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deformable_conv_grad_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/full_kernel.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
#include "paddle/phi/kernels/funcs/deformable_conv_functor.h"
namespace phi {
template <typename T>
HOSTDEVICE T DmcnGetGradientWeight(T argmax_h,
T argmax_w,
const int h,
const int w,
const int height,
const int width) {
if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 ||
argmax_w >= width) {
return 0;
}
int argmax_h_low = floor(argmax_h);
int argmax_w_low = floor(argmax_w);
int argmax_h_high = argmax_h_low + 1;
int argmax_w_high = argmax_w_low + 1;
T weight = 0;
weight = (h == argmax_h_low && w == argmax_w_low)
? (h + 1 - argmax_h) * (w + 1 - argmax_w)
: weight;
weight = (h == argmax_h_low && w == argmax_w_high)
? (h + 1 - argmax_h) * (argmax_w + 1 - w)
: weight;
weight = (h == argmax_h_high && w == argmax_w_low)
? (argmax_h + 1 - h) * (w + 1 - argmax_w)
: weight;
weight = (h == argmax_h_high && w == argmax_w_high)
? (argmax_h + 1 - h) * (argmax_w + 1 - w)
: weight;
return weight;
}
template <typename T>
HOSTDEVICE T DmcnGetCoordinateWeight(T argmax_h,
T argmax_w,
const int height,
const int width,
const T* im_data,
const int data_width,
const int bp_dir) {
if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 ||
argmax_w >= width) {
return 0;
}
int argmax_h_low = floor(argmax_h);
int argmax_w_low = floor(argmax_w);
int argmax_h_high = argmax_h_low + 1;
int argmax_w_high = argmax_w_low + 1;
T weight = 0;
if (bp_dir == 0) {
weight += (argmax_h_low >= 0 && argmax_w_low >= 0)
? -1 * (argmax_w_low + 1 - argmax_w) *
im_data[argmax_h_low * data_width + argmax_w_low]
: 0;
weight += (argmax_h_low >= 0 && argmax_w_high <= width - 1)
? -1 * (argmax_w - argmax_w_low) *
im_data[argmax_h_low * data_width + argmax_w_high]
: 0;
weight += (argmax_h_high <= height - 1 && argmax_w_low >= 0)
? (argmax_w_low + 1 - argmax_w) *
im_data[argmax_h_high * data_width + argmax_w_low]
: 0;
weight += (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
? (argmax_w - argmax_w_low) *
im_data[argmax_h_high * data_width + argmax_w_high]
: 0;
} else if (bp_dir == 1) {
weight += (argmax_h_low >= 0 && argmax_w_low >= 0)
? -1 * (argmax_h_low + 1 - argmax_h) *
im_data[argmax_h_low * data_width + argmax_w_low]
: 0;
weight += (argmax_h_low >= 0 && argmax_w_high <= width - 1)
? (argmax_h_low + 1 - argmax_h) *
im_data[argmax_h_low * data_width + argmax_w_high]
: 0;
weight += (argmax_h_high <= height - 1 && argmax_w_low >= 0)
? -1 * (argmax_h - argmax_h_low) *
im_data[argmax_h_high * data_width + argmax_w_low]
: 0;
weight += (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
? (argmax_h - argmax_h_low) *
im_data[argmax_h_high * data_width + argmax_w_high]
: 0;
}
return weight;
}
template <typename T, typename Context>
void ModulatedDeformableCol2imCoord(const Context& dev_ctx,
const T* data_col,
const T* data_im,
const T* data_offset,
const T* data_mask,
const std::vector<int64_t>& im_shape,
const std::vector<int64_t>& col_shape,
const std::vector<int64_t>& kernel_shape,
const std::vector<int>& paddings,
const std::vector<int>& strides,
const std::vector<int>& dilations,
const int deformable_groups,
T* grad_offset,
T* grad_mask);
template <typename T, typename Context>
void ModulatedDeformableCol2im(const Context& dev_ctx,
const T* data_col,
const T* data_offset,
const T* data_mask,
const std::vector<int64_t>& im_shape,
const std::vector<int64_t>& col_shape,
const std::vector<int64_t>& kernel_shape,
const std::vector<int>& pad,
const std::vector<int>& stride,
const std::vector<int>& dilation,
const int deformable_group,
T* grad_im);
template <typename T, typename Context>
void FilterGradAddup(const Context& dev_ctx,
const int nthreads,
const int n,
const int height,
const int width,
const T* dweight_3d,
T* filter_grad);
template <typename T, typename Context>
void DeformableConvGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& offset,
const DenseTensor& filter,
const paddle::optional<DenseTensor>& mask,
const DenseTensor& out_grad,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& dilations,
int deformable_groups,
int groups,
int im2col_step,
DenseTensor* dx,
DenseTensor* offset_grad,
DenseTensor* filter_grad,
DenseTensor* mask_grad) {
const int batch_size = static_cast<int>(x.dims()[0]);
DDim input_shape = common::slice_ddim(x.dims(), 1, x.dims().size());
std::vector<int64_t> input_shape_vec = common::vectorize(input_shape);
std::vector<int64_t> filter_shape_vec(common::vectorize(filter.dims()));
std::vector<int64_t> output_shape_vec(common::vectorize(out_grad.dims()));
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;
output_buffer.ShareDataWith(out_grad).Resize(
common::make_ddim(output_buffer_shape_vec));
int64_t M =
input_shape_vec[0] / groups * filter_shape_vec[2] * filter_shape_vec[3];
int64_t N = im2col_step * output_shape_vec[2] * output_shape_vec[3];
int64_t K = output_shape_vec[1] / groups;
DDim weight_3d_shape = {groups, K, M};
DDim out_grad_4d_shape = {batch_size / im2col_step, groups, K, N};
DDim col_buffer_3d_shape = {groups, M, N};
DDim filter_grad_shape = {groups, K, M};
DenseTensor weight_3d;
weight_3d.ShareDataWith(filter).Resize(weight_3d_shape);
DenseTensor out_grad_4d;
out_grad_4d.ShareDataWith(output_buffer).Resize(out_grad_4d_shape);
DenseTensor col_buffer_3d;
col_buffer_3d.ShareDataWith(col_buffer).Resize(col_buffer_3d_shape);
phi::funcs::SetConstant<Context, T> set_zero;
auto blas = phi::funcs::GetBlas<Context, T>(dev_ctx);
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;
if (filter_grad) {
Full<T>(dev_ctx,
{filter_grad_shape.Get(), filter_grad_shape.size()},
0,
filter_grad);
}
if (dx) {
dev_ctx.template Alloc<T>(dx);
set_zero(dev_ctx, dx, static_cast<T>(0));
}
if (offset_grad) {
dev_ctx.template Alloc<T>(offset_grad);
set_zero(dev_ctx, offset_grad, static_cast<T>(0));
if (mask_grad) {
dev_ctx.template Alloc<T>(mask_grad);
set_zero(dev_ctx, mask_grad, static_cast<T>(0));
}
}
for (int i = 0; i < batch_size / im2col_step; ++i) {
DenseTensor out_grad_3d = out_grad_4d.Slice(i, i + 1).Resize(
common::slice_ddim(out_grad_4d.dims(), 1, out_grad_4d.dims().size()));
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 out_grad_3d_slice = out_grad_3d.Slice(g, g + 1).Resize(
common::slice_ddim(out_grad_3d.dims(), 1, out_grad_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()));
blas.MatMul(weight_3d_slice,
true,
out_grad_3d_slice,
false,
T(1.0),
&col_buffer_3d_slice,
T(0.0));
}
col_buffer.Resize(common::make_ddim(col_buffer_shape_vec));
T* col_buffer_ptr = col_buffer.data<T>();
const T* input_ptr = x.data<T>();
const T* offset_ptr = offset.data<T>();
const T* mask_data_ptr =
mask ? mask->data<T>() + i * im2col_step * input_mask_dim : nullptr;
if (offset_grad) {
T* offset_grad_ptr = offset_grad->data<T>();
T* mask_grad_data_ptr =
mask_grad ? mask_grad->data<T>() + i * im2col_step * input_mask_dim
: nullptr;
// get grad of offset and mask
ModulatedDeformableCol2imCoord(
dev_ctx,
col_buffer_ptr,
input_ptr + i * im2col_step * input_dim,
offset_ptr + i * im2col_step * input_offset_dim,
mask_data_ptr,
input_shape_vec,
col_buffer_shape_vec,
filter_shape_vec,
paddings,
strides,
dilations,
deformable_groups,
offset_grad_ptr + i * im2col_step * input_offset_dim,
mask_grad_data_ptr);
}
if (dx) {
T* dx_ptr = dx->data<T>();
// get grad of input
ModulatedDeformableCol2im(dev_ctx,
col_buffer_ptr,
offset_ptr + i * im2col_step * input_offset_dim,
mask_data_ptr,
input_shape_vec,
col_buffer_shape_vec,
filter_shape_vec,
paddings,
strides,
dilations,
deformable_groups,
dx_ptr + i * im2col_step * input_dim);
dx->Resize(x.dims());
}
funcs::ModulatedDeformableIm2col(
dev_ctx,
input_ptr + i * im2col_step * input_dim,
offset_ptr + i * im2col_step * input_offset_dim,
mask_data_ptr,
input_shape_vec,
col_buffer_shape_vec,
filter_shape_vec,
paddings,
strides,
dilations,
deformable_groups,
col_buffer_ptr);
col_buffer_3d.Resize(col_buffer_3d_shape);
if (filter_grad) {
DenseTensor dweight_3d = Empty<T>(
dev_ctx, {filter_grad_shape.Get(), filter_grad_shape.size()});
for (int g = 0; g < groups; ++g) {
DenseTensor out_grad_3d_slice =
out_grad_3d.Slice(g, g + 1).Resize(common::slice_ddim(
out_grad_3d.dims(), 1, out_grad_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 dweight_3d_slice = dweight_3d.Slice(g, g + 1).Resize(
common::slice_ddim(dweight_3d.dims(), 1, dweight_3d.dims().size()));
blas.MatMul(out_grad_3d_slice,
false,
col_buffer_3d_slice,
true,
T(1.0),
&dweight_3d_slice,
T(0.0));
}
// update grad of weights
FilterGradAddup<T>(dev_ctx,
dweight_3d.numel(),
groups,
K,
M,
dweight_3d.data<T>(),
filter_grad->data<T>());
}
}
if (filter_grad) {
filter_grad->Resize(filter.dims());
}
}
} // namespace phi