[Streamline] Add MoveFlattenPastTopK transformation

src/finn/transformation/streamline/reorder.py

@@ -29,8 +29,10 @@
 import numpy as np
 import warnings
 from onnx import helper as oh
+from onnx import TensorProto
 
 from finn.transformation import Transformation
+import finn.core.data_layout as DataLayout
 from finn.transformation.infer_shapes import InferShapes
 from finn.core.onnx_exec import execute_node
 from finn.util.basic import get_by_name
@@ -597,3 +599,64 @@ class MoveMaxPoolPastMultiThreshold(Transformation):
 
         model = model.transform(InferShapes())
         return (model, graph_modified)
+
+
+class MoveFlattenPastTopK(Transformation):
+    """Move flatten node past a succeeding topk node, if the "axis" attribute in topk
+    is set to -1 and the data layout before the flatten is NHWC with H=W=1"""
+
+    def apply(self, model):
+        graph = model.graph
+        node_ind = 0
+        graph_modified = False
+        for n in graph.node:
+            node_ind += 1
+            if n.op_type == "Flatten":
+                consumer = model.find_consumer(n.output[0])
+                if consumer is not None and consumer.op_type == "TopK":
+                    axis = get_by_name(consumer.attribute, "axis")
+                    if axis is None or axis.i != -1:
+                        continue
+                    start_name = n.input[0]
+                    data_layout = model.get_tensor_layout(start_name)
+                    if data_layout != DataLayout.NHWC:
+                        warnings.warn(
+                            """Transformation can't be applied. The input
+                            to flatten has to have DataLayout.NHWC"""
+                        )
+                        continue
+                    (b, h, w, c) = model.get_tensor_shape(start_name)
+                    if h != 1 or w != 1:
+                        continue
+                    # get parameter k from topk
+                    k = model.get_tensor_shape(consumer.output[1])[-1]
+
+                    # swap conections
+                    # new tensor because dims change
+                    middle_name = model.make_new_valueinfo_name()
+                    topk_indices = oh.make_tensor_value_info(
+                        middle_name, TensorProto.INT64, [b, h, w, k]
+                    )
+                    end_name = consumer.output[1]
+                    graph.value_info.append(topk_indices)
+
+                    # remove old nodes
+                    graph.node.remove(n)
+                    graph.node.remove(consumer)
+
+                    # set inputs and outputs correctly
+                    consumer.input[0] = start_name
+                    consumer.output[1] = middle_name
+                    model.set_tensor_shape(consumer.output[0], (b, h, w, k))
+
+                    n.input[0] = middle_name
+                    n.output[0] = end_name
+
+                    # insert them back in
+                    graph.node.insert(node_ind - 1, consumer)
+                    graph.node.insert(node_ind, n)
+
+                    graph_modified = True
+
+        model = model.transform(InferShapes())
+        return (model, graph_modified)