How to Convert a TensorFlow Model to PyTorch?

The landscape of deep learning is rapidly evolving. While TensorFlow and PyTorch stand as two of the most prominent frameworks, each boasts its unique advantages and ecosystems.

However, transitioning between these frameworks can be daunting, often requiring tedious reimplementation and adaptation of models. Fortunately, the Open Neural Network Exchange (ONNX) format emerges as a powerful intermediary, facilitating smooth conversions between TensorFlow and PyTorch models.

In this article, we will learn how can we use ONNX to convert TensorFlow model into a Pytorch model.

Why should you convert a TensorFlow model to PyTorch?

• Ecosystem Capability
  If the project primarily uses PyTorch, converting TensorFlow models allows for seamless integration into your existing codebase without the need for additional TensorFlow dependencies.
• Preferences for the Framework
  One framework may be preferred over another by teams or individuals for reasons like functionality, community support, or ease of usage. By converting a model, practitioners can preserve the labor and expertise put into a TensorFlow model while taking advantage of PyTorch’s capabilities.
• Flexibility
  PyTorch’s dynamic computation graph allows for more flexibility during model construction and debugging compared to TensorFlow’s static graph. This can make experimentation and model development more straightforward.
• Performance Optimization
  PyTorch provides a more intuitive interface for implementing custom layers and optimizations, potentially leading to improved performance or easier implementation of specific algorithms.
• Community and Resources
  The choice of framework depends on the project’s need. PyTorch community offer more resources, libraries and support for the specific use case compared to TensorFlow.
• Research and Development
  In some research or development scenarios, certain algorithms or models may be more readily available or easier to implement in PyTorch, motivating the conversion from TensorFlow.

What is ONNX?

ONNX, or Open Neural Network Exchange, is an open-source format for representing deep learning models. It aims to enable interoperability between different deep learning frameworks by providing a common standard for model representation. Developed collaboratively by Microsoft and Facebook in 2017, ONNX allows models trained in one framework to be seamlessly transferred and deployed in another framework.

ONNX defines a common, efficient runtime inference format that can be used across platforms and devices. This reduces the overhead associated with model deployment and inference, making it easier to deploy deep learning models in production environments.

ONNX supports a wide range of neural network operators and layer types, and it can be extended to support custom operators and domain-specific operations. This flexibility enables ONNX to accommodate a broad range of model architectures and applications.

Step-by-Step Procedure of Converting TensorFlow Model to PyTorch Model

Setting Up the Environment

Let’s make sure everything is configured properly in our environment before beginning the conversion procedure. Install the required packages by using:

!pip install tensorflow torch

Create a TensorFlow Model

Python3

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Load the Iris dataset
iris = load_iris()
X = iris.data
y = iris.target.reshape(-1, 1)  # Reshape to make it a column vector

# One-hot encode the target variable
encoder = OneHotEncoder(categories='auto')
y = encoder.fit_transform(y).toarray()

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 1: Define the model
model = Sequential([
    Dense(10, activation='relu', input_shape=(X_train.shape[1],)),
    Dense(8, activation='relu'),
    Dense(3, activation='softmax')
])
model.summary()

Output:

Model: "sequential_3"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense_8 (Dense)             (None, 10)                50        
                                                                 
 dense_9 (Dense)             (None, 8)                 88        
                                                                 
 dense_10 (Dense)            (None, 3)                 27        
                                                                 
=================================================================
Total params: 165 (660.00 Byte)
Trainable params: 165 (660.00 Byte)
Non-trainable params: 0 (0.00 Byte)

Train and Save the Model

Python3

#Compile the model
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

#Train the model
model.fit(X_train, y_train, epochs=100, batch_size=4, verbose=1)

#Evaluate the model
loss, accuracy = model.evaluate(X_test, y_test)
print(f'Test Loss: {loss:.4f}')
print(f'Test Accuracy: {accuracy:.4f}')

#Save the model
model.save('iris_model.h5')

Output:

Epoch 1/100
30/30 [==============================] - 2s 2ms/step - loss: 1.1517 - accuracy: 0.3417
Epoch 2/100
30/30 [==============================] - 0s 2ms/step - loss: 1.0865 - accuracy: 0.4000
Epoch 3/100
30/30 [==============================] - 0s 2ms/step - loss: 1.0580 - accuracy: 0.4833
Epoch 4/100
30/30 [==============================] - 0s 2ms/step - loss: 1.0397 - accuracy: 0.4500
Epoch 5/100
30/30 [==============================] - 0s 2ms/step - loss: 1.0172 - accuracy: 0.3917
..
Test Loss: 0.0591 
Test Accuracy: 1.0000

Load the trained TensorFlow model

Python3

loaded_model = tf.keras.models.load_model("iris_model.h5")

Converting to PyTorch Model

Installing the Required Libraries

In order to convert TensorFlow models to ONNX format, install the tf2onnx library:

!pip install tf2onnx
!pip install onnx2pytorch

Converting to tf2onnx Model

Python3

import tf2onnx

# Convert the model to ONNX format
onnx_model, _ = tf2onnx.convert.from_keras(loaded_model)

Converting to PyTorch Model

Python3

import onnx
from onnx2pytorch import ConvertModel

# Convert ONNX model to PyTorch
pytorch_model = ConvertModel(onnx_model)
pytorch_model

Output:

ConvertModel(
  (MatMul_sequential_2/dense_5/BiasAdd:0): Linear(in_features=4, out_features=10, bias=True)
  (Relu_sequential_2/dense_5/Relu:0): ReLU(inplace=True)
  (MatMul_sequential_2/dense_6/BiasAdd:0): Linear(in_features=10, out_features=8, bias=True)
  (Relu_sequential_2/dense_6/Relu:0): ReLU(inplace=True)
  (MatMul_sequential_2/dense_7/BiasAdd:0): Linear(in_features=8, out_features=3, bias=True)
  (Softmax_dense_7): Softmax(dim=-1)
)

Best Practices in Model Conversion

When converting models between deep learning frameworks like TensorFlow and PyTorch, adhering to best practices ensure smooth and accurate transitions. Here are some key best practices to follow:

1. Before beginning the conversion process, thoroughly understand the architecture of the model you intend to convert. This includes the types of layers, activation functions, and any custom components.
2. Make sure PyTorch and TensorFlow are both available in latest versions.
3. Verify each framework’s layer compatibility twice.
4. To ensure accuracy, test the converted model thoroughly on a variety of inputs and edge cases to ensure its robustness and correctness. Consider using automated testing frameworks or validation pipelines to streamline this process.

Some of The Common Errors

1. In case of shape discrepancies during the conversion process, verify the layer configurations and input shapes twice. Apply reshaping procedures or modify the layer’s settings as necessary.
2. There might not be exact counterparts for some operations in PyTorch. Determine these processes, then either create custom layers or look for other PyTorch routines.
3. TensorFlow and PyTorch may use different tensor data formats (NHWC vs. NCHW). As necessary, change the data formats to avoid runtime issues.

Conclusion

To use PyTorch’s dynamic computing graph and its ecosystem of libraries and tools, data scientists may find it helpful to convert their TensorFlow models to PyTorch models. The process of converting a Tensorflow model to a PyTorch model was covered in this blog post. These steps include exporting the Tensorflow model to a format that PyTorch can import, loading the exported model into PyTorch, converting the weights and structure of the model to PyTorch format, and saving the PyTorch model. Data scientists can quickly convert their Tensorflow models to PyTorch models and profit from PyTorch’s features by following these steps.