code: # Copyright 2019 Google LLC
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Folding the BatchNorm op into Conv2D."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import math
  
  import numpy as np
  from tensorflow.core.framework import attr_value_pb2
  from tensorflow.core.framework import graph_pb2
  from tensorflow.core.framework import node_def_pb2
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.platform import tf_logging
  
  from tensorflowjs.converters import graph_rewrite_util
  
  INPUT_ORDER = {
      # Order of inputs for BatchNormWithGlobalNormalization.
      "BatchNormWithGlobalNormalization": [
          "conv_op", "mean_op", "var_op", "beta_op", "gamma_op"
      ],
      # Order of inputs for FusedBatchNorm.
      "FusedBatchNorm": ["conv_op", "gamma_op", "beta_op", "mean_op", "var_op"],
      # Order of inputs for FusedBatchNormV3.
      "FusedBatchNormV3": ["conv_op", "gamma_op", "beta_op", "mean_op", "var_op"]
  }
  # Name of the attribute epsilon value is stored in.
  EPSILON_ATTR = {
      "BatchNormWithGlobalNormalization": "variance_epsilon",
      "FusedBatchNorm": "epsilon",
      "FusedBatchNormV3": "epsilon"
  }
  
  # pylint: disable=R0915
  def fold_batch_norms(input_graph_def):
    """Removes batch normalization ops by folding them into convolutions.
  
    Batch normalization during training has multiple dynamic parameters that are
    updated, but once the graph is finalized these become constants. That means
    there's an opportunity to reduce the computations down to a scale and
    addition, rather than the more expensive multiple ops, and even bake the
    scaling into the convolution weights. This function identifies the typical
    pattern of batch normalization subgraphs, and performs the transformation to
    fold the computations down into a simpler form. It currently only supports
    batch normalization that's performed by the BatchNormWithGlobalNormalization
    FusedBatchNorm and FusedBatchNormV3 ops, and will need to be extended in the
    future to handle the newer style.
  
    Args:
      input_graph_def: A GraphDef containing a model.
  
    Returns:
      Modified graph with BN ops removed, and modified weights.
  
    Raises:
      ValueError: If the graph is badly formed with duplicate node names.
    """
    input_node_map = {}
    for node in input_graph_def.node:
      if node.name not in input_node_map:
        input_node_map[node.name] = node
      else:
        raise ValueError("Duplicate node names detected for ", node.name)
  
    nodes_to_skip = {}
    new_ops = []
    for node in input_graph_def.node:
      if (node.op not in ("BatchNormWithGlobalNormalization",
                          "FusedBatchNorm", "FusedBatchNormV3")):
        continue
  
      bias = None
      conv_op = graph_rewrite_util.node_from_map(
          input_node_map,
          node.input[INPUT_ORDER[node.op].index("conv_op")])
      # There might be an Add/BiasAdd op between the conv and the batchnorm,
      # which we can fold into the mean param of the batchnorm.
      if conv_op.op in ['BiasAdd', 'Add', 'AddV2']:
        add_op = conv_op
        # Follow the first input of the add to get to the conv.
        conv_op = graph_rewrite_util.node_from_map(
            input_node_map, add_op.input[0])
        bias = graph_rewrite_util.node_from_map(input_node_map, add_op.input[1])
        if conv_op.op not in ["Conv2D", "DepthwiseConv2dNative"]:
          # Follow the second input of the add to get to the conv.
          conv_op = graph_rewrite_util.node_from_map(
              input_node_map, add_op.input[1])
          bias = graph_rewrite_util.node_from_map(input_node_map, add_op.input[0])
      if bias and bias.op != 'Const':
        tf_logging.warning("The bias %s after the conv %s was not a constant. "
                           "Maybe because freeze_graph wasn't "
                           "run first?" % (bias.name, conv_op.name))
        continue
      if conv_op.op not in ["Conv2D", "DepthwiseConv2dNative"]:
        tf_logging.warning("Didn't find expected Conv2D or DepthwiseConv2dNative"
                           " input to '%s'" % node.name)
        continue
      weights_op = graph_rewrite_util.node_from_map(
          input_node_map, conv_op.input[1])
      if weights_op.op != "Const":
        tf_logging.warning("Didn't find expected conv Constant input to '%s',"
                           " found %s instead. Maybe because freeze_graph wasn't"
                           " run first?" % (conv_op.name, weights_op))
        continue
      weights = graph_rewrite_util.values_from_const(weights_op)
      if conv_op.op == "Conv2D":
        channel_count = weights.shape[3]
      elif conv_op.op == "DepthwiseConv2dNative":
        channel_count = weights.shape[2] * weights.shape[3]
  
      mean_op = graph_rewrite_util.node_from_map(
          input_node_map,
          node.input[INPUT_ORDER[node.op].index("mean_op")])
      if mean_op.op != "Const":
        tf_logging.warning("Didn't find expected mean Constant input to '%s',"
                           " found %s instead. Maybe because freeze_graph wasn't"
                           " run first?" % (node.name, mean_op))
        continue
      mean_value = graph_rewrite_util.values_from_const(mean_op)
      if bias is not None:
        # Adjust the mean of the batchnorm based on the add op in-between the conv
        # and the batchnorm.
        mean_value = mean_value - graph_rewrite_util.values_from_const(bias)
      if mean_value.shape != (channel_count,):
        tf_logging.warning("Incorrect shape for mean, found %s, expected %s,"
                           " for node %s" % (str(mean_value.shape), str(
                               (channel_count,)), node.name))
        continue
  
      var_op = graph_rewrite_util.node_from_map(
          input_node_map,
          node.input[INPUT_ORDER[node.op].index("var_op")])
      if var_op.op != "Const":
        tf_logging.warning("Didn't find expected var Constant input to '%s',"
                           " found %s instead. Maybe because freeze_graph wasn't"
                           " run first?" % (node.name, var_op))
        continue
      var_value = graph_rewrite_util.values_from_const(var_op)
      if var_value.shape != (channel_count,):
        tf_logging.warning("Incorrect shape for var, found %s, expected %s,"
                           " for node %s" % (str(var_value.shape), str(
                               (channel_count,)), node.name))
        continue
  
      beta_op = graph_rewrite_util.node_from_map(
          input_node_map,
          node.input[INPUT_ORDER[node.op].index("beta_op")])
      if beta_op.op != "Const":
        tf_logging.warning("Didn't find expected beta Constant input to '%s',"
                           " found %s instead. Maybe because freeze_graph wasn't"
                           " run first?" % (node.name, beta_op))
        continue
      beta_value = graph_rewrite_util.values_from_const(beta_op)
      if beta_value.shape != (channel_count,):
        tf_logging.warning("Incorrect shape for beta, found %s, expected %s,"
                           " for node %s" % (str(beta_value.shape), str(
                               (channel_count,)), node.name))
        continue
  
      gamma_op = graph_rewrite_util.node_from_map(
          input_node_map,
          node.input[INPUT_ORDER[node.op].index("gamma_op")])
      if gamma_op.op != "Const":
        tf_logging.warning("Didn't find expected gamma Constant input to '%s',"
                           " found %s instead. Maybe because freeze_graph wasn't"
                           " run first?" % (node.name, gamma_op))
        continue
      gamma_value = graph_rewrite_util.values_from_const(gamma_op)
      if gamma_value.shape != (channel_count,):
        tf_logging.warning("Incorrect shape for gamma, found %s, expected %s,"
                           " for node %s" % (str(gamma_value.shape), str(
                               (channel_count,)), node.name))
        continue
  
      variance_epsilon_value = node.attr[EPSILON_ATTR[node.op]].f
      nodes_to_skip[node.name] = True
      nodes_to_skip[weights_op.name] = True
      nodes_to_skip[conv_op.name] = True
      if bias is not None:
        nodes_to_skip[add_op.name] = True
  
      if scale_after_normalization(node):
        scale_value = (
            (1.0 / np.vectorize(math.sqrt)(var_value + variance_epsilon_value)) *
            gamma_value)
      else:
        scale_value = (
            1.0 / np.vectorize(math.sqrt)(var_value + variance_epsilon_value))
      offset_value = (-mean_value * scale_value) + beta_value
      scaled_weights = np.copy(weights)
      it = np.nditer(
          scaled_weights, flags=["multi_index"], op_flags=["readwrite"])
      if conv_op.op == "Conv2D":
        while not it.finished:
          current_scale = scale_value[it.multi_index[3]]
          it[0] *= current_scale
          it.iternext()
      elif conv_op.op == "DepthwiseConv2dNative":
        channel_multiplier = weights.shape[3]
        while not it.finished:
          current_scale = scale_value[it.multi_index[2] * channel_multiplier +
                                      it.multi_index[3]]
          it[0] *= current_scale
          it.iternext()
      scaled_weights_op = node_def_pb2.NodeDef()
      scaled_weights_op.op = "Const"
      scaled_weights_op.name = conv_op.name + '_weights'
      scaled_weights_op.attr["dtype"].CopyFrom(weights_op.attr["dtype"])
      scaled_weights_op.attr["value"].CopyFrom(
          attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
              scaled_weights, weights.dtype.type, weights.shape)))
      # Replace the weights node with scaled weights node
      for i, weights_node in enumerate(conv_op.input):
        if weights_node == weights_op.name:
          conv_op.input[i] = scaled_weights_op.name
  
      new_conv_op = node_def_pb2.NodeDef()
      new_conv_op.CopyFrom(conv_op)
      offset_op = node_def_pb2.NodeDef()
      offset_op.op = "Const"
      offset_op.name = conv_op.name + "_bn_offset"
      offset_op.attr["dtype"].CopyFrom(mean_op.attr["dtype"])
      offset_op.attr["value"].CopyFrom(
          attr_value_pb2.AttrValue(tensor=tensor_util.make_tensor_proto(
              offset_value, mean_value.dtype.type, offset_value.shape)))
      bias_add_op = node_def_pb2.NodeDef()
      bias_add_op.op = "BiasAdd"
      bias_add_op.name = node.name
      bias_add_op.attr["T"].CopyFrom(conv_op.attr["T"])
      bias_add_op.attr["data_format"].CopyFrom(conv_op.attr["data_format"])
      bias_add_op.input.extend([new_conv_op.name, offset_op.name])
      new_ops.extend([scaled_weights_op, new_conv_op, offset_op, bias_add_op])
  
    result_graph_def = graph_pb2.GraphDef()
    for node in input_graph_def.node:
      if node.name in nodes_to_skip:
        continue
      new_node = node_def_pb2.NodeDef()
      new_node.CopyFrom(node)
      retained_input = []
      for input_node in new_node.input:
        if not input_node.startswith('^') or input_node[1:] not in nodes_to_skip:
          retained_input.append(input_node)
      new_node.input[:] = retained_input
  
      result_graph_def.node.extend([new_node])
  
    result_graph_def.node.extend(new_ops)
    result_graph_def.library.CopyFrom(input_graph_def.library)
    result_graph_def.versions.CopyFrom(input_graph_def.versions)
    return result_graph_def
  
  # Whether to scale by gamma after normalization.
  def scale_after_normalization(node):
    if node.op == "BatchNormWithGlobalNormalization":
      return node.attr["scale_after_normalization"].b
    return True
  
apis: numpy.vectorize numpy.copy numpy.nditer tensorflow.core.framework.node_def_pb2.NodeDef tensorflow.python.framework.tensor_util.make_tensor_proto tensorflow.python.platform.tf_logging.warning tensorflow.core.framework.graph_pb2.GraphDef