From df6a26913eb0dbb5232f9a6f47ca36aa8cb7b8e4 Mon Sep 17 00:00:00 2001
From: auphelia <jakobapk@web.de>
Date: Mon, 18 May 2020 15:33:03 +0100
Subject: [PATCH] [Test] Add first draft of unit test for HLSCustomOp
SameResize_Batch
---
.../test_fpgadataflow_sameresize.py | 64 +++++++++++++++++++
1 file changed, 64 insertions(+)
create mode 100644 tests/fpgadataflow/test_fpgadataflow_sameresize.py
diff --git a/tests/fpgadataflow/test_fpgadataflow_sameresize.py b/tests/fpgadataflow/test_fpgadataflow_sameresize.py
new file mode 100644
index 000000000..8177e92ef
--- /dev/null
+++ b/tests/fpgadataflow/test_fpgadataflow_sameresize.py
@@ -0,0 +1,64 @@
+import pytest
+
+from onnx import TensorProto, helper
+from finn.core.datatype import DataType
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.infer_shapes import InferShapes
+
+
+def make_single_sameresize_modelwrapper(idim, kdim, stride, num_ch, idt, pad_style):
+ assert idim % stride == 0, "Stride must divide input dimension."
+ # number of "same" windows over the input data
+ same_windows = idim // stride
+ odim = kdim + stride * (same_windows - 1)
+ inp = helper.make_tensor_value_info(
+ "inp", TensorProto.FLOAT, [1, idim, idim, num_ch]
+ )
+ outp = helper.make_tensor_value_info(
+ "outp", TensorProto.FLOAT, [1, odim, odim, num_ch]
+ )
+
+ SameResize_node = helper.make_node(
+ "SameResize_Batch",
+ ["inp"],
+ ["outp"],
+ domain="finn",
+ backend="fpgadataflow",
+ ImgDim=idim,
+ KernelDim=kdim,
+ Stride=stride,
+ NumChannels=num_ch,
+ inputDataType=str(idt.name),
+ PaddingStyle=pad_style,
+ )
+
+ graph = helper.make_graph(
+ nodes=[SameResize_node], name="sameresize_graph", inputs=[inp], outputs=[outp]
+ )
+
+ model = helper.make_model(graph, producer_name="sameresize-model")
+ model = ModelWrapper(model)
+
+ model.set_tensor_datatype("inp", idt)
+ model.set_tensor_datatype("outp", idt)
+
+ return model
+
+
+# image dimension
+@pytest.mark.parametrize("idim", [4])
+# kernel dimension
+@pytest.mark.parametrize("kdim", [2])
+# stride
+@pytest.mark.parametrize("stride", [1])
+# number of channels
+@pytest.mark.parametrize("num_ch", [2])
+# FINN input datatype
+@pytest.mark.parametrize("idt", [DataType.INT2])
+# PaddingStyle: distribution of added values to achieve "same" padding
+@pytest.mark.parametrize("pad_style", [2])
+def test_fpgadataflow_sameresize(idim, kdim, stride, num_ch, idt, pad_style):
+ model = make_single_sameresize_modelwrapper(
+ idim, kdim, stride, num_ch, idt, pad_style
+ )
+ model = model.transform(InferShapes())
--
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