ZarrDataset

A class for handling large-volume datasets stored in OME-NGFF Zarr format. This can be used primarly with PyTorch's DataLoader in machine learning training workflows.

Usage

import zarrdataset as zds

# Open a set of Zarr files stored locally or in a S3 bucket. Must specify the
# group/component were the arrays are stored within the zarr file, and the 
# order of the axes of the dataset.
my_dataset = zds.ZarrDataset(
  dict(
    modality="images",
    filenames=["https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.1/9836839.zarr"],
    source_axes="TCZYX",
    data_group="0"
  ),
)

Integration

The ZarrDataset class is derived from PyTorch's IterableDataset class, and can be used with a DataLoader object to generate batches of inputs for machine learning training workflows.

from torch.utils.data import DataLoader
import zarrdataset as zds

my_dataset = zds.ZarrDataset(...)

# Generate batches of 16 images, uisng four threads.
# Pass the worker initialization function from zarrdataset to the DataLoader
my_dataloader = DataLoader(my_dataset,
                           batch_size=16,
                           num_workers=4,
                           worker_init_fn=zds.zarrdataset_worker_init_fn)

for x, t in my_dataloader:
    # The training loop
    ...
    output = model(x)
    loss = criterion(output, t)
    ...

Multithread data loading

Use of multiple workers through multithread requires the use of the zarrdataset_worker_init_fn function provided in this package. This allows to load only a fraction of the dataset on each worker instead of the full dataset.

Patch sampling

ZarrDataset retrieve the whole array contained in data_group by default. To retrieve patches from that array instead, use any of the two samplers provided within this package, or implement a custom one derived from the PatchSampler class.

The two existing samplers are PatchSampler and BlueNoisePatchSampler. PatchSampler retrieves patches from an evenly distributed grid of non-overlapping squared patches of side patch_size. BlueNoisePatchSampler retrieves patches of side patch_size from random locations following blue-noise sampling. The patch sampler can be integated into a ZarrDataset object as follows.

import zarrdataset as zds

# Retrieve patches of size patch_size in an evenly spaced grid from the image.
my_patch_sampler = zds.PatchSampler(patch_size)

my_dataset = zds.ZarrDataset(...,
                             patch_sampler=my_patch_sampler)

Examples of integration of the ZarrDataset class with the PyTorch's DataLoader can be found in the documentation.

Installation

This package can be installed from PyPI with the following command

pip install zarrdataset