[TEST] Add test to verify move scalar add/mul past conv only operates on linear segments

tests/transformation/test_move_scalar_past_conv.py

+import numpy as np
+import onnx.helper as oh
+import pytest
+from onnx import TensorProto
+
+import finn.core.onnx_exec as ox
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.streamline import (
+    MoveScalarAddPastConv,
+    MoveScalarMulPastConv,
+)
+
+
+@pytest.mark.parametrize(
+    "test_args", [("Add", MoveScalarAddPastConv()), ("Mul", MoveScalarMulPastConv())],
+)
+def test_move_scalar_past_conv_only_if_linear(test_args):
+    scalar_op = test_args[0]
+    transf_fxn = test_args[1]
+
+    in_feature_dim = 7
+    in_chn = 1
+    padding = False
+    stages = 3
+    kernel_size = 3
+
+    out_feature_dim = (
+        in_feature_dim if padding else in_feature_dim - (kernel_size // 2 * 2) * stages
+    )
+
+    input_shape = [1, in_chn, in_feature_dim, in_feature_dim]
+    output_shape = [1, in_chn, out_feature_dim, out_feature_dim]
+
+    conv_param_shape = [in_chn, in_chn, kernel_size, kernel_size]
+
+    conv_config = {}
+    conv_config["dilations"] = [1, 1]
+    conv_config["group"] = 1
+    conv_config["kernel_shape"] = [kernel_size, kernel_size]
+    conv_config["pads"] = [0, 0, 0, 0]
+    conv_config["strides"] = [1, 1]
+
+    top_in = oh.make_tensor_value_info("top_in", TensorProto.FLOAT, input_shape)
+    top_out = oh.make_tensor_value_info("top_out", TensorProto.FLOAT, output_shape)
+
+    value_info = [oh.make_tensor_value_info("p1", TensorProto.FLOAT, [1])]
+    value_info += [oh.make_tensor_value_info("p2", TensorProto.FLOAT, conv_param_shape)]
+    value_info += [oh.make_tensor_value_info("p3", TensorProto.FLOAT, conv_param_shape)]
+    value_info += [oh.make_tensor_value_info("p4", TensorProto.FLOAT, conv_param_shape)]
+    value_info += [oh.make_tensor_value_info("p5", TensorProto.FLOAT, conv_param_shape)]
+
+    modelproto = oh.make_model(
+        oh.make_graph(
+            name="test",
+            inputs=[top_in],
+            outputs=[top_out],
+            value_info=value_info,
+            nodes=[
+                oh.make_node("Conv", ["top_in", "p2"], ["t1"], **conv_config),
+                oh.make_node(scalar_op, ["t1", "p1"], ["t2"]),
+                oh.make_node("Conv", ["t2", "p3"], ["t3"], **conv_config),
+                oh.make_node("Conv", ["t2", "p4"], ["t4"], **conv_config),
+                oh.make_node(scalar_op, ["t3", "t4"], ["t5"]),
+                oh.make_node("Conv", ["t5", "p5"], ["top_out"], **conv_config),
+            ],
+        )
+    )
+    model = ModelWrapper(modelproto)
+    model = model.transform(InferShapes())
+
+    np.random.seed(0)
+    model.set_initializer("p1", *np.random.rand(1).astype(np.float32))
+    model.set_initializer("p2", np.random.rand(*conv_param_shape).astype(np.float32))
+    model.set_initializer("p3", np.random.rand(*conv_param_shape).astype(np.float32))
+    model.set_initializer("p4", np.random.rand(*conv_param_shape).astype(np.float32))
+    model.set_initializer("p5", np.random.rand(*conv_param_shape).astype(np.float32))
+    new_model = model.transform(transf_fxn)
+    inp_dict = {"top_in": np.random.rand(*input_shape).astype(np.float32)}
+
+    assert ox.compare_execution(model, new_model, inp_dict)
+    assert new_model.graph.node[0].op_type == "Conv"
+    assert new_model.graph.node[1].op_type == scalar_op
+    assert new_model.graph.node[2].op_type == "Conv"
+    assert new_model.graph.node[3].op_type == "Conv"
+    assert new_model.graph.node[4].op_type == scalar_op
+    assert new_model.graph.node[5].op_type == "Conv"