[Test] split ConvertToHLS tests into cnv and fc

tests/fpgadataflow/test_convert_to_hls_layers_cnv.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 pkg_resources as pk
+
+import brevitas.onnx as bo
+import numpy as np
+
+import finn.core.onnx_exec as oxe
+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
+
+export_onnx_path_cnv = "test_output_cnv.onnx"
+
+
+def test_convert_to_hls_layers_cnv_w1a1():
+    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.save("cnv-streamline.onnx")
+    # load one of the test vectors
+    fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
+    input_tensor = np.load(fn)["arr_0"].astype(np.float32)
+    assert input_tensor.shape == (1, 3, 32, 32)
+    # generate expected value from streamlined net
+    input_dict = {"global_in": input_tensor}
+    expected_ctx = oxe.execute_onnx(model, input_dict, True)
+    expected = expected_ctx[model.graph.output[0].name]
+
+    model = model.transform(LowerConvsToMatMul())
+    model = model.transform(MakeMaxPoolNHWC())
+    model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
+    model.save("cnv-lower.onnx")
+    produced_ctx = oxe.execute_onnx(model, input_dict, True)
+    produced = produced_ctx[model.graph.output[0].name]
+    assert np.isclose(expected, produced, atol=1e-3).all()
+    os.remove(export_onnx_path_cnv)

tests/fpgadataflow/test_convert_to_hls_layers.py → tests/fpgadataflow/test_convert_to_hls_layers_fc.py

@@ -38,7 +38,6 @@ import torch
 import finn.core.onnx_exec as oxe
 import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
 import finn.transformation.streamline.absorb as absorb
-from finn.transformation.streamline.reorder import MakeMaxPoolNHWC
 from finn.core.modelwrapper import ModelWrapper
 from finn.custom_op.registry import getCustomOp
 from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
@@ -51,8 +50,7 @@ from finn.transformation.infer_shapes import InferShapes
 from finn.transformation.streamline import Streamline
 from finn.transformation.streamline.round_thresholds import RoundAndClipThresholds
 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
+
 
 export_onnx_path = "test_output_tfc.onnx"
 export_onnx_path_cnv = "test_output_cnv.onnx"
@@ -127,23 +125,6 @@ def test_convert_to_hls_layers_tfc_w1a1():
     assert np.isclose(produced, expected, atol=1e-3).all()
 
 
-def test_convert_to_hls_layers_cnv_w1a1():
-    tfc = get_test_model_trained("CNV", 1, 1)
-    bo.export_finn_onnx(tfc, (1, 3, 32, 32), export_onnx_path)
-    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.save("cnv-streamline.onnx")
-    model = model.transform(LowerConvsToMatMul())
-    model = model.transform(MakeMaxPoolNHWC())
-    model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
-    model.save("cnv-lower.onnx")
-
-
 def test_convert_to_hls_layers_tfc_w1a2():
     tfc = get_test_model_trained("TFC", 1, 2)
     bo.export_finn_onnx(tfc, (1, 1, 28, 28), export_onnx_path)