text: # Copyright (c) 2020 Xilinx, Inc.
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  # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  
  import numpy as np
  import onnx.helper as helper
  
  from finn.core.datatype import DataType
  from finn.custom_op.base import CustomOp
  
  
  def multithreshold(v, thresholds, out_scale=None, out_bias=None):
      """Given a set of threshold values t={t_0, t_1 ... t_n} the successive
      thresholding maps any real number x to an integer in the interval [0, n],
      where the returned integer is the number of thresholds x is greater than
      or equal to.
  
      The output tensor will be scaled by out_scale and biased by out_bias."""
      # the inputs are expected to be in the shape (N,C,H,W) or (N, C)
      # the MultiThreshold node supports a data_layout attribute that can be set
      # to 'NHWC' to support (N,H,W,C) data layout mode for in-out as well
      # N : Batch size
      # C : Number of channels
      # H : Heigth of the input images
      # W : Width of the input images
      #
      # the thresholds are expected to be in the shape (C, B)
      # C : Number of channels (must be the same value as C in input tensor
      #     or 1 if all channels use the same threshold value)
      # B : Desired activation steps => i.e. for 4-bit activation,
      #     B=7 (2^(n)-1 and n=4)
      # the output tensor will be scaled by out_scale and biased by out_bias
      # assert threshold shape
      is_global_threshold = thresholds.shape[0] == 1
      assert (
          v.shape[1] == thresholds.shape[0]
      ) or is_global_threshold, """"Threshold
      shape incorrect"""
      # save the required shape sizes for the loops (N, C and B)
      num_batch = v.shape[0]
      num_channel = v.shape[1]
      num_act = thresholds.shape[1]
      # reshape inputs to enable channel-wise reading
      vr = v.reshape((v.shape[0], v.shape[1], -1))
      # initiate output tensor
      ret = np.zeros_like(vr)
      # iterate over thresholds channel-wise
      for t in range(num_channel):
          channel_thresh = thresholds[0] if is_global_threshold else thresholds[t]
          # iterate over batches
          for b in range(num_batch):
              # iterate over the different thresholds for one channel
              for a in range(num_act):
                  ret[b][t] += (vr[b][t] >= channel_thresh[a]).astype(int)
  
      if out_scale is None:
          out_scale = 1.0
      if out_bias is None:
          out_bias = 0.0
      return out_scale * ret.reshape(v.shape) + out_bias
  
  
  class MultiThreshold(CustomOp):
      """Class that corresponds to a multithresholding node."""
  
      def get_nodeattr_types(self):
          return {
              "out_dtype": ("s", True, ""),
              "out_scale": ("f", False, 1.0),
              "out_bias": ("f", False, 0.0),
              "data_layout": ("s", False, "NCHW", {"NCHW", "NHWC"}),
          }
  
      def make_shape_compatible_op(self, model):
          node = self.onnx_node
          return helper.make_node("Relu", [node.input[0]], [node.output[0]])
  
      def infer_node_datatype(self, model):
          node = self.onnx_node
          odt = self.get_nodeattr("out_dtype")
          is_float = False
          scale = self.get_nodeattr("out_scale")
          bias = self.get_nodeattr("out_bias")
          if scale is not None and (int(scale) != scale):
              is_float = True
          if bias is not None and (int(bias) != bias):
              is_float = True
          if is_float:
              model.set_tensor_datatype(node.output[0], DataType["FLOAT32"])
          else:
              model.set_tensor_datatype(node.output[0], DataType[odt])
  
      def execute_node(self, context, graph):
          node = self.onnx_node
          # save inputs
          v = context[node.input[0]]
          thresholds = context[node.input[1]]
          # retrieve attributes if output scaling is used
          out_scale = self.get_nodeattr("out_scale")
          out_bias = self.get_nodeattr("out_bias")
          # transpose input if NHWC data layout is chosen
          data_layout = self.get_nodeattr("data_layout")
          if data_layout == "NHWC":
              if v.ndim == 4:
                  # NHWC -> NCHW
                  v = np.transpose(v, (0, 3, 1, 2))
              elif v.ndim == 2:
                  # no HW dimension means NHWC and NCHW layouts are equivalent
                  pass
              else:
                  raise Exception(
                      "Unknown data_layout and input ndim"
                      " combination for MultiThreshold."
                  )
          # calculate output
          output = multithreshold(v, thresholds, out_scale, out_bias)
          # setting context according to output
          if data_layout == "NHWC":
              if output.ndim == 4:
                  # NCHW -> NHWC
                  output = np.transpose(output, (0, 2, 3, 1))
              elif output.ndim == 2:
                  # no HW dimension means NHWC and NCHW layouts are equivalent
                  pass
              else:
                  raise Exception(
                      "Unknown data_layout and output ndim"
                      " combination for MultiThreshold."
                  )
          context[node.output[0]] = output
  
      def verify_node(self):
          info_messages = []
  
          # verify that all necessary attributes exist
          try:
              self.get_nodeattr("out_dtype")
              info_messages.append("All necessary attributes exist")
          except Exception:
              info_messages.append(
                  """The necessary attributes do not exist.
                  MultiThreshold needs the following attributes:
                  out_scale, out_bias, out_dtype"""
              )
  
          # verify the number of inputs
          if len(self.onnx_node.input) == 2:
              info_messages.append("The number of inputs is correct")
          else:
              info_messages.append(
                  """MultiThreshold needs 2 inputs
                      (data input and threshold values)"""
              )
  
          return info_messages
  