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Update readme of mindconverter and move supported model list to single doc.
tags/v1.2.0-rc1
liuchongming
4 years ago
8 changed files with 322 additions and 288 deletions
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| @@ -136,40 +136,7 @@ Some typical image classification networks have been tested for the Graph mode. | |||||
| > 1. 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. | > 1. 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. | ||||
| > 2. The Graph-based mode will be continuously developed and optimized with further updates. | > 2. 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 1.5.0 and TensorFlow 1.15.0, X86 Ubuntu released version): | |||||
| | Supported Model | PyTorch Script | TensorFlow Script | Comment | PyTorch Weights Converted | TensorFlow Weights Converted | | |||||
| | :----: | :----: | :----: | :----: | :----: | :----: | | |||||
| | ResNet18 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | / | | TESTED | / | | |||||
| | ResNet34 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | / | | TESTED | / | | |||||
| | 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) | | TESTED | TESTED | | |||||
| | ResNet50V2 | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) | | / | TESTED | | |||||
| | 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) | | UNTESTED | TESTED | | |||||
| | ResNet101V2 | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) | | / | TESTED | | |||||
| | 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) | | TESTED | TESTED | | |||||
| | ResNet152V2 | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) | | / | TESTED | | |||||
| | Wide ResNet50 2 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | / | | TESTED | / | | |||||
| | Wide ResNet101 2 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | / | | TESTED | / | | |||||
| | VGG11/11BN | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | / | | TESTED | / | | |||||
| | VGG13/13BN | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | / | | TESTED | / | | |||||
| | 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) | | TESTED | TESTED | | |||||
| | VGG16BN | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | / | | TESTED | / | | |||||
| | 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) | | TESTED | TESTED | | |||||
| | VGG19BN | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | / | | TESTED | / | | |||||
| | AlexNet | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/alexnet.py) | / | | TESTED | / | | |||||
| | GoogLeNet | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/googlenet.py) | / | | TESTED | / | | |||||
| | Xception | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/xception.py) | | / | TESTED | | |||||
| | 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) | | TESTED | TESTED | | |||||
| | InceptionResNetV2 | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/inception_resnet_v2.py) | | / | TESTED | | |||||
| | MobileNetV1 | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/mobilenet.py) | | / | TESTED | | |||||
| | 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) | | TESTED | TESTED | | |||||
| | MNASNet | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/mnasnet.py) | / | | mnasnet0_5:TESTED mnasnet0_75:UNTESTED mnasnet1_0:TESTED mnasnet1_3:UNTESTED | / | | |||||
| | SqueezeNet | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/squeezenet.py) | / | | TESTED | / | | |||||
| | 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) | | TESTED | TESTED | | |||||
| | DenseNet161 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/densenet.py) | / | | TESTED | / | | |||||
| | NASNetMobile/Large | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/nasnet.py) | | / | TESTED | | |||||
| | EfficientNetB0~B7 | [Link](https://github.com/lukemelas/EfficientNet-PyTorch) | [TF1.15Link](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) [TF2.3Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/efficientnet.py) | | TESTED | TESTED(TF1.15) TESTED(TF2.3) | | |||||
| | Unet | [Link](https://github.com/milesial/Pytorch-UNet) | [Link](https://github.com/zhixuhao/unet) | Due to Operator `mindspore.ops.ResizeBilinear` is not implemented on GPU device for now, operator `mindspore.ops.ResizeBilinear` should be replaced by operator `mindspore.ops.ResizeNearestNeighbor`, while running in GPU device | TESTED | TESTED | | |||||
| [Supported models list (Models in below table have been tested based on PyTorch 1.5.0 and TensorFlow 1.15.0, X86 Ubuntu released version)](./docs/supported_model_list.md). | |||||
| ## Example | ## Example | ||||
| @@ -387,7 +354,7 @@ onnxruntime>=1.5.2 | |||||
| onnxoptimizer>=0.1.2 | onnxoptimizer>=0.1.2 | ||||
| ``` | ``` | ||||
| For some models, if the onnx or tf2onnx error message appears during the conversion process, please try to upgrade the onnx or tf2onnx in the environment to the latest version. | |||||
| For some models, if the onnx or tf2onnx error message appears during the conversion process, please try to upgrade the onnx, tf2onnx or onnxoptimizer in the environment to the latest version. | |||||
| ## Frequently asked questions | ## Frequently asked questions | ||||
| @@ -413,101 +380,8 @@ print(api_implementation.Type()) | |||||
| ### TensorFlow Pb model exporting | ### 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) | |||||
| ``` | |||||
| If build model with Keras API, user can refer to this [tutorial](./docs/tensorflow_model_exporting.md). | |||||
| ### MindConverter Error Code Definition | ### MindConverter Error Code Definition | ||||
| | Exception definition | Error description | Error code | Common causes | | |||||
| | :--------------------------: | :----------------------------------------------------: | :------- | ------------------------------------------------------------ | | |||||
| | MindConverterException | MindConverter base error | NAN | MindConverter base error | | |||||
| | BaseConverterError | Fail to convert because of unknown error | 0000000 | Unknown error occurred during runtime, please see the detail in MindInsight log file (default path is `~/mindinsight/log/mindconverter/`) | | |||||
| | UnKnownModelError | Fail to recognize model format | 0000001 | Generally, the given TensorFlow model or PyTorch model doesn't observe the standard | | |||||
| | ParamMissingError | Fail to get required conversion params | 0000002 | Mainly caused by missing `--shape`, `--input_nodes`, `--output_nodes` | | |||||
| | GraphInitFailError | Fail to trace the computational graph | 1000000 | Exception caused by 1000001~1000003 | | |||||
| | ModelLoadingError | Fail to load the model | 1000001 | Given `--input_nodes`, `--output_nodes`, `--shape` don't match the input model; Meanwhile, the model file can not be loaded also can cause this error. | | |||||
| | TfRuntimeError | Fail to initialize the TF runtime | 1000002 | Resources required by TensorFlow are not available | | |||||
| | RuntimeIntegrityError | Fail to locate required third party dependency | 1000003 | Caused by required third party packages are not installed | | |||||
| | TreeCreateFailError | Fail to create code hierarchical tree | 2000000 | Mainly caused by usage of `torch.nn.functional.xxx`, `torch.xxx`, `torch.Tensor.xxx` in PyTorch | | |||||
| | NodeInputMissingError | Fail to get the input node info | 2000001 | Fail to get input node info | | |||||
| | TreeNodeInsertError | Fail to insert tree node | 2000002 | Mainly caused by wrong scope name | | |||||
| | SourceFilesSaveError | Fail to generate or save converted script | 3000000 | Exception caused by 3000001~3000005 | | |||||
| | NodeInputTypeNotSupportError | Fail to recognize the input type of converted operator | 3000001 | Wrong input type set in mapper | | |||||
| | ScriptGenerationError | Fail to generate converted script | 3000002 | No left space on hard disk; Converted code is not legal; A file with the same name already exists in `--output` | | |||||
| | ReportGenerationError | Fail to generate converted script | 3000003 | No left space on hard disk; No available operator to be converted;A file with the same name already exists in `--report` | | |||||
| | CheckPointGenerationError | Fail to generate converted weight file | 3000004 | No left space on hard dist; A file with the same name already exists in `--output` | | |||||
| | WeightMapGenerationError | Fail to generate weight map file | 3000005 | No left space on hard dist; A file with the same name already exists in `--output` | | |||||
| | GeneratorError | Fail to generate code | 4000000 | Exception caused by 4000001~4000004 | | |||||
| | NodeLoadingError | Fail to load node information | 4000001 | Essential parameters are missing after conversion of a node | | |||||
| | NodeArgsTranslationError | Fail to translate the node's argument | 4000002 | Converted nodes have incorrect and conflicted information | | |||||
| | ModuleBuildError | Fail to build module instance | 4000003 | Converted nodes have incorrect and conflicted information with module | | |||||
| | CodeGenerationError | Fail to generate the code statement | 4000004 | Converted nodes have inconsistent information | | |||||
| | SubGraphSearchingError | Fail to find frequent sub-graph | 5000000 | Generally, caused by IR graph topological order error | | |||||
| Error code defined in MindConverter, please refer to [LINK](./docs/error_code_definition.md). | |||||
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| @@ -135,40 +135,7 @@ MindConverter提供两种技术方案，以应对不同脚本迁移场景： | |||||
| > 1. 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。 | > 1. 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。 | ||||
| > 2. 基于图结构的脚本生成方案持续优化中。 | > 2. 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 1.5.0以及TensorFlow 1.15.0测试通过）: | |||||
| | 模型 | PyTorch脚本 | TensorFlow脚本 | 备注 | PyTorch权重迁移 | TensorFlow权重迁移 | | |||||
| | :----: | :-----: | :----: | :----: | :----: | :----: | | |||||
| | ResNet18 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | 暂未测试 | | 已测试 | / | | |||||
| | ResNet34 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | 暂未测试 | | 已测试 | / | | |||||
| | 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) | | 已测试 | 已测试 | | |||||
| | ResNet50V2 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) | | / | 已测试 | | |||||
| | 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) | | 未测试 | 已测试 | | |||||
| | ResNet101V2 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) | | / | 已测试 | | |||||
| | 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) | | 已测试 | 已测试 | | |||||
| | ResNet152V2 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) | | / | 已测试 | | |||||
| | Wide ResNet50 2 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | 暂未测试 | | 已测试 | / | | |||||
| | Wide ResNet101 2 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | 暂未测试 | | 已测试 | / | | |||||
| | VGG11/11BN | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | 暂未测试 | | 已测试 | / | | |||||
| | VGG13/13BN | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | 暂未测试 | | 已测试 | / | | |||||
| | 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) | | 已测试 | 已测试 | | |||||
| | VGG16BN | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | 暂未测试 | | 已测试 | / | | |||||
| | 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) | | 已测试 | 已测试 | | |||||
| | VGG19BN | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | 暂未测试 | | 已测试 | / | | |||||
| | AlexNet | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/alexnet.py) | 暂未测试 | | 已测试 | / | | |||||
| | GoogLeNet | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/googlenet.py) | 暂未测试 | | 已测试 | / | | |||||
| | Xception | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/xception.py) | | / | 已测试 | | |||||
| | 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) | | 已测试 | 已测试 | | |||||
| | InceptionResNetV2 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/inception_resnet_v2.py) | | / | 已测试 | | |||||
| | MobileNetV1 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/mobilenet.py) | | / | 已测试 | | |||||
| | 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) | | 已测试 | 已测试 | | |||||
| | MNASNet | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/mnasnet.py) | 暂未测试 | | mnasnet0_5：已测试 mnasnet0_75：未测试 mnasnet1_0：已测试 mnasnet1_3：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 | | 已测试 | / | | |||||
| | 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) | | 已测试 | 已测试 | | |||||
| | DenseNet161 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/densenet.py) | 暂未测试 | | 已测试 | / | | |||||
| | NASNetMobile/Large | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/nasnet.py) | | / | 已测试 | | |||||
| | EfficientNetB0~B7 | [脚本链接](https://github.com/lukemelas/EfficientNet-PyTorch) | [TF1.15<br />脚本链接](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) <br />[TF2.3<br />脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/efficientnet.py) | | 已测试 | 已测试（TF1.15） 已测试（TF2.3）| | |||||
| | Unet | [脚本链接](https://github.com/milesial/Pytorch-UNet) | [脚本链接](https://github.com/zhixuhao/unet) | 由于算子`mindspore.ops.ResizeBilinear`在GPU上暂未实现，所以当运行在GPU设备上时，算子`mindspore.ops.ResizeBilinear`需要被替换为算子`mindspore.ops.ResizeNearestNeighbor` | 已测试 | 已测试 | | |||||
| [支持的模型列表（如下模型已基于x86 Ubuntu发行版，PyTorch 1.5.0以及TensorFlow 1.15.0测试通过）](./docs/supported_model_list_cn.md)。 | |||||
| ## 使用示例 | ## 使用示例 | ||||
| @@ -211,7 +178,8 @@ line x:y: [UnConvert] 'operator' didn't convert. ... | |||||
| #### PyTorch模型脚本生成示例 | #### PyTorch模型脚本生成示例 | ||||
| 若用户已将PyTorch模型保存为.pth格式，假设模型绝对路径为`/home/uer/model.pth`，该模型期望的输入shape为(1, 3, 224, 224) | 若用户已将PyTorch模型保存为.pth格式，假设模型绝对路径为`/home/uer/model.pth`，该模型期望的输入shape为(1, 3, 224, 224) | ||||
| ，原PyTorch脚本位于`/home/user/project/model_training`，希望将脚本、权重文件和权重映射表输出至`/home/user/output`，转换报告输出至`/home/user/output/report` | |||||
| ，原PyTorch脚本位于`/home/user/project/model_training`，希望将脚本、权重文件和权重映射表输出至`/home/user/output` | |||||
| ，转换报告输出至`/home/user/output/report` | |||||
| 。<br /> 则脚本生成命令为： | 。<br /> 则脚本生成命令为： | ||||
| ```bash | ```bash | ||||
| @@ -277,30 +245,30 @@ class Classifier(nn.Cell): | |||||
| ```json | ```json | ||||
| { | { | ||||
| "resnet50": [ | |||||
| { | |||||
| "converted_weight": { | |||||
| "name": "conv2d_0.weight", | |||||
| "shape": [ | |||||
| 64, | |||||
| 3, | |||||
| 7, | |||||
| 7 | |||||
| ], | |||||
| "data_type": "Float32" | |||||
| }, | |||||
| "source_weight": { | |||||
| "name": "conv1.weight", | |||||
| "shape": [ | |||||
| 64, | |||||
| 3, | |||||
| 7, | |||||
| 7 | |||||
| ], | |||||
| "data_type": "float32" | |||||
| } | |||||
| } | |||||
| ] | |||||
| "resnet50": [ | |||||
| { | |||||
| "converted_weight": { | |||||
| "name": "conv2d_0.weight", | |||||
| "shape": [ | |||||
| 64, | |||||
| 3, | |||||
| 7, | |||||
| 7 | |||||
| ], | |||||
| "data_type": "Float32" | |||||
| }, | |||||
| "source_weight": { | |||||
| "name": "conv1.weight", | |||||
| "shape": [ | |||||
| 64, | |||||
| 3, | |||||
| 7, | |||||
| 7 | |||||
| ], | |||||
| "data_type": "float32" | |||||
| } | |||||
| } | |||||
| ] | |||||
| } | } | ||||
| ``` | ``` | ||||
| @@ -349,7 +317,8 @@ mindconverter --model_file /home/user/xxx/model.onnx --shape 1,3,224,224 \ | |||||
| ## 注意事项 | ## 注意事项 | ||||
| 1. PyTorch、TensorFlow不作为MindInsight明确声明的依赖库。若想使用基于图结构的脚本生成工具，需要用户手动安装与生成PyTorch模型版本一致的PyTorch库（MindConverter使用PyTorch 1.5.0进行测试，不支持PyTorch 1.4.x; PyTorch 1.6.x、PyTorch 1.7.x未进行测试。），或TensorFlow。 | |||||
| 1. PyTorch、TensorFlow不作为MindInsight明确声明的依赖库。若想使用基于图结构的脚本生成工具，需要用户手动安装与生成PyTorch模型版本一致的PyTorch库（MindConverter使用PyTorch | |||||
| 1.5.0进行测试，不支持PyTorch 1.4.x; PyTorch 1.6.x、PyTorch 1.7.x未进行测试。），或TensorFlow。 | |||||
| 2. 脚本转换工具本质上为算子驱动，对于MindConverter未维护的PyTorch或ONNX算子与MindSpore算子映射，将会出现相应的算子无法转换的问题，对于该类算子，用户可手动修改，或基于MindConverter实现映射关系，向MindInsight仓库贡献。 | 2. 脚本转换工具本质上为算子驱动，对于MindConverter未维护的PyTorch或ONNX算子与MindSpore算子映射，将会出现相应的算子无法转换的问题，对于该类算子，用户可手动修改，或基于MindConverter实现映射关系，向MindInsight仓库贡献。 | ||||
| 3. 在使用基于计算图的迁移时，MindConverter会根据`--shape`参数将模型输入的批次大小（batch size）、句子长度（sequence length）、图片尺寸（image shape）等尺寸相关参数固定下来，用户需要保证基于MindSpore重训练、推理时输入shape与转换时一致；若需要调整输入尺寸，请重新指定`--shape`进行转换或修改转换后脚本中涉及张量尺寸变更操作相应的操作数。 | 3. 在使用基于计算图的迁移时，MindConverter会根据`--shape`参数将模型输入的批次大小（batch size）、句子长度（sequence length）、图片尺寸（image shape）等尺寸相关参数固定下来，用户需要保证基于MindSpore重训练、推理时输入shape与转换时一致；若需要调整输入尺寸，请重新指定`--shape`进行转换或修改转换后脚本中涉及张量尺寸变更操作相应的操作数。 | ||||
| 4. 脚本文件、权重文件和权重映射表输出于同一个目录下。 | 4. 脚本文件、权重文件和权重映射表输出于同一个目录下。 | ||||
| @@ -398,7 +367,7 @@ onnxruntime>=1.5.2 | |||||
| onnxoptimizer>=0.1.2 | onnxoptimizer>=0.1.2 | ||||
| ``` | ``` | ||||
| 对于个别模型，若在转换过程中出现onnx或tf2onnx错误信息，请尝试更新环境中onnx或tf2onnx至最新版本。 | |||||
| 对于个别模型，若在转换过程中出现onnx或tf2onnx错误信息，请尝试更新环境中onnx或tf2onnx或onnxoptimizer至最新版本。 | |||||
| ## 常见问题 | ## 常见问题 | ||||
| @@ -420,6 +389,7 @@ Q4. 为什么使用MindConverter进行模型转换需要很长时间（超过十 | |||||
| ```python | ```python | ||||
| from google.protobuf.internal import api_implementation | from google.protobuf.internal import api_implementation | ||||
| print(api_implementation.Type()) | print(api_implementation.Type()) | ||||
| ``` | ``` | ||||
| @@ -427,103 +397,8 @@ print(api_implementation.Type()) | |||||
| ### TensorFlow Pb模型导出 | ### 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) | |||||
| ``` | |||||
| 使用Keras构建模型的用户，可尝试[如下方法](./docs/tensorflow_model_exporting_cn.md)进行导出。 | |||||
| ### MindConverter错误码速查表 | ### MindConverter错误码速查表 | ||||
| | 异常声明 | 异常描述 | 异常代码 | 常见原因 | | |||||
| | :----------------------------: | :------: | :--------------- | ----------------------- | | |||||
| | MindConverterException | MindConverter异常基类 | NAN | MindConverter异常基类。 | | |||||
| | BaseConverterError | 未知错误引起的转换失败 | 0000000 | 程序运行中出现未知错误，请打开MindInsight log文件（默认位于`~/mindinsight/log/mindconverter/`目录下）查看具体错误原因。 | | |||||
| | UnKnownModelError | 识别网络模型对应的框架失败 | 0000001 | 通常为用户给定模型文件不符合TensorFlow或PyTorch标准。 | | |||||
| | ParamMissingError | 缺少转换所需参数 | 0000002 | 通常为`--shape`, `--input_nodes` , `--output_nodes`缺失导致 | | |||||
| | GraphInitFailError | 依据网络模型构建计算图失败 | 1000000 | 由1000001，1000002，1000003导致的计算图无法解析。 | | |||||
| | ModelLoadingError | 模型加载失败 | 1000001 | 给定的`--input_nodes`, `--output_nodes`, `--shape`与实际模型不符；<br />或模型文件存在问题导致模型无法加载。 | | |||||
| | TfRuntimeError | TensorFlow库执行出错 | 1000002 | TensorFlow启动申请所需资源失败导致无法正常启动，<br />请检查系统资源（进程数、内存、显存占用、CPU占用）是否充足。 | | |||||
| | RuntimeIntegrityError | 三方依赖库不完整 | 1000003 | MindConverter运行时所需的三方依赖库未安装。 | | |||||
| | TreeCreateFailError | 依据计算图构建模型树失败 | 2000000 | Tree用于生成最终代码结构，<br />通常由于PyTorch网络中存在`torch.nn.functional.xxx`, `torch.xxx`, `torch.Tensor.xxx`算子导致。 | | |||||
| | NodeInputMissingError | 网络节点输入信息丢失 | 2000001 | 节点的输入信息丢失。 | | |||||
| | TreeNodeInsertError | 树节点构建失败 | 2000002 | 由于scope name错误，无法找到该节点的父节点。 | | |||||
| | SourceFilesSaveError | 生成和保存转换后的脚本文件失败 | 3000000 | 由300000至3000005导致的脚本生成保存失败。 | | |||||
| | NodeInputTypeNotSupportError | 网络节点输入类型未知 | 3000001 | 映射关系中设置节点输入类型错误。 | | |||||
| | ScriptGenerationError | 转换脚本生成失败 | 3000002 | 空间不足；生成的脚本不符合PEP-8规范；`--output`目录下已有同名文件存在 | | |||||
| | ReportGenerationError | 转换报告生成失败 | 3000003 | 空间不足；脚本中没有需要转换的算子；`--report`目录下已有同名文件存在。 | | |||||
| | CheckPointGenerationError | 转换权重生成失败 | 3000004 | 空间不足；`--output`目录下已有同名文件存在 | | |||||
| | WeightMapGenerationError | 权重映射表生成失败 | 3000005 | 空间不足；`--output`目录下已有同名文件存在 | | |||||
| | GeneratorError | 代码生成失败 | 4000000 |由4000001至4000004引发的代码生成模块错误 | | |||||
| | NodeLoadingError | 节点读取失败 | 4000001 |转换后的节点缺少必要参数 | | |||||
| | NodeArgsTranslationError | 节点参数转换失败 | 4000002 |转换后的节点参数信息不正确 | | |||||
| | ModuleBuildError | 模块搭建失败 | 4000003 |转换后的节点信息不正确，与模块信息冲突，导致模块生成失败 | | |||||
| | CodeGenerationError | 代码生成失败 | 4000004 |转换后的节点信息前后矛盾，生成过程产生冲突 | | |||||
| | SubGraphSearchingError | 子图模式挖掘失败 | 5000000 | 通常由于模型生成对应的拓扑序错误导致。 | | |||||
| MindConverter错误码定义，请参考[链接](./docs/error_code_definition_cn.md)。 | |||||
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| @@ -0,0 +1,29 @@ | |||||
| # MindConverter Error Code Definition | |||||
| [查看中文](./error_code_definition_cn.md) | |||||
| | Exception definition | Error description | Error code | Common causes | | |||||
| | :--------------------------: | :----------------------------------------------------: | :------- | ------------------------------------------------------------ | | |||||
| | MindConverterException | MindConverter base error | NAN | MindConverter base error | | |||||
| | BaseConverterError | Fail to convert because of unknown error | 0000000 | Unknown error occurred during runtime, please see the detail in MindInsight log file (default path is `~/mindinsight/log/mindconverter/`) | | |||||
| | UnKnownModelError | Fail to recognize model format | 0000001 | Generally, the given TensorFlow model or PyTorch model doesn't observe the standard | | |||||
| | ParamMissingError | Fail to get required conversion params | 0000002 | Mainly caused by missing `--shape`, `--input_nodes`, `--output_nodes` | | |||||
| | GraphInitFailError | Fail to trace the computational graph | 1000000 | Exception caused by 1000001~1000003 | | |||||
| | ModelLoadingError | Fail to load the model | 1000001 | Given `--input_nodes`, `--output_nodes`, `--shape` don't match the input model; Meanwhile, the model file can not be loaded also can cause this error | | |||||
| | TfRuntimeError | Fail to initialize the TF runtime | 1000002 | Resources required by TensorFlow are not available | | |||||
| | RuntimeIntegrityError | Fail to locate required third party dependency | 1000003 | Caused by required third party packages are not installed | | |||||
| | TreeCreateFailError | Fail to create code hierarchical tree | 2000000 | Mainly caused by usage of `torch.nn.functional.xxx`, `torch.xxx`, `torch.Tensor.xxx` in PyTorch | | |||||
| | NodeInputMissingError | Fail to get the input node info | 2000001 | Fail to get input node info | | |||||
| | TreeNodeInsertError | Fail to insert tree node | 2000002 | Mainly caused by wrong scope name | | |||||
| | SourceFilesSaveError | Fail to generate or save converted script | 3000000 | Exception caused by 3000001~3000005 | | |||||
| | NodeInputTypeNotSupportError | Fail to recognize the input type of converted operator | 3000001 | Wrong input type set in mapper | | |||||
| | ScriptGenerationError | Fail to generate converted script | 3000002 | No left space on hard disk; Converted code is not legal; A file with the same name already exists in `--output` | | |||||
| | ReportGenerationError | Fail to generate converted script | 3000003 | No left space on hard disk; No available operator to be converted;A file with the same name already exists in `--report` | | |||||
| | CheckPointGenerationError | Fail to generate converted weight file | 3000004 | No left space on hard dist; A file with the same name already exists in `--output` | | |||||
| | WeightMapGenerationError | Fail to generate weight map file | 3000005 | No left space on hard dist; A file with the same name already exists in `--output` | | |||||
| | GeneratorError | Fail to generate code | 4000000 | Exception caused by 4000001~4000004 | | |||||
| | NodeLoadingError | Fail to load node information | 4000001 | Essential parameters are missing after conversion of a node | | |||||
| | NodeArgsTranslationError | Fail to translate the node's argument | 4000002 | Converted nodes have incorrect and conflicted information | | |||||
| | ModuleBuildError | Fail to build module instance | 4000003 | Converted nodes have incorrect and conflicted information with module | | |||||
| | CodeGenerationError | Fail to generate the code statement | 4000004 | Converted nodes have inconsistent information | | |||||
| | SubGraphSearchingError | Fail to find frequent sub-graph | 5000000 | Generally, caused by IR graph topological order error | | |||||
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| @@ -0,0 +1,29 @@ | |||||
| # MindConverter错误码速查表 | |||||
| [Switch to English version](./error_code_definition.md) | |||||
| | 异常声明 | 异常描述 | 异常代码 | 常见原因 | | |||||
| | :----------------------------: | :------: | :--------------- | ----------------------- | | |||||
| | MindConverterException | MindConverter异常基类 | NAN | MindConverter异常基类 | | |||||
| | BaseConverterError | 未知错误引起的转换失败 | 0000000 | 程序运行中出现未知错误，请打开MindInsight log文件（默认位于`~/mindinsight/log/mindconverter/`目录下）查看具体错误原因 | | |||||
| | UnKnownModelError | 识别网络模型对应的框架失败 | 0000001 | 通常为用户给定模型文件不符合TensorFlow或PyTorch标准 | | |||||
| | ParamMissingError | 缺少转换所需参数 | 0000002 | 通常为`--shape`, `--input_nodes` , `--output_nodes`缺失导致 | | |||||
| | GraphInitFailError | 依据网络模型构建计算图失败 | 1000000 | 由1000001，1000002，1000003导致的计算图无法解析 | | |||||
| | ModelLoadingError | 模型加载失败 | 1000001 | 给定的`--input_nodes`, `--output_nodes`, `--shape`与实际模型不符；<br />或模型文件存在问题导致模型无法加载 | | |||||
| | TfRuntimeError | TensorFlow库执行出错 | 1000002 | TensorFlow启动申请所需资源失败导致无法正常启动，<br />请检查系统资源（进程数、内存、显存占用、CPU占用）是否充足 | | |||||
| | RuntimeIntegrityError | 三方依赖库不完整 | 1000003 | MindConverter运行时所需的三方依赖库未安装 | | |||||
| | TreeCreateFailError | 依据计算图构建模型树失败 | 2000000 | Tree用于生成最终代码结构，<br />通常由于PyTorch网络中存在`torch.nn.functional.xxx`, `torch.xxx`, `torch.Tensor.xxx`算子导致 | | |||||
| | NodeInputMissingError | 网络节点输入信息丢失 | 2000001 | 节点的输入信息丢失 | | |||||
| | TreeNodeInsertError | 树节点构建失败 | 2000002 | 由于scope name错误，无法找到该节点的父节点 | | |||||
| | SourceFilesSaveError | 生成和保存转换后的脚本文件失败 | 3000000 | 由300000至3000005导致的脚本生成保存失败 | | |||||
| | NodeInputTypeNotSupportError | 网络节点输入类型未知 | 3000001 | 映射关系中设置节点输入类型错误 | | |||||
| | ScriptGenerationError | 转换脚本生成失败 | 3000002 | 空间不足；生成的脚本不符合PEP-8规范；`--output`目录下已有同名文件存在 | | |||||
| | ReportGenerationError | 转换报告生成失败 | 3000003 | 空间不足；脚本中没有需要转换的算子；`--report`目录下已有同名文件存在 | | |||||
| | CheckPointGenerationError | 转换权重生成失败 | 3000004 | 空间不足；`--output`目录下已有同名文件存在 | | |||||
| | WeightMapGenerationError | 权重映射表生成失败 | 3000005 | 空间不足；`--output`目录下已有同名文件存在 | | |||||
| | GeneratorError | 代码生成失败 | 4000000 |由4000001至4000004引发的代码生成模块错误 | | |||||
| | NodeLoadingError | 节点读取失败 | 4000001 |转换后的节点缺少必要参数 | | |||||
| | NodeArgsTranslationError | 节点参数转换失败 | 4000002 |转换后的节点参数信息不正确 | | |||||
| | ModuleBuildError | 模块搭建失败 | 4000003 |转换后的节点信息不正确，与模块信息冲突，导致模块生成失败 | | |||||
| | CodeGenerationError | 代码生成失败 | 4000004 |转换后的节点信息前后矛盾，生成过程产生冲突 | | |||||
| | SubGraphSearchingError | 子图模式挖掘失败 | 5000000 | 通常由于模型生成对应的拓扑序错误导致 | | |||||
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| # Verified model list of MindConverter | |||||
| [查看中文](./supported_model_list_cn.md) | |||||
| Supported models list (Models in below table have been tested based on PyTorch 1.5.0 and TensorFlow 1.15.0, X86 Ubuntu | |||||
| released version): | |||||
| | Supported Model | PyTorch Script | TensorFlow Script | Comment | PyTorch Weights Converted | TensorFlow Weights Converted | | |||||
| | :----: | :----: | :----: | :----: | :----: | :----: | | |||||
| | ResNet18 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | / | | TESTED | / | | |||||
| | ResNet34 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | / | | TESTED | / | | |||||
| | 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) | | TESTED | TESTED | | |||||
| | ResNet50V2 | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) | | / | TESTED | | |||||
| | 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) | | UNTESTED | TESTED | | |||||
| | ResNet101V2 | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) | | / | TESTED | | |||||
| | 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) | | TESTED | TESTED | | |||||
| | ResNet152V2 | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) | | / | TESTED | | |||||
| | Wide ResNet50 2 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | / | | TESTED | / | | |||||
| | Wide ResNet101 2 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | / | | TESTED | / | | |||||
| | VGG11/11BN | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | / | | TESTED | / | | |||||
| | VGG13/13BN | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | / | | TESTED | / | | |||||
| | 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) | | TESTED | TESTED | | |||||
| | VGG16BN | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | / | | TESTED | / | | |||||
| | 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) | | TESTED | TESTED | | |||||
| | VGG19BN | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | / | | TESTED | / | | |||||
| | AlexNet | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/alexnet.py) | / | | TESTED | / | | |||||
| | GoogLeNet | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/googlenet.py) | / | | TESTED | / | | |||||
| | Xception | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/xception.py) | | / | TESTED | | |||||
| | 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) | | TESTED | TESTED | | |||||
| | InceptionResNetV2 | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/inception_resnet_v2.py) | | / | TESTED | | |||||
| | MobileNetV1 | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/mobilenet.py) | | / | TESTED | | |||||
| | 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) | | TESTED | TESTED | | |||||
| | MNASNet | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/mnasnet.py) | / | | mnasnet0_5:TESTED mnasnet0_75:UNTESTED mnasnet1_0:TESTED mnasnet1_3:UNTESTED | / | | |||||
| | SqueezeNet | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/squeezenet.py) | / | | TESTED | / | | |||||
| | 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) | | TESTED | TESTED | | |||||
| | DenseNet161 | [Link](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/densenet.py) | / | | TESTED | / | | |||||
| | NASNetMobile/Large | / | [Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/nasnet.py) | | / | TESTED | | |||||
| | EfficientNetB0~B7 | [Link](https://github.com/lukemelas/EfficientNet-PyTorch) | [TF1.15Link](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) [TF2.3Link](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/efficientnet.py) | | TESTED | TESTED(TF1.15) TESTED(TF2.3) | | |||||
| | Unet | [Link](https://github.com/milesial/Pytorch-UNet) | [Link](https://github.com/zhixuhao/unet) | Due to Operator `mindspore.ops.ResizeBilinear` is not implemented on GPU device for now, operator `mindspore.ops.ResizeBilinear` should be replaced by operator `mindspore.ops.ResizeNearestNeighbor`, while running in GPU device | TESTED | TESTED | | |||||
| | Bert | [Link](https://huggingface.co/bert-base-uncased) | [Link](https://github.com/google-research/bert) | | TESTED | TESTED | | |||||
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| # MindConverter已验证的模型列表 | |||||
| [Switch to English version](./supported_model_list.md) | |||||
| 支持的模型列表（如下模型已基于x86 Ubuntu发行版，PyTorch 1.5.0以及TensorFlow 1.15.0测试通过）: | |||||
| | 模型 | PyTorch脚本 | TensorFlow脚本 | 备注 | PyTorch权重迁移 | TensorFlow权重迁移 | | |||||
| | :----: | :-----: | :----: | :----: | :----: | :----: | | |||||
| | ResNet18 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | 暂未测试 | | 已测试 | / | | |||||
| | ResNet34 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | 暂未测试 | | 已测试 | / | | |||||
| | 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) | | 已测试 | 已测试 | | |||||
| | ResNet50V2 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) | | / | 已测试 | | |||||
| | 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) | | 未测试 | 已测试 | | |||||
| | ResNet101V2 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) | | / | 已测试 | | |||||
| | 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) | | 已测试 | 已测试 | | |||||
| | ResNet152V2 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet_v2.py) | | / | 已测试 | | |||||
| | Wide ResNet50 2 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | 暂未测试 | | 已测试 | / | | |||||
| | Wide ResNet101 2 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/resnet.py) | 暂未测试 | | 已测试 | / | | |||||
| | VGG11/11BN | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | 暂未测试 | | 已测试 | / | | |||||
| | VGG13/13BN | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | 暂未测试 | | 已测试 | / | | |||||
| | 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) | | 已测试 | 已测试 | | |||||
| | VGG16BN | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | 暂未测试 | | 已测试 | / | | |||||
| | 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) | | 已测试 | 已测试 | | |||||
| | VGG19BN | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/vgg.py) | 暂未测试 | | 已测试 | / | | |||||
| | AlexNet | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/alexnet.py) | 暂未测试 | | 已测试 | / | | |||||
| | GoogLeNet | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/googlenet.py) | 暂未测试 | | 已测试 | / | | |||||
| | Xception | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/xception.py) | | / | 已测试 | | |||||
| | 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) | | 已测试 | 已测试 | | |||||
| | InceptionResNetV2 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/inception_resnet_v2.py) | | / | 已测试 | | |||||
| | MobileNetV1 | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/mobilenet.py) | | / | 已测试 | | |||||
| | 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) | | 已测试 | 已测试 | | |||||
| | MNASNet | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/mnasnet.py) | 暂未测试 | | mnasnet0_5：已测试 mnasnet0_75：未测试 mnasnet1_0：已测试 mnasnet1_3：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 | | 已测试 | / | | |||||
| | 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) | | 已测试 | 已测试 | | |||||
| | DenseNet161 | [脚本链接](https://github.com/pytorch/vision/blob/v0.5.0/torchvision/models/densenet.py) | 暂未测试 | | 已测试 | / | | |||||
| | NASNetMobile/Large | 暂未测试 | [脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/nasnet.py) | | / | 已测试 | | |||||
| | EfficientNetB0~B7 | [脚本链接](https://github.com/lukemelas/EfficientNet-PyTorch) | [TF1.15<br />脚本链接](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) <br />[TF2.3<br />脚本链接](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/efficientnet.py) | | 已测试 | 已测试（TF1.15） 已测试（TF2.3）| | |||||
| | Unet | [脚本链接](https://github.com/milesial/Pytorch-UNet) | [脚本链接](https://github.com/zhixuhao/unet) | 由于算子`mindspore.ops.ResizeBilinear`在GPU上暂未实现，所以当运行在GPU设备上时，算子`mindspore.ops.ResizeBilinear`需要被替换为算子`mindspore.ops.ResizeNearestNeighbor` | 已测试 | 已测试 | | |||||
| | Bert | [脚本链接](https://huggingface.co/bert-base-uncased) | [脚本链接](https://github.com/google-research/bert) | | 已测试 | 已测试 | | |||||
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| # TensorFlow model exporting | |||||
| [查看中文](./tensorflow_model_exporting_cn.md) | |||||
| 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 instance. | |||||
| 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) | |||||
| ``` | |||||
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| # TensorFlow 模型导出 | |||||
| [Switch to English version](./tensorflow_model_exporting.md) | |||||
| 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 instance. | |||||
| 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) | |||||
| ``` | |||||