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Go to Progress and Next Steps

Integration of PyTorch and Slicer

Key Investigators

  • Fernando Pérez-García (University College London & King's College London, UK)
  • Andrés Díaz-Pinto (King's College London, UK)
  • Andras Lasso (Queen's University, Canada)
  • Curtis Lisle (KnowledgeVis, USA)
  • Rebecca Hisey (Queen's University, Canada)
  • Steve Pieper (Isomics)
  • Tamas Ungi (Queen's University, Canada)

Project Description

Investigate the potential issues faced by users who would like to use a trained deep learning model (e.g., a convolutional neural network) inside Slicer, using PyTorch.

Objective

Issues that will be addressed:

  1. How to install PyTorch within Slicer. The main question is whether to install a version with GPU support and, if it does, which version of the CUDA toolkit to install.
  2. How to handle the necessary conversion of Slicer nodes (e.g., vtkMRMLScalarVolumeNode) to PyTorch objects (e.g., torch.Tensor) and vice versa. Look into adding tools to slicer.util.
  3. Write a tutorial with a toy example using a publicly available dataset.

Approach and Plan

  1. Investigate issues related to CUDA versions and GPU drivers, and which installation method to use depending on the platform. Maybe, write a GUI to guide the user into choosing an appropriate installation type.
  2. Once PyTorch has been installed, look into the best ways to prepare slicer nodes for inference and visualize the results in Slicer.
  3. If necessary, write a tutorial (potentially a Jupyter Notebook using SlicerJupyter)

Progress and Next Steps

PyTorch in Slicer

Optimized installation using light-the-torch

  1. Fixed light-the-torch to detect the best PyTorch version from NVIDIA drivers (link to PR)
  2. Fixed PythonQt so light-the-torch can be used within Slicer (link to PR, to be updated in Slicer fork)

PyTorch extension

The PyTorch extension has been added to the Extensions Index.

Link to pull request – Link to code

PyTorch extension in Extensions Manager

PyTorchUtils module

Demo modules

The code for these modules can be found at SlicerParcellation.

Brain Resection Cavity Segmentation

Based on Pérez-García et al., 2021, A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections. More info at the resseg-ijcars repository.

Brain Resection Cavity Segmentation

Brain Parcellation

Based on Li et al., 2017, On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task. More info at the highresnet repository.

Brain Parcellation on 3D Slicer

Parcellation run by @pieper on a synthetic 1 mm isotropic T1 MPRAGE generated from a 6.5 mm anisotropic T2 (using model from Iglesias et al. 2021):

Parcellation on T2 and synthetic T1

Parcellation on T2

This is a parcellation run through the Imaging Data Commons framework, visualized online using OHIF:

Parcellation on OHIF

Illustrations

This is a diagram of the typical usage of Python within 3D Slicer.

Example of inference using PyTorch inside Slicer

Background and References

Post on Discourse

The first discussion about this project appeared on the Slicer forum (PW35) Projects List.

Discussion on GitHub

Some issues about installing PyTorch in Slicer were discussed in the pull request to add SlicerTorchIO to the Extensions Index.

light-the-torch

This seems to be a Python package designed to help installing PyTorch easily, auto-detecting the computation backend. Probably worth looking into it: light-the-torch.

The maintainer is Philip Meier, a very active contributor to torchvision.

Also related and worth investigating, from the same author, is pytorch-pip-shim.

Example of a naive pip installation

Tried on Linux, driver 430.50 (nvidia-smi --query-gpu=driver_version --format=csv).

>>> pip_install('torch')
Collecting torch
  Downloading torch-1.9.0-cp36-cp36m-manylinux1_x86_64.whl (831.4 MB)
Collecting dataclasses
  Downloading dataclasses-0.8-py3-none-any.whl (19 kB)
Requirement already satisfied: typing-extensions in ./opt/Slicer/Nightly/lib/Python/lib/python3.6/site-packages (from torch) (3.10.0.0)
Installing collected packages: dataclasses, torch
  WARNING: The scripts convert-caffe2-to-onnx and convert-onnx-to-caffe2 are installed in '/home/fernando/opt/Slicer/Nightly/lib/Python/bin' which is not on PATH.
  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
Successfully installed dataclasses-0.8 torch-1.9.0
WARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv
>>> import torch
>>> torch.cuda.is_available()
/home/fernando/opt/Slicer/Nightly/lib/Python/lib/python3.6/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 10010). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at  /pytorch/c10/cuda/CUDAFunctions.cpp:115.)
  return torch._C._cuda_getDeviceCount() > 0
False
>>> torch._C._cuda_getCompiledVersion()
10020
$ nvidia-smi
Tue Jun 22 17:12:44 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 430.50       Driver Version: 430.50       CUDA Version: 10.1     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 1060    Off  | 00000000:01:00.0  On |                  N/A |
| N/A   67C    P0    33W /  N/A |   1694MiB /  6078MiB |     12%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0      3928      G   /usr/lib/xorg/Xorg                           576MiB |
|    0      4129      G   /usr/bin/gnome-shell                         385MiB |
|    0      4615      G   ...AAAAAAAAAAAAAAgAAAAAAAAA --shared-files    46MiB |
|    0      5099      G   ...AAAAAAAAAAAACAAAAAAAAAA= --shared-files    49MiB |
|    0      6955      G   ...AAAAAAAAAAAIAAAAAAAAAA== --shared-files   366MiB |
|    0      8016      G   ...AAgAAAAAAAAACAAAAAAAAAA= --shared-files   102MiB |
|    0      8039      G   ...o/opt/Slicer/Nightly/bin/SlicerApp-real   112MiB |
|    0     22437      G   ...AAAAAAAAAAAIAAAAAAAAAA== --shared-files    30MiB |
+-----------------------------------------------------------------------------+
$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on Sat_Aug_25_21:08:01_CDT_2018
Cuda compilation tools, release 10.0, V10.0.130

Glossary

Abbreviation Meaning
GPU Graphics Processing Unit
CUDA Compute Unified Device Architecture
NVCC NVIDIA CUDA Compiler
NVIDIA-SMI NVIDIA System Management Interface