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98075af | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 | import torch
import torch.nn as nn
import math
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=100):
super().__init__()
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0) # (1, max_len, d_model)
self.register_buffer('pe', pe)
def forward(self, x):
return x + self.pe[:, :x.size(1), :]
class TrajectoryTransformer(nn.Module):
def __init__(self):
super().__init__()
self.d_model = 64
# 1. Feature Embedding & Positional Encoding
self.embed = nn.Linear(7, self.d_model)
self.pos_enc = PositionalEncoding(self.d_model)
# 2. Transformer Sequence Encoder (Replaces LSTM)
encoder_layer = nn.TransformerEncoderLayer(
d_model=self.d_model, nhead=4, dim_feedforward=256, batch_first=True
)
self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=2)
# 3. Social Attention (Target queries Neighbors)
self.social_attn = nn.MultiheadAttention(embed_dim=self.d_model, num_heads=4, batch_first=True)
self.K = 3 # number of future modes
# 4. GOAL-CONDITIONED ARCHITECTURE
# Base hidden context: Target (64) + Social (64) = 128
self.hidden_dim = 128
self.future_len = 12 # Now predicting 6 seconds into future
# Step A: Predict exactly K distinct endpoints (goals)
self.goal_head = nn.Sequential(
nn.Linear(self.hidden_dim, 64),
nn.ReLU(),
nn.Linear(64, self.K * 2) # X, Y for K goals
)
# Step B: Given the encoded context PLUS a specific Goal, draw the path to get there
self.traj_head = nn.Sequential(
nn.Linear(self.hidden_dim + 2, 128),
nn.ReLU(),
nn.Linear(128, self.future_len * 2) # 12 steps to reach the destination
)
# 5. Probabilities of each mode
self.prob_head = nn.Linear(self.hidden_dim, self.K)
# ----------------------------
# SOCIAL POOLING
# ----------------------------
def social_pool(self, h_target, neighbor_h_list, device):
if len(neighbor_h_list) == 0:
return torch.zeros(self.d_model, device=device), None
# h_target: (64) -> query: (1, 1, 64)
query = h_target.unsqueeze(0).unsqueeze(0)
# neighbor_h_list: N x 64 -> key, value: (1, N, 64)
neighbor_h_tensor = torch.stack(neighbor_h_list).unsqueeze(0)
# apply attention
attn_output, attn_weights = self.social_attn(query, neighbor_h_tensor, neighbor_h_tensor)
return attn_output.squeeze(0).squeeze(0), attn_weights.squeeze(0)
# ----------------------------
# FORWARD PASS
# ----------------------------
def forward(self, x, neighbors):
"""
x: (B, 4, 7)
neighbors: list of length B
"""
B = x.size(0)
device = x.device
# Encode main trajectory sequence with Transformer
x_emb = self.embed(x)
x_emb = self.pos_enc(x_emb)
enc_out = self.transformer_encoder(x_emb)
h = enc_out[:, -1, :] # Grab context from last timestep (B, 64)
final_h = []
batch_attn_weights = []
# Loop through batch to handle variable size neighbors
for i in range(B):
h_target = h[i] # (64)
neighbor_h_list = []
for n in neighbors[i]:
n_tensor = torch.tensor(n, dtype=torch.float32, device=device).unsqueeze(0)
n_emb = self.pos_enc(self.embed(n_tensor))
n_enc_out = self.transformer_encoder(n_emb)
neighbor_h_list.append(n_enc_out[0, -1, :]) # (64)
# Social attention pooling
h_social, attn_weights = self.social_pool(h_target, neighbor_h_list, device)
batch_attn_weights.append(attn_weights)
# Combine Target and Social context
h_combined = torch.cat([h_target, h_social], dim=0) # (128)
final_h.append(h_combined)
h_final = torch.stack(final_h) # (B, 128)
# GOAL-CONDITIONED LOGIC
# 1. Predict Goals (End-points at t=6)
goals = self.goal_head(h_final)
goals = goals.view(B, self.K, 2) # (B, K, 2)
# 2. Condition trajectories on the predicted goals
trajs = []
for k in range(self.K):
goal_k = goals[:, k, :] # Get the k-th destination (B, 2)
# Concat the base context array with the goal coordinate!
conditioned_context = torch.cat([h_final, goal_k], dim=1) # (B, 130)
# Predict the path given the condition
traj_k = self.traj_head(conditioned_context).view(B, 1, self.future_len, 2)
trajs.append(traj_k)
traj = torch.cat(trajs, dim=1) # (B, K, 12, 2)
# 3. Mode Probabilities
probs = self.prob_head(h_final)
probs = torch.softmax(probs, dim=1)
return traj, goals, probs, batch_attn_weights |