[Transform] add fold_constants

src/finn/transformation/fold_constants.py

+import finn.core.onnx_exec as oxe
+
+
+def fold_constants(model):
+    """Replace the output of a node with const-only inputs with a precomputed
+    result."""
+    graph = model.graph
+    nodes_to_remove = []
+    node_ind = 0
+    graph_modified = False
+    execution_context = model.make_empty_exec_context()
+    for n in graph.node:
+        node_ind += 1
+        node_inp_inits = list(map(lambda x: model.get_initializer(x), n.input))
+        node_inp_dyn = list(filter(lambda x: x is None, node_inp_inits))
+        node_out = n.output[0]
+        if len(node_inp_dyn) == 0:
+            # this node has no dynamic inputs, only constant ones -- so we can
+            # do constant folding.
+            oxe.execute_node(n, execution_context, graph)
+            # use the execution result as an initializer
+            model.set_initializer(node_out, execution_context[node_out])
+            # mark computational node for removal
+            nodes_to_remove += [n]
+            graph_modified = True
+    for n in nodes_to_remove:
+        graph.node.remove(n)
+        graph_modified = True
+    # TODO remove unused tensors?
+    return (model, graph_modified)