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mindspore-mindinsight/mindinsight/mindconverter
liuchongming 425917139e Fix exception definition is not consisted with the name in code. 5 years ago
..
common Add conversion success msg, and normalize error msg. 5 years ago
graph_based_converter !1054 Fix converter for UNet in pytorch. 5 years ago
mappings fix some mapping relationships and add tests to verify the relationship 5 years ago
ops a demo case for mapping parse and save 5 years ago
README.md Fix exception definition is not consisted with the name in code. 5 years ago
README_CN.md Fix exception definition is not consisted with the name in code. 5 years ago
__init__.py add notes for init files. 5 years ago
ast_edits.py Fix AST bugs. 5 years ago
cli.py Check model file format ahead. 5 years ago
code_analysis.py Fix CI issue that code is redundant. 5 years ago
config.py The conversion report is adjusted to make the the report more reasonable and accurate. 5 years ago
converter.py Modify generated report file format and fix error message when requirements are not installed. 5 years ago
forward_call.py parse scripts by AST in converter module. 5 years ago
funcs.py fix some mapping relationships and add tests to verify the relationship 5 years ago

README.md

MindConverter tutorial

查看中文

Overview

MindConverter is a migration tool to transform the model scripts from PyTorch or TensorFlow to MindSpore. Users can migrate their PyTorch or TensorFlow models to MindSpore rapidly with minor changes according to the conversion report.

Installation

MindConverter is a submodule in MindInsight. Please follow the Guide here to install MindInsight.

Usage

MindConverter currently only provides command-line interface. Here is the manual page.

usage: mindconverter [-h] [--version] [--in_file IN_FILE]
                     [--model_file MODEL_FILE] [--shape SHAPE]
                     [--input_nodes INPUT_NODES] [--output_nodes OUTPUT_NODES]
                     [--output OUTPUT] [--report REPORT]
                     [--project_path PROJECT_PATH]

optional arguments:
  -h, --help            show this help message and exit
  --version             show program version number and exit
  --in_file IN_FILE     Specify path for script file to use AST schema to do
                        script conversation.
  --model_file MODEL_FILE
                        PyTorch .pth or TensorFlow .pb model file path to use
                        graph based schema to do script generation. When
                        `--in_file` and `--model_file` are both provided, use
                        AST schema as default.
  --shape SHAPE         Optional, expected input tensor shape of
                        `--model_file`. It is required when use graph based
                        schema. Usage: --shape 1,3,244,244
  --input_nodes INPUT_NODES
                        Optional, input node(s) name of `--model_file`. It is
                        required when use TensorFlow model. Usage:
                        --input_nodes input_1:0,input_2:0
  --output_nodes OUTPUT_NODES
                        Optional, output node(s) name of `--model_file`. It is
                        required when use TensorFlow model. Usage:
                        --output_nodes output_1:0,output_2:0
  --output OUTPUT       Optional, specify path for converted script file
                        directory. Default output directory is `output` folder
                        in the current working directory.
  --report REPORT       Optional, specify report directory. Default is
                        converted script directory.
  --project_path PROJECT_PATH
                        Optional, PyTorch scripts project path. If PyTorch
                        project is not in PYTHONPATH, please assign
                        `--project_path` when use graph based schema. Usage:
                        --project_path ~/script_file/

PyTorch Model Scripts Migration

MindConverter Provides Two Modes for PyTorch：

  1. Abstract Syntax Tree (AST) based conversion: Use the argument --in_file will enable the AST mode.
  2. Computational Graph based conversion: Use --model_file and --shape arguments will enable the Graph mode.

The AST mode will be enabled, if both --in_file and --model_file are specified.

For the Graph mode, --shape is mandatory.

For the AST mode, --shape is ignored.

--output and --report is optional. MindConverter creates an output folder under the current working directory, and outputs generated scripts and conversion reports to it.

Please note that your original PyTorch project is included in the module search path (PYTHONPATH). Use the python interpreter and test your module can be successfully loaded by import command. Use --project_path instead if your project is not in the PYTHONPATH to ensure MindConverter can load it.

Assume the project is located at /home/user/project/model_training, users can use this command to add the project to PYTHONPATH : export PYTHONPATH=/home/user/project/model_training:$PYTHONPATH
MindConverter needs the original PyTorch scripts because of the reverse serialization.

TensorFlow Model Scripts Migration

MindConverter provides computational graph based conversion for TensorFlow: Transformation will be done given --model_file, --shape, --input_nodes and --output_nodes.

AST mode is not supported for TensorFlow, only computational graph based mode is available.

Scenario

MindConverter provides two modes for different migration demands.

  1. Keep original scripts' structures, including variables, functions, and libraries.
  2. Keep extra modifications as few as possible, or no modifications are required after conversion.

The AST mode is recommended for the first demand (AST mode is only supported for PyTorch). It parses and analyzes PyTorch scripts, then replace them with the MindSpore AST to generate codes. Theoretically, The AST mode supports any model script. However, the conversion may differ due to the coding style of original scripts.

For the second demand, the Graph mode is recommended. As the computational graph is a standard descriptive language, it is not affected by user's coding style. This mode may have more operators converted as long as these operators are supported by MindConverter.

Some typical image classification networks have been tested for the Graph mode. Note that:

  1. Currently, the Graph mode does not support models with multiple inputs. Only models with a single input and single output are supported.
  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 1.4.0(TorchVision 0.5.0) and TensorFlow 1.15.0, X86 Ubuntu released version):

Supported Model PyTorch Script TensorFlow Script Comment
ResNet18 Link /
ResNet34 Link /
ResNet50 Link Link
ResNet50V2 / Link
ResNet101 Link Link
ResNet101V2 / Link
ResNet152 Link Link
ResNet152V2 / Link
Wide ResNet50 2 Link /
Wide ResNet101 2 Link /
VGG11/11BN Link /
VGG13/13BN Link /
VGG16 Link Link
VGG16BN Link /
VGG19 Link Link
VGG19BN Link /
AlexNet Link /
GoogLeNet Link /
Xception / Link
InceptionV3 Link Link
InceptionResNetV2 / Link
MobileNetV1 / Link
MobileNetV2 Link Link
MNASNet Link /
SqueezeNet Link /
DenseNet121/169/201 Link Link
DenseNet161 Link /
NASNetMobile/Large / Link
EfficientNetB0~B7 Link TF1.5Link TF2.3Link
Unet Link Link 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

Example

AST-Based Conversion

Assume the PyTorch script is located at /home/user/model.py, and outputs the transformed MindSpore script to /home/user/output, with the conversion report to /home/user/output/report. Use the following command:

mindconverter --in_file /home/user/model.py \
              --output /home/user/output \
              --report /home/user/output/report

In the conversion report, non-transformed code is listed as follows:

line <row>:<col> [UnConvert] 'operator' didn't convert. ...

For non-transformed operators, the original code keeps. Please manually migrate them. Click here for more information about operator mapping.

Here is an example of the conversion report:

 [Start Convert]
 [Insert] 'import mindspore.ops.operations as P' is inserted to the converted file.
 line 1:0: [Convert] 'import torch' is converted to 'import mindspore'.
 ...
 line 157:23: [UnConvert] 'nn.AdaptiveAvgPool2d' didn't convert. Maybe could convert to mindspore.ops.operations.ReduceMean.
 ...
 [Convert Over]

For non-transformed operators, suggestions are provided in the report. For instance, MindConverter suggests that replace torch.nn.AdaptiveAvgPool2d with mindspore.ops.operations.ReduceMean.

Graph-Based Conversion

PyTorch Model Scripts Conversion

Assume the PyTorch model (.pth file) is located at /home/user/model.pth, with input shape (1, 3, 224, 224) and the original PyTorch script is at /home/user/project/model_training. Output the transformed MindSpore script to /home/user/output, with the conversion report to /home/user/output/report. Use the following command:

mindconverter --model_file /home/user/model.pth --shape 1,3,224,224 \
              --output /home/user/output \
              --report /home/user/output/report \
              --project_path /home/user/project/model_training

The Graph mode has the same conversion report as the AST mode. However, the line number and column number refer to the transformed scripts since no original scripts are used in the process.

In addition, input and output Tensor shape of unconverted operators shows explicitly (input_shape and output_shape) as comments in converted scripts to help further manual modifications. Here is an example of the Reshape operator (Not supported in current version):

class Classifier(nn.Cell):

    def __init__(self):
        super(Classifier, self).__init__()
        ...
        self.reshape = onnx.Reshape(input_shape=(1, 1280, 1, 1),
                                    output_shape=(1, 1280))
        ...

    def construct(self, x):
        ...
        # Suppose input of `reshape` is x.
        reshape_output = self.reshape(x)
        ...

It is convenient to replace the operators according to the input_shape and output_shape parameters. The replacement is like this:

from mindspore.ops import operations as P
...

class Classifier(nn.Cell):

    def __init__(self):
        super(Classifier, self).__init__()
        ...
        self.reshape = P.Reshape(input_shape=(1, 1280, 1, 1),
                                 output_shape=(1, 1280))
        ...

    def construct(self, x):
        ...
        # Suppose input of `reshape` is x.
        reshape_output = self.reshape(x, (1, 1280))
        ...

--output and --report are optional. MindConverter creates an output folder under the current working directory, and outputs generated scripts and conversion reports to it.

TensorFlow Model Scripts Conversion

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.

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:

mindconverter --model_file /home/user/xxx/frozen_model.pb --shape 1,224,224,3 \
              --input_nodes input_1:0 \
              --output_nodes predictions/Softmax:0 \
              --output /home/user/output \
              --report /home/user/output/report

After executed MindSpore script, and report file can be found in corresponding directory.

Since the graph based scheme is a generative method, the original TensorFlow 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 the example in PyTorch Model Scripts Conversion section.

Caution

  1. PyTorch, TensorFlow are not an explicitly stated dependency libraries in MindInsight. The Graph conversion requires the consistent PyTorch or TensorFlow version as the model is trained. (MindConverter recommends PyTorch 1.4.0)
  2. This script conversion tool relies on operators which supported by MindConverter and MindSpore. Unsupported operators may not be successfully mapped to MindSpore operators. You can manually edit, or implement the mapping based on MindConverter, and contribute to our MindInsight repository. We appreciate your support for the MindSpore community.
  3. MindConverter can only guarantee that the converted model scripts require a minor revision or no revision when the inputs' shape fed to the generated model script are equal to the value of --shape (The batch size dimension is not limited).

Unsupported situation of AST mode

Situation1

Classes and functions that can't be converted:

  1. The use of .shape, .ndim and .dtype member of torch.Tensor.
  2. torch.nn.AdaptiveXXXPoolXd and torch.nn.functional.adaptive_XXX_poolXd().
  3. torch.nn.functional.Dropout.
  4. torch.unsqueeze() and torch.Tensor.unsqueeze().
  5. torch.chunk() and torch.Tensor.chunk().

Situation2

Subclassing from the subclasses of nn.Module

e.g. (code snip from torchvision.models.mobilenet)

from torch import nn

class ConvBNReLU(nn.Sequential):
    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
        padding = (kernel_size - 1) // 2
        super(ConvBNReLU, self).__init__(
            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
            nn.BatchNorm2d(out_planes),
            nn.ReLU6(inplace=True)
        )

Requirements

For users using MindConverter, in addition to install the TensorFlow that can satisfy the model loading, inference and training requirements, users also need to pip install the following third party package (tf2onnx is not required for users that convert PyTorch model definition script to MindSpore):

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):

Answer: This problem is caused by TensorFlow. First step of conversion process is loading TensorFlow model into memory using TensorFlow module, and TensorFlow starts to apply for needed resource. When required resource is unavailable, such as exceeding max process number of Linux system limit, etc., TensorFlow will raise an error from its C/C++ layer. For more detail, please refer to TensorFlow official repository. There are some known issue for reference only:
TF ISSUE 14885, TF ISSUE 37449

Q2. Can MindConverter run on ARM platform?

Answer: MindConverter supports both x86 and ARM platform. Please ensure all required dependencies and environments installed in the ARM platform.

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:

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:

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)

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
ModelNotSupportError Fail to parse .pth/.pb file 1000001 Given --input_nodes, `--output_nodes 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
ModelLoadingError Fail to load the model 1000003 Maybe cause by the wrong --input_shape value
RuntimeIntegrityError Fail to locate required third party dependency 1000004 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 3000001~3000003
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
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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MindInsight为MindSpore提供了简单易用的调优调试能力。在训练过程中，可以将标量、张量、图像、计算图、模型超参、训练耗时等数据记录到文件中，通过MindInsight可视化页面进行查看及分析。

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