wehub-resource-sync
96 lines
3.3 KiB
C++
96 lines
3.3 KiB
C++
// Copyright (c) 2023 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_grad_kernel.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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#include "paddle/phi/kernels/full_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void GridSampleGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& grid,
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const DenseTensor& out_grad,
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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* x_grad,
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DenseTensor* grid_grad) {
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if (out_grad.numel() == 0) {
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if (x_grad) {
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Full<T, Context>(dev_ctx, x_grad->dims(), 0, x_grad);
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}
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if (grid_grad) {
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Full<T, Context>(dev_ctx, grid_grad->dims(), 0, grid_grad);
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}
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return;
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}
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PADDLE_ENFORCE_EQ(
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x.dims().size(),
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4,
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common::errors::InvalidArgument(
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("XPU is only support input_dims == 4 in grid_sample_grad op.")));
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const int64_t n = grid.dims()[0];
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const int64_t out_h = grid.dims()[1];
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const int64_t out_w = grid.dims()[2];
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const int64_t c = x.dims()[1];
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const int64_t in_h = x.dims()[2];
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const int64_t in_w = x.dims()[3];
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x_grad->Resize({n, c, in_h, in_w});
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T* x_grad_ptr = dev_ctx.template Alloc<T>(x_grad);
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T* grid_grad_ptr = nullptr;
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if (grid_grad != nullptr) {
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grid_grad->Resize({n, out_h, out_w, 2});
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grid_grad_ptr = dev_ctx.template Alloc<T>(grid_grad);
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}
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bool is_nearest = false;
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if (mode == "nearest") {
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is_nearest = true;
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}
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int64_t padding_mode_type = 0;
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if (padding_mode == "border") {
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padding_mode_type = 1;
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} else if (padding_mode == "reflection") {
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padding_mode_type = 2;
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}
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int r = xpu::grid_sample_grad<T>(dev_ctx.x_context(),
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x.data<T>(),
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grid.data<T>(),
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out_grad.data<T>(),
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x_grad_ptr,
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grid_grad_ptr,
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n,
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c,
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in_h,
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in_w,
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out_h,
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out_w,
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is_nearest,
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align_corners,
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padding_mode_type,
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true);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "grid_sample_grad");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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grid_sample_grad, XPU, ALL_LAYOUT, phi::GridSampleGradKernel, float) {}
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