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from typing import List
import torch
from torch import nn as nn
from annotator.uniformer.mmcv.runner import force_fp32
from .furthest_point_sample import (furthest_point_sample,
furthest_point_sample_with_dist)
def calc_square_dist(point_feat_a, point_feat_b, norm=True):
"""Calculating square distance between a and b.
Args:
point_feat_a (Tensor): (B, N, C) Feature vector of each point.
point_feat_b (Tensor): (B, M, C) Feature vector of each point.
norm (Bool, optional): Whether to normalize the distance.
Default: True.
Returns:
Tensor: (B, N, M) Distance between each pair points.
"""
num_channel = point_feat_a.shape[-1]
# [bs, n, 1]
a_square = torch.sum(point_feat_a.unsqueeze(dim=2).pow(2), dim=-1)
# [bs, 1, m]
b_square = torch.sum(point_feat_b.unsqueeze(dim=1).pow(2), dim=-1)
corr_matrix = torch.matmul(point_feat_a, point_feat_b.transpose(1, 2))
dist = a_square + b_square - 2 * corr_matrix
if norm:
dist = torch.sqrt(dist) / num_channel
return dist
def get_sampler_cls(sampler_type):
"""Get the type and mode of points sampler.
Args:
sampler_type (str): The type of points sampler.
The valid value are "D-FPS", "F-FPS", or "FS".
Returns:
class: Points sampler type.
"""
sampler_mappings = {
'D-FPS': DFPSSampler,
'F-FPS': FFPSSampler,
'FS': FSSampler,
}
try:
return sampler_mappings[sampler_type]
except KeyError:
raise KeyError(
f'Supported `sampler_type` are {sampler_mappings.keys()}, but got \
{sampler_type}')
class PointsSampler(nn.Module):
"""Points sampling.
Args:
num_point (list[int]): Number of sample points.
fps_mod_list (list[str], optional): Type of FPS method, valid mod
['F-FPS', 'D-FPS', 'FS'], Default: ['D-FPS'].
F-FPS: using feature distances for FPS.
D-FPS: using Euclidean distances of points for FPS.
FS: using F-FPS and D-FPS simultaneously.
fps_sample_range_list (list[int], optional):
Range of points to apply FPS. Default: [-1].
"""
def __init__(self,
num_point: List[int],
fps_mod_list: List[str] = ['D-FPS'],
fps_sample_range_list: List[int] = [-1]):
super().__init__()
# FPS would be applied to different fps_mod in the list,
# so the length of the num_point should be equal to
# fps_mod_list and fps_sample_range_list.
assert len(num_point) == len(fps_mod_list) == len(
fps_sample_range_list)
self.num_point = num_point
self.fps_sample_range_list = fps_sample_range_list
self.samplers = nn.ModuleList()
for fps_mod in fps_mod_list:
self.samplers.append(get_sampler_cls(fps_mod)())
self.fp16_enabled = False
@force_fp32()
def forward(self, points_xyz, features):
"""
Args:
points_xyz (Tensor): (B, N, 3) xyz coordinates of the features.
features (Tensor): (B, C, N) Descriptors of the features.
Returns:
Tensor: (B, npoint, sample_num) Indices of sampled points.
"""
indices = []
last_fps_end_index = 0
for fps_sample_range, sampler, npoint in zip(
self.fps_sample_range_list, self.samplers, self.num_point):
assert fps_sample_range < points_xyz.shape[1]
if fps_sample_range == -1:
sample_points_xyz = points_xyz[:, last_fps_end_index:]
if features is not None:
sample_features = features[:, :, last_fps_end_index:]
else:
sample_features = None
else:
sample_points_xyz = \
points_xyz[:, last_fps_end_index:fps_sample_range]
if features is not None:
sample_features = features[:, :, last_fps_end_index:
fps_sample_range]
else:
sample_features = None
fps_idx = sampler(sample_points_xyz.contiguous(), sample_features,
npoint)
indices.append(fps_idx + last_fps_end_index)
last_fps_end_index += fps_sample_range
indices = torch.cat(indices, dim=1)
return indices
class DFPSSampler(nn.Module):
"""Using Euclidean distances of points for FPS."""
def __init__(self):
super().__init__()
def forward(self, points, features, npoint):
"""Sampling points with D-FPS."""
fps_idx = furthest_point_sample(points.contiguous(), npoint)
return fps_idx
class FFPSSampler(nn.Module):
"""Using feature distances for FPS."""
def __init__(self):
super().__init__()
def forward(self, points, features, npoint):
"""Sampling points with F-FPS."""
assert features is not None, \
'feature input to FFPS_Sampler should not be None'
features_for_fps = torch.cat([points, features.transpose(1, 2)], dim=2)
features_dist = calc_square_dist(
features_for_fps, features_for_fps, norm=False)
fps_idx = furthest_point_sample_with_dist(features_dist, npoint)
return fps_idx
class FSSampler(nn.Module):
"""Using F-FPS and D-FPS simultaneously."""
def __init__(self):
super().__init__()
def forward(self, points, features, npoint):
"""Sampling points with FS_Sampling."""
assert features is not None, \
'feature input to FS_Sampler should not be None'
ffps_sampler = FFPSSampler()
dfps_sampler = DFPSSampler()
fps_idx_ffps = ffps_sampler(points, features, npoint)
fps_idx_dfps = dfps_sampler(points, features, npoint)
fps_idx = torch.cat([fps_idx_ffps, fps_idx_dfps], dim=1)
return fps_idx
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