From a44ebfcda748ad173ce738108969c07632a9051f Mon Sep 17 00:00:00 2001
From: auphelia <jakobapk@web.de>
Date: Thu, 28 Nov 2019 16:52:58 +0000
Subject: [PATCH] [Test] Added test that creates a maxpool batch streaming
layer with two new attributes
---
tests/fpgadataflow/test_code_gen_trafo.py | 112 ++++++++++++++++++++++
1 file changed, 112 insertions(+)
create mode 100644 tests/fpgadataflow/test_code_gen_trafo.py
diff --git a/tests/fpgadataflow/test_code_gen_trafo.py b/tests/fpgadataflow/test_code_gen_trafo.py
new file mode 100644
index 000000000..de7f022a0
--- /dev/null
+++ b/tests/fpgadataflow/test_code_gen_trafo.py
@@ -0,0 +1,112 @@
+import numpy as np
+from onnx import TensorProto, helper
+
+import finn.transformation.code_gen_transformation as cg_trafo
+from finn.core.datatype import DataType
+from finn.core.modelwrapper import ModelWrapper
+
+
+def test_code_gen_trafo():
+ inp = helper.make_tensor_value_info("in", TensorProto.FLOAT, [2, 2, 4, 4])
+ outp = helper.make_tensor_value_info("out", TensorProto.FLOAT, [2, 2, 2, 2])
+
+ MaxPool_batch_node = helper.make_node(
+ "StreamingMaxPool_Batch",
+ ["in"],
+ ["out"],
+ domain="finn",
+ backend="fpgadataflow",
+ code_gen_dir="",
+ executable_path="",
+ ImgDim=4,
+ PoolDim=2,
+ NumChannels=2,
+ )
+
+ graph = helper.make_graph(
+ nodes=[MaxPool_batch_node],
+ name="max_pool_batch_graph",
+ inputs=[inp],
+ outputs=[outp],
+ )
+ model = helper.make_model(graph, producer_name="finn-hls-onnx-model")
+ model = ModelWrapper(model)
+
+ # set the tensor datatypes (in this case: all to bipolar)
+ for tensor in graph.input:
+ model.set_tensor_datatype(tensor.name, DataType["BIPOLAR"])
+ for tensor in graph.output:
+ model.set_tensor_datatype(tensor.name, DataType["BIPOLAR"])
+
+ input_tensor = np.asarray(
+ [
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ 1,
+ ],
+ dtype=np.float32,
+ ).reshape(2, 2, 4, 4)
+
+ input_dict = {"in": input_tensor}
+ # cg_trafo.
--
GitLab