From 74bee9c2fe68f9d6c55cb5078bee75ff90700795 Mon Sep 17 00:00:00 2001
From: auphelia <jakobapk@web.de>
Date: Thu, 25 Jun 2020 15:29:47 +0100
Subject: [PATCH] [Test] Add test for AbsorbScalarMulIntoTopK transformation
---
.../test_absorb_mul_into_topk.py | 110 ++++++++++++++++++
1 file changed, 110 insertions(+)
create mode 100644 tests/transformation/test_absorb_mul_into_topk.py
diff --git a/tests/transformation/test_absorb_mul_into_topk.py b/tests/transformation/test_absorb_mul_into_topk.py
new file mode 100644
index 000000000..71af5ddd9
--- /dev/null
+++ b/tests/transformation/test_absorb_mul_into_topk.py
@@ -0,0 +1,110 @@
+# 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 pytest
+
+import numpy as np
+from onnx import TensorProto, helper
+
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.general import GiveUniqueNodeNames, GiveReadableTensorNames
+from finn.transformation.insert_topk import InsertTopK
+from finn.transformation.streamline.absorb import AbsorbScalarMulIntoTopK
+import finn.core.onnx_exec as oxe
+
+# parameter to indicate if mul parameter is negative or positive
+@pytest.mark.parametrize("mul_positive", [True, False])
+# parameter to indicate if mul parameter is scalar or not
+@pytest.mark.parametrize("scalar", [True, False])
+def test_absorb_mul_into_topk(mul_positive, scalar):
+ if scalar is True:
+ shape = [1]
+ else:
+ shape = [1, 1, 1, 1000]
+ inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, [1, 1, 1, 1000])
+ a0 = helper.make_tensor_value_info("a0", TensorProto.FLOAT, shape)
+ outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, [1, 1, 1, 1000])
+
+ mul_node = helper.make_node("Mul", ["inp", "a0"], ["outp"])
+ mul_graph = helper.make_graph(
+ nodes=[mul_node],
+ name="mul-graph",
+ inputs=[inp],
+ outputs=[outp],
+ value_info=[a0],
+ )
+
+ model = helper.make_model(mul_graph, producer_name="mul_model")
+ model = ModelWrapper(model)
+ # initialize values
+ if mul_positive is True:
+ a0_values = np.random.uniform(low=0.1, high=1, size=tuple(shape)).astype(
+ np.float32
+ )
+ else:
+ a0_values = np.random.uniform(low=-1, high=-0.1, size=tuple(shape)).astype(
+ np.float32
+ )
+ model.set_initializer("a0", a0_values)
+ model = model.transform(InsertTopK())
+ model = model.transform(InferShapes())
+ model = model.transform(InferDataTypes())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(GiveReadableTensorNames())
+ model.save("test.onnx")
+ model_transformed = model.transform(AbsorbScalarMulIntoTopK())
+ model_transformed.save("test2.onnx")
+
+ # compare execution results
+ inp_values = np.random.uniform(low=-10, high=10, size=(1, 1, 1, 1000)).astype(
+ np.float32
+ )
+ idict = {"global_in": inp_values}
+ odict = oxe.execute_onnx(model, idict, True)
+ y_indices = odict["global_out"]
+ y_values = odict["TopK_0_out0"]
+ odict = oxe.execute_onnx(model_transformed, idict, True)
+ y_tr_indices = odict["global_out"]
+ y_tr_values = odict["TopK_0_out0"]
+
+ # the indices stay the same, if the model is transformed or not
+ assert (y_indices == y_tr_indices).all()
+
+ if scalar is True and mul_positive is True:
+ # the values change if the model was transformed
+ assert (y_values != y_tr_values).all()
+
+ # check for new order
+ assert model.graph != model_transformed.graph
+ assert len(model.graph.node) - 1 == len(model_transformed.graph.node)
+ assert model_transformed.graph.node[0].op_type == "TopK"
+
+ else:
+ assert (y_values == y_tr_values).all()
+ assert model.graph == model_transformed.graph
--
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