update docstring in losses

Author: franckma31

deel/torchlip/functional.py

@@ -287,9 +287,7 @@ def apply_reduction(val: torch.Tensor, reduction: str) -> torch.Tensor:
     return red(val)
 
 
-def kr_loss(
-    input: torch.Tensor, target: torch.Tensor, multi_gpu=False, true_values=None
-) -> torch.Tensor:
+def kr_loss(input: torch.Tensor, target: torch.Tensor, multi_gpu=False) -> torch.Tensor:
     r"""
     Loss to estimate the Wasserstein-1 distance using Kantorovich-Rubinstein duality,
     as per
@@ -300,12 +298,19 @@ def kr_loss(
             - \underset{\mathbf{x}\sim{}\nu}{\mathbb{E}}[f(\mathbf{x})]
 
     where :math:`\mu` and :math:`\nu` are the distributions corresponding to the
-    two possible labels as specific by ``true_values``.
+    two possible labels as specific by their sign.
+
+    `target` accepts label values in (0, 1), (-1, 1), or pre-processed with the
+    `deel.torchlip.functional.process_labels_for_multi_gpu()` function.
+
+    Using a multi-GPU/TPU strategy requires to set `multi_gpu` to True and to
+    pre-process the labels `target` with the
+    `deel.torchlip.functional.process_labels_for_multi_gpu()` function.
 
     Args:
         input: Tensor of arbitrary shape.
         target: Tensor of the same shape as input.
-        true_values: depreciated (target>0 is used)
+        multi_gpu (bool): set to True when running on multi-GPU/TPU
 
     Returns:
         The Wasserstein-1 loss between ``input`` and ``target``.
@@ -316,9 +321,7 @@ def kr_loss(
         return kr_loss_standard(input, target)
 
 
-def kr_loss_standard(
-    input: torch.Tensor, target: torch.Tensor, true_values=None
-) -> torch.Tensor:
+def kr_loss_standard(input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
     r"""
     Loss to estimate the Wasserstein-1 distance using Kantorovich-Rubinstein duality,
     as per
@@ -329,12 +332,13 @@ def kr_loss_standard(
             - \underset{\mathbf{x}\sim{}\nu}{\mathbb{E}}[f(\mathbf{x})]
 
     where :math:`\mu` and :math:`\nu` are the distributions corresponding to the
-    two possible labels as specific by ``true_values``.
+    two possible labels as specific by their sign.
+
+    `target` accepts label values in (0, 1), (-1, 1)
 
     Args:
         input: Tensor of arbitrary shape.
         target: Tensor of the same shape as input.
-        true_values: depreciated (target>0 is used)
 
     Returns:
         The Wasserstein-1 loss between ``input`` and ``target``.
@@ -384,7 +388,6 @@ def neg_kr_loss(
     input: torch.Tensor,
     target: torch.Tensor,
     multi_gpu=False,
-    true_values=None,
 ) -> torch.Tensor:
     """
     Loss to estimate the negative wasserstein-1 distance using Kantorovich-Rubinstein
@@ -393,7 +396,7 @@ def neg_kr_loss(
     Args:
         input: Tensor of arbitrary shape.
         target: Tensor of the same shape as input.
-        true_values: depreciated (target>0 is used)
+        multi_gpu (bool): set to True when running on multi-GPU/TPU
 
     Returns:
         The negative Wasserstein-1 loss between ``input`` and ``target``.
@@ -437,7 +440,6 @@ def hkr_loss(
     alpha: float,
     min_margin: float = 1.0,
     multi_gpu=False,
-    true_values=None,
 ) -> torch.Tensor:
     """
     Loss to estimate the wasserstein-1 distance with a hinge regularization using
@@ -446,9 +448,9 @@ def hkr_loss(
     Args:
         input: Tensor of arbitrary shape.
         target: Tensor of the same shape as input.
-        alpha: Regularization factor between the hinge and the KR loss.
+        alpha: Regularization factor ([0,1]) between the hinge and the KR loss.
         min_margin: Minimal margin for the hinge loss.
-        true_values: tuple containing the two label for each predicted class.
+        multi_gpu (bool): set to True when running on multi-GPU/TPU
 
     Returns:
         The regularized Wasserstein-1 loss.
@@ -478,7 +480,7 @@ def hinge_multiclass_loss(
     """
     Loss to estimate the Hinge loss in a multiclass setup. It compute the
     elementwise hinge term. Note that this formulation differs from the
-    one commonly found in tensorflow/pytorch (with marximise the difference
+    one commonly found in tensorflow/pytorch (with maximise the difference
     between the two largest logits). This formulation is consistent with the
     binary classification loss used in a multiclass fashion.
 
@@ -515,9 +517,9 @@ def hkr_multiclass_loss(
     Args:
         input: Tensor of arbitrary shape.
         target: Tensor of the same shape as input.
-        alpha: Regularization factor between the hinge and the KR loss.
+        alpha: Regularization factor ([0,1]) between the hinge and the KR loss.
         min_margin: Minimal margin for the hinge loss.
-        true_values: tuple containing the two label for each predicted class.
+        multi_gpu (bool): set to True when running on multi-GPU/TPU
 
     Returns:
         The regularized Wasserstein-1 loss.