File size: 8,363 Bytes
600bbdc | 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 | import logging
from functools import partial
from pathlib import Path
import hydra
import matplotlib
import numpy as np
import torch
from matplotlib import animation
from matplotlib import pyplot as plt
from common import load_config
from onescience.datapipes.cfd import DeepMindLagrangianDatapipe, graph_update
from onescience.distributed.manager import DistributedManager
from onescience.launch.utils import load_checkpoint
matplotlib.use("Agg")
TYPE_TO_COLOR = {
0: "green",
3: "black",
5: "blue",
6: "gold",
7: "magenta",
}
def setup_logging(rank: int):
level = logging.INFO if rank == 0 else logging.WARNING
logging.basicConfig(level=level, format="%(asctime)s - %(levelname)s - %(message)s")
return logging.getLogger("lagrangian_mgn.inference")
def resolve_device(requested: str):
if requested == "cpu":
return torch.device("cpu")
if requested in ("cuda", "gpu"):
if not torch.cuda.is_available():
raise RuntimeError("Config requested CUDA, but torch.cuda.is_available() is false.")
return torch.device("cuda:0")
return torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class MGNRollout:
def __init__(self, cfg, device, logger):
if int(cfg.test.batch_size) != 1:
raise ValueError(f"Only batch size 1 is supported, got {cfg.test.batch_size}")
self.cfg = cfg
self.device = device
self.logger = logger
self.frame_skip = int(cfg.inference.frame_skip)
self.datapipe = DeepMindLagrangianDatapipe(cfg, distributed=False)
self.dataloader = self.datapipe.test_dataloader()
self.dataset = self.datapipe.test_dataset
self.dim = self.dataset.dim
self.radius = self.dataset.radius
self.dt = self.dataset.dt
self.bounds = self.dataset.bounds
self.num_history = self.dataset.num_history
self.num_node_types = self.dataset.num_node_types
self.model = hydra.utils.instantiate(cfg.model)
if cfg.compile.enabled:
self.model = torch.compile(self.model, **cfg.compile.args)
self.model = self.model.to(device)
epoch = load_checkpoint(cfg.resume_dir, models=self.model, device=device)
if epoch == 0:
logger.warning("No checkpoint found in %s; running with random weights.", cfg.resume_dir)
self.model.eval()
def compute_boundary_feature(self, position):
dist = torch.cat(
[position - self.bounds[0], self.bounds[1] - position], dim=-1
)
feat = torch.exp(-(dist**2) / self.radius**2)
feat[dist > self.radius] = 0
return feat
def boundary_clamp(self, position):
min_bound = self.bounds[0] + 1e-3
max_bound = self.bounds[1] - 1e-3
return torch.clamp(position, min=min_bound, max=max_bound)
def pack_inputs(self, position, vel_history, node_type):
bound_feat = self.compute_boundary_feature(position)
vel_hist = vel_history.permute(1, 0, 2).flatten(1)
return torch.cat((position, vel_hist, bound_feat, node_type), dim=-1)
@torch.inference_mode()
def predict(self):
pred_pos, gt_pos, node_type = [], [], None
for graph in self.dataloader:
graph = graph.to(self.device)
if graph.ndata["t"][0].item() == 0:
if pred_pos:
yield torch.stack(pred_pos), torch.stack(gt_pos), node_type
pred_pos, gt_pos = [], []
position, vel_history, node_type = self.dataset.unpack_inputs(graph)
position = position.clone()
vel_history = vel_history.clone()
pred_pos.append(position)
gt_pos.append(position)
graph.ndata["x"] = self.pack_inputs(position, vel_history, node_type)
graph.ndata["pos"] = position
graph_update(graph, self.radius)
acceleration = self.model(graph.ndata["x"], graph.edata["x"], graph)
next_pos, next_vel = self.dataset.time_integrator(
position=position,
velocity=vel_history[-1],
acceleration=acceleration,
dt=self.dt,
denormalize=True,
)
next_pos = self.boundary_clamp(next_pos)
next_vel_norm = (
next_vel - self.dataset.vel_mean.to(self.device)
) / self.dataset.vel_std.to(self.device)
vel_history = torch.cat((vel_history[1:], next_vel_norm.unsqueeze(0)), dim=0)
position = next_pos
pred_pos.append(position)
gt_next_pos, _, _ = self.dataset.unpack_targets(graph)
gt_pos.append(gt_next_pos)
if pred_pos:
yield torch.stack(pred_pos), torch.stack(gt_pos), node_type
def init_animation(subplot_kw=None):
return plt.subplots(1, 2, figsize=(12, 6), subplot_kw=subplot_kw)
def plot_particles_2d(ax, title, position, node_color, bounds):
ax.cla()
ax.set_aspect("equal")
ax.scatter(position[:, 0], position[:, 1], c=node_color, s=10)
ax.set_xlim(bounds[0], bounds[1])
ax.set_ylim(bounds[0], bounds[1])
ax.set_title(title)
def plot_particles_3d(ax, title, position, node_color, bounds):
ax.cla()
ax.scatter(position[:, 2], position[:, 0], position[:, 1], c=node_color, s=10)
ax.set_xlim(bounds[0], bounds[1])
ax.set_ylim(bounds[0], bounds[1])
ax.set_zlim(bounds[0], bounds[1])
ax.set_title(title)
def animate_frame(num, plotter, fig, ax1, ax2, pred, gt, node_color, bounds, frame_skip):
num *= frame_skip
if num >= len(pred):
return
plotter(ax1, "Prediction", pred[num], node_color, bounds)
plotter(ax2, "Ground Truth", gt[num], node_color, bounds)
fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.1)
def save_animation(pred, gt, node_color, bounds, frame_skip, output_path, dim):
if dim == 2:
fig, (ax1, ax2) = init_animation()
plotter = plot_particles_2d
elif dim == 3:
fig, (ax1, ax2) = init_animation(subplot_kw={"projection": "3d"})
plotter = plot_particles_3d
else:
raise ValueError(f"Unsupported dim: {dim}")
ani = animation.FuncAnimation(
fig,
partial(
animate_frame,
plotter=plotter,
fig=fig,
ax1=ax1,
ax2=ax2,
pred=pred,
gt=gt,
node_color=node_color,
bounds=bounds,
frame_skip=frame_skip,
),
frames=max((pred.shape[0] - 1) // frame_skip, 1),
interval=100,
)
ani.save(output_path, writer="pillow", fps=10)
plt.close(fig)
def main():
cfg = load_config()
output_dir = Path(cfg.inference.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
DistributedManager.initialize()
manager = DistributedManager()
logger = setup_logging(manager.rank)
device = resolve_device(str(cfg.test.device))
logger.info("Starting rollout on %s", device)
rollout = MGNRollout(cfg, device, logger)
mse_list = []
for index, (pred_pos, gt_pos, node_type) in enumerate(rollout.predict()):
pred = pred_pos.cpu().numpy()
gt = gt_pos.cpu().numpy()
node_type_np = node_type.cpu().numpy()
mse = float(np.mean((pred - gt) ** 2))
mse_list.append(mse)
np.savez(output_dir / f"sequence_{index}.npz", prediction=pred, target=gt)
logger.info("Sequence %s MSE %.4e", index, mse)
if cfg.inference.save_animations:
node_color = [
TYPE_TO_COLOR.get(node_id, "gray")
for node_id in np.argmax(node_type_np, axis=1)
]
save_animation(
pred,
gt,
node_color,
rollout.bounds,
rollout.frame_skip,
output_dir / f"animation_{index}.gif",
int(cfg.dim),
)
if index + 1 >= int(cfg.inference.max_sequences):
break
np.savez(output_dir / "rollout_metrics.npz", mse=np.asarray(mse_list, dtype=np.float32))
logger.info("Average MSE %.4e", float(np.mean(mse_list)) if mse_list else float("nan"))
manager.cleanup()
if __name__ == "__main__":
main()
|