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Unverified Commit 333536f6 authored by Wenwei Zhang's avatar Wenwei Zhang Committed by GitHub
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Release v1.0.0rc1

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mmdet3d/ops/gather_points/__init__.py deleted 100644 → 0
View file @ 9c7270d0
# Copyright (c) OpenMMLab. All rights reserved.
from .gather_points import gather_points
__all__ = ['gather_points']
mmdet3d/ops/gather_points/gather_points.py deleted 100644 → 0
View file @ 9c7270d0
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch.autograd import Function
from . import gather_points_ext
class GatherPoints(Function):
"""Gather Points.
Gather points with given index.
"""
@staticmethod
def forward(ctx, features: torch.Tensor,
indices: torch.Tensor) -> torch.Tensor:
"""forward.
Args:
features (Tensor): (B, C, N) features to gather.
indices (Tensor): (B, M) where M is the number of points.
Returns:
Tensor: (B, C, M) where M is the number of points.
"""
assert features.is_contiguous()
assert indices.is_contiguous()
B, npoint = indices.size()
_, C, N = features.size()
output = features.new_zeros((B, C, npoint))
gather_points_ext.gather_points_wrapper(B, C, N, npoint, features,
indices, output)
ctx.for_backwards = (indices, C, N)
ctx.mark_non_differentiable(indices)
return output
@staticmethod
def backward(ctx, grad_out):
idx, C, N = ctx.for_backwards
B, npoint = idx.size()
grad_features = grad_out.new_zeros((B, C, N))
grad_out_data = grad_out.data.contiguous()
gather_points_ext.gather_points_grad_wrapper(B, C, N, npoint,
grad_out_data, idx,
grad_features.data)
return grad_features, None
gather_points = GatherPoints.apply
mmdet3d/ops/gather_points/src/gather_points.cpp deleted 100644 → 0
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#include <ATen/cuda/CUDAContext.h>
#include <ATen/TensorUtils.h>
#include <THC/THC.h>
#include <torch/extension.h>
#include <torch/serialize/tensor.h>
#include <vector>
extern THCState *state;
int gather_points_wrapper(int b, int c, int n, int npoints,
at::Tensor& points_tensor, at::Tensor& idx_tensor,
at::Tensor& out_tensor);
void gather_points_kernel_launcher(int b, int c, int n, int npoints,
const at::Tensor& points_tensor,
const at::Tensor& idx_tensor,
at::Tensor& out_tensor);
int gather_points_grad_wrapper(int b, int c, int n, int npoints,
at::Tensor& grad_out_tensor,
at::Tensor& idx_tensor,
at::Tensor& grad_points_tensor);
void gather_points_grad_kernel_launcher(int b, int c, int n, int npoints,
const at::Tensor& grad_out_tensor,
const at::Tensor& idx_tensor,
at::Tensor& grad_points_tensor);
int gather_points_wrapper(int b, int c, int n, int npoints,
at::Tensor& points_tensor, at::Tensor& idx_tensor,
at::Tensor& out_tensor)
{
gather_points_kernel_launcher(b, c, n, npoints, points_tensor, idx_tensor, out_tensor);
return 1;
}
int gather_points_grad_wrapper(int b, int c, int n, int npoints,
at::Tensor& grad_out_tensor,
at::Tensor& idx_tensor,
at::Tensor& grad_points_tensor)
{
gather_points_grad_kernel_launcher(b, c, n, npoints, grad_out_tensor, idx_tensor,
grad_points_tensor);
return 1;
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
{
m.def("gather_points_wrapper", &gather_points_wrapper,
"gather_points_wrapper");
m.def("gather_points_grad_wrapper", &gather_points_grad_wrapper,
"gather_points_grad_wrapper");
}
mmdet3d/ops/gather_points/src/gather_points_cuda.cu deleted 100644 → 0
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#include <stdio.h>
#include <stdlib.h>
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <torch/types.h>
#include <ATen/cuda/CUDAApplyUtils.cuh>
#define TOTAL_THREADS 1024
#define THREADS_PER_BLOCK 256
#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
template <typename scalar_t>
__global__ void gather_points_kernel(int b, int c, int n, int m,
const scalar_t *__restrict__ points,
const int *__restrict__ idx,
scalar_t *__restrict__ out) {
// points: (B, C, N)
// idx: (B, M)
// output:
// out: (B, C, M)
int bs_idx = blockIdx.z;
int c_idx = blockIdx.y;
int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (bs_idx >= b || c_idx >= c || pt_idx >= m) return;
out += bs_idx * c * m + c_idx * m + pt_idx;
idx += bs_idx * m + pt_idx;
points += bs_idx * c * n + c_idx * n;
out[0] = points[idx[0]];
}
void gather_points_kernel_launcher(int b, int c, int n, int npoints,
const at::Tensor& points_tensor,
const at::Tensor& idx_tensor,
at::Tensor& out_tensor)
{
// points: (B, C, N)
// idx: (B, npoints)
// output:
// out: (B, C, npoints)
cudaError_t err;
dim3 blocks(DIVUP(npoints, THREADS_PER_BLOCK), c,
b); // blockIdx.x(col), blockIdx.y(row)
dim3 threads(THREADS_PER_BLOCK);
cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
out_tensor.scalar_type(), "gather_points_kernel",
[&]
{
const scalar_t *points = points_tensor.data_ptr<scalar_t>();
const int *idx = idx_tensor.data_ptr<int>();
scalar_t *out = out_tensor.data_ptr<scalar_t>();
gather_points_kernel<<<blocks, threads, 0, stream>>>(b, c, n, npoints, points,
idx, out);
});
err = cudaGetLastError();
if (cudaSuccess != err)
{
fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
exit(-1);
}
}
template <typename scalar_t>
__global__ void gather_points_grad_kernel(int b, int c, int n, int m,
const scalar_t *__restrict__ grad_out,
const int *__restrict__ idx,
scalar_t *__restrict__ grad_points) {
// grad_out: (B, C, M)
// idx: (B, M)
// output:
// grad_points: (B, C, N)
int bs_idx = blockIdx.z;
int c_idx = blockIdx.y;
int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (bs_idx >= b || c_idx >= c || pt_idx >= m) return;
grad_out += bs_idx * c * m + c_idx * m + pt_idx;
idx += bs_idx * m + pt_idx;
grad_points += bs_idx * c * n + c_idx * n;
atomicAdd(grad_points + idx[0], grad_out[0]);
}
void gather_points_grad_kernel_launcher(int b, int c, int n, int npoints,
const at::Tensor& grad_out_tensor,
const at::Tensor& idx_tensor,
at::Tensor& grad_points_tensor)
{
// grad_out: (B, C, npoints)
// idx: (B, npoints)
// output:
// grad_points: (B, C, N)
cudaError_t err;
dim3 blocks(DIVUP(npoints, THREADS_PER_BLOCK), c,
b); // blockIdx.x(col), blockIdx.y(row)
dim3 threads(THREADS_PER_BLOCK);
cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
AT_DISPATCH_FLOATING_TYPES_AND_HALF(
grad_points_tensor.scalar_type(), "gather_points_grad_kernel",
[&]
{
const scalar_t *grad_out = grad_out_tensor.data_ptr<scalar_t>();
const int *idx = idx_tensor.data_ptr<int>();
scalar_t *grad_points = grad_points_tensor.data_ptr<scalar_t>();
gather_points_grad_kernel<scalar_t><<<blocks, threads, 0, stream>>>(
b, c, n, npoints, grad_out, idx, grad_points);
});
err = cudaGetLastError();
if (cudaSuccess != err)
{
fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
exit(-1);
}
}
mmdet3d/ops/group_points/__init__.py deleted 100644 → 0
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# Copyright (c) OpenMMLab. All rights reserved.
from .group_points import GroupAll, QueryAndGroup, grouping_operation
__all__ = ['QueryAndGroup', 'GroupAll', 'grouping_operation']
mmdet3d/ops/group_points/group_points.py deleted 100644 → 0
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# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
import torch
from mmcv.runner import force_fp32
from torch import nn as nn
from torch.autograd import Function
from ..ball_query import ball_query
from ..knn import knn
from . import group_points_ext
class QueryAndGroup(nn.Module):
"""Query and Group.
Groups with a ball query of radius
Args:
max_radius (float): The maximum radius of the balls.
If None is given, we will use kNN sampling instead of ball query.
sample_num (int): Maximum number of features to gather in the ball.
min_radius (float, optional): The minimum radius of the balls.
Default: 0.
use_xyz (bool, optional): Whether to use xyz.
Default: True.
return_grouped_xyz (bool, optional): Whether to return grouped xyz.
Default: False.
normalize_xyz (bool, optional): Whether to normalize xyz.
Default: False.
uniform_sample (bool, optional): Whether to sample uniformly.
Default: False
return_unique_cnt (bool, optional): Whether to return the count of
unique samples. Default: False.
return_grouped_idx (bool, optional): Whether to return grouped idx.
Default: False.
"""
def __init__(self,
max_radius,
sample_num,
min_radius=0,
use_xyz=True,
return_grouped_xyz=False,
normalize_xyz=False,
uniform_sample=False,
return_unique_cnt=False,
return_grouped_idx=False):
super(QueryAndGroup, self).__init__()
self.max_radius = max_radius
self.min_radius = min_radius
self.sample_num = sample_num
self.use_xyz = use_xyz
self.return_grouped_xyz = return_grouped_xyz
self.normalize_xyz = normalize_xyz
self.uniform_sample = uniform_sample
self.return_unique_cnt = return_unique_cnt
self.return_grouped_idx = return_grouped_idx
if self.return_unique_cnt:
assert self.uniform_sample, \
'uniform_sample should be True when ' \
'returning the count of unique samples'
if self.max_radius is None:
assert not self.normalize_xyz, \
'can not normalize grouped xyz when max_radius is None'
self.fp16_enabled = False
@force_fp32()
def forward(self, points_xyz, center_xyz, features=None):
"""forward.
Args:
points_xyz (Tensor): (B, N, 3) xyz coordinates of the features.
center_xyz (Tensor): (B, npoint, 3) Centriods.
features (Tensor): (B, C, N) Descriptors of the features.
Return：
Tensor: (B, 3 + C, npoint, sample_num) Grouped feature.
"""
# if self.max_radius is None, we will perform kNN instead of ball query
# idx is of shape [B, npoint, sample_num]
if self.max_radius is None:
idx = knn(self.sample_num, points_xyz, center_xyz, False)
idx = idx.transpose(1, 2).contiguous()
else:
idx = ball_query(self.min_radius, self.max_radius, self.sample_num,
points_xyz, center_xyz)
if self.uniform_sample:
unique_cnt = torch.zeros((idx.shape[0], idx.shape[1]))
for i_batch in range(idx.shape[0]):
for i_region in range(idx.shape[1]):
unique_ind = torch.unique(idx[i_batch, i_region, :])
num_unique = unique_ind.shape[0]
unique_cnt[i_batch, i_region] = num_unique
sample_ind = torch.randint(
0,
num_unique, (self.sample_num - num_unique, ),
dtype=torch.long)
all_ind = torch.cat((unique_ind, unique_ind[sample_ind]))
idx[i_batch, i_region, :] = all_ind
xyz_trans = points_xyz.transpose(1, 2).contiguous()
# (B, 3, npoint, sample_num)
grouped_xyz = grouping_operation(xyz_trans, idx)
grouped_xyz_diff = grouped_xyz - \
center_xyz.transpose(1, 2).unsqueeze(-1) # relative offsets
if self.normalize_xyz:
grouped_xyz_diff /= self.max_radius
if features is not None:
grouped_features = grouping_operation(features, idx)
if self.use_xyz:
# (B, C + 3, npoint, sample_num)
new_features = torch.cat([grouped_xyz_diff, grouped_features],
dim=1)
else:
new_features = grouped_features
else:
assert (self.use_xyz
), 'Cannot have not features and not use xyz as a feature!'
new_features = grouped_xyz_diff
ret = [new_features]
if self.return_grouped_xyz:
ret.append(grouped_xyz)
if self.return_unique_cnt:
ret.append(unique_cnt)
if self.return_grouped_idx:
ret.append(idx)
if len(ret) == 1:
return ret[0]
else:
return tuple(ret)
class GroupAll(nn.Module):
"""Group All.
Group xyz with feature.
Args:
use_xyz (bool): Whether to use xyz.
"""
def __init__(self, use_xyz: bool = True):
super().__init__()
self.use_xyz = use_xyz
self.fp16_enabled = False
@force_fp32()
def forward(self,
xyz: torch.Tensor,
new_xyz: torch.Tensor,
features: torch.Tensor = None):
"""forward.
Args:
xyz (Tensor): (B, N, 3) xyz coordinates of the features.
new_xyz (Tensor): Ignored.
features (Tensor): (B, C, N) features to group.
Return:
Tensor: (B, C + 3, 1, N) Grouped feature.
"""
grouped_xyz = xyz.transpose(1, 2).unsqueeze(2)
if features is not None:
grouped_features = features.unsqueeze(2)
if self.use_xyz:
new_features = torch.cat([grouped_xyz, grouped_features],
dim=1) # (B, 3 + C, 1, N)
else:
new_features = grouped_features
else:
new_features = grouped_xyz
return new_features
class GroupingOperation(Function):
"""Grouping Operation.
Group feature with given index.
"""
@staticmethod
def forward(ctx, features: torch.Tensor,
indices: torch.Tensor) -> torch.Tensor:
"""forward.
Args:
features (Tensor): (B, C, N) tensor of features to group.
indices (Tensor): (B, npoint, nsample) the indices of
features to group with.
Returns:
Tensor: (B, C, npoint, nsample) Grouped features.
"""
assert features.is_contiguous()
assert indices.is_contiguous()
B, nfeatures, nsample = indices.size()
_, C, N = features.size()
output = torch.cuda.FloatTensor(B, C, nfeatures, nsample)
group_points_ext.forward(B, C, N, nfeatures, nsample, features,
indices, output)
ctx.for_backwards = (indices, N)
return output
@staticmethod
def backward(ctx,
grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""backward.
Args:
grad_out (Tensor): (B, C, npoint, nsample) tensor of the gradients
of the output from forward.
Returns:
Tensor: (B, C, N) gradient of the features.
"""
idx, N = ctx.for_backwards
B, C, npoint, nsample = grad_out.size()
grad_features = torch.cuda.FloatTensor(B, C, N).zero_()
grad_out_data = grad_out.data.contiguous()
group_points_ext.backward(B, C, N, npoint, nsample, grad_out_data, idx,
grad_features.data)
return grad_features, None
grouping_operation = GroupingOperation.apply
mmdet3d/ops/group_points/src/group_points.cpp deleted 100644 → 0
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// Modified from
// https://github.com/sshaoshuai/Pointnet2.PyTorch/tree/master/pointnet2/src/group_points.cpp
#include <THC/THC.h>
#include <cuda.h>
#include <cuda_runtime_api.h>
#include <torch/extension.h>
#include <torch/serialize/tensor.h>
#include <vector>
extern THCState *state;
int group_points_wrapper(int b, int c, int n, int npoints, int nsample,
at::Tensor points_tensor, at::Tensor idx_tensor,
at::Tensor out_tensor);
void group_points_kernel_launcher(int b, int c, int n, int npoints, int nsample,
const float *points, const int *idx,
float *out, cudaStream_t stream);
int group_points_grad_wrapper(int b, int c, int n, int npoints, int nsample,
at::Tensor grad_out_tensor, at::Tensor idx_tensor,
at::Tensor grad_points_tensor);
void group_points_grad_kernel_launcher(int b, int c, int n, int npoints,
int nsample, const float *grad_out,
const int *idx, float *grad_points,
cudaStream_t stream);
int group_points_grad_wrapper(int b, int c, int n, int npoints, int nsample,
at::Tensor grad_out_tensor, at::Tensor idx_tensor,
at::Tensor grad_points_tensor) {
float *grad_points = grad_points_tensor.data_ptr<float>();
const int *idx = idx_tensor.data_ptr<int>();
const float *grad_out = grad_out_tensor.data_ptr<float>();
cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
group_points_grad_kernel_launcher(b, c, n, npoints, nsample, grad_out, idx,
grad_points, stream);
return 1;
}
int group_points_wrapper(int b, int c, int n, int npoints, int nsample,
at::Tensor points_tensor, at::Tensor idx_tensor,
at::Tensor out_tensor) {
const float *points = points_tensor.data_ptr<float>();
const int *idx = idx_tensor.data_ptr<int>();
float *out = out_tensor.data_ptr<float>();
cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
group_points_kernel_launcher(b, c, n, npoints, nsample, points, idx, out,
stream);
return 1;
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("forward", &group_points_wrapper, "group_points_wrapper");
m.def("backward", &group_points_grad_wrapper, "group_points_grad_wrapper");
}
mmdet3d/ops/group_points/src/group_points_cuda.cu deleted 100644 → 0
View file @ 9c7270d0
// Modified from
// https://github.com/sshaoshuai/Pointnet2.PyTorch/tree/master/pointnet2/src/group_points_gpu.cu
#include <stdio.h>
#include <stdlib.h>
#define THREADS_PER_BLOCK 256
#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
__global__ void group_points_grad_kernel(int b, int c, int n, int npoints,
int nsample,
const float *__restrict__ grad_out,
const int *__restrict__ idx,
float *__restrict__ grad_points) {
// grad_out: (B, C, npoints, nsample)
// idx: (B, npoints, nsample)
// output:
// grad_points: (B, C, N)
int bs_idx = blockIdx.z;
int c_idx = blockIdx.y;
int index = blockIdx.x * blockDim.x + threadIdx.x;
int pt_idx = index / nsample;
if (bs_idx >= b || c_idx >= c || pt_idx >= npoints) return;
int sample_idx = index % nsample;
grad_out += bs_idx * c * npoints * nsample + c_idx * npoints * nsample +
pt_idx * nsample + sample_idx;
idx += bs_idx * npoints * nsample + pt_idx * nsample + sample_idx;
atomicAdd(grad_points + bs_idx * c * n + c_idx * n + idx[0], grad_out[0]);
}
void group_points_grad_kernel_launcher(int b, int c, int n, int npoints,
int nsample, const float *grad_out,
const int *idx, float *grad_points,
cudaStream_t stream) {
// grad_out: (B, C, npoints, nsample)
// idx: (B, npoints, nsample)
// output:
// grad_points: (B, C, N)
cudaError_t err;
dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c,
b); // blockIdx.x(col), blockIdx.y(row)
dim3 threads(THREADS_PER_BLOCK);
group_points_grad_kernel<<<blocks, threads, 0, stream>>>(
b, c, n, npoints, nsample, grad_out, idx, grad_points);
err = cudaGetLastError();
if (cudaSuccess != err) {
fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
exit(-1);
}
}
__global__ void group_points_kernel(int b, int c, int n, int npoints,
int nsample,
const float *__restrict__ points,
const int *__restrict__ idx,
float *__restrict__ out) {
// points: (B, C, N)
// idx: (B, npoints, nsample)
// output:
// out: (B, C, npoints, nsample)
int bs_idx = blockIdx.z;
int c_idx = blockIdx.y;
int index = blockIdx.x * blockDim.x + threadIdx.x;
int pt_idx = index / nsample;
if (bs_idx >= b || c_idx >= c || pt_idx >= npoints) return;
int sample_idx = index % nsample;
idx += bs_idx * npoints * nsample + pt_idx * nsample + sample_idx;
int in_idx = bs_idx * c * n + c_idx * n + idx[0];
int out_idx = bs_idx * c * npoints * nsample + c_idx * npoints * nsample +
pt_idx * nsample + sample_idx;
out[out_idx] = points[in_idx];
}
void group_points_kernel_launcher(int b, int c, int n, int npoints, int nsample,
const float *points, const int *idx,
float *out, cudaStream_t stream) {
// points: (B, C, N)
// idx: (B, npoints, nsample)
// output:
// out: (B, C, npoints, nsample)
cudaError_t err;
dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c,
b); // blockIdx.x(col), blockIdx.y(row)
dim3 threads(THREADS_PER_BLOCK);
group_points_kernel<<<blocks, threads, 0, stream>>>(b, c, n, npoints, nsample,
points, idx, out);
// cudaDeviceSynchronize(); // for using printf in kernel function
err = cudaGetLastError();
if (cudaSuccess != err) {
fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
exit(-1);
}
}
mmdet3d/ops/interpolate/__init__.py deleted 100644 → 0
View file @ 9c7270d0
# Copyright (c) OpenMMLab. All rights reserved.
from .three_interpolate import three_interpolate
from .three_nn import three_nn
__all__ = ['three_nn', 'three_interpolate']
mmdet3d/ops/interpolate/src/interpolate.cpp deleted 100644 → 0
View file @ 9c7270d0
// Modified from
// https://github.com/sshaoshuai/Pointnet2.PyTorch/tree/master/pointnet2/src/interpolate.cpp
#include <THC/THC.h>
#include <cuda.h>
#include <cuda_runtime_api.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <torch/extension.h>
#include <torch/serialize/tensor.h>
#include <vector>
extern THCState *state;
void three_nn_wrapper(int b, int n, int m, at::Tensor unknown_tensor,
at::Tensor known_tensor, at::Tensor dist2_tensor,
at::Tensor idx_tensor);
void three_nn_kernel_launcher(int b, int n, int m, const float *unknown,
const float *known, float *dist2, int *idx,
cudaStream_t stream);
void three_interpolate_wrapper(int b, int c, int m, int n,
at::Tensor points_tensor, at::Tensor idx_tensor,
at::Tensor weight_tensor, at::Tensor out_tensor);
void three_interpolate_kernel_launcher(int b, int c, int m, int n,
const float *points, const int *idx,
const float *weight, float *out,
cudaStream_t stream);
void three_interpolate_grad_wrapper(int b, int c, int n, int m,
at::Tensor grad_out_tensor,
at::Tensor idx_tensor,
at::Tensor weight_tensor,
at::Tensor grad_points_tensor);
void three_interpolate_grad_kernel_launcher(int b, int c, int n, int m,
const float *grad_out,
const int *idx, const float *weight,
float *grad_points,
cudaStream_t stream);
void three_nn_wrapper(int b, int n, int m, at::Tensor unknown_tensor,
at::Tensor known_tensor, at::Tensor dist2_tensor,
at::Tensor idx_tensor) {
const float *unknown = unknown_tensor.data_ptr<float>();
const float *known = known_tensor.data_ptr<float>();
float *dist2 = dist2_tensor.data_ptr<float>();
int *idx = idx_tensor.data_ptr<int>();
cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
three_nn_kernel_launcher(b, n, m, unknown, known, dist2, idx, stream);
}
void three_interpolate_wrapper(int b, int c, int m, int n,
at::Tensor points_tensor, at::Tensor idx_tensor,
at::Tensor weight_tensor,
at::Tensor out_tensor) {
const float *points = points_tensor.data_ptr<float>();
const float *weight = weight_tensor.data_ptr<float>();
float *out = out_tensor.data_ptr<float>();
const int *idx = idx_tensor.data_ptr<int>();
cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
three_interpolate_kernel_launcher(b, c, m, n, points, idx, weight, out,
stream);
}
void three_interpolate_grad_wrapper(int b, int c, int n, int m,
at::Tensor grad_out_tensor,
at::Tensor idx_tensor,
at::Tensor weight_tensor,
at::Tensor grad_points_tensor) {
const float *grad_out = grad_out_tensor.data_ptr<float>();
const float *weight = weight_tensor.data_ptr<float>();
float *grad_points = grad_points_tensor.data_ptr<float>();
const int *idx = idx_tensor.data_ptr<int>();
cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
three_interpolate_grad_kernel_launcher(b, c, n, m, grad_out, idx, weight,
grad_points, stream);
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("three_nn_wrapper", &three_nn_wrapper, "three_nn_wrapper");
m.def("three_interpolate_wrapper", &three_interpolate_wrapper,
"three_interpolate_wrapper");
m.def("three_interpolate_grad_wrapper", &three_interpolate_grad_wrapper,
"three_interpolate_grad_wrapper");
}
mmdet3d/ops/interpolate/src/three_interpolate_cuda.cu deleted 100644 → 0
View file @ 9c7270d0
// Modified from
// https://github.com/sshaoshuai/Pointnet2.PyTorch/tree/master/pointnet2/src/interpolate_gpu.cu
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#define THREADS_PER_BLOCK 256
#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
__global__ void three_interpolate_kernel(int b, int c, int m, int n,
const float *__restrict__ points,
const int *__restrict__ idx,
const float *__restrict__ weight,
float *__restrict__ out) {
// points: (B, C, M)
// idx: (B, N, 3)
// weight: (B, N, 3)
// output:
// out: (B, C, N)
int bs_idx = blockIdx.z;
int c_idx = blockIdx.y;
int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (bs_idx >= b || c_idx >= c || pt_idx >= n) return;
weight += bs_idx * n * 3 + pt_idx * 3;
points += bs_idx * c * m + c_idx * m;
idx += bs_idx * n * 3 + pt_idx * 3;
out += bs_idx * c * n + c_idx * n;
out[pt_idx] = weight[0] * points[idx[0]] + weight[1] * points[idx[1]] +
weight[2] * points[idx[2]];
}
void three_interpolate_kernel_launcher(int b, int c, int m, int n,
const float *points, const int *idx,
const float *weight, float *out,
cudaStream_t stream) {
// points: (B, C, M)
// idx: (B, N, 3)
// weight: (B, N, 3)
// output:
// out: (B, C, N)
cudaError_t err;
dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c,
b); // blockIdx.x(col), blockIdx.y(row)
dim3 threads(THREADS_PER_BLOCK);
three_interpolate_kernel<<<blocks, threads, 0, stream>>>(b, c, m, n, points,
idx, weight, out);
err = cudaGetLastError();
if (cudaSuccess != err) {
fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
exit(-1);
}
}
__global__ void three_interpolate_grad_kernel(
int b, int c, int n, int m, const float *__restrict__ grad_out,
const int *__restrict__ idx, const float *__restrict__ weight,
float *__restrict__ grad_points) {
// grad_out: (B, C, N)
// weight: (B, N, 3)
// output:
// grad_points: (B, C, M)
int bs_idx = blockIdx.z;
int c_idx = blockIdx.y;
int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (bs_idx >= b || c_idx >= c || pt_idx >= n) return;
grad_out += bs_idx * c * n + c_idx * n + pt_idx;
weight += bs_idx * n * 3 + pt_idx * 3;
grad_points += bs_idx * c * m + c_idx * m;
idx += bs_idx * n * 3 + pt_idx * 3;
atomicAdd(grad_points + idx[0], grad_out[0] * weight[0]);
atomicAdd(grad_points + idx[1], grad_out[0] * weight[1]);
atomicAdd(grad_points + idx[2], grad_out[0] * weight[2]);
}
void three_interpolate_grad_kernel_launcher(int b, int c, int n, int m,
const float *grad_out,
const int *idx, const float *weight,
float *grad_points,
cudaStream_t stream) {
// grad_out: (B, C, N)
// weight: (B, N, 3)
// output:
// grad_points: (B, C, M)
cudaError_t err;
dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c,
b); // blockIdx.x(col), blockIdx.y(row)
dim3 threads(THREADS_PER_BLOCK);
three_interpolate_grad_kernel<<<blocks, threads, 0, stream>>>(
b, c, n, m, grad_out, idx, weight, grad_points);
err = cudaGetLastError();
if (cudaSuccess != err) {
fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
exit(-1);
}
}
mmdet3d/ops/interpolate/src/three_nn_cuda.cu deleted 100644 → 0
View file @ 9c7270d0
// Modified from
// https://github.com/sshaoshuai/Pointnet2.PyTorch/tree/master/pointnet2/src/interpolate_gpu.cu
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#define THREADS_PER_BLOCK 256
#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
__global__ void three_nn_kernel(int b, int n, int m,
const float *__restrict__ unknown,
const float *__restrict__ known,
float *__restrict__ dist2,
int *__restrict__ idx) {
// unknown: (B, N, 3)
// known: (B, M, 3)
// output:
// dist2: (B, N, 3)
// idx: (B, N, 3)
int bs_idx = blockIdx.y;
int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (bs_idx >= b || pt_idx >= n) return;
unknown += bs_idx * n * 3 + pt_idx * 3;
known += bs_idx * m * 3;
dist2 += bs_idx * n * 3 + pt_idx * 3;
idx += bs_idx * n * 3 + pt_idx * 3;
float ux = unknown[0];
float uy = unknown[1];
float uz = unknown[2];
double best1 = 1e40, best2 = 1e40, best3 = 1e40;
int besti1 = 0, besti2 = 0, besti3 = 0;
for (int k = 0; k < m; ++k) {
float x = known[k * 3 + 0];
float y = known[k * 3 + 1];
float z = known[k * 3 + 2];
float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z);
if (d < best1) {
best3 = best2;
besti3 = besti2;
best2 = best1;
besti2 = besti1;
best1 = d;
besti1 = k;
} else if (d < best2) {
best3 = best2;
besti3 = besti2;
best2 = d;
besti2 = k;
} else if (d < best3) {
best3 = d;
besti3 = k;
}
}
dist2[0] = best1;
dist2[1] = best2;
dist2[2] = best3;
idx[0] = besti1;
idx[1] = besti2;
idx[2] = besti3;
}
void three_nn_kernel_launcher(int b, int n, int m, const float *unknown,
const float *known, float *dist2, int *idx,
cudaStream_t stream) {
// unknown: (B, N, 3)
// known: (B, M, 3)
// output:
// dist2: (B, N, 3)
// idx: (B, N, 3)
cudaError_t err;
dim3 blocks(DIVUP(n, THREADS_PER_BLOCK),
b); // blockIdx.x(col), blockIdx.y(row)
dim3 threads(THREADS_PER_BLOCK);
three_nn_kernel<<<blocks, threads, 0, stream>>>(b, n, m, unknown, known,
dist2, idx);
err = cudaGetLastError();
if (cudaSuccess != err) {
fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
exit(-1);
}
}
mmdet3d/ops/interpolate/three_interpolate.py deleted 100644 → 0
View file @ 9c7270d0
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
import torch
from torch.autograd import Function
from . import interpolate_ext
class ThreeInterpolate(Function):
@staticmethod
def forward(ctx, features: torch.Tensor, indices: torch.Tensor,
weight: torch.Tensor) -> torch.Tensor:
"""Performs weighted linear interpolation on 3 features.
Args:
features (Tensor): (B, C, M) Features descriptors to be
interpolated from
indices (Tensor): (B, n, 3) index three nearest neighbors
of the target features in features
weight (Tensor): (B, n, 3) weights of interpolation
Returns:
Tensor: (B, C, N) tensor of the interpolated features
"""
assert features.is_contiguous()
assert indices.is_contiguous()
assert weight.is_contiguous()
B, c, m = features.size()
n = indices.size(1)
ctx.three_interpolate_for_backward = (indices, weight, m)
output = torch.cuda.FloatTensor(B, c, n)
interpolate_ext.three_interpolate_wrapper(B, c, m, n, features,
indices, weight, output)
return output
@staticmethod
def backward(
ctx, grad_out: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Backward of three interpolate.
Args:
grad_out (Tensor): (B, C, N) tensor with gradients of outputs
Returns:
Tensor: (B, C, M) tensor with gradients of features
"""
idx, weight, m = ctx.three_interpolate_for_backward
B, c, n = grad_out.size()
grad_features = torch.cuda.FloatTensor(B, c, m).zero_()
grad_out_data = grad_out.data.contiguous()
interpolate_ext.three_interpolate_grad_wrapper(B, c, n, m,
grad_out_data, idx,
weight,
grad_features.data)
return grad_features, None, None
three_interpolate = ThreeInterpolate.apply
mmdet3d/ops/interpolate/three_nn.py deleted 100644 → 0
View file @ 9c7270d0
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
import torch
from torch.autograd import Function
from . import interpolate_ext
class ThreeNN(Function):
@staticmethod
def forward(ctx, target: torch.Tensor,
source: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""Find the top-3 nearest neighbors of the target set from the source
set.
Args:
target (Tensor): shape (B, N, 3), points set that needs to
find the nearest neighbors.
source (Tensor): shape (B, M, 3), points set that is used
to find the nearest neighbors of points in target set.
Returns:
Tensor: shape (B, N, 3), L2 distance of each point in target
set to their corresponding nearest neighbors.
"""
assert target.is_contiguous()
assert source.is_contiguous()
B, N, _ = target.size()
m = source.size(1)
dist2 = torch.cuda.FloatTensor(B, N, 3)
idx = torch.cuda.IntTensor(B, N, 3)
interpolate_ext.three_nn_wrapper(B, N, m, target, source, dist2, idx)
ctx.mark_non_differentiable(idx)
return torch.sqrt(dist2), idx
@staticmethod
def backward(ctx, a=None, b=None):
return None, None
three_nn = ThreeNN.apply
mmdet3d/ops/iou3d/__init__.py deleted 100644 → 0
View file @ 9c7270d0
# Copyright (c) OpenMMLab. All rights reserved.
from .iou3d_utils import boxes_iou_bev, nms_gpu, nms_normal_gpu
__all__ = ['boxes_iou_bev', 'nms_gpu', 'nms_normal_gpu']
mmdet3d/ops/iou3d/iou3d_utils.py deleted 100644 → 0
View file @ 9c7270d0
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from . import iou3d_cuda
def boxes_iou_bev(boxes_a, boxes_b):
"""Calculate boxes IoU in the Bird's Eye View.
Args:
boxes_a (torch.Tensor): Input boxes a with shape (M, 5).
boxes_b (torch.Tensor): Input boxes b with shape (N, 5).
Returns:
ans_iou (torch.Tensor): IoU result with shape (M, N).
"""
ans_iou = boxes_a.new_zeros(
torch.Size((boxes_a.shape[0], boxes_b.shape[0])))
iou3d_cuda.boxes_iou_bev_gpu(boxes_a.contiguous(), boxes_b.contiguous(),
ans_iou)
return ans_iou
def nms_gpu(boxes, scores, thresh, pre_max_size=None, post_max_size=None):
"""NMS function GPU implementation (for BEV boxes). The overlap of two
boxes for IoU calculation is defined as the exact overlapping area of the
two boxes. In this function, one can also set `pre_max_size` and
`post_max_size`.
Args:
boxes (torch.Tensor): Input boxes with the shape of [N, 5]
([x1, y1, x2, y2, ry]).
scores (torch.Tensor): Scores of boxes with the shape of [N].
thresh (int): Threshold.
pre_max_size (int, optional): Max size of boxes before NMS.
Default: None.
post_max_size (int, optional): Max size of boxes after NMS.
Default: None.
Returns:
torch.Tensor: Indexes after NMS.
"""
order = scores.sort(0, descending=True)[1]
if pre_max_size is not None:
order = order[:pre_max_size]
boxes = boxes[order].contiguous()
keep = torch.zeros(boxes.size(0), dtype=torch.long)
num_out = iou3d_cuda.nms_gpu(boxes, keep, thresh, boxes.device.index)
keep = order[keep[:num_out].cuda(boxes.device)].contiguous()
if post_max_size is not None:
keep = keep[:post_max_size]
return keep
def nms_normal_gpu(boxes, scores, thresh):
"""Normal NMS function GPU implementation (for BEV boxes). The overlap of
two boxes for IoU calculation is defined as the exact overlapping area of
the two boxes WITH their yaw angle set to 0.
Args:
boxes (torch.Tensor): Input boxes with shape (N, 5).
scores (torch.Tensor): Scores of predicted boxes with shape (N).
thresh (torch.Tensor): Threshold of NMS.
Returns:
torch.Tensor: Remaining indices with scores in descending order.
"""
order = scores.sort(0, descending=True)[1]
boxes = boxes[order].contiguous()
keep = torch.zeros(boxes.size(0), dtype=torch.long)
num_out = iou3d_cuda.nms_normal_gpu(boxes, keep, thresh,
boxes.device.index)
return order[keep[:num_out].cuda(boxes.device)].contiguous()
mmdet3d/ops/iou3d/src/iou3d.cpp deleted 100644 → 0
View file @ 9c7270d0
// Modified from
// https://github.com/open-mmlab/OpenPCDet/blob/master/pcdet/ops/iou3d_nms/src/iou3d_nms.cpp
/*
3D IoU Calculation and Rotated NMS(modified from 2D NMS written by others)
Written by Shaoshuai Shi
All Rights Reserved 2019-2020.
*/
#include <cuda.h>
#include <cuda_runtime_api.h>
#include <torch/extension.h>
#include <torch/serialize/tensor.h>
#include <cstdint>
#include <vector>
#define CHECK_CUDA(x) \
TORCH_CHECK(x.device().is_cuda(), #x, " must be a CUDAtensor ")
#define CHECK_CONTIGUOUS(x) \
TORCH_CHECK(x.is_contiguous(), #x, " must be contiguous ")
#define CHECK_INPUT(x) \
CHECK_CUDA(x); \
CHECK_CONTIGUOUS(x)
#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
#define CHECK_ERROR(ans) \
{ gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, const char *file, int line,
bool abort = true) {
if (code != cudaSuccess) {
fprintf(stderr, "GPUassert: %s %s %d\n", cudaGetErrorString(code), file,
line);
if (abort) exit(code);
}
}
const int THREADS_PER_BLOCK_NMS = sizeof(unsigned long long) * 8;
void boxesoverlapLauncher(const int num_a, const float *boxes_a,
const int num_b, const float *boxes_b,
float *ans_overlap);
void boxesioubevLauncher(const int num_a, const float *boxes_a, const int num_b,
const float *boxes_b, float *ans_iou);
void nmsLauncher(const float *boxes, unsigned long long *mask, int boxes_num,
float nms_overlap_thresh);
void nmsNormalLauncher(const float *boxes, unsigned long long *mask,
int boxes_num, float nms_overlap_thresh);
int boxes_overlap_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b,
at::Tensor ans_overlap) {
// params boxes_a: (N, 5) [x1, y1, x2, y2, ry]
// params boxes_b: (M, 5)
// params ans_overlap: (N, M)
CHECK_INPUT(boxes_a);
CHECK_INPUT(boxes_b);
CHECK_INPUT(ans_overlap);
int num_a = boxes_a.size(0);
int num_b = boxes_b.size(0);
const float *boxes_a_data = boxes_a.data_ptr<float>();
const float *boxes_b_data = boxes_b.data_ptr<float>();
float *ans_overlap_data = ans_overlap.data_ptr<float>();
boxesoverlapLauncher(num_a, boxes_a_data, num_b, boxes_b_data,
ans_overlap_data);
return 1;
}
int boxes_iou_bev_gpu(at::Tensor boxes_a, at::Tensor boxes_b,
at::Tensor ans_iou) {
// params boxes_a: (N, 5) [x1, y1, x2, y2, ry]
// params boxes_b: (M, 5)
// params ans_overlap: (N, M)
CHECK_INPUT(boxes_a);
CHECK_INPUT(boxes_b);
CHECK_INPUT(ans_iou);
int num_a = boxes_a.size(0);
int num_b = boxes_b.size(0);
const float *boxes_a_data = boxes_a.data_ptr<float>();
const float *boxes_b_data = boxes_b.data_ptr<float>();
float *ans_iou_data = ans_iou.data_ptr<float>();
boxesioubevLauncher(num_a, boxes_a_data, num_b, boxes_b_data, ans_iou_data);
return 1;
}
int nms_gpu(at::Tensor boxes, at::Tensor keep,
float nms_overlap_thresh, int device_id) {
// params boxes: (N, 5) [x1, y1, x2, y2, ry]
// params keep: (N)
CHECK_INPUT(boxes);
CHECK_CONTIGUOUS(keep);
cudaSetDevice(device_id);
int boxes_num = boxes.size(0);
const float *boxes_data = boxes.data_ptr<float>();
int64_t *keep_data = keep.data_ptr<int64_t>();
const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
unsigned long long *mask_data = NULL;
CHECK_ERROR(cudaMalloc((void **)&mask_data,
boxes_num * col_blocks * sizeof(unsigned long long)));
nmsLauncher(boxes_data, mask_data, boxes_num, nms_overlap_thresh);
// unsigned long long mask_cpu[boxes_num * col_blocks];
// unsigned long long *mask_cpu = new unsigned long long [boxes_num *
// col_blocks];
std::vector<unsigned long long> mask_cpu(boxes_num * col_blocks);
// printf("boxes_num=%d, col_blocks=%d\n", boxes_num, col_blocks);
CHECK_ERROR(cudaMemcpy(&mask_cpu[0], mask_data,
boxes_num * col_blocks * sizeof(unsigned long long),
cudaMemcpyDeviceToHost));
cudaFree(mask_data);
unsigned long long *remv_cpu = new unsigned long long[col_blocks]();
int num_to_keep = 0;
for (int i = 0; i < boxes_num; i++) {
int nblock = i / THREADS_PER_BLOCK_NMS;
int inblock = i % THREADS_PER_BLOCK_NMS;
if (!(remv_cpu[nblock] & (1ULL << inblock))) {
keep_data[num_to_keep++] = i;
unsigned long long *p = &mask_cpu[0] + i * col_blocks;
for (int j = nblock; j < col_blocks; j++) {
remv_cpu[j] |= p[j];
}
}
}
delete[] remv_cpu;
if (cudaSuccess != cudaGetLastError()) printf("Error!\n");
return num_to_keep;
}
int nms_normal_gpu(at::Tensor boxes, at::Tensor keep,
float nms_overlap_thresh, int device_id) {
// params boxes: (N, 5) [x1, y1, x2, y2, ry]
// params keep: (N)
CHECK_INPUT(boxes);
CHECK_CONTIGUOUS(keep);
cudaSetDevice(device_id);
int boxes_num = boxes.size(0);
const float *boxes_data = boxes.data_ptr<float>();
int64_t *keep_data = keep.data_ptr<int64_t>();
const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
unsigned long long *mask_data = NULL;
CHECK_ERROR(cudaMalloc((void **)&mask_data,
boxes_num * col_blocks * sizeof(unsigned long long)));
nmsNormalLauncher(boxes_data, mask_data, boxes_num, nms_overlap_thresh);
// unsigned long long mask_cpu[boxes_num * col_blocks];
// unsigned long long *mask_cpu = new unsigned long long [boxes_num *
// col_blocks];
std::vector<unsigned long long> mask_cpu(boxes_num * col_blocks);
// printf("boxes_num=%d, col_blocks=%d\n", boxes_num, col_blocks);
CHECK_ERROR(cudaMemcpy(&mask_cpu[0], mask_data,
boxes_num * col_blocks * sizeof(unsigned long long),
cudaMemcpyDeviceToHost));
cudaFree(mask_data);
unsigned long long *remv_cpu = new unsigned long long[col_blocks]();
int num_to_keep = 0;
for (int i = 0; i < boxes_num; i++) {
int nblock = i / THREADS_PER_BLOCK_NMS;
int inblock = i % THREADS_PER_BLOCK_NMS;
if (!(remv_cpu[nblock] & (1ULL << inblock))) {
keep_data[num_to_keep++] = i;
unsigned long long *p = &mask_cpu[0] + i * col_blocks;
for (int j = nblock; j < col_blocks; j++) {
remv_cpu[j] |= p[j];
}
}
}
delete[] remv_cpu;
if (cudaSuccess != cudaGetLastError()) printf("Error!\n");
return num_to_keep;
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("boxes_overlap_bev_gpu", &boxes_overlap_bev_gpu,
"oriented boxes overlap");
m.def("boxes_iou_bev_gpu", &boxes_iou_bev_gpu, "oriented boxes iou");
m.def("nms_gpu", &nms_gpu, "oriented nms gpu");
m.def("nms_normal_gpu", &nms_normal_gpu, "nms gpu");
}
mmdet3d/ops/iou3d/src/iou3d_kernel.cu deleted 100644 → 0
View file @ 9c7270d0
// Modified from
// https://github.com/open-mmlab/OpenPCDet/blob/master/pcdet/ops/iou3d_nms/src/iou3d_nms_kernel.cu
/*
3D IoU Calculation and Rotated NMS(modified from 2D NMS written by others)
Written by Shaoshuai Shi
All Rights Reserved 2019-2020.
*/
#include <stdio.h>
#define THREADS_PER_BLOCK 16
#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
//#define DEBUG
const int THREADS_PER_BLOCK_NMS = sizeof(unsigned long long) * 8;
__device__ const float EPS = 1e-8;
struct Point {
float x, y;
__device__ Point() {}
__device__ Point(double _x, double _y) { x = _x, y = _y; }
__device__ void set(float _x, float _y) {
x = _x;
y = _y;
}
__device__ Point operator+(const Point &b) const {
return Point(x + b.x, y + b.y);
}
__device__ Point operator-(const Point &b) const {
return Point(x - b.x, y - b.y);
}
};
__device__ inline float cross(const Point &a, const Point &b) {
return a.x * b.y - a.y * b.x;
}
__device__ inline float cross(const Point &p1, const Point &p2,
const Point &p0) {
return (p1.x - p0.x) * (p2.y - p0.y) - (p2.x - p0.x) * (p1.y - p0.y);
}
__device__ int check_rect_cross(const Point &p1, const Point &p2,
const Point &q1, const Point &q2) {
int ret = min(p1.x, p2.x) <= max(q1.x, q2.x) &&
min(q1.x, q2.x) <= max(p1.x, p2.x) &&
min(p1.y, p2.y) <= max(q1.y, q2.y) &&
min(q1.y, q2.y) <= max(p1.y, p2.y);
return ret;
}
__device__ inline int check_in_box2d(const float *box, const Point &p) {
// params: box (5) [x1, y1, x2, y2, angle]
const float MARGIN = 1e-5;
float center_x = (box[0] + box[2]) / 2;
float center_y = (box[1] + box[3]) / 2;
float angle_cos = cos(-box[4]),
angle_sin =
sin(-box[4]); // rotate the point in the opposite direction of box
float rot_x =
(p.x - center_x) * angle_cos - (p.y - center_y) * angle_sin + center_x;
float rot_y =
(p.x - center_x) * angle_sin + (p.y - center_y) * angle_cos + center_y;
#ifdef DEBUG
printf("box: (%.3f, %.3f, %.3f, %.3f, %.3f)\n", box[0], box[1], box[2],
box[3], box[4]);
printf(
"center: (%.3f, %.3f), cossin(%.3f, %.3f), src(%.3f, %.3f), rot(%.3f, "
"%.3f)\n",
center_x, center_y, angle_cos, angle_sin, p.x, p.y, rot_x, rot_y);
#endif
return (rot_x > box[0] - MARGIN && rot_x < box[2] + MARGIN &&
rot_y > box[1] - MARGIN && rot_y < box[3] + MARGIN);
}
__device__ inline int intersection(const Point &p1, const Point &p0,
const Point &q1, const Point &q0,
Point &ans) {
// fast exclusion
if (check_rect_cross(p0, p1, q0, q1) == 0) return 0;
// check cross standing
float s1 = cross(q0, p1, p0);
float s2 = cross(p1, q1, p0);
float s3 = cross(p0, q1, q0);
float s4 = cross(q1, p1, q0);
if (!(s1 * s2 > 0 && s3 * s4 > 0)) return 0;
// calculate intersection of two lines
float s5 = cross(q1, p1, p0);
if (fabs(s5 - s1) > EPS) {
ans.x = (s5 * q0.x - s1 * q1.x) / (s5 - s1);
ans.y = (s5 * q0.y - s1 * q1.y) / (s5 - s1);
} else {
float a0 = p0.y - p1.y, b0 = p1.x - p0.x, c0 = p0.x * p1.y - p1.x * p0.y;
float a1 = q0.y - q1.y, b1 = q1.x - q0.x, c1 = q0.x * q1.y - q1.x * q0.y;
float D = a0 * b1 - a1 * b0;
ans.x = (b0 * c1 - b1 * c0) / D;
ans.y = (a1 * c0 - a0 * c1) / D;
}
return 1;
}
__device__ inline void rotate_around_center(const Point &center,
const float angle_cos,
const float angle_sin, Point &p) {
float new_x =
(p.x - center.x) * angle_cos - (p.y - center.y) * angle_sin + center.x;
float new_y =
(p.x - center.x) * angle_sin + (p.y - center.y) * angle_cos + center.y;
p.set(new_x, new_y);
}
__device__ inline int point_cmp(const Point &a, const Point &b,
const Point &center) {
return atan2(a.y - center.y, a.x - center.x) >
atan2(b.y - center.y, b.x - center.x);
}
__device__ inline float box_overlap(const float *box_a, const float *box_b) {
// params: box_a (5) [x1, y1, x2, y2, angle]
// params: box_b (5) [x1, y1, x2, y2, angle]
float a_x1 = box_a[0], a_y1 = box_a[1], a_x2 = box_a[2], a_y2 = box_a[3],
a_angle = box_a[4];
float b_x1 = box_b[0], b_y1 = box_b[1], b_x2 = box_b[2], b_y2 = box_b[3],
b_angle = box_b[4];
Point center_a((a_x1 + a_x2) / 2, (a_y1 + a_y2) / 2);
Point center_b((b_x1 + b_x2) / 2, (b_y1 + b_y2) / 2);
#ifdef DEBUG
printf(
"a: (%.3f, %.3f, %.3f, %.3f, %.3f), b: (%.3f, %.3f, %.3f, %.3f, %.3f)\n",
a_x1, a_y1, a_x2, a_y2, a_angle, b_x1, b_y1, b_x2, b_y2, b_angle);
printf("center a: (%.3f, %.3f), b: (%.3f, %.3f)\n", center_a.x, center_a.y,
center_b.x, center_b.y);
#endif
Point box_a_corners[5];
box_a_corners[0].set(a_x1, a_y1);
box_a_corners[1].set(a_x2, a_y1);
box_a_corners[2].set(a_x2, a_y2);
box_a_corners[3].set(a_x1, a_y2);
Point box_b_corners[5];
box_b_corners[0].set(b_x1, b_y1);
box_b_corners[1].set(b_x2, b_y1);
box_b_corners[2].set(b_x2, b_y2);
box_b_corners[3].set(b_x1, b_y2);
// get oriented corners
float a_angle_cos = cos(a_angle), a_angle_sin = sin(a_angle);
float b_angle_cos = cos(b_angle), b_angle_sin = sin(b_angle);
for (int k = 0; k < 4; k++) {
#ifdef DEBUG
printf("before corner %d: a(%.3f, %.3f), b(%.3f, %.3f) \n", k,
box_a_corners[k].x, box_a_corners[k].y, box_b_corners[k].x,
box_b_corners[k].y);
#endif
rotate_around_center(center_a, a_angle_cos, a_angle_sin, box_a_corners[k]);
rotate_around_center(center_b, b_angle_cos, b_angle_sin, box_b_corners[k]);
#ifdef DEBUG
printf("corner %d: a(%.3f, %.3f), b(%.3f, %.3f) \n", k, box_a_corners[k].x,
box_a_corners[k].y, box_b_corners[k].x, box_b_corners[k].y);
#endif
}
box_a_corners[4] = box_a_corners[0];
box_b_corners[4] = box_b_corners[0];
// get intersection of lines
Point cross_points[16];
Point poly_center;
int cnt = 0, flag = 0;
poly_center.set(0, 0);
for (int i = 0; i < 4; i++) {
for (int j = 0; j < 4; j++) {
flag = intersection(box_a_corners[i + 1], box_a_corners[i],
box_b_corners[j + 1], box_b_corners[j],
cross_points[cnt]);
if (flag) {
poly_center = poly_center + cross_points[cnt];
cnt++;
}
}
}
// check corners
for (int k = 0; k < 4; k++) {
if (check_in_box2d(box_a, box_b_corners[k])) {
poly_center = poly_center + box_b_corners[k];
cross_points[cnt] = box_b_corners[k];
cnt++;
}
if (check_in_box2d(box_b, box_a_corners[k])) {
poly_center = poly_center + box_a_corners[k];
cross_points[cnt] = box_a_corners[k];
cnt++;
}
}
poly_center.x /= cnt;
poly_center.y /= cnt;
// sort the points of polygon
Point temp;
for (int j = 0; j < cnt - 1; j++) {
for (int i = 0; i < cnt - j - 1; i++) {
if (point_cmp(cross_points[i], cross_points[i + 1], poly_center)) {
temp = cross_points[i];
cross_points[i] = cross_points[i + 1];
cross_points[i + 1] = temp;
}
}
}
#ifdef DEBUG
printf("cnt=%d\n", cnt);
for (int i = 0; i < cnt; i++) {
printf("All cross point %d: (%.3f, %.3f)\n", i, cross_points[i].x,
cross_points[i].y);
}
#endif
// get the overlap areas
float area = 0;
for (int k = 0; k < cnt - 1; k++) {
area += cross(cross_points[k] - cross_points[0],
cross_points[k + 1] - cross_points[0]);
}
return fabs(area) / 2.0;
}
__device__ inline float iou_bev(const float *box_a, const float *box_b) {
// params: box_a (5) [x1, y1, x2, y2, angle]
// params: box_b (5) [x1, y1, x2, y2, angle]
float sa = (box_a[2] - box_a[0]) * (box_a[3] - box_a[1]);
float sb = (box_b[2] - box_b[0]) * (box_b[3] - box_b[1]);
float s_overlap = box_overlap(box_a, box_b);
return s_overlap / fmaxf(sa + sb - s_overlap, EPS);
}
__global__ void boxes_overlap_kernel(const int num_a, const float *boxes_a,
const int num_b, const float *boxes_b,
float *ans_overlap) {
const int a_idx = blockIdx.y * THREADS_PER_BLOCK + threadIdx.y;
const int b_idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x;
if (a_idx >= num_a || b_idx >= num_b) {
return;
}
const float *cur_box_a = boxes_a + a_idx * 5;
const float *cur_box_b = boxes_b + b_idx * 5;
float s_overlap = box_overlap(cur_box_a, cur_box_b);
ans_overlap[a_idx * num_b + b_idx] = s_overlap;
}
__global__ void boxes_iou_bev_kernel(const int num_a, const float *boxes_a,
const int num_b, const float *boxes_b,
float *ans_iou) {
const int a_idx = blockIdx.y * THREADS_PER_BLOCK + threadIdx.y;
const int b_idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x;
if (a_idx >= num_a || b_idx >= num_b) {
return;
}
const float *cur_box_a = boxes_a + a_idx * 5;
const float *cur_box_b = boxes_b + b_idx * 5;
float cur_iou_bev = iou_bev(cur_box_a, cur_box_b);
ans_iou[a_idx * num_b + b_idx] = cur_iou_bev;
}
__global__ void nms_kernel(const int boxes_num, const float nms_overlap_thresh,
const float *boxes, unsigned long long *mask) {
// params: boxes (N, 5) [x1, y1, x2, y2, ry]
// params: mask (N, N/THREADS_PER_BLOCK_NMS)
const int row_start = blockIdx.y;
const int col_start = blockIdx.x;
// if (row_start > col_start) return;
const int row_size = fminf(boxes_num - row_start * THREADS_PER_BLOCK_NMS,
THREADS_PER_BLOCK_NMS);
const int col_size = fminf(boxes_num - col_start * THREADS_PER_BLOCK_NMS,
THREADS_PER_BLOCK_NMS);
__shared__ float block_boxes[THREADS_PER_BLOCK_NMS * 5];
if (threadIdx.x < col_size) {
block_boxes[threadIdx.x * 5 + 0] =
boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 0];
block_boxes[threadIdx.x * 5 + 1] =
boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 1];
block_boxes[threadIdx.x * 5 + 2] =
boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 2];
block_boxes[threadIdx.x * 5 + 3] =
boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 3];
block_boxes[threadIdx.x * 5 + 4] =
boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 4];
}
__syncthreads();
if (threadIdx.x < row_size) {
const int cur_box_idx = THREADS_PER_BLOCK_NMS * row_start + threadIdx.x;
const float *cur_box = 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 (iou_bev(cur_box, block_boxes + i * 5) > nms_overlap_thresh) {
t |= 1ULL << i;
}
}
const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
mask[cur_box_idx * col_blocks + col_start] = t;
}
}
__device__ inline float iou_normal(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, 0.f), height = fmaxf(bottom - top, 0.f);
float interS = width * height;
float Sa = (a[2] - a[0]) * (a[3] - a[1]);
float Sb = (b[2] - b[0]) * (b[3] - b[1]);
return interS / fmaxf(Sa + Sb - interS, EPS);
}
__global__ void nms_normal_kernel(const int boxes_num,
const float nms_overlap_thresh,
const float *boxes,
unsigned long long *mask) {
// params: boxes (N, 5) [x1, y1, x2, y2, ry]
// params: mask (N, N/THREADS_PER_BLOCK_NMS)
const int row_start = blockIdx.y;
const int col_start = blockIdx.x;
// if (row_start > col_start) return;
const int row_size = fminf(boxes_num - row_start * THREADS_PER_BLOCK_NMS,
THREADS_PER_BLOCK_NMS);
const int col_size = fminf(boxes_num - col_start * THREADS_PER_BLOCK_NMS,
THREADS_PER_BLOCK_NMS);
__shared__ float block_boxes[THREADS_PER_BLOCK_NMS * 5];
if (threadIdx.x < col_size) {
block_boxes[threadIdx.x * 5 + 0] =
boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 0];
block_boxes[threadIdx.x * 5 + 1] =
boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 1];
block_boxes[threadIdx.x * 5 + 2] =
boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 2];
block_boxes[threadIdx.x * 5 + 3] =
boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 3];
block_boxes[threadIdx.x * 5 + 4] =
boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 4];
}
__syncthreads();
if (threadIdx.x < row_size) {
const int cur_box_idx = THREADS_PER_BLOCK_NMS * row_start + threadIdx.x;
const float *cur_box = 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 (iou_normal(cur_box, block_boxes + i * 5) > nms_overlap_thresh) {
t |= 1ULL << i;
}
}
const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
mask[cur_box_idx * col_blocks + col_start] = t;
}
}
void boxesoverlapLauncher(const int num_a, const float *boxes_a,
const int num_b, const float *boxes_b,
float *ans_overlap) {
dim3 blocks(
DIVUP(num_b, THREADS_PER_BLOCK),
DIVUP(num_a, THREADS_PER_BLOCK)); // blockIdx.x(col), blockIdx.y(row)
dim3 threads(THREADS_PER_BLOCK, THREADS_PER_BLOCK);
boxes_overlap_kernel<<<blocks, threads>>>(num_a, boxes_a, num_b, boxes_b,
ans_overlap);
#ifdef DEBUG
cudaDeviceSynchronize(); // for using printf in kernel function
#endif
}
void boxesioubevLauncher(const int num_a, const float *boxes_a, const int num_b,
const float *boxes_b, float *ans_iou) {
dim3 blocks(
DIVUP(num_b, THREADS_PER_BLOCK),
DIVUP(num_a, THREADS_PER_BLOCK)); // blockIdx.x(col), blockIdx.y(row)
dim3 threads(THREADS_PER_BLOCK, THREADS_PER_BLOCK);
boxes_iou_bev_kernel<<<blocks, threads>>>(num_a, boxes_a, num_b, boxes_b,
ans_iou);
}
void nmsLauncher(const float *boxes, unsigned long long *mask, int boxes_num,
float nms_overlap_thresh) {
dim3 blocks(DIVUP(boxes_num, THREADS_PER_BLOCK_NMS),
DIVUP(boxes_num, THREADS_PER_BLOCK_NMS));
dim3 threads(THREADS_PER_BLOCK_NMS);
nms_kernel<<<blocks, threads>>>(boxes_num, nms_overlap_thresh, boxes, mask);
}
void nmsNormalLauncher(const float *boxes, unsigned long long *mask,
int boxes_num, float nms_overlap_thresh) {
dim3 blocks(DIVUP(boxes_num, THREADS_PER_BLOCK_NMS),
DIVUP(boxes_num, THREADS_PER_BLOCK_NMS));
dim3 threads(THREADS_PER_BLOCK_NMS);
nms_normal_kernel<<<blocks, threads>>>(boxes_num, nms_overlap_thresh, boxes,
mask);
}
mmdet3d/ops/knn/__init__.py deleted 100644 → 0
View file @ 9c7270d0
# Copyright (c) OpenMMLab. All rights reserved.
from .knn import knn
__all__ = ['knn']
mmdet3d/ops/knn/knn.py deleted 100644 → 0
View file @ 9c7270d0
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch.autograd import Function
from . import knn_ext
class KNN(Function):
r"""KNN (CUDA) based on heap data structure.
Modified from `PAConv <https://github.com/CVMI-Lab/PAConv/tree/main/
scene_seg/lib/pointops/src/knnquery_heap>`_.
Find k-nearest points.
"""
@staticmethod
def forward(ctx,
k: int,
xyz: torch.Tensor,
center_xyz: torch.Tensor = None,
transposed: bool = False) -> torch.Tensor:
"""Forward.
Args:
k (int): number of nearest neighbors.
xyz (Tensor): (B, N, 3) if transposed == False, else (B, 3, N).
xyz coordinates of the features.
center_xyz (Tensor): (B, npoint, 3) if transposed == False,
else (B, 3, npoint). centers of the knn query.
transposed (bool): whether the input tensors are transposed.
defaults to False. Should not explicitly use this keyword
when calling knn (=KNN.apply), just add the fourth param.
Returns:
Tensor: (B, k, npoint) tensor with the indices of
the features that form k-nearest neighbours.
"""
assert k > 0
if center_xyz is None:
center_xyz = xyz
if transposed:
xyz = xyz.transpose(2, 1).contiguous()
center_xyz = center_xyz.transpose(2, 1).contiguous()
assert xyz.is_contiguous() # [B, N, 3]
assert center_xyz.is_contiguous() # [B, npoint, 3]
center_xyz_device = center_xyz.get_device()
assert center_xyz_device == xyz.get_device(), \
'center_xyz and xyz should be put on the same device'
if torch.cuda.current_device() != center_xyz_device:
torch.cuda.set_device(center_xyz_device)
B, npoint, _ = center_xyz.shape
N = xyz.shape[1]
idx = center_xyz.new_zeros((B, npoint, k)).int()
dist2 = center_xyz.new_zeros((B, npoint, k)).float()
knn_ext.knn_wrapper(B, N, npoint, k, xyz, center_xyz, idx, dist2)
# idx shape to [B, k, npoint]
idx = idx.transpose(2, 1).contiguous()
ctx.mark_non_differentiable(idx)
return idx
@staticmethod
def backward(ctx, a=None):
return None, None, None
knn = KNN.apply
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