[Test] Add test for MergeONNXModels

tests/transformation/test_merge_onnx_models.py

+# 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.
+
+from pkgutil import get_data
+
+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.general import GiveReadableTensorNames, GiveUniqueNodeNames
+from finn.transformation.merge_onnx_models import MergeONNXModels
+import finn.core.onnx_exec as oxe
+
+
+def test_merge_onnx_models():
+    # load first model
+    raw_m = get_data("finn", "data/onnx/mnist-conv/model.onnx")
+    model1 = ModelWrapper(raw_m)
+    model1 = model1.transform(InferShapes())
+    model1 = model1.transform(GiveUniqueNodeNames())
+    model1 = model1.transform(GiveReadableTensorNames())
+
+    # set up second model that should be inserted before the first model
+    shape = [1, 1, 28, 28]
+    inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, shape)
+    a0 = helper.make_tensor_value_info("a0", TensorProto.FLOAT, [])
+    a1 = helper.make_tensor_value_info("a1", TensorProto.FLOAT, [])
+    outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, shape)
+
+    mul_node = helper.make_node("Mul", ["inp", "a0"], ["mul_out"])
+    div_node = helper.make_node("Div", ["mul_out", "a1"], ["outp"])
+
+    graph = helper.make_graph(
+        nodes=[mul_node, div_node],
+        name="model2-graph",
+        inputs=[inp],
+        outputs=[outp],
+        value_info=[a0, a1],
+    )
+
+    model2 = helper.make_model(graph, producer_name="model2")
+    model2 = ModelWrapper(model2)
+    # initialize model2
+    a0_value = np.random.uniform(low=0.1, high=0.99, size=(1)).astype(np.float32)
+    model2.set_initializer("a0", a0_value)
+    a1_value = 1.0 / a0_value
+    model2.set_initializer("a1", a1_value)
+    model2 = model2.transform(InferShapes())
+    model2 = model2.transform(GiveUniqueNodeNames())
+    model2 = model2.transform(GiveReadableTensorNames())
+
+    # simulate the models before the merging and pass the output of model2 to model1
+    inp_values = np.random.uniform(low=-1, high=1, size=tuple(shape)).astype(np.float32)
+    idict = {model2.graph.input[0].name: inp_values}
+    odict = oxe.execute_onnx(model2, idict)
+    temp = odict[model2.graph.output[0].name]
+
+    idict = {model1.graph.input[0].name: temp}
+    odict = oxe.execute_onnx(model1, idict)
+    outp = odict[model1.graph.output[0].name]
+    # merge models
+    model_transformed = model1.transform(MergeONNXModels(model2))
+
+    idict = {model_transformed.graph.input[0].name: inp_values}
+    odict = oxe.execute_onnx(model_transformed, idict)
+    outp_transformed = odict[model_transformed.graph.output[0].name]
+
+    assert (outp == outp_transformed).all()
+    assert len(model_transformed.graph.node) == len(model1.graph.node) + len(
+        model2.graph.node
+    )