107 lines
4.4 KiB
Python
107 lines
4.4 KiB
Python
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
import torch.nn.functional as F
|
||
|
|
|
||
|
|
try:
|
||
|
|
import torch_scatter
|
||
|
|
except Exception as e:
|
||
|
|
# Incase someone doesn't want to use dynamic pillar vfe and hasn't installed torch_scatter
|
||
|
|
pass
|
||
|
|
|
||
|
|
from .vfe_template import VFETemplate
|
||
|
|
from .dynamic_pillar_vfe import PFNLayerV2
|
||
|
|
|
||
|
|
|
||
|
|
class DynamicVoxelVFE(VFETemplate):
|
||
|
|
def __init__(self, model_cfg, num_point_features, voxel_size, grid_size, point_cloud_range, **kwargs):
|
||
|
|
super().__init__(model_cfg=model_cfg)
|
||
|
|
|
||
|
|
self.use_norm = self.model_cfg.USE_NORM
|
||
|
|
self.with_distance = self.model_cfg.WITH_DISTANCE
|
||
|
|
self.use_absolute_xyz = self.model_cfg.USE_ABSLOTE_XYZ
|
||
|
|
num_point_features += 6 if self.use_absolute_xyz else 3
|
||
|
|
if self.with_distance:
|
||
|
|
num_point_features += 1
|
||
|
|
|
||
|
|
self.num_filters = self.model_cfg.NUM_FILTERS
|
||
|
|
assert len(self.num_filters) > 0
|
||
|
|
num_filters = [num_point_features] + list(self.num_filters)
|
||
|
|
|
||
|
|
pfn_layers = []
|
||
|
|
for i in range(len(num_filters) - 1):
|
||
|
|
in_filters = num_filters[i]
|
||
|
|
out_filters = num_filters[i + 1]
|
||
|
|
pfn_layers.append(
|
||
|
|
PFNLayerV2(in_filters, out_filters, self.use_norm, last_layer=(i >= len(num_filters) - 2))
|
||
|
|
)
|
||
|
|
self.pfn_layers = nn.ModuleList(pfn_layers)
|
||
|
|
|
||
|
|
self.voxel_x = voxel_size[0]
|
||
|
|
self.voxel_y = voxel_size[1]
|
||
|
|
self.voxel_z = voxel_size[2]
|
||
|
|
self.x_offset = self.voxel_x / 2 + point_cloud_range[0]
|
||
|
|
self.y_offset = self.voxel_y / 2 + point_cloud_range[1]
|
||
|
|
self.z_offset = self.voxel_z / 2 + point_cloud_range[2]
|
||
|
|
|
||
|
|
self.scale_xyz = grid_size[0] * grid_size[1] * grid_size[2]
|
||
|
|
self.scale_yz = grid_size[1] * grid_size[2]
|
||
|
|
self.scale_z = grid_size[2]
|
||
|
|
|
||
|
|
self.grid_size = torch.tensor(grid_size).cuda()
|
||
|
|
self.voxel_size = torch.tensor(voxel_size).cuda()
|
||
|
|
self.point_cloud_range = torch.tensor(point_cloud_range).cuda()
|
||
|
|
|
||
|
|
def get_output_feature_dim(self):
|
||
|
|
return self.num_filters[-1]
|
||
|
|
|
||
|
|
def forward(self, batch_dict, **kwargs):
|
||
|
|
points = batch_dict['points'] # (batch_idx, x, y, z, i, e)
|
||
|
|
|
||
|
|
points_coords = torch.floor((points[:, [1,2,3]] - self.point_cloud_range[[0,1,2]]) / self.voxel_size[[0,1,2]]).int()
|
||
|
|
mask = ((points_coords >= 0) & (points_coords < self.grid_size[[0,1,2]])).all(dim=1)
|
||
|
|
points = points[mask]
|
||
|
|
points_coords = points_coords[mask]
|
||
|
|
points_xyz = points[:, [1, 2, 3]].contiguous()
|
||
|
|
|
||
|
|
merge_coords = points[:, 0].int() * self.scale_xyz + \
|
||
|
|
points_coords[:, 0] * self.scale_yz + \
|
||
|
|
points_coords[:, 1] * self.scale_z + \
|
||
|
|
points_coords[:, 2]
|
||
|
|
|
||
|
|
unq_coords, unq_inv, unq_cnt = torch.unique(merge_coords, return_inverse=True, return_counts=True, dim=0)
|
||
|
|
|
||
|
|
points_mean = torch_scatter.scatter_mean(points_xyz, unq_inv, dim=0)
|
||
|
|
f_cluster = points_xyz - points_mean[unq_inv, :]
|
||
|
|
|
||
|
|
f_center = torch.zeros_like(points_xyz)
|
||
|
|
f_center[:, 0] = points_xyz[:, 0] - (points_coords[:, 0].to(points_xyz.dtype) * self.voxel_x + self.x_offset)
|
||
|
|
f_center[:, 1] = points_xyz[:, 1] - (points_coords[:, 1].to(points_xyz.dtype) * self.voxel_y + self.y_offset)
|
||
|
|
# f_center[:, 2] = points_xyz[:, 2] - self.z_offset
|
||
|
|
f_center[:, 2] = points_xyz[:, 2] - (points_coords[:, 2].to(points_xyz.dtype) * self.voxel_z + self.z_offset)
|
||
|
|
|
||
|
|
if self.use_absolute_xyz:
|
||
|
|
features = [points[:, 1:], f_cluster, f_center]
|
||
|
|
else:
|
||
|
|
features = [points[:, 4:], f_cluster, f_center]
|
||
|
|
|
||
|
|
if self.with_distance:
|
||
|
|
points_dist = torch.norm(points[:, 1:4], 2, dim=1, keepdim=True)
|
||
|
|
features.append(points_dist)
|
||
|
|
features = torch.cat(features, dim=-1)
|
||
|
|
|
||
|
|
for pfn in self.pfn_layers:
|
||
|
|
features = pfn(features, unq_inv)
|
||
|
|
|
||
|
|
# generate voxel coordinates
|
||
|
|
unq_coords = unq_coords.int()
|
||
|
|
voxel_coords = torch.stack((unq_coords // self.scale_xyz,
|
||
|
|
(unq_coords % self.scale_xyz) // self.scale_yz,
|
||
|
|
(unq_coords % self.scale_yz) // self.scale_z,
|
||
|
|
unq_coords % self.scale_z), dim=1)
|
||
|
|
voxel_coords = voxel_coords[:, [0, 3, 2, 1]]
|
||
|
|
|
||
|
|
batch_dict['pillar_features'] = batch_dict['voxel_features'] = features
|
||
|
|
batch_dict['voxel_coords'] = voxel_coords
|
||
|
|
|
||
|
|
return batch_dict
|