[Test] Add test for AbsorbConsecutiveTransposes

tests/transformation/test_absorb_opposite_transposes.py

+# Copyright (c) 2020, Xilinx
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+#
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+#   list of conditions and the following disclaimer.
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+# * Redistributions in binary form must reproduce the above copyright notice,
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+#   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"
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+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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+# 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"