Release DeepTrack2 1.6.0

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):
@@ -1637,7 +1637,7 @@ def _process_and_get(self, images, **kwargs):
                 p0[0] = np.max([p0[0], 0])
                 p0[1] = np.max([p0[1], 0])
 
-                p0 = p0.astype(np.int)
+                p0 = p0.astype(int)
 
                 output_slice = output[
                     p0[0] : p0[0] + labelarg.shape[0],
@@ -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

deeptrack/models/__init__.py

@@ -7,6 +7,7 @@
 from .gans import *
 from .gnns import *
 from .vaes import *
+from .waes import *
 
 # from .mrcnn import *
 # from .yolov1 import *

deeptrack/models/gans/gan.py

@@ -3,15 +3,24 @@
 
 layers = tf.keras.layers
 
-
-@as_KerasModel
 class GAN(tf.keras.Model):
+    """Generative Adversarial Network (GAN) model.
+
+    Parameters:
+    discriminator: keras model, optional
+        The discriminator network.
+    generator: keras model, optional
+        The generator network.
+    latent_dim: int, optional
+        Dimension of the latent space for random vectors.
+    """
+
     def __init__(self, discriminator=None, generator=None, latent_dim=128):
         super(GAN, self).__init__()
 
+        # Initialize discriminator and generator, or use default if not provided
         if discriminator is None:
             discriminator = self.default_discriminator()
-
         if generator is None:
             generator = self.default_generator()
 
@@ -21,9 +30,13 @@ def __init__(self, discriminator=None, generator=None, latent_dim=128):
 
     def compile(self, d_optimizer, g_optimizer, loss_fn):
         super(GAN, self).compile()
+
+        # Set optimizers and loss function for training
         self.d_optimizer = d_optimizer
         self.g_optimizer = g_optimizer
         self.loss_fn = loss_fn
+
+        # Define metrics to track during training
         self.d_loss_metric = tf.keras.metrics.Mean(name="d_loss")
         self.g_loss_metric = tf.keras.metrics.Mean(name="g_loss")
 
@@ -36,17 +49,18 @@ def train_step(self, real_images):
         batch_size = tf.shape(real_images)[0]
         random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))
 
-        # Decode them to fake images
+        # Generate fake images using the generator
         generated_images = self.generator(random_latent_vectors)
 
-        # Combine them with real images
+        # Combine real and fake images
         combined_images = tf.concat([generated_images, real_images], axis=0)
 
-        # Assemble labels discriminating real from fake images
+        # Create labels for real and fake images
         labels = tf.concat(
             [tf.ones((batch_size, 1)), tf.zeros((batch_size, 1))], axis=0
         )
-        # Add random noise to the labels - important trick!
+
+        # Add random noise to labels to improve stability
         labels += 0.05 * tf.random.uniform(tf.shape(labels))
 
         # Train the discriminator
@@ -58,14 +72,13 @@ def train_step(self, real_images):
             zip(grads, self.discriminator.trainable_weights)
         )
 
-        # Sample random points in the latent space
+        # Generate new random latent vectors
         random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))
 
-        # Assemble labels that say "all real images"
+        # Create labels indicating "all real images" for generator training
         misleading_labels = tf.zeros((batch_size, 1))
 
-        # Train the generator (note that we should *not* update the weights
-        # of the discriminator)!
+        # Train the generator while keeping discriminator weights fixed
         with tf.GradientTape() as tape:
             predictions = self.discriminator(self.generator(random_latent_vectors))
             g_loss = self.loss_fn(misleading_labels, predictions)
@@ -75,12 +88,19 @@ def train_step(self, real_images):
         # Update metrics
         self.d_loss_metric.update_state(d_loss)
         self.g_loss_metric.update_state(g_loss)
+
+        # Return updated loss metrics
         return {
             "d_loss": self.d_loss_metric.result(),
             "g_loss": self.g_loss_metric.result(),
         }
 
+    def call(self, inputs):
+        # Run generator
+        return self.generator(inputs)
+
     def default_generator(self, latent_dim=128):
+        # Define the default generator architecture
         return tf.keras.Sequential(
             [
                 tf.keras.Input(shape=(latent_dim,)),
@@ -98,6 +118,7 @@ def default_generator(self, latent_dim=128):
         )
 
     def default_discriminator(self):
+        # Define the default discriminator architecture
         return tf.keras.Sequential(
             [
                 tf.keras.Input(shape=(64, 64, 3)),
@@ -112,4 +133,4 @@ def default_discriminator(self):
                 layers.Dense(1, activation="sigmoid"),
             ],
             name="discriminator",
-        )
+        )

deeptrack/models/vaes/vae.py

@@ -1,28 +1,39 @@
 import tensorflow as tf
 from tensorflow.keras import layers
-
 from ..utils import as_KerasModel
 
 
-@as_KerasModel
 class VAE(tf.keras.Model):
+    """Variational Autoencoder (VAE) model.
+
+    Parameters:
+    encoder: keras model, optional
+        The encoder network.
+    decoder: keras model, optional
+        The decoder network.
+    latent_dim: int, optional
+        Dimension of the latent space.
+    """
+
     def __init__(self, encoder=None, decoder=None, latent_dim=2, **kwargs):
-        super().__init__(**kwargs)
+        super(VAE, self).__init__(**kwargs)
 
-        # Dimensionality of the latent space
+        # Define encoder latent dimension
         self.latent_dim = latent_dim
 
+        # Initialize encoder and decoder, or use defaults
         if encoder is None:
             self.encoder = self.default_encoder()
 
         if decoder is None:
             self.decoder = self.default_decoder()
 
     def train_step(self, data):
-
         data, _ = data
 
+        # Gradient tape for automatic differentiation
         with tf.GradientTape() as tape:
+            # Encode input data and sample from latent space.
             # The encoder outputs the mean and log of the variance of the
             # Gaussian distribution. The log of the variance is computed
             # instead of the variance for numerical stability.
@@ -32,42 +43,37 @@ def train_step(self, data):
             epsilon = tf.random.normal(shape=tf.shape(z_mean))
             z = z_mean + tf.exp(0.5 * z_log_var) * epsilon
 
-            # Reconstruct the input image
+            # Decode latent samples and compute reconstruction loss
             rdata = self.decoder(z)
-
-            # Reconstruction loss
             rloss = self.loss(data, rdata)
 
-            # KL divergence loss
-            kl_loss = -0.5 * (
-                1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)
-            )
+            # Compute KL divergence loss
+            kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))
             kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))
 
-            # Total loss
+            # Compute total loss
             loss = rloss + kl_loss
 
-        # Compute gradients
+        # Compute gradients and update model weights
         grads = tape.gradient(loss, self.trainable_weights)
+        self.optimizer.apply_gradients(zip(grads, self.trainable_weights))
 
-        # Update weights
-        self.optimizer.apply_gradients(
-            zip(grads, self.trainable_weights),
-        )
-
-        # Update metrics
+        # Update metrics for monitoring
         self.compiled_metrics.update_state(data, rdata)
 
+        # Return loss values for visualization
         return {
             "loss": loss,
             "reconstruction_loss": rloss,
             "kl_loss": kl_loss,
         }
 
     def call(self, inputs):
+        # Use encoder to obtain latent representation
         return self.encoder(inputs)
 
     def default_encoder(self):
+        # Define the default encoder architecture
         return tf.keras.Sequential(
             [
                 tf.keras.Input(shape=(28, 28, 1)),
@@ -88,14 +94,13 @@ def default_encoder(self):
                 layers.Flatten(),
                 layers.Dense(16),
                 layers.LeakyReLU(alpha=0.2),
-                layers.Dense(
-                    self.latent_dim + self.latent_dim, name="z_mean_log_var"
-                ),
+                layers.Dense(self.latent_dim + self.latent_dim, name="z_mean_log_var"),
             ],
             name="encoder",
         )
 
     def default_decoder(self):
+        # Define the default decoder architecture
         return tf.keras.Sequential(
             [
                 tf.keras.Input(shape=(self.latent_dim,)),

deeptrack/models/waes/__init__.py

@@ -0,0 +1 @@
+from .wae import *