wehub-resource-sync
145 lines
4.7 KiB
C++
145 lines
4.7 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/kernels/grid_sample_kernel.h"
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#include "paddle/common/layout.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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template <typename T, typename Context>
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void GridSampleKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& grid,
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const std::string& mode,
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const std::string& padding_mode,
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bool align_corners,
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DenseTensor* out) {
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if (out && out->numel() == 0) {
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dev_ctx.template Alloc<T>(out);
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return;
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}
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// attrs
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// paddle.nn.functional.grid_sample(x, grid, mode='bilinear',
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// padding_mode='zeros', align_corners=True, name=None)
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const std::string data_format = DataLayoutToString(x.layout());
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// attr to real param
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bool is_nearest_bool;
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if (mode == "bilinear") {
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is_nearest_bool = false;
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} else if (mode == "nearest") {
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is_nearest_bool = true;
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} else {
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PADDLE_THROW(errors::InvalidArgument(
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"should not reach here: mode should be either 'bilinear' or "
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"'nearest', bot got %s.",
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mode));
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}
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// attention: 0: zeros, 2: reflection, 1: border according to XDNN api.
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int padding_mode_int;
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if (padding_mode == "zeros") {
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padding_mode_int = 0;
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} else if (padding_mode == "reflection") {
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padding_mode_int = 2;
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} else if (padding_mode == "border") {
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padding_mode_int = 1;
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} else {
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PADDLE_THROW(errors::InvalidArgument(
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"should not reach here: padding_mode should be either 'zeros' or "
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"'reflection' or 'border', bot got %s.",
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padding_mode));
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}
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const T* input_data = x.data<T>();
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const T* grid_data = grid.data<T>();
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int64_t n = x.dims()[0];
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int64_t c = x.dims()[1];
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if (x.dims().size() == 4) { // 2D grid sample
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int64_t h = x.dims()[2];
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int64_t w = x.dims()[3];
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int64_t out_h = grid.dims()[1];
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int64_t out_w = grid.dims()[2];
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bool is_nchw_bool;
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if (data_format == "NCHW") {
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is_nchw_bool = true;
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} else if (data_format == "NHWC") {
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is_nchw_bool = false;
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} else {
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PADDLE_THROW(errors::InvalidArgument(
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"should not reach here: data_format should be either 'NCHW' or "
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"'NHWC', bot got %s.",
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data_format));
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}
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out->Resize({n, c, out_h, out_w});
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T* output_data = dev_ctx.template Alloc<T>(out);
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int r = xpu::grid_sample(dev_ctx.x_context(),
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input_data,
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grid_data,
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output_data,
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n,
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c,
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h,
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w,
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out_h,
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out_w,
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is_nearest_bool,
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align_corners,
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padding_mode_int,
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is_nchw_bool);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "grid_sampler");
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} else { // 3D grid sample
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int64_t d = x.dims()[2];
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int64_t h = x.dims()[3];
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int64_t w = x.dims()[4];
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int64_t out_d = grid.dims()[1];
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int64_t out_h = grid.dims()[2];
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int64_t out_w = grid.dims()[3];
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out->Resize({n, c, out_d, out_h, out_w});
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T* output_data = dev_ctx.template Alloc<T>(out);
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int r = xpu::grid_sample3d(dev_ctx.x_context(),
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input_data,
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grid_data,
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output_data,
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n,
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c,
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d,
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h,
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w,
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out_d,
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out_h,
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out_w,
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is_nearest_bool,
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align_corners,
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padding_mode_int,
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true);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "grid_sampler3d");
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(grid_sample, XPU, ALL_LAYOUT, phi::GridSampleKernel, float) {
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}
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