Update MindConverter readme docs and add error codes into readme.

mindinsight/mindconverter/README.md

@@ -20,7 +20,10 @@
     - [Unsupported situation of AST mode](#unsupported-situation-of-ast-mode)
         - [Situation1](#situation1)
         - [Situation2](#situation2)
+    - [Requirements](#requirements)
     - [Frequently asked questions](#frequently-asked-questions)
+    - [Appendix](#appendix)
+        - [Tensorflow Pb Model Exporting](#tensorflow-pb-model-exporting)
 
 <!-- /TOC -->
 
@@ -120,23 +123,41 @@ Some typical image classification networks such as ResNet and VGG have been test
 > 2. The Dropout operator will be lost after conversion because the inference mode is used to load the PyTorch or TensorFlow model. Manually re-implement is necessary.
 > 3. The Graph-based mode will be continuously developed and optimized with further updates.
 
-Supported models list (Models in below table have been tested based on PyTorch 1.14.0 and TensorFlow 1.15.0, X86 Ubuntu released version):
-
-|  Supported Model | PyTorch Script | TensorFlow Script |
-| :----: | :----: | :----: |
-| ResNet18 | [link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | / |
-| ResNet34 | [link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | / |
-| ResNet50 | [link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | [link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet.py) |
-| ResNet101 | [link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | [link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet.py) |
-| ResNet152 | [link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | [link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet.py) |
-| VGG11/11BN | [link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | / |
-| VGG13/13BN | [link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | / |
-| VGG16/16BN | [link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | [link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/vgg16.py) |
-| VGG19/19BN | [link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | [link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/vgg19.py) |
-| AlexNet | [link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/alexnet.py) | / |
-| GoogLeNet | [link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/googlenet.py) | / |
-| MobileNetV2 | [link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/mobilenet.py) | [link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/mobilenet_v2.py) |
-| InceptionV3 | / | [link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/inception_v3.py) |
+Supported models list (Models in below table have been tested based on PyTorch 1.4.0(TorchVision 0.5.0) and TensorFlow 1.15.0, X86 Ubuntu released version):
+
+|  Supported Model | PyTorch Script | TensorFlow Script | Comment |
+| :----: | :----: | :----: | :----: |
+| ResNet18 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | / |  |
+| ResNet34 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | / |  |
+| ResNet50 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet.py) |  |
+| ResNet50V2 | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) |  |
+| ResNet101 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet.py) |  |
+| ResNet101V2 | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) |  |
+| ResNet152 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet.py) |  |
+| ResNet152V2 | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) |  |
+| ResNeXt50 32x4d | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | / | |
+| ResNeXt101 32x8d | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | / | |
+| Wide ResNet50 2 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | / | |
+| Wide ResNet101 2 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | / | |
+| VGG11/11BN | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | / |  |
+| VGG13/13BN | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | / |  |
+| VGG16 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/vgg16.py) |  |
+| VGG16BN | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | / |  |
+| VGG19 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/vgg19.py) |  |
+| VGG19BN | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | / |  |
+| AlexNet | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/alexnet.py) | / |  |
+| GoogLeNet | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/googlenet.py) | / |  |
+| Xception | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/xception.py) |  |
+| InceptionV3 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/inception.py) | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/inception_v3.py) |  |
+| InceptionResNetV2 | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/inception_resnet_v2.py) |  |
+| MobileNetV1 | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/mobilenet.py) |  |
+| MobileNetV2 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/mobilenet.py) | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/mobilenet_v2.py) |  |
+| MNASNet | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/mnasnet.py) | / | |
+| SqueezeNet | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/squeezenet.py) | / | |
+| DenseNet121/169/201 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/densenet.py) | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/densenet.py) |  |
+| DenseNet161 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/densenet.py) | / | |
+| NASNetMobile/Large | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/nasnet.py) |  |
+| EfficientNetB0~B7 | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/efficientnet.py) |  |
 
 ## Example
 
@@ -234,36 +255,9 @@ class Classifier(nn.Cell):
 
 #### TensorFlow Model Scripts Conversion
 
-To use TensorFlow model script migration, you need to export TensorFlow model to Pb format first, and obtain the model input node and output node name. You can refer to the following methods to export and obtain the node name:
+To use TensorFlow model script migration, users need to export TensorFlow model to Pb format first, and obtain the model input node and output node name. For exporting pb model, please refer to [TensorFlow Pb model exporting](#tensorflow-pb-model-exporting).
 
-```python
-import tensorflow as tf
-from tensorflow.python.framework import graph_io
-from tensorflow.keras.applications.inception_v3 import InceptionV3
-
-
-def freeze_graph(graph, session, output):
-    saved_path = "/home/user/xxx"
-    with graph.as_default():
-        graphdef_inf = tf.graph_util.remove_training_nodes(graph.as_graph_def())
-        graphdef_frozen = tf.graph_util.convert_variables_to_constants(session, graphdef_inf, output)
-        graph_io.write_graph(graphdef_frozen, saved_path, "frozen_model.pb", as_text=False)
-
-tf.keras.backend.set_learning_phase(0) # this line most important
-
-base_model = InceptionV3()
-session = tf.keras.backend.get_session()
-
-INPUT_NODE = base_model.inputs[0].op.name  # Get input node name of TensorFlow.
-OUTPUT_NODE = base_model.outputs[0].op.name  # Get output node name of TensorFlow.
-freeze_graph(session.graph, session, [out.op.name for out in base_model.outputs])
-print(f"Input node name: {INPUT_NODE}, output node name: {OUTPUT_NODE}")
-
-```
-
-After the above code is executed, the model will be saved to `/home/user/xxx/frozen_model.pb`. `INPUT_NODE` can be passed into `--input_nodes`, and `OUTPUT_NODE` is the corresponding `--output_nodes`.
-
-Suppose the input node name is `input_1:0`, output node name is `predictions/Softmax:0`, the input shape of model is `1,224,224,3`, the following command can be used to generate the script:
+Suppose the model is saved to `/home/user/xxx/frozen_model.pb`, corresponding input node name is `input_1:0`, output node name is `predictions/Softmax:0`, the input shape of model is `1,224,224,3`, the following command can be used to generate the script:
 
 ```bash
 mindconverter --model_file /home/user/xxx/frozen_model.pb --shape 1,224,224,3 \
@@ -277,7 +271,7 @@ After executed MindSpore script, and report file can be found in corresponding d
 
 The format of conversion report generated by script generation scheme based on graph structure is the same as that of AST scheme. However, since the graph based scheme is a generative method, the original pytorch script is not referenced in the conversion process. Therefore, the code line and column numbers involved in the generated conversion report refer to the generated script.
 
-In addition, for operators that are not converted successfully, the input and output shape of tensor of the node will be identified in the code by `input_shape` and `output_shape`. For example, please refer to [PyTorch Model Scripts Conversion](#manual_modify).
+In addition, for operators that are not converted successfully, the input and output shape of tensor of the node will be identified in the code by `input_shape` and `output_shape`. For example, please refer to the example in **PyTorch Model Scripts Conversion** section.
 
 ## Caution
 
@@ -316,6 +310,18 @@ class ConvBNReLU(nn.Sequential):
         )
 ```
 
+## Requirements
+
+For users converting PyTorch model script to MindSpore, there is no need to install other third party package.
+
+For users converting TensorFlow model script to MindSpore, in addition to install the TensorFlow can satisfy the pb model loading, inference and training, users also need to pip install the following third party package:
+
+```text
+onnx>=1.8.0
+tf2onnx>=1.7.1
+onnxruntime>=1.5.2
+```
+
 ## Frequently asked questions
 
 Q1. `terminate called after throwing an instance of 'std::system_error', what(): Resource temporarily unavailable, Aborted (core dumped)`:
@@ -324,3 +330,81 @@ Q1. `terminate called after throwing an instance of 'std::system_error', what():
 
 Q2. Can MindConverter run on ARM platform?
 > Answer: MindConverter usability on X86 Ubuntu machine has been verified, yet, on ARM has not.
+
+Q3. Why did I get message of `Error detail: [NodeInputMissing] ...` when converting PyTorch model?
+> Answer: For PyTorch model, if operations in `torch.nn.functional.xxx`, `torch.xxx`, `torch.Tensor.xxx` were used, node parsing could be failed. It's better to replace those operations with `torch.nn.xxx`.
+
+## Appendix
+
+### TensorFlow Pb model exporting
+
+If build model with Keras API, user can try the following methods.
+
+For TensorFlow 1.15.x version:
+
+```python
+import tensorflow as tf
+from tensorflow.python.framework import graph_io
+from tensorflow.python.keras.applications.inception_v3 import InceptionV3
+
+def freeze_graph(graph, session, output_nodes, output_folder: str):
+    """
+    Freeze graph for tf 1.x.x.
+
+    Args:
+        graph (tf.Graph): Graph instance.
+        session (tf.Session): Session instance.
+        output_nodes (list): Output nodes name.
+        output_folder (str): Output folder path for frozen model.
+
+    """
+    with graph.as_default():
+        graphdef_inf = tf.graph_util.remove_training_nodes(graph.as_graph_def())
+        graphdef_frozen = tf.graph_util.convert_variables_to_constants(session, graphdef_inf, output_nodes)
+        graph_io.write_graph(graphdef_frozen, output_folder, "frozen_model.pb", as_text=False)
+
+tf.keras.backend.set_learning_phase(0)
+
+keras_model = InceptionV3()
+session = tf.keras.backend.get_session()
+
+INPUT_NODES = [ipt.op.name for ipt in keras_model.inputs]
+OUTPUT_NODES = [opt.op.name for opt in keras_model.outputs]
+freeze_graph(session.graph, session, OUTPUT_NODES, "/home/user/xxx")
+print(f"Input nodes name: {INPUT_NODES}, output nodes name: {OUTPUT_NODES}")
+```
+
+For TensorFlow 2.x.x version:
+
+```python
+import tensorflow as tf
+from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
+
+
+def convert_to_froze_graph(keras_model: tf.python.keras.models.Model, model_name: str,
+                           output_folder: str):
+    """
+    Export keras model to frozen model.
+
+    Args:
+        keras_model (tensorflow.python.keras.models.Model):
+        model_name (str): Model name for the file name.
+        output_folder (str): Output folder for saving model.
+
+    """
+    full_model = tf.function(lambda x: keras_model(x))
+    full_model = full_model.get_concrete_function(
+        tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
+    )
+
+    frozen_func = convert_variables_to_constants_v2(full_model)
+    frozen_func.graph.as_graph_def()
+
+    print(f"Model inputs: {frozen_func.inputs}")
+    print(f"Model outputs: {frozen_func.outputs}")
+
+    tf.io.write_graph(graph_or_graph_def=frozen_func.graph,
+                      logdir=output_folder,
+                      name=model_name,
+                      as_text=False)
+```

mindinsight/mindconverter/README_CN.md

@@ -20,7 +20,10 @@
     - [AST方案不支持场景](#ast方案不支持场景)
         - [场景1](#场景1)
         - [场景2](#场景2)
+    - [三方库依赖](#三方库依赖)
     - [常见问题](#常见问题)
+    - [附录](#附录)
+        - [TensorFlow Pb模型导出](#tensorflow-pb模型导出)
 
 <!-- /TOC -->
 
@@ -118,23 +121,41 @@ MindConverter提供两种技术方案，以应对不同脚本迁移场景：
 > 2. 基于图结构的脚本生成方案，由于要加载PyTorch、TensorFlow模型，会导致转换后网络中Dropout算子丢失，需要用户手动补齐；
 > 3. 基于图结构的脚本生成方案持续优化中。
 
-支持的模型列表（如下模型已基于x86 Ubuntu发行版，PyTorch 1.4.0以及TensorFlow 1.15.0测试通过）:
-
-|  模型  | PyTorch脚本 | TensorFlow脚本 |
-| :----: | :----: | :----: |
-| ResNet18 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | 暂未测试 |
-| ResNet34 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | 暂未测试 |
-| ResNet50 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet.py) |
-| ResNet101 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet.py) |
-| ResNet152 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet.py) |
-| VGG11/11BN | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | 暂未测试 |
-| VGG13/13BN | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | 暂未测试 |
-| VGG16/16BN | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/vgg16.py) |
-| VGG19/19BN | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/vgg19.py) |
-| AlexNet | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/alexnet.py) | 暂未测试 |
-| GoogLeNet | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/googlenet.py) | 暂未测试 |
-| MobileNetV2 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/mobilenet.py) | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/mobilenet_v2.py) |
-| InceptionV3 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/inception_v3.py) |
+支持的模型列表（如下模型已基于x86 Ubuntu发行版，PyTorch 1.4.0(TorchVision 0.5)以及TensorFlow 1.15.0测试通过）:
+
+|  模型  | PyTorch脚本 | TensorFlow脚本 | 备注 |
+| :----: | :----: | :----: | :----: |
+| ResNet18 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | 暂未测试 |  |
+| ResNet34 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | 暂未测试 |  |
+| ResNet50 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet.py) |  |
+| ResNet50V2 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) |  |
+| ResNet101 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet.py) |  |
+| ResNet101V2 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) |  |
+| ResNet152 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet.py) |  |
+| ResNet152V2 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) |  |
+| ResNeXt50 32x4d | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | 暂未测试 | |
+| ResNeXt101 32x8d | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | 暂未测试 | |
+| Wide ResNet50 2 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | 暂未测试 | |
+| Wide ResNet101 2 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | 暂未测试 | |
+| VGG11/11BN | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | 暂未测试 |  |
+| VGG13/13BN | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | 暂未测试 |  |
+| VGG16 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/vgg16.py) |  |
+| VGG16BN | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | 暂未测试 |  |
+| VGG19 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/vgg19.py) |  |
+| VGG19BN | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | 暂未测试 |  |
+| AlexNet | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/alexnet.py) | 暂未测试 |  |
+| GoogLeNet | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/googlenet.py) | 暂未测试 |  |
+| Xception | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/xception.py) |  |
+| InceptionV3 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/inception.py) | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/inception_v3.py) |  |
+| InceptionResNetV2 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/inception_resnet_v2.py) |  |
+| MobileNetV1 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/mobilenet.py) |  |
+| MobileNetV2 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/mobilenet.py) | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/mobilenet_v2.py) |  |
+| MNASNet | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/mnasnet.py) | 暂未测试 | |
+| SqueezeNet | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/squeezenet.py) | 暂未测试 | |
+| DenseNet121/169/201 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/densenet.py) | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/densenet.py) |  |
+| DenseNet161 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/densenet.py) | 暂未测试 | |
+| NASNetMobile/Large | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/nasnet.py) |  |
+| EfficientNetB0~B7 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/efficientnet.py) |  |
 
 ## 使用示例
 
@@ -232,32 +253,7 @@ class Classifier(nn.Cell):
 
 #### TensorFlow模型脚本生成示例
 
-使用TensorFlow模型脚本迁移，需要先将TensorFlow模型导出为pb格式，并且获取模型输入节点、输出节点名称，可参考如下方法进行导出、获取节点名称：
-
-```python
-import tensorflow as tf
-from tensorflow.python.framework import graph_io
-from tensorflow.keras.applications.inception_v3 import InceptionV3
-
-def freeze_graph(graph, session, output):
-    saved_path = "/home/user/xxx"
-    with graph.as_default():
-        graphdef_inf = tf.graph_util.remove_training_nodes(graph.as_graph_def())
-        graphdef_frozen = tf.graph_util.convert_variables_to_constants(session, graphdef_inf, output)
-        graph_io.write_graph(graphdef_frozen, saved_path, "frozen_model.pb", as_text=False)
-
-tf.keras.backend.set_learning_phase(0)
-
-base_model = InceptionV3()
-session = tf.keras.backend.get_session()
-
-INPUT_NODE = base_model.inputs[0].op.name  # Get input node name of TensorFlow.
-OUTPUT_NODE = base_model.outputs[0].op.name  # Get output node name of TensorFlow.
-freeze_graph(session.graph, session, [out.op.name for out in base_model.outputs])
-print(f"Input node name: {INPUT_NODE}, output node name: {OUTPUT_NODE}")
-```
-
-上述代码执行完毕，模型将会保存至`/home/user/xxx/frozen_model.pb`。其中，`INPUT_NODE`为输入节点名称，`OUTPUT_NODE`为输出节点名称。
+使用TensorFlow模型脚本迁移，需要先将TensorFlow模型导出为pb格式，并且获取模型输入节点、输出节点名称。TensorFlow pb模型导出可参考[TensorFlow Pb模型导出](#tensorflow-pb模型导出)。
 
 假设输入节点名称为`input_1:0`、输出节点名称为`predictions/Softmax:0`，模型输入样本尺寸为`1,224,224,3`，则可使用如下命令进行脚本生成：
 
@@ -312,6 +308,18 @@ class ConvBNReLU(nn.Sequential):
         )
 ```
 
+## 三方库依赖
+
+对于PyTorch模型脚本转MindSpore的用户，无需额外安装三方依赖库。
+
+用户在使用将TensorFlow模型脚本转为MindSpore时，下列三方库未在MindInsight依赖列表（requirements.txt）中声明。用户除安装可满足导出的Pb模型加载、训练、推理的TensorFlow版本外，还需要安装（pip install）如下依赖库：
+
+```text
+onnx>=1.8.0
+tf2onnx>=1.7.1
+onnxruntime>=1.5.2
+```
+
 ## 常见问题
 
 Q1. `terminate called after throwing an instance of 'std::system_error', what(): Resource temporarily unavailable, Aborted (core dumped)`:
@@ -319,4 +327,84 @@ Q1. `terminate called after throwing an instance of 'std::system_error', what():
 > 答: 该问题由TensorFlow导致。脚本转换时，需要通过TensorFlow库加载TensorFlow的模型文件，此时TensorFlow会申请相关资源进行初始化，若申请资源失败（可能由于系统进程数超过Linux最大进程数限制），TensorFlow C/C++层会出现Core Dumped问题。详细信息请参考TensorFlow官方ISSUE，如下ISSUE仅供参考：[TF ISSUE 14885](https://github.com/tensorflow/tensorflow/issues/14885), [TF ISSUE 37449](https://github.com/tensorflow/tensorflow/issues/37449)
 
 Q2. MindConverter是否可以在ARM平台运行？
-> 答：MindConverter已基于X86 Ubuntu发行版进行测试，ARM平台未进行测试。
+
+> 答：MindConverter已基于X86 Ubuntu发行版进行测试，ARM平台未进行测试。
+
+Q3. PyTorch模型转换时为什么提示`Error detail: [NodeInputMissing] ...`?
+
+> 答：对于PyTorch模型，若网络中存在`torch.nn.functional.xxx`, `torch.xxx`, `torch.Tensor.xxx`层算子，可能存在节点解析失败的情况，需要用户手动替换为torch.nn层算子。
+
+## 附录
+
+### TensorFlow Pb模型导出
+
+使用Keras构建模型的用户，可尝试如下方法进行导出。
+
+对于TensorFlow 1.15.x版本：
+
+```python
+import tensorflow as tf
+from tensorflow.python.framework import graph_io
+from tensorflow.python.keras.applications.inception_v3 import InceptionV3
+
+def freeze_graph(graph, session, output_nodes, output_folder: str):
+    """
+    Freeze graph for tf 1.x.x.
+
+    Args:
+        graph (tf.Graph): Graph instance.
+        session (tf.Session): Session instance.
+        output_nodes (list): Output nodes name.
+        output_folder (str): Output folder path for frozen model.
+
+    """
+    with graph.as_default():
+        graphdef_inf = tf.graph_util.remove_training_nodes(graph.as_graph_def())
+        graphdef_frozen = tf.graph_util.convert_variables_to_constants(session, graphdef_inf, output_nodes)
+        graph_io.write_graph(graphdef_frozen, output_folder, "frozen_model.pb", as_text=False)
+
+tf.keras.backend.set_learning_phase(0)
+
+keras_model = InceptionV3()
+session = tf.keras.backend.get_session()
+
+INPUT_NODES = [ipt.op.name for ipt in keras_model.inputs]
+OUTPUT_NODES = [opt.op.name for opt in keras_model.outputs]
+freeze_graph(session.graph, session, OUTPUT_NODES, "/home/user/xxx")
+print(f"Input nodes name: {INPUT_NODES}, output nodes name: {OUTPUT_NODES}")
+```
+
+对于TensorFlow 2.x.x版本：
+
+```python
+import tensorflow as tf
+from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
+
+
+def convert_to_froze_graph(keras_model: tf.python.keras.models.Model, model_name: str,
+                           output_folder: str):
+    """
+    Export keras model to frozen model.
+
+    Args:
+        keras_model (tensorflow.python.keras.models.Model):
+        model_name (str): Model name for the file name.
+        output_folder (str): Output folder for saving model.
+
+    """
+    full_model = tf.function(lambda x: keras_model(x))
+    full_model = full_model.get_concrete_function(
+        tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
+    )
+
+    frozen_func = convert_variables_to_constants_v2(full_model)
+    frozen_func.graph.as_graph_def()
+
+    print(f"Model inputs: {frozen_func.inputs}")
+    print(f"Model outputs: {frozen_func.outputs}")
+
+    tf.io.write_graph(graph_or_graph_def=frozen_func.graph,
+                      logdir=output_folder,
+                      name=model_name,
+                      as_text=False)
+```