Release DeepTrack2 1.6.0 #191
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Use MultiHeadSelfAttention and MultiHeadGatedSelfAttention if want to return the attention weights
This commit adds new message-passing graph layers (MPN) and graph convolutional layers to dt, including vanilla MPN, GRUMPN, Masked-attention FGNN, and GraphTransformer.
Jp/rev magik
Fix indexing problems related to tf.gather
Allow multi-inputs in ContinuousGenerator
Fix phase correction at focus and offset calculation
Create virtual staining datasets and tools
removed pydeepimagej from deps
Update graph generators
Update vae.py
* added deterministic wae_gan, slightly modified vae (input size)) * changes to VAE, WAE_GAN, and GAN Implemented proposed changes to GAN, VAE, WAE_GAN: - optimizers of WAE_GAN as input parameters by overriding the compile method. - included documentation of input parameters for VAE and WAE_GAN. - WAE_GAN: different learning rates for the autoencoder and the discriminator, 1e-3 and 5e-4, respectively as in the original paper. - included a unit test for WAE_GAN, VAE, and GAN. - removed @as_KerasModel from GAN. - formatted the code for compatibility. * Update test_models.py * fixed typo * extra fixes * WAE generalized allows WAE-GAN and WAE-MMD * fixes * check unit test * compatibility issue removed match for compatibility, replaced with if * test unit corrected assert in test unit for GAN, VAE. and WAE * fixed dimension in test_models * added compile for GAN and WAE in test_model * defined call in gan * fixed input size in GAN
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Stable release of DeepTrack2 1.6.0
Release date: 12 September 2023
PyPi 1.6.0
https://pypi.org/project/deeptrack/1.6.0/
DeepTrack2 1.6.0 is a minor release that mainly adds WAE and provides some minor edits to the code of GAN and VAEs.
Cite as:
Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe.
"Quantitative Digital Microscopy with Deep Learning."
Applied Physics Reviews 8, 011310 (2021)
https://doi.org/10.1063/5.0034891