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f8faa82 | 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 | from itertools import cycle
import math
import os
import torch
import tyro
import numpy as np
from dataclasses import dataclass, field
from datasets.sequence import SequenceDataset
from model import TemporalUnet, CosineScheduleDiffusion, apply_conditioning
from utils.arrays import batch_to_device, to_np, to_torch, apply_dict
from utils.training import deterministic, set_cuda_visible_device, save_config
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader
import tqdm
import wandb
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.patches as patches
@dataclass
class TrainingConfig:
seed: int = 0
device: str = "cuda:0"
log_folder: str = "logs"
exp_name: str = "diffusion"
env: str = "avoiding-v0"
horizon: int = 16
normalizer: str = "LimitsNormalizer"
preprocess_fns: list = field(default_factory=lambda: [])
max_path_length: int = 200
max_n_episodes: int = 500
termination_penalty: float = 0
state_dim: int = 4
action_dim: int = 2
# training
n_train_steps: int = 1000001
save_freq: int = 50000
batch_size: int = 32
learning_rate: float = 2e-4
ema_decay: float = 0.995
sample_freq: int = 5000
n_sample_steps: int = 20
n_samples: int = 8
resume: bool = False
wandb_project: str = "direct-d3il"
wandb_entity: str = ""
@torch.no_grad()
def generate_samples(ema_model, diffusion, normalizer, dataset, cfg):
"""Generate DDIM samples from the EMA model for visualization (no conditioning)."""
ema_model.eval()
action_dim = cfg.action_dim
state_dim = cfg.state_dim
horizon = cfg.horizon
transition_dim = action_dim + state_dim
device = cfg.device
# grab a real initial observation from the dataset
sample = dataset[0]
obs_0 = sample.conditions[0] # already normalized
conditions = {0: obs_0}
x = torch.randn(cfg.n_samples, horizon, transition_dim, device=device)
x = apply_conditioning(x, to_torch(conditions, device=device), action_dim)
timesteps = torch.linspace(1.0, 0.0, cfg.n_sample_steps + 1, device=device)
for i in range(cfg.n_sample_steps):
t_now = timesteps[i]
t_next = timesteps[i + 1]
abar_now = diffusion.alpha_bar(t_now)
abar_next = diffusion.alpha_bar(t_next)
t_batch = t_now.expand(cfg.n_samples)
x0_pred = ema_model(x, t_batch)
# DDIM (deterministic): x_{t_next} = √ᾱ_{t_next} * x0_pred + √(1-ᾱ_{t_next}) * ε_pred
# where ε_pred = (x - √ᾱ_t * x0_pred) / √(1-ᾱ_t)
eps_pred = (x - math.sqrt(abar_now) * x0_pred) / math.sqrt(1.0 - abar_now)
x = math.sqrt(abar_next) * x0_pred + math.sqrt(1.0 - abar_next) * eps_pred
# only condition the start point (not the endpoint)
x = apply_conditioning(x, to_torch(conditions, device=device), action_dim)
# unnormalize observations for plotting
obs = normalizer.unnormalize(to_np(x[:, :, action_dim:]), "observations")
return obs # (n_samples, horizon, state_dim)
def plot_samples(observations, save_path):
"""Plot sampled trajectories on the obstacle field."""
fig, ax = plt.subplots(figsize=(6, 6))
ax.set_xlim([0.2, 0.8])
ax.set_ylim([-0.3, 0.4])
ax.set_facecolor([1, 1, 0.9])
ax.set_aspect("equal", adjustable="box")
ax.grid(True, alpha=0.3)
# target
ax.add_patch(patches.Rectangle((0.2, 0.35), 0.6, 0.05,
facecolor=[0.4, 1, 0.4], alpha=0.35))
# pillars
for c, r in [([0.5,-0.1],0.03), ([0.425,0.08],0.025), ([0.575,0.08],0.025),
([0.35,0.26],0.025), ([0.5,0.26],0.025), ([0.65,0.26],0.025)]:
ax.add_patch(patches.Circle(c, r, color="r", alpha=0.7))
colors = plt.cm.tab10(np.linspace(0, 1, len(observations)))
for i, obs in enumerate(observations):
x_pos = obs[:, 2] # actual_x
y_pos = obs[:, 3] # actual_y
ax.plot(x_pos, y_pos, "-o", color=colors[i], linewidth=1.5, markersize=3, alpha=0.8)
ax.plot(x_pos[0], y_pos[0], "s", color="green", markersize=5)
plt.tight_layout()
fig.savefig(save_path, dpi=100, bbox_inches="tight")
plt.close(fig)
def train(cfg: TrainingConfig, log_subfolder: str):
dataset = SequenceDataset(
env=cfg.env,
horizon=cfg.horizon,
normalizer=cfg.normalizer,
preprocess_fns=cfg.preprocess_fns,
max_path_length=cfg.max_path_length,
max_n_episodes=cfg.max_n_episodes,
termination_penalty=0,
seed=cfg.seed,
)
unet = TemporalUnet(
horizon=cfg.horizon,
transition_dim=cfg.state_dim + cfg.action_dim,
cond_dim=cfg.state_dim,
dim=32,
dim_mults=(1, 4, 8),
).to(cfg.device)
diffusion = CosineScheduleDiffusion(
model=unet,
action_dim=cfg.action_dim,
)
train_loader = cycle(DataLoader(dataset, batch_size=cfg.batch_size, shuffle=True))
optimizer = torch.optim.Adam(unet.parameters(), lr=cfg.learning_rate)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=cfg.save_freq * 2
)
ema = torch.optim.swa_utils.AveragedModel(
unet,
avg_fn=lambda avg, new, num: cfg.ema_decay * avg + (1 - cfg.ema_decay) * new,
)
# resume from latest checkpoint
start_step = 0
if cfg.resume:
import glob
ckpts = sorted(glob.glob(os.path.join(log_subfolder, "model_*.pth")))
ckpts = [c for c in ckpts if "ema" not in c]
if ckpts:
latest = ckpts[-1]
cp_idx = int(latest.split("model_")[-1].replace(".pth", ""))
start_step = cp_idx * cfg.save_freq
unet.load_state_dict(torch.load(latest))
ema_path = latest.replace("model_", "model_ema_")
if os.path.exists(ema_path):
ema.module.load_state_dict(torch.load(ema_path))
opt_path = os.path.join(log_subfolder, "optimizer.pth")
if os.path.exists(opt_path):
opt_state = torch.load(opt_path)
optimizer.load_state_dict(opt_state["optimizer"])
scheduler.load_state_dict(opt_state["scheduler"])
print(f"Resumed from checkpoint {cp_idx} (step {start_step})")
else:
print("No checkpoints found, starting from scratch")
writer = SummaryWriter(log_dir=os.path.join(log_subfolder, "tensorboard_logs"))
wandb.init(
project=cfg.wandb_project,
entity=cfg.wandb_entity or None,
name=cfg.exp_name,
config=vars(cfg),
resume="allow" if cfg.resume else None,
)
samples_dir = os.path.join(log_subfolder, "samples")
os.makedirs(samples_dir, exist_ok=True)
# skip dataloader to the right position
if start_step > 0:
print(f"Skipping dataloader to step {start_step}...")
for _ in tqdm.tqdm(range(start_step), desc="Fast-forwarding"):
next(train_loader)
for i in tqdm.tqdm(range(start_step, cfg.n_train_steps)):
batch = batch_to_device(next(train_loader), cfg.device)
loss, infos = diffusion.loss(*batch)
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
ema.update_parameters(unet)
if i % cfg.save_freq == 0:
torch.save(
unet.state_dict(),
os.path.join(log_subfolder, f"model_{i // cfg.save_freq}.pth"),
)
torch.save(
ema.module.state_dict(),
os.path.join(log_subfolder, f"model_ema_{i // cfg.save_freq}.pth"),
)
torch.save(
{"optimizer": optimizer.state_dict(), "scheduler": scheduler.state_dict()},
os.path.join(log_subfolder, "optimizer.pth"),
)
if i % cfg.sample_freq == 0:
obs = generate_samples(ema.module, diffusion, dataset.normalizer, dataset, cfg)
sample_path = os.path.join(samples_dir, f"step_{i:07d}.png")
plot_samples(obs, sample_path)
wandb.log({"samples": wandb.Image(sample_path)}, step=i)
writer.add_scalar("loss", loss, i)
wandb.log({"loss": loss.item(), "lr": scheduler.get_last_lr()[0]}, step=i)
wandb.finish()
if __name__ == "__main__":
cfg = tyro.cli(TrainingConfig)
set_cuda_visible_device(cfg)
deterministic(cfg.seed)
log_subfolder = os.path.join(cfg.log_folder, cfg.env, "diffusion", cfg.exp_name)
save_config(cfg, log_subfolder)
train(cfg, log_subfolder)
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