Some code of the implements of Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network

Last update: Nov 21, 2022

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Deep Learning 3D-GMPDCNN

Overview

3D-GMPDCNN

Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network

PyTorch implementation of "Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network"

Prerequisites

This codebase was developed and tested with pytorch 1.10.0 and CUDA 9.0 and Python 3.6.

main models

Deform_conv3D.py

This is the implementation of 3D deformable convolution in our network model

Pixel-Adaptive_Conv3D.py

This is the implementation of 3D Pixel-Adaptive convolution in our network model

linear.rar

This is the data of our experiments.

Some of the statements

As all of our code is kept secret for the time being, only the core part of the code will be shown. In addition, both modules are the reference of "Deformable Convolutional NetWorks" and "Pixel-Adaptive Convolutional Neural Networks". The whole code will be updated later.

References:

Parts of the code is based on Pixel-Adaptive Convolutional Neural Networks and Deformable Convolutional NetWorks

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Comments

Which format for „offset“ in Deform_conv3D.py in forward function?
def forward(self, x, offset): dtype = offset.data.type() ks = self.kernel_size N = offset.size(1) // 3

Hey,

I‘m trying to find a „deformable 3d convolutional layer“ for pytorch for 3d grayscale images (in my case medical ultrasound data).

I‘ve build an auto-encoder using https://github.com/kondratevakate/3d-deformable-convolutions in the encoder and decoder. Unfortunately it seems not to learn properly (compared to a typical network with non-deformable convolutional kernels it learns slowly).

Also tried this one: https://github.com/XinyiYing/D3Dnet Similar issue there.

Now I would like to try your implementation. I couldn‘t figure out how to format the „offset“-parameter of the forward function of the deformable layer.

You‘ve mentioned in the README that currently only your core part of the code is published. Is the deformable layer currently ready-to-use?

Would be glad to hear from you!

Best regards Moritz
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