cellshape-cloud 0.0.22

3D cell shape analysis using geometric deep learning on point clouds

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Cellshape-cloud is an easy-to-use tool to analyse the shapes of cells using deep learning and, in particular, graph-neural networks. The tool provides the ability to train popular graph-based autoencoders on point cloud data of 2D and 3D single cell masks as well as providing pre-trained networks for inference.

To install

pip install cellshape-cloud

Usage

Basic Usage

import torch
from cellshape_cloud import CloudAutoEncoder

model = CloudAutoEncoder(num_features=128, 
                         k=20,
                         encoder_type="dgcnn",
                         decoder_type="foldingnet")

points = torch.randn(1, 2048, 3)

recon, features = model(points)

To train an autoencoder on a set of point clouds created using cellshape-helper:

import torch
from torch.utils.data import DataLoader

import cellshape_cloud as cloud
from cellshape_cloud.vendor.chamfer_distance import ChamferLoss


input_dir = "path/to/pointcloud/files/"
batch_size = 16
learning_rate = 0.0001
num_epochs = 1
output_dir = "path/to/save/output/"

model = cloud.CloudAutoEncoder(num_features=128, 
                         k=20,
                         encoder_type="dgcnn",
                         decoder_type="foldingnet")

dataset = cloud.PointCloudDataset(input_dir)

dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

criterion = ChamferLoss()

optimizer = torch.optim.Adam(
    model.parameters(),
    lr=learning_rate * 16 / batch_size,
    betas=(0.9, 0.999),
    weight_decay=1e-6,
)

cloud.train(model, dataloader, num_epochs, criterion, optimizer, output_dir)

Parameters

• num_features: int.
  The size of the latent space of the autoencoder.
• k: int.
  The number of neightbours to use in the k-nearest-neighbours graph construction.
• encoder_type: str.
  The type of encoder: 'foldingnet' or 'dgcnn'
• decoder_type: str.
  The type of decoder: 'foldingnet' or 'dgcnn'

For developers

• Fork the repository
• Clone your fork

git clone https://github.com/USERNAME/cellshape-cloud 

• Install an editable version (-e) with the development requirements (dev)

cd cellshape-cloud
pip install -e .[dev] 

• To install pre-commit hooks to ensure formatting is correct:

pre-commit install

• To release a new version:

Firstly, update the version with bump2version (bump2version patch, bump2version minor or bump2version major). This will increment the package version (to a release candidate - e.g. 0.0.1rc0) and tag the commit. Push this tag to GitHub to run the deployment workflow:

git push --follow-tags

Once the release candidate has been tested, the release version can be created with:

bump2version release

References

[1] An Tao, 'Unsupervised Point Cloud Reconstruction for Classific Feature Learning', GitHub Repo, 2020