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 class PFNLayerV2(nn.Module): def __init__(self, in_channels, out_channels, use_norm=True, last_layer=False): super().__init__() self.last_vfe = last_layer self.use_norm = use_norm if not self.last_vfe: out_channels = out_channels // 2 if self.use_norm: self.linear = nn.Linear(in_channels, out_channels, bias=False) self.norm = nn.BatchNorm1d(out_channels, eps=1e-3, momentum=0.01) else: self.linear = nn.Linear(in_channels, out_channels, bias=True) self.relu = nn.ReLU() def forward(self, inputs, unq_inv): x = self.linear(inputs) x = self.norm(x) if self.use_norm else x x = self.relu(x) x_max = torch_scatter.scatter_max(x, unq_inv, dim=0)[0] if self.last_vfe: return x_max else: x_concatenated = torch.cat([x, x_max[unq_inv, :]], dim=1) return x_concatenated class DynamicPillarVFE(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_xy = grid_size[0] * grid_size[1] self.scale_y = grid_size[1] 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]] - self.point_cloud_range[[0,1]]) / self.voxel_size[[0,1]]).int() mask = ((points_coords >= 0) & (points_coords < self.grid_size[[0,1]])).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_xy + \ points_coords[:, 0] * self.scale_y + \ points_coords[:, 1] 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 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) # features = self.linear1(features) # features_max = torch_scatter.scatter_max(features, unq_inv, dim=0)[0] # features = torch.cat([features, features_max[unq_inv, :]], dim=1) # features = self.linear2(features) # features = torch_scatter.scatter_max(features, unq_inv, dim=0)[0] # generate voxel coordinates unq_coords = unq_coords.int() voxel_coords = torch.stack((unq_coords // self.scale_xy, (unq_coords % self.scale_xy) // self.scale_y, unq_coords % self.scale_y, torch.zeros(unq_coords.shape[0]).to(unq_coords.device).int() ), dim=1) voxel_coords = voxel_coords[:, [0, 3, 2, 1]] batch_dict['voxel_features'] = batch_dict['pillar_features'] = features batch_dict['voxel_coords'] = voxel_coords return batch_dict class DynamicPillarVFESimple2D(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 # self.use_cluster_xyz = self.model_cfg.get('USE_CLUSTER_XYZ', True) if self.use_absolute_xyz: num_point_features += 3 # if self.use_cluster_xyz: # num_point_features += 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_xy = grid_size[0] * grid_size[1] self.scale_y = grid_size[1] self.grid_size = torch.tensor(grid_size[:2]).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]] - self.point_cloud_range[[0, 1]]) / self.voxel_size[[0, 1]]).int() mask = ((points_coords >= 0) & (points_coords < self.grid_size[[0, 1]])).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_xy + \ points_coords[:, 0] * self.scale_y + \ points_coords[:, 1] unq_coords, unq_inv, unq_cnt = torch.unique(merge_coords, return_inverse=True, return_counts=True, dim=0) 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 features = [f_center] if self.use_absolute_xyz: features.append(points[:, 1:]) else: features.append(points[:, 4:]) # if self.use_cluster_xyz: # points_mean = torch_scatter.scatter_mean(points_xyz, unq_inv, dim=0) # f_cluster = points_xyz - points_mean[unq_inv, :] # features.append(f_cluster) 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() pillar_coords = torch.stack((unq_coords // self.scale_xy, (unq_coords % self.scale_xy) // self.scale_y, unq_coords % self.scale_y, ), dim=1) pillar_coords = pillar_coords[:, [0, 2, 1]] batch_dict['pillar_features'] = features batch_dict['pillar_coords'] = pillar_coords return batch_dict