!1273 [MindConverter] Fix import and format in tutorial

From: @moran3
Reviewed-by: @ouwenchang,@lixiaohui33
Signed-off-by: @lixiaohui33

mindinsight/mindconverter/tutorial/add_operator_mapper_advanced_tutorial.ipynb

@@ -266,7 +266,7 @@
    "source": [
     "import numpy as np\n",
     "from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper\n",
-    "from mindinsight.mindconverter.graph_based_converter.common.code_fragment import NewFragment\n",
+    "from mindinsight.mindconverter.graph_based_converter.common.code_fragment import Fragment\n",
     "\n",
     "\n",
     "def test_mapper(onnx_info):\n",
@@ -290,8 +290,8 @@
     "    exchange_msg['var_0']['variable_name'] = 'self_defined_operator'\n",
     "    exchange_msg['var_0']['inputs'] = ['x']\n",
     "\n",
-    "    fragment = NewFragment(data_entity=exchange_msg, code_template=template, outputs=outputs_lists,\n",
-    "                           outputs_mapping=outputs_mapping)\n",
+    "    fragment = Fragment(data_entity=exchange_msg, code_template=template, outputs=outputs_lists,\n",
+    "                        outputs_mapping=outputs_mapping)\n",
     "\n",
     "    code = fragment()\n",
     "    init_code = code[0]\n",
@@ -326,16 +326,16 @@
    ],
    "source": [
     "onnx_operator_info = {'op_name': 'onnx::AveragePool',\n",
-    "                          'attributes': {'auto_pad': 'NOTSET',\n",
-    "                                         'ceil_mode': 0,\n",
-    "                                         'count_include_pad': 0,\n",
-    "                                         'kernel_shape': (5, 5),\n",
-    "                                         'pads': (0, 0, 0, 0),\n",
-    "                                         'strides': (2, 2),\n",
-    "                                         'input_shape': (1, 3, 224, 224),\n",
-    "                                         'output_shape': (1, 3, 112, 112)\n",
-    "                                        },\n",
-    "                          'weights': []}\n",
+    "                      'attributes': {'auto_pad': 'NOTSET',\n",
+    "                                     'ceil_mode': 0,\n",
+    "                                     'count_include_pad': 0,\n",
+    "                                     'kernel_shape': (5, 5),\n",
+    "                                     'pads': (0, 0, 0, 0),\n",
+    "                                     'strides': (2, 2),\n",
+    "                                     'input_shape': (1, 3, 224, 224),\n",
+    "                                     'output_shape': (1, 3, 112, 112)\n",
+    "                                    },\n",
+    "                     'weights': []}\n",
     "test_mapper(onnx_operator_info)"
    ]
   },
@@ -481,7 +481,7 @@
    "source": [
     "import numpy as np\n",
     "from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper\n",
-    "from mindinsight.mindconverter.graph_based_converter.common.code_fragment import NewFragment\n",
+    "from mindinsight.mindconverter.graph_based_converter.common.code_fragment import Fragment\n",
     "\n",
     "\n",
     "def test_mapper(onnx_info):\n",
@@ -510,10 +510,10 @@
     "        weight = weight_inst['data']\n",
     "        weight_shape = weight.shape\n",
     "        weight_dtype = weight.dtype\n",
-    "        exchange_msg['var_0']['parameters'][weight_name] = NewFragment.create_parameter(weight_shape, weight_dtype)\n",
+    "        exchange_msg['var_0']['parameters'][weight_name] = Fragment.create_parameter(weight_shape, weight_dtype)\n",
     "\n",
-    "    fragment = NewFragment(data_entity=exchange_msg, code_template=template, outputs=outputs_lists,\n",
-    "                           outputs_mapping=outputs_mapping)\n",
+    "    fragment = Fragment(data_entity=exchange_msg, code_template=template, outputs=outputs_lists,\n",
+    "                        outputs_mapping=outputs_mapping)\n",
     "\n",
     "    code = fragment()\n",
     "    init_code = code[0]\n",
@@ -547,10 +547,10 @@
    ],
    "source": [
     "onnx_operator_info = {'op_name': 'onnx::Add',\n",
-    "                          'attributes': {},\n",
-    "                          'weights': [NodeWeight(weight_name='onnx_bias',\n",
-    "                                                 weight_value=np.ones((1, 3, 224, 224), dtype=np.int),\n",
-    "                                                 weight_location=1)]}\n",
+    "                      'attributes': {},\n",
+    "                      'weights': [NodeWeight(weight_name='onnx_bias',\n",
+    "                                             weight_value=np.ones((1, 3, 224, 224), dtype=np.int),\n",
+    "                                             weight_location=1)]}\n",
     "test_mapper(onnx_operator_info)"
    ]
   },

mindinsight/mindconverter/tutorial/add_operator_mapper_base_tutorial.ipynb

@@ -213,7 +213,7 @@
    "source": [
     "import numpy as np\n",
     "from mindinsight.mindconverter.graph_based_converter.mapper.base import ONNXToMindSporeMapper\n",
-    "from mindinsight.mindconverter.graph_based_converter.common.code_fragment import NewFragment\n",
+    "from mindinsight.mindconverter.graph_based_converter.common.code_fragment import Fragment\n",
     "from mindinsight.mindconverter.graph_based_converter.third_party_graph.onnx_utils import NodeWeight\n",
     "\n",
     "def test_mapper(onnx_info):\n",
@@ -237,8 +237,8 @@
     "    exchange_msg['var_0']['variable_name'] = 'self_defined_operator'\n",
     "    exchange_msg['var_0']['inputs'] = ['x']\n",
     "\n",
-    "    fragment = NewFragment(data_entity=exchange_msg, code_template=template, outputs=outputs_lists,\n",
-    "                           outputs_mapping=outputs_mapping)\n",
+    "    fragment = Fragment(data_entity=exchange_msg, code_template=template, outputs=outputs_lists,\n",
+    "                        outputs_mapping=outputs_mapping)\n",
     "\n",
     "    code = fragment()\n",
     "    init_code = code[0]\n",