Release 1.5.5 (#173)

* 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

---------

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

README.md

@@ -126,7 +126,7 @@ We also have examples that are specific for certain models. This includes
 - [*MAGIK*](deeptrack/models/gnns/) for graph-based particle linking and trace characterization.
 
 ## Documentation
-The detailed documentation of DeepTrack 2.1 is available at the following link: https://softmatterlab.github.io/DeepTrack-2.0/deeptrack.html
+The detailed documentation of DeepTrack 2.1 is available at the following link: https://softmatterlab.github.io/DeepTrack2/deeptrack.html
 
 ## Video Tutorials
 

deeptrack/models/gnns/graphs.py

@@ -16,6 +16,7 @@ def GetEdge(
     end: int,
     radius: int,
     parenthood: pd.DataFrame,
+    columns = [],
     **kwargs,
 ):
     """
@@ -79,7 +80,7 @@ def GetEdge(
         edges.append(combdf)
     # Concatenate the dataframes in a single
     # dataframe for the whole set of edges
-    edgedf = pd.concat(edges)
+    edgedf = pd.concat(edges) if len(edges) > 0 else pd.DataFrame(columns=columns)
 
     # Merge columns contaning the labels into a single column
     # of numpy arrays, i.e., label = [label_x, label_y]
@@ -120,6 +121,7 @@ def EdgeExtractor(nodesdf, nofframes=3, **kwargs):
     """
     # Create a copy of the dataframe to avoid overwriting
     df = nodesdf.copy()
+    columns = df.columns
 
     edgedfs = []
     sets = np.unique(df["set"])
@@ -140,7 +142,7 @@ def EdgeExtractor(nodesdf, nofframes=3, **kwargs):
             window = [elem for elem in window if elem <= df_set["frame"].max()]
 
             # Compute the edges for each frames window
-            edgedf = GetEdge(df_set, start=window[0], end=window[-1], **kwargs)
+            edgedf = GetEdge(df_set, start=window[0], end=window[-1], columns=columns, **kwargs)
             edgedf["set"] = setid
             edgedfs.append(edgedf)
 

deeptrack/models/vaes/vae.py

@@ -30,7 +30,7 @@ def train_step(self, data):
 
             # Sample a random point in the latent space
             epsilon = tf.random.normal(shape=tf.shape(z_mean))
-            z = z_mean + tf.exp(z_log_var) * epsilon
+            z = z_mean + tf.exp(0.5 * z_log_var) * epsilon
 
             # Reconstruct the input image
             rdata = self.decoder(z)

deeptrack/test/test_generators.py

@@ -8,8 +8,9 @@
 from .. import generators
 from ..optics import Fluorescence
 from ..scatterers import PointParticle
+from ..models import gnns
 import numpy as np
-
+import pandas as pd
 
 class TestGenerators(unittest.TestCase):
     def test_Generator(self):
@@ -154,7 +155,37 @@ def get_particle_position(result):
         #             a = generator[idx]
 
         #         [self.assertLess(d[-1], 8) for d in generator.data]
+    
 
+    def test_GraphGenerator(self):
+        frame = np.arange(10)
+        centroid = np.random.normal(0.5, 0.1, (10, 2))
+
+        df = pd.DataFrame(
+            {
+                'frame': frame, 
+                'centroid-0': centroid[:, 0], 
+                'centroid-1': centroid[:, 1], 
+                'label': 0, 
+                'set': 0, 
+                'solution': 0.0
+            }
+        )
+        # remove consecutive frames
+        df = df[~df["frame"].isin([3, 4, 5])]
+
+        generator = gnns.generators.GraphGenerator(
+	        nodesdf=df, 
+	        properties=["centroid"], 
+	        min_data_size=8,
+	        max_data_size=9,
+	        batch_size=8,
+	        feature_function=gnns.augmentations.GetGlobalFeature,
+	        radius=0.2,
+            nofframes=3,
+            output_type="edges"
+        )
+        self.assertIsInstance(generator, gnns.generators.ContinuousGraphGenerator)
 
 if __name__ == "__main__":
     unittest.main()