diff --git a/custom_extensions/roi_align/src/RoIAlign_cuda_3d.cu b/custom_extensions/roi_align/src/RoIAlign_cuda_3d.cu
index 26cde29..182274f 100644
--- a/custom_extensions/roi_align/src/RoIAlign_cuda_3d.cu
+++ b/custom_extensions/roi_align/src/RoIAlign_cuda_3d.cu
@@ -1,487 +1,487 @@
/*
ROIAlign implementation in CUDA from pytorch framework
(https://github.com/pytorch/vision/tree/master/torchvision/csrc/cuda on Nov 14 2019)
Adapted for additional 3D capability by G. Ramien, DKFZ Heidelberg
*/
#include <ATen/ATen.h>
#include <ATen/TensorUtils.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <ATen/cuda/CUDAApplyUtils.cuh>
#include <cstdio>
#include "cuda_helpers.h"
/*-------------- gpu kernels -----------------*/
template <typename T>
__device__ T linear_interpolate(const T xl, const T val_low, const T val_high){
T val = (val_high - val_low) * xl + val_low;
return val;
}
template <typename T>
__device__ T trilinear_interpolate(const T* input, const int height, const int width, const int depth,
T y, T x, T z, 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 || z < -1.0 || z > depth) {
// empty
return 0;
}
if (y <= 0)
y = 0;
if (x <= 0)
x = 0;
if (z <= 0)
z = 0;
int y0 = (int)y;
int x0 = (int)x;
int z0 = (int)z;
int y1;
int x1;
int z1;
if (y0 >= height - 1) {
/*if nearest gridpoint to y on the lower end is on border or border-1, set low, high, mid(=actual point) to border-1*/
y1 = y0 = height - 1;
y = (T)y0;
} else {
/* y1 is one pixel from y0, y is the actual point somewhere in between */
y1 = y0 + 1;
}
if (x0 >= width - 1) {
x1 = x0 = width - 1;
x = (T)x0;
} else {
x1 = x0 + 1;
}
if (z0 >= depth - 1) {
z1 = z0 = depth - 1;
z = (T)z0;
} else {
z1 = z0 + 1;
}
// do linear interpolation of x values
// distance of actual point to lower boundary point, already normalized since x_high - x0 = 1
T dis = x - x0;
/* accessing element b,c,y,x,z in 1D-rolled-out array of a tensor with dimensions (B, C, Y, X, Z):
tensor[b,c,y,x,z] = arr[ (((b*C+c)*Y+y)*X + x)*Z + z ] = arr[ alpha + (y*X + x)*Z + z ]
with alpha = batch&channel locator = (b*C+c)*YXZ.
hence, as current input pointer is already offset by alpha: y,x,z is at input[( y*X + x)*Z + z], where
X = width, Z = depth.
*/
T x00 = linear_interpolate(dis, input[(y0*width+ x0)*depth+z0], input[(y0*width+ x1)*depth+z0]);
T x10 = linear_interpolate(dis, input[(y1*width+ x0)*depth+z0], input[(y1*width+ x1)*depth+z0]);
T x01 = linear_interpolate(dis, input[(y0*width+ x0)*depth+z1], input[(y0*width+ x1)*depth+z1]);
T x11 = linear_interpolate(dis, input[(y1*width+ x0)*depth+z1], input[(y1*width+ x1)*depth+z1]);
// linear interpol of y values = bilinear interpol of f(x,y)
dis = y - y0;
T xy0 = linear_interpolate(dis, x00, x10);
T xy1 = linear_interpolate(dis, x01, x11);
// linear interpol of z value = trilinear interpol of f(x,y,z)
dis = z - z0;
T xyz = linear_interpolate(dis, xy0, xy1);
return xyz;
}
template <typename T>
__device__ void trilinear_interpolate_gradient(const int height, const int width, const int depth, T y, T x, T z,
T& g000, T& g001, T& g010, T& g100, T& g011, T& g101, T& g110, T& g111,
int& x0, int& x1, int& y0, int& y1, int& z0, int&z1, 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 || z < -1.0 || z > depth) {
// empty
g000 = g001 = g010 = g100 = g011 = g101 = g110 = g111 = 0.;
x0 = x1 = y0 = y1 = z0 = z1 = -1;
return;
}
if (y <= 0)
y = 0;
if (x <= 0)
x = 0;
if (z <= 0)
z = 0;
y0 = (int)y;
x0 = (int)x;
z0 = (int)z;
if (y0 >= height - 1) {
y1 = y0 = height - 1;
y = (T)y0;
} else {
y1 = y0 + 1;
}
if (x0 >= width - 1) {
x1 = x0 = width - 1;
x = (T)x0;
} else {
x1 = x0 + 1;
}
if (z0 >= depth - 1) {
z1 = z0 = depth - 1;
z = (T)z0;
} else {
z1 = z0 + 1;
}
// forward calculations are added as hints
T dis_x = x - x0;
//T x00 = linear_interpolate(dis, input[(y0*width+ x0)*depth+z0], input[(y0*width+ x1)*depth+z0]); // v000, v100
//T x10 = linear_interpolate(dis, input[(y1*width+ x0)*depth+z0], input[(y1*width+ x1)*depth+z0]); // v010, v110
//T x01 = linear_interpolate(dis, input[(y0*width+ x0)*depth+z1], input[(y0*width+ x1)*depth+z1]); // v001, v101
//T x11 = linear_interpolate(dis, input[(y1*width+ x0)*depth+z1], input[(y1*width+ x1)*depth+z1]); // v011, v111
// linear interpol of y values = bilinear interpol of f(x,y)
T dis_y = y - y0;
//T xy0 = linear_interpolate(dis, x00, x10);
//T xy1 = linear_interpolate(dis, x01, x11);
// linear interpol of z value = trilinear interpol of f(x,y,z)
T dis_z = z - z0;
//T xyz = linear_interpolate(dis, xy0, xy1);
/* need: grad_i := d(xyz)/d(v_i) with v_i = input_value_i for all i = 0,..,7 (eight input values --> eight-entry gradient)
d(lin_interp(dis,x,y))/dx = (-dis +1) and d(lin_interp(dis,x,y))/dy = dis --> derivatives are indep of x,y.
notation: gxyz = gradient for d(trilin_interp)/d(input_value_at_xyz)
below grads were calculated by hand
save time by reusing (1-dis_x) = 1-x+x0 = x1-x =: dis_x1 */
T dis_x1 = (1-dis_x), dis_y1 = (1-dis_y), dis_z1 = (1-dis_z);
g000 = dis_z1 * dis_y1 * dis_x1;
g001 = dis_z * dis_y1 * dis_x1;
g010 = dis_z1 * dis_y * dis_x1;
g100 = dis_z1 * dis_y1 * dis_x;
g011 = dis_z * dis_y * dis_x1;
g101 = dis_z * dis_y1 * dis_x;
g110 = dis_z1 * dis_y * dis_x;
g111 = dis_z * dis_y * dis_x;
return;
}
template <typename T>
__global__ void RoIAlignForward(const int nthreads, const T* input, const T spatial_scale, const int channels,
const int height, const int width, const int depth, const int pooled_height, const int pooled_width,
const int pooled_depth, const int sampling_ratio, const T* rois, T* output)
{
CUDA_1D_KERNEL_LOOP(index, nthreads) {
// (n, c, ph, pw, pd) is an element in the pooled output
int pd = index % pooled_depth;
int pw = (index / pooled_depth) % pooled_width;
int ph = (index / pooled_depth / pooled_width) % pooled_height;
int c = (index / pooled_depth / pooled_width / pooled_height) % channels;
int n = index / pooled_depth / pooled_width / pooled_height / channels;
// rois are (y1,x1,y2,x2,z1,z2) --> tensor of shape (n_rois, 6)
const T* offset_rois = rois + n * 7;
int roi_batch_ind = offset_rois[0];
// Do not use rounding; this implementation detail is critical
T roi_start_h = offset_rois[1] * spatial_scale;
T roi_start_w = offset_rois[2] * spatial_scale;
T roi_end_h = offset_rois[3] * spatial_scale;
T roi_end_w = offset_rois[4] * spatial_scale;
T roi_start_d = offset_rois[5] * spatial_scale;
T roi_end_d = offset_rois[6] * spatial_scale;
// Force malformed ROIs to be 1x1
T roi_height = max(roi_end_h - roi_start_h, (T)1.);
T roi_width = max(roi_end_w - roi_start_w, (T)1.);
T roi_depth = max(roi_end_d - roi_start_d, (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 bin_size_d = static_cast<T>(roi_depth) / static_cast<T>(pooled_depth);
const T* offset_input =
input + (roi_batch_ind * channels + c) * height * width * depth;
// We use roi_bin_grid to sample the grid and mimic integral
// roi_bin_grid == nr of sampling points per bin >= 1
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);
int roi_bin_grid_d =
(sampling_ratio > 0) ? sampling_ratio : ceil(roi_depth / pooled_depth);
// We do average (integral) pooling inside a bin
const T n_voxels = roi_bin_grid_h * roi_bin_grid_w * roi_bin_grid_d; // e.g. = 4
T output_val = 0.;
for (int iy = 0; iy < roi_bin_grid_h; iy++) // e.g., iy = 0, 1
{
const T y = roi_start_h + ph * bin_size_h +
- static_cast<T>(iy + .5f) * (bin_size_h - 1.f) / static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5, always in the middle of two grid pointsk
+ static_cast<T>(iy + .5f) * bin_size_h / static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5, always in the middle of two grid pointsk
for (int ix = 0; ix < roi_bin_grid_w; ix++)
{
const T x = roi_start_w + pw * bin_size_w +
- static_cast<T>(ix + .5f) * (bin_size_w - 1.f) / static_cast<T>(roi_bin_grid_w);
+ static_cast<T>(ix + .5f) * bin_size_w / static_cast<T>(roi_bin_grid_w);
for (int iz = 0; iz < roi_bin_grid_d; iz++)
{
const T z = roi_start_d + pd * bin_size_d +
- static_cast<T>(iz + .5f) * (bin_size_d - 1.f) / static_cast<T>(roi_bin_grid_d);
+ static_cast<T>(iz + .5f) * bin_size_d / static_cast<T>(roi_bin_grid_d);
// TODO verify trilinear interpolation
T val = trilinear_interpolate(offset_input, height, width, depth, y, x, z, index);
output_val += val;
} // z iterator and calc+add value
} // x iterator
} // y iterator
output_val /= n_voxels;
output[index] = output_val;
}
}
template <typename T>
__global__ void RoIAlignBackward(const int nthreads, const T* grad_output, const T spatial_scale, const int channels,
const int height, const int width, const int depth, const int pooled_height, const int pooled_width,
const int pooled_depth, const int sampling_ratio, T* grad_input, const T* rois,
const int n_stride, const int c_stride, const int h_stride, const int w_stride, const int d_stride)
{
CUDA_1D_KERNEL_LOOP(index, nthreads) {
// (n, c, ph, pw, pd) is an element in the pooled output
int pd = index % pooled_depth;
int pw = (index / pooled_depth) % pooled_width;
int ph = (index / pooled_depth / pooled_width) % pooled_height;
int c = (index / pooled_depth / pooled_width / pooled_height) % channels;
int n = index / pooled_depth / pooled_width / pooled_height / channels;
const T* offset_rois = rois + n * 7;
int roi_batch_ind = offset_rois[0];
// Do not using rounding; this implementation detail is critical
T roi_start_h = offset_rois[1] * spatial_scale;
T roi_start_w = offset_rois[2] * spatial_scale;
T roi_end_h = offset_rois[3] * spatial_scale;
T roi_end_w = offset_rois[4] * spatial_scale;
T roi_start_d = offset_rois[5] * spatial_scale;
T roi_end_d = offset_rois[6] * spatial_scale;
// Force malformed ROIs to be 1x1
T roi_width = max(roi_end_w - roi_start_w, (T)1.);
T roi_height = max(roi_end_h - roi_start_h, (T)1.);
T roi_depth = max(roi_end_d - roi_start_d, (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 bin_size_d = static_cast<T>(roi_depth) / static_cast<T>(pooled_depth);
// offset: index b,c,y,x,z of tensor of shape (B,C,Y,X,Z) is
// b*C*Y*X*Z + c * Y*X*Z + y * X*Z + x *Z + z = (b*C+c)Y*X*Z + ...
T* offset_grad_input =
grad_input + ((roi_batch_ind * channels + c) * height * width * depth);
// We need to index the gradient using the tensor strides to access the correct values.
int output_offset = n * n_stride + c * c_stride;
const T* offset_grad_output = grad_output + output_offset;
const T grad_output_this_bin = offset_grad_output[ph * h_stride + pw * w_stride + pd * d_stride];
// 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);
int roi_bin_grid_d = (sampling_ratio > 0) ? sampling_ratio : ceil(roi_depth / pooled_depth);
// We do average (integral) pooling inside a bin
const T n_voxels = roi_bin_grid_h * roi_bin_grid_w * roi_bin_grid_d; // e.g. = 6
for (int iy = 0; iy < roi_bin_grid_h; iy++) // e.g., iy = 0, 1
{
const T y = roi_start_h + ph * bin_size_h +
- static_cast<T>(iy + .5f) * (bin_size_h - 1.f) / static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5
+ 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 +
- static_cast<T>(ix + .5f) * (bin_size_w - 1.f) / static_cast<T>(roi_bin_grid_w);
+ static_cast<T>(ix + .5f) * bin_size_w / static_cast<T>(roi_bin_grid_w);
for (int iz = 0; iz < roi_bin_grid_d; iz++)
{
const T z = roi_start_d + pd * bin_size_d +
- static_cast<T>(iz + .5f) * (bin_size_d - 1.f) / static_cast<T>(roi_bin_grid_d);
+ static_cast<T>(iz + .5f) * bin_size_d / static_cast<T>(roi_bin_grid_d);
T g000, g001, g010, g100, g011, g101, g110, g111; // will hold the current partial derivatives
int x0, x1, y0, y1, z0, z1;
/* notation: gxyz = gradient at xyz, where x,y,z need to lie on feature-map grid (i.e., =x0,x1 etc.) */
trilinear_interpolate_gradient(height, width, depth, y, x, z,
g000, g001, g010, g100, g011, g101, g110, g111,
x0, x1, y0, y1, z0, z1, index);
/* chain rule: derivatives (i.e., the gradient) of trilin_interpolate(v1,v2,v3,v4,...) (div by n_voxels
as we actually need gradient of whole roi_align) are multiplied with gradient so far*/
g000 *= grad_output_this_bin / n_voxels;
g001 *= grad_output_this_bin / n_voxels;
g010 *= grad_output_this_bin / n_voxels;
g100 *= grad_output_this_bin / n_voxels;
g011 *= grad_output_this_bin / n_voxels;
g101 *= grad_output_this_bin / n_voxels;
g110 *= grad_output_this_bin / n_voxels;
g111 *= grad_output_this_bin / n_voxels;
if (x0 >= 0 && x1 >= 0 && y0 >= 0 && y1 >= 0 && z0 >= 0 && z1 >= 0)
{ // atomicAdd(address, content) reads content under address, adds content to it, while: no other thread
// can interfere with the memory at address during this operation (thread lock, therefore "atomic").
atomicAdd(offset_grad_input + (y0 * width + x0) * depth + z0, static_cast<T>(g000));
atomicAdd(offset_grad_input + (y0 * width + x0) * depth + z1, static_cast<T>(g001));
atomicAdd(offset_grad_input + (y1 * width + x0) * depth + z0, static_cast<T>(g010));
atomicAdd(offset_grad_input + (y0 * width + x1) * depth + z0, static_cast<T>(g100));
atomicAdd(offset_grad_input + (y1 * width + x0) * depth + z1, static_cast<T>(g011));
atomicAdd(offset_grad_input + (y0 * width + x1) * depth + z1, static_cast<T>(g101));
atomicAdd(offset_grad_input + (y1 * width + x1) * depth + z0, static_cast<T>(g110));
atomicAdd(offset_grad_input + (y1 * width + x1) * depth + z1, static_cast<T>(g111));
} // if
} // iz
} // ix
} // iy
} // CUDA_1D_KERNEL_LOOP
} // RoIAlignBackward
/*----------- wrapper functions ----------------*/
at::Tensor ROIAlign_forward_cuda(const at::Tensor& input, const at::Tensor& rois, const float spatial_scale,
const int pooled_height, const int pooled_width, const int pooled_depth, const int sampling_ratio) {
/*
input: feature-map tensor, shape (batch, n_channels, y, x(, z))
*/
AT_ASSERTM(input.device().is_cuda(), "input must be a CUDA tensor");
AT_ASSERTM(rois.device().is_cuda(), "rois must be a CUDA tensor");
at::TensorArg input_t{input, "input", 1}, rois_t{rois, "rois", 2};
at::CheckedFrom c = "ROIAlign_forward_cuda";
at::checkAllSameGPU(c, {input_t, rois_t});
at::checkAllSameType(c, {input_t, rois_t});
at::cuda::CUDAGuard device_guard(input.device());
auto num_rois = rois.size(0);
auto channels = input.size(1);
auto height = input.size(2);
auto width = input.size(3);
auto depth = input.size(4);
at::Tensor output = at::zeros(
{num_rois, channels, pooled_height, pooled_width, pooled_depth}, input.options());
auto output_size = num_rois * channels * pooled_height * pooled_width * pooled_depth;
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
dim3 grid(std::min(
at::cuda::ATenCeilDiv(static_cast<int64_t>(output_size), static_cast<int64_t>(512)), static_cast<int64_t>(4096)));
dim3 block(512);
if (output.numel() == 0) {
AT_CUDA_CHECK(cudaGetLastError());
return output;
}
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.type(), "ROIAlign forward in 3d", [&] {
RoIAlignForward<scalar_t><<<grid, block, 0, stream>>>(
output_size,
input.contiguous().data_ptr<scalar_t>(),
spatial_scale,
channels,
height,
width,
depth,
pooled_height,
pooled_width,
pooled_depth,
sampling_ratio,
rois.contiguous().data_ptr<scalar_t>(),
output.data_ptr<scalar_t>());
});
AT_CUDA_CHECK(cudaGetLastError());
return output;
}
at::Tensor ROIAlign_backward_cuda(
const at::Tensor& grad,
const at::Tensor& rois,
const float spatial_scale,
const int pooled_height,
const int pooled_width,
const int pooled_depth,
const int batch_size,
const int channels,
const int height,
const int width,
const int depth,
const int sampling_ratio)
{
AT_ASSERTM(grad.device().is_cuda(), "grad must be a CUDA tensor");
AT_ASSERTM(rois.device().is_cuda(), "rois must be a CUDA tensor");
at::TensorArg grad_t{grad, "grad", 1}, rois_t{rois, "rois", 2};
at::CheckedFrom c = "ROIAlign_backward_cuda";
at::checkAllSameGPU(c, {grad_t, rois_t});
at::checkAllSameType(c, {grad_t, rois_t});
at::cuda::CUDAGuard device_guard(grad.device());
at::Tensor grad_input =
at::zeros({batch_size, channels, height, width, depth}, grad.options());
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
dim3 grid(std::min(
at::cuda::ATenCeilDiv(
static_cast<int64_t>(grad.numel()), static_cast<int64_t>(512)),
static_cast<int64_t>(4096)));
dim3 block(512);
// handle possibly empty gradients
if (grad.numel() == 0) {
AT_CUDA_CHECK(cudaGetLastError());
return grad_input;
}
int n_stride = grad.stride(0);
int c_stride = grad.stride(1);
int h_stride = grad.stride(2);
int w_stride = grad.stride(3);
int d_stride = grad.stride(4);
AT_DISPATCH_FLOATING_TYPES_AND_HALF(grad.type(), "ROIAlign backward 3D", [&] {
RoIAlignBackward<scalar_t><<<grid, block, 0, stream>>>(
grad.numel(),
grad.data_ptr<scalar_t>(),
spatial_scale,
channels,
height,
width,
depth,
pooled_height,
pooled_width,
pooled_depth,
sampling_ratio,
grad_input.data_ptr<scalar_t>(),
rois.contiguous().data_ptr<scalar_t>(),
n_stride,
c_stride,
h_stride,
w_stride,
d_stride);
});
AT_CUDA_CHECK(cudaGetLastError());
return grad_input;
}
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