diff --git a/src/finn/transformation/streamline/absorb.py b/src/finn/transformation/streamline/absorb.py
index f04c6ba9a79457ff23bd84fbb92a37756be86fe8..8398a277443530e84632d26fbfca6d90ea4b0b9e 100644
--- a/src/finn/transformation/streamline/absorb.py
+++ b/src/finn/transformation/streamline/absorb.py
@@ -360,6 +360,7 @@ class AbsorbTransposeIntoMultiThreshold(Transformation):
model = model.transform(InferDataTypes())
return (model, graph_modified)
+
class AbsorbTransposeIntoFlatten(Transformation):
"""Absorb transpose node into succeeding flatten node, if H=W=1 and the first
dimension stays the same. Can also be applied if flatten is implemented implicitly
@@ -419,9 +420,10 @@ class AbsorbTransposeIntoFlatten(Transformation):
graph.node.insert(node_ind, node)
graph_modified = True
if graph_modified:
- model = model.transform(InferDataTypes())
+ model = model.transform(InferDataTypes())
return (model, graph_modified)
-
+
+
class AbsorbScalarMulIntoTopK(Transformation):
"""Absorb a mul node into a suceeding topk node if the mul is scalar."""
@@ -455,3 +457,84 @@ class AbsorbScalarMulIntoTopK(Transformation):
model = model.transform(InferShapes())
model = model.transform(InferDataTypes())
return (model, graph_modified)
+
+
+class AbsorbConsecutiveTransposes(Transformation):
+ """Remove (Transpose -> Transpose) patterns when the input and output
+ of the pattern have the same layout."""
+
+ def Are_opposite_permutations(self, perms1, perms2):
+ if len(perms1) != len(perms2):
+ return False
+ assert 0 <= max(perms2) < len(perms2), "invalid permutation"
+ assert 0 <= max(perms1) < len(perms1), "invalid permutation"
+
+ for i, p in enumerate(perms2):
+ if perms1[p] != i:
+ return False
+
+ return True
+
+ def apply(self, model):
+ graph = model.graph
+ graph_modified = False
+ for n in graph.node:
+ if n.op_type == "Transpose":
+ if model.is_fork_node(n):
+ next_nodes = model.find_direct_successors(n)
+ perms1 = list(get_by_name(n.attribute, "perm").ints)
+
+ # check if all nodes after fork are opposite transposes
+ all_opposite_transposes = True
+ for next_node in next_nodes:
+ if next_node is not None and next_node.op_type == "Transpose":
+ perms2 = list(get_by_name(next_node.attribute, "perm").ints)
+ if not self.Are_opposite_permutations(perms1, perms2):
+ all_opposite_transposes = False
+ break
+ else:
+ all_opposite_transposes = False
+ break
+
+ if not all_opposite_transposes:
+ continue
+
+ prod = model.find_producer(n.input[0])
+ for next_node in next_nodes:
+ # connect next_node's consumer input to n's producer output
+ # TODO implement this to allow for forks as producers and
+ # joins as consumers
+ cons = model.find_consumer(next_node.output[0])
+ cons.input[0] = prod.output[0]
+
+ # remove consumer transpose
+ graph.node.remove(next_node)
+
+ # remove producer transpose
+ graph.node.remove(n)
+ graph_modified = True
+
+ else:
+ next_node = model.find_consumer(n.output[0])
+ if next_node is not None and next_node.op_type == "Transpose":
+ perms1 = list(get_by_name(n.attribute, "perm").ints)
+ perms2 = list(get_by_name(next_node.attribute, "perm").ints)
+ if self.Are_opposite_permutations(perms1, perms2):
+
+ # connect next_node's consumer input to n's producer output
+ # TODO implement this to allow for forks as producers
+ consumers = model.find_direct_successors(next_node)
+ prod = model.find_producer(n.input[0])
+ for cons in consumers:
+ for cons_in in cons.input:
+ if cons_in == next_node.output[0]:
+ prod.output[0] = cons_in
+ break
+ # remove both transposes
+ graph.node.remove(n)
+ graph.node.remove(next_node)
+
+ graph_modified = True
+ if graph_modified:
+ model = model.transform(InferDataTypes())
+ return (model, graph_modified)
diff --git a/tests/transformation/test_absorb_opposite_transposes.py b/tests/transformation/test_absorb_opposite_transposes.py
new file mode 100644
index 0000000000000000000000000000000000000000..859e691277a261f01b559e2e166763e402c5d689
--- /dev/null
+++ b/tests/transformation/test_absorb_opposite_transposes.py
@@ -0,0 +1,76 @@
+# Copyright (c) 2020, Xilinx
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of FINN nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import numpy as np
+import onnx.helper as oh
+from onnx import TensorProto
+
+import finn.core.onnx_exec as ox
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.streamline.absorb import AbsorbConsecutiveTransposes
+
+
+def test_absorb_opposite_transposes():
+ np.random.seed(0)
+ input_shape = [1, 3, 4, 2]
+ top_in = oh.make_tensor_value_info("top_in", TensorProto.FLOAT, input_shape)
+ top_out = oh.make_tensor_value_info("top_out", TensorProto.FLOAT, input_shape)
+ value_info = [oh.make_tensor_value_info("add_param_0", TensorProto.FLOAT, [1])]
+ value_info += [oh.make_tensor_value_info("add_param_1", TensorProto.FLOAT, [1])]
+ value_info += [oh.make_tensor_value_info("mul_param_0", TensorProto.FLOAT, [1])]
+ modelproto = oh.make_model(
+ oh.make_graph(
+ name="test",
+ inputs=[top_in],
+ outputs=[top_out],
+ value_info=value_info,
+ nodes=[
+ oh.make_node("Add", ["top_in", "add_param_0"], ["t0"]),
+ oh.make_node("Transpose", ["t0"], ["t1"], perm=[0, 2, 3, 1]),
+ oh.make_node("Transpose", ["t1"], ["t2"], perm=[0, 3, 1, 2]),
+ oh.make_node("Add", ["t2", "add_param_1"], ["t3"]),
+ oh.make_node("Transpose", ["t3"], ["t4"], perm=[0, 2, 3, 1]),
+ oh.make_node("Transpose", ["t4"], ["t5"], perm=[0, 3, 1, 2]),
+ oh.make_node("Add", ["t5", "t2"], ["t6"]),
+ oh.make_node("Mul", ["t6", "mul_param_0"], ["top_out"]),
+ ],
+ )
+ )
+ model = ModelWrapper(modelproto)
+ model = model.transform(InferShapes())
+ model.set_initializer("add_param_0", np.asarray([1], dtype=np.float32))
+ model.set_initializer("add_param_1", np.asarray([3], dtype=np.float32))
+ model.set_initializer("mul_param_0", np.asarray([2], dtype=np.float32))
+ new_model = model.transform(AbsorbConsecutiveTransposes())
+ new_model = new_model.transform(InferShapes())
+ inp_dict = {"top_in": np.random.rand(*input_shape).astype(np.float32)}
+ assert ox.compare_execution(model, model, inp_dict)
+ assert len(new_model.graph.node) == 4
+ for n in new_model.graph.node:
+ assert new_model.graph.node[0].op_type != "Transpose"