wehub-resource-sync
145 lines
4.7 KiB
Plaintext
145 lines
4.7 KiB
Plaintext
// 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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#pragma once
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#include "paddle/phi/kernels/affine_grid_kernel.h"
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#include "paddle/phi/backends/all_context.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/backends/gpu/gpu_device_function.h"
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#include "paddle/phi/backends/gpu/gpu_primitives.h"
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#include "paddle/phi/common/int_array.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/bmm_kernel.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/affine_grid_utils.h"
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#include "paddle/phi/kernels/transpose_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void AffineGrid4DCUDAKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const IntArray& outputShape,
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bool align_corners,
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DenseTensor* output) {
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auto* theta = &input;
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int64_t n = theta->dims()[0];
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auto& size_attr = outputShape.GetData();
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int64_t h = size_attr[2];
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int64_t w = size_attr[3];
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if (input.numel() == 0) {
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output->Resize({n, h, w, 2});
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Full<T, Context>(dev_ctx, output->dims(), 0, output);
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return;
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}
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// Directly create the base mesh
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DenseTensor base_grid;
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base_grid.Resize({n, h, w, 3});
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T* base_grid_data = dev_ctx.template Alloc<T>(&base_grid);
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funcs::CreateBaseGridKernel_4D<T, Context>(
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dev_ctx, base_grid_data, n, h, w, align_corners);
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// Apply affine transformation
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DenseTensor base_grid_new;
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base_grid_new.ShareDataWith(base_grid);
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base_grid_new.Resize({n, h * w, 3});
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// Transpose theta: [N, 2, 3] -> [N, 3, 2]
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DenseTensor theta_transposed;
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theta_transposed.Resize({n, 3, 2});
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TransposeKernel<T, Context>(dev_ctx, input, {0, 2, 1}, &theta_transposed);
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DenseTensor grid_flat;
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grid_flat.Resize({n, h * w, 2});
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BmmKernel<T, Context>(dev_ctx, base_grid_new, theta_transposed, &grid_flat);
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// Reshaping Output
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output->ShareDataWith(grid_flat);
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output->Resize({n, h, w, 2});
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}
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template <typename T, typename Context>
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void AffineGrid5DCUDAKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const IntArray& outputShape,
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bool align_corners,
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DenseTensor* output) {
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auto* theta = &input;
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int64_t n = theta->dims()[0];
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auto& size_attr = outputShape.GetData();
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int64_t d = size_attr[2]; // depth
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int64_t h = size_attr[3]; // height
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int64_t w = size_attr[4]; // width
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if (input.numel() == 0) {
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output->Resize({n, d, h, w, 3});
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Full<T, Context>(dev_ctx, output->dims(), 0, output);
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return;
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}
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// Create a basic grid
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DenseTensor base_grid;
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base_grid.Resize({n, d, h, w, 4});
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T* base_grid_data = dev_ctx.template Alloc<T>(&base_grid);
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funcs::CreateBaseGridKernel_5D<T, Context>(
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dev_ctx, base_grid_data, n, d, h, w, align_corners);
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// Apply affine transformation
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DenseTensor base_grid_new;
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base_grid_new.ShareDataWith(base_grid);
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base_grid_new.Resize({n, d * h * w, 4});
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// Transpose theta: [N, 3, 4] -> [N, 4, 3]
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DenseTensor theta_transposed;
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theta_transposed.Resize({n, 4, 3});
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TransposeKernel<T, Context>(dev_ctx, input, {0, 2, 1}, &theta_transposed);
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// Perform batch matrix multiplication
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DenseTensor grid_flat;
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grid_flat.Resize({n, d * h * w, 3});
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BmmKernel<T, Context>(dev_ctx, base_grid_new, theta_transposed, &grid_flat);
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// Reshaping Output
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output->ShareDataWith(grid_flat);
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output->Resize({n, d, h, w, 3});
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}
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template <typename T, typename Context>
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void AffineGridCUDAKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const IntArray& outputShape,
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bool align_corners,
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DenseTensor* output) {
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auto* theta = &input;
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int64_t theta_h = theta->dims()[1];
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if (theta_h == 2) {
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AffineGrid4DCUDAKernel<T, Context>(
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dev_ctx, input, outputShape, align_corners, output);
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} else {
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AffineGrid5DCUDAKernel<T, Context>(
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dev_ctx, input, outputShape, align_corners, output);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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affine_grid, GPU, ALL_LAYOUT, phi::AffineGridCUDAKernel, float, double){};
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