// Copyright 2018 Xiaomi, Inc.  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 "gtest/gtest.h"

#include "mace/core/operator.h"
#include "mace/kernels/pooling.h"
#include "mace/ops/conv_pool_2d_base.h"
#include "mace/ops/ops_test_util.h"

namespace mace {
namespace ops {
namespace test {

class PoolingOpTest : public OpsTestBase {};

TEST_F(PoolingOpTest, MAX_VALID) {
  // Construct graph
  OpsTestNet net;
  OpDefBuilder("Pooling", "PoolingTest")
    .Input("Input")
    .Output("Output")
    .AddIntsArg("kernels", {2, 2})
    .AddIntsArg("strides", {2, 2})
    .AddIntArg("padding", Padding::VALID)
    .AddIntsArg("dilations", {1, 1})
    .AddIntArg("pooling_type", PoolingType::MAX)
    .Finalize(net.NewOperatorDef());

  // Add input data
  net.AddInputFromArray<DeviceType::CPU, float>(
    "Input", {1, 4, 4, 2},
    {0, 16, 1, 17, 2, 18, 3, 19, 4, 20, 5, 21, 6, 22, 7, 23,
     8, 24, 9, 25, 10, 26, 11, 27, 12, 28, 13, 29, 14, 30, 15, 31});

  // Run
  net.RunOp();

  // Check
  auto expected =
    CreateTensor<float>({1, 2, 2, 2}, {5, 21, 7, 23, 13, 29, 15, 31});

  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}

TEST_F(PoolingOpTest, MAX_SAME) {
  // Construct graph
  OpsTestNet net;
  OpDefBuilder("Pooling", "PoolingTest")
    .Input("Input")
    .Output("Output")
    .AddIntsArg("kernels", {2, 2})
    .AddIntsArg("strides", {2, 2})
    .AddIntArg("padding", Padding::SAME)
    .AddIntsArg("dilations", {1, 1})
    .AddIntArg("pooling_type", PoolingType::MAX)
    .Finalize(net.NewOperatorDef());

  // Add input data
  net.AddInputFromArray<DeviceType::CPU, float>("Input", {1, 3, 3, 1},
                                                {0, 1, 2, 3, 4, 5, 6, 7, 8});

  // Run
  net.RunOp();

  // Check
  auto expected = CreateTensor<float>({1, 2, 2, 1}, {4, 5, 7, 8});

  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}

TEST_F(PoolingOpTest, MAX_VALID_DILATION) {
  // Construct graph
  OpsTestNet net;
  OpDefBuilder("Pooling", "PoolingTest")
    .Input("Input")
    .Output("Output")
    .AddIntsArg("kernels", {2, 2})
    .AddIntsArg("strides", {1, 1})
    .AddIntArg("padding", Padding::VALID)
    .AddIntsArg("dilations", {2, 2})
    .AddIntArg("pooling_type", PoolingType::MAX)
    .Finalize(net.NewOperatorDef());

  // Add input data
  net.AddInputFromArray<DeviceType::CPU, float>(
    "Input", {1, 4, 4, 1},
    {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15});

  // Run
  net.RunOp();

  // Check
  auto expected = CreateTensor<float>({1, 2, 2, 1}, {10, 11, 14, 15});

  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}

TEST_F(PoolingOpTest, MAX_k2x2s2x2) {
  // Construct graph
  OpsTestNet net;
  OpDefBuilder("Pooling", "PoolingTest")
    .Input("Input")
    .Output("Output")
    .AddIntArg("pooling_type", PoolingType::MAX)
    .AddIntsArg("kernels", {2, 2})
    .AddIntsArg("strides", {2, 2})
    .AddIntArg("padding", Padding::SAME)
    .AddIntsArg("dilations", {1, 1})
    .Finalize(net.NewOperatorDef());

  // Add input data
  net.AddInputFromArray<DeviceType::CPU, float>(
    "Input", {1, 2, 9, 1},
    {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17});
  // Run
  net.RunOp();

  // Check
  auto expected = CreateTensor<float>({1, 1, 5, 1}, {10, 12, 14, 16, 17});

  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}

namespace {
template<DeviceType D>
void SimpleMaxPooling3S2() {
  // Construct graph
  OpsTestNet net;

  // Add input data
  net.AddInputFromArray<D, float>(
    "Input", {1, 3, 9, 1},
    {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
     14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26});

  if (D == DeviceType::OPENCL) {
    BufferToImage<D, float>(&net, "Input", "InputImage",
                            kernels::BufferType::IN_OUT_CHANNEL);
    OpDefBuilder("Pooling", "PoolingTest")
      .Input("InputImage")
      .Output("OutputImage")
      .AddIntArg("pooling_type", PoolingType::MAX)
      .AddIntsArg("kernels", {3, 3})
      .AddIntsArg("strides", {2, 2})
      .AddIntArg("padding", Padding::VALID)
      .AddIntsArg("dilations", {1, 1})
      .Finalize(net.NewOperatorDef());
    net.RunOp(D);
    ImageToBuffer<D, float>(&net, "OutputImage", "Output",
                            kernels::BufferType::IN_OUT_CHANNEL);
  } else {
    // Run
    OpDefBuilder("Pooling", "PoolingTest")
      .Input("Input")
      .Output("Output")
      .AddIntArg("pooling_type", PoolingType::MAX)
      .AddIntsArg("kernels", {3, 3})
      .AddIntsArg("strides", {2, 2})
      .AddIntArg("padding", Padding::VALID)
      .AddIntsArg("dilations", {1, 1})
      .Finalize(net.NewOperatorDef());
    net.RunOp(D);
  }

  // Check
  auto expected = CreateTensor<float>({1, 1, 4, 1}, {20, 22, 24, 26});

  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}
}  // namespace

TEST_F(PoolingOpTest, CPUSimpleMaxPooling3S2) { SimpleMaxPooling3S2<CPU>(); }

TEST_F(PoolingOpTest, OPENCLSimpleMaxPooling3S2) {
  SimpleMaxPooling3S2<OPENCL>();
}

namespace {
template<DeviceType D, typename T>
void MaxPooling3S2(const std::vector<index_t> &input_shape,
                   const std::vector<int> strides,
                   Padding padding) {
  // Construct graph
  OpsTestNet net;
  OpDefBuilder("Pooling", "PoolingTest")
    .Input("Input")
    .Output("Output")
    .AddIntArg("pooling_type", PoolingType::MAX)
    .AddIntsArg("kernels", {3, 3})
    .AddIntsArg("strides", strides)
    .AddIntArg("padding", padding)
    .AddIntsArg("dilations", {1, 1})
    .Finalize(net.NewOperatorDef());

  // Add input data
  net.AddRandomInput<D, float>("Input", input_shape);

  // run on cpu
  net.RunOp();
  Tensor expected;
  expected.Copy(*net.GetOutput("Output"));

  BufferToImage<D, T>(&net, "Input", "InputImage",
                      kernels::BufferType::IN_OUT_CHANNEL);
  OpDefBuilder("Pooling", "PoolingTest")
    .Input("InputImage")
    .Output("OutputImage")
    .AddIntArg("pooling_type", PoolingType::MAX)
    .AddIntsArg("kernels", {3, 3})
    .AddIntsArg("strides", strides)
    .AddIntArg("padding", padding)
    .AddIntsArg("dilations", {1, 1})
    .AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
    .Finalize(net.NewOperatorDef());
  net.RunOp(D);
  ImageToBuffer<D, float>(&net, "OutputImage", "OPENCLOutput",
                      kernels::BufferType::IN_OUT_CHANNEL);

  if (DataTypeToEnum<T>::value == DT_HALF) {
    ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"),
                            1e-3, 1e-4);
  } else {
    ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-5);
  }
}
}  // namespace

// TODO(chenghui) : there is a bug.
// TEST_F(PoolingOpTest, NEONAlignedMaxPooling3S2) {
//  AlignedMaxPooling3S2<NEON>(Padding::VALID);
//  AlignedMaxPooling3S2<NEON>(Padding::SAME);
//}

TEST_F(PoolingOpTest, OPENCLAlignedMaxPooling3S2) {
  MaxPooling3S2<OPENCL, float>({3, 64, 32, 32}, {1, 1}, Padding::VALID);
  MaxPooling3S2<OPENCL, float>({3, 64, 32, 32}, {2, 2}, Padding::VALID);
  MaxPooling3S2<OPENCL, float>({3, 64, 32, 32}, {1, 1}, Padding::SAME);
  MaxPooling3S2<OPENCL, float>({3, 64, 32, 32}, {2, 2}, Padding::SAME);
}

TEST_F(PoolingOpTest, OPENCLHalfAlignedMaxPooling3S2) {
  MaxPooling3S2<OPENCL, half>({3, 64, 32, 32}, {1, 1}, Padding::VALID);
  MaxPooling3S2<OPENCL, half>({3, 64, 32, 32}, {2, 2}, Padding::VALID);
  MaxPooling3S2<OPENCL, half>({3, 64, 32, 32}, {1, 1}, Padding::SAME);
  MaxPooling3S2<OPENCL, half>({3, 64, 32, 32}, {2, 2}, Padding::SAME);
}

TEST_F(PoolingOpTest, OPENCLUnalignedMaxPooling3S2) {
  MaxPooling3S2<OPENCL, half>({3, 41, 43, 47}, {1, 1}, Padding::VALID);
  MaxPooling3S2<OPENCL, half>({3, 41, 43, 47}, {2, 2}, Padding::VALID);
  MaxPooling3S2<OPENCL, half>({3, 41, 43, 47}, {1, 1}, Padding::SAME);
  MaxPooling3S2<OPENCL, half>({3, 41, 43, 47}, {2, 2}, Padding::SAME);
}

TEST_F(PoolingOpTest, AVG_VALID) {
  // Construct graph
  OpsTestNet net;
  OpDefBuilder("Pooling", "PoolingTest")
    .Input("Input")
    .Output("Output")
    .AddIntsArg("kernels", {2, 2})
    .AddIntsArg("strides", {2, 2})
    .AddIntArg("padding", Padding::VALID)
    .AddIntsArg("dilations", {1, 1})
    .AddIntArg("pooling_type", PoolingType::AVG)
    .Finalize(net.NewOperatorDef());

  // Add input data
  net.AddInputFromArray<DeviceType::CPU, float>(
    "Input", {1, 4, 4, 2},
    {0, 16, 1, 17, 2, 18, 3, 19, 4, 20, 5, 21, 6, 22, 7, 23,
     8, 24, 9, 25, 10, 26, 11, 27, 12, 28, 13, 29, 14, 30, 15, 31});

  // Run
  net.RunOp();

  // Check
  auto expected = CreateTensor<float>(
    {1, 2, 2, 2}, {2.5, 18.5, 4.5, 20.5, 10.5, 26.5, 12.5, 28.5});

  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}

namespace {
template<DeviceType D>
void SimpleAvgPoolingTest() {
  // Construct graph
  OpsTestNet net;

  // Add input data
  net.AddInputFromArray<D, float>(
    "Input", {1, 2, 8, 1},
    {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15});

  BufferToImage<D, float>(&net, "Input", "InputImage",
                          kernels::BufferType::IN_OUT_CHANNEL);
  OpDefBuilder("Pooling", "PoolingTest")
    .Input("InputImage")
    .Output("OutputImage")
    .AddIntArg("pooling_type", PoolingType::AVG)
    .AddIntsArg("kernels", {2, 2})
    .AddIntsArg("strides", {2, 2})
    .AddIntArg("padding", Padding::SAME)
    .AddIntsArg("dilations", {1, 1})
    .Finalize(net.NewOperatorDef());
  // Run
  net.RunOp(D);
  ImageToBuffer<D, float>(&net, "OutputImage", "Output",
                          kernels::BufferType::IN_OUT_CHANNEL);

  // Check
  auto expected = CreateTensor<float>({1, 1, 4, 1}, {4.5, 6.5, 8.5, 10.5});

  ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-5);
}
}  // namespace

TEST_F(PoolingOpTest, OPENCLSimpleAvgPooling) {
  SimpleAvgPoolingTest<OPENCL>();
}

namespace {
template<DeviceType D, typename T>
void AvgPoolingTest(const std::vector<index_t> &shape,
                    const std::vector<int> &kernels,
                    const std::vector<int> &strides,
                    Padding padding) {
  // Construct graph
  OpsTestNet net;
  OpDefBuilder("Pooling", "PoolingTest")
    .Input("Input")
    .Output("Output")
    .AddIntArg("pooling_type", PoolingType::AVG)
    .AddIntsArg("kernels", kernels)
    .AddIntsArg("strides", strides)
    .AddIntArg("padding", padding)
    .AddIntsArg("dilations", {1, 1})
    .Finalize(net.NewOperatorDef());

  // Add input data
  net.AddRandomInput<D, float>("Input", shape);

  // run on cpu
  net.RunOp();
  Tensor expected;
  expected.Copy(*net.GetOutput("Output"));

  BufferToImage<D, T>(&net, "Input", "InputImage",
                      kernels::BufferType::IN_OUT_CHANNEL);
  OpDefBuilder("Pooling", "PoolingTest")
    .Input("InputImage")
    .Output("OutputImage")
    .AddIntArg("pooling_type", PoolingType::AVG)
    .AddIntsArg("kernels", kernels)
    .AddIntsArg("strides", strides)
    .AddIntArg("padding", padding)
    .AddIntsArg("dilations", {1, 1})
    .AddIntArg("T", static_cast<int>(DataTypeToEnum<T>::value))
    .Finalize(net.NewOperatorDef());
  net.RunOp(D);
  ImageToBuffer<D, float>(&net, "OutputImage", "OPENCLOutput",
                      kernels::BufferType::IN_OUT_CHANNEL);


  if (DataTypeToEnum<T>::value == DT_HALF) {
    ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"),
                            1e-3, 1e-3);
  } else {
    ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"),
                            1e-5);
  }
}
}  // namespace

TEST_F(PoolingOpTest, OPENCLAlignedAvgPooling) {
  AvgPoolingTest<OPENCL, float>({3, 15, 15, 128}, {4, 4}, {4, 4},
                                Padding::VALID);
  AvgPoolingTest<OPENCL, float>({3, 15, 15, 128}, {4, 4}, {4, 4},
                                Padding::SAME);
}

TEST_F(PoolingOpTest, OPENCLHalfAlignedAvgPooling) {
  AvgPoolingTest<OPENCL, half>({3, 15, 15, 128}, {4, 4}, {4, 4},
                               Padding::VALID);
  AvgPoolingTest<OPENCL, half>({3, 15, 15, 128}, {4, 4}, {4, 4}, Padding::SAME);
}

TEST_F(PoolingOpTest, OPENCLAlignedLargeKernelAvgPooling) {
  AvgPoolingTest<OPENCL, float>({3, 64, 64, 128}, {16, 16}, {16, 16},
                                Padding::VALID);
  AvgPoolingTest<OPENCL, float>({3, 64, 64, 128}, {16, 16}, {16, 16},
                                Padding::SAME);
}

TEST_F(PoolingOpTest, OPENCLHalfAlignedLargeKernelAvgPooling) {
  AvgPoolingTest<OPENCL, half>({3, 64, 64, 128}, {16, 16}, {16, 16},
                               Padding::VALID);
  AvgPoolingTest<OPENCL, half>({3, 64, 64, 128}, {16, 16}, {16, 16},
                               Padding::SAME);
}

TEST_F(PoolingOpTest, OPENCLUnAlignedAvgPooling) {
  AvgPoolingTest<OPENCL, float>({3, 31, 37, 128}, {2, 2}, {2, 2},
                                Padding::VALID);
  AvgPoolingTest<OPENCL, float>({3, 31, 37, 128}, {2, 2}, {2, 2},
                                Padding::SAME);
}

TEST_F(PoolingOpTest, OPENCLUnAlignedLargeKernelAvgPooling) {
  AvgPoolingTest<OPENCL, float>({3, 31, 37, 128}, {8, 8}, {8, 8},
                                Padding::VALID);
  AvgPoolingTest<OPENCL, float>({3, 31, 37, 128}, {8, 8}, {8, 8},
                                Padding::SAME);
}

namespace {
void AvgPoolingNEONTest(const std::vector<index_t> &shape,
                        const std::vector<int> &kernels,
                        const std::vector<int> &strides,
                        Padding padding,
                        PoolingType pooling_type) {
  // Construct graph
  OpsTestNet net;
  OpDefBuilder("Pooling", "PoolingTest")
    .Input("Input")
    .Output("Output")
    .AddIntArg("pooling_type", pooling_type)
    .AddIntsArg("kernels", kernels)
    .AddIntsArg("strides", strides)
    .AddIntArg("padding", padding)
    .AddIntsArg("dilations", {1, 1})
    .Finalize(net.NewOperatorDef());

  // Add input data
  net.AddRandomInput<CPU, float>("Input", shape);

  // run on cpu
  net.RunOp();

  OpDefBuilder("Pooling", "PoolingTest")
    .Input("InputNeon")
    .Output("OutputNeon")
    .AddIntArg("pooling_type", pooling_type)
    .AddIntsArg("kernels", kernels)
    .AddIntsArg("strides", strides)
    .AddIntArg("padding", padding)
    .AddIntsArg("dilations", {1, 1})
    .Finalize(net.NewOperatorDef());

  net.FillNHWCInputToNCHWInput<DeviceType::CPU, float>("InputNeon", "Input");

  // run on neon
  net.RunOp(DeviceType::NEON);

  net.FillNHWCInputToNCHWInput<DeviceType::CPU, float>("OutputExptected",
                                                       "Output");

  ExpectTensorNear<float>(*net.GetOutput("OutputExptected"),
                          *net.GetOutput("OutputNeon"),
                          1e-5, 1e-4);
}
}  // namespace

TEST_F(PoolingOpTest, NEONTest) {
  AvgPoolingNEONTest({3, 31, 37, 128}, {8, 8}, {8, 8},
                     Padding::VALID, PoolingType::MAX);
  AvgPoolingNEONTest({3, 31, 37, 128}, {8, 8}, {8, 8},
                     Padding::SAME, PoolingType::MAX);
  AvgPoolingNEONTest({3, 31, 37, 128}, {8, 8}, {8, 8},
                     Padding::VALID, PoolingType::AVG);
  AvgPoolingNEONTest({3, 31, 37, 128}, {8, 8}, {8, 8},
                     Padding::SAME, PoolingType::AVG);
}
}  // namespace test
}  // namespace ops
}  // namespace mace