wehub-resource-sync
326 lines
11 KiB
C++
326 lines
11 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
//
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
|
|
#include "paddle/phi/kernels/interpolate_kernel.h"
|
|
|
|
#include "paddle/common/layout.h"
|
|
#include "paddle/phi/backends/xpu/enforce_xpu.h"
|
|
#include "paddle/phi/backends/xpu/xpu_context.h"
|
|
#include "paddle/phi/core/kernel_registry.h"
|
|
#include "paddle/phi/kernels/funcs/interpolate_function.h"
|
|
|
|
namespace phi {
|
|
|
|
template <typename T, typename Context>
|
|
void InterpolateKernel(
|
|
const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const optional<DenseTensor>& out_size,
|
|
const optional<std::vector<const DenseTensor*>>& size_tensor,
|
|
const optional<DenseTensor>& scale_tensor,
|
|
const std::string& data_layout_str,
|
|
int out_d,
|
|
int out_h,
|
|
int out_w,
|
|
const std::vector<double>& scale,
|
|
const std::string& interp_method,
|
|
bool align_corners,
|
|
int align_mode,
|
|
DenseTensor* output) {
|
|
if (x.numel() == 0) {
|
|
dev_ctx.template Alloc<T>(output);
|
|
return;
|
|
}
|
|
using XPUType = typename XPUTypeTrait<T>::Type;
|
|
const DataLayout data_layout = StringToDataLayout(data_layout_str);
|
|
int64_t n, c, in_d, in_h, in_w;
|
|
funcs::ExtractNCDWH(x.dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);
|
|
|
|
double scale_h = -1;
|
|
double scale_w = -1;
|
|
|
|
if (size_tensor && size_tensor->size() > 0) {
|
|
// have size tensor
|
|
auto new_size = funcs::get_new_shape(size_tensor.get());
|
|
out_h = new_size[0];
|
|
out_w = new_size[1];
|
|
} else {
|
|
if (scale_tensor) {
|
|
auto scale_data =
|
|
funcs::get_new_data_from_tensor<float>(scale_tensor.get_ptr());
|
|
if (scale_data.size() > 1) {
|
|
scale_h = scale_data[0];
|
|
scale_w = scale_data[1];
|
|
} else {
|
|
scale_h = scale_data[0];
|
|
scale_w = scale_data[0];
|
|
}
|
|
PADDLE_ENFORCE_EQ(
|
|
scale_w > 0,
|
|
true,
|
|
errors::InvalidArgument(
|
|
"The scale_w in input 'Scale' Tensor of Operator(interpolate) "
|
|
"should be greater than 0, but received value is %d.",
|
|
scale_w));
|
|
PADDLE_ENFORCE_EQ(
|
|
scale_h > 0,
|
|
true,
|
|
errors::InvalidArgument(
|
|
"The scale_h in input 'Scale' Tensor of Operator(interpolate) "
|
|
"should be greater than 0, but received value is %d.",
|
|
scale_h));
|
|
} else {
|
|
if (scale.size() > 1) {
|
|
scale_h = scale[0];
|
|
scale_w = scale[1];
|
|
|
|
PADDLE_ENFORCE_EQ(
|
|
scale_w > 0,
|
|
true,
|
|
errors::InvalidArgument(
|
|
"The scale_w in Attr(scale) of Operator(interpolate) "
|
|
"should be greater than 0, but received value is %d.",
|
|
scale_w));
|
|
PADDLE_ENFORCE_EQ(
|
|
scale_h > 0,
|
|
true,
|
|
errors::InvalidArgument(
|
|
"The scale_h in Attr(scale) of Operator(interpolate) "
|
|
"should be greater than 0, but received value is %d.",
|
|
scale_h));
|
|
}
|
|
}
|
|
if (scale_h > 0. && scale_w > 0.) {
|
|
out_h = static_cast<int>(in_h * scale_h);
|
|
out_w = static_cast<int>(in_w * scale_w);
|
|
}
|
|
if (out_size) {
|
|
auto out_size_data =
|
|
funcs::get_new_data_from_tensor<int>(out_size.get_ptr());
|
|
out_h = out_size_data[0];
|
|
out_w = out_size_data[1];
|
|
}
|
|
}
|
|
PADDLE_ENFORCE_GT(
|
|
out_h,
|
|
0,
|
|
errors::InvalidArgument("out_h in Attr(out_shape) of Op(interpolate) "
|
|
"should be greater than 0."));
|
|
PADDLE_ENFORCE_GT(
|
|
out_w,
|
|
0,
|
|
errors::InvalidArgument("out_w in Attr(out_shape) of Op(interpolate) "
|
|
"should be greater than 0."));
|
|
|
|
DDim dim_out;
|
|
if (data_layout == DataLayout::NCHW) {
|
|
dim_out = {n, c, out_h, out_w};
|
|
} else {
|
|
dim_out = {n, out_h, out_w, c};
|
|
}
|
|
output->Resize(dim_out);
|
|
dev_ctx.template Alloc<T>(output);
|
|
|
|
if (in_h == out_h && in_w == out_w) {
|
|
Copy<Context>(dev_ctx, x, dev_ctx.GetPlace(), false, output);
|
|
return;
|
|
}
|
|
float ratio_h =
|
|
funcs::AreaPixelComputeScale<float>(in_h, out_h, align_corners, scale_h);
|
|
float ratio_w =
|
|
funcs::AreaPixelComputeScale<float>(in_w, out_w, align_corners, scale_w);
|
|
if ("bicubic" == interp_method) {
|
|
if constexpr (std::is_floating_point_v<T> ||
|
|
std::is_same_v<T, phi::dtype::float16>) {
|
|
int trans_mode = (align_corners) ? (0) : ((align_mode == 0) ? (1) : (2));
|
|
int r = xpu::upsample_bicubic2d<XPUType>(
|
|
dev_ctx.x_context(),
|
|
reinterpret_cast<const XPUType*>(x.data<T>()),
|
|
reinterpret_cast<XPUType*>(output->data<T>()),
|
|
static_cast<int64_t>(n),
|
|
static_cast<int64_t>(c),
|
|
static_cast<int64_t>(in_h),
|
|
static_cast<int64_t>(in_w),
|
|
static_cast<int64_t>(out_h),
|
|
static_cast<int64_t>(out_w),
|
|
static_cast<int64_t>(trans_mode),
|
|
(data_layout == DataLayout::NCHW),
|
|
ratio_h,
|
|
ratio_w);
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "upsample_bicubic2d");
|
|
} else {
|
|
PADDLE_THROW(common::errors::Unimplemented(
|
|
"XPU Bicubic interpolation only supports float, float16 and "
|
|
"bfloat16, "
|
|
"but received other types(bfloat16 is not bound yet)"));
|
|
}
|
|
|
|
} else {
|
|
bool nearest = "nearest" == interp_method;
|
|
int trans_mode = (align_corners) ? (0) : ((align_mode == 0) ? (1) : (2));
|
|
if (nearest) {
|
|
trans_mode = (align_corners == true) ? 0 : 2;
|
|
PADDLE_ENFORCE_EQ(
|
|
(data_layout == DataLayout::NCHW),
|
|
true,
|
|
errors::InvalidArgument("XPU nearest is only support NCHW"));
|
|
}
|
|
|
|
int r = xpu::interpolate2d<XPUType>(
|
|
dev_ctx.x_context(),
|
|
reinterpret_cast<const XPUType*>(x.data<T>()),
|
|
reinterpret_cast<XPUType*>(output->data<T>()),
|
|
n,
|
|
c,
|
|
in_h,
|
|
in_w,
|
|
out_h,
|
|
out_w,
|
|
nearest,
|
|
trans_mode,
|
|
(data_layout == DataLayout::NCHW));
|
|
PADDLE_ENFORCE_XDNN_SUCCESS(r, "interpolate2d");
|
|
}
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void BilinearInterpKernel(
|
|
const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const optional<DenseTensor>& out_size,
|
|
const optional<std::vector<const DenseTensor*>>& size_tensor,
|
|
const optional<DenseTensor>& scale_tensor,
|
|
const std::string& data_layout,
|
|
int out_d,
|
|
int out_h,
|
|
int out_w,
|
|
const std::vector<double>& scale,
|
|
const std::string& interp_method,
|
|
bool align_corners,
|
|
int align_mode,
|
|
DenseTensor* output) {
|
|
InterpolateKernel<T, Context>(dev_ctx,
|
|
x,
|
|
out_size,
|
|
size_tensor,
|
|
scale_tensor,
|
|
data_layout,
|
|
out_d,
|
|
out_h,
|
|
out_w,
|
|
scale,
|
|
interp_method,
|
|
align_corners,
|
|
align_mode,
|
|
output);
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void NearestInterpKernel(
|
|
const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const optional<DenseTensor>& out_size,
|
|
const optional<std::vector<const DenseTensor*>>& size_tensor,
|
|
const optional<DenseTensor>& scale_tensor,
|
|
const std::string& data_layout,
|
|
int out_d,
|
|
int out_h,
|
|
int out_w,
|
|
const std::vector<double>& scale,
|
|
const std::string& interp_method,
|
|
bool align_corners,
|
|
int align_mode,
|
|
DenseTensor* output) {
|
|
InterpolateKernel<T, Context>(dev_ctx,
|
|
x,
|
|
out_size,
|
|
size_tensor,
|
|
scale_tensor,
|
|
data_layout,
|
|
out_d,
|
|
out_h,
|
|
out_w,
|
|
scale,
|
|
interp_method,
|
|
align_corners,
|
|
align_mode,
|
|
output);
|
|
}
|
|
|
|
template <typename T, typename Context>
|
|
void BicubicInterpKernel(
|
|
const Context& dev_ctx,
|
|
const DenseTensor& x,
|
|
const paddle::optional<DenseTensor>& out_size,
|
|
const paddle::optional<std::vector<const DenseTensor*>>& size_tensor,
|
|
const paddle::optional<DenseTensor>& scale_tensor,
|
|
const std::string& data_layout,
|
|
int out_d,
|
|
int out_h,
|
|
int out_w,
|
|
const std::vector<double>& scale,
|
|
const std::string& interp_method,
|
|
bool align_corners,
|
|
int align_mode,
|
|
DenseTensor* output) {
|
|
InterpolateKernel<T, Context>(dev_ctx,
|
|
x,
|
|
out_size,
|
|
size_tensor,
|
|
scale_tensor,
|
|
data_layout,
|
|
out_d,
|
|
out_h,
|
|
out_w,
|
|
scale,
|
|
interp_method,
|
|
align_corners,
|
|
align_mode,
|
|
output);
|
|
}
|
|
|
|
} // namespace phi
|
|
|
|
PD_REGISTER_KERNEL(bilinear_interp,
|
|
XPU,
|
|
ALL_LAYOUT,
|
|
phi::BilinearInterpKernel,
|
|
phi::float16,
|
|
float) {
|
|
kernel->InputAt(1).SetBackend(phi::Backend::ALL_BACKEND);
|
|
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
|
|
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
|
|
}
|
|
PD_REGISTER_KERNEL(nearest_interp,
|
|
XPU,
|
|
ALL_LAYOUT,
|
|
phi::NearestInterpKernel,
|
|
phi::float16,
|
|
float,
|
|
int64_t) {
|
|
kernel->InputAt(1).SetBackend(phi::Backend::ALL_BACKEND);
|
|
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
|
|
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
|
|
}
|
|
PD_REGISTER_KERNEL(bicubic_interp,
|
|
XPU,
|
|
ALL_LAYOUT,
|
|
phi::BicubicInterpKernel,
|
|
float,
|
|
phi::float16) {
|
|
kernel->InputAt(1).SetBackend(phi::Backend::ALL_BACKEND);
|
|
kernel->InputAt(2).SetBackend(phi::Backend::ALL_BACKEND);
|
|
kernel->InputAt(3).SetBackend(phi::Backend::ALL_BACKEND);
|
|
}
|