Function ConvertConvOperator

tensorflow/lite/toco/import_tensorflow.cc:789–868

}

tensorflow::Status ConvertConvOperator(
    const NodeDef& node, const TensorFlowImportFlags& tf_import_flags,
    const ModelFlags& model_flags, Model* model) {
  CHECK_EQ(node.op(), "Conv2D");
  TF_RETURN_IF_ERROR(CheckInputsCount(node, tf_import_flags, 2));

  // We only support NHWC, which is the default data_format.
  // So if data_format is not defined, we're all good.
  TF_RETURN_IF_ERROR(CheckOptionalAttr(node, "data_format", "NHWC"));
  TF_RETURN_IF_ERROR(CheckOptionalAttr(node, "T", DT_FLOAT));

  const auto& input_name = node.input(0);
  const auto& weights_name = node.input(1);
  const auto& reordered_weights_name =
      AvailableArrayName(*model, weights_name + "_reordered");
  // Check if a ReorderAxesOperator was already created for these weights
  // (that happens when multiple layers share the same weights).
  const Operator* existing_reorder =
      GetOpWithOutput(*model, reordered_weights_name);
  if (existing_reorder) {
    // Check that it is safe to rely on the _reordered naming of the output
    // array!
    CHECK(existing_reorder->type == OperatorType::kReorderAxes);
  } else {
    // Create a new ReorderAxesOperator
    auto* reorder = new ReorderAxesOperator;
    reorder->inputs = {weights_name};
    reorder->outputs = {reordered_weights_name};
    reorder->input_axes_order = AxesOrder::kHWIO;
    reorder->output_axes_order = AxesOrder::kOHWI;
    model->operators.emplace_back(reorder);
  }
  if (!HasAttr(node, "strides")) {
    return tensorflow::errors::InvalidArgument("Missing attribute 'strides'");
  }
  const auto& strides = GetListAttr(node, "strides");
  TF_RETURN_IF_ERROR(ExpectValue(strides.i_size(), 4, "number of strides"));
  TF_RETURN_IF_ERROR(ExpectValue(strides.i(0), 1, "strides(0)"));
  TF_RETURN_IF_ERROR(ExpectValue(strides.i(3), 1, "strides(3)"));
  int dilation_height_factor;
  int dilation_width_factor;
  if (HasAttr(node, "dilations")) {
    const auto& dilations = GetListAttr(node, "dilations");
    TF_RETURN_IF_ERROR(
        ExpectValue(dilations.i_size(), 4, "number of dilations"));
    if (dilations.i(0) != 1 || dilations.i(3) != 1) {
      return tensorflow::errors::InvalidArgument(absl::StrCat(
          "Can only import Conv ops with dilation along the height "
          "(1st) or width (2nd) axis. TensorFlow op \"",
          node.name(), "\" had dilations:[ ", dilations.i(0), ", ",
          dilations.i(1), ", ", dilations.i(2), ", ", dilations.i(3), "]."));
    }
    dilation_height_factor = dilations.i(1);
    dilation_width_factor = dilations.i(2);
  } else {
    dilation_height_factor = 1;
    dilation_width_factor = 1;
  }