Article:
Exporting Deformable Convolution to ONNX in PyTorch: A Comprehensive Guide
Artificial intelligence has witnessed significant progress in the field of computer vision, with convolutional neural networks (CNN) playing a crucial role. Deformable convolution is one such innovation that has further improved the performance of CNNs in various computer vision tasks. However, exporting deformable convolutions to the Open Neural Network Exchange (ONNX) format in PyTorch has been a challenge. In this article, we will explore the deform_conv2d_onnx_exporter module, an efficient solution for exporting deformable convolutions to ONNX in PyTorch.
Understanding deform_conv2d_onnx_exporter
Deform_conv2d_onnx_exporter is a powerful module that enables you to export the deform_conv2d layer to ONNX in PyTorch. As of August 2021, PyTorch 1.9.0 and torchvision 0.10.0 do not support exporting deform_conv2d to ONNX natively. However, with the deform_conv2d_onnx_exporter module, you can seamlessly export deformable convolutions to ONNX.
Installation and Usage
To get started with deform_conv2d_onnx_exporter, you first need to install the module. You can simply run the following command in your terminal:
sh
$ pip install deform_conv2d_onnx_exporter
Once the module is installed, you can use the deform_conv2d_onnx_exporter module in your PyTorch code. Here’s an example of how to use it:
“`python
import torch.onnx
from torchvision.ops.deform_conv import DeformConv2d
import deform_conv2d_onnx_exporter
deform_conv2d_onnx_exporter.register_deform_conv2d_onnx_op()
model = DeformConv2d(…)
input_names = [“input”, “offset”]
output_names = [“output”]
input_params = (
torch.rand([1, x, x, x]), # input
torch.randn([1, x, x, x]), # offset
)
torch.onnx.export(model,
input_params,
“output.onnx”,
input_names=input_names,
output_names=output_names,
opset_version=12)
“`
Make sure to set the opset_version to 12 or later. This is required for the deform_conv2d_onnx_exporter module to work properly.
Performance Considerations
While the deform_conv2d_onnx_exporter module provides an efficient solution for exporting deformable convolutions to ONNX, it is important to note that the performance may not be as optimal as the native ONNX support for other convolutional layers. The current version of ONNX, version 15, does not natively support deformable convolutions. As a result, the deform_conv2d_onnx_exporter module implements deformable convolutions using GatherND and other operators. Despite the performance trade-off, the module has been carefully implemented to minimize unnecessary or duplicated calculations and achieve good enough performance.
Future Development and License
The deform_conv2d_onnx_exporter module is an open-source project released under the MIT License. It is actively maintained by Masamitsu MURASE and welcomes contributions from the community. As the ONNX ecosystem evolves and new versions are released, it is expected that native support for deformable convolutions will be introduced. Stay tuned for future updates and enhancements to the deform_conv2d_onnx_exporter module.
In conclusion, the deform_conv2d_onnx_exporter module is a valuable tool for exporting deformable convolutions to the ONNX format in PyTorch. It enables researchers and practitioners to leverage the power of deformable convolutions in a flexible and interoperable manner. With this module, you can seamlessly integrate deformable convolutions into your computer vision pipelines and unlock new possibilities in AI research and applications.
So why wait? Start exploring the deform_conv2d_onnx_exporter module today and take your computer vision projects to the next level!
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