text: /*
  // Copyright (c) 2017 Intel Corporation
  //
  // 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 <gtest/gtest.h>
  #include "api/memory.hpp"
  #include <api/input_layout.hpp>
  #include "api/split.hpp"
  #include "api/scale.hpp"
  #include <api/topology.hpp>
  #include <api/network.hpp>
  #include <api/engine.hpp>
  #include <api/reorder.hpp>
  #include "test_utils/test_utils.h"
  
  #include <sstream>
  #include <iomanip>
  
  using namespace cldnn;
  using namespace tests;
  
  template<typename T>
  std::vector<T> generate_random_input(size_t b, size_t f, size_t y, size_t x, int min, int max) {
      static std::default_random_engine generator(random_seed);
      int k = 8; // 1/k is the resolution of the floating point numbers
      std::uniform_int_distribution<int> distribution(k * min, k * max);
      std::vector<T> v(b*f*x*y);
      for (size_t i = 0; i < b*f*x*y; ++i) {
          v[i] = (T)distribution(generator);
          v[i] /= k;
      }
      return v;
  }
  
  template<typename T>
  void check_feature_map(cldnn::pointer<T> output_ptr, std::vector<T> &input_vec, size_t batch_num, size_t feature_num, size_t y_size, size_t x_size, size_t feature_id, size_t factor)
  {
      for (size_t b = 0; b < batch_num; ++b) { //B
          for (size_t y = 0; y < y_size; ++y) { //Y
              for (size_t x = 0; x < x_size; ++x) { //X
                  auto linear_id = x + x_size * (y + y_size * (feature_id + feature_num * b));
                  auto output_linear_id = x + x_size * (y + y_size * b);
                  EXPECT_EQ(output_ptr[output_linear_id], input_vec[linear_id] * factor);
              }
          }
      }
  }
  
  template<typename T>
  void split_test(int batch_num, int feature_num, int x_size, int y_size, std::vector<cldnn::tensor> split_offsets)
  {
      const auto& engine = get_test_engine();
      cldnn::tensor reference_input_size = { batch_num, feature_num, x_size, y_size };
  
      cldnn::memory input = memory::allocate(engine, { type_to_data_type<T>::value, format::bfyx, reference_input_size });
      std::vector<std::pair<primitive_id, cldnn::tensor> > input_ids_offsets;
  
      topology topology;
      topology.add(input_layout("input", input.get_layout()));
      
      // lambda exoression to create the primitive id for the splits
      auto create_split_id = [](size_t splitNum) {
          std::stringstream ss;
          ss << std::setw(5) << std::setfill('0') << splitNum;
  
          return ss.str();
      };
  
      // Create the splits with the split ids for the topology
      for (size_t splitNum = 0; splitNum < split_offsets.size(); splitNum++) 
      {
          input_ids_offsets.push_back({ create_split_id(splitNum), split_offsets[splitNum]});
      }
  
      topology.add(split("split", "input", input_ids_offsets));
  
      std::vector<T> input_vec = generate_random_input<T>(batch_num, feature_num, y_size, x_size, -10, 10);
      set_values(input, input_vec);
  
      network network(engine, topology);
      network.set_input_data("input", input);
  
      auto outputs = network.execute();
  
      // The number of splits should match the expected number of splits
      EXPECT_EQ(outputs.size(), size_t(split_offsets.size()));
      
      std::vector<cldnn::tensor> expected_sizes;
      for (size_t splitNum = 0; splitNum < split_offsets.size(); splitNum++)  // Calculate the expected sizes
      {
          cldnn::tensor size;
  
          if (splitNum < (split_offsets.size() - 1))
          {
              size = split_offsets[splitNum + 1] - split_offsets[splitNum];
          }
          else
          {
              size = reference_input_size - split_offsets[splitNum];
          }
  
          // For all the other dimensions, copy from the split_input
          for (int dimension = 0; dimension < cldnn::tensor_dim_max; dimension++)
          {
              size.raw[dimension]
                  = (size.raw[dimension] == 0) ? reference_input_size.raw[dimension] : size.raw[dimension];
          }
  
          expected_sizes.push_back(size);
      }
  
      pointer<T> input_ptr = input.pointer<T>();
  
      for (size_t splitNum = 0; splitNum < split_offsets.size(); splitNum++)
      {
          primitive_id split_id = "split:" + create_split_id(splitNum);
          cldnn::memory output = outputs.at(split_id).get_memory();
          auto prim = output.get_layout();
          EXPECT_EQ(prim.size, expected_sizes[splitNum]);
          auto output_ptr = output.pointer<T>();
  
          // Output tensor size
          auto output_batch = prim.size.batch[0];
          auto output_feature = prim.size.feature[0];
          auto output_x = prim.size.spatial[0];
          auto output_y = prim.size.spatial[1];
  
          // Input offsets, starting from which we will compare the output
          auto input_batch_offset = split_offsets[splitNum].batch[0];
          auto input_feature_offset = split_offsets[splitNum].feature[0];
          auto input_y_offset = split_offsets[splitNum].spatial[1];
          auto input_x_offset = split_offsets[splitNum].spatial[0];
          
          // iterator to iterate through input buffer
          auto input_batch_itr = input_batch_offset;
          auto input_feature_itr = input_feature_offset;
          auto input_y_itr = input_y_offset;
          auto input_x_itr = input_x_offset;
          
          for (auto b = 0; b < output_batch; ++b) {  // B
              
                  // reset the input feature iterator
              input_feature_itr = input_feature_offset; 
              for (auto f = 0; f < output_feature; f++) {  // F
                  
                  // reset the input y iterator
                  input_y_itr = input_y_offset;  
                  for (auto y = 0; y < output_y; y++) {  // Y
                      
                      // reset the input x iterator
                      input_x_itr = input_x_offset;  
                      for (auto x = 0; x < output_x; x++) {  // X
                          auto linear_id = input_x_itr + x_size * (input_y_itr + y_size * (input_feature_itr + feature_num * input_batch_itr)); // index in input
                          auto output_linear_id = x + output_x * (y + output_y * (f + output_feature * b)); // index in output
                          EXPECT_EQ(output_ptr[output_linear_id], input_vec[linear_id]);
                          input_x_itr++;  // update the input x iterator
                      }
                      input_y_itr++;  // update the input y iterator
                  }
                  input_feature_itr++;  // update the input feature iterator
              }
              input_batch_itr++;  // update the input batch iterator
          }
      }
  }
  
  TEST(split_gpu_f32, split_1d_uneven_2_splits) {
  
      //  Input      : 2x4x3x3
      //  Output1    : 2x1x3x3
      //  Output2    : 2x3x3x3
      //  Split params:
      //  id: "out0", offsets: { 0, 0, 0, 0 }
      //  id: "out1", offsets: { 0, 1, 0, 0 }
  
      auto batch_num = 2;
      auto feature_num = 4;
      auto x_size = 3;
      auto y_size = 3;
      std::vector<cldnn::tensor> split_offsets = {
                                                  {0, 0, 0, 0},
                                                  {0, 1, 0, 0}                                                
                                                 };
  
      split_test<float>(batch_num, feature_num, x_size, y_size, split_offsets);
  }
  
  TEST(split_gpu_i64, split_1d_uneven_2_splits) {
  
      //  Input      : 2x4x3x3
      //  Output1    : 2x1x3x3
      //  Output2    : 2x3x3x3
      //  Split params:
      //  id: "out0", offsets: { 0, 0, 0, 0 }
      //  id: "out1", offsets: { 0, 1, 0, 0 }
  
      auto batch_num = 2;
      auto feature_num = 4;
      auto x_size = 3;
      auto y_size = 3;
      std::vector<cldnn::tensor> split_offsets = {
                                                  {0, 0, 0, 0},
                                                  {0, 1, 0, 0}                                                
                                                 };
  
      split_test<int64_t>(batch_num, feature_num, x_size, y_size, split_offsets);
  }
  
  TEST(split_gpu_f32, basic_split_concat_optimization) {
  
      const auto& engine = get_test_engine();
  
      auto input = memory::allocate(engine, { data_types::f32,format::bfyx,{ 1, 25, 1, 256 } });
      tests::set_random_values<float>(input);
  
      topology topology;
      topology.add(input_layout("input", input.get_layout()));
      std::vector<std::pair<primitive_id, tensor>> offsets;
      std::vector<primitive_id> ids;
      for (int i = 0; i < 25; i++)
      {
          auto id = "crop_" + std::to_string(i);
          ids.push_back("split:" + id);
          offsets.push_back({ id, {0, i, 0, 0} });
      }
  
      topology.add(split("split", "input", offsets));
      topology.add(concatenation("concat", ids, concatenation::along_f));
      topology.add(reorder("output", "concat", format::bfyx, data_types::f32));
  
      build_options opts;
      opts.set_option(build_option::optimize_data(true));
      network network(engine, topology, opts);
  
      network.set_input_data("input", input);
  
      auto outputs = network.execute();
  
      auto output = outputs.at("output").get_memory();
      auto output_ptr = output.pointer<float>();
      auto input_ptr = input.pointer<float>();
  
      for (int i = 0; i < 25*256; ++i)
      {
          EXPECT_EQ(output_ptr[i], input_ptr[i]);
      }
  }
  
  TEST(split_gpu_i64, basic_split_concat_optimization) {
  
      const auto& engine = get_test_engine();
  
      auto input = memory::allocate(engine, { data_types::i64,format::bfyx,{ 1, 25, 1, 256 } });
      tests::set_random_values<int64_t>(input);
  
      topology topology;
      topology.add(input_layout("input", input.get_layout()));
      std::vector<std::pair<primitive_id, tensor>> offsets;
      std::vector<primitive_id> ids;
      for (int i = 0; i < 25; i++)
      {
          auto id = "crop_" + std::to_string(i);
          ids.push_back("split:" + id);
          offsets.push_back({ id, {0, i, 0, 0} });
      }
  
      topology.add(split("split", "input", offsets));
      topology.add(concatenation("concat", ids, concatenation::along_f));
      topology.add(reorder("output", "concat", format::bfyx, data_types::i64));
  
      build_options opts;
      opts.set_option(build_option::optimize_data(true));
      network network(engine, topology, opts);
  
      network.set_input_data("input", input);
  
      auto outputs = network.execute();
  
      auto output = outputs.at("output").get_memory();
      auto output_ptr = output.pointer<int64_t>();
      auto input_ptr = input.pointer<int64_t>();
  
      for (int i = 0; i < 25*256; ++i)
      {
          EXPECT_EQ(output_ptr[i], input_ptr[i]);
      }
  }
  
  TEST(split_gpu_f32, split_1d_uneven_3_splits) {
  
      //  Input      : 2x8x3x3
      //  Output1    : 2x1x3x3
      //  Output2    : 2x3x3x3
      //  Output3    : 2x4x3x3
      //  Split params:
      //  id: "out0", offsets: { 0, 0, 0, 0 }
      //  id: "out1", offsets: { 0, 1, 0, 0 }
      //  id: "out2", offsets: { 0, 4, 0, 0 }
  
      auto batch_num = 2;
      auto feature_num = 8;
      auto x_size = 3;
      auto y_size = 3;
      std::vector<cldnn::tensor> split_offsets = {
                                                  {0, 0, 0, 0},
                                                  {0, 1, 0, 0},
                                                  {0, 4, 0, 0},
                                                 };
  
      split_test<float>(batch_num, feature_num, x_size, y_size, split_offsets);
  }
  
  TEST(split_gpu_i64, split_1d_uneven_3_splits) {
  
      //  Input      : 2x8x3x3
      //  Output1    : 2x1x3x3
      //  Output2    : 2x3x3x3
      //  Output3    : 2x4x3x3
      //  Split params:
      //  id: "out0", offsets: { 0, 0, 0, 0 }
      //  id: "out1", offsets: { 0, 1, 0, 0 }
      //  id: "out2", offsets: { 0, 4, 0, 0 }
  
      auto batch_num = 2;
      auto feature_num = 8;
      auto x_size = 3;
      auto y_size = 3;
      std::vector<cldnn::tensor> split_offsets = {
                                                  {0, 0, 0, 0},
                                                  {0, 1, 0, 0},
                                                  {0, 4, 0, 0},
                                                 };
  
      split_test<int64_t>(batch_num, feature_num, x_size, y_size, split_offsets);
  }
  
  TEST(split_gpu_f32, split_2d_uneven_2_splits) {
  
      //  Input      : 2x8x10x3
      //  Output1    : 2x1x4x3
      //  Output2    : 2x3x6x3
      //  Split params:
      //  id: "out0", offsets: { 0, 0, 0, 0 }
      //  id: "out1", offsets: { 0, 1, 4, 0 }
  
      auto batch_num = 2;
      auto feature_num = 8;
      auto x_size = 10;
      auto y_size = 3;
      std::vector<cldnn::tensor> split_offsets = {
                                                  {0, 0, 0, 0},
                                                  {0, 1, 4, 0}
                                                 };
  
      split_test<float>(batch_num, feature_num, x_size, y_size, split_offsets);
  }
  
  TEST(split_gpu_i64, split_2d_uneven_2_splits) {
  
      //  Input      : 2x8x10x3
      //  Output1    : 2x1x4x3
      //  Output2    : 2x3x6x3
      //  Split params:
      //  id: "out0", offsets: { 0, 0, 0, 0 }
      //  id: "out1", offsets: { 0, 1, 4, 0 }
  
      auto batch_num = 2;
      auto feature_num = 8;
      auto x_size = 10;
      auto y_size = 3;
      std::vector<cldnn::tensor> split_offsets = {
                                                  {0, 0, 0, 0},
                                                  {0, 1, 4, 0}
                                                 };
  
      split_test<int64_t>(batch_num, feature_num, x_size, y_size, split_offsets);
  }
  
  TEST(split_gpu_f32, split_2d_uneven_3_split3) {
  
      //  Input      : 2x8x10x3
      //  Output1    : 2x1x4x3
      //  Output2    : 2x3x3x3
      //  Output3    : 2x4x3x3
      //  Split params:
      //  id: "out0", offsets: { 0, 0, 0, 0 }
      //  id: "out1", offsets: { 0, 1, 4, 0 }
      //  id: "out2", offsets: { 0, 4, 7, 0 }
  
      auto batch_num = 2;
      auto feature_num = 8;
      auto x_size = 10;
      auto y_size = 3;
      std::vector<cldnn::tensor> split_offsets = {
                                                  {0, 0, 0, 0},
                                                  {0, 1, 4, 0},
                                                  {0, 4, 7, 0},
                                                 };
  
      split_test<float>(batch_num, feature_num, x_size, y_size, split_offsets);
  }
  
  TEST(split_gpu_i64, split_2d_uneven_3_split3) {
  
      //  Input      : 2x8x10x3
      //  Output1    : 2x1x4x3
      //  Output2    : 2x3x3x3
      //  Output3    : 2x4x3x3
      //  Split params:
      //  id: "out0", offsets: { 0, 0, 0, 0 }
      //  id: "out1", offsets: { 0, 1, 4, 0 }
      //  id: "out2", offsets: { 0, 4, 7, 0 }
  
      auto batch_num = 2;
      auto feature_num = 8;
      auto x_size = 10;
      auto y_size = 3;
      std::vector<cldnn::tensor> split_offsets = {
                                                  {0, 0, 0, 0},
                                                  {0, 1, 4, 0},
                                                  {0, 4, 7, 0},
                                                 };
  
      split_test<int64_t>(batch_num, feature_num, x_size, y_size, split_offsets);
  }
  
  TEST(split_gpu_f32, split_3d_uneven_2_splits) {
  
      //  Input      : 2x8x10x3
      //  Output1    : 2x1x4x1
      //  Output2    : 2x7x6x2
      //  Split params:
      //  id: "out0", offsets: { 0, 0, 0, 0 }
      //  id: "out1", offsets: { 0, 1, 4, 1 }
  
      auto batch_num = 2;
      auto feature_num = 8;
      auto x_size = 10;
      auto y_size = 3;
      std::vector<cldnn::tensor> split_offsets = {
                                                  {0, 0, 0, 0},
                                                  {0, 1, 4, 1}
                                                 };
  
      split_test<float>(batch_num, feature_num, x_size, y_size, split_offsets);
  }
  
  TEST(split_gpu_i64, split_3d_uneven_2_splits) {
  
      //  Input      : 2x8x10x3
      //  Output1    : 2x1x4x1
      //  Output2    : 2x7x6x2
      //  Split params:
      //  id: "out0", offsets: { 0, 0, 0, 0 }
      //  id: "out1", offsets: { 0, 1, 4, 1 }
  
      auto batch_num = 2;
      auto feature_num = 8;
      auto x_size = 10;
      auto y_size = 3;
      std::vector<cldnn::tensor> split_offsets = {
                                                  {0, 0, 0, 0},
                                                  {0, 1, 4, 1}
                                                 };
  
      split_test<int64_t>(batch_num, feature_num, x_size, y_size, split_offsets);
  }
  
  TEST(split_gpu_f32, split_3d_uneven_3_splits) {
  
      //  Input      : 2x8x10x5
      //  Output1    : 2x1x4x1
      //  Output2    : 2x6x4x1
      //  Output3    : 2x1x2x1
      //  Split params:
      //  id: "out0", offsets: { 0, 0, 0, 0 }
      //  id: "out1", offsets: { 0, 1, 4, 1 }
      //  id: "out2", offsets: { 0, 7, 8, 2 }
  
      auto batch_num = 2;
      auto feature_num = 8;
      auto x_size = 10;
      auto y_size = 3;
      std::vector<cldnn::tensor> split_offsets = {
                                                  {0, 0, 0, 0},
                                                  {0, 1, 4, 1},
                                                  {0, 7, 8, 2}
                                                 };
  
      split_test<float>(batch_num, feature_num, x_size, y_size, split_offsets);
  }
  
  TEST(split_gpu_i64, split_3d_uneven_3_splits) {
  
      //  Input      : 2x8x10x5
      //  Output1    : 2x1x4x1
      //  Output2    : 2x6x4x1
      //  Output3    : 2x1x2x1
      //  Split params:
      //  id: "out0", offsets: { 0, 0, 0, 0 }
      //  id: "out1", offsets: { 0, 1, 4, 1 }
      //  id: "out2", offsets: { 0, 7, 8, 2 }
  
      auto batch_num = 2;
      auto feature_num = 8;
      auto x_size = 10;
      auto y_size = 3;
      std::vector<cldnn::tensor> split_offsets = {
                                                  {0, 0, 0, 0},
                                                  {0, 1, 4, 1},
                                                  {0, 7, 8, 2}
                                                 };
  
      split_test<int64_t>(batch_num, feature_num, x_size, y_size, split_offsets);
  }
  
  TEST(split_gpu_f32, basic_in2x3x2x2_split_feature_bfyx) {
      //  Input      : 6x3x4x3
      //  3 x Outputs: 6x1x4x3
      //  Split params:
      //  id: "out0", offsets: { 0, 0, 0, 0 }
      //  id: "out1", offsets: { 0, 1, 0, 0 }
      //  id: "out2", offsets: { 0, 2, 0, 0 }
  
      const auto& engine = get_test_engine();
  
      auto batch_num = 6;
      auto feature_num = 3;
      auto x_size = 4;
      auto y_size = 3;
  
      auto input = memory::allocate(engine, { data_types::f32,format::bfyx,{ batch_num, feature_num, x_size, y_size } });
  
      topology topology;
      topology.add(input_layout("input", input.get_layout()));
      topology.add(split("split", "input",
      {
          { "out0", { 0, 0, 0, 0 } },
          { "out1", { 0, 1, 0, 0 } },
          { "out2", { 0, 2, 0, 0 } }
      } ));
  
      std::vector<float> input_vec = generate_random_input<float>(batch_num, feature_num, y_size, x_size, -10, 10);
      set_values(input, input_vec);
  
      network network(engine, topology);
  
      network.set_input_data("input", input);
  
      auto outputs = network.execute();
  
      EXPECT_EQ(outputs.size(), size_t(3));
  
      for (unsigned int i = 0; i < 3; i++)
      {
          auto split_id = "split:out" + std::to_string(i);
          auto output = outputs.at(split_id).get_memory();
          auto output_ptr = output.pointer<float>();
          check_feature_map<float>(output_ptr, input_vec, batch_num, feature_num, y_size, x_size, i, 1);
      }
  }
  
  TEST(split_gpu_i64, basic_in2x3x2x2_split_feature_bfyx) {
      //  Input      : 6x3x4x3
      //  3 x Outputs: 6x1x4x3
      //  Split params:
      //  id: "out0", offsets: { 0, 0, 0, 0 }
      //  id: "out1", offsets: { 0, 1, 0, 0 }
      //  id: "out2", offsets: { 0, 2, 0, 0 }
  
      const auto& engine = get_test_engine();
  
      auto batch_num = 6;
      auto feature_num = 3;
      auto x_size = 4;
      auto y_size = 3;
  
      auto input = memory::allocate(engine, { data_types::i64,format::bfyx,{ batch_num, feature_num, x_size, y_size } });
  
      topology topology;
      topology.add(input_layout("input", input.get_layout()));
      topology.add(split("split", "input",
      {
          { "out0", { 0, 0, 0, 0 } },
          { "out1", { 0, 1, 0, 0 } },
          { "out2", { 0, 2, 0, 0 } }
      } ));
  
      std::vector<int64_t> input_vec = generate_random_input<int64_t>(batch_num, feature_num, y_size, x_size, -10, 10);
      set_values(input, input_vec);
  
      network network(engine, topology);
  
      network.set_input_data("input", input);
  
      auto outputs = network.execute();
  
      EXPECT_EQ(outputs.size(), size_t(3));
  
      for (unsigned int i = 0; i < 3; i++)
      {
          auto split_id = "split:out" + std::to_string(i);
          auto output = outputs.at(split_id).get_memory();
          auto output_ptr = output.pointer<int64_t>();
          check_feature_map<int64_t>(output_ptr, input_vec, batch_num, feature_num, y_size, x_size, i, 1);
      }
  }
  
  TEST(split_gpu_f32, basic_in2x3x2x2_split_scale_feature_bfyx) {
      //  Input      : 6x3x4x3
      //  3 x Outputs: 6x1x4x3
      //  Split params:
      //  id: "out0", offsets: { 0, 0, 0, 0 }
      //  id: "out1", offsets: { 0, 1, 0, 0 }
      //  id: "out2", offsets: { 0, 2, 0, 0 }
      //  Additional scale layer at the end
  
      const auto& engine = get_test_engine();
  
      auto batch_num = 6;
      auto feature_num = 3;
      auto x_size = 4;
      auto y_size = 3;
  
      auto input = memory::allocate(engine, { data_types::f32,format::bfyx,{ batch_num, feature_num, x_size, y_size } });
      auto scale_input0 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 1, 1 } });
      auto scale_input1 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 1, 1 } });
      auto scale_input2 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 1, 1 } });
  
      topology topology;
      topology.add(input_layout("input", input.get_layout()));
      topology.add(input_layout("scale_input0", scale_input0.get_layout()));
      topology.add(input_layout("scale_input1", scale_input1.get_layout()));
      topology.add(input_layout("scale_input2", scale_input2.get_layout()));
      topology.add(split("split", "input",
      {
          { "out0",{ 0, 0, 0, 0 } },
          { "out1",{ 0, 1, 0, 0 } },
          { "out2",{ 0, 2, 0, 0 } }
      }));
      topology.add(scale("scale0", "split:out0", "scale_input0"));
      topology.add(scale("scale1", "split:out1", "scale_input1"));
      topology.add(scale("scale2", "split:out2", "scale_input2"));
  
      std::vector<float> scale_input_vec0 = { 1.f };
      set_values(scale_input0, scale_input_vec0);
      std::vector<float> scale_input_vec1 = { 2.f };
      set_values(scale_input1, scale_input_vec1);
      std::vector<float> scale_input_vec2 = { 3.f };
      set_values(scale_input2, scale_input_vec2);
     
      std::vector<float> input_vec = generate_random_input<float>(batch_num, feature_num, y_size, x_size, -10, 10);
      set_values(input, input_vec);
  
      network network(engine, topology);
  
      network.set_input_data("input", input);
      network.set_input_data("scale_input0", scale_input0);
      network.set_input_data("scale_input1", scale_input1);
      network.set_input_data("scale_input2", scale_input2);
  
      auto outputs = network.execute();
  
      EXPECT_EQ(outputs.size(), size_t(3));
  
      for (unsigned int i = 0; i < 3; i++)
      {
          auto split_id = "scale" + std::to_string(i);
          auto output = outputs.at(split_id).get_memory();
          auto output_ptr = output.pointer<float>();
          check_feature_map<float>(output_ptr, input_vec, batch_num, feature_num, y_size, x_size, i, i + 1);
      }
  }
  