Make activation handler guess the layout based on tensor rank if missing

src/finn/transformation/qonnx/qonnx_activation_handlers.py

@@ -402,12 +402,24 @@ def _calculate_thresholds(self):
                     else:
                         thresholds[c][t] = step / selu_scale
 
-        # First try to consider the tensor layout of the input for determining
-        # the number of output channels
+        # Get the shape of the input (should also be the output) tensor
+        # Note: Querying the input is more safe as we do not want to
+        # propagate shapes backwards by accident.
+        shape = self._model.get_tensor_shape(self._q_node.input[0])  # noqa
+        # First try to consider the tensor layout of the input for
+        # determining the number of output channels
         layout = self._model.get_tensor_layout(self._q_node.input[0])
-        # If there is a layout annotation, use this to determine the index of
-        # the channel dimension
-        if layout is not None and "C" in layout:
+        # If there is no layout annotation, guess based on rank of the
+        # tensor
+        # TODO: No support for Rank >= 5
+        if layout is None and len(shape) < 5:
+            # Maps tensor rank to layout annotation
+            rank_to_layout = {0: None, 1: "C", 2: "NC", 3: "NWC", 4: "NCHW"}
+            # Lookup the layout required by this input shape
+            layout = rank_to_layout[len(shape)]
+        # If there is a layout annotation, use this to determine the index
+        # of the channel dimension
+        if layout is not None and "C" in layout:  # noqa: Duplicate
             # Lookup the index in list
             cdim = layout.index("C")
         # If no layout has been annotated or there is no channel dimension, fall
@@ -570,12 +582,24 @@ def _calculate_thresholds(self):
                 for t in range(num_thresholds):
                     thresholds[c][t] = min_threshold[c] + step[c] * t
 
+            # Get the shape of the input (should also be the output) tensor
+            # Note: Querying the input is more safe as we do not want to
+            # propagate shapes backwards by accident.
+            shape = self._model.get_tensor_shape(self._q_node.input[0])
             # First try to consider the tensor layout of the input for
             # determining the number of output channels
-            layout = self._model.get_tensor_layout(self._q_node.input[0])
+            layout = self._model.get_tensor_layout(self._q_node.input[0])  # noqa
+            # If there is no layout annotation, guess based on rank of the
+            # tensor
+            # TODO: No support for Rank >= 5
+            if layout is None and len(shape) < 5:
+                # Maps tensor rank to layout annotation
+                rank_to_layout = {0: None, 1: "C", 2: "NC", 3: "NWC", 4: "NCHW"}
+                # Lookup the layout required by this input shape
+                layout = rank_to_layout[len(shape)]
             # If there is a layout annotation, use this to determine the index
             # of the channel dimension
-            if layout is not None and "C" in layout:
+            if layout is not None and "C" in layout:  # noqa: Duplicate
                 # Lookup the index in list
                 cdim = layout.index("C")
             # If no layout has been annotated or there is no channel dimension,