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GridSampler.cpp
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#include <ATen/native/GridSampler.h>
#include <ATen/ATen.h>
#include <ATen/Device.h>
#include <ATen/NativeFunctions.h>
#include <ATen/Parallel.h>
#include <c10/core/Layout.h>
#include <ATen/cpu/vml.h>
#include <ATen/native/IndexingUtils.h>
#include <ATen/native/UpSample.h>
#include <ATen/native/cpu/GridSamplerKernel.h>
#include <c10/util/Exception.h>
#include <c10/util/irange.h>
namespace at { namespace native {
using at::native::detail::GridSamplerInterpolation;
using at::native::detail::GridSamplerPadding;
namespace {
template<typename scalar_t>
Tensor grid_sampler_3d_cpu_impl(const Tensor& input, const Tensor& grid,
GridSamplerInterpolation interpolation_mode,
GridSamplerPadding padding_mode,
bool align_corners) {
int64_t N = input.size(0);
int64_t C = input.size(1);
int64_t inp_D = input.size(2);
int64_t inp_H = input.size(3);
int64_t inp_W = input.size(4);
int64_t out_D = grid.size(1);
int64_t out_H = grid.size(2);
int64_t out_W = grid.size(3);
auto output = at::empty({N, C, out_D, out_H, out_W}, input.options());
int64_t inp_sN = input.stride(0);
int64_t inp_sC = input.stride(1);
int64_t inp_sD = input.stride(2);
int64_t inp_sH = input.stride(3);
int64_t inp_sW = input.stride(4);
int64_t grid_sN = grid.stride(0);
int64_t grid_sD = grid.stride(1);
int64_t grid_sH = grid.stride(2);
int64_t grid_sW = grid.stride(3);
int64_t grid_sCoor = grid.stride(4);
int64_t out_sN = output.stride(0);
int64_t out_sC = output.stride(1);
int64_t out_sD = output.stride(2);
int64_t out_sH = output.stride(3);
int64_t out_sW = output.stride(4);
scalar_t *inp_ptr = input.data_ptr<scalar_t>();
scalar_t *out_ptr = output.data_ptr<scalar_t>();
scalar_t *grid_ptr = grid.data_ptr<scalar_t>();
// loop over each output pixel
at::parallel_for(0, N, 0, [&](int64_t start, int64_t end) {
for (const auto n : c10::irange(start, end)) {
scalar_t *grid_ptr_N = grid_ptr + n * grid_sN;
scalar_t *inp_ptr_N = inp_ptr + n * inp_sN;
for (const auto d : c10::irange(out_D)) {
for (const auto h : c10::irange(out_H)) {
for (const auto w : c10::irange(out_W)) {
// get the corresponding input x, y, z co-ordinates from grid
scalar_t *grid_ptr_NDHW = grid_ptr_N + d * grid_sD + h * grid_sH + w * grid_sW;
scalar_t ix = *grid_ptr_NDHW;
scalar_t iy = grid_ptr_NDHW[grid_sCoor];
scalar_t iz = grid_ptr_NDHW[2 * grid_sCoor];
ix = grid_sampler_compute_source_index(ix, inp_W, padding_mode, align_corners);
iy = grid_sampler_compute_source_index(iy, inp_H, padding_mode, align_corners);
iz = grid_sampler_compute_source_index(iz, inp_D, padding_mode, align_corners);
if (interpolation_mode == GridSamplerInterpolation::Bilinear) {
// get corner pixel values from (x, y, z)
// for 4d, we used north-east-south-west
// for 5d, we add top-bottom
int64_t ix_tnw = static_cast<int64_t>(std::floor(ix));
int64_t iy_tnw = static_cast<int64_t>(std::floor(iy));
int64_t iz_tnw = static_cast<int64_t>(std::floor(iz));
int64_t ix_tne = ix_tnw + 1;
int64_t iy_tne = iy_tnw;
int64_t iz_tne = iz_tnw;
int64_t ix_tsw = ix_tnw;
int64_t iy_tsw = iy_tnw + 1;
int64_t iz_tsw = iz_tnw;
int64_t ix_tse = ix_tnw + 1;
int64_t iy_tse = iy_tnw + 1;
int64_t iz_tse = iz_tnw;
int64_t ix_bnw = ix_tnw;
int64_t iy_bnw = iy_tnw;
int64_t iz_bnw = iz_tnw + 1;
int64_t ix_bne = ix_tnw + 1;
int64_t iy_bne = iy_tnw;
int64_t iz_bne = iz_tnw + 1;
int64_t ix_bsw = ix_tnw;
int64_t iy_bsw = iy_tnw + 1;
int64_t iz_bsw = iz_tnw + 1;
int64_t ix_bse = ix_tnw + 1;
int64_t iy_bse = iy_tnw + 1;
int64_t iz_bse = iz_tnw + 1;
// get surfaces to each neighbor:
scalar_t tnw = (ix_bse - ix) * (iy_bse - iy) * (iz_bse - iz);
scalar_t tne = (ix - ix_bsw) * (iy_bsw - iy) * (iz_bsw - iz);
scalar_t tsw = (ix_bne - ix) * (iy - iy_bne) * (iz_bne - iz);
scalar_t tse = (ix - ix_bnw) * (iy - iy_bnw) * (iz_bnw - iz);
scalar_t bnw = (ix_tse - ix) * (iy_tse - iy) * (iz - iz_tse);
scalar_t bne = (ix - ix_tsw) * (iy_tsw - iy) * (iz - iz_tsw);
scalar_t bsw = (ix_tne - ix) * (iy - iy_tne) * (iz - iz_tne);
scalar_t bse = (ix - ix_tnw) * (iy - iy_tnw) * (iz - iz_tnw);
// calculate bilinear weighted pixel value and set output pixel
scalar_t *out_ptr_NCDHW = out_ptr + n * out_sN + d * out_sD + h * out_sH + w * out_sW;
scalar_t *inp_ptr_NC = inp_ptr_N;
for (int64_t c = 0; c < C; ++c, out_ptr_NCDHW += out_sC, inp_ptr_NC += inp_sC) {
// (c, iz_tnw, iy_tnw, ix_tnw) * tnw + (c, iz_tne, iy_tne, ix_tne) * tne
// + (c, iz_tsw, iy_tsw, ix_tsw) * tsw + (c, iz_tse, iy_tse, ix_tse) * tse
// + (c, iz_bnw, iy_bnw, ix_bnw) * bnw + (c, iz_bne, iy_bne, ix_bne) * bne
// + (c, iz_bsw, iy_bsw, ix_bsw) * bsw + (c, iz_bse, iy_bse, ix_bse) * bse
*out_ptr_NCDHW = static_cast<scalar_t>(0);
if (within_bounds_3d(iz_tnw, iy_tnw, ix_tnw, inp_D, inp_H, inp_W)) {
*out_ptr_NCDHW += inp_ptr_NC[iz_tnw * inp_sD + iy_tnw * inp_sH + ix_tnw * inp_sW] * tnw;
}
if (within_bounds_3d(iz_tne, iy_tne, ix_tne, inp_D, inp_H, inp_W)) {
*out_ptr_NCDHW += inp_ptr_NC[iz_tne * inp_sD + iy_tne * inp_sH + ix_tne * inp_sW] * tne;
}
if (within_bounds_3d(iz_tsw, iy_tsw, ix_tsw, inp_D, inp_H, inp_W)) {
*out_ptr_NCDHW += inp_ptr_NC[iz_tsw * inp_sD + iy_tsw * inp_sH + ix_tsw * inp_sW] * tsw;
}
if (within_bounds_3d(iz_tse, iy_tse, ix_tse, inp_D, inp_H, inp_W)) {
*out_ptr_NCDHW += inp_ptr_NC[iz_tse * inp_sD + iy_tse * inp_sH + ix_tse * inp_sW] * tse;
}
if (within_bounds_3d(iz_bnw, iy_bnw, ix_bnw, inp_D, inp_H, inp_W)) {
*out_ptr_NCDHW += inp_ptr_NC[iz_bnw * inp_sD + iy_bnw * inp_sH + ix_bnw * inp_sW] * bnw;
}
if (within_bounds_3d(iz_bne, iy_bne, ix_bne, inp_D, inp_H, inp_W)) {
*out_ptr_NCDHW += inp_ptr_NC[iz_bne * inp_sD + iy_bne * inp_sH + ix_bne * inp_sW] * bne;
}
if (within_bounds_3d(iz_bsw, iy_bsw, ix_bsw, inp_D, inp_H, inp_W)) {
*out_ptr_NCDHW += inp_ptr_NC[iz_bsw * inp_sD + iy_bsw * inp_sH + ix_bsw * inp_sW] * bsw;
}
if (within_bounds_3d(iz_bse, iy_bse, ix_bse, inp_D, inp_H, inp_W)) {
*out_ptr_NCDHW += inp_ptr_NC[iz_bse * inp_sD + iy_bse * inp_sH + ix_bse * inp_sW] * bse;
}
}
} else if (interpolation_mode == GridSamplerInterpolation::Nearest) {
int64_t ix_nearest = static_cast<int64_t>(std::round(ix));
int64_t iy_nearest = static_cast<int64_t>(std::round(iy));
int64_t iz_nearest = static_cast<int64_t>(std::round(iz));
// assign nearest neighor pixel value to output pixel
scalar_t *out_ptr_NCDHW = out_ptr + n * out_sN + d * out_sD + h * out_sH + w * out_sW;
scalar_t *inp_ptr_NC = inp_ptr_N;
for (int64_t c = 0; c < C; ++c, out_ptr_NCDHW += out_sC, inp_ptr_NC += inp_sC) {
if (within_bounds_3d(iz_nearest, iy_nearest, ix_nearest, inp_D, inp_H, inp_W)) {
*out_ptr_NCDHW = inp_ptr_NC[iz_nearest * inp_sD + iy_nearest * inp_sH + ix_nearest * inp_sW];
} else {
*out_ptr_NCDHW = static_cast<scalar_t>(0);
}
}
}
}
}
}
}
});
return output;
}
template<typename scalar_t>
std::tuple<Tensor, Tensor>
grid_sampler_3d_backward_cpu_impl(const Tensor& grad_output,
const Tensor& input, const Tensor& grid,
GridSamplerInterpolation interpolation_mode,
GridSamplerPadding padding_mode,
bool align_corners) {
auto grad_input = at::zeros_like(input, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
auto grad_grid = at::empty_like(grid, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
// If interpolation mode is Nearest, then grad_grid is not filled in the
// loop below.
if (interpolation_mode == GridSamplerInterpolation::Nearest) {
grad_grid.zero_();
}
int64_t N = input.size(0);
int64_t C = input.size(1);
int64_t inp_D = input.size(2);
int64_t inp_H = input.size(3);
int64_t inp_W = input.size(4);
int64_t out_D = grid.size(1);
int64_t out_H = grid.size(2);
int64_t out_W = grid.size(3);
int64_t inp_sN = input.stride(0);
int64_t inp_sC = input.stride(1);
int64_t inp_sD = input.stride(2);
int64_t inp_sH = input.stride(3);
int64_t inp_sW = input.stride(4);
int64_t grid_sN = grid.stride(0);
int64_t grid_sD = grid.stride(1);
int64_t grid_sH = grid.stride(2);
int64_t grid_sW = grid.stride(3);
int64_t grid_sCoor = grid.stride(4);
int64_t gOut_sN = grad_output.stride(0);
int64_t gOut_sC = grad_output.stride(1);
int64_t gOut_sD = grad_output.stride(2);
int64_t gOut_sH = grad_output.stride(3);
int64_t gOut_sW = grad_output.stride(4);
int64_t gInp_sN = grad_input.stride(0);
int64_t gInp_sC = grad_input.stride(1);
int64_t gInp_sD = grad_input.stride(2);
int64_t gInp_sH = grad_input.stride(3);
int64_t gInp_sW = grad_input.stride(4);
int64_t gGrid_sN = grad_grid.stride(0);
int64_t gGrid_sW = grad_grid.stride(3);
scalar_t *inp_ptr = input.data_ptr<scalar_t>();
scalar_t *grid_ptr = grid.data_ptr<scalar_t>();
scalar_t *gOut_ptr = grad_output.data_ptr<scalar_t>();
scalar_t *gInp_ptr = grad_input.data_ptr<scalar_t>();
scalar_t *gGrid_ptr = grad_grid.data_ptr<scalar_t>();
// loop over each output pixel
at::parallel_for(0, N, 0, [&](int64_t start, int64_t end) {
for (const auto n : c10::irange(start, end)) {
scalar_t *grid_ptr_N = grid_ptr + n * grid_sN;
scalar_t *inp_ptr_N = inp_ptr + n * inp_sN;
scalar_t *gGrid_ptr_NDHW = gGrid_ptr + n * gGrid_sN;
for (const auto d : c10::irange(out_D)) {
for (const auto h : c10::irange(out_H)) {
for (int64_t w = 0; w < out_W; ++w, gGrid_ptr_NDHW += gGrid_sW /* grad_grid is contiguous */ ) {
// get the corresponding input x, y, z co-ordinates from grid
scalar_t *grid_ptr_NDHW = grid_ptr_N + d * grid_sD + h * grid_sH + w * grid_sW;
scalar_t ix = *grid_ptr_NDHW;
scalar_t iy = grid_ptr_NDHW[grid_sCoor];
scalar_t iz = grid_ptr_NDHW[2 * grid_sCoor];
// multipliers for gradients on ix, iy, and iz
scalar_t gix_mult, giy_mult, giz_mult;
ix = grid_sampler_compute_source_index_set_grad(ix, inp_W, padding_mode, align_corners, &gix_mult);
iy = grid_sampler_compute_source_index_set_grad(iy, inp_H, padding_mode, align_corners, &giy_mult);
iz = grid_sampler_compute_source_index_set_grad(iz, inp_D, padding_mode, align_corners, &giz_mult);
if (interpolation_mode == GridSamplerInterpolation::Bilinear) {
// get corner pixel values from (x, y, z)
// for 4d, we used north-east-south-west
// for 5d, we add top-bottom
int64_t ix_tnw = static_cast<int64_t>(std::floor(ix));
int64_t iy_tnw = static_cast<int64_t>(std::floor(iy));
int64_t iz_tnw = static_cast<int64_t>(std::floor(iz));
int64_t ix_tne = ix_tnw + 1;
int64_t iy_tne = iy_tnw;
int64_t iz_tne = iz_tnw;
int64_t ix_tsw = ix_tnw;
int64_t iy_tsw = iy_tnw + 1;
int64_t iz_tsw = iz_tnw;
int64_t ix_tse = ix_tnw + 1;
int64_t iy_tse = iy_tnw + 1;
int64_t iz_tse = iz_tnw;
int64_t ix_bnw = ix_tnw;
int64_t iy_bnw = iy_tnw;
int64_t iz_bnw = iz_tnw + 1;
int64_t ix_bne = ix_tnw + 1;
int64_t iy_bne = iy_tnw;
int64_t iz_bne = iz_tnw + 1;
int64_t ix_bsw = ix_tnw;
int64_t iy_bsw = iy_tnw + 1;
int64_t iz_bsw = iz_tnw + 1;
int64_t ix_bse = ix_tnw + 1;
int64_t iy_bse = iy_tnw + 1;
int64_t iz_bse = iz_tnw + 1;
// get surfaces to each neighbor:
scalar_t tnw = (ix_bse - ix) * (iy_bse - iy) * (iz_bse - iz);
scalar_t tne = (ix - ix_bsw) * (iy_bsw - iy) * (iz_bsw - iz);
scalar_t tsw = (ix_bne - ix) * (iy - iy_bne) * (iz_bne - iz);
scalar_t tse = (ix - ix_bnw) * (iy - iy_bnw) * (iz_bnw - iz);
scalar_t bnw = (ix_tse - ix) * (iy_tse - iy) * (iz - iz_tse);
scalar_t bne = (ix - ix_tsw) * (iy_tsw - iy) * (iz - iz_tsw);
scalar_t bsw = (ix_tne - ix) * (iy - iy_tne) * (iz - iz_tne);
scalar_t bse = (ix - ix_tnw) * (iy - iy_tnw) * (iz - iz_tnw);
scalar_t gix = static_cast<scalar_t>(0), giy = static_cast<scalar_t>(0), giz = static_cast<scalar_t>(0);
scalar_t *gOut_ptr_NCDHW = gOut_ptr + n * gOut_sN + d * gOut_sD + h * gOut_sH + w * gOut_sW;
scalar_t *gInp_ptr_NC = gInp_ptr + n * gInp_sN;
scalar_t *inp_ptr_NC = inp_ptr_N;
// calculate bilinear weighted pixel value and set output pixel
for (int64_t c = 0; c < C; ++c, gOut_ptr_NCDHW += gOut_sC, gInp_ptr_NC += gInp_sC, inp_ptr_NC += inp_sC) {
scalar_t gOut = *gOut_ptr_NCDHW;
// calculate and set grad_input
safe_add_3d(gInp_ptr_NC, iz_tnw, iy_tnw, ix_tnw, gInp_sD, gInp_sH, gInp_sW, inp_D, inp_H, inp_W, tnw * gOut);
safe_add_3d(gInp_ptr_NC, iz_tne, iy_tne, ix_tne, gInp_sD, gInp_sH, gInp_sW, inp_D, inp_H, inp_W, tne * gOut);
safe_add_3d(gInp_ptr_NC, iz_tsw, iy_tsw, ix_tsw, gInp_sD, gInp_sH, gInp_sW, inp_D, inp_H, inp_W, tsw * gOut);
safe_add_3d(gInp_ptr_NC, iz_tse, iy_tse, ix_tse, gInp_sD, gInp_sH, gInp_sW, inp_D, inp_H, inp_W, tse * gOut);
safe_add_3d(gInp_ptr_NC, iz_bnw, iy_bnw, ix_bnw, gInp_sD, gInp_sH, gInp_sW, inp_D, inp_H, inp_W, bnw * gOut);
safe_add_3d(gInp_ptr_NC, iz_bne, iy_bne, ix_bne, gInp_sD, gInp_sH, gInp_sW, inp_D, inp_H, inp_W, bne * gOut);
safe_add_3d(gInp_ptr_NC, iz_bsw, iy_bsw, ix_bsw, gInp_sD, gInp_sH, gInp_sW, inp_D, inp_H, inp_W, bsw * gOut);
safe_add_3d(gInp_ptr_NC, iz_bse, iy_bse, ix_bse, gInp_sD, gInp_sH, gInp_sW, inp_D, inp_H, inp_W, bse * gOut);
// calculate grad_grid
if (within_bounds_3d(iz_tnw, iy_tnw, ix_tnw, inp_D, inp_H, inp_W)) {
scalar_t tnw_val = inp_ptr_NC[iz_tnw * inp_sD + iy_tnw * inp_sH + ix_tnw * inp_sW];
gix -= tnw_val * (iy_bse - iy) * (iz_bse - iz) * gOut;
giy -= tnw_val * (ix_bse - ix) * (iz_bse - iz) * gOut;
giz -= tnw_val * (ix_bse - ix) * (iy_bse - iy) * gOut;
}
if (within_bounds_3d(iz_tne, iy_tne, ix_tne, inp_D, inp_H, inp_W)) {
scalar_t tne_val = inp_ptr_NC[iz_tne * inp_sD + iy_tne * inp_sH + ix_tne * inp_sW];
gix += tne_val * (iy_bsw - iy) * (iz_bsw - iz) * gOut;
giy -= tne_val * (ix - ix_bsw) * (iz_bsw - iz) * gOut;
giz -= tne_val * (ix - ix_bsw) * (iy_bsw - iy) * gOut;
}
if (within_bounds_3d(iz_tsw, iy_tsw, ix_tsw, inp_D, inp_H, inp_W)) {
scalar_t tsw_val = inp_ptr_NC[iz_tsw * inp_sD + iy_tsw * inp_sH + ix_tsw * inp_sW];
gix -= tsw_val * (iy - iy_bne) * (iz_bne - iz) * gOut;
giy += tsw_val * (ix_bne - ix) * (iz_bne - iz) * gOut;
giz -= tsw_val * (ix_bne - ix) * (iy - iy_bne) * gOut;
}
if (within_bounds_3d(iz_tse, iy_tse, ix_tse, inp_D, inp_H, inp_W)) {
scalar_t tse_val = inp_ptr_NC[iz_tse * inp_sD + iy_tse * inp_sH + ix_tse * inp_sW];
gix += tse_val * (iy - iy_bnw) * (iz_bnw - iz) * gOut;
giy += tse_val * (ix - ix_bnw) * (iz_bnw - iz) * gOut;
giz -= tse_val * (ix - ix_bnw) * (iy - iy_bnw) * gOut;
}
if (within_bounds_3d(iz_bnw, iy_bnw, ix_bnw, inp_D, inp_H, inp_W)) {
scalar_t bnw_val = inp_ptr_NC[iz_bnw * inp_sD + iy_bnw * inp_sH + ix_bnw * inp_sW];
gix -= bnw_val * (iy_tse - iy) * (iz - iz_tse) * gOut;
giy -= bnw_val * (ix_tse - ix) * (iz - iz_tse) * gOut;
giz += bnw_val * (ix_tse - ix) * (iy_tse - iy) * gOut;
}
if (within_bounds_3d(iz_bne, iy_bne, ix_bne, inp_D, inp_H, inp_W)) {
scalar_t bne_val = inp_ptr_NC[iz_bne * inp_sD + iy_bne * inp_sH + ix_bne * inp_sW];
gix += bne_val * (iy_tsw - iy) * (iz - iz_tsw) * gOut;
giy -= bne_val * (ix - ix_tsw) * (iz - iz_tsw) * gOut;
giz += bne_val * (ix - ix_tsw) * (iy_tsw - iy) * gOut;
}
if (within_bounds_3d(iz_bsw, iy_bsw, ix_bsw, inp_D, inp_H, inp_W)) {
scalar_t bsw_val = inp_ptr_NC[iz_bsw * inp_sD + iy_bsw * inp_sH + ix_bsw * inp_sW];
gix -= bsw_val * (iy - iy_tne) * (iz - iz_tne) * gOut;
giy += bsw_val * (ix_tne - ix) * (iz - iz_tne) * gOut;
giz += bsw_val * (ix_tne - ix) * (iy - iy_tne) * gOut;
}
if (within_bounds_3d(iz_bse, iy_bse, ix_bse, inp_D, inp_H, inp_W)) {
scalar_t bse_val = inp_ptr_NC[iz_bse * inp_sD + iy_bse * inp_sH + ix_bse * inp_sW];
gix += bse_val * (iy - iy_tnw) * (iz - iz_tnw) * gOut;
giy += bse_val * (ix - ix_tnw) * (iz - iz_tnw) * gOut;
giz += bse_val * (ix - ix_tnw) * (iy - iy_tnw) * gOut;
}
}
// assuming grad_grid is contiguous
gGrid_ptr_NDHW[0] = gix_mult * gix;
gGrid_ptr_NDHW[1] = giy_mult * giy;
gGrid_ptr_NDHW[2] = giz_mult * giz;
} else if (interpolation_mode == GridSamplerInterpolation::Nearest) {
int64_t ix_nearest = static_cast<int64_t>(std::round(ix));
int64_t iy_nearest = static_cast<int64_t>(std::round(iy));
int64_t iz_nearest = static_cast<int64_t>(std::round(iz));
// assign nearest neighor pixel value to output pixel
scalar_t *gOut_ptr_NCDHW = gOut_ptr + n * gOut_sN + d * gOut_sD + h * gOut_sH + w * gOut_sW;
scalar_t *gInp_ptr_NC = gInp_ptr + n * gInp_sN;
for (int64_t c = 0; c < C; ++c, gOut_ptr_NCDHW += gOut_sC, gInp_ptr_NC += gInp_sC) {
// calculate and set grad_input
safe_add_3d(gInp_ptr_NC, iz_nearest, iy_nearest, ix_nearest,
gInp_sD, gInp_sH, gInp_sW, inp_D, inp_H, inp_W, *gOut_ptr_NCDHW);
}
}
}
}
}
}
});
return std::make_tuple(grad_input, grad_grid);
}
} // namespace
Tensor _grid_sampler_2d_cpu_quantized(
const Tensor& input,
const Tensor& grid,
int64_t interpolation_mode_,
int64_t padding_mode_,
bool align_corners) {
auto interpolation_mode =
static_cast<GridSamplerInterpolation>(interpolation_mode_);
/* Bilinear interpolation is supported using the fact that we can perform
* linear interpolations on quantized values without rescaling. */
TORCH_CHECK(
interpolation_mode == GridSamplerInterpolation::Bilinear,
"_grid_sampler_2d_cpu_quantized(): only bilinear interpolation supported")
auto padding_mode = static_cast<GridSamplerPadding>(padding_mode_);
int64_t N = input.size(0);
int64_t C = input.size(1);
int64_t inp_H = input.size(2);
int64_t inp_W = input.size(3);
int64_t out_H = grid.size(1);
int64_t out_W = grid.size(2);
uint8_t zero_point = input.q_zero_point();
auto output = at::_empty_affine_quantized(
{N, C, out_H, out_W},
at::device(c10::kCPU).dtype(c10::kQUInt8),
input.q_scale(),
zero_point);
int64_t inp_sN = input.stride(0);
int64_t inp_sC = input.stride(1);
int64_t inp_sH = input.stride(2);
int64_t inp_sW = input.stride(3);
int64_t grid_sN = grid.stride(0);
int64_t grid_sH = grid.stride(1);
int64_t grid_sW = grid.stride(2);
int64_t grid_sCoor = grid.stride(3);
int64_t out_sN = output.stride(0);
int64_t out_sC = output.stride(1);
int64_t out_sH = output.stride(2);
int64_t out_sW = output.stride(3);
uint8_t* inp_ptr = (uint8_t*)input.data_ptr<quint8>();
uint8_t* out_ptr = (uint8_t*)output.data_ptr<quint8>();
float* grid_ptr = grid.data_ptr<float>();
at::parallel_for(0, N, 0, [&](int64_t start, int64_t end) {
for (const auto n : c10::irange(start, end)) {
float* grid_ptr_N = grid_ptr + n * grid_sN;
uint8_t* inp_ptr_N = inp_ptr + n * inp_sN;
for (const auto h : c10::irange(out_H)) {
for (const auto w : c10::irange(out_W)) {
// get the corresponding input x, y, z co-ordinates from grid
float* grid_ptr_NHW = grid_ptr_N + h * grid_sH + w * grid_sW;
float x = *grid_ptr_NHW;
float y = grid_ptr_NHW[grid_sCoor];
float ix = grid_sampler_compute_source_index(
x, inp_W, padding_mode, align_corners);
float iy = grid_sampler_compute_source_index(
y, inp_H, padding_mode, align_corners);
// get corner pixel values from (x, y)
// for 4d, we use north-east-south-west
int64_t ix_nw = static_cast<int64_t>(std::floor(ix));
int64_t iy_nw = static_cast<int64_t>(std::floor(iy));
int64_t ix_ne = ix_nw + 1;
int64_t iy_ne = iy_nw;
int64_t ix_sw = ix_nw;
int64_t iy_sw = iy_nw + 1;
int64_t ix_se = ix_nw + 1;
int64_t iy_se = iy_nw + 1;
// get surfaces to each neighbor:
float nw = (ix_se - ix) * (iy_se - iy);
float ne = (ix - ix_sw) * (iy_sw - iy);
float sw = (ix_ne - ix) * (iy - iy_ne);
float se = (ix - ix_nw) * (iy - iy_nw);
// calculate bilinear weighted pixel value and set output pixel
uint8_t* inp_ptr_NC = inp_ptr_N;
uint8_t* out_ptr_NCHW =
out_ptr + n * out_sN + h * out_sH + w * out_sW;
for (int64_t c = 0; c < C;
++c, out_ptr_NCHW += out_sC, inp_ptr_NC += inp_sC) {
float res = 0;
res += within_bounds_2d(iy_nw, ix_nw, inp_H, inp_W)
? inp_ptr_NC[iy_nw * inp_sH + ix_nw * inp_sW] * nw
: zero_point * nw;
res += within_bounds_2d(iy_ne, ix_ne, inp_H, inp_W)
? inp_ptr_NC[iy_ne * inp_sH + ix_ne * inp_sW] * ne
: zero_point * ne;
res += within_bounds_2d(iy_sw, ix_sw, inp_H, inp_W)
? inp_ptr_NC[iy_sw * inp_sH + ix_sw * inp_sW] * sw
: zero_point * sw;
res += within_bounds_2d(iy_se, ix_se, inp_H, inp_W)
? inp_ptr_NC[iy_se * inp_sH + ix_se * inp_sW] * se
: zero_point * se;
*out_ptr_NCHW = std::round(res);
}
}
}
}
});
return output;
}
Tensor _grid_sampler_2d_cpu_fallback(const Tensor& input, const Tensor& grid,
int64_t interpolation_mode_,
int64_t padding_mode_,
bool align_corners) {
auto interpolation_mode = static_cast<GridSamplerInterpolation>(interpolation_mode_);
auto padding_mode = static_cast<GridSamplerPadding>(padding_mode_);
using scalar_t = float;
int64_t N = input.size(0);
int64_t C = input.size(1);
int64_t inp_H = input.size(2);
int64_t inp_W = input.size(3);
int64_t out_H = grid.size(1);
int64_t out_W = grid.size(2);
auto output = at::empty({N, C, out_H, out_W}, input.options());
int64_t inp_sN = input.stride(0);
int64_t inp_sC = input.stride(1);
int64_t inp_sH = input.stride(2);
int64_t inp_sW = input.stride(3);
int64_t grid_sN = grid.stride(0);
int64_t grid_sH = grid.stride(1);
int64_t grid_sW = grid.stride(2);
int64_t grid_sCoor = grid.stride(3);
int64_t out_sN = output.stride(0);
int64_t out_sC = output.stride(1);
int64_t out_sH = output.stride(2);
int64_t out_sW = output.stride(3);
scalar_t *inp_ptr = input.data_ptr<scalar_t>();
scalar_t *out_ptr = output.data_ptr<scalar_t>();
scalar_t *grid_ptr = grid.data_ptr<scalar_t>();
// loop over each output pixel
at::parallel_for(0, N, 0, [&](int64_t start, int64_t end) {
for (const auto n : c10::irange(start, end)) {
scalar_t *grid_ptr_N = grid_ptr + n * grid_sN;
scalar_t *inp_ptr_N = inp_ptr + n * inp_sN;
for (const auto h : c10::irange(out_H)) {
for (const auto w : c10::irange(out_W)) {
// get the corresponding input x, y, z co-ordinates from grid
scalar_t *grid_ptr_NHW = grid_ptr_N + h * grid_sH + w * grid_sW;
scalar_t x = *grid_ptr_NHW;
scalar_t y = grid_ptr_NHW[grid_sCoor];
scalar_t ix = grid_sampler_compute_source_index(x, inp_W, padding_mode, align_corners);
scalar_t iy = grid_sampler_compute_source_index(y, inp_H, padding_mode, align_corners);
if (interpolation_mode == GridSamplerInterpolation::Bilinear) {
// get corner pixel values from (x, y)
// for 4d, we use north-east-south-west
int64_t ix_nw = static_cast<int64_t>(std::floor(ix));
int64_t iy_nw = static_cast<int64_t>(std::floor(iy));
int64_t ix_ne = ix_nw + 1;
int64_t iy_ne = iy_nw;
int64_t ix_sw = ix_nw;
int64_t iy_sw = iy_nw + 1;
int64_t ix_se = ix_nw + 1;
int64_t iy_se = iy_nw + 1;
// get surfaces to each neighbor:
scalar_t nw = (ix_se - ix) * (iy_se - iy);
scalar_t ne = (ix - ix_sw) * (iy_sw - iy);
scalar_t sw = (ix_ne - ix) * (iy - iy_ne);
scalar_t se = (ix - ix_nw) * (iy - iy_nw);
// calculate bilinear weighted pixel value and set output pixel
scalar_t *inp_ptr_NC = inp_ptr_N;
scalar_t *out_ptr_NCHW = out_ptr + n * out_sN + h * out_sH + w * out_sW;
for (int64_t c = 0; c < C; ++c, out_ptr_NCHW += out_sC, inp_ptr_NC += inp_sC) {
auto res = static_cast<scalar_t>(0);
if (within_bounds_2d(iy_nw, ix_nw, inp_H, inp_W)) {
res += inp_ptr_NC[iy_nw * inp_sH + ix_nw * inp_sW] * nw;
}
if (within_bounds_2d(iy_ne, ix_ne, inp_H, inp_W)) {
res += inp_ptr_NC[iy_ne * inp_sH + ix_ne * inp_sW] * ne;
}
if (within_bounds_2d(iy_sw, ix_sw, inp_H, inp_W)) {
res += inp_ptr_NC[iy_sw * inp_sH + ix_sw * inp_sW] * sw;
}
if (within_bounds_2d(iy_se, ix_se, inp_H, inp_W)) {
res += inp_ptr_NC[iy_se * inp_sH + ix_se * inp_sW] * se;
}
*out_ptr_NCHW = res;
}
} else if (interpolation_mode == GridSamplerInterpolation::Nearest) {
int64_t ix_nearest = static_cast<int64_t>(std::nearbyint(ix));
int64_t iy_nearest = static_cast<int64_t>(std::nearbyint(iy));
// assign nearest neighor pixel value to output pixel
scalar_t *out_ptr_NCHW = out_ptr + n * out_sN + h * out_sH + w * out_sW;
scalar_t *inp_ptr_NC = inp_ptr_N;
for (int64_t c = 0; c < C; ++c, out_ptr_NCHW += out_sC, inp_ptr_NC += inp_sC) {
if (within_bounds_2d(iy_nearest, ix_nearest, inp_H, inp_W)) {
*out_ptr_NCHW = inp_ptr_NC[iy_nearest * inp_sH + ix_nearest * inp_sW];
} else {
*out_ptr_NCHW = static_cast<scalar_t>(0);
}
}
} else if (interpolation_mode == GridSamplerInterpolation::Bicubic) {
// grid_sampler_compute_source_index will "clip the value" of idx depends on the padding,
// which would cause calculation to be wrong,
// for example x = -0.1 -> ix = 0 for zero padding, but in bicubic ix = floor(x) = -1
// There would be more problem in reflection padding, since the -1 and +1 direction is not fixed in boundary condition
ix = grid_sampler_unnormalize(x, inp_W, align_corners);
iy = grid_sampler_unnormalize(y, inp_H, align_corners);
scalar_t ix_nw = std::floor(ix);
scalar_t iy_nw = std::floor(iy);
const scalar_t tx = ix - ix_nw;
const scalar_t ty = iy - iy_nw;
scalar_t *inp_ptr_NC = inp_ptr_N;
scalar_t *out_ptr_NCHW = out_ptr + n * out_sN + h * out_sH + w * out_sW;
for (int64_t c = 0; c < C; ++c, out_ptr_NCHW += out_sC, inp_ptr_NC += inp_sC) {
// NOLINTNEXTLINE(modernize-avoid-c-arrays,cppcoreguidelines-avoid-c-arrays)
scalar_t coefficients[4];
// Interpolate 4 values in the x directon
for (const auto i : c10::irange(4)) {
coefficients[i] = cubic_interp1d<scalar_t>(
get_value_bounded<scalar_t>(inp_ptr_NC, ix_nw - 1, iy_nw - 1 + i, inp_W, inp_H, inp_sW, inp_sH, padding_mode, align_corners),
get_value_bounded<scalar_t>(inp_ptr_NC, ix_nw + 0, iy_nw - 1 + i, inp_W, inp_H, inp_sW, inp_sH, padding_mode, align_corners),
get_value_bounded<scalar_t>(inp_ptr_NC, ix_nw + 1, iy_nw - 1 + i, inp_W, inp_H, inp_sW, inp_sH, padding_mode, align_corners),
get_value_bounded<scalar_t>(inp_ptr_NC, ix_nw + 2, iy_nw - 1 + i, inp_W, inp_H, inp_sW, inp_sH, padding_mode, align_corners),
tx);
}
// Interpolate in the y direction
*out_ptr_NCHW = cubic_interp1d<scalar_t>(
coefficients[0],
coefficients[1],
coefficients[2],
coefficients[3],
ty);
}
}
}
}
}
});
return output;
}
std::tuple<Tensor, Tensor>
_grid_sampler_2d_cpu_fallback_backward(const Tensor& grad_output,
const Tensor& input, const Tensor& grid,
int64_t interpolation_mode_,
int64_t padding_mode_,
bool align_corners) {
const auto interpolation_mode = static_cast<GridSamplerInterpolation>(interpolation_mode_);
const auto padding_mode = static_cast<GridSamplerPadding>(padding_mode_);
using scalar_t = float;
auto grad_input = at::zeros_like(input, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
auto grad_grid = at::empty_like(grid, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
// If interpolation mode is Nearest, then grad_grid is not filled in the
// loop below.
if (interpolation_mode == GridSamplerInterpolation::Nearest) {
grad_grid.zero_();
}
int64_t N = input.size(0);
int64_t C = input.size(1);
int64_t inp_H = input.size(2);
int64_t inp_W = input.size(3);
int64_t out_H = grid.size(1);
int64_t out_W = grid.size(2);
int64_t inp_sN = input.stride(0);
int64_t inp_sC = input.stride(1);
int64_t inp_sH = input.stride(2);
int64_t inp_sW = input.stride(3);
int64_t grid_sN = grid.stride(0);
int64_t grid_sH = grid.stride(1);
int64_t grid_sW = grid.stride(2);
int64_t grid_sCoor = grid.stride(3);
int64_t gOut_sN = grad_output.stride(0);
int64_t gOut_sC = grad_output.stride(1);
int64_t gOut_sH = grad_output.stride(2);
int64_t gOut_sW = grad_output.stride(3);
int64_t gInp_sN = grad_input.stride(0);
int64_t gInp_sC = grad_input.stride(1);
int64_t gInp_sH = grad_input.stride(2);
int64_t gInp_sW = grad_input.stride(3);
int64_t gGrid_sN = grad_grid.stride(0);
int64_t gGrid_sW = grad_grid.stride(2);
scalar_t *inp_ptr = input.data_ptr<scalar_t>();
scalar_t *grid_ptr = grid.data_ptr<scalar_t>();
scalar_t *gOut_ptr = grad_output.data_ptr<scalar_t>();
scalar_t *gInp_ptr = grad_input.data_ptr<scalar_t>();
scalar_t *gGrid_ptr = grad_grid.data_ptr<scalar_t>();
// loop over each output pixel
at::parallel_for(0, N, 0, [&](int64_t start, int64_t end) {
for (const auto n : c10::irange(start, end)) {
scalar_t *grid_ptr_N = grid_ptr + n * grid_sN;
scalar_t *inp_ptr_N = inp_ptr + n * inp_sN;
scalar_t *gGrid_ptr_NHW = gGrid_ptr + n * gGrid_sN;
for (const auto h : c10::irange(out_H)) {
for (int64_t w = 0; w < out_W; ++w, gGrid_ptr_NHW += gGrid_sW /* grad_grid is contiguous */ ) {
// get the corresponding input x, y co-ordinates from grid
scalar_t *grid_ptr_NHW = grid_ptr_N + h * grid_sH + w * grid_sW;
scalar_t x = *grid_ptr_NHW;
scalar_t y = grid_ptr_NHW[grid_sCoor];
// multipliers for gradients on ix, iy
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
scalar_t gix_mult, giy_mult;
scalar_t ix = grid_sampler_compute_source_index_set_grad(x, inp_W, padding_mode, align_corners, &gix_mult);
scalar_t iy = grid_sampler_compute_source_index_set_grad(y, inp_H, padding_mode, align_corners, &giy_mult);
if (interpolation_mode == GridSamplerInterpolation::Bilinear) {
// get corner pixel values from (x, y)
// for 4d, we use north-east-south-west
int64_t ix_nw = static_cast<int64_t>(std::floor(ix));
int64_t iy_nw = static_cast<int64_t>(std::floor(iy));
int64_t ix_ne = ix_nw + 1;
int64_t iy_ne = iy_nw;
int64_t ix_sw = ix_nw;
int64_t iy_sw = iy_nw + 1;
int64_t ix_se = ix_nw + 1;
int64_t iy_se = iy_nw + 1;
// get surfaces to each neighbor:
scalar_t nw = (ix_se - ix) * (iy_se - iy);
scalar_t ne = (ix - ix_sw) * (iy_sw - iy);
scalar_t sw = (ix_ne - ix) * (iy - iy_ne);
scalar_t se = (ix - ix_nw) * (iy - iy_nw);
scalar_t gix = static_cast<scalar_t>(0), giy = static_cast<scalar_t>(0);
scalar_t *gOut_ptr_NCHW = gOut_ptr + n * gOut_sN + h * gOut_sH + w * gOut_sW;
scalar_t *gInp_ptr_NC = gInp_ptr + n * gInp_sN;
scalar_t *inp_ptr_NC = inp_ptr_N;
// calculate bilinear weighted pixel value and set output pixel
for (int64_t c = 0; c < C; ++c, gOut_ptr_NCHW += gOut_sC, gInp_ptr_NC += gInp_sC, inp_ptr_NC += inp_sC) {
scalar_t gOut = *gOut_ptr_NCHW;
// calculate and set grad_input
safe_add_2d(gInp_ptr_NC, iy_nw, ix_nw, gInp_sH, gInp_sW, inp_H, inp_W, nw * gOut);
safe_add_2d(gInp_ptr_NC, iy_ne, ix_ne, gInp_sH, gInp_sW, inp_H, inp_W, ne * gOut);
safe_add_2d(gInp_ptr_NC, iy_sw, ix_sw, gInp_sH, gInp_sW, inp_H, inp_W, sw * gOut);
safe_add_2d(gInp_ptr_NC, iy_se, ix_se, gInp_sH, gInp_sW, inp_H, inp_W, se * gOut);
// calculate grad_grid
if (within_bounds_2d(iy_nw, ix_nw, inp_H, inp_W)) {
scalar_t nw_val = inp_ptr_NC[iy_nw * inp_sH + ix_nw * inp_sW];
gix -= nw_val * (iy_se - iy) * gOut;
giy -= nw_val * (ix_se - ix) * gOut;
}
if (within_bounds_2d(iy_ne, ix_ne, inp_H, inp_W)) {
scalar_t ne_val = inp_ptr_NC[iy_ne * inp_sH + ix_ne * inp_sW];
gix += ne_val * (iy_sw - iy) * gOut;
giy -= ne_val * (ix - ix_sw) * gOut;
}
if (within_bounds_2d(iy_sw, ix_sw, inp_H, inp_W)) {
scalar_t sw_val = inp_ptr_NC[iy_sw * inp_sH + ix_sw * inp_sW];
gix -= sw_val * (iy - iy_ne) * gOut;
giy += sw_val * (ix_ne - ix) * gOut;
}
if (within_bounds_2d(iy_se, ix_se, inp_H, inp_W)) {
scalar_t se_val = inp_ptr_NC[iy_se * inp_sH + ix_se * inp_sW];
gix += se_val * (iy - iy_nw) * gOut;
giy += se_val * (ix - ix_nw) * gOut;
}
}
// assuming grad_grid is contiguous
gGrid_ptr_NHW[0] = gix_mult * gix;
gGrid_ptr_NHW[1] = giy_mult * giy;
} else if (interpolation_mode == GridSamplerInterpolation::Nearest) {
int64_t ix_nearest = static_cast<int64_t>(std::nearbyint(ix));
int64_t iy_nearest = static_cast<int64_t>(std::nearbyint(iy));
// assign nearest neighor pixel value to output pixel
scalar_t *gOut_ptr_NCHW = gOut_ptr + n * gOut_sN + h * gOut_sH + w * gOut_sW;
scalar_t *gInp_ptr_NC = gInp_ptr + n * gInp_sN;
for (int64_t c = 0; c < C; ++c, gOut_ptr_NCHW += gOut_sC, gInp_ptr_NC += gInp_sC) {
// calculate and set grad_input
safe_add_2d(gInp_ptr_NC, iy_nearest, ix_nearest, gInp_sH, gInp_sW,
inp_H, inp_W, *gOut_ptr_NCHW);
}
} else if (interpolation_mode == GridSamplerInterpolation::Bicubic) {
ix = grid_sampler_unnormalize_set_grad(x, inp_W, align_corners, &gix_mult);
iy = grid_sampler_unnormalize_set_grad(y, inp_H, align_corners, &giy_mult);
scalar_t ix_nw = std::floor(ix);
scalar_t iy_nw = std::floor(iy);
const scalar_t tx = ix - ix_nw;
const scalar_t ty = iy - iy_nw;
// NOLINTNEXTLINE(modernize-avoid-c-arrays,cppcoreguidelines-avoid-c-arrays)
scalar_t x_coeffs[4];
// NOLINTNEXTLINE(modernize-avoid-c-arrays,cppcoreguidelines-avoid-c-arrays)
scalar_t y_coeffs[4];
// NOLINTNEXTLINE(modernize-avoid-c-arrays,cppcoreguidelines-avoid-c-arrays)
scalar_t x_coeffs_grad[4];
// NOLINTNEXTLINE(modernize-avoid-c-arrays,cppcoreguidelines-avoid-c-arrays)
scalar_t y_coeffs_grad[4];
get_cubic_upsample_coefficients<scalar_t>(x_coeffs, tx);
get_cubic_upsample_coefficients<scalar_t>(y_coeffs, ty);
get_cubic_coefficients_grad<scalar_t>(x_coeffs_grad, tx);
get_cubic_coefficients_grad<scalar_t>(y_coeffs_grad, ty);
scalar_t gix = static_cast<scalar_t>(0);
scalar_t giy = static_cast<scalar_t>(0);
scalar_t *gOut_ptr_NCHW = gOut_ptr + n * gOut_sN + h * gOut_sH + w * gOut_sW;
scalar_t *gInp_ptr_NC = gInp_ptr + n * gInp_sN;
scalar_t *inp_ptr_NC = inp_ptr_N;
for (int64_t c = 0; c < C; ++c, gOut_ptr_NCHW += gOut_sC, gInp_ptr_NC += gInp_sC, inp_ptr_NC+= inp_sC) {
scalar_t gOut = *gOut_ptr_NCHW;
for (const auto i : c10::irange(4)) {
for (const auto j : c10::irange(4)) {
// set input gradient
add_value_bounded<scalar_t>(gInp_ptr_NC, ix_nw - 1 + i, iy_nw - 1 + j,
inp_W, inp_H, gInp_sW, gInp_sH, gOut * x_coeffs[i] * y_coeffs[j], padding_mode, align_corners);
// set grid gradient
scalar_t val = get_value_bounded<scalar_t>(inp_ptr_NC, ix_nw - 1 + i, iy_nw - 1 + j,
inp_W, inp_H, inp_sW, inp_sH, padding_mode, align_corners);
gix -= val * x_coeffs_grad[i] * y_coeffs[j] * gOut;
giy -= val * y_coeffs_grad[j] * x_coeffs[i] * gOut;
}
}
}
gGrid_ptr_NHW[0] = gix_mult * gix;
gGrid_ptr_NHW[1] = giy_mult * giy;
}
}
}
}
});
return std::make_tuple(grad_input, grad_grid);
}
// No shape checking needed here. See # NOTE [ grid_sampler Native Functions ].
Tensor grid_sampler_2d_cpu(const Tensor& input, const Tensor& grid,
int64_t interpolation_mode, int64_t padding_mode,
bool align_corners) {
if (input.scalar_type() == kQUInt8) {
return native::_grid_sampler_2d_cpu_quantized(
input, grid, interpolation_mode, padding_mode, align_corners);
}
// AVX gather instructions use signed 32-bit offsets to gather float values.
// Check for possible overflow and fallback to scalar implementation
if (input.scalar_type() != kDouble) {
TORCH_CHECK(input.scalar_type() == kFloat,
"grid_sampler_2d_cpu not implemented for ", input.scalar_type());
auto sizes = input.sizes();
auto strides = input.strides();
const auto grid_sW = grid.strides()[2];
// NOTE: Gather offsets are only used for the input H, W dimensions
// or only for strided access to the grid tensor
auto max_gather_offset = std::max(
(sizes[2] - 1) * strides[2] + (sizes[3] - 1) * strides[3],
grid_sW * (vec::Vectorized<float>::size() - 1));
if (max_gather_offset > std::numeric_limits<int32_t>::max()) {
return native::_grid_sampler_2d_cpu_fallback(
input, grid, interpolation_mode, padding_mode, align_corners);
}
}
return grid_sampler_2d_cpu_kernel(
kCPU, input, grid, interpolation_mode, padding_mode, align_corners);
}
DEFINE_DISPATCH(grid_sampler_2d_cpu_kernel);
// No shape checking needed here. See # NOTE [ grid_sampler Native Functions ].
Tensor grid_sampler_3d_cpu(const Tensor& input, const Tensor& grid,
int64_t interpolation_mode, int64_t padding_mode,
bool align_corners) {
return AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "grid_sampler3d_cpu", [&] {
return grid_sampler_3d_cpu_impl<scalar_t>(
input, grid, static_cast<GridSamplerInterpolation>(interpolation_mode),
static_cast<GridSamplerPadding>(padding_mode), align_corners);
});
}
// No shape checking needed here. See # NOTE [ grid_sampler Native Functions ].
std::tuple<Tensor, Tensor>
grid_sampler_2d_backward_cpu(const Tensor& grad_output, const Tensor& input, const Tensor& grid,
int64_t interpolation_mode, int64_t padding_mode, bool align_corners,
std::array<bool,2> output_mask) {
// AVX gather instructions use signed 32-bit offsets to gather float values.
// Check for possible overflow and fallback to scalar implementation
if (input.scalar_type() != kDouble) {
TORCH_CHECK(input.scalar_type() == kFloat,
"grid_sampler_2d_backward_cpu not implemented for ", input.scalar_type());
auto isizes = input.sizes();
auto istrides = input.strides();
auto gsizes = grad_output.sizes();
auto gstrides = grad_output.strides();
const auto grid_sW = grid.strides()[2];
// NOTE: Gather offsets are only used for the height and width dimensions
auto max_gather_offset = std::max(
std::max(
(isizes[2] - 1) * istrides[2] + (isizes[3] - 1) * istrides[3],
(gsizes[2] - 1) * gstrides[2] + (gsizes[3] - 1) * gstrides[3]),
grid_sW * (vec::Vectorized<float>::size() - 1));
if (max_gather_offset > std::numeric_limits<int32_t>::max()) {
return native::_grid_sampler_2d_cpu_fallback_backward(
grad_output, input, grid, interpolation_mode, padding_mode, align_corners);
}
}
return grid_sampler_2d_backward_cpu_kernel(
kCPU, grad_output, input, grid, interpolation_mode, padding_mode, align_corners, output_mask);
}
DEFINE_DISPATCH(grid_sampler_2d_backward_cpu_kernel);
// No shape checking needed here. See # NOTE [ grid_sampler Native Functions ].
std::tuple<Tensor, Tensor>
grid_sampler_3d_backward_cpu(const Tensor& grad_output, const Tensor& input, const Tensor& grid,
int64_t interpolation_mode, int64_t padding_mode, bool align_corners) {
return AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "grid_sampler_3d_backward_cpu", [&] {
return grid_sampler_3d_backward_cpu_impl<scalar_t>(
grad_output, input, grid,
static_cast<GridSamplerInterpolation>(interpolation_mode),
static_cast<GridSamplerPadding>(padding_mode), align_corners);
});
}
Tensor grid_sampler(const Tensor& input, const Tensor& grid,
int64_t interpolation_mode, int64_t padding_mode,
bool align_corners) {
TORCH_CHECK(
input.defined() && grid.defined(),
"grid_sampler(): expected input and grid to not be undefined, but input "
"is ", input, " and grid is ", grid);
auto input_opt = input.options();
auto grid_opt = grid.options();
TORCH_CHECK(
input_opt.device() == grid_opt.device(),
"grid_sampler(): expected input and grid to be on same device, but input "
"is on ", input_opt.device(), " and grid is on ", grid_opt.device());
TORCH_CHECK(
input_opt.layout() == kStrided && grid_opt.layout() == kStrided,
"grid_sampler(): expected input and grid to have torch.strided layout, but "
"input has ", input_opt.layout(), " and grid has ", grid_opt.layout());
TORCH_CHECK(
(input.dim() == 4 || input.dim() == 5) && input.dim() == grid.dim(),
"grid_sampler(): expected 4D or 5D input and grid with same number of "
"dimensions, but got input with sizes ", input.sizes(),
" and grid with sizes ", grid.sizes());
TORCH_CHECK(
input.size(0) == grid.size(0),
"grid_sampler(): expected grid and input to have same batch size, but got "
"input with sizes ", input.sizes(), " and grid with sizes ", grid.sizes());
TORCH_CHECK(
grid.size(-1) == input.dim() - 2,
"grid_sampler(): expected grid to have size ", input.dim() - 2, " in last "
"dimension, but got grid with sizes ", grid.sizes());
TORCH_CHECK(
!(input.dim() == 5 && static_cast<GridSamplerInterpolation>(interpolation_mode) == GridSamplerInterpolation::Bicubic),
"grid_sampler(): bicubic interpolation only supports 4D input"
);
for (const auto i : c10::irange(2, input.dim())) {
TORCH_CHECK(input.size(i) > 0,
"grid_sampler(): expected input to have non-empty spatial dimensions, "
"but input has sizes ", input.sizes(), " with dimension ", i, " being "
"empty");
}
// cudnn does not support inputs larger than 1024
if (at::native::cudnn_is_acceptable(input) &&
at::native::cudnn_is_acceptable(grid) &&
at::native::canUse32BitIndexMath(input) &&
at::native::canUse32BitIndexMath(grid) &&
static_cast<GridSamplerInterpolation>(interpolation_mode) == GridSamplerInterpolation::Bilinear &&
static_cast<GridSamplerPadding>(padding_mode) == GridSamplerPadding::Zeros &&
align_corners &&
input.dim() == 4 &&
input.size(1) <= 1024) {
return cudnn_grid_sampler(input, grid);
}
if (input.dim() == 4) {
return at::grid_sampler_2d(input, grid, interpolation_mode, padding_mode, align_corners);
} else {
return at::grid_sampler_3d(input, grid, interpolation_mode, padding_mode, align_corners);
}
}
}} // namespace at::native