// conv_op_impl.h is the templated implementation of the conv_op.h file.
#ifndef CAFFE2_OPERATORS_DEFORM_CONV_OP_IMPL_H_
#define CAFFE2_OPERATORS_DEFORM_CONV_OP_IMPL_H_

#include "caffe2/core/context.h"
#include "caffe2/core/flags.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/operator.h"
#include "caffe2/operators/conv_pool_op_base.h"
#include "caffe2/operators/deform_conv_op.h"
#include "caffe2/utils/math.h"

namespace caffe2 {

template <typename T, class Context>
bool DeformConvOp<T, Context>::RunOnDeviceWithOrderNCHW() {
  const Tensor& X = Input(INPUT);
  const Tensor& offset = Input(OFFSET);
  auto& filter = Input(FILTER);
  Tensor* Y = Output(0);
  const int N = X.dim32(0), C = X.dim32(1);
  CAFFE_ENFORCE_EQ(X.dim(), filter.dim());
  const int M = filter.dim32(0);
  CAFFE_ENFORCE(
      C == filter.dim32(1) * group_,
      "Convolution op: input channels does not match: # of input channels ",
      C,
      " is not equal to kernel channels * group:",
      filter.dim32(1),
      "*",
      group_);
  CAFFE_ENFORCE(
      M % group_ == 0,
      "The number of output channels is not divisible by group.");
  CAFFE_ENFORCE(
      kernel_.size() == 2,
      "Deformable convolution only supports 2d kernel, has ",
      kernel_.size(),
      "d kernel.");
  CAFFE_ENFORCE(
      offset.dim() == 4,
      "Deformable convolution only supports 4d offset, has ",
      offset.dim(),
      "d offset.");
  CAFFE_ENFORCE_EQ(offset.dim32(0), N);
  CAFFE_ENFORCE(
      C % deformable_group_ == 0,
      "The number of input channels ",
      C,
      " is not divisible by deformable group ",
      deformable_group_);
  CAFFE_ENFORCE(
      M % deformable_group_ == 0,
      "The number of output channels ",
      M,
      " is not divisible by deformable group ",
      deformable_group_);
  CAFFE_ENFORCE(
      offset.dim32(1) == 2 * kernel_h() * kernel_w() * deformable_group_,
      "Deformable convolution: offset 1st dimension must equal "
      "2 * kernel_h * kernel_w * deformable_group: 2 * ",
      kernel_h(),
      " * ",
      kernel_w(),
      " * ",
      deformable_group_);

  CAFFE_ENFORCE_EQ(
      offset.dim32(2),
      (X.dim32(2) + pad_t() + pad_b() - (dilation_h() * (kernel_h() - 1) + 1)) /
              stride_h() +
          1);
  CAFFE_ENFORCE_EQ(
      offset.dim32(3),
      (X.dim32(3) + pad_l() + pad_r() - (dilation_w() * (kernel_w() - 1) + 1)) /
              stride_w() +
          1);

  int kernel_dims_size = 1;
  for (int i = 0; i < kernel_.size(); ++i) {
    CAFFE_ENFORCE(filter.dim32(i + 2) == kernel_[i]);
    kernel_dims_size *= kernel_[i];
  }

  ConvPoolOpBase<Context>::SetOutputSize(X, Y, filter.dim32(0));

  const vector<int> input_dims = GetDims(X);
  const vector<int> output_dims = GetDims(*Y);
  const int input_image_size = this->GetDimsSize(X);
  const int output_image_size = this->GetDimsSize(*Y);

  vector<int> img_shape;
  img_shape.assign(X.sizes().begin() + 1, X.sizes().end());

  vector<int> buffer_shape;
  buffer_shape.push_back(C / group_ * kernel_dims_size);
  buffer_shape.insert(
      buffer_shape.end(), output_dims.begin(), output_dims.end());

  // The dimension of each kernel
  const int kernel_dim = C / group_ * kernel_dims_size;
  // The offset corresponding to a single input image, and a single output
  // image.
  const int input_offset = C / group_ * input_image_size;
  const int output_offset = M / group_ * output_image_size;
  const int offset_offset = offset.numel() / offset.dim32(0);
  const int filter_offset = filter.numel() / group_;

  // The col buffer is stored in CHW order as well - kernel_dim, and the height
  // and width.
  const T* Xdata = X.template data<T>();
  const T* offset_data = offset.template data<T>();

  if (InputSize() == 4) {
    auto& bias = Input(BIAS);
    CAFFE_ENFORCE(bias.dim() == 1);
    CAFFE_ENFORCE(bias.dim32(0) == M);
    if (bias_multiplier_.numel() != output_image_size) {
      // If the helper bias multiplier is not image size, reshape and fill it
      // with
      // one.
      ReinitializeTensor(
          &bias_multiplier_,
          vector<int64_t>(1, output_image_size),
          at::dtype<T>().device(Context::GetDeviceType()));
      math::Set<T, Context>(
          output_image_size,
          static_cast<T>(1),
          bias_multiplier_.template mutable_data<T>(),
          &context_);
    }
  }
  T* Ydata = Y->template mutable_data<T>();
  const T* bias_data = nullptr;
  if (InputSize() == 4) {
    bias_data = Input(BIAS).template data<T>();
  }

  auto f = [this,
            &filter_offset,
            &bias_data,
            &X,
            &buffer_shape,
            &N,
            &Xdata,
            &offset_data,
            &M,
            &filter,
            &output_image_size,
            &kernel_dim,
            &Ydata,
            &input_offset,
            &offset_offset,
            &output_offset](Tensor* col_buffer) {
    col_buffer->Resize(buffer_shape);
    T* col_buffer_data = col_buffer->template mutable_data<T>();
    // Im2col, followed by gemm.
    for (int image_id = 0; image_id < N; ++image_id) {
      for (int group_id = 0; group_id < group_; ++group_id) {
        DeformableIm2col(
            Xdata + group_id * input_offset,
            offset_data,
            X.sizes(),
            col_buffer->sizes(),
            col_buffer_data);
        // Weight term
        math::Gemm<T, Context>(
            CblasNoTrans,
            CblasNoTrans,
            M / group_,
            output_image_size,
            kernel_dim,
            1,
            filter.template data<T>() + group_id * filter_offset,
            col_buffer_data,
            0,
            Ydata + group_id * output_offset,
            &context_);
      }
      if (bias_data) {
        math::Gemm<T, Context>(
            CblasNoTrans,
            CblasNoTrans,
            M,
            output_image_size,
            1,
            1,
            bias_data,
            bias_multiplier_.template data<T>(),
            1,
            Ydata,
            &context_);
      }
      Xdata += input_offset * group_;
      Ydata += output_offset * group_;
      offset_data += offset_offset;
    }
  };

  if (FLAGS_caffe2_force_shared_col_buffer || shared_buffer_) {
    runWithSharedBuffer<Context>(ws_, f);
  } else {
    f(&col_buffer_);
  }
  return true;
}

template <typename T, class Context>
bool DeformConvGradientOp<T, Context>::RunOnDeviceWithOrderNCHW() {
  auto& X = Input(INPUT);
  auto& offset = Input(OFFSET);
  auto& filter = Input(FILTER);
  auto& dY = Input(OUTPUT_GRAD);

  const int N = X.dim32(0), C = X.dim32(1);

  const vector<int> input_dims = this->GetDims(X);
  const int input_image_size = this->GetDimsSize(X);

  const vector<int> output_dims = this->GetDims(dY);
  // The output image size is the spatial size of the output.
  const int output_image_size = this->GetDimsSize(dY);

  ConvPoolOpBase<Context>::ComputePads(input_dims);
  CAFFE_ENFORCE_EQ(X.dim(), filter.dim());
  const int M = filter.dim32(0);
  CAFFE_ENFORCE(filter.dim32(1) * group_ == C);

  CAFFE_ENFORCE(
      kernel_.size() == 2,
      "Deformable convolution only supports 2d kernel, has ",
      kernel_.size(),
      "d kernel.");
  CAFFE_ENFORCE(
      offset.dim() == 4,
      "Deformable convolution only supports 4d offset, has ",
      offset.dim(),
      "d offset.");
  CAFFE_ENFORCE_EQ(offset.dim32(0), N);
  CAFFE_ENFORCE(
      C % deformable_group_ == 0,
      "The number of input channels ",
      C,
      " is not divisible by deformable group ",
      deformable_group_);
  CAFFE_ENFORCE(
      M % deformable_group_ == 0,
      "The number of output channels ",
      M,
      " is not divisible by deformable group ",
      deformable_group_);
  CAFFE_ENFORCE(
      offset.dim32(1) == 2 * kernel_h() * kernel_w() * deformable_group_,
      "Deformable convolution: offset 1st dimension must equal "
      "2 * kernel_h * kernel_w * deformable_group: 2 * ",
      kernel_h(),
      " * ",
      kernel_w(),
      " * ",
      deformable_group_);

  CAFFE_ENFORCE_EQ(
      offset.dim32(2),
      (X.dim32(2) + pad_t() + pad_b() - (dilation_h() * (kernel_h() - 1) + 1)) /
              stride_h() +
          1);
  CAFFE_ENFORCE_EQ(
      offset.dim32(3),
      (X.dim32(3) + pad_l() + pad_r() - (dilation_w() * (kernel_w() - 1) + 1)) /
              stride_w() +
          1);

  int kernel_dims_size = 1;
  for (int i = 0; i < kernel_.size(); ++i) {
    CAFFE_ENFORCE(filter.dim32(i + 2) == kernel_[i]);
    kernel_dims_size *= kernel_[i];
  }

  CAFFE_ENFORCE(M % group_ == 0);
  auto* dfilter = Output(FILTER_GRAD, filter.sizes(), at::dtype<T>());
  auto* doffset = Output(OFFSET_GRAD, offset.sizes(), at::dtype<T>());

  // The dimension of each kernel
  const int kernel_dim = C / group_ * kernel_dims_size;
  // The offset corresponding to a single input image, and a single output
  // image.
  const int input_offset = C / group_ * input_image_size;
  const int output_offset = M / group_ * output_image_size;
  const int offset_offset = offset.numel() / offset.dim32(0);
  const int filter_offset = filter.numel() / group_;

  // The col buffer is stored in CHW order as well - kernel_dim, and the
  // height and width.
  vector<int64_t> img_shape;
  img_shape.assign(X.sizes().begin() + 1, X.sizes().end());
  vector<int64_t> col_buffer_shape;
  col_buffer_shape.push_back(C * kernel_dims_size);
  col_buffer_shape.insert(
      col_buffer_shape.end(), output_dims.begin(), output_dims.end());
  ReinitializeTensor(
      &col_buffer_,
      col_buffer_shape,
      at::dtype<T>().device(Context::GetDeviceType()));

  const int col_buffer_offset = col_buffer_.numel() / group_;

  const T* Xdata = X.template data<T>();
  const T* filter_data = filter.template data<T>();
  const T* offset_data = offset.template data<T>();
  const T* dYdata = dY.template data<T>();
  T* col_buffer_data = col_buffer_.template mutable_data<T>();
  T* dfilter_data = dfilter->template mutable_data<T>();
  T* doffset_data = doffset->template mutable_data<T>();

  // Pre-setting the gradients to zero.
  math::Set<T, Context>(dfilter->numel(), 0, dfilter_data, &context_);

  T* dbias_data = nullptr;
  if (!no_bias_) {
    auto* dbias = Output(BIAS_OR_INPUT_GRAD, {M}, at::dtype<T>());
    if (bias_multiplier_.numel() != output_image_size) {
      // If the helper bias multiplier is not M, reshape and fill it with one.
      ReinitializeTensor(
          &bias_multiplier_,
          vector<int64_t>(1, output_image_size),
          at::dtype<T>().device(Context::GetDeviceType()));
      math::Set<T, Context>(
          output_image_size,
          static_cast<T>(1),
          bias_multiplier_.template mutable_data<T>(),
          &context_);
    }
    dbias_data = dbias->template mutable_data<T>();
    math::Set<T, Context>(dbias->numel(), 0, dbias_data, &context_);
  }

  T* dXdata = nullptr;
  if (OutputSize() == 4 || (no_bias_ && (OutputSize() == 3))) {
    auto* dX = Output(
        no_bias_ ? BIAS_OR_INPUT_GRAD : INPUT_GRAD, X.sizes(), at::dtype<T>());
    dXdata = dX->template mutable_data<T>();
    math::Set<T, Context>(dX->numel(), 0, dXdata, &context_);
  }

  for (int image_id = 0; image_id < N; ++image_id) {