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Release 1.5.2 (#164)
* chore: autopublish 2022-07-26T13:54:44Z * Remove create-badges job * Delete test.py * Add multi-head masked attention * Update multi-head gated attention to match parent layer * Update documentation * Test multi-head masked attention * allow gated attention layers to use bias * test bias in gated attention layers * set return_attention_weights to False to avoid multi-outputs Use MultiHeadSelfAttention and MultiHeadGatedSelfAttention if want to return the attention weights * reformat gnns/layers.py This commit adds new message-passing graph layers (MPN) and graph convolutional layers to dt, including vanilla MPN, GRUMPN, Masked-attention FGNN, and GraphTransformer. * Update layers.py * Update test_layers.py * Update models.py * Update test_models.py * Update test_models.py * Fix indexing problems related to tf.gather * Allow multi-inputs in ContinuousGenerator * Fix bad conversion to integer * version bump * Fix phase correction at focus and offset calculation * Fix phase correction in propagation * Fix mie phase out of foucs * Fix mie phase out of foucs * Update README.md * Bm/version 1.4.0 (#137) * Update layers.py * Update convolutional.py Transformer-based models can now be reused and expanded quickly and easily * Update documentation * Update Transformer-based models * Delete classifying_MNIST_vit_tutorial.ipynb * Create classifying_MNIST_vit_tutorial.ipynb * Update datasets.py * Allows kwargs as inputs in single_layer_call * Update embeddings.py * masked transformers * reformat transformer models * Create trajectory_analysis_tutorial.ipynb * Add Variational autoencoders * Add variational autoencoders * Update vae.py * Create MNIST_VAE_tutorial.ipynb * Update MNIST_VAE_tutorial.ipynb * Create folder for course examples * Update README.md * Update README.md * Update examples * Update README.md * Update README.md * Update MNIST VAE examples * Added MLP regression example * Update README.md * Create image_segmentation_Unet.ipynb * Update README.md * Documented and tested cell_counting_tutorial.ipynb * improve dnn example * Shift variant mie * Position mie scatterer correctly * implement set z * implement mnist v1 * implement z dependence * remove logging * Implement flattening methods * Implement pooling and resizing * Implement TensorflowDataset * Finalize MNIST * Implement Malaria classification * alpha0 release * fix batchsize in fit * implement dataset.take * Implement datasets * fix phase in mie * Fix mie positioning and focusing * Commit to new branch * add tensorflow datasets dependence * remove test Co-authored-by: Jesús Pineda <[email protected]> Co-authored-by: Jesús Pineda <[email protected]> Co-authored-by: Benjamin Midtvedt <[email protected]> Co-authored-by: Ccx55 <[email protected]> * Add tensorflow datasets to the list of dependencies. * Read requirements.txt into setup.py * remove sphinx from build * remove create badges * Create CITATION.cff * Create .zenodo.json * Update transformer models * Update pint_definition.py * Update requirements.txt * create TimeDistributed CNN * small fixes to lodestar * Update layers.py * Update test_layers.py * remove direct getter of properties * Update scatterers.py Coherence length fix for MieScatterer * Update scatterers.py Added coherence length to the conversion table * mie phase fix * removed pydeepimagej from deps * Change loss input order of CGAN and PCGAN * Create dmdataset (dataset for graph-level regression tasks) * Update gnns/__init__.py * Add detection_linking_hela dataset * Update dmdataset.py * Create the regression_diffusion_landscape * Update scatterers.py CuPy fix for coherence length * Update test_scatterers.py Added a new method for testing MieSphere when coherence length parameter is provided. * Update augmentations.py * Update test_scatterers.py * Update test_scatterers.py * Create endothelial_vs dataset * Update layers.py * Update utils.py * Update docs link * Update README.md * version bump * version bump * Update README.md Co-authored-by: BenjaminMidtvedt <[email protected]> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Jesús Pineda <[email protected]> Co-authored-by: Benjamin Midtvedt <[email protected]> Co-authored-by: Jesús Pineda <[email protected]> Co-authored-by: Ccx55 <[email protected]> Co-authored-by: Harshith Bachimanchi <[email protected]> Co-authored-by: gideon <[email protected]> Co-authored-by: Benjamin Midtvedt <[email protected]>
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‎README.md‎

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<p align="center">
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<img width="350" src=https://github.com/softmatterlab/DeepTrack-2.0/blob/develop/assets/logo.png?raw=true>
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<img width="350" src=https://github.com/softmatterlab/DeepTrack2/blob/develop/assets/logo.png?raw=true>
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</p>
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<h3 align="center">A comprehensive deep learning framework for digital microscopy.</h3>
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<p align="center">
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<a href="/LICENSE" alt="licence"><img src="https://img.shields.io/github/license/softmatterlab/DeepTrack-2.0"></a>
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<a href="https://badge.fury.io/py/deeptrack"><img src="https://badge.fury.io/py/deeptrack.svg" alt="PyPI version"></a>
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<a href="https://softmatterlab.github.io/DeepTrack-2.0/deeptrack.html"><img src="https://img.shields.io/badge/docs-passing-green" alt="PyPI version"></a>
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<a href="https://softmatterlab.github.io/DeepTrack2/deeptrack.html"><img src="https://img.shields.io/badge/docs-passing-green" alt="PyPI version"></a>
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<a href="https://badge.fury.io/py/deeptrack"><img src="https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-blue" alt="Python version"></a>
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<a href="https://doi.org/10.1063/5.0034891" alt="DOI">
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<img src="https://img.shields.io/badge/DOI-10.1063%2F5.0034891-blue">
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<br/>
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<a href="https://colab.research.google.com/github/softmatterlab/DeepTrack-2.0/blob/master/examples/paper-examples/2-single_particle_tracking.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg"> Training a CNN-based single particle tracker using simulated data </a>
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<br/>
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<a href="https://doi.org/10.48550/arXiv.2202.13546" alt="DOI lodestar">
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<img src="https://img.shields.io/badge/DOI-10.48550%2FarXiv.2202.13546-blue">
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<a href="https://doi.org/10.1038/s41467-022-35004-y" alt="DOI lodestar">
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<img src="https://img.shields.io/badge/DOI-10.1038%2Fs41467--022--35004--y-blue">
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</a>
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<a href="https://colab.research.google.com/github/softmatterlab/DeepTrack-2.0/blob/master/examples/LodeSTAR/02.%20tracking_particles_of_various_shapes.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg"> Unsupervised training of a single particle tracker using LodeSTAR </a>
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<p align="left">
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<img width="600" src=/assets/MPT-packed.gif?raw=true>
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<br/>
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<a href="https://doi.org/10.48550/arXiv.2202.13546" alt="DOI lodestar">
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<img src="https://img.shields.io/badge/DOI-10.48550%2FarXiv.2202.13546-blue">
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<a href="https://doi.org/10.1038/s41467-022-35004-y" alt="DOI lodestar">
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<img src="https://img.shields.io/badge/DOI-10.1038%2Fs41467--022--35004--y-blue">
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</a> <a href="https://colab.research.google.com/github/softmatterlab/DeepTrack-2.0/blob/master/examples/LodeSTAR/03.track_BF-C2DL-HSC.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg"> Training LodeSTAR to detect multiple cells from a single image </a>
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<a href="https://colab.research.google.com/github/softmatterlab/DeepTrack-2.0/blob/master/examples/paper-examples/4-multi-molecule-tracking.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg"> Training a UNet-based multi-particle tracker using simulated data </a>
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See also:
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<https://www.nature.com/articles/s41467-022-35004-y>:
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```
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Midtvedt, B., Pineda, J., Skärberg, F. et al.
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"Single-shot self-supervised object detection in microscopy."
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Nat Commun 13, 7492 (2022).
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```
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<https://arxiv.org/abs/2202.06355>:
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```
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Jesús Pineda, Benjamin Midtvedt, Harshith Bachimanchi, Sergio Noé, Daniel Midtvedt, Giovanni Volpe,1 and Carlo Manzo

‎deeptrack/datasets/__init__.py‎

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regression_holography_nanoparticles,
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segmentation_fluorescence_u2os,
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detection_holography_nanoparticles,
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detection_linking_hela,
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dmdataset,
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regression_diffusion_landscape,
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endothelial_vs,
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)

‎deeptrack/datasets/detection_linking_hela/__init__.py‎

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"""detection_linking_hela dataset."""
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from .detection_linking_hela import DetectionLinkingHela

‎deeptrack/datasets/detection_linking_hela/checksums.tsv‎

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# TODO(detection_linking_hela): If your dataset downloads files, then the checksums
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# will be automatically added here when running
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# `tfds build --register_checksums`.

‎deeptrack/datasets/detection_linking_hela/detection_linking_hela.py‎

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import tensorflow as tf
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import tensorflow_datasets as tfds
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import numpy as np
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import pandas as pd
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_DESCRIPTION = """
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This dataset includes tracking data from DIC-C2DH-HELA (provided by the sixth edition of the Cell Tracking Challenge).
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It consists of two dataframes: ``nodes`` and ``parenthood``. ``nodes`` contains information about the individual
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cells, while "parenthood" includes information on the lineage of the cells.
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"""
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_CITATION = """
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@article{pineda2022geometric,
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title={Geometric deep learning reveals the spatiotemporal fingerprint of microscopic motion},
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author={Pineda, Jes{\'u}s and Midtvedt, Benjamin and Bachimanchi, Harshith and No{\'e}, Sergio and Midtvedt, Daniel and Volpe, Giovanni and Manzo, Carlo},
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journal={arXiv preprint arXiv:2202.06355},
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year={2022}
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}
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"""
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class DetectionLinkingHela(tfds.core.GeneratorBasedBuilder):
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"""DatasetBuilder for detection_linking_Hela dataset."""
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VERSION = tfds.core.Version("1.0.0")
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RELEASE_NOTES = {
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"1.0.0": "Initial release.",
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}
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def _info(self) -> tfds.core.DatasetInfo:
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"""Returns the dataset metadata."""
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NODE_FEATURES = self.get_node_features()
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return tfds.core.DatasetInfo(
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builder=self,
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description=_DESCRIPTION,
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features=tfds.features.FeaturesDict(
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{
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"nodes": tfds.features.FeaturesDict(
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{
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**{
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key: tfds.features.Tensor(
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shape=(None,), dtype=NODE_FEATURES[key]
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)
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for key in NODE_FEATURES.keys()
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},
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}
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),
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"parenthood": tfds.features.FeaturesDict(
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{
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"child": tfds.features.Tensor(
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shape=(None,), dtype=tf.int32
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),
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"parent": tfds.features.Tensor(
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shape=(None,), dtype=tf.int32
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),
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}
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),
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"images": tfds.features.Tensor(
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shape=(84, 512, 512, 1), dtype=tf.float64
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),
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"masks": tfds.features.Tensor(
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shape=(84, 512, 512, 1), dtype=tf.float64
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),
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}
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),
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supervised_keys=None,
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homepage="https://dataset-homepage/",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager: tfds.download.DownloadManager):
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"""Returns SplitGenerators."""
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# Downloads the data and defines the splits
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path = dl_manager.download_and_extract(
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"https://drive.google.com/u/1/uc?id=1itHz4KmrUqDCKpGNyHUiHE4AFhwiJ5XR&export=download"
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)
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# Returns the Dict[split names, Iterator[Key, Example]]
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return {
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"train": self._generate_examples(
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path / "detection_linking_hela", "train"
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),
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"test": self._generate_examples(
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path / "detection_linking_hela", "test"
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),
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}
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def _generate_examples(self, path, split):
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"""Yields examples."""
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# Load data
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nodes, parenthood, images, masks = (
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pd.read_csv(path / split / "nodesdf.csv"),
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pd.read_csv(path / split / "parenthood.csv"),
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np.load(path / split / "images.npy"),
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np.load(path / split / "masks.npy"),
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)
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yield "_", {
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"nodes": {**nodes.to_dict("list")},
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"parenthood": {**parenthood.to_dict("list")},
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"images": images * 1.0,
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"masks": masks * 1.0,
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}
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def get_node_features(self):
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return {
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"frame": tf.int32,
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"label": tf.int32,
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"centroid-0": tf.float32,
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"centroid-1": tf.float32,
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"area": tf.float32,
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"mean_intensity": tf.float32,
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"perimeter": tf.float32,
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"eccentricity": tf.float32,
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"solidity": tf.float32,
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"set": tf.float32,
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"parent": tf.int32,
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"solution": tf.float32,
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}

‎deeptrack/datasets/detection_linking_hela/dummy_data/TODO-add_fake_data_in_this_directory.txt‎

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‎deeptrack/datasets/dmdataset/__init__.py‎

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"""dmdataset dataset."""
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from .dmdataset import Dmdataset

‎deeptrack/datasets/dmdataset/checksums.tsv‎

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# TODO(dmdataset): If your dataset downloads files, then the checksums
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# will be automatically added here when running
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# `tfds build --register_checksums`.

‎deeptrack/datasets/dmdataset/dmdataset.py‎

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"""dmdataset dataset."""
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import tensorflow_datasets as tfds
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import tensorflow as tf
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import os
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import scipy
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_DESCRIPTION = """
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"""
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_CITATION = """
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"""
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class Dmdataset(tfds.core.GeneratorBasedBuilder):
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"""DatasetBuilder for dmdataset dataset."""
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VERSION = tfds.core.Version("1.0.0")
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RELEASE_NOTES = {
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"1.0.0": "Initial release.",
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}
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def _info(self) -> tfds.core.DatasetInfo:
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"""Returns the dataset metadata."""
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NODE_FEATURES = self.get_features()
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return tfds.core.DatasetInfo(
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builder=self,
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description=_DESCRIPTION,
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features=tfds.features.FeaturesDict(
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{
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"graph": tfds.features.FeaturesDict(
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{
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**{
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key: tfds.features.Tensor(
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shape=feature[0],
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dtype=feature[1],
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)
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for key, feature in NODE_FEATURES[
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"graph"
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].items()
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},
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}
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),
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"labels": tfds.features.FeaturesDict(
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{
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**{
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key: tfds.features.Tensor(
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shape=feature[0],
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dtype=feature[1],
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)
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for key, feature in NODE_FEATURES[
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"labels"
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].items()
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},
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}
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),
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"sets": tfds.features.FeaturesDict(
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{
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**{
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key: tfds.features.Tensor(
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shape=feature[0],
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dtype=feature[1],
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)
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for key, feature in NODE_FEATURES[
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"sets"
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].items()
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},
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}
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),
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}
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),
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supervised_keys=None,
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homepage="https://dataset-homepage/",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager: tfds.download.DownloadManager):
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"""Returns SplitGenerators."""
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path = dl_manager.download_and_extract(
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"https://drive.google.com/u/1/uc?id=19vplN2lbKo4KAmv4NRU2qr3NSlzxFzrx&export=download"
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)
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return {
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"train": self._generate_examples(
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os.path.join(path, "dmdataset", "training")
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),
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"test": self._generate_examples(
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os.path.join(path, "dmdataset", "validation")
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),
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}
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def _generate_examples(self, path):
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"""Yields examples."""
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data = [{}, {}, {}]
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for i, subdict in enumerate(self.get_features().values()):
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files = (*subdict.keys(),)
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for file in files:
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data_elem = scipy.sparse.load_npz(
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os.path.join(path, file + ".npz")
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).toarray()
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data_elem = (
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data_elem[0] if data_elem.shape[0] == 1 else data_elem
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)
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data[i][file] = data_elem
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yield "key", {
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"graph": data[0],
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"labels": data[1],
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"sets": data[2],
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}
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def get_features(self):
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return {
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"graph": {
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"frame": [(None, 1), tf.float64],
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"node_features": [(None, 3), tf.float64],
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"edge_features": [(None, 1), tf.float64],
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"edge_indices": [(None, 2), tf.int64],
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"edge_dropouts": [(None, 2), tf.float64],
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},
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"labels": {
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"node_labels": [(None,), tf.float64],
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"edge_labels": [(None,), tf.float64],
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"global_labels": [(None, 3), tf.float64],
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},
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"sets": {
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"node_sets": [(None, 2), tf.int64],
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"edge_sets": [(None, 3), tf.int64],
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},
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}

‎deeptrack/datasets/endothelial_vs/__init__.py‎

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"""endothelial_vs dataset."""
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from .endothelial_vs import EndothelialVs

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