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Unverified Commit 8016d880 authored by pc's avatar pc Committed by GitHub
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fix dcn compile error in parrots (#1378)

parent fa22d9db
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Showing with 1771 additions and 172 deletions
mmcv/ops/csrc/parrots/deform_conv.cpp
View file @ 8016d880
......@@ -2,61 +2,152 @@
#include "pytorch_cpp_helper.hpp"
#ifdef MMCV_WITH_CUDA
void DeformConvForwardCUDAKernelLauncher(Tensor input, Tensor weight,
Tensor offset, Tensor output,
Tensor columns, Tensor ones, int kW,
int kH, int dW, int dH, int padW,
int padH, int dilationW, int dilationH,
int group, int deformable_group,
int im2col_step);
void DeformConvBackwardInputCUDAKernelLauncher(
Tensor input, Tensor offset, Tensor gradOutput, Tensor gradInput,
Tensor gradOffset, Tensor weight, Tensor columns, int kW, int kH, int dW,
int dH, int padW, int padH, int dilationW, int dilationH, int group,
int deformable_group, int im2col_step);
void DeformConvBackwardParametersCUDAKernelLauncher(
Tensor input, Tensor offset, Tensor gradOutput, Tensor gradWeight,
Tensor columns, Tensor ones, int kW, int kH, int dW, int dH, int padW,
int padH, int dilationW, int dilationH, int group, int deformable_group,
float scale, int im2col_step);
void deform_conv_forward_cuda(Tensor input, Tensor weight, Tensor offset,
Tensor output, Tensor columns, Tensor ones,
int kW, int kH, int dW, int dH, int padW,
int padH, int dilationW, int dilationH, int group,
int deformable_group, int im2col_step) {
DeformConvForwardCUDAKernelLauncher(
input, weight, offset, output, columns, ones, kW, kH, dW, dH, padW, padH,
dilationW, dilationH, group, deformable_group, im2col_step);
}
void deform_conv_backward_input_cuda(Tensor input, Tensor offset,
Tensor gradOutput, Tensor gradInput,
Tensor gradOffset, Tensor weight,
Tensor columns, int kW, int kH, int dW,
int dH, int padW, int padH, int dilationW,
int dilationH, int group,
int deformable_group, int im2col_step) {
DeformConvBackwardInputCUDAKernelLauncher(
input, offset, gradOutput, gradInput, gradOffset, weight, columns, kW, kH,
dW, dH, padW, padH, dilationW, dilationH, group, deformable_group,
im2col_step);
}
void deformable_im2col(Tensor data_im, Tensor data_offset, const int channels,
const int height, const int width, const int ksize_h,
const int ksize_w, const int pad_h, const int pad_w,
const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w,
const int parallel_imgs, const int deformable_group,
Tensor data_col);
void deformable_col2im(Tensor data_col, Tensor data_offset, const int channels,
const int height, const int width, const int ksize_h,
const int ksize_w, const int pad_h, const int pad_w,
const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w,
const int parallel_imgs, const int deformable_group,
Tensor grad_im);
void deformable_col2im_coord(
Tensor data_col, Tensor data_im, Tensor data_offset, const int channels,
const int height, const int width, const int ksize_h, const int ksize_w,
const int pad_h, const int pad_w, const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w, const int parallel_imgs,
const int deformable_group, Tensor grad_offset);
void deform_conv_backward_parameters_cuda(
Tensor input, Tensor offset, Tensor gradOutput, Tensor gradWeight,
Tensor columns, Tensor ones, int kW, int kH, int dW, int dH, int padW,
int padH, int dilationW, int dilationH, int group, int deformable_group,
float scale, int im2col_step) {
DeformConvBackwardParametersCUDAKernelLauncher(
input, offset, gradOutput, gradWeight, columns, ones, kW, kH, dW, dH,
padW, padH, dilationW, dilationH, group, deformable_group, scale,
im2col_step);
}
#endif
void deformable_im2col_cpu(Tensor data_im, Tensor data_offset,
const int channels, const int height,
const int width, const int ksize_h,
const int ksize_w, const int pad_h, const int pad_w,
const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w,
const int parallel_imgs, const int deformable_group,
Tensor data_col);
void deformable_col2im_cpu(Tensor data_col, Tensor data_offset,
const int channels, const int height,
const int width, const int ksize_h,
const int ksize_w, const int pad_h, const int pad_w,
const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w,
const int parallel_imgs, const int deformable_group,
Tensor grad_im);
void deformable_col2im_coord_cpu(
Tensor data_col, Tensor data_im, Tensor data_offset, const int channels,
const int height, const int width, const int ksize_h, const int ksize_w,
const int pad_h, const int pad_w, const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w, const int parallel_imgs,
const int deformable_group, Tensor grad_offset);
void deform_conv_shape_check(at::Tensor input, at::Tensor offset,
at::Tensor *gradOutput, at::Tensor weight, int kH,
int kW, int dH, int dW, int padH, int padW,
int dilationH, int dilationW, int group,
int deformable_group) {
TORCH_CHECK(
weight.ndimension() == 4,
"4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, but got: %s",
weight.ndimension());
TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous");
TORCH_CHECK(kW > 0 && kH > 0,
"kernel size should be greater than zero, but got kH: %d kW: %d",
kH, kW);
TORCH_CHECK((weight.size(2) == kH && weight.size(3) == kW),
"kernel size should be consistent with weight, ",
"but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d",
kH, kW, weight.size(2), weight.size(3));
TORCH_CHECK(dW > 0 && dH > 0,
"stride should be greater than zero, but got dH: %d dW: %d", dH,
dW);
TORCH_CHECK(
dilationW > 0 && dilationH > 0,
"dilation should be greater than 0, but got dilationH: %d dilationW: %d",
dilationH, dilationW);
int ndim = input.ndimension();
int dimf = 0;
int dimh = 1;
int dimw = 2;
if (ndim == 4) {
dimf++;
dimh++;
dimw++;
}
TORCH_CHECK(ndim == 3 || ndim == 4,
"3D or 4D input tensor expected but got: %s", ndim);
long nInputPlane = weight.size(1) * group;
long inputHeight = input.size(dimh);
long inputWidth = input.size(dimw);
long nOutputPlane = weight.size(0);
long outputHeight =
(inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
long outputWidth =
(inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
TORCH_CHECK(nInputPlane % deformable_group == 0,
"input channels must divide deformable group size");
if (outputWidth < 1 || outputHeight < 1)
AT_ERROR(
"Given input size: (%ld x %ld x %ld). "
"Calculated output size: (%ld x %ld x %ld). Output size is too small",
nInputPlane, inputHeight, inputWidth, nOutputPlane, outputHeight,
outputWidth);
TORCH_CHECK(input.size(1) == nInputPlane,
"invalid number of input planes, expected: %d, but got: %d",
nInputPlane, input.size(1));
TORCH_CHECK((inputHeight >= kH && inputWidth >= kW),
"input image is smaller than kernel");
TORCH_CHECK(
(offset.size(2) == outputHeight && offset.size(3) == outputWidth),
"invalid spatial size of offset, expected height: %d width: %d, but "
"got height: %d width: %d",
outputHeight, outputWidth, offset.size(2), offset.size(3));
TORCH_CHECK((offset.size(1) == deformable_group * 2 * kH * kW),
"invalid number of channels of offset");
if (gradOutput != NULL) {
TORCH_CHECK(
gradOutput->size(dimf) == nOutputPlane,
"invalid number of gradOutput planes, expected: %d, but got: %d",
nOutputPlane, gradOutput->size(dimf));
TORCH_CHECK(
(gradOutput->size(dimh) == outputHeight &&
gradOutput->size(dimw) == outputWidth),
"invalid size of gradOutput, expected height: %d width: %d , but "
"got height: %d width: %d",
outputHeight, outputWidth, gradOutput->size(dimh),
gradOutput->size(dimw));
}
}
void deform_conv_forward(Tensor input, Tensor weight, Tensor offset,
Tensor output, Tensor columns, Tensor ones, int kW,
int kH, int dW, int dH, int padW, int padH,
......@@ -70,15 +161,118 @@ void deform_conv_forward(Tensor input, Tensor weight, Tensor offset,
CHECK_CUDA_INPUT(output);
CHECK_CUDA_INPUT(columns);
CHECK_CUDA_INPUT(ones);
deform_conv_forward_cuda(input, weight, offset, output, columns, ones, kW,
kH, dW, dH, padW, padH, dilationW, dilationH,
group, deformable_group, im2col_step);
#else
AT_ERROR("DeformConv is not compiled with GPU support");
#endif
} else {
AT_ERROR("DeformConv is not implemented on CPU");
CHECK_CPU_INPUT(input);
CHECK_CPU_INPUT(offset);
CHECK_CPU_INPUT(weight);
CHECK_CPU_INPUT(output);
CHECK_CPU_INPUT(columns);
CHECK_CPU_INPUT(ones);
}
deform_conv_shape_check(input, offset, NULL, weight, kH, kW, dH, dW, padH,
padW, dilationH, dilationW, group, deformable_group);
at::DeviceGuard guard(input.device());
int batch = 1;
if (input.ndimension() == 3) {
// Force batch
batch = 0;
input.unsqueeze_(0);
offset.unsqueeze_(0);
}
// todo: assert batchsize dividable by im2col_step
long batchSize = input.size(0);
long nInputPlane = input.size(1);
long inputHeight = input.size(2);
long inputWidth = input.size(3);
long nOutputPlane = weight.size(0);
long outputWidth =
(inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
long outputHeight =
(inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset");
output = output.view({batchSize / im2col_step, im2col_step, nOutputPlane,
outputHeight, outputWidth});
columns = at::zeros(
{nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
input.options());
if (ones.ndimension() != 2 ||
ones.size(0) * ones.size(1) < outputHeight * outputWidth) {
ones = at::ones({outputHeight, outputWidth}, input.options());
}
input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
inputHeight, inputWidth});
offset =
offset.view({batchSize / im2col_step, im2col_step,
deformable_group * 2 * kH * kW, outputHeight, outputWidth});
Tensor output_buffer = at::zeros({batchSize / im2col_step, nOutputPlane,
im2col_step * outputHeight, outputWidth},
output.options());
output_buffer = output_buffer.view(
{output_buffer.size(0), group, output_buffer.size(1) / group,
output_buffer.size(2), output_buffer.size(3)});
for (int elt = 0; elt < batchSize / im2col_step; elt++) {
if (input.device().is_cuda()) {
#ifdef MMCV_WITH_CUDA
deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight,
inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
dilationW, im2col_step, deformable_group, columns);
#endif
} else {
deformable_im2col_cpu(input[elt], offset[elt], nInputPlane, inputHeight,
inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
dilationW, im2col_step, deformable_group, columns);
}
columns = columns.view({group, columns.size(0) / group, columns.size(1)});
weight = weight.view({group, weight.size(0) / group, weight.size(1),
weight.size(2), weight.size(3)});
for (int g = 0; g < group; g++) {
output_buffer[elt][g] = output_buffer[elt][g]
.flatten(1)
.addmm_(weight[g].flatten(1), columns[g])
.view_as(output_buffer[elt][g]);
}
columns =
columns.view({columns.size(0) * columns.size(1), columns.size(2)});
weight = weight.view({weight.size(0) * weight.size(1), weight.size(2),
weight.size(3), weight.size(4)});
}
output_buffer = output_buffer.view(
{output_buffer.size(0), output_buffer.size(1) * output_buffer.size(2),
output_buffer.size(3), output_buffer.size(4)});
output_buffer = output_buffer.view({batchSize / im2col_step, nOutputPlane,
im2col_step, outputHeight, outputWidth});
output_buffer.transpose_(1, 2);
output.copy_(output_buffer);
output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth});
input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
offset = offset.view(
{batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
if (batch == 0) {
output = output.view({nOutputPlane, outputHeight, outputWidth});
input = input.view({nInputPlane, inputHeight, inputWidth});
offset = offset.view({offset.size(1), offset.size(2), offset.size(3)});
}
}
......@@ -97,16 +291,134 @@ void deform_conv_backward_input(Tensor input, Tensor offset, Tensor gradOutput,
CHECK_CUDA_INPUT(gradOffset);
CHECK_CUDA_INPUT(weight);
CHECK_CUDA_INPUT(columns);
deform_conv_backward_input_cuda(input, offset, gradOutput, gradInput,
gradOffset, weight, columns, kW, kH, dW, dH,
padW, padH, dilationW, dilationH, group,
deformable_group, im2col_step);
#else
AT_ERROR("DeformConv is not compiled with GPU support");
#endif
} else {
AT_ERROR("DeformConv is not implemented on CPU");
CHECK_CPU_INPUT(input);
CHECK_CPU_INPUT(offset);
CHECK_CPU_INPUT(gradOutput);
CHECK_CPU_INPUT(gradInput);
CHECK_CPU_INPUT(gradOffset);
CHECK_CPU_INPUT(weight);
CHECK_CPU_INPUT(columns);
}
deform_conv_shape_check(input, offset, &gradOutput, weight, kH, kW, dH, dW,
padH, padW, dilationH, dilationW, group,
deformable_group);
at::DeviceGuard guard(input.device());
int batch = 1;
if (input.ndimension() == 3) {
// Force batch
batch = 0;
input = input.view({1, input.size(0), input.size(1), input.size(2)});
offset = offset.view({1, offset.size(0), offset.size(1), offset.size(2)});
gradOutput = gradOutput.view(
{1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)});
}
long batchSize = input.size(0);
long nInputPlane = input.size(1);
long inputHeight = input.size(2);
long inputWidth = input.size(3);
long nOutputPlane = weight.size(0);
long outputWidth =
(inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
long outputHeight =
(inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
TORCH_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset");
gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth});
columns = at::zeros(
{nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
input.options());
// change order of grad output
gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step,
nOutputPlane, outputHeight, outputWidth});
gradOutput.transpose_(1, 2);
gradInput = gradInput.view({batchSize / im2col_step, im2col_step, nInputPlane,
inputHeight, inputWidth});
input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
inputHeight, inputWidth});
gradOffset = gradOffset.view({batchSize / im2col_step, im2col_step,
deformable_group * 2 * kH * kW, outputHeight,
outputWidth});
offset =
offset.view({batchSize / im2col_step, im2col_step,
deformable_group * 2 * kH * kW, outputHeight, outputWidth});
for (int elt = 0; elt < batchSize / im2col_step; elt++) {
// divide into groups
columns = columns.view({group, columns.size(0) / group, columns.size(1)});
weight = weight.view({group, weight.size(0) / group, weight.size(1),
weight.size(2), weight.size(3)});
gradOutput = gradOutput.view(
{gradOutput.size(0), group, gradOutput.size(1) / group,
gradOutput.size(2), gradOutput.size(3), gradOutput.size(4)});
for (int g = 0; g < group; g++) {
columns[g] = columns[g].addmm_(weight[g].flatten(1).transpose(0, 1),
gradOutput[elt][g].flatten(1), 0.0f, 1.0f);
}
columns =
columns.view({columns.size(0) * columns.size(1), columns.size(2)});
gradOutput = gradOutput.view(
{gradOutput.size(0), gradOutput.size(1) * gradOutput.size(2),
gradOutput.size(3), gradOutput.size(4), gradOutput.size(5)});
if (input.device().is_cuda()) {
#ifdef MMCV_WITH_CUDA
deformable_col2im_coord(columns, input[elt], offset[elt], nInputPlane,
inputHeight, inputWidth, kH, kW, padH, padW, dH,
dW, dilationH, dilationW, im2col_step,
deformable_group, gradOffset[elt]);
deformable_col2im(columns, offset[elt], nInputPlane, inputHeight,
inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
dilationW, im2col_step, deformable_group,
gradInput[elt]);
#endif
} else {
deformable_col2im_coord_cpu(columns, input[elt], offset[elt], nInputPlane,
inputHeight, inputWidth, kH, kW, padH, padW,
dH, dW, dilationH, dilationW, im2col_step,
deformable_group, gradOffset[elt]);
deformable_col2im_cpu(columns, offset[elt], nInputPlane, inputHeight,
inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
dilationW, im2col_step, deformable_group,
gradInput[elt]);
}
weight = weight.view({weight.size(0) * weight.size(1), weight.size(2),
weight.size(3), weight.size(4)});
}
gradOutput.transpose_(1, 2);
gradOutput =
gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth});
gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth});
input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
gradOffset = gradOffset.view(
{batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
offset = offset.view(
{batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
if (batch == 0) {
gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth});
input = input.view({nInputPlane, inputHeight, inputWidth});
gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth});
offset = offset.view({offset.size(1), offset.size(2), offset.size(3)});
gradOffset =
gradOffset.view({offset.size(1), offset.size(2), offset.size(3)});
}
}
......@@ -125,15 +437,122 @@ void deform_conv_backward_parameters(Tensor input, Tensor offset,
CHECK_CUDA_INPUT(gradWeight);
CHECK_CUDA_INPUT(columns);
CHECK_CUDA_INPUT(ones);
deform_conv_backward_parameters_cuda(input, offset, gradOutput, gradWeight,
columns, ones, kW, kH, dW, dH, padW,
padH, dilationW, dilationH, group,
deformable_group, scale, im2col_step);
#else
AT_ERROR("DeformConv is not compiled with GPU support");
#endif
} else {
AT_ERROR("DeformConv is not implemented on CPU");
CHECK_CPU_INPUT(input);
CHECK_CPU_INPUT(offset);
CHECK_CPU_INPUT(gradOutput);
CHECK_CPU_INPUT(gradWeight);
CHECK_CPU_INPUT(columns);
CHECK_CPU_INPUT(ones);
}
deform_conv_shape_check(input, offset, &gradOutput, gradWeight, kH, kW, dH,
dW, padH, padW, dilationH, dilationW, group,
deformable_group);
at::DeviceGuard guard(input.device());
int batch = 1;
if (input.ndimension() == 3) {
// Force batch
batch = 0;
input = input.view(
at::IntList({1, input.size(0), input.size(1), input.size(2)}));
gradOutput = gradOutput.view(
{1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)});
}
long batchSize = input.size(0);
long nInputPlane = input.size(1);
long inputHeight = input.size(2);
long inputWidth = input.size(3);
long nOutputPlane = gradWeight.size(0);
long outputWidth =
(inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
long outputHeight =
(inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset");
columns = at::zeros(
{nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth},
input.options());
gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step,
nOutputPlane, outputHeight, outputWidth});
gradOutput.transpose_(1, 2);
Tensor gradOutputBuffer = at::zeros_like(gradOutput);
gradOutputBuffer =
gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, im2col_step,
outputHeight, outputWidth});
gradOutputBuffer = gradOutputBuffer.contiguous();
gradOutputBuffer.copy_(gradOutput);
gradOutputBuffer =
gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane,
im2col_step * outputHeight, outputWidth});
gradOutput.transpose_(1, 2);
gradOutput =
gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth});
input = input.view({batchSize / im2col_step, im2col_step, nInputPlane,
inputHeight, inputWidth});
offset =
offset.view({batchSize / im2col_step, im2col_step,
deformable_group * 2 * kH * kW, outputHeight, outputWidth});
for (int elt = 0; elt < batchSize / im2col_step; elt++) {
if (input.device().is_cuda()) {
#ifdef MMCV_WITH_CUDA
deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight,
inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
dilationW, im2col_step, deformable_group, columns);
#endif
} else {
deformable_im2col_cpu(input[elt], offset[elt], nInputPlane, inputHeight,
inputWidth, kH, kW, padH, padW, dH, dW, dilationH,
dilationW, im2col_step, deformable_group, columns);
}
// divide into group
gradOutputBuffer = gradOutputBuffer.view(
{gradOutputBuffer.size(0), group, gradOutputBuffer.size(1) / group,
gradOutputBuffer.size(2), gradOutputBuffer.size(3)});
columns = columns.view({group, columns.size(0) / group, columns.size(1)});
gradWeight =
gradWeight.view({group, gradWeight.size(0) / group, gradWeight.size(1),
gradWeight.size(2), gradWeight.size(3)});
for (int g = 0; g < group; g++) {
gradWeight[g] = gradWeight[g]
.flatten(1)
.addmm_(gradOutputBuffer[elt][g].flatten(1),
columns[g].transpose(1, 0), 1.0, scale)
.view_as(gradWeight[g]);
}
gradOutputBuffer = gradOutputBuffer.view(
{gradOutputBuffer.size(0),
gradOutputBuffer.size(1) * gradOutputBuffer.size(2),
gradOutputBuffer.size(3), gradOutputBuffer.size(4)});
columns =
columns.view({columns.size(0) * columns.size(1), columns.size(2)});
gradWeight = gradWeight.view({gradWeight.size(0) * gradWeight.size(1),
gradWeight.size(2), gradWeight.size(3),
gradWeight.size(4)});
}
input = input.view({batchSize, nInputPlane, inputHeight, inputWidth});
offset = offset.view(
{batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth});
if (batch == 0) {
gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth});
input = input.view({nInputPlane, inputHeight, inputWidth});
}
}
mmcv/ops/csrc/parrots/deform_conv_cpu.cpp 0 → 100644
View file @ 8016d880
// Copyright (c) OpenMMLab. All rights reserved
#include "pytorch_cpp_helper.hpp"
template <typename T>
T deformable_im2col_bilinear_cpu(const T *input, const int data_width,
const int height, const int width, T h, T w) {
if (h <= -1 || height <= h || w <= -1 || width <= w) {
return 0;
}
int h_low = floor(h);
int w_low = floor(w);
int h_high = h_low + 1;
int w_high = w_low + 1;
T lh = h - h_low;
T lw = w - w_low;
T hh = 1 - lh, hw = 1 - lw;
T v1 = 0;
if (h_low >= 0 && w_low >= 0) v1 = input[h_low * data_width + w_low];
T v2 = 0;
if (h_low >= 0 && w_high <= width - 1)
v2 = input[h_low * data_width + w_high];
T v3 = 0;
if (h_high <= height - 1 && w_low >= 0)
v3 = input[h_high * data_width + w_low];
T v4 = 0;
if (h_high <= height - 1 && w_high <= width - 1)
v4 = input[h_high * data_width + w_high];
T w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
return val;
}
template <typename T>
T get_gradient_weight_cpu(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) {
// empty
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 (h == argmax_h_low && w == argmax_w_low)
weight = (h + 1 - argmax_h) * (w + 1 - argmax_w);
if (h == argmax_h_low && w == argmax_w_high)
weight = (h + 1 - argmax_h) * (argmax_w + 1 - w);
if (h == argmax_h_high && w == argmax_w_low)
weight = (argmax_h + 1 - h) * (w + 1 - argmax_w);
if (h == argmax_h_high && w == argmax_w_high)
weight = (argmax_h + 1 - h) * (argmax_w + 1 - w);
return weight;
}
template <typename T>
T get_coordinate_weight_cpu(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) {
// empty
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) {
if (argmax_h_low >= 0 && argmax_w_low >= 0)
weight += -1 * (argmax_w_low + 1 - argmax_w) *
im_data[argmax_h_low * data_width + argmax_w_low];
if (argmax_h_low >= 0 && argmax_w_high <= width - 1)
weight += -1 * (argmax_w - argmax_w_low) *
im_data[argmax_h_low * data_width + argmax_w_high];
if (argmax_h_high <= height - 1 && argmax_w_low >= 0)
weight += (argmax_w_low + 1 - argmax_w) *
im_data[argmax_h_high * data_width + argmax_w_low];
if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
weight += (argmax_w - argmax_w_low) *
im_data[argmax_h_high * data_width + argmax_w_high];
} else if (bp_dir == 1) {
if (argmax_h_low >= 0 && argmax_w_low >= 0)
weight += -1 * (argmax_h_low + 1 - argmax_h) *
im_data[argmax_h_low * data_width + argmax_w_low];
if (argmax_h_low >= 0 && argmax_w_high <= width - 1)
weight += (argmax_h_low + 1 - argmax_h) *
im_data[argmax_h_low * data_width + argmax_w_high];
if (argmax_h_high <= height - 1 && argmax_w_low >= 0)
weight += -1 * (argmax_h - argmax_h_low) *
im_data[argmax_h_high * data_width + argmax_w_low];
if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
weight += (argmax_h - argmax_h_low) *
im_data[argmax_h_high * data_width + argmax_w_high];
}
return weight;
}
template <typename T>
void deformable_im2col_cpu_kernel(
const int n, const T *data_im, const T *data_offset, const int height,
const int width, const int kernel_h, const int kernel_w, const int pad_h,
const int pad_w, const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w,
const int channel_per_deformable_group, const int batch_size,
const int num_channels, const int deformable_group, const int height_col,
const int width_col, T *data_col) {
for (int index = 0; index < n; index++) {
// index index of output matrix
const int w_col = index % width_col;
const int h_col = (index / width_col) % height_col;
const int b_col = (index / width_col / height_col) % batch_size;
const int c_im = (index / width_col / height_col) / batch_size;
const int c_col = c_im * kernel_h * kernel_w;
// compute deformable group index
const int deformable_group_index = c_im / channel_per_deformable_group;
const int h_in = h_col * stride_h - pad_h;
const int w_in = w_col * stride_w - pad_w;
T *data_col_ptr =
data_col +
((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col;
const T *data_im_ptr =
data_im + (b_col * num_channels + c_im) * height * width;
const T *data_offset_ptr =
data_offset + (b_col * deformable_group + deformable_group_index) * 2 *
kernel_h * kernel_w * height_col * width_col;
for (int i = 0; i < kernel_h; ++i) {
for (int j = 0; j < kernel_w; ++j) {
const int data_offset_h_ptr =
((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col;
const int data_offset_w_ptr =
((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col +
w_col;
const T offset_h = data_offset_ptr[data_offset_h_ptr];
const T offset_w = data_offset_ptr[data_offset_w_ptr];
T val = static_cast<T>(0);
const T h_im = h_in + i * dilation_h + offset_h;
const T w_im = w_in + j * dilation_w + offset_w;
if (h_im > -1 && w_im > -1 && h_im < height && w_im < width)
val = deformable_im2col_bilinear_cpu(data_im_ptr, width, height,
width, h_im, w_im);
*data_col_ptr = val;
data_col_ptr += batch_size * height_col * width_col;
}
}
}
}
template <typename T>
void deformable_col2im_cpu_kernel(
const int n, const T *data_col, const T *data_offset, const int channels,
const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w,
const int channel_per_deformable_group, const int batch_size,
const int deformable_group, const int height_col, const int width_col,
T *grad_im) {
for (int index = 0; index < n; index++) {
const int j = (index / width_col / height_col / batch_size) % kernel_w;
const int i =
(index / width_col / height_col / batch_size / kernel_w) % kernel_h;
const int c =
index / width_col / height_col / batch_size / kernel_w / kernel_h;
// compute the start and end of the output
const int deformable_group_index = c / channel_per_deformable_group;
int w_out = index % width_col;
int h_out = (index / width_col) % height_col;
int b = (index / width_col / height_col) % batch_size;
int w_in = w_out * stride_w - pad_w;
int h_in = h_out * stride_h - pad_h;
const T *data_offset_ptr =
data_offset + (b * deformable_group + deformable_group_index) * 2 *
kernel_h * kernel_w * height_col * width_col;
const int data_offset_h_ptr =
((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out;
const int data_offset_w_ptr =
((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out;
const T offset_h = data_offset_ptr[data_offset_h_ptr];
const T offset_w = data_offset_ptr[data_offset_w_ptr];
const T cur_inv_h_data = h_in + i * dilation_h + offset_h;
const T cur_inv_w_data = w_in + j * dilation_w + offset_w;
const T cur_top_grad = data_col[index];
const int cur_h = (int)cur_inv_h_data;
const int cur_w = (int)cur_inv_w_data;
for (int dy = -2; dy <= 2; dy++) {
for (int dx = -2; dx <= 2; dx++) {
if (cur_h + dy >= 0 && cur_h + dy < height && cur_w + dx >= 0 &&
cur_w + dx < width && abs(cur_inv_h_data - (cur_h + dy)) < 1 &&
abs(cur_inv_w_data - (cur_w + dx)) < 1) {
int cur_bottom_grad_pos =
((b * channels + c) * height + cur_h + dy) * width + cur_w + dx;
T weight =
get_gradient_weight_cpu(cur_inv_h_data, cur_inv_w_data,
cur_h + dy, cur_w + dx, height, width);
*(grad_im + cur_bottom_grad_pos) += weight * cur_top_grad;
}
}
}
}
}
template <typename T>
void deformable_col2im_coord_cpu_kernel(
const int n, const T *data_col, const T *data_im, const T *data_offset,
const int channels, const int height, const int width, const int kernel_h,
const int kernel_w, const int pad_h, const int pad_w, const int stride_h,
const int stride_w, const int dilation_h, const int dilation_w,
const int channel_per_deformable_group, const int batch_size,
const int offset_channels, const int deformable_group, const int height_col,
const int width_col, T *grad_offset) {
for (int index = 0; index < n; index++) {
T val = 0;
int w = index % width_col;
int h = (index / width_col) % height_col;
int c = (index / width_col / height_col) % offset_channels;
int b = (index / width_col / height_col) / offset_channels;
// compute the start and end of the output
const int deformable_group_index = c / (2 * kernel_h * kernel_w);
const int col_step = kernel_h * kernel_w;
int cnt = 0;
const T *data_col_ptr = data_col + deformable_group_index *
channel_per_deformable_group *
batch_size * width_col * height_col;
const T *data_im_ptr =
data_im + (b * deformable_group + deformable_group_index) *
channel_per_deformable_group / kernel_h / kernel_w *
height * width;
const T *data_offset_ptr =
data_offset + (b * deformable_group + deformable_group_index) * 2 *
kernel_h * kernel_w * height_col * width_col;
const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w;
for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group;
col_c += col_step) {
const int col_pos =
(((col_c * batch_size + b) * height_col) + h) * width_col + w;
const int bp_dir = offset_c % 2;
int j = (col_pos / width_col / height_col / batch_size) % kernel_w;
int i =
(col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h;
int w_out = col_pos % width_col;
int h_out = (col_pos / width_col) % height_col;
int w_in = w_out * stride_w - pad_w;
int h_in = h_out * stride_h - pad_h;
const int data_offset_h_ptr =
(((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out);
const int data_offset_w_ptr =
(((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col +
w_out);
const T offset_h = data_offset_ptr[data_offset_h_ptr];
const T offset_w = data_offset_ptr[data_offset_w_ptr];
T inv_h = h_in + i * dilation_h + offset_h;
T inv_w = w_in + j * dilation_w + offset_w;
if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width)
inv_h = inv_w = -2;
const T weight = get_coordinate_weight_cpu(
inv_h, inv_w, height, width, data_im_ptr + cnt * height * width,
width, bp_dir);
val += weight * data_col_ptr[col_pos];
cnt += 1;
}
grad_offset[index] = val;
}
}
void deformable_im2col_cpu(Tensor data_im, Tensor data_offset,
const int channels, const int height,
const int width, const int ksize_h,
const int ksize_w, const int pad_h, const int pad_w,
const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w,
const int parallel_imgs, const int deformable_group,
Tensor data_col) {
int height_col =
(height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1;
int width_col =
(width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1;
int num_kernels = channels * height_col * width_col * parallel_imgs;
int channel_per_deformable_group = channels / deformable_group;
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
data_im.scalar_type(), "deformable_im2col_cpu", [&] {
deformable_im2col_cpu_kernel<scalar_t>(
num_kernels, data_im.data_ptr<scalar_t>(),
data_offset.data_ptr<scalar_t>(), height, width, ksize_h, ksize_w,
pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w,
channel_per_deformable_group, parallel_imgs, channels,
deformable_group, height_col, width_col,
data_col.data_ptr<scalar_t>());
});
}
void deformable_col2im_cpu(Tensor data_col, Tensor data_offset,
const int channels, const int height,
const int width, const int ksize_h,
const int ksize_w, const int pad_h, const int pad_w,
const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w,
const int parallel_imgs, const int deformable_group,
Tensor grad_im) {
// todo: make sure parallel_imgs is passed in correctly
int height_col =
(height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1;
int width_col =
(width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1;
int num_kernels =
channels * ksize_h * ksize_w * height_col * width_col * parallel_imgs;
int channel_per_deformable_group = channels / deformable_group;
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
data_col.scalar_type(), "deformable_col2im_gpu", ([&] {
const scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
scalar_t *grad_im_ = grad_im.data_ptr<scalar_t>();
deformable_col2im_cpu_kernel<scalar_t>(
num_kernels, data_col_, data_offset_, channels, height, width,
ksize_h, ksize_w, pad_h, pad_w, stride_h, stride_w, dilation_h,
dilation_w, channel_per_deformable_group, parallel_imgs,
deformable_group, height_col, width_col, grad_im_);
}));
}
void deformable_col2im_coord_cpu(
Tensor data_col, Tensor data_im, Tensor data_offset, const int channels,
const int height, const int width, const int ksize_h, const int ksize_w,
const int pad_h, const int pad_w, const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w, const int parallel_imgs,
const int deformable_group, Tensor grad_offset) {
int height_col =
(height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1;
int width_col =
(width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1;
int num_kernels = height_col * width_col * 2 * ksize_h * ksize_w *
deformable_group * parallel_imgs;
int channel_per_deformable_group =
channels * ksize_h * ksize_w / deformable_group;
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
data_col.scalar_type(), "deformable_col2im_coord_cpu", ([&] {
const scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
const scalar_t *data_im_ = data_im.data_ptr<scalar_t>();
const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
scalar_t *grad_offset_ = grad_offset.data_ptr<scalar_t>();
deformable_col2im_coord_cpu_kernel<scalar_t>(
num_kernels, data_col_, data_im_, data_offset_, channels, height,
width, ksize_h, ksize_w, pad_h, pad_w, stride_h, stride_w,
dilation_h, dilation_w, channel_per_deformable_group, parallel_imgs,
2 * ksize_h * ksize_w * deformable_group, deformable_group,
height_col, width_col, grad_offset_);
}));
}
mmcv/ops/csrc/parrots/deform_conv_parrots.cpp
View file @ 8016d880
......@@ -8,12 +8,6 @@
using namespace parrots;
#ifdef MMCV_WITH_CUDA
/*void deform_conv_forward_cuda(Tensor input, Tensor weight, Tensor offset,
* Tensor output, Tensor columns, Tensor ones,
* int kW, int kH, int dW, int dH, int padW,
* int padH, int dilationW, int dilationH, int
* group, int deformable_group, int im2col_step);
*/
void deform_conv_forward_cuda_parrots(CudaContext& ctx, const SSElement& attr,
const OperatorBase::in_list_t& ins,
OperatorBase::out_list_t& outs) {
......@@ -41,18 +35,11 @@ void deform_conv_forward_cuda_parrots(CudaContext& ctx, const SSElement& attr,
auto columns = buildATensor(ctx, outs[1]);
auto ones = buildATensor(ctx, outs[2]);
deform_conv_forward_cuda(input, weight, offset, output, columns, ones, kW, kH,
dW, dH, padW, padH, dilationW, dilationH, group,
deformable_group, im2col_step);
deform_conv_forward(input, weight, offset, output, columns, ones, kW, kH, dW,
dH, padW, padH, dilationW, dilationH, group,
deformable_group, im2col_step);
}
/*void deform_conv_backward_input_cuda(Tensor input, Tensor offset,
* Tensor gradOutput, Tensor gradInput,
* Tensor gradOffset, Tensor weight,
* Tensor columns, int kW, int kH, int dW,
* int dH, int padW, int padH, int
* dilationW, int dilationH, int group, int deformable_group, int im2col_step);
*/
void deform_conv_backward_input_cuda_parrots(CudaContext& ctx,
const SSElement& attr,
const OperatorBase::in_list_t& ins,
......@@ -82,18 +69,12 @@ void deform_conv_backward_input_cuda_parrots(CudaContext& ctx,
auto weight = buildATensor(ctx, outs[2]);
auto columns = buildATensor(ctx, outs[3]);
deform_conv_backward_input_cuda(input, offset, gradOutput, gradInput,
gradOffset, weight, columns, kW, kH, dW, dH,
padW, padH, dilationW, dilationH, group,
deformable_group, im2col_step);
deform_conv_backward_input(input, offset, gradOutput, gradInput, gradOffset,
weight, columns, kW, kH, dW, dH, padW, padH,
dilationW, dilationH, group, deformable_group,
im2col_step);
}
/*void deform_conv_backward_parameters_cuda(
* Tensor input, Tensor offset, Tensor gradOutput, Tensor gradWeight,
* Tensor columns, Tensor ones, int kW, int kH, int dW, int dH, int padW,
* int padH, int dilationW, int dilationH, int group, int deformable_group,
* float scale, int im2col_step);
*/
void deform_conv_backward_parameters_cuda_parrots(
CudaContext& ctx, const SSElement& attr, const OperatorBase::in_list_t& ins,
OperatorBase::out_list_t& outs) {
......@@ -122,10 +103,112 @@ void deform_conv_backward_parameters_cuda_parrots(
auto gradWeight = buildATensor(ctx, outs[0]);
auto columns = buildATensor(ctx, outs[1]);
auto ones = buildATensor(ctx, outs[2]);
deform_conv_backward_parameters_cuda(input, offset, gradOutput, gradWeight,
columns, ones, kW, kH, dW, dH, padW,
padH, dilationW, dilationH, group,
deformable_group, scale, im2col_step);
deform_conv_backward_parameters(input, offset, gradOutput, gradWeight,
columns, ones, kW, kH, dW, dH, padW, padH,
dilationW, dilationH, group, deformable_group,
scale, im2col_step);
}
#endif
void deform_conv_forward_cpu_parrots(HostContext& ctx, const SSElement& attr,
const OperatorBase::in_list_t& ins,
OperatorBase::out_list_t& outs) {
int kW, kH, dW, dH, padW, padH, dilationW, dilationH, group, deformable_group,
im2col_step;
SSAttrs(attr)
.get<int>("kW", kW)
.get<int>("kH", kH)
.get<int>("dW", dW)
.get<int>("dH", dH)
.get<int>("padW", padW)
.get<int>("padH", padH)
.get<int>("dilationW", dilationW)
.get<int>("dilationH", dilationH)
.get<int>("group", group)
.get<int>("deformable_group", deformable_group)
.get<int>("im2col_step", im2col_step)
.done();
const auto& input = buildATensor(ctx, ins[0]);
const auto& weight = buildATensor(ctx, ins[1]);
const auto& offset = buildATensor(ctx, ins[2]);
auto output = buildATensor(ctx, outs[0]);
auto columns = buildATensor(ctx, outs[1]);
auto ones = buildATensor(ctx, outs[2]);
deform_conv_forward(input, weight, offset, output, columns, ones, kW, kH, dW,
dH, padW, padH, dilationW, dilationH, group,
deformable_group, im2col_step);
}
void deform_conv_backward_input_cpu_parrots(HostContext& ctx,
const SSElement& attr,
const OperatorBase::in_list_t& ins,
OperatorBase::out_list_t& outs) {
int kW, kH, dW, dH, padW, padH, dilationW, dilationH, group, deformable_group,
im2col_step;
SSAttrs(attr)
.get<int>("kW", kW)
.get<int>("kH", kH)
.get<int>("dW", dW)
.get<int>("dH", dH)
.get<int>("padW", padW)
.get<int>("padH", padH)
.get<int>("dilationW", dilationW)
.get<int>("dilationH", dilationH)
.get<int>("group", group)
.get<int>("deformable_group", deformable_group)
.get<int>("im2col_step", im2col_step)
.done();
const auto& input = buildATensor(ctx, ins[0]);
const auto& offset = buildATensor(ctx, ins[1]);
const auto& gradOutput = buildATensor(ctx, ins[2]);
auto gradInput = buildATensor(ctx, outs[0]);
auto gradOffset = buildATensor(ctx, outs[1]);
auto weight = buildATensor(ctx, outs[2]);
auto columns = buildATensor(ctx, outs[3]);
deform_conv_backward_input(input, offset, gradOutput, gradInput, gradOffset,
weight, columns, kW, kH, dW, dH, padW, padH,
dilationW, dilationH, group, deformable_group,
im2col_step);
}
void deform_conv_backward_parameters_cpu_parrots(
HostContext& ctx, const SSElement& attr, const OperatorBase::in_list_t& ins,
OperatorBase::out_list_t& outs) {
int kW, kH, dW, dH, padW, padH, dilationW, dilationH, group, deformable_group,
im2col_step;
float scale;
SSAttrs(attr)
.get<int>("kW", kW)
.get<int>("kH", kH)
.get<int>("dW", dW)
.get<int>("dH", dH)
.get<int>("padW", padW)
.get<int>("padH", padH)
.get<int>("dilationW", dilationW)
.get<int>("dilationH", dilationH)
.get<int>("group", group)
.get<int>("deformable_group", deformable_group)
.get<float>("scale", scale)
.get<int>("im2col_step", im2col_step)
.done();
const auto& input = buildATensor(ctx, ins[0]);
const auto& offset = buildATensor(ctx, ins[1]);
const auto& gradOutput = buildATensor(ctx, ins[2]);
auto gradWeight = buildATensor(ctx, outs[0]);
auto columns = buildATensor(ctx, outs[1]);
auto ones = buildATensor(ctx, outs[2]);
deform_conv_backward_parameters(input, offset, gradOutput, gradWeight,
columns, ones, kW, kH, dW, dH, padW, padH,
dilationW, dilationH, group, deformable_group,
scale, im2col_step);
}
PARROTS_EXTENSION_REGISTER(deform_conv_forward)
......@@ -142,7 +225,10 @@ PARROTS_EXTENSION_REGISTER(deform_conv_forward)
.attr("im2col_step")
.input(3)
.output(3)
.apply(deform_conv_forward_cpu_parrots)
#ifdef MMCV_WITH_CUDA
.apply(deform_conv_forward_cuda_parrots)
#endif
.done();
PARROTS_EXTENSION_REGISTER(deform_conv_backward_input)
......@@ -159,7 +245,10 @@ PARROTS_EXTENSION_REGISTER(deform_conv_backward_input)
.attr("im2col_step")
.input(3)
.output(4)
.apply(deform_conv_backward_input_cpu_parrots)
#ifdef MMCV_WITH_CUDA
.apply(deform_conv_backward_input_cuda_parrots)
#endif
.done();
PARROTS_EXTENSION_REGISTER(deform_conv_backward_parameters)
......@@ -177,6 +266,8 @@ PARROTS_EXTENSION_REGISTER(deform_conv_backward_parameters)
.attr("im2col_step")
.input(3)
.output(3)
.apply(deform_conv_backward_parameters_cpu_parrots)
#ifdef MMCV_WITH_CUDA
.apply(deform_conv_backward_parameters_cuda_parrots)
.done();
#endif
.done();
mmcv/ops/csrc/parrots/deform_conv_pytorch.h
View file @ 8016d880
......@@ -4,24 +4,25 @@
#include <torch/extension.h>
using namespace at;
void deform_conv_forward_cuda(Tensor input, Tensor weight, Tensor offset,
Tensor output, Tensor columns, Tensor ones,
int kW, int kH, int dW, int dH, int padW,
int padH, int dilationW, int dilationH, int group,
int deformable_group, int im2col_step);
void deform_conv_forward(Tensor input, Tensor weight, Tensor offset,
Tensor output, Tensor columns, Tensor ones, int kW,
int kH, int dW, int dH, int padW, int padH,
int dilationW, int dilationH, int group,
int deformable_group, int im2col_step);
void deform_conv_backward_input_cuda(Tensor input, Tensor offset,
Tensor gradOutput, Tensor gradInput,
Tensor gradOffset, Tensor weight,
Tensor columns, int kW, int kH, int dW,
int dH, int padW, int padH, int dilationW,
int dilationH, int group,
int deformable_group, int im2col_step);
void deform_conv_backward_input(Tensor input, Tensor offset, Tensor gradOutput,
Tensor gradInput, Tensor gradOffset,
Tensor weight, Tensor columns, int kW, int kH,
int dW, int dH, int padW, int padH,
int dilationW, int dilationH, int group,
int deformable_group, int im2col_step);
void deform_conv_backward_parameters_cuda(
Tensor input, Tensor offset, Tensor gradOutput, Tensor gradWeight,
Tensor columns, Tensor ones, int kW, int kH, int dW, int dH, int padW,
int padH, int dilationW, int dilationH, int group, int deformable_group,
float scale, int im2col_step);
void deform_conv_backward_parameters(Tensor input, Tensor offset,
Tensor gradOutput, Tensor gradWeight,
Tensor columns, Tensor ones, int kW,
int kH, int dW, int dH, int padW, int padH,
int dilationW, int dilationH, int group,
int deformable_group, float scale,
int im2col_step);
#endif // DEFORM_CONV_PYTORCH_H
mmcv/ops/csrc/parrots/modulated_deform_conv.cpp
View file @ 8016d880
......@@ -2,48 +2,59 @@
#include "pytorch_cpp_helper.hpp"
#ifdef MMCV_WITH_CUDA
void ModulatedDeformConvForwardCUDAKernelLauncher(
Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset,
Tensor mask, Tensor output, Tensor columns, int kernel_h, int kernel_w,
const int stride_h, const int stride_w, const int pad_h, const int pad_w,
const int dilation_h, const int dilation_w, const int group,
const int deformable_group, const bool with_bias);
void ModulatedDeformConvBackwardCUDAKernelLauncher(
Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset,
Tensor mask, Tensor columns, Tensor grad_input, Tensor grad_weight,
Tensor grad_bias, Tensor grad_offset, Tensor grad_mask, Tensor grad_output,
int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h,
int pad_w, int dilation_h, int dilation_w, int group, int deformable_group,
const bool with_bias);
void modulated_deformable_im2col_cuda(
const Tensor data_im, const Tensor data_offset, const Tensor data_mask,
const int batch_size, const int channels, const int height_im,
const int width_im, const int height_col, const int width_col,
const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w,
const int stride_h, const int stride_w, const int dilation_h,
const int dilation_w, const int deformable_group, Tensor data_col);
void modulated_deform_conv_forward_cuda(
Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset,
Tensor mask, Tensor output, Tensor columns, int kernel_h, int kernel_w,
const int stride_h, const int stride_w, const int pad_h, const int pad_w,
const int dilation_h, const int dilation_w, const int group,
const int deformable_group, const bool with_bias) {
ModulatedDeformConvForwardCUDAKernelLauncher(
input, weight, bias, ones, offset, mask, output, columns, kernel_h,
kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, group,
deformable_group, with_bias);
}
void modulated_deformable_col2im_cuda(
const Tensor data_col, const Tensor data_offset, const Tensor data_mask,
const int batch_size, const int channels, const int height_im,
const int width_im, const int height_col, const int width_col,
const int kernel_h, const int kernel_w, const int pad_h, const int pad_w,
const int stride_h, const int stride_w, const int dilation_h,
const int dilation_w, const int deformable_group, Tensor grad_im);
void modulated_deformable_col2im_coord_cuda(
const Tensor data_col, const Tensor data_im, const Tensor data_offset,
const Tensor data_mask, const int batch_size, const int channels,
const int height_im, const int width_im, const int height_col,
const int width_col, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w, const int deformable_group,
Tensor grad_offset, Tensor grad_mask);
void modulated_deform_conv_backward_cuda(
Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset,
Tensor mask, Tensor columns, Tensor grad_input, Tensor grad_weight,
Tensor grad_bias, Tensor grad_offset, Tensor grad_mask, Tensor grad_output,
int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h,
int pad_w, int dilation_h, int dilation_w, int group, int deformable_group,
const bool with_bias) {
ModulatedDeformConvBackwardCUDAKernelLauncher(
input, weight, bias, ones, offset, mask, columns, grad_input, grad_weight,
grad_bias, grad_offset, grad_mask, grad_output, kernel_h, kernel_w,
stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, group,
deformable_group, with_bias);
}
#endif
void modulated_deformable_im2col_cpu(
const Tensor data_im, const Tensor data_offset, const Tensor data_mask,
const int batch_size, const int channels, const int height_im,
const int width_im, const int height_col, const int width_col,
const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w,
const int stride_h, const int stride_w, const int dilation_h,
const int dilation_w, const int deformable_group, Tensor data_col);
void modulated_deformable_col2im_cpu(
const Tensor data_col, const Tensor data_offset, const Tensor data_mask,
const int batch_size, const int channels, const int height_im,
const int width_im, const int height_col, const int width_col,
const int kernel_h, const int kernel_w, const int pad_h, const int pad_w,
const int stride_h, const int stride_w, const int dilation_h,
const int dilation_w, const int deformable_group, Tensor grad_im);
void modulated_deformable_col2im_coord_cpu(
const Tensor data_col, const Tensor data_im, const Tensor data_offset,
const Tensor data_mask, const int batch_size, const int channels,
const int height_im, const int width_im, const int height_col,
const int width_col, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w, const int deformable_group,
Tensor grad_offset, Tensor grad_mask);
void modulated_deform_conv_forward(
Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset,
Tensor mask, Tensor output, Tensor columns, int kernel_h, int kernel_w,
......@@ -61,15 +72,98 @@ void modulated_deform_conv_forward(
CHECK_CUDA_INPUT(output);
CHECK_CUDA_INPUT(columns);
modulated_deform_conv_forward_cuda(
input, weight, bias, ones, offset, mask, output, columns, kernel_h,
kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w,
group, deformable_group, with_bias);
#else
AT_ERROR("ModulatedDeformConv is not compiled with GPU support");
#endif
} else {
AT_ERROR("ModulatedDeformConv is not implemented on CPU");
CHECK_CPU_INPUT(input);
CHECK_CPU_INPUT(weight);
CHECK_CPU_INPUT(bias);
CHECK_CPU_INPUT(ones);
CHECK_CPU_INPUT(offset);
CHECK_CPU_INPUT(mask);
CHECK_CPU_INPUT(output);
CHECK_CPU_INPUT(columns);
}
at::DeviceGuard guard(input.device());
const int batch = input.size(0);
const int channels = input.size(1);
const int height = input.size(2);
const int width = input.size(3);
const int channels_out = weight.size(0);
const int channels_kernel = weight.size(1);
const int kernel_h_ = weight.size(2);
const int kernel_w_ = weight.size(3);
if (kernel_h_ != kernel_h || kernel_w_ != kernel_w)
AT_ERROR("Input shape and kernel shape wont match: (%d x %d vs %d x %d).",
kernel_h_, kernel_w, kernel_h_, kernel_w_);
if (channels != channels_kernel * group)
AT_ERROR("Input shape and kernel channels wont match: (%d vs %d).",
channels, channels_kernel * group);
const int height_out =
(height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
const int width_out =
(width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
if (ones.ndimension() != 2 ||
ones.size(0) * ones.size(1) < height_out * width_out) {
// Resize plane and fill with ones...
ones = at::ones({height_out, width_out}, input.options());
}
// resize output
output = output.view({batch, channels_out, height_out, width_out}).zero_();
// resize temporary columns
columns =
at::zeros({channels * kernel_h * kernel_w, 1 * height_out * width_out},
input.options());
output = output.view({output.size(0), group, output.size(1) / group,
output.size(2), output.size(3)});
for (int b = 0; b < batch; b++) {
if (input.device().is_cuda()) {
#ifdef MMCV_WITH_CUDA
modulated_deformable_im2col_cuda(
input[b], offset[b], mask[b], 1, channels, height, width, height_out,
width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
dilation_h, dilation_w, deformable_group, columns);
#endif
} else {
modulated_deformable_im2col_cpu(
input[b], offset[b], mask[b], 1, channels, height, width, height_out,
width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
dilation_h, dilation_w, deformable_group, columns);
}
// divide into group
weight = weight.view({group, weight.size(0) / group, weight.size(1),
weight.size(2), weight.size(3)});
columns = columns.view({group, columns.size(0) / group, columns.size(1)});
for (int g = 0; g < group; g++) {
output[b][g] = output[b][g]
.flatten(1)
.addmm_(weight[g].flatten(1), columns[g])
.view_as(output[b][g]);
}
weight = weight.view({weight.size(0) * weight.size(1), weight.size(2),
weight.size(3), weight.size(4)});
columns =
columns.view({columns.size(0) * columns.size(1), columns.size(2)});
}
output = output.view({output.size(0), output.size(1) * output.size(2),
output.size(3), output.size(4)});
if (with_bias) {
output += bias.view({1, bias.size(0), 1, 1});
}
}
......@@ -96,15 +190,149 @@ void modulated_deform_conv_backward(
CHECK_CUDA_INPUT(grad_mask);
CHECK_CUDA_INPUT(grad_output);
modulated_deform_conv_backward_cuda(
input, weight, bias, ones, offset, mask, columns, grad_input,
grad_weight, grad_bias, grad_offset, grad_mask, grad_output, kernel_h,
kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w,
group, deformable_group, with_bias);
#else
AT_ERROR("ModulatedDeformConv is not compiled with GPU support");
#endif
} else {
AT_ERROR("ModulatedDeformConv is not implemented on CPU");
CHECK_CPU_INPUT(input);
CHECK_CPU_INPUT(weight);
CHECK_CPU_INPUT(bias);
CHECK_CPU_INPUT(ones);
CHECK_CPU_INPUT(offset);
CHECK_CPU_INPUT(mask);
CHECK_CPU_INPUT(columns);
CHECK_CPU_INPUT(grad_input);
CHECK_CPU_INPUT(grad_weight);
CHECK_CPU_INPUT(grad_bias);
CHECK_CPU_INPUT(grad_offset);
CHECK_CPU_INPUT(grad_mask);
CHECK_CPU_INPUT(grad_output);
}
at::DeviceGuard guard(input.device());
const int batch = input.size(0);
const int channels = input.size(1);
const int height = input.size(2);
const int width = input.size(3);
const int channels_kernel = weight.size(1);
const int kernel_h_ = weight.size(2);
const int kernel_w_ = weight.size(3);
if (kernel_h_ != kernel_h || kernel_w_ != kernel_w)
AT_ERROR("Input shape and kernel shape wont match: (%d x %d vs %d x %d).",
kernel_h_, kernel_w, kernel_h_, kernel_w_);
if (channels != channels_kernel * group)
AT_ERROR("Input shape and kernel channels wont match: (%d vs %d).",
channels, channels_kernel * group);
const int height_out =
(height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
const int width_out =
(width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
if (ones.ndimension() != 2 ||
ones.size(0) * ones.size(1) < height_out * width_out) {
// Resize plane and fill with ones...
ones = at::ones({height_out, width_out}, input.options());
}
grad_input = grad_input.view({batch, channels, height, width});
columns = at::zeros({channels * kernel_h * kernel_w, height_out * width_out},
input.options());
grad_output =
grad_output.view({grad_output.size(0), group, grad_output.size(1) / group,
grad_output.size(2), grad_output.size(3)});
for (int b = 0; b < batch; b++) {
// divide int group
columns = columns.view({group, columns.size(0) / group, columns.size(1)});
weight = weight.view({group, weight.size(0) / group, weight.size(1),
weight.size(2), weight.size(3)});
for (int g = 0; g < group; g++) {
columns[g].addmm_(weight[g].flatten(1).transpose(0, 1),
grad_output[b][g].flatten(1), 0.0f, 1.0f);
}
columns =
columns.view({columns.size(0) * columns.size(1), columns.size(2)});
weight = weight.view({weight.size(0) * weight.size(1), weight.size(2),
weight.size(3), weight.size(4)});
if (input.device().is_cuda()) {
#ifdef MMCV_WITH_CUDA
// gradient w.r.t. input coordinate data
modulated_deformable_col2im_coord_cuda(
columns, input[b], offset[b], mask[b], 1, channels, height, width,
height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h,
stride_w, dilation_h, dilation_w, deformable_group, grad_offset[b],
grad_mask[b]);
// gradient w.r.t. input data
modulated_deformable_col2im_cuda(
columns, offset[b], mask[b], 1, channels, height, width, height_out,
width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
dilation_h, dilation_w, deformable_group, grad_input[b]);
// gradient w.r.t. weight, dWeight should accumulate across the batch and
// group
modulated_deformable_im2col_cuda(
input[b], offset[b], mask[b], 1, channels, height, width, height_out,
width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
dilation_h, dilation_w, deformable_group, columns);
#endif
} else {
// gradient w.r.t. input coordinate data
modulated_deformable_col2im_coord_cpu(
columns, input[b], offset[b], mask[b], 1, channels, height, width,
height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h,
stride_w, dilation_h, dilation_w, deformable_group, grad_offset[b],
grad_mask[b]);
// gradient w.r.t. input data
modulated_deformable_col2im_cpu(
columns, offset[b], mask[b], 1, channels, height, width, height_out,
width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
dilation_h, dilation_w, deformable_group, grad_input[b]);
// gradient w.r.t. weight, dWeight should accumulate across the batch and
// group
modulated_deformable_im2col_cpu(
input[b], offset[b], mask[b], 1, channels, height, width, height_out,
width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w,
dilation_h, dilation_w, deformable_group, columns);
}
columns = columns.view({group, columns.size(0) / group, columns.size(1)});
grad_weight = grad_weight.view({group, grad_weight.size(0) / group,
grad_weight.size(1), grad_weight.size(2),
grad_weight.size(3)});
if (with_bias)
grad_bias = grad_bias.view({group, grad_bias.size(0) / group});
for (int g = 0; g < group; g++) {
grad_weight[g] =
grad_weight[g]
.flatten(1)
.addmm_(grad_output[b][g].flatten(1), columns[g].transpose(0, 1))
.view_as(grad_weight[g]);
if (with_bias) {
grad_bias[g] =
grad_bias[g]
.view({-1, 1})
.addmm_(grad_output[b][g].flatten(1), ones.view({-1, 1}))
.view(-1);
}
}
columns =
columns.view({columns.size(0) * columns.size(1), columns.size(2)});
grad_weight = grad_weight.view({grad_weight.size(0) * grad_weight.size(1),
grad_weight.size(2), grad_weight.size(3),
grad_weight.size(4)});
if (with_bias)
grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)});
}
grad_output = grad_output.view({grad_output.size(0) * grad_output.size(1),
grad_output.size(2), grad_output.size(3),
grad_output.size(4)});
}
mmcv/ops/csrc/parrots/modulated_deform_conv_cpu.cpp 0 → 100644
View file @ 8016d880
// Copyright (c) OpenMMLab. All rights reserved
#include "pytorch_cpp_helper.hpp"
template <typename T>
T dmcn_im2col_bilinear_cpu(const T *input, const int data_width,
const int height, const int width, T h, T w) {
int h_low = floorf(h);
int w_low = floorf(w);
int h_high = h_low + 1;
int w_high = w_low + 1;
T lh = h - h_low;
T lw = w - w_low;
T hh = 1 - lh, hw = 1 - lw;
T v1 = 0;
if (h_low >= 0 && w_low >= 0) v1 = input[h_low * data_width + w_low];
T v2 = 0;
if (h_low >= 0 && w_high <= width - 1)
v2 = input[h_low * data_width + w_high];
T v3 = 0;
if (h_high <= height - 1 && w_low >= 0)
v3 = input[h_high * data_width + w_low];
T v4 = 0;
if (h_high <= height - 1 && w_high <= width - 1)
v4 = input[h_high * data_width + w_high];
T w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
return val;
}
template <typename T>
T dmcn_get_gradient_weight_cpu(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) {
// empty
return 0;
}
int argmax_h_low = floorf(argmax_h);
int argmax_w_low = floorf(argmax_w);
int argmax_h_high = argmax_h_low + 1;
int argmax_w_high = argmax_w_low + 1;
T weight = 0;
if (h == argmax_h_low && w == argmax_w_low)
weight = (h + 1 - argmax_h) * (w + 1 - argmax_w);
if (h == argmax_h_low && w == argmax_w_high)
weight = (h + 1 - argmax_h) * (argmax_w + 1 - w);
if (h == argmax_h_high && w == argmax_w_low)
weight = (argmax_h + 1 - h) * (w + 1 - argmax_w);
if (h == argmax_h_high && w == argmax_w_high)
weight = (argmax_h + 1 - h) * (argmax_w + 1 - w);
return weight;
}
template <typename T>
T dmcn_get_coordinate_weight_cpu(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) {
// empty
return 0;
}
int argmax_h_low = floorf(argmax_h);
int argmax_w_low = floorf(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) {
if (argmax_h_low >= 0 && argmax_w_low >= 0)
weight += -1 * (argmax_w_low + 1 - argmax_w) *
im_data[argmax_h_low * data_width + argmax_w_low];
if (argmax_h_low >= 0 && argmax_w_high <= width - 1)
weight += -1 * (argmax_w - argmax_w_low) *
im_data[argmax_h_low * data_width + argmax_w_high];
if (argmax_h_high <= height - 1 && argmax_w_low >= 0)
weight += (argmax_w_low + 1 - argmax_w) *
im_data[argmax_h_high * data_width + argmax_w_low];
if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
weight += (argmax_w - argmax_w_low) *
im_data[argmax_h_high * data_width + argmax_w_high];
} else if (bp_dir == 1) {
if (argmax_h_low >= 0 && argmax_w_low >= 0)
weight += -1 * (argmax_h_low + 1 - argmax_h) *
im_data[argmax_h_low * data_width + argmax_w_low];
if (argmax_h_low >= 0 && argmax_w_high <= width - 1)
weight += (argmax_h_low + 1 - argmax_h) *
im_data[argmax_h_low * data_width + argmax_w_high];
if (argmax_h_high <= height - 1 && argmax_w_low >= 0)
weight += -1 * (argmax_h - argmax_h_low) *
im_data[argmax_h_high * data_width + argmax_w_low];
if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1)
weight += (argmax_h - argmax_h_low) *
im_data[argmax_h_high * data_width + argmax_w_high];
}
return weight;
}
template <typename T>
void modulated_deformable_im2col_cpu_kernel(
const int n, const T *data_im, const T *data_offset, const T *data_mask,
const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w,
const int channel_per_deformable_group, const int batch_size,
const int num_channels, const int deformable_group, const int height_col,
const int width_col, T *data_col) {
for (int index = 0; index < n; index++) {
// index index of output matrix
const int w_col = index % width_col;
const int h_col = (index / width_col) % height_col;
const int b_col = (index / width_col / height_col) % batch_size;
const int c_im = (index / width_col / height_col) / batch_size;
const int c_col = c_im * kernel_h * kernel_w;
// compute deformable group index
const int deformable_group_index = c_im / channel_per_deformable_group;
const int h_in = h_col * stride_h - pad_h;
const int w_in = w_col * stride_w - pad_w;
T *data_col_ptr =
data_col +
((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col;
const T *data_im_ptr =
data_im + (b_col * num_channels + c_im) * height * width;
const T *data_offset_ptr =
data_offset + (b_col * deformable_group + deformable_group_index) * 2 *
kernel_h * kernel_w * height_col * width_col;
const T *data_mask_ptr =
data_mask + (b_col * deformable_group + deformable_group_index) *
kernel_h * kernel_w * height_col * width_col;
for (int i = 0; i < kernel_h; ++i) {
for (int j = 0; j < kernel_w; ++j) {
const int data_offset_h_ptr =
((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col;
const int data_offset_w_ptr =
((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col +
w_col;
const int data_mask_hw_ptr =
((i * kernel_w + j) * height_col + h_col) * width_col + w_col;
const T offset_h = data_offset_ptr[data_offset_h_ptr];
const T offset_w = data_offset_ptr[data_offset_w_ptr];
const T mask = data_mask_ptr[data_mask_hw_ptr];
T val = static_cast<T>(0);
const T h_im = h_in + i * dilation_h + offset_h;
const T w_im = w_in + j * dilation_w + offset_w;
if (h_im > -1 && w_im > -1 && h_im < height && w_im < width)
val = dmcn_im2col_bilinear_cpu(data_im_ptr, width, height, width,
h_im, w_im);
*data_col_ptr = val * mask;
data_col_ptr += batch_size * height_col * width_col;
}
}
}
}
template <typename T>
void modulated_deformable_col2im_cpu_kernel(
const int n, const T *data_col, const T *data_offset, const T *data_mask,
const int channels, const int height, const int width, const int kernel_h,
const int kernel_w, const int pad_h, const int pad_w, const int stride_h,
const int stride_w, const int dilation_h, const int dilation_w,
const int channel_per_deformable_group, const int batch_size,
const int deformable_group, const int height_col, const int width_col,
T *grad_im) {
for (int index = 0; index < n; index++) {
const int j = (index / width_col / height_col / batch_size) % kernel_w;
const int i =
(index / width_col / height_col / batch_size / kernel_w) % kernel_h;
const int c =
index / width_col / height_col / batch_size / kernel_w / kernel_h;
// compute the start and end of the output
const int deformable_group_index = c / channel_per_deformable_group;
int w_out = index % width_col;
int h_out = (index / width_col) % height_col;
int b = (index / width_col / height_col) % batch_size;
int w_in = w_out * stride_w - pad_w;
int h_in = h_out * stride_h - pad_h;
const T *data_offset_ptr =
data_offset + (b * deformable_group + deformable_group_index) * 2 *
kernel_h * kernel_w * height_col * width_col;
const T *data_mask_ptr =
data_mask + (b * deformable_group + deformable_group_index) * kernel_h *
kernel_w * height_col * width_col;
const int data_offset_h_ptr =
((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out;
const int data_offset_w_ptr =
((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out;
const int data_mask_hw_ptr =
((i * kernel_w + j) * height_col + h_out) * width_col + w_out;
const T offset_h = data_offset_ptr[data_offset_h_ptr];
const T offset_w = data_offset_ptr[data_offset_w_ptr];
const T mask = data_mask_ptr[data_mask_hw_ptr];
const T cur_inv_h_data = h_in + i * dilation_h + offset_h;
const T cur_inv_w_data = w_in + j * dilation_w + offset_w;
const T cur_top_grad = data_col[index] * mask;
const int cur_h = (int)cur_inv_h_data;
const int cur_w = (int)cur_inv_w_data;
for (int dy = -2; dy <= 2; dy++) {
for (int dx = -2; dx <= 2; dx++) {
if (cur_h + dy >= 0 && cur_h + dy < height && cur_w + dx >= 0 &&
cur_w + dx < width && abs(cur_inv_h_data - (cur_h + dy)) < 1 &&
abs(cur_inv_w_data - (cur_w + dx)) < 1) {
int cur_bottom_grad_pos =
((b * channels + c) * height + cur_h + dy) * width + cur_w + dx;
T weight = dmcn_get_gradient_weight_cpu(cur_inv_h_data,
cur_inv_w_data, cur_h + dy,
cur_w + dx, height, width);
*(grad_im + cur_bottom_grad_pos) += weight * cur_top_grad;
}
}
}
}
}
template <typename T>
void modulated_deformable_col2im_coord_cpu_kernel(
const int n, const T *data_col, const T *data_im, const T *data_offset,
const T *data_mask, const int channels, const int height, const int width,
const int kernel_h, const int kernel_w, const int pad_h, const int pad_w,
const int stride_h, const int stride_w, const int dilation_h,
const int dilation_w, const int channel_per_deformable_group,
const int batch_size, const int offset_channels, const int deformable_group,
const int height_col, const int width_col, T *grad_offset, T *grad_mask) {
for (int index = 0; index < n; index++) {
T val = 0, mval = 0;
int w = index % width_col;
int h = (index / width_col) % height_col;
int c = (index / width_col / height_col) % offset_channels;
int b = (index / width_col / height_col) / offset_channels;
// compute the start and end of the output
const int deformable_group_index = c / (2 * kernel_h * kernel_w);
const int col_step = kernel_h * kernel_w;
int cnt = 0;
const T *data_col_ptr = data_col + deformable_group_index *
channel_per_deformable_group *
batch_size * width_col * height_col;
const T *data_im_ptr =
data_im + (b * deformable_group + deformable_group_index) *
channel_per_deformable_group / kernel_h / kernel_w *
height * width;
const T *data_offset_ptr =
data_offset + (b * deformable_group + deformable_group_index) * 2 *
kernel_h * kernel_w * height_col * width_col;
const T *data_mask_ptr =
data_mask + (b * deformable_group + deformable_group_index) * kernel_h *
kernel_w * height_col * width_col;
const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w;
for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group;
col_c += col_step) {
const int col_pos =
(((col_c * batch_size + b) * height_col) + h) * width_col + w;
const int bp_dir = offset_c % 2;
int j = (col_pos / width_col / height_col / batch_size) % kernel_w;
int i =
(col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h;
int w_out = col_pos % width_col;
int h_out = (col_pos / width_col) % height_col;
int w_in = w_out * stride_w - pad_w;
int h_in = h_out * stride_h - pad_h;
const int data_offset_h_ptr =
(((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out);
const int data_offset_w_ptr =
(((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col +
w_out);
const int data_mask_hw_ptr =
(((i * kernel_w + j) * height_col + h_out) * width_col + w_out);
const T offset_h = data_offset_ptr[data_offset_h_ptr];
const T offset_w = data_offset_ptr[data_offset_w_ptr];
const T mask = data_mask_ptr[data_mask_hw_ptr];
T inv_h = h_in + i * dilation_h + offset_h;
T inv_w = w_in + j * dilation_w + offset_w;
if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width)
inv_h = inv_w = -2;
else
mval += data_col_ptr[col_pos] *
dmcn_im2col_bilinear_cpu(data_im_ptr + cnt * height * width,
width, height, width, inv_h, inv_w);
const T weight = dmcn_get_coordinate_weight_cpu(
inv_h, inv_w, height, width, data_im_ptr + cnt * height * width,
width, bp_dir);
val += weight * data_col_ptr[col_pos] * mask;
cnt += 1;
}
// KERNEL_ASSIGN(grad_offset[index], offset_req, val);
grad_offset[index] = val;
if (offset_c % 2 == 0)
// KERNEL_ASSIGN(grad_mask[(((b * deformable_group +
// deformable_group_index) * kernel_h * kernel_w + offset_c / 2) *
// height_col + h) * width_col + w], mask_req, mval);
grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h *
kernel_w +
offset_c / 2) *
height_col +
h) *
width_col +
w] = mval;
}
}
void modulated_deformable_im2col_cpu(
const Tensor data_im, const Tensor data_offset, const Tensor data_mask,
const int batch_size, const int channels, const int height_im,
const int width_im, const int height_col, const int width_col,
const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w,
const int stride_h, const int stride_w, const int dilation_h,
const int dilation_w, const int deformable_group, Tensor data_col) {
// num_axes should be smaller than block size
const int channel_per_deformable_group = channels / deformable_group;
const int num_kernels = channels * batch_size * height_col * width_col;
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
data_im.scalar_type(), "modulated_deformable_im2col_cpu", ([&] {
const scalar_t *data_im_ = data_im.data_ptr<scalar_t>();
const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
const scalar_t *data_mask_ = data_mask.data_ptr<scalar_t>();
scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
modulated_deformable_im2col_cpu_kernel(
num_kernels, data_im_, data_offset_, data_mask_, height_im,
width_im, kernel_h, kenerl_w, pad_h, pad_w, stride_h, stride_w,
dilation_h, dilation_w, channel_per_deformable_group, batch_size,
channels, deformable_group, height_col, width_col, data_col_);
}));
}
void modulated_deformable_col2im_cpu(
const Tensor data_col, const Tensor data_offset, const Tensor data_mask,
const int batch_size, const int channels, const int height_im,
const int width_im, const int height_col, const int width_col,
const int kernel_h, const int kernel_w, const int pad_h, const int pad_w,
const int stride_h, const int stride_w, const int dilation_h,
const int dilation_w, const int deformable_group, Tensor grad_im) {
const int channel_per_deformable_group = channels / deformable_group;
const int num_kernels =
channels * kernel_h * kernel_w * batch_size * height_col * width_col;
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
data_col.scalar_type(), "modulated_deformable_col2im_cpu", ([&] {
const scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
const scalar_t *data_mask_ = data_mask.data_ptr<scalar_t>();
scalar_t *grad_im_ = grad_im.data_ptr<scalar_t>();
modulated_deformable_col2im_cpu_kernel(
num_kernels, data_col_, data_offset_, data_mask_, channels,
height_im, width_im, kernel_h, kernel_w, pad_h, pad_w, stride_h,
stride_w, dilation_h, dilation_w, channel_per_deformable_group,
batch_size, deformable_group, height_col, width_col, grad_im_);
}));
}
void modulated_deformable_col2im_coord_cpu(
const Tensor data_col, const Tensor data_im, const Tensor data_offset,
const Tensor data_mask, const int batch_size, const int channels,
const int height_im, const int width_im, const int height_col,
const int width_col, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w, const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w, const int deformable_group,
Tensor grad_offset, Tensor grad_mask) {
const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h *
kernel_w * deformable_group;
const int channel_per_deformable_group =
channels * kernel_h * kernel_w / deformable_group;
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
data_col.scalar_type(), "modulated_deformable_col2im_coord_cpu", ([&] {
const scalar_t *data_col_ = data_col.data_ptr<scalar_t>();
const scalar_t *data_im_ = data_im.data_ptr<scalar_t>();
const scalar_t *data_offset_ = data_offset.data_ptr<scalar_t>();
const scalar_t *data_mask_ = data_mask.data_ptr<scalar_t>();
scalar_t *grad_offset_ = grad_offset.data_ptr<scalar_t>();
scalar_t *grad_mask_ = grad_mask.data_ptr<scalar_t>();
modulated_deformable_col2im_coord_cpu_kernel(
num_kernels, data_col_, data_im_, data_offset_, data_mask_,
channels, height_im, width_im, kernel_h, kernel_w, pad_h, pad_w,
stride_h, stride_w, dilation_h, dilation_w,
channel_per_deformable_group, batch_size,
2 * kernel_h * kernel_w * deformable_group, deformable_group,
height_col, width_col, grad_offset_, grad_mask_);
}));
}
mmcv/ops/csrc/parrots/modulated_deform_conv_parrots.cpp
View file @ 8016d880
......@@ -37,10 +37,10 @@ void modulated_deform_conv_forward_cuda_parrots(
auto output = buildATensor(ctx, outs[0]);
auto columns = buildATensor(ctx, outs[1]);
modulated_deform_conv_forward_cuda(
input, weight, bias, ones, offset, mask, output, columns, kernel_h,
kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, group,
deformable_group, with_bias);
modulated_deform_conv_forward(input, weight, bias, ones, offset, mask, output,
columns, kernel_h, kernel_w, stride_h, stride_w,
pad_h, pad_w, dilation_h, dilation_w, group,
deformable_group, with_bias);
}
void modulated_deform_conv_backward_cuda_parrots(
......@@ -76,13 +76,88 @@ void modulated_deform_conv_backward_cuda_parrots(
auto grad_offset = buildATensor(ctx, outs[4]);
auto grad_mask = buildATensor(ctx, outs[5]);
auto grad_output = buildATensor(ctx, outs[6]);
modulated_deform_conv_backward_cuda(
modulated_deform_conv_backward(
input, weight, bias, ones, offset, mask, columns, grad_input, grad_weight,
grad_bias, grad_offset, grad_mask, grad_output, kernel_h, kernel_w,
stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, group,
deformable_group, with_bias);
}
#endif
void modulated_deform_conv_forward_cpu_parrots(
HostContext& ctx, const SSElement& attr, const OperatorBase::in_list_t& ins,
OperatorBase::out_list_t& outs) {
int kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h,
dilation_w, group, deformable_group, with_bias;
SSAttrs(attr)
.get<int>("kernel_h", kernel_h)
.get<int>("kernel_w", kernel_w)
.get<int>("stride_h", stride_h)
.get<int>("stride_w", stride_w)
.get<int>("pad_h", pad_h)
.get<int>("pad_w", pad_w)
.get<int>("dilation_h", dilation_h)
.get<int>("dilation_w", dilation_w)
.get<int>("group", group)
.get<int>("deformable_group", deformable_group)
.get<int>("with_bias", with_bias)
.done();
const auto& input = buildATensor(ctx, ins[0]);
const auto& weight = buildATensor(ctx, ins[1]);
const auto& bias = buildATensor(ctx, ins[2]);
const auto& ones = buildATensor(ctx, ins[3]);
const auto& offset = buildATensor(ctx, ins[4]);
const auto& mask = buildATensor(ctx, ins[5]);
auto output = buildATensor(ctx, outs[0]);
auto columns = buildATensor(ctx, outs[1]);
modulated_deform_conv_forward(input, weight, bias, ones, offset, mask, output,
columns, kernel_h, kernel_w, stride_h, stride_w,
pad_h, pad_w, dilation_h, dilation_w, group,
deformable_group, with_bias);
}
void modulated_deform_conv_backward_cpu_parrots(
HostContext& ctx, const SSElement& attr, const OperatorBase::in_list_t& ins,
OperatorBase::out_list_t& outs) {
int kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h,
dilation_w, group, deformable_group, with_bias;
SSAttrs(attr)
.get<int>("kernel_h", kernel_h)
.get<int>("kernel_w", kernel_w)
.get<int>("stride_h", stride_h)
.get<int>("stride_w", stride_w)
.get<int>("pad_h", pad_h)
.get<int>("pad_w", pad_w)
.get<int>("dilation_h", dilation_h)
.get<int>("dilation_w", dilation_w)
.get<int>("group", group)
.get<int>("deformable_group", deformable_group)
.get<int>("with_bias", with_bias)
.done();
const auto& input = buildATensor(ctx, ins[0]);
const auto& weight = buildATensor(ctx, ins[1]);
const auto& bias = buildATensor(ctx, ins[2]);
const auto& ones = buildATensor(ctx, ins[3]);
const auto& offset = buildATensor(ctx, ins[4]);
const auto& mask = buildATensor(ctx, ins[5]);
auto columns = buildATensor(ctx, outs[0]);
auto grad_input = buildATensor(ctx, outs[1]);
auto grad_weight = buildATensor(ctx, outs[2]);
auto grad_bias = buildATensor(ctx, outs[3]);
auto grad_offset = buildATensor(ctx, outs[4]);
auto grad_mask = buildATensor(ctx, outs[5]);
auto grad_output = buildATensor(ctx, outs[6]);
modulated_deform_conv_backward(
input, weight, bias, ones, offset, mask, columns, grad_input, grad_weight,
grad_bias, grad_offset, grad_mask, grad_output, kernel_h, kernel_w,
stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, group,
deformable_group, with_bias);
}
PARROTS_EXTENSION_REGISTER(modulated_deform_conv_forward)
.attr("kernel_h")
.attr("kernel_w")
......@@ -97,7 +172,10 @@ PARROTS_EXTENSION_REGISTER(modulated_deform_conv_forward)
.attr("with_bias")
.input(6)
.output(2)
.apply(modulated_deform_conv_forward_cpu_parrots)
#ifdef MMCV_WITH_CUDA
.apply(modulated_deform_conv_forward_cuda_parrots)
#endif
.done();
PARROTS_EXTENSION_REGISTER(modulated_deform_conv_backward)
......@@ -114,6 +192,8 @@ PARROTS_EXTENSION_REGISTER(modulated_deform_conv_backward)
.attr("with_bias")
.input(6)
.output(7)
.apply(modulated_deform_conv_backward_cpu_parrots)
#ifdef MMCV_WITH_CUDA
.apply(modulated_deform_conv_backward_cuda_parrots)
.done();
#endif
.done();
mmcv/ops/csrc/parrots/modulated_deform_conv_pytorch.h
View file @ 8016d880
......@@ -4,14 +4,14 @@
#include <torch/extension.h>
using namespace at;
void modulated_deform_conv_forward_cuda(
void modulated_deform_conv_forward(
Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset,
Tensor mask, Tensor output, Tensor columns, int kernel_h, int kernel_w,
const int stride_h, const int stride_w, const int pad_h, const int pad_w,
const int dilation_h, const int dilation_w, const int group,
const int deformable_group, const bool with_bias);
void modulated_deform_conv_backward_cuda(
void modulated_deform_conv_backward(
Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset,
Tensor mask, Tensor columns, Tensor grad_input, Tensor grad_weight,
Tensor grad_bias, Tensor grad_offset, Tensor grad_mask, Tensor grad_output,
......
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