diff --git a/custom_extensions/roi_align/setup.py b/custom_extensions/roi_align/setup.py
deleted file mode 100644
index ebe50f4..0000000
--- a/custom_extensions/roi_align/setup.py
+++ /dev/null
@@ -1,28 +0,0 @@
-"""
-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}
- )
-
-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/src/RoIAlign_cuda.cu b/custom_extensions/roi_align/src/RoIAlign_cuda.cu
deleted file mode 100644
index 39426bf..0000000
--- a/custom_extensions/roi_align/src/RoIAlign_cuda.cu
+++ /dev/null
@@ -1,422 +0,0 @@
-/*
-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/src/RoIAlign_cuda_3d.cu b/custom_extensions/roi_align/src/RoIAlign_cuda_3d.cu
deleted file mode 100644
index 182274f..0000000
--- a/custom_extensions/roi_align/src/RoIAlign_cuda_3d.cu
+++ /dev/null
@@ -1,487 +0,0 @@
-/*
-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/src/RoIAlign_interface.cpp b/custom_extensions/roi_align/src/RoIAlign_interface.cpp
deleted file mode 100644
index 41d5fdf..0000000
--- a/custom_extensions/roi_align/src/RoIAlign_interface.cpp
+++ /dev/null
@@ -1,104 +0,0 @@
-/* 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/src/RoIAlign_interface_3d.cpp b/custom_extensions/roi_align/src/RoIAlign_interface_3d.cpp
deleted file mode 100644
index bb680fa..0000000
--- a/custom_extensions/roi_align/src/RoIAlign_interface_3d.cpp
+++ /dev/null
@@ -1,112 +0,0 @@
-/* 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/src/cuda_helpers.h b/custom_extensions/roi_align/src/cuda_helpers.h
deleted file mode 100644
index af32f60..0000000
--- a/custom_extensions/roi_align/src/cuda_helpers.h
+++ /dev/null
@@ -1,5 +0,0 @@
-#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/sandbox/setup.py b/custom_extensions/sandbox/setup.py
deleted file mode 100644
index 71ac616..0000000
--- a/custom_extensions/sandbox/setup.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""
-Created at 07.11.19 19:12
-@author: gregor
-
-"""
-
-
-from setuptools import setup
-from torch.utils import cpp_extension
-
-setup(name='sandbox_cuda',
- ext_modules=[cpp_extension.CUDAExtension('sandbox', ['src/sandbox.cpp', 'src/sandbox_cuda.cu'])],
- cmdclass={'build_ext': cpp_extension.BuildExtension})
\ No newline at end of file
diff --git a/custom_extensions/sandbox/src/sandbox.cpp b/custom_extensions/sandbox/src/sandbox.cpp
deleted file mode 100644
index e1fbe12..0000000
--- a/custom_extensions/sandbox/src/sandbox.cpp
+++ /dev/null
@@ -1,82 +0,0 @@
-// ------------------------------------------------------------------
-// Faster R-CNN
-// Copyright (c) 2015 Microsoft
-// Licensed under The MIT License [see fast-rcnn/LICENSE for details]
-// Written by Shaoqing Ren, rewritten in C++ by Gregor Ramien
-// ------------------------------------------------------------------
-//#include <THC/THC.h>
-//#include <TH/TH.h>
-
-#include <torch/extension.h>
-#include <iostream>
-
-#include "sandbox.h"
-
-//#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0)) // divide m by n, add +1 if there is a remainder
-#define getNBlocks(m,n) ( (m+n-1) / (n) ) // m = nr of total (required) threads, n = nr of threads per block.
-int const threadsPerBlock = sizeof(unsigned long long) * 8;
-
-//---- declarations that will be defined in cuda kernel
-void add_cuda(int n=1<<3);
-void nms_cuda(at::Tensor *boxes, at::Tensor *scores, float thresh);
-//-----------------------------------------------------------------
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-void sandbox() {
-
- //std::cout<< "number: "<< number << std::endl;
-
- torch::Tensor tensor = torch::tensor({0,1,2,3,4,5}, at::kLong).view({2,3});
- std::cout<< "tensor: " << tensor << std::endl;
- std::cout<< "tensor shape: " << at::size(tensor,0) << ", " << at::size(tensor,1) << std::endl;
- return;
-}
-
-void add(){
- //tutorial function: add two arrays (x,y) of length n
- add_cuda();
-}
-
-//void nms(at::Tensor boxes, at::Tensor scores, float thresh) {
-void nms() {
-
- // x1, y1, x2, y2
- at::Tensor boxes = torch::tensor({
- {20, 10, 60, 40},
- {10, 20, 40, 60},
- {20, 20, 80, 50}
- }, at::TensorOptions().dtype(at::kInt).device(at::kCUDA));
- std::cout << "boxes: \n" << boxes << std::endl;
- at::Tensor scores = torch::tensor({
- 0.5,
- 0.6,
- 0.7
- }, at::TensorOptions().dtype(at::kFloat).device(at::kCUDA));
- std::cout<< "scores: \n" << scores << std::endl;
-
- CHECK_INPUT(boxes); CHECK_INPUT(scores);
-
- int boxes_num = at::size(boxes,0);
- int boxes_dim = at::size(boxes,1);
-
- std::cout << "boxes shape: " << boxes_num << ", " << boxes_dim << std::endl;
-
- float * boxes_dev; unsigned long long * mask_dev; float thresh = 0.05;
-
- dim3 blocks((boxes_num, threadsPerBlock);
- int threads(threadsPerBlock);
-
-
-}
-
-
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("sandbox", &sandbox, "Sandy Box Function");
- m.def("nms", &nms, "NMS in cpp");
- m.def("add", &add, "tutorial add function");
-
-}
\ No newline at end of file
diff --git a/custom_extensions/sandbox/src/sandbox.h b/custom_extensions/sandbox/src/sandbox.h
deleted file mode 100644
index c67254f..0000000
--- a/custom_extensions/sandbox/src/sandbox.h
+++ /dev/null
@@ -1,3 +0,0 @@
-
-
-#define getNBlocks(m,n) ( (m+n-1) / (n) ) // m = nr of total (required) threads, n = nr of threads per block.
\ No newline at end of file
diff --git a/custom_extensions/sandbox/src/sandbox_cuda.cu b/custom_extensions/sandbox/src/sandbox_cuda.cu
deleted file mode 100644
index 8bb6764..0000000
--- a/custom_extensions/sandbox/src/sandbox_cuda.cu
+++ /dev/null
@@ -1,130 +0,0 @@
-// ------------------------------------------------------------------
-// Faster R-CNN
-// Copyright (c) 2015 Microsoft
-// Licensed under The MIT License [see fast-rcnn/LICENSE for details]
-// Written by Shaoqing Ren
-// ------------------------------------------------------------------
-
-#include <cuda.h>
-#include <cuda_runtime.h>
-
-#include <math.h>
-#include <stdio.h>
-#include <iostream>
-#include <float.h>
-
-#include "sandbox.h"
-//tutorial cuda function add
-
-__global__ void add_kernel(float *x, float *y, int n){
- printf("block %d: threadIdx.x %d, threadIdx.y %d, threadIdx.z %d.\n", blockIdx.x, threadIdx.x, threadIdx.y, threadIdx.z);
- int index = blockIdx.x * blockDim.x + threadIdx.x;
- int stride = blockDim.x * gridDim.x;
- for (int i=index; i<n; i+=stride)
- y[i] = x[i] + y[i];
-}
-
-void add_cuda(int n=1<<3){
- float *x, *y;
- std::cout << "n: " << n << std::endl;
- cudaMallocManaged(&x, n*sizeof(float));
- cudaMallocManaged(&y, n*sizeof(float));
-
- for (int i=0; i<n;i++){
- x[i] = 1.0f;
- y[i] = 2.0f;
- }
-
- int blockSize = 256;
- int numBlocks = getNBlocks(n, blockSize);
- std::cout << "numBlocks " << numBlocks << std::endl;
- add_kernel<<<numBlocks, blockSize>>>(x, y, n);
-
- cudaDeviceSynchronize();
-
- float maxError = 0.0f;
- for (int i = 0; i < n; i++)
- maxError = fmax(maxError, fabs(y[i]-3.0f));
- std::cout << "Max error: " << maxError << std::endl;
-
- cudaFree(x);
- cudaFree(y);
-
-}
-
-/*
-__device__ inline float devIoU(float const * const a, float const * const b) {
- float left = fmaxf(a[0], b[0]), right = fminf(a[2], b[2]);
- float top = fmaxf(a[1], b[1]), bottom = fminf(a[3], b[3]);
- float width = fmaxf(right - left + 1, 0.f), height = fmaxf(bottom - top + 1, 0.f);
- float interS = width * height;
- float Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1);
- float Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1);
- return interS / (Sa + Sb - interS);
-}
-
-__global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh,
- const float *dev_boxes, unsigned long long *dev_mask) {
- const int row_start = blockIdx.y;
- const int col_start = blockIdx.x;
-
- // if (row_start > col_start) return;
-
- const int row_size =
- fminf(n_boxes - row_start * threadsPerBlock, threadsPerBlock);
- const int col_size =
- fminf(n_boxes - col_start * threadsPerBlock, threadsPerBlock);
-
- __shared__ float block_boxes[threadsPerBlock * 5];
- if (threadIdx.x < col_size) {
- block_boxes[threadIdx.x * 5 + 0] =
- dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0];
- block_boxes[threadIdx.x * 5 + 1] =
- dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1];
- block_boxes[threadIdx.x * 5 + 2] =
- dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2];
- block_boxes[threadIdx.x * 5 + 3] =
- dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3];
- block_boxes[threadIdx.x * 5 + 4] =
- dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4];
- }
- __syncthreads();
-
- if (threadIdx.x < row_size) {
- const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x;
- const float *cur_box = dev_boxes + cur_box_idx * 5;
- int i = 0;
- unsigned long long t = 0;
- int start = 0;
- if (row_start == col_start) {
- start = threadIdx.x + 1;
- }
- for (i = start; i < col_size; i++) {
- if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) {
- t |= 1ULL << i;
- }
- }
- const int col_blocks = DIVUP(n_boxes, threadsPerBlock);
- dev_mask[cur_box_idx * col_blocks + col_start] = t;
- }
-}
-//
-*/
-
-__global__ void nms_kernel(){
-
-}
-
-void nms_cuda(){
-
-
-
-}
-
-
-int main(void){
-
- nms_cuda();
-
- return 0;
-}
\ No newline at end of file