wehub-resource-sync
240 lines
8.0 KiB
C++
240 lines
8.0 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/interpolate_grad_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/backends/xpu/xpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/interpolate_function.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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template <typename T, typename Context>
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void InterpolateGradKernel(
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const Context& dev_ctx,
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const DenseTensor& x,
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const optional<DenseTensor>& out_size,
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const optional<std::vector<const DenseTensor*>>& size_tensor,
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const optional<DenseTensor>& scale_tensor,
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const DenseTensor& output_grad,
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const std::string& data_layout_str,
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int out_d,
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int out_h,
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int out_w,
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const std::vector<double>& scale,
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const std::string& interp_method,
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bool align_corners,
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int align_mode,
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DenseTensor* x_grad) {
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if (x_grad && x_grad->numel() == 0) {
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dev_ctx.template Alloc<T>(x_grad);
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return;
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}
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const DataLayout data_layout = StringToDataLayout(data_layout_str);
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int64_t n, c, in_d, in_h, in_w;
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funcs::ExtractNCDWH(x.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);
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double scale_h = -1;
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double scale_w = -1;
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if (scale_tensor) {
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auto scale_data =
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funcs::get_new_data_from_tensor<float>(scale_tensor.get_ptr());
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if (scale_data.size() > 1) {
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scale_h = scale_data[0];
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scale_w = scale_data[1];
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} else {
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scale_w = scale_data[0];
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scale_h = scale_data[0];
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}
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PADDLE_ENFORCE_EQ(
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scale_w > 0,
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true,
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errors::InvalidArgument(
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"The scale_w in input 'Scale' Tensor of Operator(interpolate) "
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"should be greater than 0, but received value is %d.",
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scale_w));
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PADDLE_ENFORCE_EQ(
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scale_h > 0,
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true,
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errors::InvalidArgument(
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"The scale_h in input 'Scale' Tensor of Operator(interpolate) "
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"should be greater than 0, but received value is %d.",
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scale_h));
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} else {
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if (scale.size() > 1) {
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scale_h = scale[0];
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scale_w = scale[1];
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PADDLE_ENFORCE_EQ(
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scale_w > 0,
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true,
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errors::InvalidArgument(
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"The scale_w in Attr(scale) of Operator(interpolate) "
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"should be greater than 0, but received value is %d.",
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scale_w));
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PADDLE_ENFORCE_EQ(
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scale_h > 0,
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true,
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errors::InvalidArgument(
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"The scale_h in Attr(scale) of Operator(interpolate) "
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"should be greater than 0, but received value is %d.",
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scale_h));
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}
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}
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if (scale_h > 0. && scale_w > 0.) {
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out_h = static_cast<int>(in_h * scale_h);
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out_w = static_cast<int>(in_w * scale_w);
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}
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if (out_size) {
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auto out_size_data =
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funcs::get_new_data_from_tensor<int>(out_size.get_ptr());
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out_h = out_size_data[0];
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out_w = out_size_data[1];
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}
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if (size_tensor && size_tensor->size() > 0) {
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// have size tensor
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auto new_size = funcs::get_new_shape(size_tensor.get());
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out_h = new_size[0];
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out_w = new_size[1];
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}
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DDim dim_grad;
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if (data_layout == DataLayout::NCHW) {
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dim_grad = {n, c, in_h, in_w};
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} else {
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dim_grad = {n, in_h, in_w, c};
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}
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x_grad->Resize(dim_grad);
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dev_ctx.template Alloc<T>(x_grad);
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int r = 0;
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r = xpu::constant<T>(dev_ctx.x_context(),
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x_grad->data<T>(),
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x_grad->numel(),
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static_cast<T>(0.0));
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
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if (in_h == out_h && in_w == out_w) {
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Copy<Context>(dev_ctx, output_grad, dev_ctx.GetPlace(), false, x_grad);
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return;
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}
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bool nearest = "nearest" == interp_method;
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int trans_mode = (align_corners) ? (0) : ((align_mode == 0) ? (1) : (2));
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if (nearest) {
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trans_mode = (align_corners) ? (0) : (2);
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}
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r = xpu::interpolate2d_grad<T>(dev_ctx.x_context(),
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output_grad.data<T>(),
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x_grad->data<T>(),
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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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nearest,
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trans_mode,
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(data_layout == DataLayout::NCHW));
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "interpolate2d_grad");
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}
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template <typename T, typename Context>
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void BilinearInterpGradKernel(
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const Context& dev_ctx,
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const DenseTensor& x,
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const optional<DenseTensor>& out_size,
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const optional<std::vector<const DenseTensor*>>& size_tensor,
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const optional<DenseTensor>& scale_tensor,
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const DenseTensor& out_grad,
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const std::string& data_layout,
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int out_d,
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int out_h,
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int out_w,
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const std::vector<double>& scale,
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const std::string& interp_method,
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bool align_corners,
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int align_mode,
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DenseTensor* x_grad) {
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InterpolateGradKernel<T, Context>(dev_ctx,
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x,
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out_size,
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size_tensor,
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scale_tensor,
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out_grad,
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data_layout,
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out_d,
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out_h,
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out_w,
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scale,
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interp_method,
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align_corners,
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align_mode,
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x_grad);
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}
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template <typename T, typename Context>
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void NearestInterpGradKernel(
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const Context& dev_ctx,
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const DenseTensor& x,
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const optional<DenseTensor>& out_size,
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const optional<std::vector<const DenseTensor*>>& size_tensor,
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const optional<DenseTensor>& scale_tensor,
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const DenseTensor& out_grad,
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const std::string& data_layout,
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int out_d,
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int out_h,
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int out_w,
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const std::vector<double>& scale,
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const std::string& interp_method,
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bool align_corners,
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int align_mode,
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DenseTensor* x_grad) {
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InterpolateGradKernel<T, Context>(dev_ctx,
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x,
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out_size,
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size_tensor,
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scale_tensor,
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out_grad,
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data_layout,
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out_d,
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out_h,
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out_w,
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scale,
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interp_method,
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align_corners,
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align_mode,
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x_grad);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(bilinear_interp_grad,
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XPU,
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ALL_LAYOUT,
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phi::BilinearInterpGradKernel,
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float) {
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kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
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kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
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}
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PD_REGISTER_KERNEL(
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nearest_interp_grad, XPU, ALL_LAYOUT, phi::NearestInterpGradKernel, float) {
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kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
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kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
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}
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