from typing import Dict, Mapping, Optional import math import torch import torch.nn as nn from torch_cluster import radius, radius_graph from torch_geometric.data import HeteroData, Batch from torch_geometric.utils import dense_to_sparse, subgraph from dev.modules.layers import * from dev.modules.map_decoder import discretize_neighboring from dev.utils.geometry import angle_between_2d_vectors, wrap_angle from dev.utils.weight_init import weight_init def cal_polygon_contour(x, y, theta, width, length): left_front_x = x + 0.5 * length * math.cos(theta) - 0.5 * width * math.sin(theta) left_front_y = y + 0.5 * length * math.sin(theta) + 0.5 * width * math.cos(theta) left_front = (left_front_x, left_front_y) right_front_x = x + 0.5 * length * math.cos(theta) + 0.5 * width * math.sin(theta) right_front_y = y + 0.5 * length * math.sin(theta) - 0.5 * width * math.cos(theta) right_front = (right_front_x, right_front_y) right_back_x = x - 0.5 * length * math.cos(theta) + 0.5 * width * math.sin(theta) right_back_y = y - 0.5 * length * math.sin(theta) - 0.5 * width * math.cos(theta) right_back = (right_back_x, right_back_y) left_back_x = x - 0.5 * length * math.cos(theta) - 0.5 * width * math.sin(theta) left_back_y = y - 0.5 * length * math.sin(theta) + 0.5 * width * math.cos(theta) left_back = (left_back_x, left_back_y) polygon_contour = [left_front, right_front, right_back, left_back] return polygon_contour class SMARTAgentDecoder(nn.Module): def __init__(self, dataset: str, input_dim: int, hidden_dim: int, num_historical_steps: int, num_interaction_steps: int, time_span: Optional[int], pl2a_radius: float, pl2seed_radius: float, a2a_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=[], predict_motion: bool=False, predict_state: bool=False, predict_map: bool=False, state_token: Dict[str, int]=None, seed_size: int=5) -> None: super(SMARTAgentDecoder, self).__init__() self.dataset = dataset self.input_dim = input_dim self.hidden_dim = hidden_dim self.num_historical_steps = num_historical_steps self.num_interaction_steps = num_interaction_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.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 # 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_x_a = 2 input_dim_r_t = 4 input_dim_r_pt2a = 3 input_dim_r_a2a = 3 input_dim_token = 8 # tokens: (token_size, 4, 2) self.seed_size = seed_size self.all_agent_type = ['veh', 'ped', 'cyc', 'background', 'invalid', 'seed'] self.seed_agent_type = ['veh', 'ped', 'cyc', 'seed'] self.type_a_emb = nn.Embedding(len(self.all_agent_type), hidden_dim) self.shape_emb = MLPLayer(3, hidden_dim, hidden_dim) if self.predict_state: self.state_a_emb = nn.Embedding(len(self.state_type), hidden_dim) self.invalid_shape_value = .1 self.motion_gap = 1. self.heading_gap = 1. self.x_a_emb = FourierEmbedding(input_dim=input_dim_x_a, hidden_dim=hidden_dim, num_freq_bands=num_freq_bands) self.r_t_emb = FourierEmbedding(input_dim=input_dim_r_t, hidden_dim=hidden_dim, num_freq_bands=num_freq_bands) 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_emb_veh = MLPEmbedding(input_dim=input_dim_token, hidden_dim=hidden_dim) self.token_emb_ped = MLPEmbedding(input_dim=input_dim_token, hidden_dim=hidden_dim) self.token_emb_cyc = MLPEmbedding(input_dim=input_dim_token, hidden_dim=hidden_dim) self.no_token_emb = nn.Embedding(1, hidden_dim) self.bos_token_emb = nn.Embedding(1, hidden_dim) # FIXME: do we need this??? self.token_emb_offset = MLPEmbedding(input_dim=2, hidden_dim=hidden_dim) num_inputs = 2 if self.predict_state: num_inputs = 3 self.fusion_emb = MLPEmbedding(input_dim=self.hidden_dim * num_inputs, hidden_dim=self.hidden_dim) self.t_attn_layers = nn.ModuleList( [AttentionLayer(hidden_dim=hidden_dim, num_heads=num_heads, head_dim=head_dim, dropout=dropout, bipartite=False, has_pos_emb=True) for _ in range(num_layers)] ) self.pt2a_attn_layers = nn.ModuleList( [AttentionLayer(hidden_dim=hidden_dim, num_heads=num_heads, head_dim=head_dim, dropout=dropout, bipartite=True, has_pos_emb=True) for _ in range(num_layers)] ) self.a2a_attn_layers = nn.ModuleList( [AttentionLayer(hidden_dim=hidden_dim, num_heads=num_heads, head_dim=head_dim, dropout=dropout, bipartite=False, has_pos_emb=True) for _ in range(num_layers)] ) self.token_size = token_size # 2048 # agent motion prediction head self.token_predict_head = MLPLayer(input_dim=hidden_dim, hidden_dim=hidden_dim, output_dim=self.token_size) # agent state prediction head if self.predict_state: self.seed_feature = nn.Embedding(self.seed_size, self.hidden_dim) self.state_predict_head = MLPLayer(input_dim=hidden_dim, hidden_dim=hidden_dim, output_dim=len(self.valid_state_type)) self.seed_head = MLPLayer(input_dim=hidden_dim, hidden_dim=hidden_dim, output_dim=hidden_dim) self.seed_state_predict_head = MLPLayer(input_dim=hidden_dim, hidden_dim=hidden_dim, output_dim=len(self.seed_state_type)) self.seed_type_predict_head = MLPLayer(input_dim=hidden_dim, hidden_dim=hidden_dim, output_dim=len(self.seed_agent_type)) # entering token prediction # FIXME: this is just under test!!! # self.bos_pl_predict_head = MLPLayer(input_dim=hidden_dim, hidden_dim=hidden_dim, # output_dim=200) # self.bos_offset_predict_head = MLPLayer(input_dim=hidden_dim, hidden_dim=hidden_dim, # output_dim=2601) 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 = True self.temporal_attn_seed = False # FIXME: This is just under test!!! # self.mapping_network = MappingNetwork(z_dim=hidden_dim, w_dim=hidden_dim, num_layers=num_layers) 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, 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) # set the position of invalid agents to the position of ego agent # note here we only set invalid steps BEFORE the bos token! # is_invalid = agent_state == self.invalid_state # is_bos = agent_state == self.enter_state # bos_index = torch.where(is_bos.any(dim=1), torch.argmax(is_bos.long(), dim=1), torch.tensor(0)) # bos_mask = torch.arange(num_step).expand(num_agent, -1).to(agent_state.device) < bos_index[:, None] # is_invalid[~bos_mask] = False # ego_pos_a = pos_a[av_index].clone() # ego_head_vector_a = head_vector_a[av_index].clone() # pos_a[is_invalid] = ego_pos_a[None, :].repeat(pos_a.shape[0], 1, 1)[is_invalid] # head_vector_a[is_invalid] = ego_head_vector_a[None, :].repeat(head_vector_a.shape[0], 1, 1)[is_invalid] 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())]) 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]] # 'vehicle', 'pedestrian', 'cyclist', 'background' is_invalid = (agent_state == self.invalid_state) & (agent_state != self.enter_state) agent_types = data['agent']['type'][filter_mask].long().repeat_interleave(repeats=num_step, dim=0) agent_types[is_invalid.reshape(-1)] = self.all_agent_type.index('invalid') agent_shapes = data['agent']['shape'][filter_mask, self.num_historical_steps - 1, :].repeat_interleave(repeats=num_step, dim=0) agent_shapes[is_invalid.reshape(-1)] = self.invalid_shape_value categorical_embs = [self.type_a_emb(agent_types), self.shape_emb(agent_shapes)] feature_a = torch.stack( [torch.norm(motion_vector_a[:, :, :2], p=2, dim=-1), angle_between_2d_vectors(ctr_vector=head_vector_a, nbr_vector=motion_vector_a[:, :, :2]), ], dim=-1) # (num_agent, num_shifted_step, 2) x_a = self.x_a_emb(continuous_inputs=feature_a.view(-1, feature_a.size(-1)), categorical_embs=categorical_embs) x_a = x_a.view(-1, num_step, self.hidden_dim) # (num_agent, num_step, hidden_dim) s_a = self.state_a_emb(agent_state.reshape(-1).long()).reshape(num_agent, num_step, self.hidden_dim) feat_a = torch.cat((agent_token_emb, x_a, s_a), dim=-1) # (num_agent, num_step, hidden_dim * 3) feat_a = self.fusion_emb(feat_a) # (num_agent, num_step, hidden_dim) # seed agent feature motion_vector_seed = motion_vector_a[av_index : av_index + 1] head_vector_seed = head_vector_a[av_index : av_index + 1] feat_seed = self.build_invalid_agent_feature(num_step, pos_a.device, type_index=self.all_agent_type.index('seed'), motion_vector=motion_vector_seed, head_vector=head_vector_seed) # replace the features of steps before bos of valid agents with the corresponding invalid agent features # is_bos = agent_state == self.enter_state # is_eos = agent_state == self.exit_state # bos_index = torch.where(is_bos.any(dim=1), torch.argmax(is_bos.long(), dim=1), torch.tensor(0)) # eos_index = torch.where(is_eos.any(dim=1), torch.argmax(is_eos.long(), dim=1), torch.tensor(num_step - 1)) # is_before_bos = torch.arange(num_step).expand(num_agent, -1).to(agent_state.device) < bos_index[:, None] # is_after_eos = torch.arange(num_step).expand(num_agent, -1).to(agent_state.device) > eos_index[:, None] + 1 # feat_ina = self.build_invalid_agent_feature(num_step, pos_a.device) # feat_a[is_before_bos | is_after_eos] = feat_ina.repeat(num_agent, 1, 1)[is_before_bos | is_after_eos] # print("train") # is_bos = agent_state == self.enter_state # is_eos = agent_state == self.exit_state # bos_index = torch.where(is_bos.any(dim=1), torch.argmax(is_bos.long(), dim=1), torch.tensor(0)) # eos_index = torch.where(is_eos.any(dim=1), torch.argmax(is_eos.long(), dim=1), torch.tensor(num_step - 1)) # mask = torch.arange(num_step).expand(num_agent, -1).to(agent_state.device) # mask = (mask >= bos_index[:, None]) & (mask <= eos_index[:, None] + 1) # is_invalid[mask] = False # print(feat_a.sum(dim=-1)[is_invalid]) feat_a = torch.cat([feat_a, feat_seed], dim=0) # (num_agent + 1, num_step, hidden_dim) # feat_a_sum = feat_a.sum(dim=-1) # for a in range(num_agent): # print(f"agent {a}:") # print(f"state: {agent_state[a, :]}") # print(f"feat_a_sum: {feat_a_sum[a, :]}") # exit(1) if inference: return feat_a, head_vector_a, agent_token_traj_all, agent_token_emb, categorical_embs else: return feat_a, head_vector_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) # update the relative motion/head vectors is_bos = state_a == self.enter_state motion_vector_a[is_bos] = self.motion_gap is_last_eos = state_a.roll(shifts=1, dims=1) == self.exit_state is_last_eos[:, 0] = False motion_vector_a[is_last_eos] = -self.motion_gap head_vector_a = torch.stack([head_a.cos(), head_a.sin()], dim=-1) return motion_vector_a, head_vector_a def build_invalid_agent_feature(self, num_step, device, motion_vector=None, head_vector=None, type_index=None, shape_value=None): invalid_agent_token_emb = self.no_token_emb(torch.zeros(1, device=device).long())[:, None].repeat(1, num_step, 1) if motion_vector is None or head_vector is None: motion_vector = torch.zeros((1, num_step, 2), device=device) head_vector = torch.stack([torch.cos(torch.zeros(1, device=device)), torch.sin(torch.zeros(1, device=device))], dim=-1)[:, None, :].repeat(1, num_step, 1) feature_ina = torch.stack( [torch.norm(motion_vector[:, :, :2], p=2, dim=-1), angle_between_2d_vectors(ctr_vector=head_vector, nbr_vector=motion_vector[:, :, :2]), ], dim=-1) if type_index is None: type_index = self.all_agent_type.index('invalid') if shape_value is None: shape_value = torch.full((1, 3), self.invalid_shape_value, device=device) categorical_embs_ina = [self.type_a_emb(torch.tensor([type_index], device=device)), self.shape_emb(shape_value)] x_ina = self.x_a_emb(continuous_inputs=feature_ina.view(-1, feature_ina.size(-1)), categorical_embs=categorical_embs_ina) x_ina = x_ina.view(-1, num_step, self.hidden_dim) # (1, num_step, hidden_dim) s_ina = self.state_a_emb(torch.tensor([self.invalid_state], device=device))[:, None].repeat(1, num_step, 1) # NOTE: do not use `expand` feat_ina = torch.cat((invalid_agent_token_emb, x_ina, s_ina), dim=-1) feat_ina = self.fusion_emb(feat_ina) # (1, num_step, hidden_dim) return feat_ina def build_temporal_edge(self, pos_a, head_a, head_vector_a, state_a, mask, inference_mask=None, av_index=None): num_agent = pos_a.shape[0] hist_mask = mask.clone() if not self.temporal_attn_to_invalid: hist_mask[state_a == self.invalid_state] = False # set the position of invalid agents to the position of ego agent ego_pos_a = pos_a[av_index].clone() # (num_step, 2) ego_head_a = head_a[av_index].clone() ego_head_vector_a = head_vector_a[av_index].clone() ego_state_a = state_a[av_index].clone() # is_invalid = state_a == self.invalid_state # pos_a[is_invalid] = ego_pos_a[None, :].repeat(pos_a.shape[0], 1, 1)[is_invalid] # head_a[is_invalid] = ego_head_a[None, :].repeat(head_a.shape[0], 1)[is_invalid] # add seed agent pos_a = torch.cat([pos_a, ego_pos_a[None]], dim=0) head_a = torch.cat([head_a, ego_head_a[None]], dim=0) state_a = torch.cat([state_a, ego_state_a[None]], dim=0) head_vector_a = torch.cat([head_vector_a, ego_head_vector_a[None]], dim=0) hist_mask = torch.cat([hist_mask, torch.ones_like(hist_mask[0:1])], dim=0).bool() if not self.temporal_attn_seed: hist_mask[-1:] = False if inference_mask is not None: inference_mask[-1:] = False 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[-1] = 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]]) # FIXME relative motion/head for bos/eos token # is_next_bos = state_a.roll(shifts=-1, dims=1) == self.enter_state # is_next_bos[:, -1] = False # the last step # is_next_bos_t = is_next_bos.reshape(-1) # rel_pos_t[is_next_bos_t[edge_index_t[0]]] = -self.bos_motion # rel_pos_t[is_next_bos_t[edge_index_t[1]]] = self.bos_motion # rel_head_t[is_next_bos_t[edge_index_t[0]]] = -torch.pi # rel_head_t[is_next_bos_t[edge_index_t[1]]] = torch.pi # is_last_eos = state_a.roll(shifts=1, dims=1) == self.exit_state # is_last_eos[:, 0] = False # the first step # is_last_eos_t = is_last_eos.reshape(-1) # rel_pos_t[is_last_eos_t[edge_index_t[0]]] = -self.bos_motion # rel_pos_t[is_last_eos_t[edge_index_t[1]]] = self.bos_motion # rel_head_t[is_last_eos_t[edge_index_t[0]]] = -torch.pi # rel_head_t[is_last_eos_t[edge_index_t[1]]] = torch.pi # handle the bos token of ego agent # is_invalid = state_a == self.invalid_state # is_invalid_t = is_invalid.reshape(-1) # is_ego_bos = (ego_state_a == self.enter_state)[None, :].expand(num_agent + 1, -1) # is_ego_bos_t = is_ego_bos.reshape(-1) # rel_pos_t[is_invalid_t[edge_index_t[0]] & is_ego_bos_t[edge_index_t[0]]] = 0. # rel_pos_t[is_invalid_t[edge_index_t[1]] & is_ego_bos_t[edge_index_t[1]]] = 0. # rel_head_t[is_invalid_t[edge_index_t[0]] & is_ego_bos_t[edge_index_t[0]]] = 0. # rel_head_t[is_invalid_t[edge_index_t[1]] & is_ego_bos_t[edge_index_t[1]]] = 0. # 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]]] = -self.motion_gap rel_pos_t[is_invalid_t[edge_index_t[1]]] = self.motion_gap rel_head_t[is_invalid_t[edge_index_t[0]]] = -self.heading_gap rel_head_t[is_invalid_t[edge_index_t[1]]] = self.heading_gap 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, pos_a, head_a, head_vector_a, state_a, batch_s, mask_a, inference_mask=None, av_index=None): num_agent, num_step, _ = pos_a.shape pos_a = torch.cat([pos_a, pos_a[av_index][None]], dim=0) head_a = torch.cat([head_a, head_a[av_index][None]], dim=0) state_a = torch.cat([state_a, state_a[av_index][None]], dim=0) head_vector_a = torch.cat([head_vector_a, head_vector_a[av_index][None]], dim=0) 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_s = mask_a.transpose(0, 1).reshape(-1) # seed agent mask_seed = state_a[av_index] != self.invalid_state pos_seed = pos_a[av_index] edge_index_seed2a = radius(x=pos_seed[:, :2], y=pos_s[:, :2], r=self.pl2seed_radius, batch_x=torch.arange(num_step).to(pos_s.device), batch_y=batch_s, max_num_neighbors=300) edge_index_seed2a = edge_index_seed2a[:, mask_s[edge_index_seed2a[0]] & mask_seed[edge_index_seed2a[1]]] # convert to global index (must be unilateral connection) edge_index_seed2a[1, :] = (edge_index_seed2a[1, :] + 1) * (num_agent + 1) - 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, edge_index=edge_index_a2a)[0] # add the edges which connect seed agents edge_index_a2a = torch.cat([edge_index_a2a, edge_index_seed2a], dim=-1) # set the position of invalid agents to the position of ego agent # ego_pos_a = pos_a[av_index].clone() # ego_head_a = head_a[av_index].clone() # is_invalid = state_a == self.invalid_state # pos_a[is_invalid] = ego_pos_a[None, :].repeat(pos_a.shape[0], 1, 1)[is_invalid] # head_a[is_invalid] = ego_head_a[None, :].repeat(head_a.shape[0], 1)[is_invalid] 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]]) # relative motion/head for bos/eos token # is_bos = state_a == self.enter_state # is_bos_s = is_bos.transpose(0, 1).reshape(-1) # rel_pos_a2a[is_bos_s[edge_index_a2a[0]]] = -self.bos_motion # rel_pos_a2a[is_bos_s[edge_index_a2a[1]]] = self.bos_motion # rel_head_a2a[is_bos_s[edge_index_a2a[0]]] = -torch.pi # rel_head_a2a[is_bos_s[edge_index_a2a[1]]] = torch.pi # is_last_eos = state_a.roll(shifts=-1, dims=1) == self.exit_state # is_last_eos[:, 0] = False # first step # is_last_eos_s = is_last_eos.transpose(0, 1).reshape(-1) # rel_pos_a2a[is_last_eos_s[edge_index_a2a[0]]] = -self.bos_motion # rel_pos_a2a[is_last_eos_s[edge_index_a2a[1]]] = self.bos_motion # rel_head_a2a[is_last_eos_s[edge_index_a2a[0]]] = -torch.pi # rel_head_a2a[is_last_eos_s[edge_index_a2a[1]]] = torch.pi # handle the bos token of ego agent # is_invalid = state_a == self.invalid_state # is_invalid_s = is_invalid.transpose(0, 1).reshape(-1) # is_ego_bos = (state_a[av_index] == self.enter_state)[None, :].expand(num_agent + 1, -1) # is_ego_bos_s = is_ego_bos.transpose(0, 1).reshape(-1) # rel_pos_a2a[is_invalid_s[edge_index_a2a[0]] & is_ego_bos_s[edge_index_a2a[0]]] = 0. # rel_pos_a2a[is_invalid_s[edge_index_a2a[1]] & is_ego_bos_s[edge_index_a2a[1]]] = 0. # rel_head_a2a[is_invalid_s[edge_index_a2a[0]] & is_ego_bos_s[edge_index_a2a[0]]] = 0. # rel_head_a2a[is_invalid_s[edge_index_a2a[1]] & is_ego_bos_s[edge_index_a2a[1]]] = 0. # 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]]] = -self.motion_gap rel_pos_a2a[is_invalid_s[edge_index_a2a[1]]] = self.motion_gap rel_head_a2a[is_invalid_s[edge_index_a2a[0]]] = -self.heading_gap rel_head_a2a[is_invalid_s[edge_index_a2a[1]]] = self.heading_gap 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], dim=-1) r_a2a = self.r_a2a_emb(continuous_inputs=r_a2a, categorical_embs=None) return edge_index_a2a, r_a2a def build_map2agent_edge(self, data, num_step, pos_a, head_a, head_vector_a, state_a, batch_s, batch_pl, mask, inference_mask=None, av_index=None): num_agent, num_step, _ = pos_a.shape mask_pl2a = mask.clone() if inference_mask is not None: mask_pl2a = mask_pl2a & inference_mask mask_pl2a = mask_pl2a.transpose(0, 1).reshape(-1) pos_a = torch.cat([pos_a, pos_a[av_index][None]], dim=0) state_a = torch.cat([state_a, state_a[av_index][None]], dim=0) head_a = torch.cat([head_a, head_a[av_index][None]], dim=0) head_vector_a = torch.cat([head_vector_a, head_vector_a[av_index][None]], dim=0) 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) # seed agent mask_seed = state_a[av_index] != self.invalid_state pos_seed = pos_a[av_index] edge_index_pl2seed = radius(x=pos_seed[:, :2], y=pos_pl[:, :2], r=self.pl2seed_radius, batch_x=torch.arange(num_step).to(pos_s.device), batch_y=batch_pl, max_num_neighbors=600) edge_index_pl2seed = edge_index_pl2seed[:, mask_seed[edge_index_pl2seed[1]]] # convert to global index edge_index_pl2seed[1, :] = (edge_index_pl2seed[1, :] + 1) * (num_agent + 1) - 1 # build map2agent directed graph # edge_index_pl2a[0]: pl token; edge_index_pl2a[1]: agent token 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) # We force invalid agents to interact with **all** (visible in current window) map tokens # invalid_node_index_a = torch.where(bos_state_s.bool())[0] # sampled_node_index_m = torch.arange(ori_pos_pl.shape[0]).to(pos_pl.device) # if kwargs.get('sample_pt_indices', None) is not None: # sampled_node_index_m = sampled_node_index_m[kwargs['sample_pt_indices'].long()] # grid_a, grid_b = torch.meshgrid(sampled_node_index_m, invalid_node_index_a, indexing='ij') # invalid_edge_index_pl2a = torch.stack([grid_a.reshape(-1), grid_b.reshape(-1)], dim=0) # edge_index_pl2a = torch.concat([edge_index_pl2a, invalid_edge_index_pl2a], dim=-1) # remove the edges which connect with motion-invalid agents edge_index_pl2a = edge_index_pl2a[:, mask_pl2a[edge_index_pl2a[1]]] # add the edges which connect seed agents with map tokens edge_index_pl2a = torch.cat([edge_index_pl2a, edge_index_pl2seed], dim=-1) # set the position of invalid agents to the position of ego agent # ego_pos_a = pos_a[av_index].clone() # ego_head_a = head_a[av_index].clone() # is_invalid = state_a == self.invalid_state # pos_a[is_invalid] = ego_pos_a[None, :].repeat(pos_a.shape[0], 1, 1)[is_invalid] # head_a[is_invalid] = ego_head_a[None, :].repeat(head_a.shape[0], 1)[is_invalid] 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) return edge_index_pl2a, r_pl2a def get_inputs(self, data: HeteroData) -> Dict[str, torch.Tensor]: pos_a = data['agent']['token_pos'] head_a = data['agent']['token_heading'] agent_category = data['agent']['category'] agent_token_index = data['agent']['token_idx'] agent_state_index = data['agent']['state_idx'] mask = data['agent']['raw_agent_valid_mask'].clone() # mask[agent_category != 3] = False if not self.predict_state: agent_state_index = None next_token_index_gt = agent_token_index.roll(shifts=-1, dims=1) next_state_index_gt = agent_state_index.roll(shifts=-1, dims=1) if self.predict_state: next_token_eval_mask = mask.clone() next_token_eval_mask = next_token_eval_mask * next_token_eval_mask.roll(shifts=1, dims=1) bos_token_index = torch.nonzero(agent_state_index == 2) eos_token_index = torch.nonzero(agent_state_index == 3) next_token_eval_mask[bos_token_index[:, 0], bos_token_index[:, 1]] = 1 for eos_token_index_ in eos_token_index: if not next_token_eval_mask[eos_token_index_[0], eos_token_index_[1]]: next_token_eval_mask[eos_token_index_[0], eos_token_index_[1]:] = 0 next_token_eval_mask = next_token_eval_mask.roll(shifts=-1, dims=1) # TODO: next_state_eval_mask !!! if next_token_index_gt[next_token_eval_mask].min() < 0: raise RuntimeError() next_token_eval_mask[:, -1] = False return {'token_pos': pos_a, 'token_heading': head_a, 'agent_category': agent_category, 'next_token_idx_gt': next_token_index_gt, 'next_state_idx_gt': next_state_index_gt, 'next_token_eval_mask': next_token_eval_mask, 'raw_agent_valid_mask': data['agent']['raw_agent_valid_mask'], } def forward(self, data: HeteroData, map_enc: Mapping[str, torch.Tensor]) -> Dict[str, torch.Tensor]: pos_a = data['agent']['token_pos'].clone() # (num_agent, num_shifted_step, 2) head_a = data['agent']['token_heading'].clone() # (num_agent, num_shifted_step) num_agent, num_step, traj_dim = pos_a.shape # e.g. (50, 18, 2) agent_category = data['agent']['category'].clone() # (num_agent,) agent_token_index = data['agent']['token_idx'].clone() # (num_agent, num_step) agent_state_index = data['agent']['state_idx'].clone() # (num_agent, num_step) agent_type_index = data['agent']['type'].clone() # (num_agent, num_step) agent_enter_pl_token_idx = None agent_enter_offset_token_idx = None device = pos_a.device seed_step_mask = agent_state_index[:, 1:] == self.enter_state if torch.any(seed_step_mask.sum(dim=0) > self.seed_size): print(agent_state_index) print(agent_state_index.shape) print(seed_step_mask.long()) print(seed_step_mask.sum(dim=0)) raise RuntimeError(f"Seed size {self.seed_size} is too small.") # fix pos and head of invalid agents av_index = int(data['agent']['av_index']) # ego_pos_a = pos_a[av_index].clone() # (num_shifted_step, 2) # ego_head_vector_a = head_vector_a[av_index] # (num_shifted_step, 2) # is_invalid = agent_state_index == self.invalid_state # pos_a[is_invalid] = ego_pos_a[None, :].expand(pos_a.shape[0], -1, -1)[is_invalid] # head_vector_a[is_invalid] = ego_head_vector_a[None, :].expand(head_vector_a.shape[0], -1, -1)[is_invalid] if not self.predict_state: agent_state_index = None feat_a, head_vector_a = self.agent_token_embedding(data, agent_token_index, agent_state_index, pos_a, head_a, av_index=av_index) # build masks mask = data['agent']['raw_agent_valid_mask'].clone() temporal_mask = mask.clone() interact_mask = mask.clone() if self.predict_state: agent_enter_offset_token_idx = data['agent']['neighbor_token_idx'] agent_enter_pl_token_idx = data['agent']['map_bos_token_idx'] agent_enter_pl_token_id = data['agent']['map_bos_token_id'] is_bos = agent_state_index == self.enter_state is_eos = agent_state_index == self.exit_state bos_index = torch.where(is_bos.any(dim=1), torch.argmax(is_bos.long(), dim=1), torch.tensor(0)) eos_index = torch.where(is_eos.any(dim=1), torch.argmax(is_eos.long(), dim=1), torch.tensor(num_step - 1)) # not `-1` temporal_mask = torch.ones_like(mask) motion_mask = torch.arange(mask.shape[1]).expand(mask.shape[0], -1).to(device) motion_mask = (motion_mask > bos_index[:, None]) & (motion_mask <= eos_index[:, None]) temporal_mask[motion_mask] = mask[motion_mask] interact_mask[agent_state_index == self.enter_state] = True interact_mask = torch.cat([interact_mask, torch.ones_like(interact_mask[:1])]).bool() # placeholder edge_index_t, r_t = self.build_temporal_edge(pos_a, head_a, head_vector_a, agent_state_index, temporal_mask, av_index=av_index) # +1: placeholder for seed agent # if isinstance(data, Batch): # print(data['agent']['batch'], data.num_graphs) # batch_s = torch.cat([data['agent']['batch'] + data.num_graphs * t for t in range(num_step)], dim=0) # batch_pl = torch.cat([data['pt_token']['batch'] + data.num_graphs * t for t in range(num_step)], dim=0) # else: batch_s = torch.arange(num_step, device=device).repeat_interleave(data['agent']['num_nodes'] + 1) batch_pl = torch.arange(num_step, device=device).repeat_interleave(data['pt_token']['num_nodes']) edge_index_a2a, r_a2a = self.build_interaction_edge(pos_a, head_a, head_vector_a, agent_state_index, batch_s, interact_mask, av_index=av_index) agent_category = torch.cat([agent_category, torch.full(agent_category[-1:].shape, 3, device=device)]) interact_mask[agent_category != 3] = False edge_index_pl2a, r_pl2a = self.build_map2agent_edge(data, num_step, pos_a, head_a, head_vector_a, agent_state_index, batch_s, batch_pl, interact_mask, av_index=av_index) # mapping network # z = torch.randn(num_agent, self.hidden_dim).to(feat_a.device) # w = self.mapping_network(z) for i in range(self.num_layers): # feat_a = feat_a + w[:, None] feat_a = feat_a.reshape(-1, self.hidden_dim) # (num_agent, num_step, hidden_dim) -> (seq_len, hidden_dim) feat_a = self.t_attn_layers[i](feat_a, r_t, edge_index_t) feat_a = feat_a.reshape(-1, num_step, self.hidden_dim).transpose(0, 1).reshape(-1, self.hidden_dim) feat_a = self.pt2a_attn_layers[i](( map_enc['x_pt'].repeat_interleave(repeats=num_step, dim=0).reshape(-1, num_step, self.hidden_dim).transpose(0, 1).reshape( -1, self.hidden_dim), feat_a), r_pl2a, edge_index_pl2a) feat_a = self.a2a_attn_layers[i](feat_a, r_a2a, edge_index_a2a) feat_a = feat_a.reshape(num_step, -1, self.hidden_dim).transpose(0, 1) # next motion token next_token_prob = self.token_predict_head(feat_a[:-1]) # (num_agent, num_step, token_size) next_token_prob_softmax = torch.softmax(next_token_prob, dim=-1) _, next_token_idx = torch.topk(next_token_prob_softmax, k=10, dim=-1) # (num_agent, num_step, 10) next_token_index_gt = agent_token_index.roll(shifts=-1, dims=1) # next state token next_state_prob = self.state_predict_head(feat_a[:-1]) next_state_idx = next_state_prob.softmax(dim=-1).argmax(dim=-1, keepdim=True) # (num_agent, num_step, 1) next_state_index_gt = agent_state_index.roll(shifts=-1, dims=1) # (invalid, valid, exit) # seed agent feat_seed = self.seed_head(feat_a[-1:]) + self.seed_feature.weight[:, None] next_state_prob_seed = self.seed_state_predict_head(feat_seed) next_state_idx_seed = next_state_prob_seed.softmax(dim=-1).argmax(dim=-1, keepdim=True) # (self.seed_size, num_step, 1) next_type_prob_seed = self.seed_type_predict_head(feat_seed) next_type_idx_seed = next_type_prob_seed.softmax(dim=-1).argmax(dim=-1, keepdim=True) next_type_index_gt = agent_type_index[:, None].expand(-1, num_step).roll(shifts=-1, dims=1) # polygon token for bos token # next_bos_pl_prob = self.bos_pl_predict_head(feat_a) # next_bos_pl_prob_softmax = torch.softmax(next_bos_pl_prob, dim=-1) # _, next_bos_pl_idx = torch.topk(next_bos_pl_prob_softmax, k=1, dim=-1) # (num_agent, num_step, 1) # next_bos_pl_index_gt = agent_enter_pl_token_id.roll(shifts=-1, dims=-1) # offset token for bos token # next_bos_offset_prob = self.bos_offset_predict_head(feat_a) # next_bos_offset_prob_softmax = torch.softmax(next_bos_offset_prob, dim=-1) # _, next_bos_offset_idx = torch.topk(next_bos_offset_prob_softmax, k=1, dim=-1) # next_bos_offset_index_gt = agent_enter_offset_token_idx.roll(shifts=-1, dims=-1) # next token prediction mask bos_token_index = torch.nonzero(agent_state_index == self.enter_state) eos_token_index = torch.nonzero(agent_state_index == self.exit_state) # mask for motion tokens next_token_eval_mask = mask.clone() next_token_eval_mask = next_token_eval_mask * next_token_eval_mask.roll(shifts=-1, dims=1) * next_token_eval_mask.roll(shifts=1, dims=1) for bos_token_index_ in bos_token_index: next_token_eval_mask[bos_token_index_[0], bos_token_index_[1] : bos_token_index_[1] + 1] = 1 next_token_eval_mask[bos_token_index_[0], bos_token_index_[1] + 1 : bos_token_index_[1] + 2] = \ mask[bos_token_index_[0], bos_token_index_[1] + 2 : bos_token_index_[1] + 3] next_token_eval_mask[eos_token_index[:, 0], eos_token_index[:, 1]] = 0 # mask for state tokens next_state_eval_mask = mask.clone() next_state_eval_mask = next_state_eval_mask * next_state_eval_mask.roll(shifts=-1, dims=1) * next_state_eval_mask.roll(shifts=1, dims=1) for bos_token_index_ in bos_token_index: next_state_eval_mask[bos_token_index_[0], :bos_token_index_[1]] = 0 next_state_eval_mask[bos_token_index_[0], bos_token_index_[1] : bos_token_index_[1] + 1] = 1 next_state_eval_mask[bos_token_index_[0], bos_token_index_[1] + 1 : bos_token_index_[1] + 2] = \ mask[bos_token_index_[0], bos_token_index_[1] + 2 : bos_token_index_[1] + 3] for eos_token_index_ in eos_token_index: next_state_eval_mask[eos_token_index_[0], eos_token_index_[1] + 1:] = 1 next_state_eval_mask[eos_token_index_[0], eos_token_index_[1] : eos_token_index_[1] + 1] = \ mask[eos_token_index_[0], eos_token_index_[1] - 1 : eos_token_index_[1]] # seed agents next_bos_token_index = torch.nonzero(next_state_index_gt == self.enter_state) next_bos_token_index = next_bos_token_index[next_bos_token_index[:, 1] < num_step - 1] next_state_index_gt_seed = torch.full((self.seed_size, num_step), self.seed_state_type.index('invalid'), device=next_state_index_gt.device) next_type_index_gt_seed = torch.full((self.seed_size, num_step), self.seed_agent_type.index('seed'), device=next_state_index_gt.device) next_eval_mask_seed = torch.ones_like(next_state_index_gt_seed) num_seed = torch.zeros(num_step, device=next_state_index_gt.device).long() for next_bos_token_index_ in next_bos_token_index: if num_seed[next_bos_token_index_[1]] < self.seed_size: next_state_index_gt_seed[num_seed[next_bos_token_index_[1]], next_bos_token_index_[1]] = self.seed_state_type.index('enter') next_type_index_gt_seed[num_seed[next_bos_token_index_[1]], next_bos_token_index_[1]] = next_type_index_gt[next_bos_token_index_[0], next_bos_token_index_[1]] num_seed[next_bos_token_index_[1]] += 1 # the last timestep is the beginning of the sequence (also the input) next_token_eval_mask[:, -1] = 0 next_state_eval_mask[:, -1] = 0 next_eval_mask_seed[:, -1] = 0 # next_bos_token_eval_mask[:, -1] = False # no invalid motion token will be supervised if (next_token_index_gt[next_token_eval_mask] < 0).any(): raise RuntimeError() next_state_index_gt[next_state_index_gt == self.exit_state] = self.valid_state_type.index('exit') return {'x_a': feat_a, # motion token 'next_token_idx': next_token_idx, 'next_token_prob': next_token_prob, 'next_token_idx_gt': next_token_index_gt, 'next_token_eval_mask': next_token_eval_mask.bool(), # state token 'next_state_idx': next_state_idx, 'next_state_prob': next_state_prob, 'next_state_idx_gt': next_state_index_gt, 'next_state_eval_mask': next_state_eval_mask.bool(), # seed agent 'next_state_idx_seed': next_state_idx_seed, 'next_state_prob_seed': next_state_prob_seed, 'next_state_idx_gt_seed': next_state_index_gt_seed, 'next_type_idx_seed': next_type_idx_seed, 'next_type_prob_seed': next_type_prob_seed, 'next_type_idx_gt_seed': next_type_index_gt_seed, 'next_eval_mask_seed': next_eval_mask_seed.bool(), # pl token for bos # 'next_bos_pl_idx': next_bos_pl_idx, # 'next_bos_pl_prob': next_bos_pl_prob, # 'next_bos_pl_index_gt': next_bos_pl_index_gt, # offset token for bos # 'next_bos_offset_idx': next_bos_offset_idx, # 'next_bos_offset_prob': next_bos_offset_prob, # 'next_bos_offset_index_gt': next_bos_offset_index_gt, # 'next_bos_token_eval_mask': next_bos_token_eval_mask, } def inference(self, data: HeteroData, map_enc: Mapping[str, torch.Tensor]) -> Dict[str, torch.Tensor]: start_state_idx = data['agent']['state_idx'][:, (self.num_historical_steps - 1) // self.shift] filter_mask = (start_state_idx == self.valid_state) | (start_state_idx == self.exit_state) seed_step_mask = data['agent']['state_idx'][:, (self.num_historical_steps - 1) // self.shift:] == self.enter_state seed_agent_index_per_step = [torch.nonzero(seed_step_mask[:, t]).squeeze(dim=-1) for t in range(seed_step_mask.shape[1])] if torch.any(seed_step_mask.sum(dim=0) > self.seed_size): raise RuntimeError(f"Seed size {self.seed_size} is too small.") # num_historical_steps=11 eval_mask = data['agent']['valid_mask'][filter_mask, self.num_historical_steps - 1] if self.predict_state: eval_mask = torch.ones_like(eval_mask).bool() # agent attributes pos_a = data['agent']['token_pos'][filter_mask].clone() # (num_agent, num_step, 2) state_a = data['agent']['state_idx'][filter_mask].clone() # (num_agent, num_step) head_a = data['agent']['token_heading'][filter_mask].clone() # (num_agent, num_step) gt_traj = data['agent']['position'][filter_mask, self.num_historical_steps:, :self.input_dim].contiguous() num_agent, num_step, traj_dim = pos_a.shape av_index = int(data['agent']['av_index']) av_index -= (~filter_mask[:av_index]).sum() # map attributes pos_pl = data['pt_token']['position'][:, :2].clone() # (num_pl, 2) # make future steps to zero pos_a[:, (self.num_historical_steps - 1) // self.shift:] = 0 state_a[:, (self.num_historical_steps - 1) // self.shift:] = 0 head_a[:, (self.num_historical_steps - 1) // self.shift:] = 0 agent_valid_mask = data['agent']['raw_agent_valid_mask'][filter_mask].clone() # token_valid_mask agent_valid_mask[:, (self.num_historical_steps - 1) // self.shift:] = True agent_valid_mask[~eval_mask] = False agent_token_index = data['agent']['token_idx'][filter_mask] agent_state_index = data['agent']['state_idx'][filter_mask] agent_type = data['agent']['type'][filter_mask] agent_category = data['agent']['category'][filter_mask] feat_a, head_vector_a, agent_token_traj_all, agent_token_emb, categorical_embs = self.agent_token_embedding(data, agent_token_index, agent_state_index, pos_a, head_a, inference=True, filter_mask=filter_mask, av_index=av_index, ) feat_seed = feat_a[-1:] feat_a = feat_a[:-1] agent_type = data["agent"]["type"][filter_mask] veh_mask = agent_type == 0 cyc_mask = agent_type == 2 ped_mask = agent_type == 1 # self.num_recurrent_steps_val = 91 - 11 = 80 self.num_recurrent_steps_val = data["agent"]['position'].shape[1] - self.num_historical_steps pred_traj = torch.zeros(pos_a.shape[0], self.num_recurrent_steps_val, 2, device=feat_a.device) # (num_agent, 80, 2) pred_head = torch.zeros(pos_a.shape[0], self.num_recurrent_steps_val, device=feat_a.device) pred_type = agent_type.clone() pred_state = torch.zeros(pos_a.shape[0], self.num_recurrent_steps_val, device=feat_a.device) pred_prob = torch.zeros(pos_a.shape[0], self.num_recurrent_steps_val // self.shift, device=feat_a.device) # (num_agent, 80 // 5 = 16) next_token_idx_list = [] next_state_idx_list = [] next_bos_pl_idx_list = [] next_bos_offset_idx_list = [] feat_a_t_dict = {} feat_sa_t_dict = {} # build masks (init) mask = agent_valid_mask.clone() temporal_mask = mask.clone() interact_mask = mask.clone() if self.predict_state: # find bos and eos index is_bos = agent_state_index == self.enter_state is_eos = agent_state_index == self.exit_state bos_index = torch.where(is_bos.any(dim=1), torch.argmax(is_bos.long(), dim=1), torch.tensor(0)) eos_index = torch.where(is_eos.any(dim=1), torch.argmax(is_eos.long(), dim=1), torch.tensor(num_step - 1)) temporal_mask = torch.ones_like(mask) motion_mask = torch.arange(mask.shape[1]).expand(mask.shape[0], mask.shape[1]).to(mask.device) motion_mask = (motion_mask > bos_index[:, None]) & (motion_mask <= eos_index[:, None]) motion_mask[:, self.num_historical_steps // self.shift:] = False temporal_mask[motion_mask] = mask[motion_mask] interact_mask = torch.ones_like(mask) non_motion_mask = ~motion_mask non_motion_mask[:, self.num_historical_steps // self.shift:] = False interact_mask[non_motion_mask] = False interact_mask[agent_state_index == self.enter_state] = True temporal_mask[:, (self.num_historical_steps - 1) // self.shift:] = True interact_mask[:, (self.num_historical_steps - 1) // self.shift:] = True # mapping network # z = torch.randn(num_agent, self.hidden_dim).to(feat_a.device) # w = self.mapping_network(z) # we only need to predict 16 next tokens for t in range(self.num_recurrent_steps_val // self.shift): # feat_a = feat_a + w[:, None] num_agent = pos_a.shape[0] if t == 0: inference_mask = temporal_mask.clone() inference_mask = torch.cat([inference_mask, torch.ones_like(inference_mask[-1:])]) inference_mask[:, (self.num_historical_steps - 1) // self.shift + t:] = False else: inference_mask = torch.zeros_like(temporal_mask) inference_mask = torch.cat([inference_mask, torch.zeros_like(inference_mask[-1:])]) inference_mask[:, max((self.num_historical_steps - 1) // self.shift + t - (self.num_interaction_steps // self.shift), 0) : (self.num_historical_steps - 1) // self.shift + t] = True interact_mask = torch.cat([interact_mask, torch.ones_like(interact_mask[:1])]).bool() # placeholder edge_index_t, r_t = self.build_temporal_edge(pos_a, head_a, head_vector_a, state_a, temporal_mask, inference_mask, av_index=av_index) # +1: placeholder for seed agent batch_s = torch.arange(num_step, device=pos_a.device).repeat_interleave(num_agent + 1) batch_pl = torch.arange(num_step, device=pos_a.device).repeat_interleave(data['pt_token']['num_nodes']) # In the inference stage, we only infer the current stage for recurrent edge_index_a2a, r_a2a = self.build_interaction_edge(pos_a, head_a, head_vector_a, state_a, batch_s, interact_mask, inference_mask, av_index=av_index) edge_index_pl2a, r_pl2a = self.build_map2agent_edge(data, num_step, pos_a, head_a, head_vector_a, state_a, batch_s, batch_pl, interact_mask, inference_mask, av_index=av_index) interact_mask = interact_mask[:-1] # if t > 0: # feat_a_sum = feat_a.sum(dim=-1) # for a in range(pos_a.shape[0]): # t_1 = (self.num_historical_steps - 1) // self.shift + t - 1 # print(f"agent {a} t_1 {t_1}") # print(f"token: {next_token_idx[a]}") # print(f"state: {next_state_idx[a]}") # print(f"feat_a_sum: {feat_a_sum[a, t_1]}") for i in range(self.num_layers): if (i in feat_a_t_dict) and (i in feat_sa_t_dict): feat_a = feat_a_t_dict[i] feat_seed = feat_sa_t_dict[i] feat_a = torch.cat([feat_a, feat_seed], dim=0) feat_a = feat_a.reshape(-1, self.hidden_dim) feat_a = self.t_attn_layers[i](feat_a, r_t, edge_index_t) feat_a = feat_a.reshape(-1, num_step, self.hidden_dim).transpose(0, 1).reshape(-1, self.hidden_dim) feat_a = self.pt2a_attn_layers[i](( map_enc['x_pt'].repeat_interleave(repeats=num_step, dim=0).reshape(-1, num_step, self.hidden_dim).transpose(0, 1).reshape( -1, self.hidden_dim), feat_a), r_pl2a, edge_index_pl2a) feat_a = self.a2a_attn_layers[i](feat_a, r_a2a, edge_index_a2a) feat_a = feat_a.reshape(num_step, -1, self.hidden_dim).transpose(0, 1) feat_seed = feat_a[-1:] # (1, num_step, hidden_dim) feat_a = feat_a[:-1] # (num_agent, num_step, hidden_dim) if t == 0: feat_a_t_dict[i + 1] = feat_a feat_sa_t_dict[i + 1] = feat_seed else: # update agent features at current step n = feat_a_t_dict[i + 1].shape[0] feat_a_t_dict[i + 1][:n, (self.num_historical_steps - 1) // self.shift - 1 + t] = feat_a[:n, (self.num_historical_steps - 1) // self.shift - 1 + t] # add newly inserted agent features (only when t changed) if feat_a.shape[0] > n: m = feat_a.shape[0] - n feat_a_t_dict[i + 1] = torch.cat([feat_a_t_dict[i + 1], feat_a[-m:]]) # update seed agent features at current step feat_sa_t_dict[i + 1][:, (self.num_historical_steps - 1) // self.shift - 1 + t] = feat_seed[:, (self.num_historical_steps - 1) // self.shift - 1 + t] # next motion token next_token_prob = self.token_predict_head(feat_a[:, (self.num_historical_steps - 1) // self.shift - 1 + t]) next_token_prob_softmax = torch.softmax(next_token_prob, dim=-1) topk_token_prob, next_token_idx = torch.topk(next_token_prob_softmax, k=self.beam_size, dim=-1) # both (num_agent, beam_size) e.g. (31, 5) # next state token next_state_prob = self.state_predict_head(feat_a[:, (self.num_historical_steps - 1) // self.shift - 1 + t]) next_state_idx = next_state_prob.softmax(dim=-1).argmax(dim=-1) next_state_idx[next_state_idx == self.valid_state_type.index('exit')] = self.exit_state # seed agent feat_seed = self.seed_head(feat_seed) + self.seed_feature.weight[:, None] next_state_prob_seed = self.seed_state_predict_head(feat_seed[:, (self.num_historical_steps - 1) // self.shift - 1 + t]) next_state_idx_seed = next_state_prob_seed.softmax(dim=-1).argmax(dim=-1, keepdim=True) next_state_idx_seed[next_state_idx_seed == self.seed_state_type.index('enter')] = self.enter_state next_type_prob_seed = self.seed_type_predict_head(feat_seed[:, (self.num_historical_steps - 1) // self.shift - 1 + t]) next_type_idx_seed = next_type_prob_seed.softmax(dim=-1).argmax(dim=-1, keepdim=True) # print(f"t: {t}") # print(next_type_idx_seed[..., 0].tolist()) # bos pl prediction # next_bos_pl_prob = self.bos_pl_predict_head(feat_a[:, (self.num_historical_steps - 1) // self.shift - 1 + t]) # next_bos_pl_prob_softmax = torch.softmax(next_bos_pl_prob, dim=-1) # next_bos_pl_idx = torch.argmax(next_bos_pl_prob_softmax, dim=-1) # bos offset prediction # next_bos_offset_prob = self.bos_offset_predict_head(feat_a[:, (self.num_historical_steps - 1) // self.shift - 1 + t]) # next_bos_offset_prob_softmax = torch.softmax(next_bos_offset_prob, dim=-1) # next_bos_offset_idx = torch.argmax(next_bos_offset_prob_softmax, dim=-1) # convert the predicted token to a 0.5s (6 timesteps) trajectory expanded_token_index = next_token_idx[..., None, None, None].expand(-1, -1, 6, 4, 2) next_token_traj = torch.gather(agent_token_traj_all, 1, expanded_token_index) # (num_agent, beam_size, 6, 4, 2) # apply rotation and translation on 'next_token_traj' theta = head_a[:, (self.num_historical_steps - 1) // self.shift - 1 + t] cos, sin = theta.cos(), theta.sin() rot_mat = torch.zeros((num_agent, 2, 2), device=theta.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(next_token_traj.view(-1, 4, 2), rot_mat[:, None, None, ...].repeat(1, self.beam_size, self.shift + 1, 1, 1).view( -1, 2, 2)).view(num_agent, self.beam_size, self.shift + 1, 4, 2) agent_pred_rel = agent_diff_rel + pos_a[:, (self.num_historical_steps - 1) // self.shift - 1 + t, :][:, None, None, None, ...] # sample 1 most probable index of top beam_size tokens, (num_agent, beam_size) -> (num_agent, 1) # then sample the agent_pred_rel, (num_agent, beam_size, 6, 4, 2) -> (num_agent, 6, 4, 2) sample_token_index = torch.multinomial(topk_token_prob, 1).to(agent_pred_rel.device) next_token_idx = next_token_idx.gather(dim=1, index=sample_token_index).squeeze(-1) agent_pred_rel = agent_pred_rel.gather(dim=1, index=sample_token_index[..., None, None, None].expand(-1, -1, 6, 4, 2))[:, 0, ...] # get predicted position and heading of current shifted timesteps diff_xy = agent_pred_rel[:, 1:, 0, :] - agent_pred_rel[:, 1:, 3, :] pred_traj[:num_agent, t * 5 : (t + 1) * 5] = agent_pred_rel[:, 1:, ...].clone().mean(dim=2) pred_head[:num_agent, t * 5 : (t + 1) * 5] = torch.arctan2(diff_xy[:, :, 1], diff_xy[:, :, 0]) pred_state[:num_agent, t * 5 : (t + 1) * 5] = next_state_idx[:, None].repeat(1, 5) # pred_prob[:num_agent, t] = topk_token_prob.gather(dim=-1, index=sample_token_index)[:, 0] # (num_agent, beam_size) -> (num_agent,) # update pos/head/state of current step pos_a[:, (self.num_historical_steps - 1) // self.shift + t] = agent_pred_rel[:, -1, ...].clone().mean(dim=1) diff_xy = agent_pred_rel[:, -1, 0, :] - agent_pred_rel[:, -1, 3, :] theta = torch.arctan2(diff_xy[:, 1], diff_xy[:, 0]) head_a[:, (self.num_historical_steps - 1) // self.shift + t] = theta state_a[:, (self.num_historical_steps - 1) // self.shift + t] = next_state_idx # the case that the current predicted state token is invalid/exit is_eos = next_state_idx == self.exit_state is_invalid = next_state_idx == self.invalid_state next_token_idx[is_invalid] = -1 pos_a[is_invalid, (self.num_historical_steps - 1) // self.shift + t] = 0. head_a[is_invalid, (self.num_historical_steps - 1) // self.shift + t] = 0. mask[is_invalid, (self.num_historical_steps - 1) // self.shift + t] = False # to handle those newly-added agents interact_mask[is_invalid, (self.num_historical_steps - 1) // self.shift + t] = False agent_token_emb[is_invalid, (self.num_historical_steps - 1) // self.shift + t] = self.no_token_emb(torch.zeros(1, device=pos_a.device).long()) type_emb = categorical_embs[0].reshape(num_agent, num_step, -1) shape_emb = categorical_embs[1].reshape(num_agent, num_step, -1) type_emb[is_invalid, (self.num_historical_steps - 1) // self.shift + t] = self.type_a_emb(torch.tensor(self.all_agent_type.index('invalid'), device=pos_a.device).long()) shape_emb[is_invalid, (self.num_historical_steps - 1) // self.shift + t] = self.shape_emb(torch.full((1, 3), self.invalid_shape_value, device=pos_a.device)) categorical_embs = [type_emb.reshape(-1, self.hidden_dim), shape_emb.reshape(-1, self.hidden_dim)] # FIXME: need to discuss!!! # if is_eos.any(): # pos_a[is_eos, (self.num_historical_steps - 1) // self.shift + t + 1:] = 0. # head_a[is_eos, (self.num_historical_steps - 1) // self.shift + t + 1:] = 0. # mask[is_eos, (self.num_historical_steps - 1) // self.shift + t + 1:] = False # to handle those newly-added agents # interact_mask[torch.cat([is_eos, torch.zeros(1, device=is_eos.device).bool()]), (self.num_historical_steps - 1) // self.shift + t + 1:] = False # agent_token_emb[is_eos, (self.num_historical_steps - 1) // self.shift + t + 1:] = self.no_token_emb(torch.zeros(1, device=pos_a.device).long()) # type_emb = categorical_embs[0].reshape(num_agent, num_step, -1) # shape_emb = categorical_embs[1].reshape(num_agent, num_step, -1) # type_emb[is_eos, (self.num_historical_steps - 1) // self.shift + t + 1:] = self.type_a_emb(torch.tensor(self.all_agent_type.index('invalid'), device=pos_a.device).long()) # shape_emb[is_eos, (self.num_historical_steps - 1) // self.shift + t + 1:] = self.shape_emb(torch.full((1, 3), self.invalid_shape_value, device=pos_a.device)) # categorical_embs = [type_emb.reshape(-1, self.hidden_dim), shape_emb.reshape(-1, self.hidden_dim)] # for sa in range(next_state_idx_seed.shape[0]): # if next_state_idx_seed[sa] == self.enter_state: # print(f"agent {sa} is entering at step {t}") # insert new agents (from seed agent) seed_agent_index_cur_step = seed_agent_index_per_step[t] num_new_agent = min(len(seed_agent_index_cur_step), next_state_idx_seed.bool().sum()) new_agent_mask = next_state_idx_seed.bool() next_state_idx_seed = next_state_idx_seed[new_agent_mask] next_state_idx_seed = next_state_idx_seed[:num_new_agent] next_type_idx_seed = next_type_idx_seed[new_agent_mask] next_type_idx_seed = next_type_idx_seed[:num_new_agent] selected_agent_index_cur_step = seed_agent_index_cur_step[:num_new_agent] agent_token_index = torch.cat([agent_token_index, data['agent']['token_idx'][selected_agent_index_cur_step]]) agent_state_index = torch.cat([agent_state_index, data['agent']['state_idx'][selected_agent_index_cur_step]]) agent_category = torch.cat([agent_category, data['agent']['category'][selected_agent_index_cur_step]]) agent_valid_mask = torch.cat([agent_valid_mask, data['agent']['raw_agent_valid_mask'][selected_agent_index_cur_step]]) gt_traj = torch.cat([gt_traj, data['agent']['position'][selected_agent_index_cur_step, self.num_historical_steps:, :self.input_dim]]) # FIXME: under test!!! bos token index is -2 next_state_idx = torch.cat([next_state_idx, next_state_idx_seed], dim=0).long() next_token_idx = torch.cat([next_token_idx, torch.zeros(num_new_agent, device=next_token_idx.device) - 2], dim=0).long() mask = torch.cat([mask, torch.ones(num_new_agent, num_step, device=mask.device)], dim=0).bool() temporal_mask = torch.cat([temporal_mask, torch.ones(num_new_agent, num_step, device=temporal_mask.device)], dim=0).bool() interact_mask = torch.cat([interact_mask, torch.ones(num_new_agent, num_step, device=interact_mask.device)], dim=0).bool() # new_pos_a = ego_pos_a[None].repeat(num_new_agent, 1, 1) # new_head_a = ego_head_a[None].repeat(num_new_agent, 1) new_pos_a = torch.zeros(num_new_agent, num_step, 2, device=pos_a.device) new_head_a = torch.zeros(num_new_agent, num_step, device=pos_a.device) new_state_a = torch.zeros(num_new_agent, num_step, device=state_a.device) new_shape_a = torch.full((num_new_agent, num_step, 3), self.invalid_shape_value, device=pos_a.device) new_type_a = torch.full((num_new_agent, num_step), self.all_agent_type.index('invalid'), device=pos_a.device) if num_new_agent > 0: gt_bos_pos_a = data['agent']['position'][seed_agent_index_cur_step[:num_new_agent], (self.num_historical_steps - 1) // self.shift + t] new_pos_a[:, (self.num_historical_steps - 1) // self.shift + t] = gt_bos_pos_a[:, :2].clone() pos_a = torch.cat([pos_a, new_pos_a], dim=0) gt_bos_head_a = data['agent']['heading'][seed_agent_index_cur_step[:num_new_agent], (self.num_historical_steps - 1) // self.shift + t] new_head_a[:, (self.num_historical_steps - 1) // self.shift + t] = gt_bos_head_a.clone() head_a = torch.cat([head_a, new_head_a], dim=0) gt_bos_shape_a = data['agent']['shape'][seed_agent_index_cur_step[:num_new_agent], self.num_historical_steps - 1] gt_bos_type_a = data['agent']['type'][seed_agent_index_cur_step[:num_new_agent]] new_shape_a[:, (self.num_historical_steps - 1) // self.shift + t:] = gt_bos_shape_a.clone()[:, None] new_type_a[:, (self.num_historical_steps - 1) // self.shift + t:] = gt_bos_type_a.clone()[:, None] # new_type_a[:, (self.num_historical_steps - 1) // self.shift + t] = next_type_idx_seed pred_type = torch.cat([pred_type, new_type_a[:, (self.num_historical_steps - 1) // self.shift + t]]) new_state_a[:, (self.num_historical_steps - 1) // self.shift + t] = self.enter_state state_a = torch.cat([state_a, new_state_a], dim=0) mask[-num_new_agent:, :(self.num_historical_steps - 1) // self.shift + t + 1] = 0 interact_mask[-num_new_agent:, :(self.num_historical_steps - 1) // self.shift + t] = 0 # update all steps new_pred_traj = torch.zeros(num_new_agent, self.num_recurrent_steps_val, 2, device=pos_a.device) new_pred_traj[:, t * 5 : (t + 1) * 5] = new_pos_a[:, (self.num_historical_steps - 1) // self.shift + t][:, None].repeat(1, 5, 1) pred_traj = torch.cat([pred_traj, new_pred_traj], dim=0) new_pred_head = torch.zeros(num_new_agent, self.num_recurrent_steps_val, device=pos_a.device) new_pred_head[:, t * 5 : (t + 1) * 5] = new_head_a[:, (self.num_historical_steps - 1) // self.shift + t][:, None].repeat(1, 5) pred_head = torch.cat([pred_head, new_pred_head], dim=0) new_pred_state = torch.zeros(num_new_agent, self.num_recurrent_steps_val, device=pos_a.device) new_pred_state[:, t * 5 : (t + 1) * 5] = next_state_idx_seed[:, None].repeat(1, 5) pred_state = torch.cat([pred_state, new_pred_state], dim=0) # handle the position/heading of bos token # bos_pl_pos = pos_pl[next_bos_pl_idx[is_bos].long()] # bos_offset_pos = discretize_neighboring(neighbor_index=next_bos_offset_idx[is_bos]) # pos_a[is_bos, (self.num_historical_steps - 1) // self.shift + t] += (bos_pl_pos + bos_offset_pos) # # headings before bos token remains 0 which align with training process # head_a[is_bos, (self.num_historical_steps - 1) // self.shift + t] += 0. # add new agents token embeddings agent_token_emb = torch.cat([agent_token_emb, self.no_token_emb(torch.zeros(1, device=pos_a.device).long())[None, :].repeat(num_new_agent, num_step, 1)]) veh_mask = torch.cat([veh_mask, next_type_idx_seed == self.seed_agent_type.index('veh')]) ped_mask = torch.cat([ped_mask, next_type_idx_seed == self.seed_agent_type.index('ped')]) cyc_mask = torch.cat([cyc_mask, next_type_idx_seed == self.seed_agent_type.index('cyc')]) # add new agents trajectory embeddings 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) new_agent_token_traj_all = torch.zeros((num_new_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) new_agent_token_traj_all[next_type_idx_seed == 0] = torch.cat( [trajectory_token_all_veh[:, :self.shift], trajectory_token_veh[:, None, ...]], dim=1) new_agent_token_traj_all[next_type_idx_seed == 1] = torch.cat( [trajectory_token_all_ped[:, :self.shift], trajectory_token_ped[:, None, ...]], dim=1) new_agent_token_traj_all[next_type_idx_seed == 2] = torch.cat( [trajectory_token_all_cyc[:, :self.shift], trajectory_token_cyc[:, None, ...]], dim=1) agent_token_traj_all = torch.cat([agent_token_traj_all, new_agent_token_traj_all], dim=0) # add new agents categorical embeddings new_categorical_embs = [self.type_a_emb(new_type_a.reshape(-1).long()), self.shape_emb(new_shape_a.reshape(-1, 3))] categorical_embs = [torch.cat([categorical_embs[0], new_categorical_embs[0]], dim=0), torch.cat([categorical_embs[1], new_categorical_embs[1]], dim=0)] # update token embeddings of current step agent_token_emb[veh_mask, (self.num_historical_steps - 1) // self.shift + t] = self.agent_token_emb_veh[ next_token_idx[veh_mask]] agent_token_emb[ped_mask, (self.num_historical_steps - 1) // self.shift + t] = self.agent_token_emb_ped[ next_token_idx[ped_mask]] agent_token_emb[cyc_mask, (self.num_historical_steps - 1) // self.shift + t] = self.agent_token_emb_cyc[ next_token_idx[cyc_mask]] motion_vector_a, head_vector_a = self.build_vector_a(pos_a, head_a, state_a) motion_vector_a[:, (self.num_historical_steps - 1) // self.shift + 1 + t:] = 0. head_vector_a[:, (self.num_historical_steps - 1) // self.shift + 1 + t:] = 0. x_a = torch.stack( [torch.norm(motion_vector_a[:, :, :2], p=2, dim=-1), angle_between_2d_vectors(ctr_vector=head_vector_a, nbr_vector=motion_vector_a[:, :, :2])], dim=-1) x_b = x_a.clone() x_a = self.x_a_emb(continuous_inputs=x_a.view(-1, x_a.size(-1)), categorical_embs=categorical_embs) x_a = x_a.view(-1, num_step, self.hidden_dim) s_a = self.state_a_emb(state_a.reshape(-1).long()).reshape(num_agent + num_new_agent, num_step, self.hidden_dim) feat_a = torch.cat((agent_token_emb, x_a, s_a), dim=-1) feat_a = self.fusion_emb(feat_a) # if t >= 15: # print(f"inference {t}") # is_invalid = state_a == self.invalid_state # is_bos = state_a == self.enter_state # is_eos = state_a == self.exit_state # bos_index = torch.where(is_bos.any(dim=1), torch.argmax(is_bos.long(), dim=1), torch.tensor(0)) # eos_index = torch.where(is_eos.any(dim=1), torch.argmax(is_eos.long(), dim=1), torch.tensor(num_step - 1)) # mask = torch.arange(num_step).expand(num_agent + num_new_agent, -1).to(state_a.device) # mask = (mask >= bos_index[:, None]) & (mask <= eos_index[:, None] + 1) # is_invalid[mask] = False # is_invalid[:, (self.num_historical_steps - 1) // self.shift + 1 + t:] = False # print(pos_a[:, :((self.num_historical_steps - 1) // self.shift + 1 + t)]) # print(state_a[:, :((self.num_historical_steps - 1) // self.shift + 1 + t)]) # print(pos_a[is_invalid][:, 0]) # print(head_a[is_invalid]) # print(categorical_embs[0].sum(dim=-1)[is_invalid.reshape(-1)]) # print(categorical_embs[1].sum(dim=-1)[is_invalid.reshape(-1)]) # print(motion_vector_a[is_invalid][:, 0]) # print(head_vector_a[is_invalid][:, 0]) # print(x_b.sum(dim=-1)[is_invalid]) # print(x_a.sum(dim=-1)[is_invalid]) # for a in range(state_a.shape[0]): # print(f"agent: {a}") # print(state_a[a]) # print(is_invalid[a].long()) # print(pos_a[a, :, 0]) # print(motion_vector_a[a, :, 0]) # print(s_a.sum(dim=-1)[is_invalid]) # print(feat_a.sum(dim=-1)[is_invalid]) # replace the features of steps before bos of valid agents with the corresponding seed agent features # 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(num_step)) # before_bos_mask = torch.arange(num_step).expand(num_agent + num_new_agent, -1).to(state_a.device) < bos_index[:, None] # feat_a[before_bos_mask] = feat_seed.repeat(num_agent + num_new_agent, 1, 1)[before_bos_mask] # build seed agent features motion_vector_seed = motion_vector_a[av_index : av_index + 1] head_vector_seed = head_vector_a[av_index : av_index + 1] feat_seed = self.build_invalid_agent_feature(num_step, pos_a.device, type_index=self.all_agent_type.index('seed'), motion_vector=motion_vector_seed, head_vector=head_vector_seed) # print(f"inference {t}") # print(feat_seed.sum(dim=-1)) next_token_idx_list.append(next_token_idx[:, None]) next_state_idx_list.append(next_state_idx[:, None]) # next_bos_pl_idx_list.append(next_bos_pl_idx[:, None]) # next_bos_offset_idx_list.append(next_bos_offset_idx[:, None]) # TODO: check this # agent_valid_mask[agent_category != 3] = False # print("inference") # is_invalid = state_a == self.invalid_state # is_bos = state_a == self.enter_state # is_eos = state_a == self.exit_state # bos_index = torch.where(is_bos.any(dim=1), torch.argmax(is_bos.long(), dim=1), torch.tensor(0)) # eos_index = torch.where(is_eos.any(dim=1), torch.argmax(is_eos.long(), dim=1), torch.tensor(num_step - 1)) # mask = torch.arange(num_step).expand(num_agent, -1).to(state_a.device) # mask = (mask >= bos_index[:, None]) & (mask <= eos_index[:, None] + 1) # is_invalid[mask] = False # print(feat_a.sum(dim=-1)[is_invalid]) # print(pos_a[is_invalid][: 0]) # print(head_a[is_invalid]) # exit(1) num_agent = pos_a.shape[0] for i in range(len(next_token_idx_list)): next_token_idx_list[i] = torch.cat([next_token_idx_list[i], torch.zeros(num_agent - next_token_idx_list[i].shape[0], 1, device=next_token_idx_list[i].device) - 1], dim=0).long() next_state_idx_list[i] = torch.cat([next_state_idx_list[i], torch.zeros(num_agent - next_state_idx_list[i].shape[0], 1, device=next_state_idx_list[i].device)], dim=0).long() # eval mask next_token_eval_mask = agent_valid_mask.clone() next_state_eval_mask = agent_valid_mask.clone() bos_token_index = torch.nonzero(agent_state_index == self.enter_state) eos_token_index = torch.nonzero(agent_state_index == self.exit_state) next_token_eval_mask[bos_token_index[:, 0], bos_token_index[:, 1]] = 1 for bos_token_index_i in bos_token_index: next_state_eval_mask[bos_token_index_i[0], :bos_token_index_i[1] + 2] = 1 for eos_token_index_i in eos_token_index: next_state_eval_mask[eos_token_index_i[0], eos_token_index_i[1]:] = 1 # add history attributes num_agent = pred_traj.shape[0] num_init_agent = filter_mask.sum() pred_traj = torch.cat([pred_traj, torch.zeros(num_agent, self.num_historical_steps - 1, *(pred_traj.shape[2:]), device=pred_traj.device)], dim=1) pred_head = torch.cat([pred_head, torch.zeros(num_agent, self.num_historical_steps - 1, *(pred_head.shape[2:]), device=pred_head.device)], dim=1) pred_state = torch.cat([pred_state, torch.zeros(num_agent, self.num_historical_steps - 1, *(pred_state.shape[2:]), device=pred_state.device)], dim=1) pred_state[:num_init_agent, :self.num_historical_steps - 1] = data['agent']['state_idx'][filter_mask, :(self.num_historical_steps - 1) // self.shift].repeat_interleave(repeats=self.shift, dim=1) historical_token_idx = data['agent']['token_idx'][filter_mask, :(self.num_historical_steps - 1) // self.shift] historical_token_idx[historical_token_idx < 0] = 0 historical_token_traj_all = torch.gather(agent_token_traj_all, 1, historical_token_idx[..., None, None, None].expand(-1, -1, 6, 4, 2)) init_theta = head_a[:num_init_agent, 0] cos, sin = init_theta.cos(), init_theta.sin() rot_mat = torch.zeros((num_init_agent, 2, 2), device=init_theta.device) rot_mat[:, 0, 0] = cos rot_mat[:, 0, 1] = sin rot_mat[:, 1, 0] = -sin rot_mat[:, 1, 1] = cos historical_token_traj_all = torch.bmm(historical_token_traj_all.view(-1, 4, 2), rot_mat[:, None, None, ...].repeat(1, (self.num_historical_steps - 1) // self.shift, self.shift + 1, 1, 1).view( -1, 2, 2)).view(num_init_agent, (self.num_historical_steps - 1) // self.shift, self.shift + 1, 4, 2) historical_token_traj_all = historical_token_traj_all + pos_a[:num_init_agent, 0, :][:, None, None, None, ...] pred_traj[:num_init_agent, :self.num_historical_steps - 1] = historical_token_traj_all[:, :, 1:, ...].clone().mean(dim=3).reshape(num_init_agent, -1, 2) diff_xy = historical_token_traj_all[..., 1:, 0, :] - historical_token_traj_all[..., 1:, 3, :] pred_head[:num_init_agent, :self.num_historical_steps - 1] = torch.arctan2(diff_xy[..., 1], diff_xy[..., 0]).reshape(num_init_agent, -1) return { 'av_index': av_index, 'valid_mask': agent_valid_mask[:, self.num_historical_steps:], 'pos_a': pos_a[:, (self.num_historical_steps - 1) // self.shift:], 'head_a': head_a[:, (self.num_historical_steps - 1) // self.shift:], 'gt_traj': gt_traj, 'pred_traj': pred_traj, 'pred_head': pred_head, 'pred_type': list(map(lambda i: self.seed_agent_type[i], pred_type.tolist())), 'pred_state': pred_state, 'next_token_idx': torch.cat(next_token_idx_list, dim=-1), # (num_agent, num_step) 'next_token_idx_gt': agent_token_index, 'next_state_idx': torch.cat(next_state_idx_list, dim=-1) if len(next_state_idx_list) > 0 else None, 'next_state_idx_gt': agent_state_index, 'next_token_eval_mask': next_token_eval_mask, 'next_state_eval_mask': next_state_eval_mask, # 'next_bos_pl_idx': torch.cat(next_bos_pl_idx_list, dim=-1), # 'next_bos_offset_idx': torch.cat(next_bos_offset_idx_list, dim=-1), }