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dbc6675 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | import glob
import os
import sys
from collections.abc import Iterable
import numpy as np
import torch
from torch.utils.data import Dataset
from tqdm import tqdm
def _progress_enabled() -> bool:
return bool(sys.stdout.isatty())
def _normalize_domains(domain) -> list[str]:
if isinstance(domain, str):
return [domain]
if isinstance(domain, Iterable):
return [str(d) for d in domain]
raise TypeError(f"Unsupported domain spec: {domain!r}")
class PlanningTrajectoryDataset(Dataset):
def __init__(self, data_dir, domain, split="train"):
"""
data_dir: root data dir (e.g., 'data/encodings/graphs')
"""
self.files = []
self.domains = _normalize_domains(domain)
self.traj_files = []
for domain_name in self.domains:
target_dir = os.path.join(data_dir, domain_name, split)
if not os.path.exists(target_dir):
print(f"Warning: Directory {target_dir} does not exist.")
continue
all_files = glob.glob(os.path.join(target_dir, "*.npy"))
filtered_files = [f for f in all_files if not f.endswith("_goal.npy")]
self.traj_files.extend(sorted(filtered_files))
self.traj_files = sorted(self.traj_files)
def __len__(self):
return len(self.traj_files)
def __getitem__(self, idx):
traj_path = self.traj_files[idx]
goal_path = traj_path.replace(".npy", "_goal.npy")
# Load numpy arrays
# Traj: [T, D]
# Goal: [D]
traj = np.load(traj_path).astype(np.float32)
goal = np.load(goal_path).astype(np.float32)
# Ensure Goal is at least 1D [D]
if goal.ndim == 0:
goal = goal.reshape(1)
# Ensure Traj is 2D [T, D]
if traj.ndim == 1:
# Ambiguity: Is it [T] (D=1) or [D] (T=1)?
# We use Goal dimension D to decide.
D = goal.shape[0]
if traj.shape[0] == D:
# Likely T=1, D=D
traj = traj.reshape(1, D)
else:
# Likely T=T, D=1
traj = traj.reshape(-1, 1)
return torch.from_numpy(traj), torch.from_numpy(goal)
def collate_trajectories(batch):
"""
Custom collate function to handle variable length trajectories.
Pads sequences to the longest in the batch.
Returns:
padded_trajs: [B, MaxT, D]
goals: [B, D]
lengths: [B]
"""
trajs, goals = zip(*batch)
# Lengths for packing
lengths = torch.tensor([t.shape[0] for t in trajs])
# Pad trajectories: [B, MaxT, D]
padded_trajs = torch.nn.utils.rnn.pad_sequence(trajs, batch_first=True)
# Stack goals: [B, D]
goals = torch.stack(goals)
return padded_trajs, goals, lengths
def load_flat_dataset_for_xgboost(data_dir, domain, split="train", delta=False):
"""
Loads trajectories and flattens them into (X, y) pairs for XGBoost.
X: Concatenation of [State_t, Goal]
y: State_{t+1} (if delta=False) OR (State_{t+1} - State_t) (if delta=True)
"""
domains = _normalize_domains(domain)
traj_files = []
for domain_name in domains:
target_dir = os.path.join(data_dir, domain_name, split)
if not os.path.exists(target_dir):
print(f"Warning: Directory {target_dir} does not exist.")
continue
all_files = glob.glob(os.path.join(target_dir, "*.npy"))
traj_files.extend(sorted([f for f in all_files if not f.endswith("_goal.npy")]))
traj_files = sorted(traj_files)
if not traj_files:
return None, None
X_list = []
y_list = []
print(
f"Loading {len(traj_files)} trajectories for {split} "
f"across domains: {', '.join(domains)}"
)
for traj_path in tqdm(
traj_files,
desc=f"Flattening {split}",
disable=(not _progress_enabled()),
):
goal_path = traj_path.replace(".npy", "_goal.npy")
# Load raw numpy (skip torch conversion)
traj = np.load(traj_path).astype(np.float32) # [T, D]
goal = np.load(goal_path).astype(np.float32) # [D]
# Fix dimensions
if goal.ndim == 0:
goal = goal.reshape(1)
D = goal.shape[0]
if traj.ndim == 1:
if traj.shape[0] == D:
traj = traj.reshape(1, D)
else:
traj = traj.reshape(-1, 1)
# Need at least 2 steps to form a pair
T = traj.shape[0]
if T < 2:
continue
# Inputs: S_0 ... S_{T-1}
states_in = traj[:-1, :] # [T-1, D]
# Targets: S_1 ... S_T
states_out = traj[1:, :] # [T-1, D]
# Expand Goal: [T-1, D]
goals_in = np.tile(goal, (T - 1, 1))
# Create X: [S_t, G]
# Shape: [T-1, 2D]
x_chunk = np.hstack([states_in, goals_in])
# Create y
if delta:
y_chunk = states_out - states_in
else:
y_chunk = states_out
X_list.append(x_chunk)
y_list.append(y_chunk)
if not X_list:
return None, None
X = np.vstack(X_list)
y = np.vstack(y_list)
return X, y
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