[Transform] use DataType.signed() in MoveMaxPoolPastMultiThreshold

added dt inference to test case

src/finn/transformation/streamline/reorder.py

@@ -27,6 +27,7 @@
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 import numpy as np
+import warnings
 from onnx import helper as oh
 
 from finn.transformation import Transformation
@@ -546,16 +547,10 @@ class MoveMaxPoolPastMultiThreshold(Transformation):
             if n.op_type == "MaxPool" and not model.is_fork_node(n):
                 consumer = model.find_consumer(n.output[0])
                 if consumer is not None and consumer.op_type == "MultiThreshold":
-                    is_signed = True
-                    for attr in consumer.attribute:
-                        if (
-                            attr.name == "out_dtype"
-                            and len(attr.s) >= 5
-                            and attr.s[:4] == b"UINT"
-                        ):
-                            is_signed = False
-
-                    if is_signed:
+                    mt_out = consumer.output[0]
+                    mt_odt = model.get_tensor_datatype(mt_out)
+                    if mt_odt.signed():
+                        warnings.warn("Skipping signed-output MultiThreshold")
                         continue
 
                     # remove old nodes

tests/transformation/test_move_maxpool_past_multithreshold.py

@@ -5,6 +5,7 @@ import finn.core.onnx_exec as oxe
 from finn.core.modelwrapper import ModelWrapper
 from finn.transformation.streamline.reorder import MoveMaxPoolPastMultiThreshold
 from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
 
 
 def get_multithreshold_rand_params(channels, num_of_thres, seed=None):
@@ -79,6 +80,7 @@ def test_move_maxpool_past_multithreshold():
     )
     model = ModelWrapper(modelproto)
     model = model.transform(InferShapes())
+    model = model.transform(InferDataTypes())
 
     model.set_initializer("thres1", np.array([[0]]))
     model.set_initializer(