[Streamline] Update MoveMulPastDWConv transformation

src/finn/transformation/streamline/reorder.py

@@ -32,6 +32,7 @@ from onnx import helper as oh
 
 from finn.transformation import Transformation
 from finn.transformation.infer_shapes import InferShapes
+from finn.core.datatype import DataType
 from finn.core.onnx_exec import execute_node
 from finn.util.basic import get_by_name
 from finn.custom_op.registry import getCustomOp
@@ -361,29 +362,36 @@ class MoveMulPastDWConv(Transformation):
                 ):
                     mul_weight_name = n.input[1]
                     A = model.get_initializer(mul_weight_name)
-                    assert A is not None, "Initializer for mul weights is not set."
+                    if A is None:
+                        continue
                     conv_node = consumer
                     mul_node = n
                     start_name = mul_node.input[0]
                     conv_in_name = conv_node.input[0]
                     conv_in_shape = model.get_tensor_shape(conv_in_name)
                     ifm_ch = conv_in_shape[1]
-                    group_attribute = get_by_name(consumer.attribute, "group").i
+                    group_attribute = get_by_name(consumer.attribute, "group")
+                    if group_attribute is None:
+                        continue
+                    group_attribute = group_attribute.i
                     conv_out_name = conv_node.output[0]
                     conv_out_shape = model.get_tensor_shape(conv_out_name)
-                    if np.prod(A.shape) == ifm_ch == group_attribute:
+                    if A.shape == (1, ifm_ch, 1, 1) and ifm_ch == group_attribute:
                         # if the mul is channelwise and conv is depthwise,
                         # we can simply swap the order of ops
                         # rewire mul input to be conv input
                         conv_node.input[0] = start_name
                         model.set_tensor_shape(start_name, conv_in_shape)
+                        model.set_tensor_datatype(start_name, DataType.FLOAT32)
                         # use old conv input tensor as conv output
                         conv_node.output[0] = conv_in_name
                         model.set_tensor_shape(conv_in_name, conv_out_shape)
+                        model.set_tensor_datatype(conv_in_name, DataType.FLOAT32)
                         # use new conv output as new mul node input
                         mul_node.input[0] = conv_in_name
                         # use old conv output as new mul node output
                         mul_node.output[0] = conv_out_name
+                        model.set_tensor_datatype(conv_out_name, DataType.FLOAT32)
                         # move mul node past conv node
                         graph.node.remove(mul_node)
                         graph.node.insert(node_ind, mul_node)