[Test] Add test to test MoveFlattenPastTopK trafo

tests/transformation/test_move_flatten_past_topk.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.
+import pytest
+
+from onnx import TensorProto, helper
+
+from finn.core.modelwrapper import ModelWrapper
+from finn.core.datatype import DataType
+import finn.core.data_layout as DataLayout
+from finn.util.basic import gen_finn_dt_tensor
+from finn.transformation.insert_topk import InsertTopK
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.general import GiveUniqueNodeNames, GiveReadableTensorNames
+from finn.transformation.streamline.reorder import MoveFlattenPastTopK
+import finn.core.onnx_exec as oxe
+
+# data layout
+@pytest.mark.parametrize("data_layout", [DataLayout.NHWC, DataLayout.NCHW])
+# batch size
+@pytest.mark.parametrize("batch_size", [1, 2])
+def test_move_flatten_past_affine(data_layout, batch_size):
+    if data_layout == DataLayout.NHWC:
+        ishape = [batch_size, 1, 1, 1024]
+        oshape = [batch_size, 1024]
+    else:
+        ishape = [batch_size, 1024, 1, 1]
+        oshape = [batch_size, 1024]
+
+    inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, ishape)
+    outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, oshape)
+
+    flatten_node = helper.make_node("Flatten", ["inp"], ["outp"])
+
+    graph = helper.make_graph(
+        nodes=[flatten_node], name="move-flatten-graph", inputs=[inp], outputs=[outp],
+    )
+
+    model = helper.make_model(graph, producer_name="move_flatten_model")
+    model = ModelWrapper(model)
+
+    model.set_tensor_datatype("inp", DataType.INT2)
+    model.set_tensor_layout("inp", data_layout)
+    model = model.transform(InsertTopK())
+    model = model.transform(InferShapes())
+    model = model.transform(InferDataTypes())
+    model = model.transform(InferDataLayouts())
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(GiveReadableTensorNames())
+
+    # compare execution before and after transformation
+    inp_values = gen_finn_dt_tensor(DataType.INT2, ishape)
+    idict = {model.graph.input[0].name: inp_values}
+    model_transformed = model.transform(MoveFlattenPastTopK())
+    assert oxe.compare_execution(model, model_transformed, idict)
+
+    # depending on data layout check if graph is transformed or not
+    if data_layout == DataLayout.NHWC:
+        # check if nodes have new order in transformed graph
+        assert model.graph != model_transformed.graph
+        assert model_transformed.graph.node[-1].op_type == "Flatten"
+    else:
+        assert model.graph == model_transformed.graph