[Test] introduce test_infer_data_layouts

tests/transformation/test_infer_data_layouts.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 os
+
+import brevitas.onnx as bo
+import finn.transformation.streamline.absorb as absorb
+from finn.transformation.streamline.reorder import MakeMaxPoolNHWC
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.fold_constants import FoldConstants
+from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.streamline import Streamline
+from finn.util.test import get_test_model_trained
+from finn.transformation.double_to_single_float import DoubleToSingleFloat
+from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
+from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
+import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
+from finn.transformation.infer_data_layouts import InferDataLayouts
+import finn.core.data_layout as DataLayout
+
+export_onnx_path_cnv = "test_output_cnv.onnx"
+
+
+def test_infer_data_layouts():
+    cnv = get_test_model_trained("CNV", 1, 1)
+    bo.export_finn_onnx(cnv, (1, 3, 32, 32), export_onnx_path_cnv)
+    model = ModelWrapper(export_onnx_path_cnv)
+    model = model.transform(DoubleToSingleFloat())
+    model = model.transform(InferShapes())
+    model = model.transform(FoldConstants())
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(GiveReadableTensorNames())
+    model = model.transform(Streamline())
+    model = model.transform(InferDataLayouts())
+
+    assert model.get_tensor_layout("global_in") == DataLayout.NCHW
+    assert model.get_tensor_layout("Conv_0_out0") == DataLayout.NCHW
+    assert model.get_tensor_layout("MaxPool_0_out0") == DataLayout.NCHW
+    assert model.get_tensor_layout("MultiThreshold_6_out0") == DataLayout.NCHW
+    assert model.get_tensor_layout("Reshape_0_out0") == DataLayout.NC
+    assert model.get_tensor_layout("MatMul_0_out0") == DataLayout.NC
+    assert model.get_tensor_layout("global_out") == DataLayout.NC
+
+    model = model.transform(LowerConvsToMatMul())
+    model = model.transform(MakeMaxPoolNHWC())
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(GiveReadableTensorNames())
+    model = model.transform(InferDataLayouts())
+
+    assert model.get_tensor_layout("global_in") == DataLayout.NCHW
+    assert model.get_tensor_layout("Transpose_0_out0") == DataLayout.NHWC
+    assert model.get_tensor_layout("Im2Col_0_out0") == DataLayout.NHWC
+    # note: im2col output isn't really NHWC or any other common layout
+    # since the concept of channels changes with lowering... but it is
+    # conceptually close to NHWC since the innermost dim gets multiplied
+    assert model.get_tensor_layout("MatMul_0_out0") == DataLayout.NHWC
+    assert model.get_tensor_layout("Transpose_1_out0") == DataLayout.NCHW
+    assert model.get_tensor_layout("Transpose_2_out0") == DataLayout.NHWC
+    assert model.get_tensor_layout("MaxPoolNHWC_0_out0") == DataLayout.NHWC
+    assert model.get_tensor_layout("Reshape_0_out0") == DataLayout.NC
+    assert model.get_tensor_layout("global_out") == DataLayout.NC
+
+    model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
+    model = model.transform(ConvertBipolarMatMulToXnorPopcount())
+    model = model.transform(Streamline())
+    model = model.transform(to_hls.InferBinaryStreamingFCLayer())
+    model = model.transform(to_hls.InferQuantizedStreamingFCLayer())
+    model = model.transform(to_hls.InferConvInpGen())
+    model = model.transform(to_hls.InferStreamingMaxPool())
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(GiveReadableTensorNames())
+    model = model.transform(InferDataLayouts())
+
+    assert model.get_tensor_layout("global_in") == DataLayout.NCHW
+    assert model.get_tensor_layout("Transpose_0_out0") == DataLayout.NHWC
+    # note: im2col output isn't really NHWC or any other common layout
+    # since the concept of channels changes with lowering... but it is
+    # conceptually close to NHWC since the innermost dim gets multiplied
+    assert (
+        model.get_tensor_layout("ConvolutionInputGenerator_0_out0") == DataLayout.NHWC
+    )
+    assert model.get_tensor_layout("StreamingFCLayer_Batch_3_out0") == DataLayout.NHWC
+    assert model.get_tensor_layout("Reshape_0_out0") == DataLayout.NC
+    assert model.get_tensor_layout("StreamingFCLayer_Batch_6_out0") == DataLayout.NC
+    assert model.get_tensor_layout("global_out") == DataLayout.NC
+
+    os.remove(export_onnx_path_cnv)