File size: 8,428 Bytes
32502a8 | 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 | """Train the stable human-motion-to-Microduck-command policy used by the show."""
from __future__ import annotations
import argparse
import csv
import json
from pathlib import Path
import random
import time
import numpy as np
import onnx
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
class CommandPolicy(nn.Module):
def __init__(
self,
input_dim: int,
feature_mean: torch.Tensor,
feature_std: torch.Tensor,
command_mean: torch.Tensor,
command_std: torch.Tensor,
) -> None:
super().__init__()
self.register_buffer("feature_mean", feature_mean)
self.register_buffer("feature_std", feature_std)
self.register_buffer("command_mean", command_mean)
self.register_buffer("command_std", command_std)
self.net = nn.Sequential(
nn.Linear(input_dim, 128),
nn.ELU(),
nn.Linear(128, 128),
nn.ELU(),
nn.Linear(128, 64),
nn.ELU(),
nn.Linear(64, 13),
)
def forward(self, features: torch.Tensor) -> torch.Tensor:
normalized = (features - self.feature_mean) / self.feature_std
return self.net(normalized) * self.command_std + self.command_mean
def normalized_command(self, features: torch.Tensor) -> torch.Tensor:
normalized = (features - self.feature_mean) / self.feature_std
return self.net(normalized)
def run(args: argparse.Namespace) -> dict:
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
started = time.time()
source = np.load(args.retarget.resolve(), allow_pickle=True)
features = source["human_features_14d"].astype(np.float32)
targets = source["command_13d"].astype(np.float32)
names = [str(value) for value in source["human_feature_names"]]
val_mask = np.arange(len(features)) % 5 == 0
train_mask = ~val_mask
x_train, y_train = features[train_mask], targets[train_mask]
x_val, y_val = features[val_mask], targets[val_mask]
feature_mean_np = x_train.mean(axis=0)
feature_std_np = np.maximum(x_train.std(axis=0), 1e-4)
command_mean_np = y_train.mean(axis=0)
command_std_np = np.maximum(y_train.std(axis=0), 1e-4)
feature_mean = torch.tensor(feature_mean_np)
feature_std = torch.tensor(feature_std_np)
command_mean = torch.tensor(command_mean_np)
command_std = torch.tensor(command_std_np)
input_dim = features.shape[1]
model = CommandPolicy(input_dim, feature_mean, feature_std, command_mean, command_std)
train_y_normalized = (y_train - command_mean_np) / command_std_np
loader = DataLoader(
TensorDataset(torch.tensor(x_train), torch.tensor(train_y_normalized)),
batch_size=args.batch_size,
shuffle=True,
generator=torch.Generator().manual_seed(args.seed),
)
optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=1e-6)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
noise_std = torch.tensor(
[0.001, 0.001, 0.005, 0.005, 0.005, 0.015, 0.015, 0.01, 0.01, 0.005, 0.005, 0.0, 0.0, 0.0]
)
loss_fn = nn.SmoothL1Loss(beta=0.08)
x_val_t = torch.tensor(x_val)
y_val_t = torch.tensor(y_val)
rows: list[dict] = []
best_rmse = float("inf")
best_state = None
for epoch in range(1, args.epochs + 1):
model.train()
total = 0.0
count = 0
for batch_x, batch_y in loader:
augmented = batch_x + torch.randn_like(batch_x) * noise_std
predicted = model.normalized_command(augmented)
loss = loss_fn(predicted, batch_y)
optimizer.zero_grad(set_to_none=True)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
total += float(loss.detach()) * len(batch_x)
count += len(batch_x)
scheduler.step()
model.eval()
with torch.no_grad():
predicted = model(x_val_t)
rmse = torch.sqrt(torch.mean((predicted - y_val_t) ** 2))
mae = torch.mean(torch.abs(predicted - y_val_t))
row = {
"epoch": epoch,
"train_smooth_l1": total / max(1, count),
"val_rmse_command": float(rmse),
"val_mae_command": float(mae),
"learning_rate": optimizer.param_groups[0]["lr"],
}
rows.append(row)
if row["val_rmse_command"] < best_rmse:
best_rmse = row["val_rmse_command"]
best_state = {
key: value.detach().cpu().clone()
for key, value in model.state_dict().items()
}
if epoch == 1 or epoch % 50 == 0 or epoch == args.epochs:
print(json.dumps(row))
assert best_state is not None
model.load_state_dict(best_state)
model.eval()
output = args.output.resolve()
output.mkdir(parents=True, exist_ok=True)
torch.save({
"state_dict": best_state,
"seed": args.seed,
"feature_names": names,
"architecture": [input_dim, 128, 128, 64, 13],
}, output / "human_motion_command_policy.pt")
with (output / "training_metrics.csv").open("w", newline="", encoding="utf-8") as stream:
writer = csv.DictWriter(stream, fieldnames=rows[0].keys())
writer.writeheader()
writer.writerows(rows)
np.savez_compressed(
output / "normalization.npz",
feature_mean=feature_mean_np,
feature_std=feature_std_np,
command_mean=command_mean_np,
command_std=command_std_np,
feature_names=np.asarray(names),
)
onnx_path = output / "human_motion_command_policy.onnx"
torch.onnx.export(
model,
torch.zeros((1, input_dim), dtype=torch.float32),
str(onnx_path),
input_names=["human_motion_features"],
output_names=["microduck_command_13d"],
dynamic_axes={
"human_motion_features": {0: "batch"},
"microduck_command_13d": {0: "batch"},
},
opset_version=17,
dynamo=False,
)
graph = onnx.load(str(onnx_path))
for key, value in {
"input_features": ",".join(names),
"output_contract": "twist(3),head_pose(4),body_pose(6)",
"control_hz": "50",
"target_bpm": "111",
"low_level_controller": "official Pollen Microduck policy family",
}.items():
item = graph.metadata_props.add()
item.key = key
item.value = value
onnx.save(graph, str(onnx_path))
with torch.no_grad():
full_pred = model(torch.tensor(features)).numpy()
error = full_pred - targets
summary = {
"schema_version": "1.0",
"training_method": "supervised human-motion feature to deployable command policy",
"seed": args.seed,
"epochs": args.epochs,
"frames": len(features),
"best_validation_rmse_command": best_rmse,
"full_sequence_rmse_command": float(np.sqrt(np.mean(error ** 2))),
"max_abs_command_error": np.max(np.abs(error), axis=0).round(7).tolist(),
"elapsed_seconds": time.time() - started,
"input_features": names,
"output_contract": "[twist(3), head_pose(4), body_pose(6)]",
"safety_architecture": "learned choreography command policy + official Pollen balancing policies",
"files": {
"checkpoint": "human_motion_command_policy.pt",
"onnx": "human_motion_command_policy.onnx",
"metrics": "training_metrics.csv",
"normalization": "normalization.npz",
},
}
(output / "training_summary.json").write_text(
json.dumps(summary, indent=2),
encoding="utf-8",
)
print(json.dumps(summary, indent=2))
return summary
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--retarget", type=Path, required=True)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--epochs", type=int, default=500)
parser.add_argument("--batch-size", type=int, default=256)
parser.add_argument("--learning-rate", type=float, default=1e-3)
parser.add_argument("--seed", type=int, default=20260830)
run(parser.parse_args())
if __name__ == "__main__":
main()
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