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eb4fc81 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 | """Linear probes for FlowMo latent state and drift context."""
from __future__ import annotations
import argparse
import importlib
import json
from dataclasses import dataclass
from pathlib import Path
import numpy as np
import torch
from torch.utils.data import DataLoader, Dataset
from experiments.shared.src.vision.clean_renderer import render_clean_boat_history_tensor
from experiments.train_image_world_models import autocast_context, configure_training_runtime, selected_history_indices
@dataclass
class ProbeSet:
z: np.ndarray
c: np.ndarray
momentum: np.ndarray
local_flow: np.ndarray
episode_drift: np.ndarray
class FlowMoProbeDataset(Dataset):
def __init__(
self,
source_npz: str,
history_len: int,
episodes: int,
max_windows: int,
seed: int,
):
src = np.load(source_npz, allow_pickle=False)
self.states = src["states"][:episodes].astype(np.float32)
self.actions = src["actions"][:episodes].astype(np.float32)
self.true_flow = src["true_flow"][:episodes].astype(np.float32)
self.boat_ids = src["boat_ids"][:episodes].astype(np.int64)
self.history_len = int(history_len)
steps = self.actions.shape[1]
all_indices = [(ep, t) for ep in range(episodes) for t in range(self.history_len - 1, steps)]
rng = np.random.default_rng(seed)
selected = rng.choice(len(all_indices), size=min(max_windows, len(all_indices)), replace=False)
self.indices = [all_indices[int(i)] for i in selected]
self.episode_drift = self.true_flow.mean(axis=1).astype(np.float32)
def __len__(self) -> int:
return len(self.indices)
def __getitem__(self, index: int):
ep, t = self.indices[index]
state_hist = self.states[ep, t - self.history_len + 1 : t + 1, :6].copy()
padded_actions = np.zeros((self.actions.shape[1] + 1, self.actions.shape[2]), dtype=np.float32)
padded_actions[1:] = self.actions[ep]
action_hist = padded_actions[t - self.history_len + 1 : t + 1].copy()
return (
torch.from_numpy(state_hist),
torch.from_numpy(action_hist),
torch.tensor(self.boat_ids[ep], dtype=torch.long),
torch.from_numpy(self.states[ep, t, 3:6].copy()),
torch.from_numpy(self.true_flow[ep, t].copy()),
torch.from_numpy(self.episode_drift[ep].copy()),
)
def build_flowmo():
config_module = importlib.import_module("experiments.flowmo.src.config")
model_module = importlib.import_module("experiments.flowmo.src.model")
cfg = config_module.default_config()
return cfg, model_module.build_model(cfg)
def loader_kwargs(num_workers: int) -> dict:
if num_workers <= 0:
return {}
return {"multiprocessing_context": "spawn", "persistent_workers": True, "prefetch_factor": 4}
@torch.no_grad()
def collect_probe_set(model, source: str, episodes: int, max_windows: int, args, seed: int) -> ProbeSet:
dataset = FlowMoProbeDataset(source, args.history_len, episodes, max_windows, seed)
loader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=torch.device(args.device).type == "cuda",
**loader_kwargs(args.num_workers),
)
device = torch.device(args.device)
history_indices = selected_history_indices(model, args.history_len)
zs, cs, momentum, local_flow, episode_drift = [], [], [], [], []
model.eval()
for states, actions, boat_ids, batch_momentum, batch_flow, batch_drift in loader:
states = states[:, history_indices].to(device, non_blocking=True)
actions = actions[:, history_indices].to(device, non_blocking=True)
boat_ids = boat_ids.to(device, non_blocking=True)
images = render_clean_boat_history_tensor(
states,
boat_ids,
image_size=args.image_size,
visual_scale=args.visual_scale,
)
with autocast_context(device, args.precision):
z, c = model.encode(images, actions)
zs.append(z.float().cpu().numpy())
cs.append(c.float().cpu().numpy())
momentum.append(batch_momentum.numpy())
local_flow.append(batch_flow.numpy())
episode_drift.append(batch_drift.numpy())
return ProbeSet(
z=np.concatenate(zs, axis=0),
c=np.concatenate(cs, axis=0),
momentum=np.concatenate(momentum, axis=0),
local_flow=np.concatenate(local_flow, axis=0),
episode_drift=np.concatenate(episode_drift, axis=0),
)
def fit_ridge(features: np.ndarray, targets: np.ndarray, alpha: float) -> dict[str, np.ndarray]:
x_mean = features.mean(axis=0, keepdims=True)
x_std = features.std(axis=0, keepdims=True) + 1.0e-6
x = (features - x_mean) / x_std
x = np.concatenate([x, np.ones((x.shape[0], 1), dtype=x.dtype)], axis=1)
penalty = np.eye(x.shape[1], dtype=np.float64) * float(alpha)
penalty[-1, -1] = 0.0
weights = np.linalg.solve(x.T @ x + penalty, x.T @ targets)
return {"x_mean": x_mean, "x_std": x_std, "weights": weights}
def predict_ridge(model: dict[str, np.ndarray], features: np.ndarray) -> np.ndarray:
x = (features - model["x_mean"]) / model["x_std"]
x = np.concatenate([x, np.ones((x.shape[0], 1), dtype=x.dtype)], axis=1)
return x @ model["weights"]
def regression_metrics(prediction: np.ndarray, target: np.ndarray, dims: list[str]) -> dict:
error = prediction - target
mse_per_dim = np.mean(error * error, axis=0)
rmse_per_dim = np.sqrt(mse_per_dim)
baseline_mse = np.mean((target - target.mean(axis=0, keepdims=True)) ** 2, axis=0) + 1.0e-12
r2_per_dim = 1.0 - mse_per_dim / baseline_mse
return {
"rmse": float(np.sqrt(np.mean(error * error))),
"r2_mean": float(np.mean(r2_per_dim)),
"rmse_by_dim": {dim: float(value) for dim, value in zip(dims, rmse_per_dim)},
"r2_by_dim": {dim: float(value) for dim, value in zip(dims, r2_per_dim)},
}
def feature_matrix(probe_set: ProbeSet, feature_name: str) -> np.ndarray:
if feature_name == "z":
return probe_set.z
if feature_name == "c":
return probe_set.c
if feature_name == "z_c":
return np.concatenate([probe_set.z, probe_set.c], axis=1)
raise ValueError(f"unknown feature set: {feature_name}")
def target_matrix(probe_set: ProbeSet, target_name: str) -> tuple[np.ndarray, list[str]]:
if target_name == "momentum":
return probe_set.momentum, ["vx", "vy", "omega"]
if target_name == "local_flow":
return probe_set.local_flow, ["flow_x", "flow_y"]
if target_name == "episode_drift":
return probe_set.episode_drift, ["drift_x", "drift_y"]
raise ValueError(f"unknown target: {target_name}")
def train_probe_bank(train_set: ProbeSet, alpha: float) -> dict:
bank = {}
for target_name in ["momentum", "local_flow", "episode_drift"]:
targets, _dims = target_matrix(train_set, target_name)
bank[target_name] = {}
for feature_name in ["z", "c", "z_c"]:
bank[target_name][feature_name] = fit_ridge(feature_matrix(train_set, feature_name), targets, alpha)
return bank
def evaluate_probe_bank(bank: dict, probe_set: ProbeSet) -> dict:
payload = {}
for target_name in ["momentum", "local_flow", "episode_drift"]:
targets, dims = target_matrix(probe_set, target_name)
payload[target_name] = {}
for feature_name in ["z", "c", "z_c"]:
prediction = predict_ridge(bank[target_name][feature_name], feature_matrix(probe_set, feature_name))
payload[target_name][feature_name] = regression_metrics(prediction, targets, dims)
return payload
def parse_eval_split(text: str) -> tuple[str, str, int]:
name, source, episodes = text.split(":", maxsplit=2)
return name, source, int(episodes)
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--train-source", default="data/paper/train.npz")
parser.add_argument("--train-episodes", type=int, default=2400)
parser.add_argument("--train-windows", type=int, default=32768)
parser.add_argument("--eval-splits", nargs="+", default=[
"test:data/paper/test.npz:480",
])
parser.add_argument("--eval-windows", type=int, default=8192)
parser.add_argument("--history-len", type=int, default=32)
parser.add_argument("--batch-size", type=int, default=256)
parser.add_argument("--ridge-alpha", type=float, default=1.0e-3)
parser.add_argument("--checkpoint-name", default="paper.pt")
parser.add_argument("--image-size", type=int, default=160)
parser.add_argument("--visual-scale", type=float, default=2.5)
parser.add_argument("--num-workers", type=int, default=4)
parser.add_argument("--seed", type=int, default=909)
parser.add_argument("--device", default="cuda")
parser.add_argument("--precision", choices=["fp32", "bf16", "fp16"], default="bf16")
parser.add_argument("--out", default="experiments/reports/paper_flowmo_latent_probes.json")
args = parser.parse_args()
device = torch.device(args.device)
configure_training_runtime(device)
_cfg, model = build_flowmo()
state = torch.load(Path("experiments/flowmo/checkpoint") / args.checkpoint_name, map_location="cpu")
model.load_state_dict(state)
model.to(device)
if device.type == "cuda":
model.to(memory_format=torch.channels_last)
train_set = collect_probe_set(model, args.train_source, args.train_episodes, args.train_windows, args, args.seed)
bank = train_probe_bank(train_set, args.ridge_alpha)
split_results = {}
for index, split in enumerate(args.eval_splits):
name, source, episodes = parse_eval_split(split)
eval_set = collect_probe_set(model, source, episodes, args.eval_windows, args, args.seed + 100 + index)
split_results[name] = evaluate_probe_bank(bank, eval_set)
payload = {
"method": "flowmo",
"checkpoint": args.checkpoint_name,
"train_source": args.train_source,
"train_windows": args.train_windows,
"eval_windows": args.eval_windows,
"ridge_alpha": args.ridge_alpha,
"feature_sets": {
"z": "short-history object-motion latent",
"c": "long-history ambient-drift context",
"z_c": "concatenated state and context",
},
"targets": {
"momentum": ["vx", "vy", "omega"],
"local_flow": ["flow_x", "flow_y"],
"episode_drift": ["mean_flow_x", "mean_flow_y"],
},
"splits": split_results,
}
out = Path(args.out)
out.parent.mkdir(parents=True, exist_ok=True)
out.write_text(json.dumps(payload, indent=2))
print(json.dumps(payload, indent=2))
if __name__ == "__main__":
main()
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