indentation changes

optax/losses/_self_supervised.py

@@ -89,70 +89,69 @@ def ntxent(
 
 
 def _pairwise_distance(x: chex.Array, y: chex.Array, p: int = 2, eps: float = 1e-6) -> chex.Array:
-    diff = x - y
-    dist = jnp.sum(jnp.abs(diff) ** p + eps, axis=-1) ** (1.0 / p)
-    return dist
-  
+  diff = x - y
+  dist = jnp.sum(jnp.abs(diff) ** p + eps, axis=-1) ** (1.0 / p)
+  return dist
+
 
 def triplet_margin_loss(
-    anchor: chex.Array,
-    positive: chex.Array,
-    negative: chex.Array,
-    *,
-    margin: float = 1.0,
-    p: int = 2,
-    eps: float = 1e-6,
-    swap: bool = False,
-    reduction: str = 'mean',
+  anchor: chex.Array,
+  positive: chex.Array,
+  negative: chex.Array,
+  *,
+  margin: float = 1.0,
+  p: int = 2,
+  eps: float = 1e-6,
+  swap: bool = False,
+  reduction: str = 'mean',
 ) -> chex.Array:
-    """Triplet margin loss function.
-    
-    Measures the relative similarity between an anchor point, a positive point, and
-    a negative point using the distance metric specified by p-norm. The loss encourages
-    the distance between the anchor and positive points to be smaller than the distance
-    between the anchor and negative points by at least the margin amount.
-    
-    Args:
-        anchor: The anchor embeddings. Shape: [batch_size, feature_dim].
-        positive: The positive embeddings. Shape: [batch_size, feature_dim].
-        negative: The negative embeddings. Shape: [batch_size, feature_dim].
-        margin: The margin value. Default: 1.0.
-        p: The norm degree for pairwise distance. Default: 2.
-        eps: Small epsilon value to avoid numerical issues. Default: 1e-6.
-        swap: Use the distance swap optimization from "Learning shallow convolutional 
-            feature descriptors with triplet losses" by V. Balntas et al. Default: False.
-        reduction: Specifies the reduction to apply to the output:
-            'none' | 'mean' | 'sum'. Default: 'mean'.
-    
-    Returns:
-        The triplet margin loss value.
-        If reduction is 'none': tensor of shape [batch_size]
-        If reduction is 'mean' or 'sum': scalar tensor.
-    """
-    chex.assert_equal_shape([anchor, positive, negative])
-
-    if not(anchor.ndim == 2 and positive.ndim == 2 and negative.ndim == 2):
-        raise ValueError("Inputs must be 2D tensors")
-
-    # Calculate distances between pairs
-    dist_pos = _pairwise_distance(anchor, positive, p, eps)
-    dist_neg = _pairwise_distance(anchor, negative, p, eps)
-
-    # Implement distance swap if enabled
-    if swap:
-        dist_swap = _pairwise_distance(positive, negative)
-        dist_neg = jnp.minimum(dist_neg, dist_swap)
-
-    # Calculate loss with margin
-    losses = jnp.maximum(margin + dist_pos - dist_neg, 0.0)
-
-    # Apply reduction
-    if reduction == 'none':
-        return losses
-    elif reduction == 'mean':
-        return jnp.mean(losses)
-    elif reduction == 'sum':
-        return jnp.sum(losses)
-    else:
-        raise ValueError(f"Invalid reduction mode: {reduction}")
-
+  """Triplet margin loss function.
+
+  Measures the relative similarity between an anchor point, a positive point, and
+  a negative point using the distance metric specified by p-norm. The loss encourages
+  the distance between the anchor and positive points to be smaller than the distance
+  between the anchor and negative points by at least the margin amount.
+
+  Args:
+      anchor: The anchor embeddings. Shape: [batch_size, feature_dim].
+      positive: The positive embeddings. Shape: [batch_size, feature_dim].
+      negative: The negative embeddings. Shape: [batch_size, feature_dim].
+      margin: The margin value. Default: 1.0.
+      p: The norm degree for pairwise distance. Default: 2.
+      eps: Small epsilon value to avoid numerical issues. Default: 1e-6.
+      swap: Use the distance swap optimization from "Learning shallow convolutional
+          feature descriptors with triplet losses" by V. Balntas et al. Default: False.
+      reduction: Specifies the reduction to apply to the output:
+          'none' | 'mean' | 'sum'. Default: 'mean'.
+
+  Returns:
+      The triplet margin loss value.
+      If reduction is 'none': tensor of shape [batch_size]
+      If reduction is 'mean' or 'sum': scalar tensor.
+  """
+  chex.assert_equal_shape([anchor, positive, negative])
+
+  if not(anchor.ndim == 2 and positive.ndim == 2 and negative.ndim == 2):
+      raise ValueError("Inputs must be 2D tensors")
+
+  # Calculate distances between pairs
+  dist_pos = _pairwise_distance(anchor, positive, p, eps)
+  dist_neg = _pairwise_distance(anchor, negative, p, eps)
+
+  # Implement distance swap if enabled
+  if swap:
+      dist_swap = _pairwise_distance(positive, negative)
+      dist_neg = jnp.minimum(dist_neg, dist_swap)
+
+  # Calculate loss with margin
+  losses = jnp.maximum(margin + dist_pos - dist_neg, 0.0)
+
+  # Apply reduction
+  if reduction == 'none':
+      return losses
+  elif reduction == 'mean':
+      return jnp.mean(losses)
+  elif reduction == 'sum':
+      return jnp.sum(losses)
+  else:
+      raise ValueError(f"Invalid reduction mode: {reduction}")