Fix mixing layer when sampling/mpe with cls>1

simple_einet/einet.py

@@ -357,15 +357,28 @@ def sample(
             torch.Tensor: Samples generated according to the distribution specified by the SPN.
 
         """
-        assert class_index is None or evidence is None, "Cannot provide both, evidence and class indices."
+        class_is_given = class_index is not None
+        evidence_is_given = evidence is not None
+        is_multiclass = self.config.num_classes > 1
+
+        assert not (class_is_given and evidence_is_given), "Cannot provide both, evidence and class indices."
         assert (
-            num_samples is None or evidence is None
+            num_samples is None or not evidence_is_given
         ), "Cannot provide both, number of samples to generate (num_samples) and evidence."
-        assert ((class_index is not None) and (self.config.num_classes > 1)) or (
-            (class_index is None) and (self.config.num_classes == 1)
-        )
 
-        # Check if evidence contains nans
+        if num_samples is not None:
+            assert num_samples > 0, "Number of samples must be > 0."
+
+        # if not is_mpe:
+        #     assert ((class_index is not None) and (self.config.num_classes > 1)) or (
+        #         (class_index is None) and (self.config.num_classes == 1)
+        #     ), "Class index must be given if the number of classes is > 1 or must be none if the number of classes is 1."
+
+        if class_is_given:
+            assert (
+                self.config.num_classes > 1
+            ), f"Class indices are only supported when the number of classes for this model is > 1."
+
         if evidence is not None:
             # Set n to the number of samples in the evidence
             num_samples = evidence.shape[0]
@@ -423,6 +436,9 @@ def sample(
                             indices.requires_grad_(True)  # Enable gradients
 
                     ctx.indices_out = indices
+                else:
+                    # Sample class
+                    ctx = self._class_sampling_root.sample(ctx=ctx)
 
             # Save parent indices that were sampled from the sampling root
             if self.config.num_repetitions > 1:

simple_einet/layers/mixing.py

@@ -81,14 +81,14 @@ def _sample_from_weights(self, ctx: SamplingContext, log_weights: Tensor):
     def _condition_weights_on_evidence(self, ctx: SamplingContext, log_weights: Tensor):
         lls = self._input_cache["in"]
 
-        # Index repetition
+        # Index lls at correct repetitions
         if ctx.is_differentiable:
-            r_idxs = ctx.indices_repetition.view(ctx.num_samples, 1, 1, 1)
-            lls = index_one_hot(lls, index=r_idxs, dim=2)
+            p_idxs = ctx.indices_out.view(ctx.num_samples, 1, self.num_sums_out, 1)
+            lls = index_one_hot(lls, index=p_idxs, dim=2)
         else:
-            r_idxs = ctx.indices_repetition[..., None, None, None]
-            r_idxs = r_idxs.expand(-1, 1, self.num_sums_out, self.num_sums_in)
-            lls = lls.gather(dim=2, index=r_idxs).squeeze(2)
+            p_idxs = ctx.indices_out[..., None, None]
+            p_idxs = p_idxs.expand(-1, 1, 1, self.num_sums_in)
+            lls = lls.gather(dim=2, index=p_idxs).squeeze(2)
 
         log_prior = log_weights
         log_posterior = log_prior + lls