void furthest_point_sampling_kernel_wrapper(int, int, int, const float*, float*, int*) at L:233 in _ext_src/src/sampling_gpu.cu
时间: 2023-12-14 12:04:49 浏览: 98
这是一个函数的声明,名为"furthest_point_sampling_kernel_wrapper",在 "sampling_gpu.cu" 文件的第 233 行。该函数接受多个参数,包括三个整数参数和两个指向浮点数和整数数组的指针参数。根据函数名和参数列表,它可能是一个用于计算最远点采样的 GPU 核函数的包装器。
相关问题
if new_xyz is None: new_xyz = pointnet2_utils.gather_operation( xyz_flipped, pointnet2_utils.furthest_point_sample(xyz, self.npoint) ).transpose(1, 2).contiguous() if self.npoint is not None else None
这是一个Python的代码块,主要是在使用PointNet++网络时,对输入点云进行下采样操作。具体来说,如果输入点云数量较多,会先通过PointNet2的工具函数`furthest_point_sample`对点云进行下采样,得到一定数量的关键点,然后再通过`gather_operation`函数将这些关键点的坐标提取出来,作为新的点云数据。如果`npoint`参数为`None`,则不进行下采样操作,直接使用原始的点云数据。
class PointnetSAModuleMSG(_PointnetSAModuleBase): """Pointnet set abstraction layer with multiscale grouping""" def __init__(self, *, npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], bn: bool = True, use_xyz: bool = True, pool_method='max_pool', instance_norm=False): """ :param npoint: int :param radii: list of float, list of radii to group with :param nsamples: list of int, number of samples in each ball query :param mlps: list of list of int, spec of the pointnet before the global pooling for each scale :param bn: whether to use batchnorm :param use_xyz: :param pool_method: max_pool / avg_pool :param instance_norm: whether to use instance_norm """ super().__init__() assert len(radii) == len(nsamples) == len(mlps) self.npoint = npoint self.groupers = nn.ModuleList() self.mlps = nn.ModuleList() for i in range(len(radii)): radius = radii[i] nsample = nsamples[i] self.groupers.append( pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz) if npoint is not None else pointnet2_utils.GroupAll(use_xyz) ) mlp_spec = mlps[i] if use_xyz: mlp_spec[0] += 3 self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn, instance_norm=instance_norm)) self.pool_method = pool_method我想将以上模块输出的张量的shape在模块尾部打印出来方便检查,你可以提供详细的代码吗?
当模块输出的张量维度较多时,可以使用 `torch.Size` 或者 `tuple` 将它们打印出来。以下是添加打印输出的代码示例:
```
class PointnetSAModuleMSG(_PointnetSAModuleBase):
"""Pointnet set abstraction layer with multiscale grouping"""
def __init__(
self,
*,
npoint: int,
radii: List[float],
nsamples: List[int],
mlps: List[List[int]],
bn: bool = True,
use_xyz: bool = True,
pool_method='max_pool',
instance_norm=False
):
"""
:param npoint: int
:param radii: list of float, list of radii to group with
:param nsamples: list of int, number of samples in each ball query
:param mlps: list of list of int, spec of the pointnet before the global pooling for each scale
:param bn: whether to use batchnorm
:param use_xyz:
:param pool_method: max_pool / avg_pool
:param instance_norm: whether to use instance_norm
"""
super().__init__()
assert len(radii) == len(nsamples) == len(mlps)
self.npoint = npoint
self.groupers = nn.ModuleList()
self.mlps = nn.ModuleList()
for i in range(len(radii)):
radius = radii[i]
nsample = nsamples[i]
self.groupers.append(
pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz)
if npoint is not None else pointnet2_utils.GroupAll(use_xyz)
)
mlp_spec = mlps[i]
if use_xyz:
mlp_spec[0] += 3
self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn, instance_norm=instance_norm))
self.pool_method = pool_method
def forward(self, xyz, features=None):
"""
:param xyz: (batch_size, num_points, 3) tensor
:param features: (batch_size, num_points, dim) tensor, optional
:return:
new_xyz: (batch_size, npoint, 3) tensor
new_features: (batch_size, npoint, \sum_k(mlps[k][-1])) tensor
"""
B, N, C = xyz.shape
if self.npoint is not None:
fps_idx = pointnet2_utils.furthest_point_sample(xyz, self.npoint)
new_xyz = pointnet2_utils.gather_operation(xyz.transpose(1, 2).contiguous(), fps_idx).transpose(1, 2).contiguous()
else:
new_xyz = None
new_features_list = []
for i, grouper in enumerate(self.groupers):
grouped_xyz, grouped_features, idx = grouper(new_xyz, xyz, features)
new_features = self.mlps[i](grouped_features)
if self.pool_method == 'max_pool':
new_features = F.max_pool1d(new_features, new_features.size(2)).squeeze(2) # (B, C, N) -> (B, C)
elif self.pool_method == 'avg_pool':
new_features = F.avg_pool1d(new_features, new_features.size(2)).squeeze(2) # (B, C, N) -> (B, C)
new_features_list.append(new_features)
new_features = torch.cat(new_features_list, dim=1)
# 打印输出张量的形状
print("new_xyz shape:", new_xyz.shape)
print("new_features shape:", new_features.shape)
return new_xyz, new_features
```
在 `forward` 方法中添加了打印输出,可以直接输出张量的形状信息。
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