Welcome to dlsia's documentation!

dlsia (Deep Learning for Scientific Image Analysis) provides easy access to a number of segmentation and denoising methods using convolution neural networks. The tools available are build for microscopy and synchrotron-imaging/scattering data in mind, but can be used elsewhere as well.

The easiest way to start playing with the code is to install dlsia and perform denoising/segmenting using custom neural networks in our tutorial notebooks located in the dlsia/tutorials folder.

Install dlsia

We offer several methods for installation.

pip: Python package installer

We are currently working on a stable release.

From source

dlsia may be directly downloaded and installed into your machine by cloning the public repository into an empty directory using:

$ git clone https://github.com/phzwart/dlsia.git

Once cloned, move to the newly minted dlsia directory and install dlsia using:

$ cd dlsia
$ pip install -e .

Further documentation & tutorial download

For more in-depth documentation and end-to-end training workflows, please visit our readthedocs page for more support. To download only the tutorials in a new folder, use the following terminal input for a sparse git checkout:

mkdir dlsiaTutorials
cd dlsiaTutorials
git init
git config core.sparseCheckout true
git remote add -f origin https://github.com/phzwart/dlsia.git
echo "dlsia/tutorials/*" > .git/info/sparse-checkout
git checkout main

Getting started

We start with some basic imports - we import a network and some training scripts:

from dlsia.core.networks import msdnet
from dlsia.core import train_scripts

Mixed-Scale dense networks (MSDNet)

A plain 2d mixed-scale dense network is constructed as follows:

from dlsia.core.networks import msdnet

msdnet_model = msdnet.MixedScaleDenseNetwork(in_channels=1,
                                             out_channels=1,
                                             num_layers=20,
                                             max_dilation=10)

while 3d network types for volumetric images can be built passing in equivalent kernels for 3 dimensions:

import torch
from torch import nn

msdnet3d_model = msdnet.MixedScaleDenseNetwork(in_channels=1,
                                               out_channels=1,
                                               num_layers=20,
                                               max_dilation=10,
                                               normalization=nn.BatchNorm3d,
                                               convolution=nn.Conv3d)

Note that each instance of a convolution operator is followed by ReLU activation and batch normalization. To turn these off, simply pass in the parameters

activation=None,
normalization=None

Sparse mixed-scale dense network (SMSNet)

The dlsia suite also provides ways and means to build random, sparse mixed scale networks. SMSNets contain more sparsely connected nodes than a standard MSDNet and are useful to alleviate overfitting and multi-network aggregation. Controlling sparsity is possible, see full documentation for more details.

from dlsia.core.networks import smsnet

smsnet_model = smsnet.random_SMS_network(in_channels=1,
                                         out_channels=1,
                                         layers=20,
                                         dilation_choices=[1, 2, 4, 8],
                                         hidden_out_channels=[1, 2, 3])

Tunable U-Nets

An alternative network choice is to construct a UNet. Classic U-Nets can easily explode in the number of parameters it requires; here we make it a bit easier to tune desired architecture-governing parameters:

from dlsia.core.networks import tunet

tunet_model = tunet.TUNet(image_shape=(64, 128),
                          in_channels=1,
                          out_channels=4,
                          base_channels=4,
                          depth=3,
                          growth_rate=1.5)

Training

Data preparation

To prep data for training, we make liberal use of PyTorch DataLoader classes. This allows for easy handling of data in the training process and automates the iterative loading of batch sizes.

In the example below, we take pair two numpy arrays of shape [num_images, num_channels, x_size, y_size] consisting of training images and masks, convert them into PyTorch tensors, then initialize the DataLoader class.

import torch
from torch.utils.data import TensorDataset, DataLoader

train_data = TensorDataset(torch.Tensor(training_imgs), 
                           torch.Tensor(training_masks))

train_loader_params = {'batch_size': 20,
                       'shuffle': True}

train_loader = DataLoader(train_data, **train_loader_params)

Training loop

Once your DataLoaders are constructed, the training of these networks is as simple as defining a torch.nn optimizer, and calling the training script:

from torch import optim, nn
from dlsia.core import helpers, train_scripts

criterion = nn.CrossEntropyLoss()   # For segmenting
optimizer = optim.Adam(tunet_model.parameters(), lr=1e-2)

device = helpers.get_device()
tunet_model = tunet_model.to(device)

tunet_model, results = train_scripts.train_segmentation(net=tunet_model,
                                                        trainloader=train_loader,
                                                        validationloader=test_loader,
                                                        NUM_EPOCHS=epochs, 
                                                        criterion=criterion,
                                                        optimizer=optimizer,
                                                        device=device,
                                                        show=1)

The output of the training scripts is the trained network and a dictionary with training losses and evaluation metrics. You can view them as follows:

from dlsia.viz_tools import plots

fig = plots.plot_training_results_segmentation(results)
fig.show()

Saving and loading models

Each dlsia network library contains submodules for saving trained networks and loading them from file. Using the conventional PyTorch .pt model file extension, the TUNet above may be saved with

savepath = 'this_tunet.pt'
tunet_model.save_network_parameters(savepath)

and reloaded for future use with

copy_of_tunet = tunet.TUNetwork_from_file(savepath)

License and Legal Stuff

This software has been developed from funds that originate from the US tax payer and is free for academics. Please have a look at the license agreement for more details. Commercial usage will require some extra steps. Please contact ipo@lbl.gov for more details.

Final Thoughts

This documentation is far from complete, but have some notebooks as part of the codebase, which could provide a good entry point.

More to come!