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Move coarse rasterization to new file

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Summary: In preparation for sharing coarse rasterization between point clouds and meshes, move the functions to a new file. No code changes.

Reviewed By: bottler

Differential Revision: D30367812

fbshipit-source-id: 9e73835a26c4ac91f5c9f61ff682bc8218e36c6a
This commit is contained in:
Justin Johnson
2021-09-08 16:15:36 -07:00
committed by Facebook GitHub Bot
parent f2c44e3540
commit 62dbf371ae
8 changed files with 534 additions and 480 deletions
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79
pytorch3d/csrc/rasterize_points/bitmask.cuh
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@@ -1,79 +0,0 @@
/*
* Copyright (c) Facebook, Inc. and its affiliates.
* All rights reserved.
*
* This source code is licensed under the BSD-style license found in the
* LICENSE file in the root directory of this source tree.
*/
#pragma once
#define BINMASK_H
// A BitMask represents a bool array of shape (H, W, N). We pack values into
// the bits of unsigned ints; a single unsigned int has B = 32 bits, so to hold
// all values we use H * W * (N / B) = H * W * D values. We want to store
// BitMasks in shared memory, so we assume that the memory has already been
// allocated for it elsewhere.
class BitMask {
public:
__device__ BitMask(unsigned int* data, int H, int W, int N)
: data(data), H(H), W(W), B(8 * sizeof(unsigned int)), D(N / B) {
// TODO: check if the data is null.
N = ceilf(N % 32); // take ceil incase N % 32 != 0
block_clear(); // clear the data
}
// Use all threads in the current block to clear all bits of this BitMask
__device__ void block_clear() {
for (int i = threadIdx.x; i < H * W * D; i += blockDim.x) {
data[i] = 0;
}
__syncthreads();
}
__device__ int _get_elem_idx(int y, int x, int d) {
return y * W * D + x * D + d / B;
}
__device__ int _get_bit_idx(int d) {
return d % B;
}
// Turn on a single bit (y, x, d)
__device__ void set(int y, int x, int d) {
int elem_idx = _get_elem_idx(y, x, d);
int bit_idx = _get_bit_idx(d);
const unsigned int mask = 1U << bit_idx;
atomicOr(data + elem_idx, mask);
}
// Turn off a single bit (y, x, d)
__device__ void unset(int y, int x, int d) {
int elem_idx = _get_elem_idx(y, x, d);
int bit_idx = _get_bit_idx(d);
const unsigned int mask = ~(1U << bit_idx);
atomicAnd(data + elem_idx, mask);
}
// Check whether the bit (y, x, d) is on or off
__device__ bool get(int y, int x, int d) {
int elem_idx = _get_elem_idx(y, x, d);
int bit_idx = _get_bit_idx(d);
return (data[elem_idx] >> bit_idx) & 1U;
}
// Compute the number of bits set in the row (y, x, :)
__device__ int count(int y, int x) {
int total = 0;
for (int i = 0; i < D; ++i) {
int elem_idx = y * W * D + x * D + i;
unsigned int elem = data[elem_idx];
total += __popc(elem);
}
return total;
}
private:
unsigned int* data;
int H, W, B, D;
};

226
pytorch3d/csrc/rasterize_points/rasterize_points.cu
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@@ -13,7 +13,6 @@
#include <cstdio>
#include <sstream>
#include <tuple>
#include "rasterize_points/bitmask.cuh"
#include "rasterize_points/rasterization_utils.cuh"
namespace {
@@ -217,231 +216,6 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> RasterizePointsNaiveCuda(
return std::make_tuple(point_idxs, zbuf, pix_dists);
}
// ****************************************************************************
// * COARSE RASTERIZATION *
// ****************************************************************************
__global__ void RasterizePointsCoarseCudaKernel(
const float* points, // (P, 3)
const int64_t* cloud_to_packed_first_idx, // (N)
const int64_t* num_points_per_cloud, // (N)
const float* radius,
const int N,
const int P,
const int H,
const int W,
const int bin_size,
const int chunk_size,
const int max_points_per_bin,
int* points_per_bin,
int* bin_points) {
extern __shared__ char sbuf[];
const int M = max_points_per_bin;
// Integer divide round up
const int num_bins_x = 1 + (W - 1) / bin_size;
const int num_bins_y = 1 + (H - 1) / bin_size;
// NDC range depends on the ratio of W/H
// The shorter side from (H, W) is given an NDC range of 2.0 and
// the other side is scaled by the ratio of H:W.
const float NDC_x_half_range = NonSquareNdcRange(W, H) / 2.0f;
const float NDC_y_half_range = NonSquareNdcRange(H, W) / 2.0f;
// Size of half a pixel in NDC units is the NDC half range
// divided by the corresponding image dimension
const float half_pix_x = NDC_x_half_range / W;
const float half_pix_y = NDC_y_half_range / H;
// This is a boolean array of shape (num_bins_y, num_bins_x, chunk_size)
// stored in shared memory that will track whether each point in the chunk
// falls into each bin of the image.
BitMask binmask((unsigned int*)sbuf, num_bins_y, num_bins_x, chunk_size);
// Have each block handle a chunk of points and build a 3D bitmask in
// shared memory to mark which points hit which bins. In this first phase,
// each thread processes one point at a time. After processing the chunk,
// one thread is assigned per bin, and the thread counts and writes the
// points for the bin out to global memory.
const int chunks_per_batch = 1 + (P - 1) / chunk_size;
const int num_chunks = N * chunks_per_batch;
for (int chunk = blockIdx.x; chunk < num_chunks; chunk += gridDim.x) {
const int batch_idx = chunk / chunks_per_batch;
const int chunk_idx = chunk % chunks_per_batch;
const int point_start_idx = chunk_idx * chunk_size;
binmask.block_clear();
// Using the batch index of the thread get the start and stop
// indices for the points.
const int64_t cloud_point_start_idx = cloud_to_packed_first_idx[batch_idx];
const int64_t cloud_point_stop_idx =
cloud_point_start_idx + num_points_per_cloud[batch_idx];
// Have each thread handle a different point within the chunk
for (int p = threadIdx.x; p < chunk_size; p += blockDim.x) {
const int p_idx = point_start_idx + p;
// Check if point index corresponds to the cloud in the batch given by
// batch_idx.
if (p_idx >= cloud_point_stop_idx || p_idx < cloud_point_start_idx) {
continue;
}
const float px = points[p_idx * 3 + 0];
const float py = points[p_idx * 3 + 1];
const float pz = points[p_idx * 3 + 2];
const float p_radius = radius[p_idx];
if (pz < 0)
continue; // Don't render points behind the camera.
const float px0 = px - p_radius;
const float px1 = px + p_radius;
const float py0 = py - p_radius;
const float py1 = py + p_radius;
// Brute-force search over all bins; TODO something smarter?
// For example we could compute the exact bin where the point falls,
// then check neighboring bins. This way we wouldn't have to check
// all bins (however then we might have more warp divergence?)
for (int by = 0; by < num_bins_y; ++by) {
// Get y extent for the bin. PixToNonSquareNdc gives us the location of
// the center of each pixel, so we need to add/subtract a half
// pixel to get the true extent of the bin.
const float by0 = PixToNonSquareNdc(by * bin_size, H, W) - half_pix_y;
const float by1 =
PixToNonSquareNdc((by + 1) * bin_size - 1, H, W) + half_pix_y;
const bool y_overlap = (py0 <= by1) && (by0 <= py1);
if (!y_overlap) {
continue;
}
for (int bx = 0; bx < num_bins_x; ++bx) {
// Get x extent for the bin; again we need to adjust the
// output of PixToNonSquareNdc by half a pixel.
const float bx0 = PixToNonSquareNdc(bx * bin_size, W, H) - half_pix_x;
const float bx1 =
PixToNonSquareNdc((bx + 1) * bin_size - 1, W, H) + half_pix_x;
const bool x_overlap = (px0 <= bx1) && (bx0 <= px1);
if (x_overlap) {
binmask.set(by, bx, p);
}
}
}
}
__syncthreads();
// Now we have processed every point in the current chunk. We need to
// count the number of points in each bin so we can write the indices
// out to global memory. We have each thread handle a different bin.
for (int byx = threadIdx.x; byx < num_bins_y * num_bins_x;
byx += blockDim.x) {
const int by = byx / num_bins_x;
const int bx = byx % num_bins_x;
const int count = binmask.count(by, bx);
const int points_per_bin_idx =
batch_idx * num_bins_y * num_bins_x + by * num_bins_x + bx;
// This atomically increments the (global) number of points found
// in the current bin, and gets the previous value of the counter;
// this effectively allocates space in the bin_points array for the
// points in the current chunk that fall into this bin.
const int start = atomicAdd(points_per_bin + points_per_bin_idx, count);
// Now loop over the binmask and write the active bits for this bin
// out to bin_points.
int next_idx = batch_idx * num_bins_y * num_bins_x * M +
by * num_bins_x * M + bx * M + start;
for (int p = 0; p < chunk_size; ++p) {
if (binmask.get(by, bx, p)) {
// TODO: Throw an error if next_idx >= M -- this means that
// we got more than max_points_per_bin in this bin
// TODO: check if atomicAdd is needed in line 265.
bin_points[next_idx] = point_start_idx + p;
next_idx++;
}
}
}
__syncthreads();
}
}
at::Tensor RasterizePointsCoarseCuda(
const at::Tensor& points, // (P, 3)
const at::Tensor& cloud_to_packed_first_idx, // (N)
const at::Tensor& num_points_per_cloud, // (N)
const std::tuple<int, int> image_size,
const at::Tensor& radius,
const int bin_size,
const int max_points_per_bin) {
TORCH_CHECK(
points.ndimension() == 2 && points.size(1) == 3,
"points must have dimensions (num_points, 3)");
// Check inputs are on the same device
at::TensorArg points_t{points, "points", 1},
cloud_to_packed_first_idx_t{
cloud_to_packed_first_idx, "cloud_to_packed_first_idx", 2},
num_points_per_cloud_t{num_points_per_cloud, "num_points_per_cloud", 3};
at::CheckedFrom c = "RasterizePointsCoarseCuda";
at::checkAllSameGPU(
c, {points_t, cloud_to_packed_first_idx_t, num_points_per_cloud_t});
// Set the device for the kernel launch based on the device of the input
at::cuda::CUDAGuard device_guard(points.device());
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
const int H = std::get<0>(image_size);
const int W = std::get<1>(image_size);
const int P = points.size(0);
const int N = num_points_per_cloud.size(0);
const int M = max_points_per_bin;
// Integer divide round up.
const int num_bins_y = 1 + (H - 1) / bin_size;
const int num_bins_x = 1 + (W - 1) / bin_size;
if (num_bins_y >= kMaxItemsPerBin || num_bins_x >= kMaxItemsPerBin) {
// Make sure we do not use too much shared memory.
std::stringstream ss;
ss << "In Coarse Rasterizer got num_bins_y: " << num_bins_y
<< ", num_bins_x: " << num_bins_x << ", "
<< "; that's too many!";
AT_ERROR(ss.str());
}
auto opts = num_points_per_cloud.options().dtype(at::kInt);
at::Tensor points_per_bin = at::zeros({N, num_bins_y, num_bins_x}, opts);
at::Tensor bin_points = at::full({N, num_bins_y, num_bins_x, M}, -1, opts);
if (bin_points.numel() == 0) {
AT_CUDA_CHECK(cudaGetLastError());
return bin_points;
}
const int chunk_size = 512;
const size_t shared_size = num_bins_y * num_bins_x * chunk_size / 8;
const size_t blocks = 64;
const size_t threads = 512;
RasterizePointsCoarseCudaKernel<<<blocks, threads, shared_size, stream>>>(
points.contiguous().data_ptr<float>(),
cloud_to_packed_first_idx.contiguous().data_ptr<int64_t>(),
num_points_per_cloud.contiguous().data_ptr<int64_t>(),
radius.contiguous().data_ptr<float>(),
N,
P,
H,
W,
bin_size,
chunk_size,
M,
points_per_bin.contiguous().data_ptr<int32_t>(),
bin_points.contiguous().data_ptr<int32_t>());
AT_CUDA_CHECK(cudaGetLastError());
return bin_points;
}
// ****************************************************************************
// * FINE RASTERIZATION *
// ****************************************************************************

13
pytorch3d/csrc/rasterize_points/rasterize_points.h
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@@ -10,6 +10,7 @@
#include <torch/extension.h>
#include <cstdio>
#include <tuple>
#include "rasterize_coarse/rasterize_coarse.h"
#include "utils/pytorch3d_cutils.h"
// ****************************************************************************
@@ -104,6 +105,8 @@ std::tuple<torch::Tensor, torch::Tensor, torch::Tensor> RasterizePointsNaive(
// * COARSE RASTERIZATION *
// ****************************************************************************
// RasterizePointsCoarseCuda in rasterize_coarse/rasterize_coarse.h
torch::Tensor RasterizePointsCoarseCpu(
const torch::Tensor& points,
const torch::Tensor& cloud_to_packed_first_idx,
@@ -113,16 +116,6 @@ torch::Tensor RasterizePointsCoarseCpu(
const int bin_size,
const int max_points_per_bin);
#ifdef WITH_CUDA
torch::Tensor RasterizePointsCoarseCuda(
const torch::Tensor& points,
const torch::Tensor& cloud_to_packed_first_idx,
const torch::Tensor& num_points_per_cloud,
const std::tuple<int, int> image_size,
const torch::Tensor& radius,
const int bin_size,
const int max_points_per_bin);
#endif
// Args:
// points: Tensor of shape (P, 3) giving (packed) positions for
// points in all N pointclouds in the batch where P is the total