Improved previously found solution for convolution input constraint (clearly mark when data is needed from bordering regions)

Author: diaconuccalin

Deeploy/Targets/PULPOpen/TileConstraints/ConvTileConstraint.py

@@ -308,13 +308,15 @@ def addGeometricalConstraint(tilerModel: TilerModel, parseDict: Dict, ctxt: Netw
         #   (Depends on output height and width, kernel size, padding, dilations, and strides.
         #   For more information on the connections, see ONNX and/or Torch Conv2D documentation).
         #   Assume worst case scenario (data padding on all sides) when tiling on a ceratin dimension.
-        effectiveHeight = inputHeightVar + ((pads[0] + pads[2]) * (inputHeightVar == inputBuffer.shape[1]))
-        effectiveWidth = inputWidthVar + ((pads[1] + pads[3]) * (inputWidthVar == inputBuffer.shape[2]))
+        effectiveHeight = inputHeightVar + ((pads[0] + pads[2]) * (inputHeightVar == inputBuffer.shape[1])) - (
+            (weightHeightVar - 1) * (inputHeightVar != inputBuffer.shape[1]))
+        effectiveWidth = inputWidthVar + ((pads[1] + pads[3]) * (inputWidthVar == inputBuffer.shape[2])) - (
+            (weightWidthVar - 1) * (inputWidthVar != inputBuffer.shape[2]))
 
         tilerModel.addConstraint(
-            (outputHeightVar == (effectiveHeight - dilations[0] * (weightHeightVar - 1) - 1) // strides[0]))
+            (outputHeightVar == (effectiveHeight - dilations[0] * (weightHeightVar - 1) - 1) // strides[0] + 1))
         tilerModel.addConstraint(
-            (outputWidthVar == (effectiveWidth - dilations[1] * (weightWidthVar - 1) - 1) // strides[1]))
+            (outputWidthVar == (effectiveWidth - dilations[1] * (weightWidthVar - 1) - 1) // strides[1] + 1))
 
         #   Add constraint for input channel size match
         #   (Depends on weight output channel and conv grouping)