PyTorch implementation of HighResNet
Project description
HighRes3DNet
PyTorch implementation of HighRes3DNet from Li et al. 2017, On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task.
All the information about how the weights were ported from NiftyNet can be found in my submission to the MICCAI Educational Challenge 2019.
A 2D version (HighRes2DNet) is also available.
Installation
1. Install PyTorch
Within a conda environment:
$ conda create -n deepgif python -y
$ conda activate deepgif
(deepgif) $ conda install pytorch torchvision cudatoolkit=10.0 -c pytorch -y
2. Install the pip package
(deepgif) $ pip install highresnet
>>> from highresnet import HighRes3DNet
>>> model = HighRes3DNet(in_channels=1, out_channels=160)
Usage
PyTorch Hub
If you are using pytorch>=1.2.0, you can import the model
directly from this repository using PyTorch Hub.
>>> import torch
>>> repo = 'fepegar/highresnet'
>>> model_name = 'highres3dnet'
>>> print(torch.hub.help(repo, model_name))
"HighRes3DNet by Li et al. 2017 for T1-MRI brain parcellation"
"pretrained (bool): load parameters from pretrained model"
>>> model = torch.hub.load(repo, model_name, pretrained=True)
Command line interface
(deepgif) $ deepgif t1_mri.nii.gz parcellation.nii.gz
Using cache found in /home/fernando/.cache/torch/hub/fepegar_highresnet_master
100%|███████████████████████████████████████████| 36/36 [01:13<00:00, 2.05s/it]
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