text: /**
   * Copyright 2020 Huawei Technologies Co., Ltd
   *
   * 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.
   */
  
  #include "ops/unsqueeze.h"
  #include "utils/check_convert_utils.h"
  #include "abstract/primitive_infer_map.h"
  #include "ops/op_utils.h"
  
  namespace mindspore {
  namespace ops {
  void Unsqueeze::Init(const std::vector<int64_t> axis) { this->set_axis(axis); }
  
  void Unsqueeze::set_axis(std::vector<int64_t> axis) { this->AddAttr(kAxis, MakeValue(axis)); }
  
  std::vector<int64_t> Unsqueeze::get_axis() const {
    auto value_ptr = this->GetAttr(kAxis);
    return GetValue<std::vector<int64_t>>(value_ptr);
  }
  AbstractBasePtr UnsqueezeInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                 const std::vector<AbstractBasePtr> &input_args) {
    MS_EXCEPTION_IF_NULL(primitive);
    auto unsqueeze_prim = primitive->cast<PrimUnsqueezePtr>();
    MS_EXCEPTION_IF_NULL(unsqueeze_prim);
    auto prim_name = unsqueeze_prim->name();
    CheckAndConvertUtils::CheckInteger("unsqueeze_infer", input_args.size(), kEqual, 1, prim_name);
    MS_EXCEPTION_IF_NULL(input_args[0]);
    auto input = input_args[0];
  
    // Infer type
    auto input_type = input->BuildType()->cast<TensorTypePtr>()->element();
  
    // Infer shape
    auto dims = unsqueeze_prim->get_axis();
    auto input_shape = CheckAndConvertUtils::ConvertShapePtrToShape("input", input->BuildShape(), prim_name);
    auto input_rank = input_shape.size();
    auto dim_rank = dims.size();
    std::vector<int64_t> out_shape;
    if (dim_rank == 0) {
      std::copy_if(input_shape.begin(), input_shape.end(), out_shape.begin(), [](const auto item) { return item == 1; });
    } else {
      auto sz = input_rank + dim_rank;
      size_t in_itr = 0;
      size_t ax_itr = 0;
      for (size_t i = 0; i < sz; i++) {
        if (ax_itr < dim_rank && dims[ax_itr] == (int64_t)i) {
          out_shape.emplace_back(1);
          ax_itr++;
        } else if (ax_itr < dim_rank && dims[ax_itr] + sz == i) {
          out_shape.emplace_back(1);
          ax_itr++;
        } else {
          out_shape.emplace_back(input_shape[in_itr]);
          in_itr++;
        }
      }
    }
    return std::make_shared<abstract::AbstractTensor>(input_type, out_shape);
  }
  REGISTER_PRIMITIVE_C(kNameUnsqueeze, Unsqueeze);
  }  // namespace ops
  }  // namespace mindspore
  