/* Copyright (c) 2018 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. */

#ifdef FUSION_CONVBNRELU_OP

#include "operators/kernel/conv_bn_relu_kernel.h"
#include <cmath>
#include "operators/kernel/cl/cl-kernel-func/conv_func.h"

namespace paddle_mobile {
namespace operators {

template <>
bool ConvBNReluKernel<GPU_CL, float>::Init(
    FusionConvBNReluParam<GPU_CL> *param) {
  PADDLE_MOBILE_ENFORCE(
      param->Filter()->dims()[2] == param->Filter()->dims()[3] &&
          param->Paddings()[0] == param->Paddings()[1],
      "need equal");
  const framework::CLImage *mean = param->InputMean();
  const framework::CLImage *variance = param->InputVariance();
  const framework::CLImage *scale = param->InputScale();
  const framework::CLImage *bias = param->InputBias();
  const float epsilon = param->Epsilon();

  const int C = mean->numel();

  auto mean_ptr = mean->data<float>();
  auto variance_ptr = variance->data<float>();
  auto scale_ptr = scale->data<float>();
  auto bias_ptr = bias->data<float>();

  float inv_std_ptr[C];
  for (int i = 0; i < C; i++) {
    inv_std_ptr[i] =
        1 / static_cast<float>(pow((variance_ptr[i] + epsilon), 0.5));
  }
  float *new_scale_ptr = new float[C];
  float *new_bias_ptr = new float[C];

  for (int i = 0; i < C; i++) {
    new_scale_ptr[i] = inv_std_ptr[i] * scale_ptr[i];
    new_bias_ptr[i] = bias_ptr[i] - mean_ptr[i] * inv_std_ptr[i] * scale_ptr[i];
  }

  framework::CLImage *new_scale = new framework::CLImage();

  //  for (int j = 0; j < C; ++j) {
  //    DLOG << " new scale - " << j << new_scale_ptr[j];
  //  }
  //
  //  for (int j = 0; j < C; ++j) {
  //    DLOG << " new bias - " << j << new_bias_ptr[j];
  //  }

  new_scale->SetTensorData(new_scale_ptr, variance->dims());
  new_scale->InitCLImage(this->cl_helper_.CLContext(),
                         cl_helper_.CLCommandQueue());

  //  DLOG << " climage - y bias: " << *(param->Bias());
  //
  //  DLOG << " climage - new scale: " << *new_scale;

  framework::CLImage *new_bias = new framework::CLImage();

  new_bias->SetTensorData(new_bias_ptr, variance->dims());
  new_bias->InitCLImage(this->cl_helper_.CLContext(),
                        cl_helper_.CLCommandQueue());

  //  DLOG << " climage - new bias: " << *new_bias;
  //
  //  DLOG << " climage - filter: " << *(param->Filter());

  param->SetNewScale(new_scale);
  param->SetNewBias(new_bias);

  delete[](new_scale_ptr);
  delete[](new_bias_ptr);

  PADDLE_MOBILE_ENFORCE(
      param->Filter()->dims()[2] == param->Filter()->dims()[3] &&
          param->Paddings()[0] == param->Paddings()[1],
      "need equal");

  int offset = static_cast<int>(param->Filter()->dims()[2]) / 2 -
               static_cast<int>(param->Paddings()[1]);

  param->SetOffset(offset);

  const std::string conv_kernel_file = "conv_kernel.cl";
  const std::string wino_kernel_file = "winograd_transform.cl";
  const std::string build_options = "-DBATCH_NORM -DRELU";

  if (param->Filter()->dims()[2] == 1 && param->Filter()->dims()[3] == 1) {
    param->ExecMode() = ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW1x1_FLOAT;
    param->Filter()->InitNImage(cl_helper_.CLContext(),
                                cl_helper_.CLCommandQueue());

    if (param->Input()->dims()[1] % 4 == 0) {
      this->cl_helper_.AddKernel("conv_1x1_simple", conv_kernel_file,
                                 build_options);
    } else {
      this->cl_helper_.AddKernel("conv_1x1_wrapped", conv_kernel_file,
                                 build_options);
    }
  } else if (param->Filter()->dims()[1] == 1 &&
             param->Input()->dims()[1] == param->Output()->dims()[1] &&
             param->Filter()->dims()[2] == 3) {
    param->Filter()->InitDWImage(cl_helper_.CLContext(),
                                 cl_helper_.CLCommandQueue());
    if (param->Strides()[0] == 1 && param->Dilations()[0] == 1) {
      param->ExecMode() = ConvParam<GPU_CL>::EXEC_DEPTHWISE3x3S1_FLOAT;
      this->cl_helper_.AddKernel("depth_conv_3x3s1", conv_kernel_file,
                                 build_options);
    } else {
      param->ExecMode() = ConvParam<GPU_CL>::EXEC_DEPTHWISE3x3_FLOAT;
      this->cl_helper_.AddKernel("depth_conv_3x3", conv_kernel_file,
                                 build_options);
    }

  } else if (param->Filter()->dims()[1] == 1 &&
             param->Input()->dims()[1] == param->Output()->dims()[1] &&
             param->Filter()->dims()[2] != 3) {
    param->Filter()->InitDWImage(cl_helper_.CLContext(),
                                 cl_helper_.CLCommandQueue());

    param->ExecMode() = ConvParam<GPU_CL>::EXEC_DEPTHWISEBASIC_FLOAT;
    this->cl_helper_.AddKernel("depth_conv", conv_kernel_file, build_options);
  } else if (param->Filter()->dims()[2] == 3 &&
             param->Filter()->dims()[3] == 3) {
    //    if (param->Strides()[0] == param->Strides()[1] &&
    //        param->Strides()[0] == 1 && param->Input()->dims()[2] >= 32) {
    //      param->ExecMode() = ConvParam<GPU_CL>::EXEC_WINOGRAD3X3_FLOAT;
    //      this->cl_helper_.AddKernel("winograd_filter_transform_2x2",
    //                                 wino_kernel_file, build_options);
    //      this->cl_helper_.AddKernel("winograd_input_transform_2x2",
    //                                 wino_kernel_file, build_options);
    //      this->cl_helper_.AddKernel("matmul", "matmul.cl");
    //      this->cl_helper_.AddKernel("winograd_output_transform_2x2",
    //                                 wino_kernel_file, build_options);
    //
    //      winograd_transform_weight<4, 3>(&this->cl_helper_, param->Filter());
    //
    //    } else {
    param->ExecMode() = ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW3x3_FLOAT;
    param->Filter()->InitCLImage(cl_helper_.CLContext(),
                                 cl_helper_.CLCommandQueue());

    this->cl_helper_.AddKernel("conv_3x3", conv_kernel_file, build_options);
    //    }
  } else {
    PADDLE_MOBILE_THROW_EXCEPTION(" not support ");
  }
  return true;
}

template <>
void ConvBNReluKernel<GPU_CL, float>::Compute(
    const FusionConvBNReluParam<GPU_CL> &param) {
  switch (param.ExecMode()) {
    case ConvParam<GPU_CL>::EXEC_WINOGRAD3X3_FLOAT:
      WinogradConv3x3<4, 3>(&this->cl_helper_, param, true, nullptr,
                            param.NewScale(), param.NewBias());
      break;
    case ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW1x1_FLOAT:
    case ConvParam<GPU_CL>::EXEC_SLIDINGWINDOW3x3_FLOAT:
    case ConvParam<GPU_CL>::EXEC_DEPTHWISE3x3_FLOAT:
    case ConvParam<GPU_CL>::EXEC_DEPTHWISEBASIC_FLOAT:
      ConvAddBnRelu(&this->cl_helper_, param, true, nullptr, param.NewScale(),
                    param.NewBias());
      break;
    case ConvParam<GPU_CL>::EXEC_DEPTHWISE3x3S1_FLOAT:
      DWConvAddBnRelu(&this->cl_helper_, param, true, nullptr, param.NewScale(),
                      param.NewBias());
      break;
    default:
      PADDLE_MOBILE_THROW_EXCEPTION("Invalid convolution execute mode %d",
                                    param.ExecMode());
  }
}
template class ConvBNReluKernel<GPU_CL, float>;

}  // namespace operators
}  // namespace paddle_mobile

#endif
