[feat] changes requested in LSL

tensorflow_similarity/losses/lifted_structure_loss.py

@@ -24,7 +24,7 @@
 from tensorflow_similarity.algebra import build_masks
 from tensorflow_similarity.distances import Distance, distance_canonicalizer
 from tensorflow_similarity.types import FloatTensor, IntTensor
-from typing import Callable
+from tensorflow_similarity import losses as tfsim_losses
 from .metric_loss import MetricLoss
 from .utils import compute_loss, negative_distances, positive_distances
 
@@ -35,28 +35,9 @@ def lifted_struct_loss(
     distance: Distance,
     positive_mining_strategy: str = "hard",
     negative_mining_strategy: str = "easy",
-    soft_margin: bool = False,
     margin: float = 1.0,
 ) -> FloatTensor:
-    """Lifted Struct loss computations
-
-    Args:
-        labels: labels associated with the embed
-        embeddings: Embedded examples.
-        distance: Which distance function to use to compute the pairwise
-            distances between embeddings.
-        positive_mining_strategy: What mining strategy to use to select
-            embedding from the same class. Defaults to 'hard'.
-            Available: {'easy', 'hard'}
-        negative_mining_strategy: What mining strategy to use for select the
-            embedding from the different class. Defaults to 'easy'.
-            Available: {'hard', 'semi-hard', 'easy'}
-        soft_margin: Use a soft margin instead of an explicit one.
-        margin: Use an explicit value for the margin term.
-
-    Returns:
-        Loss: The loss value for the current batch.
-    """
+    """Lifted Struct loss computations"""
 
     # Compute pairwise distances
     pairwise_distances = distance(embeddings)
@@ -70,26 +51,21 @@ def lifted_struct_loss(
     )
 
     # Get negative distances
-    negative_dists = negative_distances(
+    negative_dists, _ = negative_distances(
         negative_mining_strategy, pairwise_distances, negative_mask
     )
 
     # Reorder pairwise distances and negative mask based on positive indices
     reordered_pairwise_distances = tf.gather(pairwise_distances, positive_indices, axis=1)
     reordered_negative_mask = tf.gather(negative_mask, positive_indices, axis=1)
 
-    # Concatenate pairwise distances and negative masks along axis=1
-    concatenated_distances = tf.concat([pairwise_distances, reordered_pairwise_distances], axis=1)
-    concatenated_negative_mask = tf.concat([negative_mask, reordered_negative_mask], axis=1)
-
-    # Compute log sum exp with concatenated distances and negative mask
-    logsumexp_result = tf.reduce_logsumexp(concatenated_distances, axis=1, keepdims=True)
-    masked_logsumexp = tf.where(concatenated_negative_mask, logsumexp_result, tf.zeros_like(concatenated_distances))
+    # Compute (margin - neg_dist) logsum_exp values for each row (equation 4 in the paper)
+    neg_logsumexp = tfsim_losses.utils.logsumexp(margin - reordered_pairwise_distances, reordered_negative_mask)
 
     # Calculate the loss
-    pairwise_diff = pairwise_distances - masked_logsumexp
-    j_values = tf.reduce_sum(tf.maximum(0.0, pairwise_diff), axis=1)
-    loss = tf.reduce_mean(j_values) / (2 * tf.reduce_sum(positive_mask))
+    j_values = neg_logsumexp + positive_dists
+
+    loss = j_values / 2.0
 
     return loss
 
@@ -117,7 +93,6 @@ def __init__(
         distance: Distance | str = "cosine",
         positive_mining_strategy: str = "hard",
         negative_mining_strategy: str = "easy",
-        soft_margin: bool = False,
         margin: float = 1.0,
         name: str = "LiftedStructLoss",
         **kwargs,
@@ -133,7 +108,6 @@ def __init__(
             negative_mining_strategy: What mining strategy to use for select the
                 embedding from the different class. Defaults to 'easy'.
                 Available: {'hard', 'semi-hard', 'easy'}
-            soft_margin: Use a soft margin instead of an explicit one.
             margin: Use an explicit value for the margin term.
             name: Loss name. Defaults to "LiftedStructLoss".
 
@@ -159,7 +133,6 @@ def __init__(
             distance=distance,
             positive_mining_strategy=positive_mining_strategy,
             negative_mining_strategy=negative_mining_strategy,
-            soft_margin=soft_margin,
             margin=margin,
             **kwargs,
         )