[Thresholding] Generalize data layouts for node execution

See fastmachinelearning/qonnx#143 for the
similar generalization applied to QONNX MultiThreshold

Author: iksnagreb

src/finn/custom_op/fpgadataflow/thresholding.py

@@ -243,16 +243,29 @@ def execute_node(self, context, graph):
         inp_values = context[node.input[0]]
         th_val = context[node.input[1]]
         out_bias = self.get_nodeattr("ActVal")
-        # MT expects inputs to be in the shape (N,C,H,W) or (N, C)
-        # if 4D then input values in context are (N,H,W,C) and need to
-        # be transposed.
-        # if 2D then inputs can be passed directly to MT function
-        is_4d = len(inp_values.shape) == 4
-        if is_4d:
-            inp_values = np.transpose(inp_values, (0, 3, 1, 2))
+
+        # Consider the data layout for transposing the input into the format
+        # accepted by the multithreshold function above, i.e, the channel
+        # dimension is along the axis with index 1.
+        data_layout = None
+        # If there is no layout annotation, guess based on rank of the tensor
+        # TODO: Currently there is no mechanism here to get the layout
+        #  annotation, we allways guess, but this matches the previous behavior.
+        if len(inp_values.shape) < 5:
+            # Maps tensor rank to layout annotation
+            rank_to_layout = {0: None, 1: "C", 2: "NC", 3: "NWC", 4: "NHWC"}
+            # Lookup the layout required by this input shape
+            data_layout = rank_to_layout[len(inp_values.shape)]
+        # Lookup the index of the channel dimension in the data layout
+        # Note: Assumes there is at most one "C" which denotes the channel
+        # dimension
+        cdim = data_layout.index("C") if "C" in data_layout else 1
+        # Rearrange the input to the expected (N, C, ...) layout
+        inp_values = inp_values.swapaxes(cdim, 1)
         y = multithreshold(inp_values, th_val, out_bias=out_bias)
-        if is_4d:
-            y = y.transpose(0, 2, 3, 1)
+        # Rearrange the output back to the original layout
+        y = y.swapaxes(cdim, 1)
+
         act = DataType[self.get_nodeattr("outputDataType")]
         if act == DataType["BIPOLAR"]:
             # binary to bipolar