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bevfusion_changes.patch
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bevfusion_changes.patch
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diff --git a/.gitignore b/.gitignore
index b6e4761..fe21cf8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,6 +5,11 @@ __pycache__/
# C extensions
*.so
+data/
+
+*.pkl
+*.pth
+.vscode/
# Distribution / packaging
.Python
diff --git a/extend/__init__.py b/extend/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/extend/custom_func.py b/extend/custom_func.py
new file mode 100644
index 0000000..94e5057
--- /dev/null
+++ b/extend/custom_func.py
@@ -0,0 +1,102 @@
+# only for bevfusion
+import torch
+import torchvision.transforms as transforms
+import torch.nn.functional as F
+import torchvision
+from torchvision.utils import save_image
+import copy
+
+
+def custom_data_preprocess(data):
+ return data
+
+
+def custom_data_postprocess_eval(data):
+ return data
+
+
+def custom_data_work(data):
+ metas = data["metas"]._data[0][0]
+ img_path_list = metas["filename"]
+ img_org_np = metas["img_org"]
+ img_processed = data["img"]._data[0].clone()
+ gt_labels_3d = data["gt_labels_3d"]._data[0][0]
+ return metas, img_path_list, img_org_np, img_processed, gt_labels_3d
+
+
+def custom_data_work_point(data):
+ metas = data["metas"]._data[0][0]
+ img_path_list = metas["filename"]
+ img_org_np = metas["img_org"]
+ img_processed = data["img"]._data[0].clone()
+ gt_labels_3d = data["gt_labels_3d"]._data[0][0]
+ points_tensor = data["points"]._data[0][0].clone()
+ return metas, img_path_list, img_org_np, img_processed, gt_labels_3d, points_tensor
+
+
+def custom_result_postprocess(result):
+ return result
+
+
+def custom_img_read_from_img_org(img_org_np, device):
+ img_org_np_255_rgb_hwcn_uint8 = img_org_np # PIL读取 RGB 直接转 numpy
+ img_org_tensor_rgb_255_hwcn = torch.from_numpy(
+ img_org_np_255_rgb_hwcn_uint8
+ ).float()
+ img_org_tensor_rgb_255 = img_org_tensor_rgb_255_hwcn.permute(3, 2, 0, 1)
+ img_tensor_rgb_6chw_0to1 = (img_org_tensor_rgb_255 / 255.0).to(device)
+ return img_tensor_rgb_6chw_0to1
+
+
+def custom_differentiable_transform(img_tensor_rgb_6chw_0to1, img_metas):
+ """Alternative Data Preparation for Original Model
+
+ Args:
+ img_tensor (torch.tensor): (6xCxHxW), tensors of original imgs
+ """
+
+ assert len(img_tensor_rgb_6chw_0to1.shape) == 4
+ assert img_tensor_rgb_6chw_0to1.shape[0] == 6
+ assert img_tensor_rgb_6chw_0to1.shape[1] == 3
+ assert img_tensor_rgb_6chw_0to1.max() <= 1.0
+ assert img_tensor_rgb_6chw_0to1.min() >= 0.0
+ assert img_tensor_rgb_6chw_0to1.dtype == torch.float32
+ assert img_tensor_rgb_6chw_0to1.is_cuda
+ img_tensor = img_tensor_rgb_6chw_0to1
+
+ device = img_tensor_rgb_6chw_0to1.device
+ mean = [0.485, 0.456, 0.406]
+ std = [0.229, 0.224, 0.225]
+ mean = torch.tensor(mean).to(device)[None, None, :, None, None]
+ std = torch.tensor(std).to(device)[None, None, :, None, None]
+
+ ############ resize norm pad
+ ######## resize
+ resize_size = (432, 768)
+ img_tensor_resize = F.interpolate(
+ img_tensor, resize_size, mode="bilinear", align_corners=False
+ )
+
+ ######## crop
+ crop_size = (32, 176, 736, 432)
+ img_tensor_crop = img_tensor_resize[
+ ..., crop_size[1] : crop_size[3], crop_size[0] : crop_size[2]
+ ]
+
+ ######## norm
+ img_tensor_norm = (img_tensor_crop - mean) / std
+
+ return img_tensor_norm
+
+
+def custom_image_data_give(data, image_ready):
+ data_copy = copy.deepcopy(data)
+ data_copy["img"]._data[0] = image_ready
+ return data_copy
+
+
+def custom_image_data_give_point(data, image_ready, points_ready):
+ data_copy = copy.deepcopy(data)
+ data_copy["img"]._data[0] = image_ready
+ data_copy["points"]._data[0][0] = points_ready
+ return data_copy
diff --git a/extend_common b/extend_common
new file mode 120000
index 0000000..0633dd9
--- /dev/null
+++ b/extend_common
@@ -0,0 +1 @@
+../extend_common/
\ No newline at end of file
diff --git a/mmdet3d/apis_common b/mmdet3d/apis_common
new file mode 120000
index 0000000..b44e193
--- /dev/null
+++ b/mmdet3d/apis_common
@@ -0,0 +1 @@
+../../apis_common/
\ No newline at end of file
diff --git a/mmdet3d/datasets/pipelines/formating.py b/mmdet3d/datasets/pipelines/formating.py
index 3e781ce..34ac202 100644
--- a/mmdet3d/datasets/pipelines/formating.py
+++ b/mmdet3d/datasets/pipelines/formating.py
@@ -157,6 +157,8 @@ class Collect3D:
"pcd_rotation",
"lidar_path",
"transformation_3d_flow",
+ # zzj api
+ 'img_org'
),
):
self.keys = keys
diff --git a/mmdet3d/datasets/pipelines/loading.py b/mmdet3d/datasets/pipelines/loading.py
index cac5b6d..8b57313 100644
--- a/mmdet3d/datasets/pipelines/loading.py
+++ b/mmdet3d/datasets/pipelines/loading.py
@@ -70,6 +70,9 @@ class LoadMultiViewImageFromFiles:
results["pad_shape"] = images[0].size
results["scale_factor"] = 1.0
+ # zzj add
+ results["img_org"] = np.stack([np.asarray(image) for image in images],axis=-1)
+
return results
def __repr__(self):
diff --git a/mmdet3d/models/fusion_models/bevfusion.py b/mmdet3d/models/fusion_models/bevfusion.py
index 2931b48..301f5dd 100644
--- a/mmdet3d/models/fusion_models/bevfusion.py
+++ b/mmdet3d/models/fusion_models/bevfusion.py
@@ -126,13 +126,20 @@ class BEVFusion(Base3DFusionModel):
)
return x
+ # def extract_lidar_features(self, x) -> torch.Tensor:
+ # feats, coords, sizes = self.voxelize(x)
+ # batch_size = coords[-1, 0] + 1
+ # x = self.encoders["lidar"]["backbone"](feats, coords, batch_size, sizes=sizes)
+ # return x
def extract_lidar_features(self, x) -> torch.Tensor:
feats, coords, sizes = self.voxelize(x)
+ # zzj api voxelization
+ coords = coords[:,[0,3,2,1]] # z,y,x -->> x,y,z plz check mmcvfull1.6.1: mmcv\ops\csrc\common\cuda\voxelization_cuda_kernel.cuh L-45
batch_size = coords[-1, 0] + 1
- x = self.encoders["lidar"]["backbone"](feats, coords, batch_size, sizes=sizes)
+ x = self.encoders["lidar"]["backbone"](feats, coords, batch_size, sizes=sizes) # SparseEncoder
return x
- @torch.no_grad()
+ # @torch.no_grad()
@force_fp32()
def voxelize(self, points):
feats, coords, sizes = [], [], []
@@ -261,7 +268,8 @@ class BEVFusion(Base3DFusionModel):
x = self.decoder["backbone"](x)
x = self.decoder["neck"](x)
- if self.training:
+ # if self.training:
+ if kwargs['return_loss']:
outputs = {}
for type, head in self.heads.items():
if type == "object":
diff --git a/mmdet3d/models/vtransforms/base.py b/mmdet3d/models/vtransforms/base.py
index 400054c..2094e18 100644
--- a/mmdet3d/models/vtransforms/base.py
+++ b/mmdet3d/models/vtransforms/base.py
@@ -240,7 +240,7 @@ class BaseDepthTransform(BaseTransform):
)
for b in range(batch_size):
- cur_coords = points[b][:, :3]
+ cur_coords = points[b][:, :3].detach()
cur_img_aug_matrix = img_aug_matrix[b]
cur_lidar_aug_matrix = lidar_aug_matrix[b]
cur_lidar2image = lidar2image[b]
@@ -272,7 +272,7 @@ class BaseDepthTransform(BaseTransform):
& (cur_coords[..., 1] < self.image_size[1])
& (cur_coords[..., 1] >= 0)
)
- for c in range(on_img.shape[0]):
+ for c in range(on_img.shape[0]): # 把 有lidar的点 在depth 上 打出来，数值为真实深度（m）
masked_coords = cur_coords[c, on_img[c]].long()
masked_dist = dist[c, on_img[c]]
depth[b, c, 0, masked_coords[:, 0], masked_coords[:, 1]] = masked_dist
diff --git a/mmdet3d/models/vtransforms/depth_lss.py b/mmdet3d/models/vtransforms/depth_lss.py
index b7cd45d..263aa4a 100644
--- a/mmdet3d/models/vtransforms/depth_lss.py
+++ b/mmdet3d/models/vtransforms/depth_lss.py
@@ -82,11 +82,11 @@ class DepthLSSTransform(BaseDepthTransform):
def get_cam_feats(self, x, d):
B, N, C, fH, fW = x.shape
- d = d.view(B * N, *d.shape[2:])
+ d = d.view(B * N, *d.shape[2:]) # 这里的d 可是从LiDAR 投影过来的，是GT啊！
x = x.view(B * N, C, fH, fW)
- d = self.dtransform(d)
- x = torch.cat([d, x], dim=1)
+ d = self.dtransform(d) # 但gt的 d 还是被投入 模型运算 了
+ x = torch.cat([d, x], dim=1) # 处理后的d 作为特征的一部分，供深度预测了
x = self.depthnet(x)
depth = x[:, : self.D].softmax(dim=1)
diff --git a/mmdet3d/ops/voxel/src/voxelization.h b/mmdet3d/ops/voxel/src/voxelization.h
index 765b30a..1e96c4e 100644
--- a/mmdet3d/ops/voxel/src/voxelization.h
+++ b/mmdet3d/ops/voxel/src/voxelization.h
@@ -27,6 +27,11 @@ int hard_voxelize_gpu(const at::Tensor &points, at::Tensor &voxels,
const std::vector<float> voxel_size,
const std::vector<float> coors_range,
const int max_points, const int max_voxels,
+ // zzj api 20220224
+ // : add input valuables for return
+ at::Tensor &point_to_pointidx,
+ at::Tensor &point_to_voxelidx,
+ at::Tensor &coor_to_voxelidx,
const int NDim = 3);
int nondisterministic_hard_voxelize_gpu(const at::Tensor &points, at::Tensor &voxels,
@@ -60,12 +65,22 @@ inline int hard_voxelize(const at::Tensor &points, at::Tensor &voxels,
const std::vector<float> voxel_size,
const std::vector<float> coors_range,
const int max_points, const int max_voxels,
+ // zzj api 20220224
+ // : add input valuables for return
+ at::Tensor &point_to_pointidx,
+ at::Tensor &point_to_voxelidx,
+ at::Tensor &coor_to_voxelidx,
const int NDim = 3, const bool deterministic = true) {
if (points.device().is_cuda()) {
#ifdef WITH_CUDA
if (deterministic) {
return hard_voxelize_gpu(points, voxels, coors, num_points_per_voxel,
voxel_size, coors_range, max_points, max_voxels,
+ // zzj api 20220224
+ // : add input valuables for return
+ point_to_pointidx,
+ point_to_voxelidx,
+ coor_to_voxelidx,
NDim);
}
return nondisterministic_hard_voxelize_gpu(points, voxels, coors, num_points_per_voxel,
diff --git a/mmdet3d/ops/voxel/src/voxelization_cpu.cpp b/mmdet3d/ops/voxel/src/voxelization_cpu.cpp
index 1f87e26..6bcec40 100644
--- a/mmdet3d/ops/voxel/src/voxelization_cpu.cpp
+++ b/mmdet3d/ops/voxel/src/voxelization_cpu.cpp
@@ -27,7 +27,7 @@ void dynamic_voxelize_kernel(const torch::TensorAccessor<T, 2> points,
failed = true;
break;
}
- coor[j] = c;
+ coor[ndim_minus_1 - j] = c;
}
for (int k = 0; k < NDim; ++k) {
diff --git a/mmdet3d/ops/voxel/src/voxelization_cuda.cu b/mmdet3d/ops/voxel/src/voxelization_cuda.cu
index 8191cba..b3b4af5 100644
--- a/mmdet3d/ops/voxel/src/voxelization_cuda.cu
+++ b/mmdet3d/ops/voxel/src/voxelization_cuda.cu
@@ -53,9 +53,9 @@ __global__ void dynamic_voxelize_kernel(
coors_offset[1] = -1;
coors_offset[2] = -1;
} else {
- coors_offset[0] = c_x;
+ coors_offset[0] = c_z;
coors_offset[1] = c_y;
- coors_offset[2] = c_z;
+ coors_offset[2] = c_x;
}
}
}
@@ -166,10 +166,10 @@ __global__ void determin_voxel_num(
int voxelidx = voxel_num[0];
if (voxel_num[0] >= max_voxels) continue;
voxel_num[0] += 1;
- coor_to_voxelidx[i] = voxelidx;
+ coor_to_voxelidx[i] = voxelidx; // coor_to_voxelidx 应该改名为 point_to_voxelidx
num_points_per_voxel[voxelidx] = 1;
} else {
- int point_idx = point_to_pointidx[i];
+ int point_idx = point_to_pointidx[i]; // 从当前点，跳到体素的第一个点，再用第一个点查询体素的index
int voxelidx = coor_to_voxelidx[point_idx];
if (voxelidx != -1) {
coor_to_voxelidx[i] = voxelidx;
@@ -233,6 +233,11 @@ int hard_voxelize_gpu(const at::Tensor& points, at::Tensor& voxels,
const std::vector<float> voxel_size,
const std::vector<float> coors_range,
const int max_points, const int max_voxels,
+ // zzj api 20220224
+ // : add for return
+ at::Tensor &point_to_pointidx,
+ at::Tensor &point_to_voxelidx,
+ at::Tensor &coor_to_voxelidx,
const int NDim = 3) {
// current version tooks about 0.04s for one frame on cpu
// check device
@@ -280,16 +285,20 @@ int hard_voxelize_gpu(const at::Tensor& points, at::Tensor& voxels,
// 2. map point to the idx of the corresponding voxel, find duplicate coor
// create some temporary variables
- auto point_to_pointidx = -at::ones(
- {
- num_points,
- },
- points.options().dtype(at::kInt));
- auto point_to_voxelidx = -at::ones(
- {
- num_points,
- },
- points.options().dtype(at::kInt));
+
+ // zzj api 20220224
+ // : use outside coming values defined in python
+ // not defined in C
+ // auto point_to_pointidx = -at::ones(
+ // {
+ // num_points,
+ // },
+ // points.options().dtype(at::kInt));
+ // auto point_to_voxelidx = -at::ones(
+ // {
+ // num_points,
+ // },
+ // points.options().dtype(at::kInt));
dim3 map_grid(std::min(at::cuda::ATenCeilDiv(num_points, 512), 4096));
dim3 map_block(512);
@@ -307,11 +316,16 @@ int hard_voxelize_gpu(const at::Tensor& points, at::Tensor& voxels,
// 3. determin voxel num and voxel's coor index
// make the logic in the CUDA device could accelerate about 10 times
- auto coor_to_voxelidx = -at::ones(
- {
- num_points,
- },
- points.options().dtype(at::kInt));
+
+ // zzj api 20220224
+ // : use outside coming values defined in python
+ // not defined in C
+ // auto coor_to_voxelidx = -at::ones(
+ // {
+ // num_points,
+ // },
+ // points.options().dtype(at::kInt));
+
auto voxel_num = at::zeros(
{
1,
diff --git a/mmdet3d/ops/voxel/src_backup/scatter_points_cpu.cpp b/mmdet3d/ops/voxel/src_backup/scatter_points_cpu.cpp
new file mode 100644
index 0000000..c22b8ae
--- /dev/null
+++ b/mmdet3d/ops/voxel/src_backup/scatter_points_cpu.cpp
@@ -0,0 +1,122 @@
+#include <ATen/TensorUtils.h>
+#include <torch/extension.h>
+// #include "voxelization.h"
+
+namespace {
+
+template <typename T_int>
+void determin_max_points_kernel(
+ torch::TensorAccessor<T_int, 2> coor,
+ torch::TensorAccessor<T_int, 1> point_to_voxelidx,
+ torch::TensorAccessor<T_int, 1> num_points_per_voxel,
+ torch::TensorAccessor<T_int, 3> coor_to_voxelidx, int& voxel_num,
+ int& max_points, const int num_points) {
+ int voxelidx, num;
+ for (int i = 0; i < num_points; ++i) {
+ if (coor[i][0] == -1) continue;
+ voxelidx = coor_to_voxelidx[coor[i][0]][coor[i][1]][coor[i][2]];
+
+ // record voxel
+ if (voxelidx == -1) {
+ voxelidx = voxel_num;
+ voxel_num += 1;
+ coor_to_voxelidx[coor[i][0]][coor[i][1]][coor[i][2]] = voxelidx;
+ }
+
+ // put points into voxel
+ num = num_points_per_voxel[voxelidx];
+ point_to_voxelidx[i] = num;
+ num_points_per_voxel[voxelidx] += 1;
+
+ // update max points per voxel
+ max_points = std::max(max_points, num + 1);
+ }
+
+ return;
+}
+
+template <typename T, typename T_int>
+void scatter_point_to_voxel_kernel(
+ const torch::TensorAccessor<T, 2> points,
+ torch::TensorAccessor<T_int, 2> coor,
+ torch::TensorAccessor<T_int, 1> point_to_voxelidx,
+ torch::TensorAccessor<T_int, 3> coor_to_voxelidx,
+ torch::TensorAccessor<T, 3> voxels,
+ torch::TensorAccessor<T_int, 2> voxel_coors, const int num_features,
+ const int num_points, const int NDim) {
+ for (int i = 0; i < num_points; ++i) {
+ int num = point_to_voxelidx[i];
+ int voxelidx = coor_to_voxelidx[coor[i][0]][coor[i][1]][coor[i][2]];
+ for (int k = 0; k < num_features; ++k) {
+ voxels[voxelidx][num][k] = points[i][k];
+ }
+ for (int k = 0; k < NDim; ++k) {
+ voxel_coors[voxelidx][k] = coor[i][k];
+ }
+ }
+}
+
+} // namespace
+
+namespace voxelization {
+
+std::vector<at::Tensor> dynamic_point_to_voxel_cpu(
+ const at::Tensor& points, const at::Tensor& voxel_mapping,
+ const std::vector<float> voxel_size, const std::vector<float> coors_range) {
+ // current version tooks about 0.02s_0.03s for one frame on cpu
+ // check device
+ AT_ASSERTM(points.device().is_cpu(), "points must be a CPU tensor");
+
+ const int NDim = voxel_mapping.size(1);
+ const int num_points = points.size(0);
+ const int num_features = points.size(1);
+
+ std::vector<int> grid_size(NDim);
+ for (int i = 0; i < NDim; ++i) {
+ grid_size[i] =
+ round((coors_range[NDim + i] - coors_range[i]) / voxel_size[i]);
+ }
+
+ at::Tensor num_points_per_voxel = at::zeros(
+ {
+ num_points,
+ },
+ voxel_mapping.options());
+ at::Tensor coor_to_voxelidx = -at::ones(
+ {grid_size[2], grid_size[1], grid_size[0]}, voxel_mapping.options());
+ at::Tensor point_to_voxelidx = -at::ones(
+ {
+ num_points,
+ },
+ voxel_mapping.options());
+
+ int voxel_num = 0;
+ int max_points = 0;
+ AT_DISPATCH_ALL_TYPES(voxel_mapping.scalar_type(), "determin_max_point", [&] {
+ determin_max_points_kernel<scalar_t>(
+ voxel_mapping.accessor<scalar_t, 2>(),
+ point_to_voxelidx.accessor<scalar_t, 1>(),
+ num_points_per_voxel.accessor<scalar_t, 1>(),
+ coor_to_voxelidx.accessor<scalar_t, 3>(), voxel_num, max_points,
+ num_points);
+ });
+
+ at::Tensor voxels =
+ at::zeros({voxel_num, max_points, num_features}, points.options());
+ at::Tensor voxel_coors =
+ at::zeros({voxel_num, NDim}, points.options().dtype(at::kInt));
+
+ AT_DISPATCH_ALL_TYPES(points.scalar_type(), "scatter_point_to_voxel", [&] {
+ scatter_point_to_voxel_kernel<scalar_t, int>(
+ points.accessor<scalar_t, 2>(), voxel_mapping.accessor<int, 2>(),
+ point_to_voxelidx.accessor<int, 1>(),
+ coor_to_voxelidx.accessor<int, 3>(), voxels.accessor<scalar_t, 3>(),
+ voxel_coors.accessor<int, 2>(), num_features, num_points, NDim);
+ });
+
+ at::Tensor num_points_per_voxel_out =
+ num_points_per_voxel.slice(/*dim=*/0, /*start=*/0, /*end=*/voxel_num);
+ return {voxels, voxel_coors, num_points_per_voxel_out};
+}
+
+} // namespace voxelization
diff --git a/mmdet3d/ops/voxel/src_backup/scatter_points_cuda.cu b/mmdet3d/ops/voxel/src_backup/scatter_points_cuda.cu
new file mode 100644
index 0000000..2ed1869
--- /dev/null
+++ b/mmdet3d/ops/voxel/src_backup/scatter_points_cuda.cu
@@ -0,0 +1,310 @@
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <torch/types.h>
+
+#include <ATen/cuda/CUDAApplyUtils.cuh>
+
+typedef enum { SUM = 0, MEAN = 1, MAX = 2 } reduce_t;
+
+#define CHECK_CUDA(x) \
+ TORCH_CHECK(x.device().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)
+
+namespace {
+int const threadsPerBlock = 512;
+int const maxGridDim = 50000;
+} // namespace
+
+__device__ __forceinline__ static void reduceMax(float *address, float val) {
+ int *address_as_i = reinterpret_cast<int *>(address);
+ int old = *address_as_i, assumed;
+ do {
+ assumed = old;
+ old = atomicCAS(address_as_i, assumed,
+ __float_as_int(fmaxf(val, __int_as_float(assumed))));
+ } while (assumed != old || __int_as_float(old) < val);
+}
+
+__device__ __forceinline__ static void reduceMax(double *address, double val) {
+ unsigned long long *address_as_ull =
+ reinterpret_cast<unsigned long long *>(address);
+ unsigned long long old = *address_as_ull, assumed;
+ do {
+ assumed = old;
+ old = atomicCAS(
+ address_as_ull, assumed,
+ __double_as_longlong(fmax(val, __longlong_as_double(assumed))));
+ } while (assumed != old || __longlong_as_double(old) < val);
+}
+
+// get rid of meaningless warnings when compiling host code
+#ifdef __CUDA_ARCH__
+__device__ __forceinline__ static void reduceAdd(float *address, float val) {
+#if (__CUDA_ARCH__ < 200)
+#warning \
+ "compute capability lower than 2.x. fall back to use CAS version of atomicAdd for float32"
+ int *address_as_i = reinterpret_cast<int *>(address);
+ int old = *address_as_i, assumed;
+ do {
+ assumed = old;
+ old = atomicCAS(address_as_i, assumed,
+ __float_as_int(val + __int_as_float(assumed)));
+ } while (assumed != old);
+#else
+ atomicAdd(address, val);
+#endif
+}
+
+__device__ __forceinline__ static void reduceAdd(double *address, double val) {
+#if (__CUDA_ARCH__ < 600)
+#warning \
+ "compute capability lower than 6.x. fall back to use CAS version of atomicAdd for float64"
+ unsigned long long *address_as_ull =
+ reinterpret_cast<unsigned long long *>(address);
+ unsigned long long old = *address_as_ull, assumed;
+ do {
+ assumed = old;
+ old = atomicCAS(address_as_ull, assumed,
+ __double_as_longlong(val + __longlong_as_double(assumed)));
+ } while (assumed != old);
+#else
+ atomicAdd(address, val);
+#endif
+}
+#endif
+
+template <typename T>
+__global__ void
+feats_reduce_kernel(const T *feats, const int32_t *coors_map,
+ T *reduced_feats, // shall be 0 at initialization
+ const int num_input, const int num_feats,
+ const reduce_t reduce_type) {
+ for (int x = blockIdx.x * blockDim.x + threadIdx.x; x < num_input;
+ x += gridDim.x * blockDim.x) {
+ int32_t reduce_to = coors_map[x];
+ if (reduce_to == -1) continue;
+
+ const T *feats_offset = feats + x * num_feats;
+ T *reduced_feats_offset = reduced_feats + reduce_to * num_feats;
+ if (reduce_type == reduce_t::MAX) {
+ for (int i = 0; i < num_feats; i++) {
+ reduceMax(&reduced_feats_offset[i], feats_offset[i]);
+ }
+ } else {
+ for (int i = 0; i < num_feats; i++) {
+ reduceAdd(&reduced_feats_offset[i], feats_offset[i]);
+ }
+ }
+ }
+}
+
+template <typename T>
+__global__ void add_reduce_traceback_grad_kernel(
+ T *grad_feats, const T *grad_reduced_feats, const int32_t *coors_map,
+ const int32_t *reduce_count, const int num_input, const int num_feats,
+ const reduce_t reduce_type) {
+ for (int x = blockIdx.x * blockDim.x + threadIdx.x; x < num_input;
+ x += gridDim.x * blockDim.x) {
+ int32_t reduce_to = coors_map[x];
+ if (reduce_to == -1) {
+ continue;
+ }
+
+ const int input_offset = x * num_feats;
+ T *grad_feats_offset = grad_feats + input_offset;
+ const int reduced_offset = reduce_to * num_feats;
+ const T *grad_reduced_feats_offset = grad_reduced_feats + reduced_offset;
+
+ if (reduce_type == reduce_t::SUM) {
+ for (int i = 0; i < num_feats; i++) {
+ grad_feats_offset[i] = grad_reduced_feats_offset[i];
+ }
+ } else if (reduce_type == reduce_t::MEAN) {
+ for (int i = 0; i < num_feats; i++) {
+ grad_feats_offset[i] = grad_reduced_feats_offset[i] /
+ static_cast<T>(reduce_count[reduce_to]);
+ }
+ }
+ }
+}
+
+template <typename T>
+__global__ void max_reduce_traceback_scatter_idx_kernel(
+ const T *feats, const T *reduced_feats, int32_t *reduce_from,
+ const int32_t *coors_map, const int num_input, const int num_feats) {
+ for (int x = blockIdx.x * blockDim.x + threadIdx.x; x < num_input;
+ x += gridDim.x * blockDim.x) {
+ int32_t reduce_to = coors_map[x];
+
+ const int input_offset = x * num_feats;
+ const T *feats_offset = feats + input_offset;
+
+ if (reduce_to == -1) {
+ continue;
+ }
+
+ const int reduced_offset = reduce_to * num_feats;
+ const T *reduced_feats_offset = reduced_feats + reduced_offset;
+ int32_t *reduce_from_offset = reduce_from + reduced_offset;
+
+ for (int i = 0; i < num_feats; i++) {
+ if (feats_offset[i] == reduced_feats_offset[i]) {
+ atomicMin(&reduce_from_offset[i], static_cast<int32_t>(x));
+ }
+ }
+ }
+}
+
+template <typename T>
+__global__ void max_reduce_scatter_grad_kernel(T *grad_feats,
+ const T *grad_reduced_feats,
+ const int32_t *reduce_from,
+ const int num_reduced,
+ const int num_feats) {
+ for (int x = blockIdx.x * blockDim.x + threadIdx.x; x < num_reduced;
+ x += gridDim.x * blockDim.x) {
+ const int reduced_offset = x * num_feats;
+ const int32_t *scatter_to_offset = reduce_from + reduced_offset;
+ const T *grad_reduced_feats_offset = grad_reduced_feats + reduced_offset;
+
+ for (int i = 0; i < num_feats; i++) {
+ grad_feats[scatter_to_offset[i] * num_feats + i] =
+ grad_reduced_feats_offset[i];
+ }
+ }
+}
+
+namespace voxelization {
+
+std::vector<at::Tensor> dynamic_point_to_voxel_forward_gpu(
+ const at::Tensor &feats, const at::Tensor &coors,
+ const reduce_t reduce_type) {
+ CHECK_INPUT(feats);
+ CHECK_INPUT(coors);
+
+ const int num_input = feats.size(0);
+ const int num_feats = feats.size(1);
+
+ if (num_input == 0)
+ return {feats.clone().detach(),
+ coors.clone().detach(),
+ coors.new_empty({0}, torch::kInt32),
+ coors.new_empty({0}, torch::kInt32)};
+
+ at::Tensor out_coors;
+ at::Tensor coors_map;
+ at::Tensor reduce_count;
+
+ auto coors_clean = coors.masked_fill(coors.lt(0).any(-1, true), -1);
+
+ std::tie(out_coors, coors_map, reduce_count) =
+ at::unique_dim(coors_clean, 0, true, true, true);
+
+ if (out_coors.index({0, 0}).lt(0).item<bool>()) {
+ // the first element of out_coors (-1,-1,-1) and should be removed
+ out_coors = out_coors.slice(0, 1);
+ reduce_count = reduce_count.slice(0, 1);
+ coors_map = coors_map - 1;
+ }
+
+ coors_map = coors_map.to(torch::kInt32);
+ reduce_count = reduce_count.to(torch::kInt32);
+
+ auto reduced_feats =
+ at::empty({out_coors.size(0), num_feats}, feats.options());
+
+ AT_DISPATCH_FLOATING_TYPES(
+ feats.scalar_type(), "feats_reduce_kernel", ([&] {
+ if (reduce_type == reduce_t::MAX)
+ reduced_feats.fill_(-std::numeric_limits<scalar_t>::infinity());
+ else
+ reduced_feats.fill_(static_cast<scalar_t>(0));
+
+ dim3 blocks(std::min(at::cuda::ATenCeilDiv(num_input, threadsPerBlock),
+ maxGridDim));
+ dim3 threads(threadsPerBlock);
+ feats_reduce_kernel<<<blocks, threads>>>(
+ feats.data_ptr<scalar_t>(), coors_map.data_ptr<int32_t>(),
+ reduced_feats.data_ptr<scalar_t>(), num_input, num_feats, reduce_type);
+ if (reduce_type == reduce_t::MEAN)
+ reduced_feats /= reduce_count.unsqueeze(-1).to(reduced_feats.dtype());
+ }));
+ AT_CUDA_CHECK(cudaGetLastError());
+
+ return {reduced_feats, out_coors, coors_map, reduce_count};
+}
+
+void dynamic_point_to_voxel_backward_gpu(at::Tensor &grad_feats,
+ const at::Tensor &grad_reduced_feats,
+ const at::Tensor &feats,
+ const at::Tensor &reduced_feats,
+ const at::Tensor &coors_map,
+ const at::Tensor &reduce_count,
+ const reduce_t reduce_type) {
+ CHECK_INPUT(grad_feats);
+ CHECK_INPUT(grad_reduced_feats);
+ CHECK_INPUT(feats);
+ CHECK_INPUT(reduced_feats);
+ CHECK_INPUT(coors_map);
+ CHECK_INPUT(reduce_count);
+
+ const int num_input = feats.size(0);
+ const int num_reduced = reduced_feats.size(0);
+ const int num_feats = feats.size(1);
+
+ grad_feats.fill_(0);
+ // copy voxel grad to points
+
+ if (num_input == 0 || num_reduced == 0) return;
+
+ if (reduce_type == reduce_t::MEAN || reduce_type == reduce_t::SUM) {
+ AT_DISPATCH_FLOATING_TYPES(
+ grad_reduced_feats.scalar_type(), "add_reduce_traceback_grad_kernel",
+ ([&] {
+ dim3 blocks(std::min(
+ at::cuda::ATenCeilDiv(num_input, threadsPerBlock), maxGridDim));
+ dim3 threads(threadsPerBlock);
+ add_reduce_traceback_grad_kernel<<<blocks, threads>>>(
+ grad_feats.data_ptr<scalar_t>(),
+ grad_reduced_feats.data_ptr<scalar_t>(),
+ coors_map.data_ptr<int32_t>(), reduce_count.data_ptr<int32_t>(),
+ num_input, num_feats, reduce_type);
+ }));
+ AT_CUDA_CHECK(cudaGetLastError());
+ } else {
+ auto reduce_from = at::full({num_reduced, num_feats}, num_input,
+ coors_map.options().dtype(torch::kInt32));
+ AT_DISPATCH_FLOATING_TYPES(
+ grad_reduced_feats.scalar_type(),
+ "max_reduce_traceback_scatter_idx_kernel", ([&] {
+ dim3 blocks(std::min(
+ at::cuda::ATenCeilDiv(num_input, threadsPerBlock), maxGridDim));
+ dim3 threads(threadsPerBlock);
+ max_reduce_traceback_scatter_idx_kernel<<<blocks, threads>>>(
+ feats.data_ptr<scalar_t>(), reduced_feats.data_ptr<scalar_t>(),
+ reduce_from.data_ptr<int32_t>(), coors_map.data_ptr<int32_t>(),
+ num_input, num_feats);
+ }));
+ AT_CUDA_CHECK(cudaGetLastError());
+
+ AT_DISPATCH_FLOATING_TYPES(
+ grad_reduced_feats.scalar_type(),
+ "max_reduce_traceback_scatter_idx_kernel", ([&] {
+ dim3 blocks(std::min(
+ at::cuda::ATenCeilDiv(num_reduced, threadsPerBlock), maxGridDim));
+ dim3 threads(threadsPerBlock);
+ max_reduce_scatter_grad_kernel<<<blocks, threads>>>(
+ grad_feats.data_ptr<scalar_t>(),
+ grad_reduced_feats.data_ptr<scalar_t>(),
+ reduce_from.data_ptr<int32_t>(), num_reduced, num_feats);
+ }));
+ AT_CUDA_CHECK(cudaGetLastError());
+ }
+ return;
+}
+
+} // namespace voxelization
diff --git a/mmdet3d/ops/voxel/src_backup/voxelization.cpp b/mmdet3d/ops/voxel/src_backup/voxelization.cpp
new file mode 100644
index 0000000..f83348e
--- /dev/null
+++ b/mmdet3d/ops/voxel/src_backup/voxelization.cpp
@@ -0,0 +1,13 @@
+#include <torch/extension.h>
+#include "voxelization.h"
+
+namespace voxelization {
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+ m.def("hard_voxelize", &hard_voxelize, "hard voxelize");
+ m.def("dynamic_voxelize", &dynamic_voxelize, "dynamic voxelization");
+ m.def("dynamic_point_to_voxel_forward", &dynamic_point_to_voxel_forward, "dynamic point to voxel forward");
+ m.def("dynamic_point_to_voxel_backward", &dynamic_point_to_voxel_backward, "dynamic point to voxel backward");
+}
+
+} // namespace voxelization
diff --git a/mmdet3d/ops/voxel/src_backup/voxelization.h b/mmdet3d/ops/voxel/src_backup/voxelization.h
new file mode 100644
index 0000000..765b30a
--- /dev/null
+++ b/mmdet3d/ops/voxel/src_backup/voxelization.h
@@ -0,0 +1,142 @@
+#pragma once
+#include <torch/extension.h>
+
+typedef enum { SUM = 0, MEAN = 1, MAX = 2 } reduce_t;
+
+namespace voxelization {
+
+int hard_voxelize_cpu(const at::Tensor &points, at::Tensor &voxels,
+ at::Tensor &coors, at::Tensor &num_points_per_voxel,
+ const std::vector<float> voxel_size,
+ const std::vector<float> coors_range,
+ const int max_points, const int max_voxels,
+ const int NDim = 3);
+
+void dynamic_voxelize_cpu(const at::Tensor &points, at::Tensor &coors,
+ const std::vector<float> voxel_size,
+ const std::vector<float> coors_range,
+ const int NDim = 3);
+
+std::vector<at::Tensor> dynamic_point_to_voxel_cpu(
+ const at::Tensor &points, const at::Tensor &voxel_mapping,
+ const std::vector<float> voxel_size, const std::vector<float> coors_range);
+
+#ifdef WITH_CUDA
+int hard_voxelize_gpu(const at::Tensor &points, at::Tensor &voxels,
+ at::Tensor &coors, at::Tensor &num_points_per_voxel,
+ const std::vector<float> voxel_size,
+ const std::vector<float> coors_range,
+ const int max_points, const int max_voxels,
+ const int NDim = 3);
+
+int nondisterministic_hard_voxelize_gpu(const at::Tensor &points, at::Tensor &voxels,
+ at::Tensor &coors, at::Tensor &num_points_per_voxel,
+ const std::vector<float> voxel_size,
+ const std::vector<float> coors_range,
+ const int max_points, const int max_voxels,
+ const int NDim = 3);
+
+void dynamic_voxelize_gpu(const at::Tensor &points, at::Tensor &coors,
+ const std::vector<float> voxel_size,
+ const std::vector<float> coors_range,
+ const int NDim = 3);
+
+std::vector<torch::Tensor> dynamic_point_to_voxel_forward_gpu(const torch::Tensor &feats,
+ const torch::Tensor &coors,
+ const reduce_t reduce_type);
+
+void dynamic_point_to_voxel_backward_gpu(torch::Tensor &grad_feats,
+ const torch::Tensor &grad_reduced_feats,
+ const torch::Tensor &feats,
+ const torch::Tensor &reduced_feats,
+ const torch::Tensor &coors_idx,
+ const torch::Tensor &reduce_count,
+ const reduce_t reduce_type);
+#endif
+
+// Interface for Python
+inline int hard_voxelize(const at::Tensor &points, at::Tensor &voxels,
+ at::Tensor &coors, at::Tensor &num_points_per_voxel,
+ const std::vector<float> voxel_size,
+ const std::vector<float> coors_range,
+ const int max_points, const int max_voxels,
+ const int NDim = 3, const bool deterministic = true) {
+ if (points.device().is_cuda()) {
+#ifdef WITH_CUDA
+ if (deterministic) {
+ return hard_voxelize_gpu(points, voxels, coors, num_points_per_voxel,
+ voxel_size, coors_range, max_points, max_voxels,
+ NDim);
+ }
+ return nondisterministic_hard_voxelize_gpu(points, voxels, coors, num_points_per_voxel,
+ voxel_size, coors_range, max_points, max_voxels,
+ NDim);
+#else
+ AT_ERROR("Not compiled with GPU support");
+#endif
+ }
+ return hard_voxelize_cpu(points, voxels, coors, num_points_per_voxel,
+ voxel_size, coors_range, max_points, max_voxels,
+ NDim);
+}
+
+inline void dynamic_voxelize(const at::Tensor &points, at::Tensor &coors,
+ const std::vector<float> voxel_size,
+ const std::vector<float> coors_range,
+ const int NDim = 3) {
+ if (points.device().is_cuda()) {
+#ifdef WITH_CUDA
+ return dynamic_voxelize_gpu(points, coors, voxel_size, coors_range, NDim);
+#else
+ AT_ERROR("Not compiled with GPU support");
+#endif
+ }
+ return dynamic_voxelize_cpu(points, coors, voxel_size, coors_range, NDim);
+}
+
+inline reduce_t convert_reduce_type(const std::string &reduce_type) {
+ if (reduce_type == "max")
+ return reduce_t::MAX;
+ else if (reduce_type == "sum")
+ return reduce_t::SUM;
+ else if (reduce_type == "mean")
+ return reduce_t::MEAN;
+ else TORCH_CHECK(false, "do not support reduce type " + reduce_type)
+ return reduce_t::SUM;
+}
+
+inline std::vector<torch::Tensor> dynamic_point_to_voxel_forward(const torch::Tensor &feats,
+ const torch::Tensor &coors,
+ const std::string &reduce_type) {
+ if (feats.device().is_cuda()) {
+#ifdef WITH_CUDA
+ return dynamic_point_to_voxel_forward_gpu(feats, coors, convert_reduce_type(reduce_type));
+#else
+ TORCH_CHECK(false, "Not compiled with GPU support");
+#endif
+ }
+ TORCH_CHECK(false, "do not support cpu yet");
+ return std::vector<torch::Tensor>();
+}
+
+inline void dynamic_point_to_voxel_backward(torch::Tensor &grad_feats,
+ const torch::Tensor &grad_reduced_feats,
+ const torch::Tensor &feats,
+ const torch::Tensor &reduced_feats,
+ const torch::Tensor &coors_idx,
+ const torch::Tensor &reduce_count,
+ const std::string &reduce_type) {
+ if (grad_feats.device().is_cuda()) {
+#ifdef WITH_CUDA
+ dynamic_point_to_voxel_backward_gpu(
+ grad_feats, grad_reduced_feats, feats, reduced_feats, coors_idx, reduce_count,
+ convert_reduce_type(reduce_type));
+ return;
+#else
+ TORCH_CHECK(false, "Not compiled with GPU support");
+#endif