import os import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from typing import Dict, Mapping, Optional, Literal from torch_cluster import radius, radius_graph from torch_geometric.data import HeteroData, Batch from torch_geometric.utils import dense_to_sparse, subgraph from scipy.optimize import linear_sum_assignment from dev.modules.attr_tokenizer import Attr_Tokenizer from dev.modules.layers import * from dev.utils.visualization import * from dev.utils.func import angle_between_2d_vectors, wrap_angle, weight_init class SMARTOccDecoder(nn.Module): def __init__(self, dataset: str, input_dim: int, hidden_dim: int, num_historical_steps: int, time_span: Optional[int], pl2a_radius: float, pl2seed_radius: float, a2a_radius: float, a2sa_radius: float, pl2sa_radius: float, num_freq_bands: int, num_layers: int, num_heads: int, head_dim: int, dropout: float, token_data: Dict, token_size: int, special_token_index: list=[], attr_tokenizer: Attr_Tokenizer=None, predict_motion: bool=False, predict_state: bool=False, predict_map: bool=False, predict_occ: bool=False, state_token: Dict[str, int]=None, seed_size: int=5, buffer_size: int=32, loss_weight: dict=None, logger=None) -> None: super(SMARTOccDecoder, self).__init__() self.dataset = dataset self.input_dim = input_dim self.hidden_dim = hidden_dim self.num_historical_steps = num_historical_steps self.time_span = time_span if time_span is not None else num_historical_steps self.pl2a_radius = pl2a_radius self.pl2seed_radius = pl2seed_radius self.a2a_radius = a2a_radius self.a2sa_radius = a2sa_radius self.pl2sa_radius = pl2sa_radius self.num_freq_bands = num_freq_bands self.num_layers = num_layers self.num_heads = num_heads self.head_dim = head_dim self.dropout = dropout self.special_token_index = special_token_index self.predict_motion = predict_motion self.predict_state = predict_state self.predict_map = predict_map self.predict_occ = predict_occ self.loss_weight = loss_weight self.logger = logger self.attr_tokenizer = attr_tokenizer # state tokens self.state_type = list(state_token.keys()) self.state_token = state_token self.invalid_state = int(state_token['invalid']) self.valid_state = int(state_token['valid']) self.enter_state = int(state_token['enter']) self.exit_state = int(state_token['exit']) self.seed_state_type = ['invalid', 'enter'] self.valid_state_type = ['invalid', 'valid', 'exit'] input_dim_r_pt2a = 3 input_dim_r_a2a = 3 self.seed_size = seed_size self.buffer_size = buffer_size self.agent_type = ['veh', 'ped', 'cyc', 'seed'] self.type_a_emb = nn.Embedding(len(self.agent_type), hidden_dim) self.shape_emb = MLPEmbedding(input_dim=3, hidden_dim=hidden_dim) self.state_a_emb = nn.Embedding(len(self.state_type), hidden_dim) self.motion_gap = 1. self.heading_gap = 1. self.invalid_shape_value = .1 self.invalid_motion_value = -2. self.invalid_head_value = -2. self.r_pt2a_emb = FourierEmbedding(input_dim=input_dim_r_pt2a, hidden_dim=hidden_dim, num_freq_bands=num_freq_bands) self.r_a2a_emb = FourierEmbedding(input_dim=input_dim_r_a2a, hidden_dim=hidden_dim, num_freq_bands=num_freq_bands) self.token_size = token_size # 2048 self.grid_size = self.attr_tokenizer.grid_size self.angle_size = self.attr_tokenizer.angle_size self.agent_limit = 3 self.pt_limit = 10 self.grid_agent_occ_head = MLPLayer(input_dim=hidden_dim, hidden_dim=self.grid_size, output_dim=self.agent_limit * self.grid_size) self.grid_pt_occ_head = MLPLayer(input_dim=hidden_dim, hidden_dim=self.grid_size, output_dim=self.pt_limit * self.grid_size) # self.num_seed_feature = 1 # self.num_seed_feature = self.seed_size self.num_seed_feature = 10 self.trajectory_token = token_data['token'] # dict('veh', 'ped', 'cyc') (2048, 4, 2) self.trajectory_token_traj = token_data['traj'] # (2048, 6, 3) self.trajectory_token_all = token_data['token_all'] # (2048, 6, 4, 2) self.apply(weight_init) self.shift = 5 self.beam_size = 5 self.hist_mask = True self.temporal_attn_to_invalid = False self.use_rel = False # seed agent self.temporal_attn_seed = False self.seed_attn_to_av = True self.seed_use_ego_motion = False def transform_rel(self, token_traj, prev_pos, prev_heading=None): if prev_heading is None: diff_xy = prev_pos[:, :, -1, :] - prev_pos[:, :, -2, :] prev_heading = torch.arctan2(diff_xy[:, :, 1], diff_xy[:, :, 0]) num_agent, num_step, traj_num, traj_dim = token_traj.shape cos, sin = prev_heading.cos(), prev_heading.sin() rot_mat = torch.zeros((num_agent, num_step, 2, 2), device=prev_heading.device) rot_mat[:, :, 0, 0] = cos rot_mat[:, :, 0, 1] = -sin rot_mat[:, :, 1, 0] = sin rot_mat[:, :, 1, 1] = cos agent_diff_rel = torch.bmm(token_traj.view(-1, traj_num, 2), rot_mat.view(-1, 2, 2)).view(num_agent, num_step, traj_num, traj_dim) agent_pred_rel = agent_diff_rel + prev_pos[:, :, -1:, :] return agent_pred_rel def _agent_token_embedding(self, data, agent_token_index, agent_state, agent_offset_token_idx, pos_a, head_a, inference=False, filter_mask=None, av_index=None): if filter_mask is None: filter_mask = torch.ones_like(agent_state[:, 2], dtype=torch.bool) num_agent, num_step, traj_dim = pos_a.shape # traj_dim=2 agent_type = data['agent']['type'][filter_mask] veh_mask = (agent_type == 0) ped_mask = (agent_type == 1) cyc_mask = (agent_type == 2) motion_vector_a, head_vector_a = self._build_vector_a(pos_a, head_a, agent_state) trajectory_token_veh = torch.from_numpy(self.trajectory_token['veh']).clone().to(pos_a.device).to(torch.float) trajectory_token_ped = torch.from_numpy(self.trajectory_token['ped']).clone().to(pos_a.device).to(torch.float) trajectory_token_cyc = torch.from_numpy(self.trajectory_token['cyc']).clone().to(pos_a.device).to(torch.float) self.agent_token_emb_veh = self.token_emb_veh(trajectory_token_veh.view(trajectory_token_veh.shape[0], -1)) # (token_size, 8) self.agent_token_emb_ped = self.token_emb_ped(trajectory_token_ped.view(trajectory_token_ped.shape[0], -1)) self.agent_token_emb_cyc = self.token_emb_cyc(trajectory_token_cyc.view(trajectory_token_cyc.shape[0], -1)) # add bos token embedding self.agent_token_emb_veh = torch.cat([self.agent_token_emb_veh, self.bos_token_emb(torch.zeros(1, device=pos_a.device).long())]) self.agent_token_emb_ped = torch.cat([self.agent_token_emb_ped, self.bos_token_emb(torch.zeros(1, device=pos_a.device).long())]) self.agent_token_emb_cyc = torch.cat([self.agent_token_emb_cyc, self.bos_token_emb(torch.zeros(1, device=pos_a.device).long())]) # add invalid token embedding self.agent_token_emb_veh = torch.cat([self.agent_token_emb_veh, self.no_token_emb(torch.zeros(1, device=pos_a.device).long())]) self.agent_token_emb_ped = torch.cat([self.agent_token_emb_ped, self.no_token_emb(torch.zeros(1, device=pos_a.device).long())]) self.agent_token_emb_cyc = torch.cat([self.agent_token_emb_cyc, self.no_token_emb(torch.zeros(1, device=pos_a.device).long())]) # self.grid_token_emb = self.token_emb_grid(torch.stack([self.attr_tokenizer.dist, # self.attr_tokenizer.dir], dim=-1).to(pos_a.device)) self.grid_token_emb = self.token_emb_grid(self.attr_tokenizer.grid) self.grid_token_emb = torch.cat([self.grid_token_emb, self.invalid_offset_token_emb(torch.zeros(1, device=pos_a.device).long())]) if inference: agent_token_traj_all = torch.zeros((num_agent, self.token_size, self.shift + 1, 4, 2), device=pos_a.device) trajectory_token_all_veh = torch.from_numpy(self.trajectory_token_all['veh']).clone().to(pos_a.device).to(torch.float) trajectory_token_all_ped = torch.from_numpy(self.trajectory_token_all['ped']).clone().to(pos_a.device).to(torch.float) trajectory_token_all_cyc = torch.from_numpy(self.trajectory_token_all['cyc']).clone().to(pos_a.device).to(torch.float) agent_token_traj_all[veh_mask] = torch.cat( [trajectory_token_all_veh[:, :self.shift], trajectory_token_veh[:, None, ...]], dim=1) agent_token_traj_all[ped_mask] = torch.cat( [trajectory_token_all_ped[:, :self.shift], trajectory_token_ped[:, None, ...]], dim=1) agent_token_traj_all[cyc_mask] = torch.cat( [trajectory_token_all_cyc[:, :self.shift], trajectory_token_cyc[:, None, ...]], dim=1) # additional token embeddings are already added -> -1: invalid, -2: bos agent_token_emb = torch.zeros((num_agent, num_step, self.hidden_dim), device=pos_a.device) agent_token_emb[veh_mask] = self.agent_token_emb_veh[agent_token_index[veh_mask]] agent_token_emb[ped_mask] = self.agent_token_emb_ped[agent_token_index[ped_mask]] agent_token_emb[cyc_mask] = self.agent_token_emb_cyc[agent_token_index[cyc_mask]] offset_token_emb = self.grid_token_emb[agent_offset_token_idx] # 'vehicle', 'pedestrian', 'cyclist', 'background' is_invalid = agent_state == self.invalid_state agent_types = data['agent']['type'].clone()[filter_mask].long().repeat_interleave(repeats=num_step, dim=0) agent_types[is_invalid.reshape(-1)] = self.agent_type.index('seed') agent_shapes = data['agent']['shape'].clone()[filter_mask, self.num_historical_steps - 1, :].repeat_interleave(repeats=num_step, dim=0) agent_shapes[is_invalid.reshape(-1)] = self.invalid_shape_value # TODO: fix ego_pos in inference mode offset_pos = pos_a - pos_a[av_index].repeat_interleave(repeats=data['batch_size_a'], dim=0) feat_a, categorical_embs = self._build_agent_feature(num_step, pos_a.device, motion_vector_a, head_vector_a, agent_token_emb, offset_token_emb, offset_pos=offset_pos, type=agent_types, shape=agent_shapes, state=agent_state, n=num_agent) if inference: return feat_a, agent_token_traj_all, agent_token_emb, categorical_embs else: # seed agent feature if self.seed_use_ego_motion: motion_vector_seed = motion_vector_a[av_index].repeat_interleave(repeats=self.num_seed_feature, dim=0) head_vector_seed = head_vector_a[av_index].repeat_interleave(repeats=self.num_seed_feature, dim=0) else: motion_vector_seed = head_vector_seed = None feat_seed, _ = self._build_agent_feature(num_step, pos_a.device, motion_vector_seed, head_vector_seed, state_index=self.invalid_state, n=data.num_graphs * self.num_seed_feature) feat_a = torch.cat([feat_a, feat_seed], dim=0) # (a + n, t, d) return feat_a def _build_vector_a(self, pos_a, head_a, state_a): num_agent = pos_a.shape[0] motion_vector_a = torch.cat([pos_a.new_zeros(num_agent, 1, self.input_dim), pos_a[:, 1:] - pos_a[:, :-1]], dim=1) motion_vector_a[state_a == self.invalid_state] = self.invalid_motion_value # invalid -> valid is_last_invalid = (state_a.roll(shifts=1, dims=1) == self.invalid_state) & (state_a != self.invalid_state) is_last_invalid[:, 0] = state_a[:, 0] == self.enter_state motion_vector_a[is_last_invalid] = self.motion_gap # valid -> invalid is_last_valid = (state_a.roll(shifts=1, dims=1) != self.invalid_state) & (state_a == self.invalid_state) is_last_valid[:, 0] = False motion_vector_a[is_last_valid] = -self.motion_gap head_a[state_a == self.invalid_state] == self.invalid_head_value head_vector_a = torch.stack([head_a.cos(), head_a.sin()], dim=-1) return motion_vector_a, head_vector_a def _build_agent_feature(self, num_step, device, motion_vector=None, head_vector=None, agent_token_emb=None, agent_grid_emb=None, offset_pos=None, type=None, shape=None, categorical_embs_a=None, state=None, state_index=None, n=1): if agent_token_emb is None: agent_token_emb = self.no_token_emb(torch.zeros(1, device=device).long())[:, None].repeat(n, num_step, 1) if state is not None: agent_token_emb[state == self.enter_state] = self.bos_token_emb(torch.zeros(1, device=device).long()) if agent_grid_emb is None: agent_grid_emb = self.grid_token_emb[None, None, self.grid_size // 2].repeat(n, num_step, 1) if motion_vector is None or head_vector is None: pos_a = torch.zeros((n, num_step, 2), device=device) head_a = torch.zeros((n, num_step), device=device) if state is None: state = torch.full((n, num_step), self.invalid_state, device=device) motion_vector, head_vector = self._build_vector_a(pos_a, head_a, state) if offset_pos is None: offset_pos = torch.zeros_like(motion_vector) feature_a = torch.stack( [torch.norm(motion_vector[:, :, :2], p=2, dim=-1), angle_between_2d_vectors(ctr_vector=head_vector, nbr_vector=motion_vector[:, :, :2]), # torch.norm(offset_pos[:, :, :2], p=2, dim=-1), ], dim=-1) if categorical_embs_a is None: if type is None: type = torch.tensor([self.agent_type.index('seed')], device=device) if shape is None: shape = torch.full((1, 3), self.invalid_shape_value, device=device) categorical_embs_a = [self.type_a_emb(type.reshape(-1)), self.shape_emb(shape.reshape(-1, shape.shape[-1]))] x_a = self.x_a_emb(continuous_inputs=feature_a.view(-1, feature_a.size(-1)), categorical_embs=categorical_embs_a) x_a = x_a.view(-1, num_step, self.hidden_dim) # (a, t, d) if state is None: assert state_index is not None, f"state index need to be set when state tensor is None!" state = torch.tensor([state_index], device=device)[:, None].repeat(n, num_step, 1) # do not use `expand` s_a = self.state_a_emb(state.reshape(-1).long()).reshape(n, num_step, self.hidden_dim) feat_a = torch.cat((agent_token_emb, x_a, s_a, agent_grid_emb), dim=-1) feat_a = self.fusion_emb(feat_a) # (a, t, d) return feat_a, categorical_embs_a def _pad_feat(self, num_graph, av_index, *feats, num_seed_feature=None): if num_seed_feature is None: num_seed_feature = self.num_seed_feature padded_feats = tuple() for i in range(len(feats)): padded_feats += (torch.cat([feats[i], feats[i][av_index].repeat_interleave( repeats=num_seed_feature, dim=0)], dim=0 ),) pad_mask = torch.ones(*padded_feats[0].shape[:2], device=feats[0].device).bool() # (a, t) pad_mask[-num_graph * num_seed_feature:] = False return padded_feats + (pad_mask,) def _build_seed_feat(self, data, pos_a, head_a, state_a, head_vector_a, mask, sort_indices, av_index): seed_mask = sort_indices != av_index.repeat_interleave(repeats=data['batch_size_a'], dim=0)[:, None] # TODO: fix batch_size!!! print(mask.shape, sort_indices.shape, seed_mask.shape) mask[-data.num_graphs * self.num_seed_feature:] = seed_mask[:self.num_seed_feature] insert_pos_a = torch.gather(pos_a, dim=0, index=sort_indices[:self.num_seed_feature, :, None].expand(-1, -1, pos_a.shape[-1])) pos_a[mask] = insert_pos_a[mask[-self.num_seed_feature:]] state_a[-data.num_graphs * self.num_seed_feature:] = self.enter_state return pos_a, head_a, state_a, head_vector_a, mask def _build_temporal_edge(self, data, pos_a, head_a, state_a, head_vector_a, mask, inference_mask=None): num_graph = data.num_graphs num_agent = pos_a.shape[0] hist_mask = mask.clone() if not self.temporal_attn_to_invalid: is_bos = state_a == self.enter_state bos_index = torch.where(is_bos.any(dim=1), torch.argmax(is_bos.long(), dim=1), torch.tensor(0)) history_invalid_mask = torch.arange(mask.shape[1]).expand(mask.shape[0], mask.shape[1]).to(mask.device) history_invalid_mask = (history_invalid_mask < bos_index[:, None]) hist_mask[history_invalid_mask] = False if not self.temporal_attn_seed: hist_mask[-num_graph * self.num_seed_feature:] = False if inference_mask is not None: inference_mask[-num_graph * self.num_seed_feature:] = False else: # WARNING: if use temporal attn to seed # we need to fix the pos/head of seed!!! raise RuntimeError("Wrong settings!") pos_t = pos_a.reshape(-1, self.input_dim) # (num_agent * num_step, ...) head_t = head_a.reshape(-1) head_vector_t = head_vector_a.reshape(-1, 2) # for those invalid agents won't predict any motion token, we don't attend to them is_bos = state_a == self.enter_state is_bos[-num_graph * self.num_seed_feature:] = False bos_index = torch.where(is_bos.any(dim=1), torch.argmax(is_bos.long(), dim=1), torch.tensor(0)) motion_predict_start_index = torch.clamp(bos_index - self.time_span / self.shift + 1, min=0) motion_predict_mask = torch.arange(hist_mask.shape[1]).expand(hist_mask.shape[0], -1).to(hist_mask.device) motion_predict_mask = motion_predict_mask >= motion_predict_start_index[:, None] hist_mask[~motion_predict_mask] = False if self.hist_mask and self.training: hist_mask[ torch.arange(mask.shape[0]).unsqueeze(1), torch.randint(0, mask.shape[1], (num_agent, 10))] = False mask_t = hist_mask.unsqueeze(2) & hist_mask.unsqueeze(1) elif inference_mask is not None: mask_t = hist_mask.unsqueeze(2) & inference_mask.unsqueeze(1) else: mask_t = hist_mask.unsqueeze(2) & hist_mask.unsqueeze(1) # mask_t: (num_agent, 18, 18), edge_index_t: (2, num_edge) edge_index_t = dense_to_sparse(mask_t)[0] edge_index_t = edge_index_t[:, (edge_index_t[1] - edge_index_t[0] > 0) & (edge_index_t[1] - edge_index_t[0] <= self.time_span / self.shift)] rel_pos_t = pos_t[edge_index_t[0]] - pos_t[edge_index_t[1]] rel_head_t = wrap_angle(head_t[edge_index_t[0]] - head_t[edge_index_t[1]]) # handle the invalid steps is_invalid = state_a == self.invalid_state is_invalid_t = is_invalid.reshape(-1) rel_pos_t[is_invalid_t[edge_index_t[0]] & ~is_invalid_t[edge_index_t[1]]] = -self.motion_gap rel_pos_t[~is_invalid_t[edge_index_t[0]] & is_invalid_t[edge_index_t[1]]] = self.motion_gap rel_head_t[is_invalid_t[edge_index_t[0]] & ~is_invalid_t[edge_index_t[1]]] = -self.heading_gap rel_head_t[~is_invalid_t[edge_index_t[1]] & is_invalid_t[edge_index_t[1]]] = self.heading_gap rel_pos_t[is_invalid_t[edge_index_t[0]] & is_invalid_t[edge_index_t[1]]] = self.invalid_motion_value rel_head_t[is_invalid_t[edge_index_t[0]] & is_invalid_t[edge_index_t[1]]] = self.invalid_head_value r_t = torch.stack( [torch.norm(rel_pos_t[:, :2], p=2, dim=-1), angle_between_2d_vectors(ctr_vector=head_vector_t[edge_index_t[1]], nbr_vector=rel_pos_t[:, :2]), rel_head_t, edge_index_t[0] - edge_index_t[1]], dim=-1) r_t = self.r_t_emb(continuous_inputs=r_t, categorical_embs=None) return edge_index_t, r_t def _build_interaction_edge(self, data, pos_a, head_a, state_a, head_vector_a, batch_s, mask, pad_mask=None, inference_mask=None, av_index=None, seq_mask=None, seq_index=None, grid_index_a=None, **plot_kwargs): num_graph = data.num_graphs num_agent, num_step, _ = pos_a.shape is_training = inference_mask is None mask_a = mask.clone() if pad_mask is None: pad_mask = torch.ones_like(state_a).bool() pos_s = pos_a.transpose(0, 1).reshape(-1, self.input_dim) head_s = head_a.transpose(0, 1).reshape(-1) head_vector_s = head_vector_a.transpose(0, 1).reshape(-1, 2) pad_mask_s = pad_mask.transpose(0, 1).reshape(-1) if inference_mask is not None: mask_a = mask_a & inference_mask mask_s = mask_a.transpose(0, 1).reshape(-1) # build agent2agent bilateral connection edge_index_a2a = radius_graph(x=pos_s[:, :2], r=self.a2a_radius, batch=batch_s, loop=False, max_num_neighbors=300) edge_index_a2a = subgraph(subset=mask_s & pad_mask_s, edge_index=edge_index_a2a)[0] if os.getenv('PLOT_EDGE', False): plot_interact_edge(edge_index_a2a, data['scenario_id'], data['batch_size_a'].cpu(), self.num_seed_feature, num_step, av_index=av_index, **plot_kwargs) rel_pos_a2a = pos_s[edge_index_a2a[0]] - pos_s[edge_index_a2a[1]] rel_head_a2a = wrap_angle(head_s[edge_index_a2a[0]] - head_s[edge_index_a2a[1]]) # handle the invalid steps is_invalid = state_a == self.invalid_state is_invalid_s = is_invalid.transpose(0, 1).reshape(-1) rel_pos_a2a[is_invalid_s[edge_index_a2a[0]] & ~is_invalid_s[edge_index_a2a[1]]] = -self.motion_gap rel_pos_a2a[~is_invalid_s[edge_index_a2a[0]] & is_invalid_s[edge_index_a2a[1]]] = self.motion_gap rel_head_a2a[is_invalid_s[edge_index_a2a[0]] & ~is_invalid_s[edge_index_a2a[1]]] = -self.heading_gap rel_head_a2a[~is_invalid_s[edge_index_a2a[1]] & is_invalid_s[edge_index_a2a[1]]] = self.heading_gap rel_pos_a2a[is_invalid_s[edge_index_a2a[0]] & is_invalid_s[edge_index_a2a[1]]] = self.invalid_motion_value rel_head_a2a[is_invalid_s[edge_index_a2a[0]] & is_invalid_s[edge_index_a2a[1]]] = self.invalid_head_value r_a2a = torch.stack( [torch.norm(rel_pos_a2a[:, :2], p=2, dim=-1), angle_between_2d_vectors(ctr_vector=head_vector_s[edge_index_a2a[1]], nbr_vector=rel_pos_a2a[:, :2]), rel_head_a2a, torch.zeros_like(edge_index_a2a[0])], dim=-1) r_a2a = self.r_a2a_emb(continuous_inputs=r_a2a, categorical_embs=None) # add the edges which connect seed agents if is_training: mask_av = torch.ones_like(mask_a).bool() if not self.seed_attn_to_av: mask_av[av_index] = False mask_a &= mask_av edge_index_seed2a, r_seed2a = self._build_a2sa_edge(data, pos_a, head_a, head_vector_a, batch_s, mask_a.clone(), ~pad_mask.clone(), inference_mask=inference_mask, r=self.pl2seed_radius, max_num_neighbors=300, seq_mask=seq_mask, seq_index=seq_index, grid_index_a=grid_index_a, mode='grid') if os.getenv('PLOT_EDGE', False): plot_interact_edge(edge_index_seed2a, data['scenario_id'], data['batch_size_a'].cpu(), self.num_seed_feature, num_step, 'interact_edge_map_seed', av_index=av_index, **plot_kwargs) edge_index_a2a = torch.cat([edge_index_a2a, edge_index_seed2a], dim=-1) r_a2a = torch.cat([r_a2a, r_seed2a]) return edge_index_a2a, r_a2a, (edge_index_a2a.shape[1], edge_index_seed2a.shape[1]) #, nearest_dict return edge_index_a2a, r_a2a def _build_map2agent_edge(self, data, pos_a, head_a, state_a, head_vector_a, batch_s, batch_pl, mask, pad_mask=None, inference_mask=None, av_index=None, **kwargs): num_graph = data.num_graphs num_agent, num_step, _ = pos_a.shape is_training = inference_mask is None mask_pl2a = mask.clone() if pad_mask is None: pad_mask = torch.ones_like(state_a).bool() pos_s = pos_a.transpose(0, 1).reshape(-1, self.input_dim) head_s = head_a.transpose(0, 1).reshape(-1) head_vector_s = head_vector_a.transpose(0, 1).reshape(-1, 2) pad_mask_s = pad_mask.transpose(0, 1).reshape(-1) if inference_mask is not None: mask_pl2a = mask_pl2a & inference_mask mask_pl2a = mask_pl2a.transpose(0, 1).reshape(-1) ori_pos_pl = data['pt_token']['position'][:, :self.input_dim].contiguous() ori_orient_pl = data['pt_token']['orientation'].contiguous() pos_pl = ori_pos_pl.repeat(num_step, 1) # not `repeat_interleave` orient_pl = ori_orient_pl.repeat(num_step) # build map2agent directed graph # edge_index_pl2a = radius(x=pos_s[:, :2], y=pos_pl[:, :2], r=self.pl2a_radius, # batch_x=batch_s, batch_y=batch_pl, max_num_neighbors=300) edge_index_pl2a = radius(x=pos_pl[:, :2], y=pos_s[:, :2], r=self.pl2a_radius, batch_x=batch_pl, batch_y=batch_s, max_num_neighbors=5) edge_index_pl2a = edge_index_pl2a[[1, 0]] edge_index_pl2a = edge_index_pl2a[:, mask_pl2a[edge_index_pl2a[1]] & pad_mask_s[edge_index_pl2a[1]]] rel_pos_pl2a = pos_pl[edge_index_pl2a[0]] - pos_s[edge_index_pl2a[1]] rel_orient_pl2a = wrap_angle(orient_pl[edge_index_pl2a[0]] - head_s[edge_index_pl2a[1]]) # handle the invalid steps is_invalid = state_a == self.invalid_state is_invalid_s = is_invalid.transpose(0, 1).reshape(-1) rel_pos_pl2a[is_invalid_s[edge_index_pl2a[1]]] = self.motion_gap rel_orient_pl2a[is_invalid_s[edge_index_pl2a[1]]] = self.heading_gap r_pl2a = torch.stack( [torch.norm(rel_pos_pl2a[:, :2], p=2, dim=-1), angle_between_2d_vectors(ctr_vector=head_vector_s[edge_index_pl2a[1]], nbr_vector=rel_pos_pl2a[:, :2]), rel_orient_pl2a], dim=-1) r_pl2a = self.r_pt2a_emb(continuous_inputs=r_pl2a, categorical_embs=None) # add the edges which connect seed agents if is_training: edge_index_pl2seed, r_pl2seed = self._build_map2sa_edge(data, pos_a, head_a, head_vector_a, batch_s, batch_pl, ~pad_mask.clone(), inference_mask=inference_mask, r=self.pl2seed_radius, max_num_neighbors=2048, mode='grid') # sanity check # pl2a_index = torch.zeros(pos_a.shape[0], num_step) # pl2a_r = torch.zeros(pos_a.shape[0], num_step) # for src_index in torch.unique(edge_index_pl2seed[1]): # src_row = src_index % pos_a.shape[0] # src_col = src_index // pos_a.shape[0] # pl2a_index[src_row, src_col] = edge_index_pl2seed[0, edge_index_pl2seed[1] == src_index].sum() # pl2a_r[src_row, src_col] = r_pl2seed[edge_index_pl2seed[1] == src_index].sum() # print(pl2a_index) # print(pl2a_r) # exit(1) if os.getenv('PLOT_EDGE', False): plot_interact_edge(edge_index_pl2seed, data['scenario_id'], data['batch_size_a'].cpu(), self.num_seed_feature, num_step, 'interact_edge_map_seed', av_index=av_index) edge_index_pl2a = torch.cat([edge_index_pl2a, edge_index_pl2seed], dim=-1) r_pl2a = torch.cat([r_pl2a, r_pl2seed]) return edge_index_pl2a, r_pl2a, (edge_index_pl2a.shape[1], edge_index_pl2seed.shape[1]) return edge_index_pl2a, r_pl2a def _build_a2sa_edge(self, data, pos_a, head_a, head_vector_a, batch_s, mask_a, mask_sa, inference_mask=None, r=None, max_num_neighbors=8, seq_mask=None, seq_index=None, grid_index_a=None, mode: Literal['grid', 'heading']='heading', **plot_kwargs): num_agent, num_step, _ = pos_a.shape is_training = inference_mask is None pos_s = pos_a.transpose(0, 1).reshape(-1, self.input_dim) head_s = head_a.transpose(0, 1).reshape(-1) head_vector_s = head_vector_a.transpose(0, 1).reshape(-1, 2) if inference_mask is not None: mask_a = mask_a & inference_mask mask_sa = mask_sa & inference_mask mask_s = mask_a.transpose(0, 1).reshape(-1) mask_s_sa = mask_sa.transpose(0, 1).reshape(-1) # build seed_agent2agent unilateral connection assert r is not None, "r needs to be specified!" # edge_index_a2sa = radius(x=pos_s[mask_s_sa, :2], y=pos_s[:, :2], r=r, # batch_x=batch_s[mask_s_sa], batch_y=batch_s, max_num_neighbors=max_num_neighbors) edge_index_a2sa = radius(x=pos_s[:, :2], y=pos_s[mask_s_sa, :2], r=r, batch_x=batch_s, batch_y=batch_s[mask_s_sa], max_num_neighbors=max_num_neighbors) edge_index_a2sa = edge_index_a2sa[[1, 0]] edge_index_a2sa = edge_index_a2sa[:, ~mask_s_sa[edge_index_a2sa[0]] & mask_s[edge_index_a2sa[0]]] # only for seed agent sequence training if seq_mask is not None: edge_mask = seq_mask[edge_index_a2sa[1]] edge_mask = torch.gather(edge_mask, dim=1, index=edge_index_a2sa[0, :, None] % num_agent)[:, 0] edge_index_a2sa = edge_index_a2sa[:, edge_mask] if seq_index is None: seq_index = torch.zeros(num_agent, device=pos_a.device).long() if seq_index.dim() == 1: seq_index = seq_index[:, None].repeat(1, num_step) seq_index = seq_index.transpose(0, 1).reshape(-1) assert seq_index.shape[0] == pos_s.shape[0], f"Inconsistent lenght {seq_index.shape[0]} and {pos_s.shape[0]}!" # convert to global index all_index = torch.arange(pos_s.shape[0], device=pos_a.device).long() sa_index = all_index[mask_s_sa] edge_index_a2sa[1] = sa_index[edge_index_a2sa[1]] # plot edge index TODO: now only support bs=1 if os.getenv('PLOT_EDGE_INFERENCE', False) and not is_training: num_agent, num_step, _ = pos_a.shape # plot_interact_edge(edge_index_a2sa, data['scenario_id'], data['batch_size_a'].cpu(), 1, num_step, # 'interact_a2sa_edge_map', **plot_kwargs) plot_interact_edge(edge_index_a2sa, data['scenario_id'], torch.tensor([num_agent - 1]), 1, num_step, f"interact_a2sa_edge_map_infer_{plot_kwargs['tag']}", **plot_kwargs) rel_pos_a2sa = pos_s[edge_index_a2sa[0]] - pos_s[edge_index_a2sa[1]] rel_head_a2sa = wrap_angle(head_s[edge_index_a2sa[0]] - head_s[edge_index_a2sa[1]]) r_a2sa = torch.stack( [torch.norm(rel_pos_a2sa[:, :2], p=2, dim=-1), angle_between_2d_vectors(ctr_vector=head_vector_s[edge_index_a2sa[1]], nbr_vector=rel_pos_a2sa[:, :2]), rel_head_a2sa, seq_index[edge_index_a2sa[0]] - seq_index[edge_index_a2sa[1]]], dim=-1) r_a2sa = self.r_a2sa_emb(continuous_inputs=r_a2sa, categorical_embs=None) return edge_index_a2sa, r_a2sa def _build_map2sa_edge(self, data, pos_a, head_a, head_vector_a, batch_s, batch_pl, mask_sa, inference_mask=None, r=None, max_num_neighbors=32, mode: Literal['grid', 'heading']='heading'): _, num_step, _ = pos_a.shape mask_pl2sa = torch.ones_like(mask_sa).bool() if inference_mask is not None: mask_pl2sa = mask_pl2sa & inference_mask mask_pl2sa = mask_pl2sa.transpose(0, 1).reshape(-1) mask_s_sa = mask_sa.transpose(0, 1).reshape(-1) pos_s = pos_a.transpose(0, 1).reshape(-1, self.input_dim) head_s = head_a.transpose(0, 1).reshape(-1) head_vector_s = head_vector_a.transpose(0, 1).reshape(-1, 2) ori_pos_pl = data['pt_token']['position'][:, :self.input_dim].contiguous() ori_orient_pl = data['pt_token']['orientation'].contiguous() pos_pl = ori_pos_pl.repeat(num_step, 1) # not `repeat_interleave` orient_pl = ori_orient_pl.repeat(num_step) # build map2agent directed graph assert r is not None, "r needs to be specified!" # edge_index_pl2sa = radius(x=pos_s[mask_s_sa, :2], y=pos_pl[:, :2], r=r, # batch_x=batch_s[mask_s_sa], batch_y=batch_pl, max_num_neighbors=max_num_neighbors) edge_index_pl2sa = radius(x=pos_pl[:, :2], y=pos_s[mask_s_sa, :2], r=r, batch_x=batch_pl, batch_y=batch_s[mask_s_sa], max_num_neighbors=max_num_neighbors) edge_index_pl2sa = edge_index_pl2sa[[1, 0]] edge_index_pl2sa = edge_index_pl2sa[:, mask_pl2sa[mask_s_sa][edge_index_pl2sa[1]]] # convert to global index all_index = torch.arange(pos_s.shape[0], device=pos_a.device).long() sa_index = all_index[mask_s_sa] edge_index_pl2sa[1] = sa_index[edge_index_pl2sa[1]] # plot edge map # if os.getenv('PLOT_EDGE', False): # plot_map_edge(edge_index_pl2sa, pos_s[:, :2], data, save_path='map2sa_edge_map') rel_pos_pl2sa = pos_pl[edge_index_pl2sa[0]] - pos_s[edge_index_pl2sa[1]] rel_orient_pl2sa = wrap_angle(orient_pl[edge_index_pl2sa[0]] - head_s[edge_index_pl2sa[1]]) r_pl2sa = torch.stack( [torch.norm(rel_pos_pl2sa[:, :2], p=2, dim=-1), angle_between_2d_vectors(ctr_vector=head_vector_s[edge_index_pl2sa[1]], nbr_vector=rel_pos_pl2sa[:, :2]), rel_orient_pl2sa], dim=-1) r_pl2sa = self.r_pt2sa_emb(continuous_inputs=r_pl2sa, categorical_embs=None) return edge_index_pl2sa, r_pl2sa def _build_sa2sa_edge(self, data, pos_a, head_a, state_a, head_vector_a, batch_s, mask, inference_mask=None, **plot_kwargs): num_agent = pos_a.shape[0] pos_t = pos_a.transpose(0, 1).reshape(-1, self.input_dim) head_t = head_a.reshape(-1) head_vector_t = head_vector_a.reshape(-1, 2) if inference_mask is not None: mask_t = mask.unsqueeze(2) & inference_mask.unsqueeze(1) else: mask_t = mask.unsqueeze(2) & mask.unsqueeze(1) edge_index_sa2sa = dense_to_sparse(mask_t)[0] edge_index_sa2sa = edge_index_sa2sa[:, edge_index_sa2sa[1] - edge_index_sa2sa[0] > 0] rel_pos_t = pos_t[edge_index_sa2sa[0]] - pos_t[edge_index_sa2sa[1]] rel_head_t = wrap_angle(head_t[edge_index_sa2sa[0]] - head_t[edge_index_sa2sa[1]]) r_t = torch.stack( [torch.norm(rel_pos_t[:, :2], p=2, dim=-1), angle_between_2d_vectors(ctr_vector=head_vector_t[edge_index_sa2sa[1]], nbr_vector=rel_pos_t[:, :2]), rel_head_t, edge_index_sa2sa[0] - edge_index_sa2sa[1]], dim=-1) r_sa2sa = self.r_sa2sa_emb(continuous_inputs=r_t, categorical_embs=None) return edge_index_sa2sa, r_sa2sa def _build_seq(self, device, num_agent, num_step, av_index, sort_indices): """ Args: sort_indices (torch.Tensor): shape (num_agent, num_atep) """ # sort_indices = sort_indices[:self.num_seed_feature] seq_mask = torch.ones(self.num_seed_feature, num_step, num_agent + self.num_seed_feature, device=device).bool() seq_mask[..., -self.num_seed_feature:] = False for t in range(num_step): for s in range(self.num_seed_feature): seq_mask[s, t, sort_indices[s:, t].flatten().long()] = False if self.seed_attn_to_av: seq_mask[..., av_index] = True seq_mask = seq_mask.transpose(0, 1).reshape(-1, num_agent + self.num_seed_feature) seq_index = torch.cat([torch.zeros(num_agent), torch.arange(self.num_seed_feature) + 1]).to(device) seq_index = seq_index[:, None].repeat(1, num_step) for t in range(num_step): for s in range(self.num_seed_feature): seq_index[sort_indices[s : s + 1, t].flatten().long(), t] = s + 1 seq_index[av_index] = 0 return seq_mask, seq_index def _build_occ_gt(self, data, seq_mask, pos_rel_index_gt, pos_rel_index_gt_seed, mask_seed, edge_index=None, mode='edge_index'): """ Args: seq_mask (torch.Tensor): shape (num_step * num_seed_feature, num_agent + self.num_seed_feature) pos_rel_index_gt (torch.Tensor): shape (num_agent, num_step) pos_rel_index_gt_seed (torch.Tensor): shape (num_seed, num_step) """ num_agent = data['agent']['state_idx'].shape[0] + self.num_seed_feature num_step = data['agent']['state_idx'].shape[1] data['agent']['agent_occ'] = torch.zeros(data.num_graphs * self.num_seed_feature, num_step, self.attr_tokenizer.grid_size, device=data['agent']['state_idx'].device).long() data['agent']['map_occ'] = torch.zeros(data.num_graphs, num_step, self.attr_tokenizer.grid_size, device=data['agent']['state_idx'].device).long() if mode == 'edge_index': assert edge_index is not None, f"Need edge_index input!" for src_index in torch.unique(edge_index[1]): # decode src src_row = src_index % num_agent - (num_agent - self.num_seed_feature) src_col = src_index // num_agent # decode tgt tgt_indexes = edge_index[0, edge_index[1] == src_index] tgt_rows = tgt_indexes % num_agent tgt_cols = tgt_indexes // num_agent assert tgt_rows.max() < num_agent - self.num_seed_feature, f"Invalid {tgt_rows}" assert torch.unique(tgt_cols).shape[0] == 1 and torch.unique(tgt_cols)[0] == src_col data['agent']['agent_occ'][src_row, src_col, pos_rel_index_gt[tgt_rows, tgt_cols]] = 1 else: seq_mask = seq_mask.reshape(num_step, self.num_seed_feature, -1).transpose(0, 1)[..., :-self.num_seed_feature] for s in range(self.num_seed_feature): for t in range(num_step): index = pos_rel_index_gt[seq_mask[s, t], t] data['agent']['agent_occ'][s, t, index[index != -1]] = 1 if t > 0 and s < pos_rel_index_gt_seed.shape[0] and mask_seed[s, t - 1]: # insert agents data['agent']['agent_occ'][s, t, pos_rel_index_gt_seed[s, t - 1]] = -1 # TODO: fix batch_size!!! pt_grid_token_idx = data['agent']['pt_grid_token_idx'] # (t, num_pt) for t in range(num_step): data['agent']['map_occ'][:, t, pt_grid_token_idx[t][pt_grid_token_idx[t] != -1]] = 1 data['agent']['map_occ'] = data['agent']['map_occ'].repeat_interleave(repeats=self.num_seed_feature, dim=0) def forward(self, data: HeteroData, map_enc: Mapping[str, torch.Tensor]) -> Dict[str, torch.Tensor]: pos_a = data['agent']['token_pos'].clone() # (a, t, 2) head_a = data['agent']['token_heading'].clone() # (a, t) num_agent, num_step, traj_dim = pos_a.shape # e.g. (50, 18, 2) num_pt = data['pt_token']['position'].shape[0] agent_category = data['agent']['category'].clone() # (a,) agent_shape = data['agent']['shape'][:, self.num_historical_steps - 1].clone() # (a, 3) agent_token_index = data['agent']['token_idx'].clone() # (a, t) agent_state_index = data['agent']['state_idx'].clone() agent_type_index = data['agent']['type'].clone() av_index = data['agent']['av_index'].long() ego_pos = pos_a[av_index] ego_head = head_a[av_index] _, head_vector_a = self._build_vector_a(pos_a, head_a, agent_state_index) agent_grid_token_idx = data['agent']['grid_token_idx'] agent_grid_offset_xy = data['agent']['grid_offset_xy'] agent_head_token_idx = data['agent']['heading_token_idx'] sort_indices = data['agent']['sort_indices'] pt_grid_token_idx = data['agent']['pt_grid_token_idx'] ori_pos_pl = data['pt_token']['position'][:, :self.input_dim].contiguous() ori_orient_pl = data['pt_token']['orientation'].contiguous() pos_pl = ori_pos_pl.repeat(num_step, 1) orient_pl = ori_orient_pl.repeat(num_step) # build relative 3d descriptors pos_s = pos_a.transpose(0, 1).reshape(-1, self.input_dim) head_s = head_a.transpose(0, 1).reshape(-1) ego_pos_a = ego_pos.repeat_interleave(repeats=data['batch_size_a'], dim=0) ego_head_a = ego_head.repeat_interleave(repeats=data['batch_size_a'], dim=0) ego_pos_s = ego_pos_a.transpose(0, 1).reshape(-1, self.input_dim) ego_head_s = ego_head_a.transpose(0, 1).reshape(-1) rel_pos_a2a = pos_s - ego_pos_s rel_head_a2a = head_s - ego_head_s ego_pos_pl = ego_pos.repeat_interleave(repeats=data['batch_size_pl'], dim=0) ego_head_pl = ego_head.repeat_interleave(repeats=data['batch_size_pl'], dim=0) ego_pos_s = ego_pos_pl.transpose(0, 1).reshape(-1, self.input_dim) ego_head_s = ego_head_pl.transpose(0, 1).reshape(-1) rel_pos_pl2a = pos_pl - ego_pos_s rel_head_pl2a = orient_pl - ego_head_s # releative encodings ego_head_vector_a = head_vector_a[av_index].repeat_interleave(repeats=data['batch_size_a'], dim=0) ego_head_vector_s = ego_head_vector_a.transpose(0, 1).reshape(-1, 2) r_a2a = torch.stack( [torch.norm(rel_pos_a2a[:, :2], p=2, dim=-1), angle_between_2d_vectors(ctr_vector=ego_head_vector_s, nbr_vector=rel_pos_a2a[:, :2]), rel_head_a2a], dim=-1) r_a2a = self.r_a2a_emb(continuous_inputs=r_a2a, categorical_embs=None) # [N, hidden_dim] ego_head_vector_a = head_vector_a[av_index].repeat_interleave(repeats=data['batch_size_pl'], dim=0) ego_head_vector_s = ego_head_vector_a.transpose(0, 1).reshape(-1, 2) r_pl2a = torch.stack( [torch.norm(rel_pos_pl2a[:, :2], p=2, dim=-1), angle_between_2d_vectors(ctr_vector=ego_head_vector_s, nbr_vector=rel_pos_pl2a[:, :2]), rel_head_pl2a], dim=-1) r_pl2a = self.r_pt2a_emb(continuous_inputs=r_pl2a, categorical_embs=None) # [M, d] r_a2a = r_a2a.reshape(num_step, num_agent, -1).transpose(0, 1) r_pl2a = r_pl2a.reshape(num_step, num_pt, -1).transpose(0, 1) select_agent = torch.randperm(num_agent)[:self.agent_limit] select_pt = torch.randperm(num_pt)[:self.pt_limit] r_a2a = r_a2a[select_agent] r_pl2a = r_pl2a[select_pt] # aggregate to global feature r_a2a = r_a2a.mean(dim=0) # [t, d] r_pl2a = r_pl2a.mean(dim=0) # decode grid index of neighbor agents agent_occ = self.grid_agent_occ_head(r_a2a) # [t, grid_size] pt_occ = self.grid_pt_occ_head(r_pl2a) # 1. # agent_occ_gt = torch.zeros_like(agent_occ).long() # pt_occ_gt = torch.zeros_like(pt_occ).long() # for t in range(num_step): # agent_occ_gt[t, agent_grid_token_idx[:, t][agent_grid_token_idx[:, t] != -1]] = 1 # pt_occ_gt[t, pt_grid_token_idx[t][pt_grid_token_idx[t] != -1]] = 1 # agent_occ_gt[:, self.grid_size // 2] = 0 # pt_occ_gt[:, self.grid_size // 2] = 0 # agent_occ_eval_mask = torch.ones_like(agent_occ_gt) # agent_occ_eval_mask[0] = 0 # agent_occ_eval_mask[:, self.grid_size // 2] = 0 # pt_occ_eval_mask = torch.ones_like(pt_occ_gt) # pt_occ_eval_mask[0] = 0 # pt_occ_eval_mask[:, self.grid_size // 2] = 0 # 2. # agent_occ_gt = agent_grid_token_idx.transpose(0, 1).reshape(-1) # pt_occ_gt = pt_grid_token_idx.reshape(-1) # agent_occ_eval_mask = torch.zeros_like(agent_occ_gt) # agent_occ_eval_mask[torch.randperm(agent_occ_gt.shape[0])[:(num_step * 10)]] = 1 # agent_occ_eval_mask[agent_occ_gt == -1] = 0 # pt_occ_eval_mask = torch.zeros_like(pt_occ_gt) # pt_occ_eval_mask[torch.randperm(pt_occ_gt.shape[0])[:(num_step * 300)]] = 1 # pt_occ_eval_mask[pt_occ_gt == -1] = 0 # 3. agent_occ = agent_occ.reshape(num_step, self.agent_limit, -1) pt_occ = pt_occ.reshape(num_step, self.pt_limit, -1) agent_occ_gt = agent_grid_token_idx[select_agent].transpose(0, 1) pt_occ_gt = pt_grid_token_idx[:, select_pt] agent_occ_eval_mask = agent_occ_gt != -1 pt_occ_eval_mask = pt_occ_gt != -1 agent_occ = agent_occ[:, :agent_occ_gt.shape[1]] pt_occ = pt_occ[:, :pt_occ_gt.shape[1]] return {'occ_decoder': True, 'num_step': num_step, 'num_agent': self.agent_limit, # num_agent 'num_pt': self.pt_limit, # num_pt 'agent_occ': agent_occ, 'agent_occ_gt': agent_occ_gt, 'agent_occ_eval_mask': agent_occ_eval_mask.bool(), 'pt_occ': pt_occ, 'pt_occ_gt': pt_occ_gt, 'pt_occ_eval_mask': pt_occ_eval_mask.bool(), } def inference(self, *args, **kwargs): return self(*args, **kwargs)