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deformable_conv_kernel_impl.h
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deformable_conv_kernel_impl.h
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// 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.
#pragma once
#include "paddle/common/hostdevice.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/kernels/empty_kernel.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
#include "paddle/phi/kernels/funcs/deformable_conv_functor.h"
#include "paddle/phi/kernels/transpose_kernel.h"
#include "paddle/utils/optional.h"
namespace phi {
template <typename T, typename Context>
void DeformableConvKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& offset,
const DenseTensor& filter,
const paddle::optional<DenseTensor>& mask,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& dilations,
int deformable_groups,
int groups,
int im2col_step,
DenseTensor* out) {
const int batch_size = static_cast<int>(x.dims()[0]);
int temp_step = std::min(64, batch_size);
if (batch_size % temp_step == 0) {
im2col_step = temp_step;
}
std::vector<int64_t> filter_shape_vec(common::vectorize(filter.dims()));
std::vector<int64_t> output_shape_vec(common::vectorize(out->dims()));
// col_shape_vec: {c_i * k_h * k_w, im2col_step, o_h, o_w}
std::vector<int64_t> col_buffer_shape_vec(filter_shape_vec.size());
col_buffer_shape_vec[0] = x.dims()[1] * filter.dims()[2] * filter.dims()[3];
col_buffer_shape_vec[1] = im2col_step;
for (size_t j = 0; j < filter_shape_vec.size() - 2; ++j) {
col_buffer_shape_vec[j + 2] = output_shape_vec[j + 2];
}
std::vector<int64_t> output_buffer_shape_vec(1);
output_buffer_shape_vec[0] = batch_size * output_shape_vec[1] *
output_shape_vec[2] * output_shape_vec[3];
DenseTensor col_buffer = Empty<T>(dev_ctx, col_buffer_shape_vec);
DenseTensor output_buffer = Empty<T>(dev_ctx, output_buffer_shape_vec);
int64_t M = output_shape_vec[1] / groups;
int64_t N = im2col_step * output_shape_vec[2] * output_shape_vec[3];
int64_t K = x.dims()[1] * filter_shape_vec[2] * filter_shape_vec[3] / groups;
DenseTensor weight_3d;
weight_3d.ShareDataWith(filter).Resize(common::make_ddim({groups, M, K}));
DenseTensor col_buffer_3d;
col_buffer_3d.ShareDataWith(col_buffer)
.Resize(common::make_ddim({groups, K, N}));
DenseTensor output_4d;
output_4d.ShareDataWith(output_buffer)
.Resize(common::make_ddim({batch_size / im2col_step, groups, M, N}));
DDim input_shape = common::slice_ddim(x.dims(), 1, x.dims().size());
std::vector<int64_t> input_shape_vec = common::vectorize(input_shape);
int input_dim = x.numel() / x.dims()[0];
int input_offset_dim = offset.numel() / offset.dims()[0];
int input_mask_dim = mask ? mask->numel() / mask->dims()[0] : 0;
const T* input_ptr = x.data<T>();
const T* offset_ptr = offset.data<T>();
const T* mask_ptr = mask ? mask->data<T>() : nullptr;
T* col_buffer_ptr = col_buffer.data<T>();
auto blas = phi::funcs::GetBlas<Context, T>(dev_ctx);
for (int i = 0; i < batch_size / im2col_step; ++i) {
const T* temp_mask_ptr =
mask_ptr ? mask_ptr + i * im2col_step * input_mask_dim : nullptr;
funcs::ModulatedDeformableIm2col(
dev_ctx,
input_ptr + i * im2col_step * input_dim,
offset_ptr + i * im2col_step * input_offset_dim,
temp_mask_ptr,
input_shape_vec,
col_buffer_shape_vec,
filter_shape_vec,
paddings,
strides,
dilations,
deformable_groups,
col_buffer_ptr);
DenseTensor output_3d = output_4d.Slice(i, i + 1).Resize(common::slice_ddim(
output_4d.dims(),
1,
output_4d.dims().size())); // group * C/group * (im2step * H * W)
// get the product of pixel and weight
for (int g = 0; g < groups; ++g) {
DenseTensor weight_3d_slice = weight_3d.Slice(g, g + 1).Resize(
common::slice_ddim(weight_3d.dims(), 1, weight_3d.dims().size()));
DenseTensor col_buffer_3d_slice =
col_buffer_3d.Slice(g, g + 1).Resize(common::slice_ddim(
col_buffer_3d.dims(), 1, col_buffer_3d.dims().size()));
DenseTensor output_3d_slice =
output_3d.Slice(g, g + 1).Resize(common::slice_ddim(
output_3d.dims(),
1,
output_3d.dims().size())); // C * ((im2col_step)*H*W))
blas.MatMul(weight_3d_slice,
false,
col_buffer_3d_slice,
false,
T(1.0),
&output_3d_slice,
T(0.0));
}
}
// swap axis to get the right result when im2col_step is greater than 1
if (im2col_step > 1) {
std::vector<int> axis(4);
axis[0] = 0;
axis[1] = 2;
axis[2] = 1;
axis[3] = 3;
DenseTensor real_output_buffer = phi::Transpose<T, Context>(
dev_ctx,
output_4d.Resize(
common::make_ddim({batch_size / im2col_step,
output_shape_vec[1],
im2col_step,
output_shape_vec[2] * output_shape_vec[3]})),
axis);
out->ShareDataWith(real_output_buffer)
.Resize(common::make_ddim(output_shape_vec));
} else {
out->ShareDataWith(output_buffer)
.Resize(common::make_ddim(output_shape_vec));
}
}
} // namespace phi