_stats, call_stats
+
+    @staticmethod
+    def _rewrite(var, data, template: str) -> str:
+        """
+        Backfill data into code template.
+
+        Args:
+            var (str): Current operation variable name.
+            data (dict): Data to be written.
+            template (str): Code template.
+
+        Returns:
+            str, single code line.
+        """
+        rewrite_data = {var: data[ExchangeMessageKeywords.VariableScope.value.VARIABLE_NAME.value]}
+        if ExchangeMessageKeywords.VariableScope.value.INPUTS.value in data:
+            rewrite_data[ExchangeMessageKeywords.VariableScope.value.INPUTS.value] = ", ".join(
+                data[ExchangeMessageKeywords.VariableScope.value.INPUTS.value])
+        if ExchangeMessageKeywords.VariableScope.value.TRAINABLE_PARAMS.value in data:
+            rewrite_data.update(data[ExchangeMessageKeywords.VariableScope.value.TRAINABLE_PARAMS.value])
+        rewrite_data.update(data[ExchangeMessageKeywords.VariableScope.value.ARGS.value])
+        return template.format(**{
+            k: str(rewrite_data[k]) for k in rewrite_data
+        })

mindinsight/mindconverter/graph_based_converter/common/utils.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -209,3 +209,10 @@ def get_framework_type(model_path):
         raise error
 
     return framework_type
+
+
+def reset_init_or_construct(template, variable_slot, new_data, scope):
+    """Reset init statement."""
+    template[variable_slot][scope].clear()
+    template[variable_slot][scope] += new_data
+    return template

mindinsight/mindconverter/graph_based_converter/constant.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -42,6 +42,47 @@ ONNX_MIN_VER = "1.8.0"
 TF2ONNX_MIN_VER = "1.7.1"
 ONNXRUNTIME_MIN_VER = "1.5.2"
 
+
+@unique
+class TemplateKeywords(Enum):
+    """Define keywords in template message."""
+    INIT = "init"
+    CONSTRUCT = "construct"
+
+
+@unique
+class ExchangeMessageKeywords(Enum):
+    """Define keywords in exchange message."""
+    METADATA = "metadata"
+
+    @unique
+    class MetadataScope(Enum):
+        """Define metadata scope keywords in exchange message."""
+        SOURCE = "source"
+        OPERATION = "operation"
+        INPUTS = "inputs"
+        INPUTS_SHAPE = "inputs_shape"
+        OUTPUTS = "outputs"
+        OUTPUTS_SHAPE = "outputs_shape"
+        PRECURSOR = "precursor_nodes"
+        SUCCESSOR = "successor_nodes"
+        ATTRS = "attributes"
+        SCOPE = "scope"
+
+    @unique
+    class VariableScope(Enum):
+        """Define variable scope keywords in exchange message."""
+        OPERATION = "operation"
+        VARIABLE_NAME = "variable_name"
+        OUTPUT_TYPE = "output_type"
+        TSR_TYPE = "tensor"
+        ARR_TYPE = "array"
+        INPUTS = "inputs"
+        ARGS = "args"
+        WEIGHTS = "weights"
+        TRAINABLE_PARAMS = "trainable_params"
+
+
 BINARY_HEADER_PYTORCH_FILE = \
     b'\x80\x02\x8a\nl\xfc\x9cF\xf9 j\xa8P\x19.\x80\x02M\xe9\x03.\x80\x02}q\x00(X\x10\x00\x00\x00'
 TENSORFLOW_MODEL_SUFFIX = "pb"

mindinsight/mindconverter/graph_based_converter/generator/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -18,8 +18,9 @@ __all__ = ["batch_add_nodes"]
 import re
 import copy
 
+from mindinsight.mindconverter.graph_based_converter.common.code_fragment import CodeFragment
+from mindinsight.mindconverter.graph_based_converter.constant import ExchangeMessageKeywords
 from .generator import Generator, CodeStruct
-from ..common.code_fragment import CodeFragment
 
 
 def _tf_model_node_name_reformat(node, node_name):
@@ -34,7 +35,6 @@ def _tf_model_node_name_reformat(node, node_name):
         str, re-formatted node name.
     """
     scope_name = node.scope_name
-    new_name = None
     regex = r"(?P<parent>.+/)(?P<op>\w+)"
     match = re.match(regex, scope_name)
     parent = match.group("parent")
@@ -79,6 +79,32 @@ def batch_add_nodes(graph_obj, mapper) -> Generator:
     return generator_inst
 
 
+def _supply_graph_info(node, external_inputs):
+    """
+    Supply IR graph node info into metadata.
+
+    Args:
+        node (GraphNode): Graph node instance.
+        external_inputs (list[str]): External inputs in ONNX ir.
+
+    Returns:
+        dict, metadata.
+    """
+    precursors = _combine_external_inputs_with_precursor_nodes(node, external_inputs)
+    return {
+        ExchangeMessageKeywords.MetadataScope.value.SOURCE.value: node.ir_node_name,
+        ExchangeMessageKeywords.MetadataScope.value.OPERATION.value: node.ir_node_operation,
+        ExchangeMessageKeywords.MetadataScope.value.SCOPE.value: node.scope_name,
+        ExchangeMessageKeywords.MetadataScope.value.INPUTS.value: node.ir_node_inputs,
+        ExchangeMessageKeywords.MetadataScope.value.INPUTS_SHAPE.value: node.input_shape,
+        ExchangeMessageKeywords.MetadataScope.value.OUTPUTS.value: node.ir_node_outputs,
+        ExchangeMessageKeywords.MetadataScope.value.OUTPUTS_SHAPE.value: node.output_shape,
+        ExchangeMessageKeywords.MetadataScope.value.PRECURSOR.value: precursors,
+        ExchangeMessageKeywords.MetadataScope.value.SUCCESSOR.value: node.ir_node_successor,
+        ExchangeMessageKeywords.MetadataScope.value.ATTRS.value: node.node_params,
+    }
+
+
 def _convert_params(node, mapper):
     """
     Call mapper to convert node's params from ONNX to MindSpore.
@@ -88,10 +114,8 @@ def _convert_params(node, mapper):
         mapper (Mapper): The mapper instance which indicating conversion method.
 
     Returns:
-        str, op name in MindSpore
-        dict, MindSpore parameters
-        dict, MindSpore settings
-        dict, weights of the node
+        tuple[str, dict, dict, dict], op name in MindSpore, MindSpore parameters,
+        MindSpore settings and weights of the node.
     """
     params = copy.deepcopy(node.node_params)
     params.update({"input_shape": node.input_shape,
@@ -109,3 +133,24 @@ def _convert_params(node, mapper):
         return op_in_ms, ms_params, ms_settings, weights
 
     return node.op_name, node.node_params, dict(), dict()
+
+
+def _combine_external_inputs_with_precursor_nodes(node, external_inputs):
+    """
+    User_provided_input_nodes.
+
+    Args:
+        node (OnnxGraphNode): Node instance.
+        external_inputs (list[str]): Inputs in onnx ir.
+
+    Returns:
+        list[str], precursor nodes list.
+    """
+    inputs = set(node.ir_node_inputs)
+    to_be_added = list(inputs & set(external_inputs))
+    precursor = node.ir_node_precursor
+    # Add external inputs to precursor as the order of its inputs.
+    for item in to_be_added:
+        node_idx = node.ir_node_inputs.index(item)
+        precursor.insert(node_idx, item)
+    return precursor

mindinsight/mindconverter/graph_based_converter/mapper/base.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -19,6 +19,7 @@ import json
 import os
 from typing import Dict
 from mindinsight.mindconverter.common.log import logger as log
+from mindinsight.mindconverter.graph_based_converter.constant import ExchangeMessageKeywords, TemplateKeywords
 
 CONFIG_JSON = "onnx_to_ms.json"
 OPERATION_TABLE = os.path.join(
@@ -36,6 +37,7 @@ GET_OP_NAME = "_operation_name_in_ms"
 GET_OP_PARAMS = "_convert_params"
 GET_OP_WEIGHTS = "_convert_trained_weights"
 GET_OP_SETTINGS = "_convert_settings"
+GET_OP_TEMPLATE = "_generate_snippet_template"
 
 
 class Mapper(metaclass=abc.ABCMeta):
@@ -44,7 +46,7 @@ class Mapper(metaclass=abc.ABCMeta):
     @staticmethod
     @abc.abstractmethod
     def _operation_name_in_ms(*args, **kwargs):
-        """Corresponding operation name in mindspore."""
+        """Corresponding operation name in MindSpore."""
 
     @staticmethod
     @abc.abstractmethod
@@ -66,6 +68,11 @@ class Mapper(metaclass=abc.ABCMeta):
     def convert(cls, op_name: str, params: Dict, weights: Dict = None):
         """Convert third party operation's param into MindSpore operation."""
 
+    @staticmethod
+    @abc.abstractmethod
+    def _generate_snippet_template(**kwargs):
+        """Generate code template according to node info."""
+
 
 class ONNXToMindSporeMapper(Mapper, abc.ABC):
     """ONNX operation to MindSpore."""
@@ -131,3 +138,36 @@ class ONNXToMindSporeMapper(Mapper, abc.ABC):
     @staticmethod
     def _convert_settings(**kwargs):
         raise NotImplementedError
+
+    @staticmethod
+    def _generate_snippet_template(**kwargs):
+        op = kwargs.get("operation")
+        args = kwargs.get("converted_params")
+        weights = kwargs.get("weights")
+        if not op:
+            raise ValueError("Can not get MindSpore operation name.")
+        variable_slot = "var_0"
+        init_template = f"self.{{{variable_slot}}} = {op}({', '.join(['%s={%s}' % (p, p) for p in args])})"
+        construct_template = f"opt_{{{variable_slot}}} = self.{{{variable_slot}}}" \
+                             f"({{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}})"
+        template = {
+            variable_slot: {
+                TemplateKeywords.INIT.value: [init_template],
+                TemplateKeywords.CONSTRUCT.value: [construct_template]
+            }
+        }
+        exchange_msg = {
+            variable_slot: {
+                ExchangeMessageKeywords.VariableScope.value.OPERATION.value: op,
+                ExchangeMessageKeywords.VariableScope.value.VARIABLE_NAME.value: None,
+                ExchangeMessageKeywords.VariableScope.value.OUTPUT_TYPE.value:
+                    ExchangeMessageKeywords.VariableScope.value.TSR_TYPE.value,
+                ExchangeMessageKeywords.VariableScope.value.INPUTS.value: [],
+                ExchangeMessageKeywords.VariableScope.value.ARGS.value: args,
+                ExchangeMessageKeywords.VariableScope.value.WEIGHTS.value: weights,
+                ExchangeMessageKeywords.VariableScope.value.TRAINABLE_PARAMS.value: {}
+            }
+        }
+        outputs_list = [f"opt_{{{variable_slot}}}"]
+        outputs_mapping = ((0, 0),)
+        return template, exchange_msg, outputs_list, outputs_mapping

mindinsight/mindconverter/graph_based_converter/mapper/impl/nn/batch_norm_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,8 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
 
 
 class BatchNormMapper(ONNXToMindSporeMapper):

mindinsight/mindconverter/graph_based_converter/mapper/impl/nn/conv_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -14,9 +14,9 @@
 # ==============================================================================
 """Mapper module."""
 import numpy as np
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
-from ....common import utils
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.common.utils import convert_bytes_string_to_string
 
 
 def _convert_padding(**kwargs):
@@ -83,7 +83,7 @@ class ConvMapper(ONNXToMindSporeMapper):
 
         auto_pad = None
         if params.get("auto_pad") is not None:
-            auto_pad = utils.convert_bytes_string_to_string(params.get("auto_pad"))
+            auto_pad = convert_bytes_string_to_string(params.get("auto_pad"))
 
         # tmp tf translated ver. mapping
         if isinstance(params.get('dilations'), list):

mindinsight/mindconverter/graph_based_converter/mapper/impl/nn/dense_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,8 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
 
 
 class DenseMapper(ONNXToMindSporeMapper):

mindinsight/mindconverter/graph_based_converter/mapper/impl/nn/flatten_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,8 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
 
 
 class FlattenMapper(ONNXToMindSporeMapper):

mindinsight/mindconverter/graph_based_converter/mapper/impl/nn/global_pool_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,8 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
 
 
 class GlobalPoolMapper(ONNXToMindSporeMapper):

mindinsight/mindconverter/graph_based_converter/mapper/impl/nn/mat_mul_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,10 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting, Tensor, get_dtype
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting, Tensor, get_dtype
+from mindinsight.mindconverter.graph_based_converter.common.utils import reset_init_or_construct
+from mindinsight.mindconverter.graph_based_converter.constant import ExchangeMessageKeywords, TemplateKeywords
 
 
 class MatMulMapper(ONNXToMindSporeMapper):
@@ -43,3 +45,30 @@ class MatMulMapper(ONNXToMindSporeMapper):
             ref = t_name
         return Setting(op_extra_tensor=Tensor(shape=tensor.shape,
                                               dtype=get_dtype(tensor), reference=ref))
+
+    @staticmethod
+    def _generate_snippet_template(**kwargs):
+        template, exchange_msg, outputs_list, outputs_mapping = ONNXToMindSporeMapper._generate_snippet_template(
+            **kwargs)
+        op = kwargs.get("operation")
+        args = kwargs.get("converted_params")
+        weights = kwargs.get("weights")
+        if not weights:
+            return template, exchange_msg, outputs_list, outputs_mapping
+
+        weight = list(weights.items())[0]
+        _, tensor = weight
+
+        variable_slot = "var_0"
+        init_template = f"self.{{{variable_slot}}} = {op}({', '.join(['%s={%s}' % (p, p) for p in args])})"
+        init_tensor = f"self.{{{variable_slot}}}_w = " \
+                      f"Tensor(np.random.uniform(0, 1, {tensor.shape}).astype(np.{tensor.dtype}))"
+        construct_template = f"opt_{{{variable_slot}}} = self.{{{variable_slot}}}" \
+                             f"({{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}}," \
+                             f"self.{{{variable_slot}}}_w)"
+        template = reset_init_or_construct(template, variable_slot, [init_template, init_tensor],
+                                           TemplateKeywords.INIT.value)
+        template = reset_init_or_construct(template, variable_slot, [construct_template],
+                                           TemplateKeywords.CONSTRUCT.value)
+
+        return template, exchange_msg, outputs_list, outputs_mapping

mindinsight/mindconverter/graph_based_converter/mapper/impl/nn/pad_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,8 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
 
 
 def _padding_format_convert(padding: list):

mindinsight/mindconverter/graph_based_converter/mapper/impl/nn/pool_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,8 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
 
 
 class PoolMapper(ONNXToMindSporeMapper):

mindinsight/mindconverter/graph_based_converter/mapper/impl/nn/relu_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,8 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
 
 
 class ReLUMapper(ONNXToMindSporeMapper):

mindinsight/mindconverter/graph_based_converter/mapper/impl/nn/sigmoid_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,8 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
 
 
 class SigmoidMapper(ONNXToMindSporeMapper):

mindinsight/mindconverter/graph_based_converter/mapper/impl/nn/softmax_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,8 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
 
 
 class SoftmaxMapper(ONNXToMindSporeMapper):

mindinsight/mindconverter/graph_based_converter/mapper/impl/ops/add_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,10 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting, Tensor, get_dtype
+from mindinsight.mindconverter.graph_based_converter.common.utils import reset_init_or_construct
+from mindinsight.mindconverter.graph_based_converter.constant import ExchangeMessageKeywords, TemplateKeywords
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting, Tensor, get_dtype
 
 
 class AddMapper(ONNXToMindSporeMapper):
@@ -43,3 +45,30 @@ class AddMapper(ONNXToMindSporeMapper):
             ref = t_name
         return Setting(op_extra_tensor=Tensor(shape=tensor.shape,
                                               dtype=get_dtype(tensor), reference=ref))
+
+    @staticmethod
+    def _generate_snippet_template(**kwargs):
+        template, exchange_msg, outputs_list, outputs_mapping = ONNXToMindSporeMapper._generate_snippet_template(
+            **kwargs)
+        op = kwargs.get("operation")
+        args = kwargs.get("converted_params")
+        weights = kwargs.get("weights")
+        if not weights:
+            return template, exchange_msg, outputs_list, outputs_mapping
+
+        bias = list(weights.items())[0]
+        _, tensor = bias
+
+        variable_slot = "var_0"
+        init_template = f"self.{{{variable_slot}}} = {op}({', '.join(['%s={%s}' % (p, p) for p in args])})"
+        init_tensor = f"self.{{{variable_slot}}}_bias = " \
+                      f"Tensor(np.random.uniform(0, 1, {tensor.shape}).astype(np.{tensor.dtype}))"
+        construct_template = f"opt_{{{variable_slot}}} = self.{{{variable_slot}}}" \
+                             f"({{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}}," \
+                             f"self.{{{variable_slot}}}_bias)"
+        template = reset_init_or_construct(template, variable_slot, [init_template, init_tensor],
+                                           TemplateKeywords.INIT.value)
+        template = reset_init_or_construct(template, variable_slot, [construct_template],
+                                           TemplateKeywords.CONSTRUCT.value)
+
+        return template, exchange_msg, outputs_list, outputs_mapping

mindinsight/mindconverter/graph_based_converter/mapper/impl/ops/concat_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -14,8 +14,10 @@
 # ==============================================================================
 """Mapper module."""
 from mindinsight.mindconverter.graph_based_converter.constant import InputType
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.common.utils import reset_init_or_construct
+from mindinsight.mindconverter.graph_based_converter.constant import ExchangeMessageKeywords, TemplateKeywords
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
 
 
 class ConcatMapper(ONNXToMindSporeMapper):
@@ -38,3 +40,14 @@ class ConcatMapper(ONNXToMindSporeMapper):
     def _convert_settings(**kwargs):
         input_type = InputType.LIST.value
         return Setting(op_ipt_type=input_type)
+
+    @staticmethod
+    def _generate_snippet_template(**kwargs):
+        template, exchange_msg, outputs_list, outputs_mapping = ONNXToMindSporeMapper._generate_snippet_template(
+            **kwargs)
+        variable_slot = "var_0"
+        construct_template = f"opt_{{{variable_slot}}} = self.{{{variable_slot}}}" \
+                             f"(({{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}},))"
+        template = reset_init_or_construct(template, variable_slot, [construct_template],
+                                           TemplateKeywords.CONSTRUCT.value)
+        return template, exchange_msg, outputs_list, outputs_mapping

mindinsight/mindconverter/graph_based_converter/mapper/impl/ops/mul_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,10 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting, Tensor, get_dtype
+from mindinsight.mindconverter.graph_based_converter.common.utils import reset_init_or_construct
+from mindinsight.mindconverter.graph_based_converter.constant import ExchangeMessageKeywords, TemplateKeywords
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting, Tensor, get_dtype
 
 
 class MulMapper(ONNXToMindSporeMapper):
@@ -40,3 +42,29 @@ class MulMapper(ONNXToMindSporeMapper):
         ref, tensor = list(weights.items())[0]
         return Setting(op_extra_tensor=Tensor(shape=tensor.shape,
                                               dtype=get_dtype(tensor), reference=ref))
+
+    @staticmethod
+    def _generate_snippet_template(**kwargs):
+        template, exchange_msg, outputs_list, outputs_mapping = ONNXToMindSporeMapper._generate_snippet_template(
+            **kwargs)
+        op = kwargs.get("operation")
+        args = kwargs.get("converted_params")
+        weights = kwargs.get("weights")
+        if not weights:
+            return template, exchange_msg, outputs_list, outputs_mapping
+
+        weight = list(weights.items())[0]
+        _, tensor = weight
+
+        variable_slot = "var_0"
+        init_template = f"self.{{{variable_slot}}} = {op}({', '.join(['%s={%s}' % (p, p) for p in args])})"
+        init_tensor = f"self.{{{variable_slot}}}_w = Tensor(np.random.uniform(0, 1, {tensor.shape})" \
+                      f".astype(np.{tensor.dtype}))"
+        construct_template = f"opt_{{{variable_slot}}} = self.{{{variable_slot}}}" \
+                             f"({{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}}," \
+                             f"self.{{{variable_slot}}}_w)"
+        template = reset_init_or_construct(template, variable_slot, [init_template, init_tensor],
+                                           TemplateKeywords.INIT.value)
+        template = reset_init_or_construct(template, variable_slot, [construct_template],
+                                           TemplateKeywords.CONSTRUCT.value)
+        return template, exchange_msg, outputs_list, outputs_mapping

mindinsight/mindconverter/graph_based_converter/mapper/impl/ops/reduce_mean_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,10 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.common.utils import reset_init_or_construct
+from mindinsight.mindconverter.graph_based_converter.constant import ExchangeMessageKeywords, TemplateKeywords
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
 
 
 class ReduceMeanMapper(ONNXToMindSporeMapper):
@@ -42,3 +44,20 @@ class ReduceMeanMapper(ONNXToMindSporeMapper):
         else:
             axis = tuple()
         return Setting(op_extra_input={'axis': axis})
+
+    @staticmethod
+    def _generate_snippet_template(**kwargs):
+        template, exchange_msg, outputs_list, outputs_mapping = ONNXToMindSporeMapper._generate_snippet_template(
+            **kwargs)
+        raw_params = kwargs.get("raw_params")
+        if raw_params.get('axes'):
+            axis = raw_params['axes'][0] if len(raw_params['axes']) == 1 else tuple(raw_params['axes'])
+        else:
+            axis = tuple()
+        variable_slot = "var_0"
+        construct_template = f"opt_{{{variable_slot}}} = self.{{{variable_slot}}}" \
+                             f"({{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}}, {axis})"
+        template = reset_init_or_construct(template, variable_slot, [construct_template],
+                                           TemplateKeywords.CONSTRUCT.value)
+
+        return template, exchange_msg, outputs_list, outputs_mapping

mindinsight/mindconverter/graph_based_converter/mapper/impl/ops/reshape_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,10 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.common.utils import reset_init_or_construct
+from mindinsight.mindconverter.graph_based_converter.constant import ExchangeMessageKeywords, TemplateKeywords
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
 
 
 class ReshapeMapper(ONNXToMindSporeMapper):
@@ -52,3 +54,20 @@ class ReshapeMapper(ONNXToMindSporeMapper):
         shape = [-1]
         shape += list(weights.values())[0][1:].tolist()
         return Setting(op_extra_input={"shape": tuple(shape)})
+
+    @staticmethod
+    def _generate_snippet_template(**kwargs):
+        template, exchange_msg, outputs_list, outputs_mapping = ONNXToMindSporeMapper._generate_snippet_template(
+            **kwargs)
+        weights = kwargs.get("weights")
+        if len(weights) > 1:
+            raise ValueError("For reshape, `weights` length should equal to 1.")
+        shape = [-1]
+        shape += list(weights.values())[0][1:].tolist()
+        variable_slot = "var_0"
+        construct_template = f"opt_{{{variable_slot}}} = self.{{{variable_slot}}}" \
+                             f"({{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}}, {tuple(shape)})"
+        template = reset_init_or_construct(template, variable_slot, [construct_template],
+                                           TemplateKeywords.CONSTRUCT.value)
+
+        return template, exchange_msg, outputs_list, outputs_mapping

mindinsight/mindconverter/graph_based_converter/mapper/impl/ops/resize_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,9 +13,9 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
-from ....common import utils
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.common.utils import convert_bytes_string_to_string
 
 
 class ResizeMapper(ONNXToMindSporeMapper):
@@ -26,11 +26,11 @@ class ResizeMapper(ONNXToMindSporeMapper):
         params = kwargs.get("params")
         onnx_coordinate_transform = params.get("coordinate_transformation_mode")
         if onnx_coordinate_transform is not None:
-            onnx_coordinate_transform = utils.convert_bytes_string_to_string(onnx_coordinate_transform)
+            onnx_coordinate_transform = convert_bytes_string_to_string(onnx_coordinate_transform)
 
         interpolation_mode = params.get("mode")
         if interpolation_mode is not None:
-            interpolation_mode = utils.convert_bytes_string_to_string(interpolation_mode)
+            interpolation_mode = convert_bytes_string_to_string(interpolation_mode)
 
         # Define which MindSpore Resize operator to be used
         if interpolation_mode == "linear":
@@ -54,7 +54,7 @@ class ResizeMapper(ONNXToMindSporeMapper):
 
         onnx_coordinate_transform = params.get("coordinate_transformation_mode")
         if onnx_coordinate_transform is not None:
-            onnx_coordinate_transform = utils.convert_bytes_string_to_string(onnx_coordinate_transform)
+            onnx_coordinate_transform = convert_bytes_string_to_string(onnx_coordinate_transform)
 
         if onnx_coordinate_transform == "align_corners" or "half_pixel" in onnx_coordinate_transform:
             align_corners = True

mindinsight/mindconverter/graph_based_converter/mapper/impl/ops/slice_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,10 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.common.utils import reset_init_or_construct
+from mindinsight.mindconverter.graph_based_converter.constant import ExchangeMessageKeywords, TemplateKeywords
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
 
 
 class SliceMapper(ONNXToMindSporeMapper):
@@ -41,3 +43,21 @@ class SliceMapper(ONNXToMindSporeMapper):
         starts = sorted(zip(weights[0].tolist(), weights[2].tolist()), key=lambda x: x[1], reverse=False)
         return Setting(op_extra_input={"begin": tuple([i[0] for i in starts]),
                                        "size": tuple(opt_shape)})
+
+    @staticmethod
+    def _generate_snippet_template(**kwargs):
+        template, exchange_msg, outputs_list, outputs_mapping = ONNXToMindSporeMapper._generate_snippet_template(
+            **kwargs)
+        weights = list(kwargs.get("weights").values())  # start, end, axis
+        opt_shape = kwargs["raw_params"]["output_shape"]
+        if not weights:
+            raise ValueError("Cannot get required params from slice.")
+        starts = sorted(zip(weights[0].tolist(), weights[2].tolist()), key=lambda x: x[1], reverse=False)
+        variable_slot = "var_0"
+        construct_template = f"opt_{{{variable_slot}}} = self.{{{variable_slot}}}" \
+                             f"({{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}}, " \
+                             f"{tuple([i[0] for i in starts])}, {tuple(opt_shape)})"
+        template = reset_init_or_construct(template, variable_slot, [construct_template],
+                                           TemplateKeywords.CONSTRUCT.value)
+
+        return template, exchange_msg, outputs_list, outputs_mapping

mindinsight/mindconverter/graph_based_converter/mapper/impl/ops/split_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,8 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
 
 
 class SplitMapper(ONNXToMindSporeMapper):

mindinsight/mindconverter/graph_based_converter/mapper/impl/ops/transpose_mapper.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -13,8 +13,10 @@
 # limitations under the License.
 # ==============================================================================
 """Mapper module."""
-from ...base import ONNXToMindSporeMapper
-from ...gen_setting import Setting
+from mindinsight.mindconverter.graph_based_converter.common.utils import reset_init_or_construct
+from mindinsight.mindconverter.graph_based_converter.constant import ExchangeMessageKeywords, TemplateKeywords
+from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper
+from mindinsight.mindconverter.graph_based_converter.mapper.gen_setting import Setting
 
 
 class TransposeMapper(ONNXToMindSporeMapper):
@@ -42,3 +44,17 @@ class TransposeMapper(ONNXToMindSporeMapper):
             converted_params['input_perm'] = perm
 
         return Setting(op_extra_input=converted_params)
+
+    @staticmethod
+    def _generate_snippet_template(**kwargs):
+        template, exchange_msg, outputs_list, outputs_mapping = ONNXToMindSporeMapper._generate_snippet_template(
+            **kwargs)
+        raw_params = kwargs.get("raw_params")
+        perm = raw_params["perm"]
+        variable_slot = "var_0"
+        construct_template = f"opt_{{{variable_slot}}} = self.{{{variable_slot}}}" \
+                             f"({{{ExchangeMessageKeywords.VariableScope.value.INPUTS.value}}}, {tuple(perm)})"
+        template = reset_init_or_construct(template, variable_slot, [construct_template],
+                                           TemplateKeywords.CONSTRUCT.value)
+
+        return template, exchange_msg, outputs_list, outputs_mapping

mindinsight/mindconverter/graph_based_converter/third_party_graph/base.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+# Copyright 2020-2021 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.
@@ -17,11 +17,13 @@ import abc
 from collections import OrderedDict
 from copy import deepcopy
 
+from typing import List
+
 from mindinsight.mindconverter.common.log import logger as log
-from ..common.code_fragment import CodeFragment
-from ..constant import NodeType, InputType
-from ..mapper.base import Mapper
-from ...common.exceptions import NodeInputTypeNotSupportError
+from mindinsight.mindconverter.graph_based_converter.common.code_fragment import CodeFragment
+from mindinsight.mindconverter.graph_based_converter.constant import NodeType, InputType
+from mindinsight.mindconverter.graph_based_converter.mapper.base import Mapper
+from mindinsight.mindconverter.common.exceptions import NodeInputTypeNotSupportError
 
 
 class GraphParser(metaclass=abc.ABCMeta):
@@ -97,7 +99,6 @@ class Graph(BaseGraph, abc.ABC):
         self.model = model
         self._raw_input_nodes = kwargs.get("input_nodes")
         self._raw_output_nodes = kwargs.get("output_nodes")
-        self.checkpoint = kwargs.get("checkpoint", None)
         self._nodes_collection = OrderedDict()
         self._nodes_record = dict()
         self._shape_dict = dict()
@@ -107,6 +108,13 @@ class Graph(BaseGraph, abc.ABC):
         self._input_shape = dict()
         self._is_multi_opt_graph = False
 
+    @property
+    def user_provided_input_nodes(self) -> List[str]:
+        """User provided input_nodes in CLI."""
+        if not isinstance(self._raw_input_nodes, list):
+            return [self._raw_input_nodes]
+        return self._raw_input_nodes
+
     def get_input_shape(self, name):
         """
         Get node input shape.
@@ -285,9 +293,9 @@ class GraphNode(abc.ABC):
         self.successor_nodes = []
         # Control dependency.
         self._deleted_in_edge = 0
-        # Source node in pytorch.
-        self._src_node = str(node) if node else None
-        # Original operation name in pytorch.
+        # Source node in ONNX.
+        self._src_node = node if node else None
+        # Original operation name in ONNX.
         self._op_name = None
         self._op_params = dict()
         self._scope_name = None
@@ -311,6 +319,40 @@ class GraphNode(abc.ABC):
         # Is in multi output graph.
         self._is_in_multi_opt_graph = False
 
+    @property
+    def ir_node_name(self):
+        """Getter of ir node's name."""
+        return self._src_node.name
+
+    @property
+    def ir_node_operation(self):
+        """Getter of ir node's operation."""
+        return self._src_node.op_type
+
+    @property
+    def ir_node_inputs(self):
+        """Getter of ir node's inputs."""
+        return list(self._src_node.input_name_list)
+
+    @property
+    def ir_node_outputs(self):
+        """Getter of ir node's outputs."""
+        return list(self._src_node.output_name_list)
+
+    @property
+    def ir_node_precursor(self):
+        """Getter of ir node's precursor."""
+        return [
+            v.name for _, v in self._src_node.precursor_onnx_node_dict.items()
+        ]
+
+    @property
+    def ir_node_successor(self):
+        """Getter of ir node's successor."""
+        return [
+            v.name for _, v in self._src_node.successor_onnx_node_dict.items()
+        ]
+
     @property
     def weight(self):
         return self._weight

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

@@ -0,0 +1,15 @@
+# Copyright 2021 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.
+# ==============================================================================
+"""Unit test for mindconverter.graph_based_converter.common interface."""

tests/ut/mindconverter/graph_based_converter/common/test_fragment.py

@@ -0,0 +1,145 @@
+# Copyright 2021 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 fragment."""
+from unittest import TestCase
+from mindinsight.mindconverter.graph_based_converter.common.code_fragment import NewFragment as Fragment
+
+
+class TestFragment(TestCase):
+    """Tester of fragment."""
+
+    def test_matmul(self):
+        """Test matmul like operation's template."""
+        template = {
+            'var_0': {
+                'init': [
+                    'self.{var_0} = nn.MatMul()',
+                    'self.{var_0}_w = Tensor(np.random.rand(*(2048, 1000)).astype(np.float32))'
+                ],
+                'construct': ['opt_{var_0} = self.{var_0}({inputs},self.{var_0}_w)']
+            }
+        }
+        rewrite_data = {
+            'var_0': {
+                'operation': 'nn.MatMul',
+                'output_type': 'tensor',
+                'variable_name': "matmul", 'inputs': ["x"], 'args': {},
+                'weights': {},
+                'trainable_params': {}
+            },
+            'metadata': {
+                'source': 'probs/MatMul', 'operation': 'MatMul', 'scope': 'Model/MatMul',
+                'inputs': ['avg_pool/Mean:0', 'probs/MatMul/ReadVariableOp:0'],
+                'inputs_shape': (1, 2048), 'outputs': ['probs/MatMul:0'], 'outputs_shape': [1, 1000],
+                'precursor_nodes': ['avg_pool/Mean'], 'successor_nodes': ['probs/BiasAdd'],
+                'attributes': {}
+            }
+        }
+        fragment = Fragment(data_entity=rewrite_data, code_template=template, outputs=["opt_{var_0}"],
+                            outputs_mapping=((0, 0),))
+        code = fragment()
+        init = code[0]
+        construct = code[1]
+        self.assertEqual(init, ['self.matmul = nn.MatMul()',
+                                'self.matmul_w = Tensor(np.random.rand(*(2048, 1000)).astype(np.float32))'])
+        self.assertEqual(construct, ['opt_matmul = self.matmul(x,self.matmul_w)'])
+        self.assertEqual(fragment.get_outputs_by_idx(0), "opt_matmul")
+
+    def test_biasadd(self):
+        """Test biasadd like operation's template."""
+        template = {
+            'var_0': {
+                'init': [
+                    'self.{var_0} = P.TensorAdd()',
+                    'self.{var_0}_bias = Tensor(np.random.rand(*(1000,)).astype(np.float32))'
+                ],
+                'construct': ['opt_{var_0} = self.{var_0}({inputs},self.{var_0}_bias)']
+            }
+        }
+        rewrite_data = {
+            'var_0': {
+                'operation': 'P.TensorAdd',
+                'output_type': 'tensor',
+                'variable_name': "add", 'inputs': ["x"], 'args': {}, 'weights': {},
+                'trainable_params': {}
+            },
+            'metadata': {
+                'source': 'probs/BiasAdd', 'operation': 'Add', 'scope': 'Model/Add',
+                'inputs': ['probs/MatMul:0', 'probs/BiasAdd/ReadVariableOp:0'], 'inputs_shape': (1, 1000),
+                'outputs': ['probs/BiasAdd:0'], 'outputs_shape': [1, 1000], 'precursor_nodes': ['probs/MatMul'],
+                'successor_nodes': ['probs/Softmax'], 'attributes': {}
+            }
+        }
+        fragment = Fragment(data_entity=rewrite_data, code_template=template, outputs=["opt_{var_0}"],
+                            outputs_mapping=((0, 0),))
+        code = fragment()
+        init = code[0]
+        construct = code[1]
+        self.assertEqual(init, ['self.add = P.TensorAdd()',
+                                'self.add_bias = Tensor(np.random.rand(*(1000,)).astype(np.float32))'])
+        self.assertEqual(construct, ['opt_add = self.add(x,self.add_bias)'])
+        self.assertEqual(fragment.get_outputs_by_idx(0), "opt_add")
+
+    def test_transpose(self):
+        """Test transpose like operation's template."""
+        template = {
+            'var_0': {
+                'init': ['self.{var_0} = P.Transpose()'],
+                'construct': ['opt_{var_0} = self.{var_0}({inputs}, (0, 3, 1, 2))']
+            }
+        }
+        rewrite_data = {
+            'var_0': {
+                'operation': 'P.Transpose',
+                'output_type': 'tensor',
+                'variable_name': "transpose", 'inputs': ["x"], 'args': {}, 'weights': {},
+                'trainable_params': {}
+            }
+        }
+        fragment = Fragment(data_entity=rewrite_data, code_template=template, outputs=["opt_{var_0}"],
+                            outputs_mapping=((0, 0),))
+        code = fragment()
+        init = code[0]
+        construct = code[1]
+        self.assertEqual(init, ['self.transpose = P.Transpose()'])
+        self.assertEqual(construct, ['opt_transpose = self.transpose(x, (0, 3, 1, 2))'])
+        self.assertEqual(fragment.get_outputs_by_idx(0), "opt_transpose")
+
+    def test_split(self):
+        """Test split like operation's template."""
+        template = {
+            'var_0': {
+                'init': ['self.{var_0} = P.Split(axis={axis}, output_num={output_num})'],
+                'construct': ['opt_{var_0} = self.{var_0}({inputs})']
+            }
+        }
+        rewrite_data = {
+            'var_0': {
+                'operation': 'P.Split',
+                'variable_name': "split",
+                'output_type': 'array',
+                'inputs': ["x"],
+                'args': {"axis": 1, "output_num": 2}, 'weights': {},
+                'trainable_params': {}
+            }
+        }
+        fragment = Fragment(data_entity=rewrite_data, code_template=template, outputs=["opt_{var_0}"],
+                            outputs_mapping=((0, 0),))
+        code = fragment()
+        init = code[0]
+        construct = code[1]
+        self.assertEqual(init, ['self.split = P.Split(axis=1, output_num=2)'])
+        self.assertEqual(construct, ['opt_split = self.split(x)'])
+        self.assertEqual(fragment.get_outputs_by_idx(0, 1), 'opt_split[1]')