[Core] add canonical data layouts in own file

src/finn/core/data_layout.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.
+
+# predefined lists of strings to have a cannonical way of expresing data layout
+# annotations
+
+NHWC = ["N", "H", "W", "C"]
+NCHW = ["N", "C", "H", "W"]
+NC = ["N", "C"]

tests/core/test_modelwrapper.py

@@ -31,6 +31,7 @@ import onnx
 from collections import Counter
 import brevitas.onnx as bo
 import numpy as np
+import finn.core.data_layout as data_layout
 
 from finn.core.modelwrapper import ModelWrapper
 from finn.util.test import get_test_model_trained
@@ -69,7 +70,7 @@ def test_modelwrapper():
     assert out_prod.op_type == "MultiThreshold"
     inp_layout = model.get_tensor_layout(inp_name)
     assert inp_layout is None
-    inp_layout = ["N", "C", "H", "W"]
+    inp_layout = data_layout.NCHW
     model.set_tensor_layout(inp_name, inp_layout)
     assert model.get_tensor_layout(inp_name) == inp_layout
     os.remove(export_onnx_path)