diff --git a/custom_extensions/roi_align/2D/setup.py b/custom_extensions/roi_align/2D/setup.py
new file mode 100644
index 0000000..921913f
--- /dev/null
+++ b/custom_extensions/roi_align/2D/setup.py
@@ -0,0 +1,22 @@
+"""
+Created at 07.11.19 19:12
+@author: gregor
+
+"""
+
+import os, sys, site
+from pathlib import Path
+
+# recognise newly installed packages in path
+site.main()
+
+from setuptools import setup
+from torch.utils import cpp_extension
+
+dir_ = Path(os.path.dirname(sys.argv[0]))
+
+setup(name='RoIAlign extension 2D',
+ ext_modules=[cpp_extension.CUDAExtension('roi_al_extension', [str(dir_/'src/RoIAlign_interface.cpp'),
+ str(dir_/'src/RoIAlign_cuda.cu')])],
+ cmdclass={'build_ext': cpp_extension.BuildExtension}
+ )
diff --git a/custom_extensions/roi_align/2D/src/RoIAlign_cuda.cu b/custom_extensions/roi_align/2D/src/RoIAlign_cuda.cu
new file mode 100644
index 0000000..39426bf
--- /dev/null
+++ b/custom_extensions/roi_align/2D/src/RoIAlign_cuda.cu
@@ -0,0 +1,422 @@
+/*
+ROIAlign implementation in CUDA from pytorch framework
+(https://github.com/pytorch/vision/tree/master/torchvision/csrc/cuda on Nov 14 2019)
+
+*/
+
+#include <ATen/ATen.h>
+#include <ATen/TensorUtils.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+#include <ATen/cuda/CUDAApplyUtils.cuh>
+#include <typeinfo>
+#include "cuda_helpers.h"
+
+template <typename T>
+__device__ T bilinear_interpolate(
+ const T* input,
+ const int height,
+ const int width,
+ T y,
+ T x,
+ 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
+ return 0;
+ }
+
+ if (y <= 0)
+ y = 0;
+ if (x <= 0)
+ x = 0;
+
+ int y_low = (int)y;
+ int x_low = (int)x;
+ int y_high;
+ int x_high;
+
+ 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;
+
+ // do bilinear interpolation
+ T v1 = input[y_low * width + x_low];
+ T v2 = input[y_low * width + x_high];
+ T v3 = input[y_high * width + x_low];
+ T v4 = input[y_high * width + x_high];
+ T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
+
+ T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+
+ return val;
+}
+
+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 pooled_height,
+ const int pooled_width,
+ const int sampling_ratio,
+ const T* rois,
+ T* output) {
+ CUDA_1D_KERNEL_LOOP(index, nthreads) {
+ // (n, c, ph, pw) is an element in the pooled output
+ const int pw = index % pooled_width;
+ const int ph = (index / pooled_width) % pooled_height;
+ const int c = (index / pooled_width / pooled_height) % channels;
+ const int n = index / pooled_width / pooled_height / channels;
+
+ const T* offset_rois = rois + n * 5;
+ 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;
+
+ // 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 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);
+
+ const T* offset_input =
+ input + (roi_batch_ind * channels + c) * height * width;
+
+ // 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
+ 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 / 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 / static_cast<T>(roi_bin_grid_w);
+ T val = bilinear_interpolate(offset_input, height, width, y, x, index);
+ output_val += val;
+ }
+ }
+ output_val /= count;
+
+ output[index] = output_val;
+ }
+}
+
+template <typename T>
+__device__ 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 = input[y_low * width + x_low];
+ // T v2 = input[y_low * width + x_high];
+ // T v3 = input[y_high * width + x_low];
+ // T v4 = input[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 <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 pooled_height,
+ const int pooled_width,
+ 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)
+{
+ CUDA_1D_KERNEL_LOOP(index, nthreads) {
+ // (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_rois = rois + n * 5;
+ 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;
+
+ // 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 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_grad_input =
+ grad_input + ((roi_batch_ind * channels + c) * height * width);
+
+ // 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];
+
+ // 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++) // e.g., iy = 0, 1
+ {
+ const T y = roi_start_h + ph * bin_size_h +
+ 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 / static_cast<T>(roi_bin_grid_w);
+
+ T w1, w2, w3, w4;
+ 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 = grad_output_this_bin * w1 / count;
+ T g2 = grad_output_this_bin * w2 / count;
+ T g3 = grad_output_this_bin * w3 / count;
+ T g4 = grad_output_this_bin * w4 / count;
+
+ if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) {
+ atomicAdd(
+ offset_grad_input + y_low * width + x_low, static_cast<T>(g1));
+ atomicAdd(
+ offset_grad_input + y_low * width + x_high, static_cast<T>(g2));
+ atomicAdd(
+ offset_grad_input + y_high * width + x_low, static_cast<T>(g3));
+ atomicAdd(
+ offset_grad_input + y_high * width + x_high, static_cast<T>(g4));
+ } // if
+ } // ix
+ } // iy
+ } // CUDA_1D_KERNEL_LOOP
+} // RoIAlignBackward
+
+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 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());
+
+ int num_rois = rois.size(0);
+ int channels = input.size(1);
+ int height = input.size(2);
+ int width = input.size(3);
+
+ at::Tensor output = at::zeros(
+ {num_rois, channels, pooled_height, pooled_width}, input.options());
+
+ auto output_size = num_rois * pooled_height * pooled_width * channels;
+ 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", [&] {
+ RoIAlignForward<scalar_t><<<grid, block, 0, stream>>>(
+ output_size,
+ input.contiguous().data_ptr<scalar_t>(),
+ spatial_scale,
+ channels,
+ height,
+ width,
+ pooled_height,
+ pooled_width,
+ 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 batch_size,
+ const int channels,
+ const int height,
+ const int width,
+ 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}, 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);
+
+ AT_DISPATCH_FLOATING_TYPES_AND_HALF(grad.type(), "ROIAlign_backward", [&] {
+ RoIAlignBackward<scalar_t><<<grid, block, 0, stream>>>(
+ grad.numel(),
+ grad.data_ptr<scalar_t>(),
+ spatial_scale,
+ channels,
+ height,
+ width,
+ pooled_height,
+ pooled_width,
+ sampling_ratio,
+ grad_input.data_ptr<scalar_t>(),
+ rois.contiguous().data_ptr<scalar_t>(),
+ n_stride,
+ c_stride,
+ h_stride,
+ w_stride);
+ });
+ AT_CUDA_CHECK(cudaGetLastError());
+ return grad_input;
+}
\ No newline at end of file
diff --git a/custom_extensions/roi_align/2D/src/RoIAlign_interface.cpp b/custom_extensions/roi_align/2D/src/RoIAlign_interface.cpp
new file mode 100644
index 0000000..41d5fdf
--- /dev/null
+++ b/custom_extensions/roi_align/2D/src/RoIAlign_interface.cpp
@@ -0,0 +1,104 @@
+/* adopted from pytorch framework
+ https://github.com/pytorch/vision/blob/master/torchvision/csrc/ROIAlign.h on Nov 15 2019.
+
+ does not include CPU support but could be added with this interface.
+*/
+
+#include <torch/extension.h>
+
+// Declarations that are initialized in cuda file
+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 sampling_ratio);
+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 batch_size, const int channels,
+ const int height, const int width, const int sampling_ratio);
+
+// Interface for Python
+at::Tensor ROIAlign_forward(
+ const at::Tensor& input, // Input feature map.
+ const at::Tensor& rois, // List of ROIs to pool over.
+ const double spatial_scale, // The scale of the image features. ROIs will be scaled to this.
+ const int64_t pooled_height, // The height of the pooled feature map.
+ const int64_t pooled_width, // The width of the pooled feature
+ const int64_t sampling_ratio) // The number of points to sample in each bin along each axis.
+{
+ if (input.type().is_cuda()) {
+ return ROIAlign_forward_cuda(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio);
+ }
+ AT_ERROR("Not compiled with CPU support");
+ //return ROIAlign_forward_cpu(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio);
+}
+
+at::Tensor ROIAlign_backward(const at::Tensor& grad, const at::Tensor& rois, const float spatial_scale,
+ const int pooled_height, const int pooled_width, const int batch_size, const int channels,
+ const int height, const int width, const int sampling_ratio) {
+ if (grad.type().is_cuda()) {
+ return ROIAlign_backward_cuda( grad, rois, spatial_scale, pooled_height, pooled_width, batch_size, channels,
+ height, width, sampling_ratio);
+ }
+ AT_ERROR("Not compiled with CPU support");
+ //return ROIAlign_backward_cpu(grad, rois, spatial_scale, pooled_height, pooled_width, batch_size, channels,
+ // height, width, sampling_ratio);
+}
+
+using namespace at;
+using torch::Tensor;
+using torch::autograd::AutogradContext;
+using torch::autograd::Variable;
+using torch::autograd::variable_list;
+
+class ROIAlignFunction : public torch::autograd::Function<ROIAlignFunction> {
+ public:
+ static variable_list forward(
+ AutogradContext* ctx,
+ Variable input,
+ Variable rois,
+ const double spatial_scale,
+ const int64_t pooled_height,
+ const int64_t pooled_width,
+ const int64_t sampling_ratio) {
+ ctx->saved_data["spatial_scale"] = spatial_scale;
+ ctx->saved_data["pooled_height"] = pooled_height;
+ ctx->saved_data["pooled_width"] = pooled_width;
+ ctx->saved_data["sampling_ratio"] = sampling_ratio;
+ ctx->saved_data["input_shape"] = input.sizes();
+ ctx->save_for_backward({rois});
+ auto result = ROIAlign_forward(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio);
+ return {result};
+ }
+
+ static variable_list backward(
+ AutogradContext* ctx,
+ variable_list grad_output) {
+ // Use data saved in forward
+ auto saved = ctx->get_saved_variables();
+ auto rois = saved[0];
+ auto input_shape = ctx->saved_data["input_shape"].toIntList();
+ auto grad_in = ROIAlign_backward(
+ grad_output[0],
+ rois,
+ ctx->saved_data["spatial_scale"].toDouble(),
+ ctx->saved_data["pooled_height"].toInt(),
+ ctx->saved_data["pooled_width"].toInt(),
+ input_shape[0], //b
+ input_shape[1], //c
+ input_shape[2], //h
+ input_shape[3], //w
+ ctx->saved_data["sampling_ratio"].toInt());
+ return {
+ grad_in, Variable(), Variable(), Variable(), Variable(), Variable()};
+ }
+};
+
+Tensor roi_align(const Tensor& input, const Tensor& rois, const double spatial_scale, const int64_t pooled_height,
+ const int64_t pooled_width, const int64_t sampling_ratio) {
+
+ return ROIAlignFunction::apply(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio)[0];
+
+}
+
+
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+ m.def("roi_align", &roi_align, "ROIAlign 2D in c++ and/or cuda");
+}
\ No newline at end of file
diff --git a/custom_extensions/roi_align/2D/src/cuda_helpers.h b/custom_extensions/roi_align/2D/src/cuda_helpers.h
new file mode 100644
index 0000000..af32f60
--- /dev/null
+++ b/custom_extensions/roi_align/2D/src/cuda_helpers.h
@@ -0,0 +1,5 @@
+#pragma once
+
+#define CUDA_1D_KERNEL_LOOP(i, n) \
+ for (int i = (blockIdx.x * blockDim.x) + threadIdx.x; i < (n); \
+ i += (blockDim.x * gridDim.x))
diff --git a/custom_extensions/roi_align/3D/setup.py b/custom_extensions/roi_align/3D/setup.py
new file mode 100644
index 0000000..f2d164b
--- /dev/null
+++ b/custom_extensions/roi_align/3D/setup.py
@@ -0,0 +1,22 @@
+"""
+Created at 07.11.19 19:12
+@author: gregor
+
+"""
+
+import os, sys, site
+from pathlib import Path
+
+# recognise newly installed packages in path
+site.main()
+
+from setuptools import setup
+from torch.utils import cpp_extension
+
+dir_ = Path(os.path.dirname(sys.argv[0]))
+
+setup(name='RoIAlign extension 3D',
+ ext_modules=[cpp_extension.CUDAExtension('roi_al_extension_3d', [str(dir_/'src/RoIAlign_interface_3d.cpp'),
+ str(dir_/'src/RoIAlign_cuda_3d.cu')])],
+ cmdclass={'build_ext': cpp_extension.BuildExtension}
+ )
\ No newline at end of file
diff --git a/custom_extensions/roi_align/3D/src/RoIAlign_cuda_3d.cu b/custom_extensions/roi_align/3D/src/RoIAlign_cuda_3d.cu
new file mode 100644
index 0000000..182274f
--- /dev/null
+++ b/custom_extensions/roi_align/3D/src/RoIAlign_cuda_3d.cu
@@ -0,0 +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 / 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 / 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 / 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 / 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 / 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 / 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;
+}
\ No newline at end of file
diff --git a/custom_extensions/roi_align/3D/src/RoIAlign_interface_3d.cpp b/custom_extensions/roi_align/3D/src/RoIAlign_interface_3d.cpp
new file mode 100644
index 0000000..bb680fa
--- /dev/null
+++ b/custom_extensions/roi_align/3D/src/RoIAlign_interface_3d.cpp
@@ -0,0 +1,112 @@
+/* adopted from pytorch framework
+ https://github.com/pytorch/vision/blob/master/torchvision/csrc/ROIAlign.h on Nov 15 2019.
+
+ does not include CPU support but could be added with this interface.
+*/
+
+#include <torch/extension.h>
+
+/*---------------- 3D implementation ---------------------------*/
+
+// Declarations that are initialized in cuda file
+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);
+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);
+
+// Interface for Python
+at::Tensor ROIAlign_forward(
+ const at::Tensor& input, // Input feature map.
+ const at::Tensor& rois, // List of ROIs to pool over.
+ const double spatial_scale, // The scale of the image features. ROIs will be scaled to this.
+ const int64_t pooled_height, // The height of the pooled feature map.
+ const int64_t pooled_width, // The width of the pooled feature
+ const int64_t pooled_depth,
+ const int64_t sampling_ratio) // The number of points to sample in each bin along each axis.
+{
+ if (input.type().is_cuda()) {
+ return ROIAlign_forward_cuda(input, rois, spatial_scale, pooled_height, pooled_width, pooled_depth, sampling_ratio);
+ }
+ AT_ERROR("Not compiled with CPU support");
+ //return ROIAlign_forward_cpu(input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio);
+}
+
+at::Tensor ROIAlign_backward(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) {
+ if (grad.type().is_cuda()) {
+ return ROIAlign_backward_cuda( grad, rois, spatial_scale, pooled_height, pooled_width, pooled_depth, batch_size,
+ channels, height, width, depth, sampling_ratio);
+ }
+ AT_ERROR("Not compiled with CPU support");
+ //return ROIAlign_backward_cpu(grad, rois, spatial_scale, pooled_height, pooled_width, batch_size, channels,
+ // height, width, sampling_ratio);
+}
+
+using namespace at;
+using torch::Tensor;
+using torch::autograd::AutogradContext;
+using torch::autograd::Variable;
+using torch::autograd::variable_list;
+
+class ROIAlignFunction : public torch::autograd::Function<ROIAlignFunction> {
+ public:
+ static variable_list forward(
+ AutogradContext* ctx,
+ Variable input,
+ Variable rois,
+ const double spatial_scale,
+ const int64_t pooled_height,
+ const int64_t pooled_width,
+ const int64_t pooled_depth,
+ const int64_t sampling_ratio) {
+ ctx->saved_data["spatial_scale"] = spatial_scale;
+ ctx->saved_data["pooled_height"] = pooled_height;
+ ctx->saved_data["pooled_width"] = pooled_width;
+ ctx->saved_data["pooled_depth"] = pooled_depth;
+ ctx->saved_data["sampling_ratio"] = sampling_ratio;
+ ctx->saved_data["input_shape"] = input.sizes();
+ ctx->save_for_backward({rois});
+ auto result = ROIAlign_forward(input, rois, spatial_scale, pooled_height, pooled_width, pooled_depth,
+ sampling_ratio);
+ return {result};
+ }
+
+ static variable_list backward(
+ AutogradContext* ctx,
+ variable_list grad_output) {
+ // Use data saved in forward
+ auto saved = ctx->get_saved_variables();
+ auto rois = saved[0];
+ auto input_shape = ctx->saved_data["input_shape"].toIntList();
+ auto grad_in = ROIAlign_backward(
+ grad_output[0],
+ rois,
+ ctx->saved_data["spatial_scale"].toDouble(),
+ ctx->saved_data["pooled_height"].toInt(),
+ ctx->saved_data["pooled_width"].toInt(),
+ ctx->saved_data["pooled_depth"].toInt(),
+ input_shape[0],
+ input_shape[1],
+ input_shape[2],
+ input_shape[3],
+ input_shape[4],
+ ctx->saved_data["sampling_ratio"].toInt());
+ return {
+ grad_in, Variable(), Variable(), Variable(), Variable(), Variable(), Variable()};
+ }
+};
+
+Tensor roi_align(const Tensor& input, const Tensor& rois, const double spatial_scale, const int64_t pooled_height,
+ const int64_t pooled_width, const int64_t pooled_depth, const int64_t sampling_ratio) {
+
+ return ROIAlignFunction::apply(input, rois, spatial_scale, pooled_height, pooled_width, pooled_depth,
+ sampling_ratio)[0];
+
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+ m.def("roi_align", &roi_align, "ROIAlign 3D in c++ and/or cuda");
+}
\ No newline at end of file
diff --git a/custom_extensions/roi_align/3D/src/cuda_helpers.h b/custom_extensions/roi_align/3D/src/cuda_helpers.h
new file mode 100644
index 0000000..af32f60
--- /dev/null
+++ b/custom_extensions/roi_align/3D/src/cuda_helpers.h
@@ -0,0 +1,5 @@
+#pragma once
+
+#define CUDA_1D_KERNEL_LOOP(i, n) \
+ for (int i = (blockIdx.x * blockDim.x) + threadIdx.x; i < (n); \
+ i += (blockDim.x * gridDim.x))