Fixed formatting

Author: diaconuccalin

Deeploy/CommonExtensions/NetworkDeployers/SignPropDeployer.py

@@ -25,16 +25,16 @@ def __init__(self,
                  inputOffsets: Dict[str, int] = {},
                  n_cores: int = 8):
         super().__init__(
-            graph=graph,
-            deploymentPlatform=deploymentPlatform,
-            inputTypes=inputTypes,
-            loweringOptimizer=loweringOptimizer,
-            scheduler=scheduler,
-            name=name,
-            default_channels_first=default_channels_first,
-            deeployStateDir=deeployStateDir,
-            n_cores=n_cores,
-            )
+            graph = graph,
+            deploymentPlatform = deploymentPlatform,
+            inputTypes = inputTypes,
+            loweringOptimizer = loweringOptimizer,
+            scheduler = scheduler,
+            name = name,
+            default_channels_first = default_channels_first,
+            deeployStateDir = deeployStateDir,
+            n_cores = n_cores,
+        )
 
         if inputOffsets == {}:
             for key in inputTypes.keys():

Deeploy/Targets/PULPOpen/TileConstraints/DWConvTileConstraint.py

@@ -245,7 +245,7 @@ def addGeometricalConstraint(tilerModel: TilerModel, parseDict: Dict, ctxt: Netw
 
         # ===== GET NECESSARY INFORMATION =====
         # Get to-be-tiled tensor's buffers
-        inputBufferName = parseDict['data_in']        
+        inputBufferName = parseDict['data_in']
         outputBufferName = parseDict['data_out']
 
         weightBufferName = parseDict['weight']
@@ -307,8 +307,10 @@ def addGeometricalConstraint(tilerModel: TilerModel, parseDict: Dict, ctxt: Netw
         #   Add constraint for input width and height sizes match
         #   (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).
-        tilerModel.addConstraint(outputHeightVar == (((inputHeightVar + pads[0] + pads[2] - dilations[0] * (weightHeightVar - 1) -1) // strides[0]) + 1))
-        tilerModel.addConstraint(outputWidthVar == (((inputWidthVar + pads[1] + pads[3] - dilations[1] * (weightWidthVar - 1) -1) // strides[1]) + 1))
+        tilerModel.addConstraint(outputHeightVar == (((inputHeightVar + pads[0] + pads[2] - dilations[0] *
+                                                       (weightHeightVar - 1) - 1) // strides[0]) + 1))
+        tilerModel.addConstraint(outputWidthVar == (((inputWidthVar + pads[1] + pads[3] - dilations[1] *
+                                                      (weightWidthVar - 1) - 1) // strides[1]) + 1))
 
         #   Add constraint for input channel size match
         #   (Depends on weight output channel and conv grouping)
@@ -335,7 +337,7 @@ def addGeometricalConstraint(tilerModel: TilerModel, parseDict: Dict, ctxt: Netw
     def addPolicyConstraint(tilerModel: TilerModel, parseDict: Dict, ctxt: NetworkContext) -> TilerModel:
         # ===== GET NECESSARY INFORMATION =====
         # Get to-be-tiled tensor's buffers
-        inputBufferName = parseDict['data_in']        
+        inputBufferName = parseDict['data_in']
         outputBufferName = parseDict['data_out']
 
         weightBufferName = parseDict['weight']

Deeploy/Targets/PULPOpen/TileConstraints/MatMulTileConstraint.py

@@ -47,7 +47,6 @@ def addGeometricalConstraint(tilerModel: TilerModel, parseDict: Dict, ctxt: Netw
                                                          dimIdx = (tensorsShapeLenB - 1) - parseDict['transB'])
 
         #   Output dims
-        outBatchDimsVars = [tilerModel.getTensorDimVar(tensorName=outputBuffer.name, dimIdx=idx) for idx in range(tensorsShapeLenOutput - 2)]
         outputMatrixFirstDimVar = tilerModel.getTensorDimVar(tensorName = outputBuffer.name,
                                                              dimIdx = (tensorsShapeLenOutput - 2))
         outputMatrixSecondDimVar = tilerModel.getTensorDimVar(tensorName = outputBuffer.name,