wehub-resource-sync
72 lines
2.6 KiB
C++
72 lines
2.6 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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#pragma once
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#include <vector>
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/kernels/funcs/im2col.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/funcs/unfold_functor.h"
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namespace phi {
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template <typename T, typename Context>
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void FoldGradKernel(const Context& dev_ctx,
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const DenseTensor& x UNUSED,
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const DenseTensor& out_grad,
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const std::vector<int>& output_sizes,
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const std::vector<int>& kernel_sizes,
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const std::vector<int>& strides,
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const std::vector<int>& paddings,
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const std::vector<int>& dilations,
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DenseTensor* x_grad) {
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dev_ctx.template Alloc<T>(x_grad);
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if (!x_grad) return;
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const auto& x_dims = x_grad->dims();
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const int64_t batch_size = x_dims[0];
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int output_height = (output_sizes[0] + 2 * paddings[0] -
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(dilations[0] * (kernel_sizes[0] - 1) + 1)) /
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strides[0] +
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1;
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int output_width = (output_sizes[1] + 2 * paddings[1] -
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(dilations[1] * (kernel_sizes[1] - 1) + 1)) /
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strides[1] +
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1;
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int64_t n_input_plane = x_dims[1];
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int64_t n_output_plane = n_input_plane / (kernel_sizes[0] * kernel_sizes[1]);
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DDim out_shape =
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make_ddim({n_output_plane, output_sizes[0], output_sizes[1]});
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DDim input_matrix_shape = make_ddim(
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{1, kernel_sizes[0], kernel_sizes[1], output_height, output_width});
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funcs::Im2ColFunctor<funcs::ColFormat::CFO, Context, T> im2col;
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for (int64_t i = 0; i < batch_size; i++) {
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DenseTensor out_grad_batch = out_grad.Slice(i, i + 1).Resize(out_shape);
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DenseTensor x_grad_batch =
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x_grad->Slice(i, i + 1).Resize(input_matrix_shape);
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im2col(
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dev_ctx, out_grad_batch, dilations, strides, paddings, &x_grad_batch);
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}
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}
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} // namespace phi
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