Hotfix Version 2.0.1

bayesflow/networks/flow_matching/flow_matching.py

@@ -39,11 +39,11 @@ class FlowMatching(InferenceNetwork):
     }
 
     OPTIMAL_TRANSPORT_DEFAULT_CONFIG = {
-        "method": "sinkhorn",
-        "cost": "euclidean",
+        "method": "log_sinkhorn",
         "regularization": 0.1,
         "max_steps": 100,
-        "tolerance": 1e-4,
+        "atol": 1e-5,
+        "rtol": 1e-4,
     }
 
     INTEGRATE_DEFAULT_CONFIG = {

bayesflow/utils/optimal_transport/log_sinkhorn.py

@@ -1,7 +1,6 @@
 import keras
 
 from .. import logging
-from ..tensor_utils import is_symbolic_tensor
 
 from .euclidean import euclidean
 
@@ -27,9 +26,6 @@ def log_sinkhorn_plan(x1, x2, regularization: float = 1.0, rtol=1e-5, atol=1e-8,
 
     log_plan = cost / -(regularization * keras.ops.mean(cost) + 1e-16)
 
-    if is_symbolic_tensor(log_plan):
-        return log_plan
-
     def contains_nans(plan):
         return keras.ops.any(keras.ops.isnan(plan))
 
@@ -59,7 +55,7 @@ def do_nothing():
     def log_steps():
         msg = "Log-Sinkhorn-Knopp converged after {:d} steps."
 
-        logging.info(msg, steps)
+        logging.debug(msg, steps)
 
     def warn_convergence():
         marginals = keras.ops.logsumexp(log_plan, axis=0)

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "bayesflow"
-version = "2.0.0"
+version = "2.0.1"
 authors = [{ name = "The BayesFlow Team" }]
 classifiers = [
     "Development Status :: 5 - Production/Stable",