1.5.0 (#148)

* 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 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]>
DeepTrackAI · Nov 1, 2022 · 8685af1 · 8685af1
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diff --git a/.zenodo.json b/.zenodo.json
@@ -0,0 +1,51 @@
+{
+    "creators": [
+        {
+            "orcid": "0000-0001-9386-4753",
+            "affiliation": "Gothenburg University",
+            "name": "Midtvedt, Benjamin"
+        }, 
+        {
+            "orcid": "0000-0002-9197-3451",
+            "affiliation": "Gothenburg University",
+            "name": "Pineda, Jesus"
+        }, 
+        {
+            "orcid": "0000-0001-7275-6921",
+            "affiliation": "Chalmers University of Technology",
+            "name": "Klein Morberg, Henrik"
+        }, 
+        {
+            "orcid": "0000-0002-8625-0996",
+            "affiliation": "University of Vic",
+            "name": "Manzo, Carlo"
+        }, 
+        {
+            "orcid": "0000-0001-5057-1846",
+            "affiliation": "Gothenburg University",
+            "name": "Volpe, Giovanni"
+        }
+    ],
+
+    "title": "DeepTrack2",
+
+    "related_identifiers": [
+        {
+            "scheme": "doi",
+            "identifier": "10.1063/5.0034891",
+            "relation": "isDocumentedBy",
+            "resource_type": "publication-article"
+        }
+    ],
+
+    "description": "A Python software platform for microscopy enhanced by deep learning." ,
+
+    "keywords": ["Deep Learning", "Software", "Microscopy", "Particle Tracking", "Python"],
+
+    "upload_type": "software",
+
+    "communities": [
+        {"identifier": "www.deeptrack.org"},
+        {"identifier": "https://github.com/softmatterlab/DeepTrack2"}
+    ]
+}
diff --git a/CITATION.cff b/CITATION.cff
@@ -0,0 +1,30 @@
+# This CITATION.cff file was generated with cffinit.
+# Visit https://bit.ly/cffinit to generate yours today!
+
+cff-version: 1.2.0
+title: DeepTrack2
+message: >-
+  If you use this software, please cite it through
+  this publication: Benjamin Midtvedt, Saga
+  Helgadottir, Aykut Argun, Jesús Pineda, Daniel
+  Midtvedt, Giovanni Volpe. "Quantitative Digital
+  Microscopy with Deep Learning." Applied Physics
+  Reviews 8 (2021), 011310.
+  https://doi.org/10.1063/5.0034891
+type: software
+authors:
+  - given-names: Benjamin
+    family-names: Midtvedt
+    orcid: 'https://orcid.org/0000-0001-9386-4753'
+  - given-names: Jesus
+    family-names: Pineda
+    orcid: 'https://orcid.org/0000-0002-9197-3451'
+  - given-names: Henrik
+    family-names: Klein Morberg
+    orcid: 'https://orcid.org/0000-0001-7275-6921'
+  - given-names: Carlo
+    family-names: Manzo
+    orcid: 'https://orcid.org/0000-0002-8625-0996'
+  - given-names: Giovanni
+    family-names: Volpe
+    orcid: 'https://orcid.org/0000-0001-5057-1846'
diff --git a/deeptrack/backend/pint_definition.py b/deeptrack/backend/pint_definition.py
@@ -306,7 +306,8 @@
 reciprocal_centimeter = 1 / cm = cm_1 = kayser
 
 # Velocity
-[velocity] = [length] / [time] = [speed]
+[velocity] = [length] / [time]
+[speed] = [velocity]
 knot = nautical_mile / hour = kt = knot_international = international_knot
 mile_per_hour = mile / hour = mph = MPH
 kilometer_per_hour = kilometer / hour = kph = KPH

diff --git a/deeptrack/datasets/__init__.py b/deeptrack/datasets/__init__.py
@@ -3,4 +3,5 @@
     segmentation_ssTEM_drosophila,
     regression_holography_nanoparticles,
     segmentation_fluorescence_u2os,
+    detection_holography_nanoparticles,
 )
diff --git a/deeptrack/datasets/detection_holography_nanoparticles/__init__.py b/deeptrack/datasets/detection_holography_nanoparticles/__init__.py
@@ -0,0 +1,3 @@
+"""detection_holography_nanoparticles dataset."""
+
+from .detection_holography_nanoparticles import DetectionHolographyNanoparticles
diff --git a/deeptrack/datasets/detection_holography_nanoparticles/checksums.tsv b/deeptrack/datasets/detection_holography_nanoparticles/checksums.tsv
@@ -0,0 +1,3 @@
+# TODO(detection_holography_nanoparticles): If your dataset downloads files, then the checksums
+# will be automatically added here when running
+# `tfds build --register_checksums`.
diff --git a/deeptrack/datasets/detection_holography_nanoparticles/detection_holography_nanoparticles.py b/deeptrack/datasets/detection_holography_nanoparticles/detection_holography_nanoparticles.py
@@ -0,0 +1,77 @@
+"""detection_holography_nanoparticles dataset."""
+
+import tensorflow_datasets as tfds
+import tensorflow as tf
+import numpy as np
+
+# TODO(detection_holography_nanoparticles): Markdown description  that will appear on the catalog page.
+_DESCRIPTION = """
+"""
+
+# TODO(detection_holography_nanoparticles): BibTeX citation
+_CITATION = """
+"""
+
+
+class DetectionHolographyNanoparticles(tfds.core.GeneratorBasedBuilder):
+    """DatasetBuilder for detection_holography_nanoparticles dataset."""
+
+    VERSION = tfds.core.Version("1.0.2")
+    RELEASE_NOTES = {
+        "1.0.0": "Initial release.",
+    }
+
+    def _info(self) -> tfds.core.DatasetInfo:
+        """Returns the dataset metadata."""
+        # TODO(detection_holography_nanoparticles): Specifies the tfds.core.DatasetInfo object
+        return tfds.core.DatasetInfo(
+            builder=self,
+            description=_DESCRIPTION,
+            features=tfds.features.FeaturesDict(
+                {
+                    # These are the features of your dataset like images, labels ...
+                    "image": tfds.features.Tensor(
+                        shape=(972, 729, 2), dtype=tf.float64
+                    ),
+                    "label": tfds.features.Tensor(shape=(None, 7), dtype=tf.float64),
+                }
+            ),
+            # If there's a common (input, target) tuple from the
+            # features, specify them here. They'll be used if
+            # `as_supervised=True` in `builder.as_dataset`.
+            supervised_keys=("image", "label"),  # Set to `None` to disable
+            homepage="https://dataset-homepage/",
+            citation=_CITATION,
+            disable_shuffling=True,
+        )
+
+    def _split_generators(self, dl_manager: tfds.download.DownloadManager):
+        """Returns SplitGenerators."""
+        # TODO(detection_holography_nanoparticles): Downloads the data and defines the splits
+        path = dl_manager.download_and_extract(
+            "https://drive.google.com/u/1/uc?id=1uAZVr9bldhZhxuXAXvdd1-Ks4m9HPRtM&export=download"
+        )
+
+        # TODO(detection_holography_nanoparticles): Returns the Dict[split names, Iterator[Key, Example]]
+        return {
+            "train": self._generate_examples(path),
+        }
+
+    def _generate_examples(self, path):
+        """Yields examples."""
+        # TODO(detection_holography_nanoparticles): Yields (key, example) tuples from the dataset
+
+        fields = path.glob("f*.npy")
+        labels = path.glob("d*.npy")
+
+        # sort the files
+        fields = sorted(fields, key=lambda x: int(x.stem[1:]))
+        labels = sorted(labels, key=lambda x: int(x.stem[1:]))
+
+        for field, label in zip(fields, labels):
+            field_data = np.load(field)
+            field_data = np.stack((field_data.real, field_data.imag), axis=-1)
+            yield field.stem, {
+                "image": field_data,
+                "label": np.load(label),
+            }
diff --git a/...ck/datasets/detection_holography_nanoparticles/detection_holography_nanoparticles_test.py b/...ck/datasets/detection_holography_nanoparticles/detection_holography_nanoparticles_test.py
@@ -0,0 +1,24 @@
+"""detection_holography_nanoparticles dataset."""
+
+import tensorflow_datasets as tfds
+from . import detection_holography_nanoparticles
+
+
+class DetectionHolographyNanoparticlesTest(tfds.testing.DatasetBuilderTestCase):
+  """Tests for detection_holography_nanoparticles dataset."""
+  # TODO(detection_holography_nanoparticles):
+  DATASET_CLASS = detection_holography_nanoparticles.DetectionHolographyNanoparticles
+  SPLITS = {
+      'train': 3,  # Number of fake train example
+      'test': 1,  # Number of fake test example
+  }
+
+  # If you are calling `download/download_and_extract` with a dict, like:
+  #   dl_manager.download({'some_key': 'http://a.org/out.txt', ...})
+  # then the tests needs to provide the fake output paths relative to the
+  # fake data directory
+  # DL_EXTRACT_RESULT = {'some_key': 'output_file1.txt', ...}
+
+
+if __name__ == '__main__':
+  tfds.testing.test_main()
diff --git a/...ts/detection_holography_nanoparticles/dummy_data/TODO-add_fake_data_in_this_directory.txt b/...ts/detection_holography_nanoparticles/dummy_data/TODO-add_fake_data_in_this_directory.txt
diff --git a/deeptrack/extras/datasets.py b/deeptrack/extras/datasets.py
@@ -46,7 +46,12 @@
     "CellData": ("1CJW7msDiI7xq7oMce4l9tRkNN6O5eKtj", "CellData", ""),
     "CellMigData": ("1vRsWcxjbTz6rffCkrwOfs_ezPvUjPwGw", "CellMigData", ""),
     "BFC2Cells": ("1lHgJdG5I3vRnU_DRFwTr_c69nx1Xkd3X", "BFC2Cells", ""),
-    "STrajCh": ("1wXCSzvHuLwz1dywxUu2aQXlqbgf2V8r3", "STrajCh", "")
+    "STrajCh": ("1wXCSzvHuLwz1dywxUu2aQXlqbgf2V8r3", "STrajCh", ""),
+    "TrajectoryDiffusion": (
+        "1YhECLQrWPZgc_TVY2Sl2OwDcNxmA_jR5",
+        "TrajectoryDiffusion",
+        "",
+    ),
 }
 
 
@@ -109,7 +114,9 @@ def load(key):
 
     # If the extracted folder is another folder with the same name, move it.
     if os.path.isdir(f"datasets/{folder_name}/{folder_name}"):
-        os.rename(f"datasets/{folder_name}/{folder_name}", f"datasets/{folder_name}")
+        os.rename(
+            f"datasets/{folder_name}/{folder_name}", f"datasets/{folder_name}"
+        )
 
 
 def load_model(key):
@@ -171,7 +178,9 @@ def load_model(key):
 
     # If the extracted folder is another folder with the same name, move it.
     if os.path.isdir(f"models/{folder_name}/{folder_name}"):
-        os.rename(f"models/{folder_name}/{folder_name}", f"models/{folder_name}")
+        os.rename(
+            f"models/{folder_name}/{folder_name}", f"models/{folder_name}"
+        )
 
     return f"models/{folder_name}"
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,3 @@
		"""detection_holography_nanoparticles dataset."""

		from .detection_holography_nanoparticles import DetectionHolographyNanoparticles