mindspore-mindinsight/mindinsight/mindconverter

• MindConverter tutorial
• • Overview
  • Installation
  • Usage
  • • PyTorch Model Scripts Migration
    • TensorFlow Model Scripts Migration
  • Scenario
  • Example
  • • AST-Based Conversion
    • Graph-Based Conversion
    • • PyTorch Model Scripts Conversion
      • TensorFlow Model Scripts Conversion
  • Caution
  • Unsupported situation of AST mode
  • • Situation1
    • Situation2
  • Frequently asked questions

MindConverter tutorial

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• MindConverter tutorial
  • Overview
  • Installation
  • Usage
    • PyTorch Model Scripts Migration
    • TensorFlow Model Scripts Migration
  • Scenario
  • Example
    • AST-Based Conversion
    • Graph-Based Conversion
      • PyTorch Model Scripts Conversion
      • TensorFlow Model Scripts Conversion
  • Caution
  • Unsupported situation of AST mode
    • Situation1
    • Situation2
  • Frequently asked questions

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_node INPUT_NODE] [--output_node OUTPUT_NODE]
                     [--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_NODE
                        Optional, input node(s) name of `--model_file`. It is
                        required when use Tensorflow model. Usage:
                        --input_node input_1:0,input_2:0
  --output_nodes OUTPUT_NODE
                        Optional, output node(s) name of `--model_file`. It is
                        required when use Tensorflow model. Usage:
                        --output_node 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_node and --output_node.

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 such as ResNet and VGG 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:

Supported Model	PyTorch Script	TensorFlow Script
ResNet18	link	/
ResNet34	link	/
ResNet50	link	link
ResNet101	link	link
ResNet152	link	link
VGG11/11BN	link	/
VGG13/13BN	link	/
VGG16/16BN	link	link
VGG19/19BN	link	link
AlexNet	link	/
GoogLeNet	link	/
MobileNetV2	link	link
InceptionV3	/	link

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, 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:

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:

mindconverter --model_file /home/user/xxx/frozen_model.pb --shape 1,224,224,3 \
              --input_node input_1:0 \
              --output_node 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.

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_ shape, output_ For example, please refer to PyTorch Model Scripts Conversion.

Caution

1. PyTorch, TensorFlow, TF2ONNX(1.7.1) 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 or 1.6.0)
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:

• The use of shape, ndim and dtype member of torch.Tensor.
• torch.nn.AdaptiveXXXPoolXd and torch.nn.functional.adaptive_XXX_poolXd()
• torch.nn.functional.Dropout
• torch.unsqueeze() and torch.Tensor.unsqueeze()
• 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)
        )

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