sigma type & doc (#72)

* docstrings OTPlanSampler

* ensure sigma type to be float

torchcfm/conditional_flow_matching.py

@@ -6,6 +6,7 @@
 # License: MIT License
 
 import math
+from typing import Union
 
 import torch
 
@@ -31,7 +32,7 @@ def pad_t_like_x(t, x):
     t: Vector (bs)
     pad_t_like_x(t, x): Tensor (bs, 1, 1, 1)
     """
-    if isinstance(t, float):
+    if isinstance(t, (float, int)):
         return t
     return t.reshape(-1, *([1] * (x.dim() - 1)))
 
@@ -47,12 +48,12 @@ class ConditionalFlowMatcher:
     - score function $\nabla log p_t(x|x0, x1)$
     """
 
-    def __init__(self, sigma: float = 0.0):
+    def __init__(self, sigma: Union[float, int] = 0.0):
         r"""Initialize the ConditionalFlowMatcher class. It requires the hyper-parameter $\sigma$.
 
         Parameters
         ----------
-        sigma : float
+        sigma : Union[float, int]
         """
         self.sigma = sigma
 
@@ -215,15 +216,15 @@ class ExactOptimalTransportConditionalFlowMatcher(ConditionalFlowMatcher):
     It overrides the sample_location_and_conditional_flow.
     """
 
-    def __init__(self, sigma: float = 0.0):
+    def __init__(self, sigma: Union[float, int] = 0.0):
         r"""Initialize the ConditionalFlowMatcher class. It requires the hyper-parameter $\sigma$.
 
         Parameters
         ----------
-        sigma : float
+        sigma : Union[float, int]
         ot_sampler: exact OT method to draw couplings (x0, x1) (see Eq.(17) [1]).
         """
-        self.sigma = sigma
+        super().__init__(sigma)
         self.ot_sampler = OTPlanSampler(method="exact")
 
     def sample_location_and_conditional_flow(self, x0, x1, return_noise=False):
@@ -382,16 +383,16 @@ class SchrodingerBridgeConditionalFlowMatcher(ConditionalFlowMatcher):
     sample_location_and_conditional_flow functions.
     """
 
-    def __init__(self, sigma: float = 1.0, ot_method="exact"):
+    def __init__(self, sigma: Union[float, int] = 1.0, ot_method="exact"):
         r"""Initialize the SchrodingerBridgeConditionalFlowMatcher class. It requires the hyper-
         parameter $\sigma$ and the entropic OT map.
 
         Parameters
         ----------
-        sigma : float
+        sigma : Union[float, int]
         ot_sampler: exact OT method to draw couplings (x0, x1) (see Eq.(17) [1]).
         """
-        self.sigma = sigma
+        super().__init__(sigma)
         self.ot_method = ot_method
         self.ot_sampler = OTPlanSampler(method=ot_method, reg=2 * self.sigma**2)
 

torchcfm/optimal_transport.py

@@ -19,6 +19,21 @@ def __init__(
         normalize_cost=False,
         **kwargs,
     ):
+        r"""Initialize the OTPlanSampler class.
+
+        Parameters
+        ----------
+        method : str
+            The method used to compute the OT plan. Can be one of "exact", "sinkhorn",
+            "unbalanced", or "partial".
+        reg : float (default : 0.05)
+            Entropic regularization coefficients.
+        reg_m : float (default : 1.0)
+            Marginal relaxation term for unbalanced OT (`method='unbalanced'`).
+        normalize_cost : bool (default : False)
+            Whether to normalize the cost matrix by its maximum value.
+            It should be set to `False` when using minibatches.
+        """
         # ot_fn should take (a, b, M) as arguments where a, b are marginals and
         # M is a cost matrix
         if method == "exact":