DeepTrack2 1.5.6 (#174)

* 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

* Update README.md

* Update graphs.py

* Update test_generators.py

* Update generators.py

* fix test

* Update vae.py

* Bugfix in endothelial_vs dataset

* Fix issue with repeated oneof-features (#169)

* Update cell_migration_analysis.ipynb

---------

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]>

Author: 8 people

deeptrack/datasets/endothelial_vs/endothelial_vs.py

@@ -2,6 +2,7 @@
 
 import tensorflow_datasets as tfds
 import numpy as np
+import tensorflow as tf
 
 _DESCRIPTION = """
 """
@@ -34,10 +35,10 @@ def _info(self) -> tfds.core.DatasetInfo:
             features=tfds.features.FeaturesDict(
                 {
                     "image": tfds.features.Image(
-                        shape=(None, None, 1), dtype="uint16"
+                        shape=(None, None, 1), dtype=tf.uint16
                     ),
                     "label": tfds.features.Image(
-                        shape=(None, None, 1), dtype="uint16"
+                        shape=(None, None, 1), dtype=tf.uint16
                     ),
                 }
             ),

deeptrack/features.py

@@ -1307,8 +1307,8 @@ def _process_properties(self, propertydict) -> dict:
 
         return propertydict
 
-    def get(self, image, key, **kwargs):
-        return self.collection[key](image)
+    def get(self, image, key, _ID=(), **kwargs):
+        return self.collection[key](image, _ID=_ID)
 
 
 class OneOfDict(Feature):
@@ -1338,8 +1338,8 @@ def _process_properties(self, propertydict) -> dict:
 
         return propertydict
 
-    def get(self, image, key, **kwargs):
-        return self.collection[key](image)
+    def get(self, image, key, _ID=(), **kwargs):
+        return self.collection[key](image, _ID=_ID)
 
 
 # class Dataset(Feature):
@@ -2109,7 +2109,7 @@ def _check_volumes_non_overlapping(self, volume_1, volume_2, min_distance):
         )
 
     def _resample_volume_position(self, volume):
-        """ Draws a new position for the volume. """
+        """Draws a new position for the volume."""
 
         for pdict in volume.properties:
             if "position" in pdict and "_position_sampler" in pdict:
@@ -2122,4 +2122,4 @@ def _resample_volume_position(self, volume):
 
 
 # Alias
-Dataset = TensorflowDataset
+Dataset = TensorflowDataset