text: // Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
  //
  // 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 "lite/operators/pool_op.h"
  #include <gtest/gtest.h>
  #include <random>
  #include "lite/core/op_registry.h"
  #include "lite/kernels/npu/bridges/registry.h"
  #include "lite/kernels/npu/bridges/test_helper.h"
  
  namespace paddle {
  namespace lite {
  namespace kernels {
  namespace npu {
  namespace bridges {
  
  void pool_ref(const std::shared_ptr<operators::PoolOpLite> op) {
    Scope* scope = op->scope();
    const OpInfo* op_info = op->op_info();
    auto x = scope->FindVar(op_info->Input("X").front())->GetMutable<Tensor>();
    auto out =
        scope->FindVar(op_info->Output("Out").front())->GetMutable<Tensor>();
    auto& in_dims = x->dims();
    auto& out_dims = out->dims();
  
    const float* src_ptr = x->data<const float>();
    float* dst_ptr = out->mutable_data<float>();
  
    std::vector<int> ksize = op_info->GetAttr<std::vector<int>>("ksize");
    std::vector<int> strides = op_info->GetAttr<std::vector<int>>("strides");
    std::vector<int> paddings = op_info->GetAttr<std::vector<int>>("paddings");
    bool exclusive = op_info->GetAttr<bool>("exclusive");
    std::string pooling_type = op_info->GetAttr<std::string>("pooling_type");
    bool global_pooling = op_info->GetAttr<bool>("global_pooling");
  
    int in_n = in_dims[0];
    int in_c = in_dims[1];
    int in_h = in_dims[2];
    int in_w = in_dims[3];
    int size_in_n = in_c * in_h * in_w;
    int size_in_c = in_h * in_w;
  
    int out_h = out_dims[2];
    int out_w = out_dims[3];
    int size_out_n = in_c * out_h * out_w;
    int size_out_c = out_h * out_w;
  
    int window_h = ksize[0];
    int window_w = ksize[1];
    int stride_h = strides[0];
    int stride_w = strides[1];
    int pad_h = paddings[0];
    int pad_w = paddings[1];
  
    if (global_pooling == true) {
      for (int n = 0; n < in_n; ++n) {
        for (int c = 0; c < in_c; ++c) {
          const float* src = src_ptr + n * size_in_n + c * size_in_c;
          float res = src[0];
          if (pooling_type == "max") {
            for (int i = 1; i < size_in_c; ++i) {
              float cur_val = src[i];
              res = cur_val > res ? cur_val : res;
            }
          } else if (pooling_type == "avg") {
            for (int i = 1; i < size_in_c; ++i) {
              float cur_val = src[i];
              res += cur_val;
            }
            res /= size_in_c;
          }
          dst_ptr[n * size_out_n + c] = res;
        }
      }
    } else {
      for (int n = 0; n < in_n; ++n) {
        for (int c = 0; c < in_c; ++c) {
          for (int h = 0; h < out_h; ++h) {
            int sh = h * stride_h;
            int eh = sh + window_h;
            sh = (sh - pad_h) < 0 ? 0 : sh - pad_h;
            eh = (eh - pad_h) > in_h ? in_h : eh - pad_h;
            for (int w = 0; w < out_w; ++w) {
              int sw = w * stride_w;
              int ew = sw + window_w;
              sw = (sw - pad_w) < 0 ? 0 : sw - pad_w;
              ew = (ew - pad_w) > in_w ? in_w : ew - pad_w;
              int pooling_size = (ew - sw) * (eh - sh);
              if (pooling_size == 0) continue;
              float res = 0.f;
              for (int kh = sh; kh < eh; ++kh) {
                for (int kw = sw; kw < ew; ++kw) {
                  int src_idx = n * size_in_n + c * size_in_c + kh * in_w + kw;
                  if (kh == sh && kw == sw) {
                    res = src_ptr[src_idx];
                  } else {
                    if (pooling_type == "max") {
                      res = res >= src_ptr[src_idx] ? res : src_ptr[src_idx];
                    }
                    if (pooling_type == "avg") {
                      res += src_ptr[src_idx];
                    }
                  }
                }
              }
              if (pooling_type == "avg") {
                if (exclusive) {
                  res /= pooling_size;
                } else {
                  res /= window_h * window_w;
                }
              }
              dst_ptr[n * size_out_n + c * size_out_c + h * out_w + w] = res;
            }
          }
        }
      }
    }
  }
  
  void test_pool(int bs,
                 int ic,
                 int ih,
                 int iw,
                 std::string pooling_type,
                 bool ceil_mode,
                 bool global_pooling,
                 bool exclusive,
                 int ksize,
                 int stride,
                 int padding) {
    // prepare input&output variables
    Scope scope;
    std::string x_var_name = "x";
    std::string out_var_name = "out";
    std::string out_ref_var_name = "out_ref";
    auto* x = scope.Var(x_var_name)->GetMutable<Tensor>();
    auto* out = scope.Var(out_var_name)->GetMutable<Tensor>();
    auto* out_ref = scope.Var(out_ref_var_name)->GetMutable<Tensor>();
    x->Resize({bs, ic, ih, iw});
  
    // initialize input&output data
    FillTensor<float>(x);
  
    // initialize op desc
    cpp::OpDesc opdesc;
    opdesc.SetType("pool2d");
    opdesc.SetInput("X", {x_var_name});
    opdesc.SetOutput("Out", {out_var_name});
    opdesc.SetAttr("pooling_type", pooling_type);
    opdesc.SetAttr("ksize", std::vector<int>({ksize, ksize}));
    opdesc.SetAttr("global_pooling", global_pooling);
    opdesc.SetAttr("exclusive", exclusive);
    opdesc.SetAttr("strides", std::vector<int>({stride, stride}));
    opdesc.SetAttr("paddings", std::vector<int>({padding, padding}));
  
    // create and convert op to NPU model, then run it on NPU
    auto op = CreateOp<operators::PoolOpLite>(opdesc, &scope);
    LauchOp(op, {x_var_name}, {out_var_name});
    out_ref->CopyDataFrom(*out);
  
    // execute reference implementation and save to output tensor
    pool_ref(op);
  
    // compare results
    auto* out_data = out->mutable_data<float>();
    auto* out_ref_data = out_ref->mutable_data<float>();
    for (int i = 0; i < out->dims().production(); i++) {
      EXPECT_NEAR(out_data[i], out_ref_data[i], 1e-2);
    }
  }
  
  TEST(NPUBridges, pool) {
    for (auto pooling_type : {"max", "avg"}) {
      for (auto ceil_mode : {true, false}) {
        for (auto global_pooling : {/*true, */ false}) {
          for (auto exclusive : {true /*, false*/}) {
            for (auto ksize : {2, 3}) {
              for (auto stride : {1, 2}) {
                for (auto padding : {0, 1}) {
                  for (auto bs : {1, 3}) {
                    for (auto ic : {1, 3}) {
                      for (auto ih : {3, 7}) {
                        for (auto iw : {3, 7}) {
                          test_pool(bs,
                                    ic,
                                    ih,
                                    iw,
                                    pooling_type,
                                    ceil_mode,
                                    global_pooling,
                                    exclusive,
                                    ksize,
                                    stride,
                                    padding);
                        }
                      }
                    }
                  }
                }
              }
            }
          }
        }
      }
    }
    for (auto pooling_type : {"max", "avg"}) {
      for (auto ceil_mode : {true, false}) {
        bool global_pooling = true;
        bool exclusive = true;
        int ksize = 2;
        int stride = 1;
        int padding = 0;
        int bs = 6;
        int ic = 6;
        int ih = 6;
        int iw = 6;
        test_pool(bs,
                  ic,
                  ih,
                  iw,
                  pooling_type,
                  ceil_mode,
                  global_pooling,
                  exclusive,
                  ksize,
                  stride,
                  padding);
      }
    }
  }
  
  }  // namespace bridges
  }  // namespace npu
  }  // namespace kernels
  }  // namespace lite
  }  // namespace paddle
  
  USE_LITE_OP(pool2d);
  USE_NPU_BRIDGE(pool2d);
  