286 lines
8.3 KiB
C++
286 lines
8.3 KiB
C++
|
#include "roi_align_gradient_op.h"
|
||
|
|
||
|
#include "caffe2/utils/eigen_utils.h"
|
||
|
#include "caffe2/utils/math.h"
|
||
|
|
||
|
namespace caffe2 {
|
||
|
namespace {
|
||
|
|
||
|
template <typename T>
|
||
|
void bilinear_interpolate_gradient(
|
||
|
const int height,
|
||
|
const int width,
|
||
|
T y,
|
||
|
T x,
|
||
|
T& w1,
|
||
|
T& w2,
|
||
|
T& w3,
|
||
|
T& w4,
|
||
|
int& x_low,
|
||
|
int& x_high,
|
||
|
int& y_low,
|
||
|
int& y_high,
|
||
|
const int /*index*/ /* index for debug only*/) {
|
||
|
// deal with cases that inverse elements are out of feature map boundary
|
||
|
if (y < -1.0 || y > height || x < -1.0 || x > width) {
|
||
|
// empty
|
||
|
w1 = w2 = w3 = w4 = 0.;
|
||
|
x_low = x_high = y_low = y_high = -1;
|
||
|
return;
|
||
|
}
|
||
|
|
||
|
if (y <= 0) {
|
||
|
y = 0;
|
||
|
}
|
||
|
if (x <= 0) {
|
||
|
x = 0;
|
||
|
}
|
||
|
|
||
|
y_low = (int)y;
|
||
|
x_low = (int)x;
|
||
|
|
||
|
if (y_low >= height - 1) {
|
||
|
y_high = y_low = height - 1;
|
||
|
y = (T)y_low;
|
||
|
} else {
|
||
|
y_high = y_low + 1;
|
||
|
}
|
||
|
|
||
|
if (x_low >= width - 1) {
|
||
|
x_high = x_low = width - 1;
|
||
|
x = (T)x_low;
|
||
|
} else {
|
||
|
x_high = x_low + 1;
|
||
|
}
|
||
|
|
||
|
T ly = y - y_low;
|
||
|
T lx = x - x_low;
|
||
|
T hy = 1. - ly, hx = 1. - lx;
|
||
|
|
||
|
// reference in forward
|
||
|
// T v1 = bottom_data[y_low * width + x_low];
|
||
|
// T v2 = bottom_data[y_low * width + x_high];
|
||
|
// T v3 = bottom_data[y_high * width + x_low];
|
||
|
// T v4 = bottom_data[y_high * width + x_high];
|
||
|
// T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
|
||
|
|
||
|
w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
|
||
|
|
||
|
return;
|
||
|
}
|
||
|
|
||
|
template <class T>
|
||
|
inline void add(const T& val, T* address) {
|
||
|
*address += val;
|
||
|
}
|
||
|
|
||
|
template <typename T>
|
||
|
void ROIAlignBackwardFeature(
|
||
|
const int nthreads,
|
||
|
const T* top_diff,
|
||
|
const int /*num_rois*/,
|
||
|
const T& spatial_scale,
|
||
|
const int channels,
|
||
|
const int height,
|
||
|
const int width,
|
||
|
const int pooled_height,
|
||
|
const int pooled_width,
|
||
|
const int sampling_ratio,
|
||
|
T* bottom_diff,
|
||
|
const T* bottom_rois,
|
||
|
int rois_cols,
|
||
|
bool continuous_coordinate) {
|
||
|
DCHECK(rois_cols == 4 || rois_cols == 5);
|
||
|
|
||
|
for (int index = 0; index < nthreads; index++) {
|
||
|
// (n, c, ph, pw) is an element in the pooled output
|
||
|
int pw = index % pooled_width;
|
||
|
int ph = (index / pooled_width) % pooled_height;
|
||
|
int c = (index / pooled_width / pooled_height) % channels;
|
||
|
int n = index / pooled_width / pooled_height / channels;
|
||
|
|
||
|
const T* offset_bottom_rois = bottom_rois + n * rois_cols;
|
||
|
int roi_batch_ind = 0;
|
||
|
if (rois_cols == 5) {
|
||
|
roi_batch_ind = offset_bottom_rois[0];
|
||
|
offset_bottom_rois++;
|
||
|
}
|
||
|
|
||
|
// Do not using rounding; this implementation detail is critical
|
||
|
T roi_offset = continuous_coordinate ? T(0.5) : 0;
|
||
|
T roi_start_w = offset_bottom_rois[0] * spatial_scale - roi_offset;
|
||
|
T roi_start_h = offset_bottom_rois[1] * spatial_scale - roi_offset;
|
||
|
T roi_end_w = offset_bottom_rois[2] * spatial_scale - roi_offset;
|
||
|
T roi_end_h = offset_bottom_rois[3] * spatial_scale - roi_offset;
|
||
|
|
||
|
T roi_width = roi_end_w - roi_start_w;
|
||
|
T roi_height = roi_end_h - roi_start_h;
|
||
|
if (continuous_coordinate) {
|
||
|
CAFFE_ENFORCE(
|
||
|
roi_width >= 0 && roi_height >= 0,
|
||
|
"ROIs in ROIAlign do not have non-negative size!");
|
||
|
} else { // backward compatibility
|
||
|
// Force malformed ROIs to be 1x1
|
||
|
roi_width = std::max(roi_width, (T)1.);
|
||
|
roi_height = std::max(roi_height, (T)1.);
|
||
|
}
|
||
|
T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
|
||
|
T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
|
||
|
|
||
|
T* offset_bottom_diff =
|
||
|
bottom_diff + (roi_batch_ind * channels + c) * height * width;
|
||
|
|
||
|
int top_offset = (n * channels + c) * pooled_height * pooled_width;
|
||
|
const T* offset_top_diff = top_diff + top_offset;
|
||
|
const T top_diff_this_bin = offset_top_diff[ph * pooled_width + pw];
|
||
|
|
||
|
// We use roi_bin_grid to sample the grid and mimic integral
|
||
|
int roi_bin_grid_h = (sampling_ratio > 0)
|
||
|
? sampling_ratio
|
||
|
: ceil(roi_height / pooled_height); // e.g., = 2
|
||
|
int roi_bin_grid_w =
|
||
|
(sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width);
|
||
|
|
||
|
// We do average (integral) pooling inside a bin
|
||
|
const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4
|
||
|
|
||
|
for (int iy = 0; iy < roi_bin_grid_h; iy++) {
|
||
|
const T y = roi_start_h + ph * bin_size_h +
|
||
|
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
|
||
|
static_cast<T>(iy + .5f) * bin_size_h /
|
||
|
static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5
|
||
|
for (int ix = 0; ix < roi_bin_grid_w; ix++) {
|
||
|
const T x = roi_start_w + pw * bin_size_w +
|
||
|
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
|
||
|
static_cast<T>(ix + .5f) * bin_size_w /
|
||
|
static_cast<T>(roi_bin_grid_w);
|
||
|
|
||
|
T w1, w2, w3, w4;
|
||
|
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
|
||
|
int x_low, x_high, y_low, y_high;
|
||
|
|
||
|
bilinear_interpolate_gradient(
|
||
|
height,
|
||
|
width,
|
||
|
y,
|
||
|
x,
|
||
|
w1,
|
||
|
w2,
|
||
|
w3,
|
||
|
w4,
|
||
|
x_low,
|
||
|
x_high,
|
||
|
y_low,
|
||
|
y_high,
|
||
|
index);
|
||
|
|
||
|
T g1 = top_diff_this_bin * w1 / count;
|
||
|
T g2 = top_diff_this_bin * w2 / count;
|
||
|
T g3 = top_diff_this_bin * w3 / count;
|
||
|
T g4 = top_diff_this_bin * w4 / count;
|
||
|
|
||
|
if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) {
|
||
|
// atomic add is not needed for now since it is single threaded
|
||
|
add(static_cast<T>(g1), offset_bottom_diff + y_low * width + x_low);
|
||
|
add(static_cast<T>(g2), offset_bottom_diff + y_low * width + x_high);
|
||
|
add(static_cast<T>(g3), offset_bottom_diff + y_high * width + x_low);
|
||
|
add(static_cast<T>(g4), offset_bottom_diff + y_high * width + x_high);
|
||
|
} // if
|
||
|
} // ix
|
||
|
} // iy
|
||
|
} // for
|
||
|
} // ROIAlignBackward
|
||
|
|
||
|
} // namespace
|
||
|
|
||
|
template <>
|
||
|
C10_EXPORT bool RoIAlignGradientOp<float, CPUContext>::RunOnDevice() {
|
||
|
auto& X = Input(0); // Input data to pool
|
||
|
auto& R = Input(1); // RoIs
|
||
|
auto& dY = Input(2); // Gradient of net w.r.t. output of "forward" op
|
||
|
// (aka "gradOutput")
|
||
|
|
||
|
CAFFE_ENFORCE_EQ(R.dim(), 2);
|
||
|
// if R has 5 columns, the first column is the index, otherwise 0
|
||
|
CAFFE_ENFORCE(R.dim32(1) == 4 || R.dim32(1) == 5);
|
||
|
|
||
|
auto* dX = Output(
|
||
|
0,
|
||
|
X.sizes(),
|
||
|
at::dtype<float>()); // Gradient of net w.r.t. input to "forward" op (aka
|
||
|
// "gradInput")
|
||
|
|
||
|
// Must zero-out dX before accumulating gradients
|
||
|
// (TODO): Kaiming - is this safe?
|
||
|
math::Set<float, CPUContext>(
|
||
|
dX->numel(), 0.f, dX->template mutable_data<float>(), &context_);
|
||
|
|
||
|
if (dY.numel() > 0) { // Handle possibly empty gradient if there were no rois
|
||
|
ROIAlignBackwardFeature<float>(
|
||
|
dY.numel(),
|
||
|
dY.data<float>(),
|
||
|
R.dim32(0),
|
||
|
spatial_scale_,
|
||
|
X.dim32(1),
|
||
|
X.dim32(2),
|
||
|
X.dim32(3),
|
||
|
pooled_height_,
|
||
|
pooled_width_,
|
||
|
sampling_ratio_,
|
||
|
dX->template mutable_data<float>(),
|
||
|
R.data<float>(),
|
||
|
R.dim32(1),
|
||
|
aligned_);
|
||
|
}
|
||
|
return true;
|
||
|
}
|
||
|
|
||
|
REGISTER_CPU_OPERATOR(RoIAlignGradient, RoIAlignGradientOp<float, CPUContext>);
|
||
|
|
||
|
// Input: X, rois, dY (aka "gradOutput");
|
||
|
// Output: dX (aka "gradInput")
|
||
|
OPERATOR_SCHEMA(RoIAlignGradient)
|
||
|
.NumInputs(3)
|
||
|
.NumOutputs(1)
|
||
|
.Input(0, "X", "See RoIPoolF.")
|
||
|
.Input(1, "RoIs", "See RoIPoolF.")
|
||
|
.Input(2, "dY", "Gradient of forward output 0 (Y)")
|
||
|
.Output(0, "dX", "Gradient of forward input 0 (X)");
|
||
|
|
||
|
namespace {
|
||
|
|
||
|
class GetRoIAlignGradient : public GradientMakerBase {
|
||
|
using GradientMakerBase::GradientMakerBase;
|
||
|
vector<OperatorDef> GetGradientDefs() override {
|
||
|
return SingleGradientDef(
|
||
|
"RoIAlignGradient",
|
||
|
"",
|
||
|
vector<string>{I(0), I(1), GO(0)},
|
||
|
vector<string>{GI(0)});
|
||
|
}
|
||
|
};
|
||
|
|
||
|
} // namespace
|
||
|
|
||
|
REGISTER_GRADIENT(RoIAlign, GetRoIAlignGradient);
|
||
|
|
||
|
template <typename T>
|
||
|
using RoIAlignGradientCPUOp = RoIAlignGradientOp<T, CPUContext>;
|
||
|
|
||
|
} // namespace caffe2
|
||
|
|
||
|
C10_EXPORT_CAFFE2_OP_TO_C10_CPU(
|
||
|
RoIAlignGradient,
|
||
|
"_caffe2::RoIAlignGradient("
|
||
|
" Tensor features,"
|
||
|
" Tensor rois,"
|
||
|
" Tensor grad,"
|
||
|
" str order,"
|
||
|
" float spatial_scale,"
|
||
|
" int pooled_h,"
|
||
|
" int pooled_w,"
|
||
|
" int sampling_ratio,"
|
||
|
" bool aligned"
|
||
|
") -> Tensor",
|
||
|
caffe2::RoIAlignGradientCPUOp<float>);
|