diff --git a/src/finn/custom_op/fpgadataflow/fmpadding.py b/src/finn/custom_op/fpgadataflow/fmpadding.py
index 675bbfee67d99dfa8ee288e717d52ea5dad9e397..fa321dfa65d14b67fa218fb6a49f602ddab8d57e 100644
--- a/src/finn/custom_op/fpgadataflow/fmpadding.py
+++ b/src/finn/custom_op/fpgadataflow/fmpadding.py
@@ -7,26 +7,39 @@ from finn.util.data_packing import npy_to_rtlsim_input, rtlsim_output_to_npy
 
 
 class FMPadding_Batch(HLSCustomOp):
-    """Class that corresponds to finn-hlslib FMPadding_Batch function.
-    Implements 'same' padding on a given input image."""
+    """Corresponds to finn-hlslib FMPadding_Batch function.
+    Pads input image by given amount."""
 
     def __init__(self, onnx_node):
         super().__init__(onnx_node)
 
     def get_nodeattr_types(self):
         my_attrs = {
+            # spatial size of input images
             "ImgDim": ("i", True, 0),
-            "OutputDim": ("i", True, 0),
+            # total padding (per dimension) to apply
             "Padding": ("i", True, 2),
+            # number of channels in input image
             "NumChannels": ("i", True, 0),
             # FINN input datatype
             "inputDataType": ("s", True, ""),
+            # controls distribution of padded pixels
+            # in case of uneven padding -- see FMPadding fxn
+            # in hlslib
             "PaddingStyle": ("i", False, 2),
+            # shape describing input vecs per execution
             "numInputVectors": ("i", False, 1),
         }
         my_attrs.update(super().get_nodeattr_types())
         return my_attrs
 
+    def get_padded_odim(self):
+        "Return the padded spatial size of the output."
+
+        idim = self.get_nodeattr("ImgDim")
+        pad = self.get_nodeattr("Padding")
+        return idim + pad
+
     def get_normal_input_shape(self):
         idim = self.get_nodeattr("ImgDim")
         num_ch = self.get_nodeattr("NumChannels")
@@ -35,7 +48,7 @@ class FMPadding_Batch(HLSCustomOp):
         return ishape
 
     def get_normal_output_shape(self):
-        odim = self.get_nodeattr("OutputDim")
+        odim = self.get_padded_odim()
         num_ch = self.get_nodeattr("NumChannels")
 
         oshape = (1, odim, odim, num_ch)
@@ -124,7 +137,7 @@ class FMPadding_Batch(HLSCustomOp):
             #define Padding1 {}\n#define NumChannels1 {}\n
             #define PaddingStyle1 {}\n#define numReps {}\n""".format(
                 self.get_nodeattr("ImgDim"),
-                self.get_nodeattr("OutputDim"),
+                self.get_padded_odim(),
                 self.get_nodeattr("Padding"),
                 self.get_nodeattr("NumChannels"),
                 self.get_nodeattr("PaddingStyle"),
diff --git a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
index 6f45d498cc7d6fd663cd6b507cfd780b69d51dd8..d421a5f3ef8ca980b399087de1482b2ae913da1b 100644
--- a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
+++ b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
@@ -91,7 +91,6 @@ class InferConvInpGen(Transformation):
                         domain="finn",
                         backend="fpgadataflow",
                         ImgDim=ifm_dim,
-                        OutputDim=odim_padding,
                         Padding=2 * pad,
                         NumChannels=ifm_ch,
                         inputDataType=dt.name,
diff --git a/tests/fpgadataflow/test_fpgadataflow_fmpadding.py b/tests/fpgadataflow/test_fpgadataflow_fmpadding.py
index 76ba9ced592927740df46e1508899801bac74c32..9d6390b2673e5d2c0e72748183ac04ed222d078e 100644
--- a/tests/fpgadataflow/test_fpgadataflow_fmpadding.py
+++ b/tests/fpgadataflow/test_fpgadataflow_fmpadding.py
@@ -42,7 +42,6 @@ def make_single_fmpadding_modelwrapper(idim, padding, num_ch, idt, pad_style):
         domain="finn",
         backend="fpgadataflow",
         ImgDim=idim,
-        OutputDim=odim,
         Padding=padding,
         NumChannels=num_ch,
         inputDataType=str(idt.name),