From c2c6f1085fb7b77ef82e5aa191c06b9024f75b0b Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <maltanar@gmail.com>
Date: Mon, 23 Mar 2020 23:14:55 +0000
Subject: [PATCH] [Test] split ConvertToHLS tests into cnv and fc
---
.../test_convert_to_hls_layers_cnv.py | 77 +++++++++++++++++++
...rs.py => test_convert_to_hls_layers_fc.py} | 21 +----
2 files changed, 78 insertions(+), 20 deletions(-)
create mode 100644 tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
rename tests/fpgadataflow/{test_convert_to_hls_layers.py => test_convert_to_hls_layers_fc.py} (90%)
diff --git a/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py b/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
new file mode 100644
index 000000000..33a3f1926
--- /dev/null
+++ b/tests/fpgadataflow/test_convert_to_hls_layers_cnv.py
@@ -0,0 +1,77 @@
+# 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)
diff --git a/tests/fpgadataflow/test_convert_to_hls_layers.py b/tests/fpgadataflow/test_convert_to_hls_layers_fc.py
similarity index 90%
rename from tests/fpgadataflow/test_convert_to_hls_layers.py
rename to tests/fpgadataflow/test_convert_to_hls_layers_fc.py
index 365e684ed..1a2d65de0 100644
--- a/tests/fpgadataflow/test_convert_to_hls_layers.py
+++ b/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)
--
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