diff --git a/src/finn/core/execute_custom_node.py b/src/finn/core/execute_custom_node.py
index 1d7c0f6b28be6dde803ccb6d0370eb8c69154e6a..744ecf1b72abfd4bf2e60c5e8d258e16f72363fd 100644
--- a/src/finn/core/execute_custom_node.py
+++ b/src/finn/core/execute_custom_node.py
@@ -9,14 +9,16 @@ def execute_custom_node(node, context, graph):
if node.op_type == "MultiThreshold":
+ # save inputs
v = context[node.input[0]]
thresholds = context[node.input[1]]
-
+
# calculate output
output = multiThresh.execute(v, thresholds)
# setting context according to output
context[node.output[0]] = output
+ print(output)
else:
# exception if op_type is not supported
diff --git a/tests/test_mixed_onnx_exec.py b/tests/test_mixed_onnx_exec.py
index 06fa7514fae3ec51fa0066976f5b02b6acc2b321..912d62b5cb3962b881f5267c3c425231f0f8ee32 100644
--- a/tests/test_mixed_onnx_exec.py
+++ b/tests/test_mixed_onnx_exec.py
@@ -1,15 +1,20 @@
import numpy as np
+import onnx
from onnx import TensorProto, helper
+import finn.transformation.infer_shapes as si
+from finn.core.modelwrapper import ModelWrapper
+import finn.core.onnx_exec as oxe
+
def test_execute_mixed_model():
- out0 = helper.make_tensor_value_info("out", TensorProto.FLOAT, [6, 3, 2, 2])
+ out0 = helper.make_tensor_value_info("out0", TensorProto.FLOAT, [6, 3, 2, 2])
graph_def = helper.make_graph(
nodes=[
helper.make_node(
- "MultiThreshold", ["v", "thresholds"], ["out"], domain="finn"
+ "MultiThreshold", ["v", "thresholds"], ["out0"], domain="finn"
),
helper.make_node("Relu", ["out0"], ["out1"]),
],
@@ -18,5 +23,202 @@ def test_execute_mixed_model():
helper.make_tensor_value_info("v", TensorProto.FLOAT, [6, 3, 2, 2]),
helper.make_tensor_value_info("thresholds", TensorProto.FLOAT, [3, 7]),
],
- outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, [6, 3, 2, 2])],
+ outputs=[helper.make_tensor_value_info("out1", TensorProto.FLOAT, [6, 3, 2, 2])],
+ value_info = [out0],
)
+ model_def = helper.make_model(graph_def, producer_name='onnx-example')
+
+ model = ModelWrapper(model_def)
+ model = model.transform_single(si.infer_shapes)
+
+ inputs = np.asarray(
+ [
+ 4.8,
+ 3.2,
+ 1.2,
+ 4.9,
+ 7.8,
+ 2.4,
+ 3.1,
+ 4.7,
+ 6.2,
+ 5.1,
+ 4.9,
+ 2.2,
+ 6.2,
+ 0.0,
+ 0.8,
+ 4.7,
+ 0.2,
+ 5.6,
+ 8.9,
+ 9.2,
+ 9.1,
+ 4.0,
+ 3.3,
+ 4.9,
+ 2.3,
+ 1.7,
+ 1.3,
+ 2.2,
+ 4.6,
+ 3.4,
+ 3.7,
+ 9.8,
+ 4.7,
+ 4.9,
+ 2.8,
+ 2.7,
+ 8.3,
+ 6.7,
+ 4.2,
+ 7.1,
+ 2.8,
+ 3.1,
+ 0.8,
+ 0.6,
+ 4.4,
+ 2.7,
+ 6.3,
+ 6.1,
+ 1.4,
+ 5.3,
+ 2.3,
+ 1.9,
+ 4.7,
+ 8.1,
+ 9.3,
+ 3.7,
+ 2.7,
+ 5.1,
+ 4.2,
+ 1.8,
+ 4.1,
+ 7.3,
+ 7.1,
+ 0.4,
+ 0.2,
+ 1.3,
+ 4.3,
+ 8.9,
+ 1.4,
+ 1.6,
+ 8.3,
+ 9.4,
+ ]
+ , dtype=np.float32,
+ ).reshape(6, 3, 2, 2)
+
+ threshold_values = np.asarray(
+ [
+ 0.8,
+ 1.4,
+ 1.7,
+ 3.5,
+ 5.2,
+ 6.8,
+ 8.2,
+ 0.2,
+ 2.2,
+ 3.5,
+ 4.5,
+ 6.6,
+ 8.6,
+ 9.2,
+ 1.3,
+ 4.1,
+ 4.5,
+ 6.5,
+ 7.8,
+ 8.1,
+ 8.9,
+ ]
+ , dtype=np.float32,
+ ).reshape(3, 7)
+
+ input_dict = {}
+ input_dict['v']=inputs
+ input_dict['thresholds']=threshold_values
+
+ output_dict = oxe.execute_onnx(model, input_dict)
+
+ outputs = np.asarray(
+ [
+ 4.0,
+ 3.0,
+ 1.0,
+ 4.0,
+ 5.0,
+ 2.0,
+ 2.0,
+ 4.0,
+ 3.0,
+ 3.0,
+ 3.0,
+ 1.0,
+ 5.0,
+ 0.0,
+ 1.0,
+ 4.0,
+ 1.0,
+ 4.0,
+ 6.0,
+ 7.0,
+ 7.0,
+ 1.0,
+ 1.0,
+ 3.0,
+ 3.0,
+ 3.0,
+ 1.0,
+ 3.0,
+ 4.0,
+ 2.0,
+ 3.0,
+ 7.0,
+ 3.0,
+ 3.0,
+ 1.0,
+ 1.0,
+ 7.0,
+ 5.0,
+ 4.0,
+ 6.0,
+ 2.0,
+ 2.0,
+ 1.0,
+ 1.0,
+ 2.0,
+ 1.0,
+ 3.0,
+ 3.0,
+ 2.0,
+ 5.0,
+ 3.0,
+ 3.0,
+ 4.0,
+ 5.0,
+ 7.0,
+ 3.0,
+ 1.0,
+ 3.0,
+ 2.0,
+ 1.0,
+ 4.0,
+ 6.0,
+ 6.0,
+ 0.0,
+ 1.0,
+ 1.0,
+ 3.0,
+ 6.0,
+ 1.0,
+ 1.0,
+ 6.0,
+ 7.0,
+ ]
+ , dtype=np.float32,
+ ).reshape(6, 3, 2, 2)
+
+ assert (output_dict["out1"] == outputs).all()
+