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7087ec2 | 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 | import json
import logging
import os
import sys
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
PROJECT_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PROJECT_ROOT))
from model import HeteroGNS
from onescience.datapipes.cfd import BENODatapipe
from onescience.distributed.manager import DistributedManager
from onescience.utils.YParams import YParams
from onescience.utils.beno.util import record_data, to_np_array
from onescience.utils.beno.utilities import LpLoss, plot_data
logging.basicConfig(level=logging.INFO)
LOGGER = logging.getLogger("BENO_Inference")
def set_default_data_env():
os.environ.setdefault("ONESCIENCE_BENO_DATA_DIR", str(PROJECT_ROOT / "data"))
def resolve_path(path_value):
path = Path(path_value)
return path if path.is_absolute() else PROJECT_ROOT / path
def load_config():
set_default_data_env()
cfg = YParams(str(PROJECT_ROOT / "conf" / "config.yaml"), "root")
cfg.datapipe.source.data_dir = str(resolve_path(cfg.datapipe.source.data_dir))
cfg.datapipe.source.cache_dir = str(resolve_path(cfg.datapipe.source.cache_dir))
cfg.inference.checkpoint_dir = str(resolve_path(cfg.inference.checkpoint_dir))
cfg.inference.result_dir = str(resolve_path(cfg.inference.result_dir))
cfg.inference.picture_dir = str(resolve_path(cfg.inference.picture_dir))
cfg.inference.metrics_path = str(resolve_path(cfg.inference.metrics_path))
if cfg.inference.checkpoint_path:
cfg.inference.checkpoint_path = str(resolve_path(cfg.inference.checkpoint_path))
return cfg
def activation_from_name(name):
name = str(name).lower()
if name == "relu":
return nn.ReLU
if name == "elu":
return nn.ELU
if name == "leakyrelu":
return nn.LeakyReLU
return nn.SiLU
def build_model(model_cfg):
return HeteroGNS(
nnode_in_features=model_cfg.nnode_in_features,
nnode_out_features=model_cfg.nnode_out_features,
nedge_in_features=model_cfg.nedge_in_features,
latent_dim=model_cfg.get("latent_dim", model_cfg.get("width", 128)),
nmessage_passing_steps=model_cfg.get("nmessage_passing_steps", 10),
nmlp_layers=model_cfg.nmlp_layers,
mlp_hidden_dim=model_cfg.get("mlp_hidden_dim", model_cfg.get("width", 128)),
activation=activation_from_name(model_cfg.act),
boundary_dim=model_cfg.boundary_dim,
trans_layer=model_cfg.trans_layer,
)
def select_device(device_name, dist):
device_name = str(device_name).lower()
if device_name == "cpu":
return torch.device("cpu")
if device_name == "cuda":
if not torch.cuda.is_available():
raise RuntimeError("inference.device is cuda, but CUDA is not available.")
return torch.device(f"cuda:{dist.local_rank}")
return dist.device
def find_checkpoint(cfg):
if cfg.inference.checkpoint_path:
path = Path(cfg.inference.checkpoint_path)
return path if path.exists() else None
checkpoints = sorted(Path(cfg.inference.checkpoint_dir).glob("model_epoch_*.pt"))
return checkpoints[-1] if checkpoints else None
def main():
cfg = load_config()
DistributedManager.initialize()
dist = DistributedManager()
device = select_device(cfg.inference.get("device", "auto"), dist)
datapipe = BENODatapipe(cfg, distributed=(dist.world_size > 1))
test_loader, _ = datapipe.test_dataloader()
u_normalizer = datapipe.u_normalizer.to(device)
a_normalizer = datapipe.a_normalizer.to(device)
resolution = int(cfg.datapipe.data.resolution)
model = build_model(cfg.model).to(device)
checkpoint = find_checkpoint(cfg)
if checkpoint is not None:
LOGGER.info("Loading checkpoint: %s", checkpoint)
state_dict = torch.load(checkpoint, map_location=device)
model.load_state_dict(state_dict)
else:
LOGGER.warning("No checkpoint found. Running with random weights.")
model.eval()
result_dir = Path(cfg.inference.result_dir)
picture_dir = Path(cfg.inference.picture_dir)
result_dir.mkdir(parents=True, exist_ok=True)
picture_dir.mkdir(parents=True, exist_ok=True)
myloss = LpLoss(size_average=False)
analysis_record = {}
out_all = np.array([])
label_all = np.array([])
a_ori_all = np.array([])
mask_all = np.array([])
grid_all = np.array([])
with torch.no_grad():
for sample_id, data in enumerate(test_loader):
data = data.to(device)
out_indomain = model(data)
full_out = torch.zeros((resolution * resolution, 1), device=device)
full_label = torch.zeros((resolution * resolution), device=device)
full_input_a = torch.zeros((resolution * resolution, 10), device=device)
full_grid = torch.zeros((resolution * resolution, 2), device=device)
indices = data["G1"].sample_idx
full_out[indices] = out_indomain
full_label[indices] = data["G1+2"].y
full_input_a[indices, :] = data["G1"].x
full_grid[indices, :] = data["G1"].x[:, :2]
pred_decoded = u_normalizer.decode(full_out.view(1, -1))
a_decoded = a_normalizer.decode(full_input_a[:, 2].view(1, -1))
label_reshaped = full_label.view(1, -1)
cell_state_full = torch.zeros((1, resolution * resolution), device=device)
cell_state_full[0, :] = data["G1"].cell_state
l2_item = myloss(pred_decoded, label_reshaped).item()
mae_item = nn.L1Loss()(pred_decoded, label_reshaped).item()
record_data(analysis_record, [l2_item, mae_item], ["L2", "MAE"])
np.savez(
result_dir / f"sample_{sample_id:04d}.npz",
predict=to_np_array(pred_decoded).reshape(resolution, resolution),
label=to_np_array(label_reshaped).reshape(resolution, resolution),
forcing=to_np_array(a_decoded).reshape(resolution, resolution),
mask=to_np_array(cell_state_full).reshape(resolution, resolution),
)
out_all = np.append(out_all, to_np_array(pred_decoded))
label_all = np.append(label_all, to_np_array(label_reshaped))
a_ori_all = np.append(a_ori_all, to_np_array(a_decoded))
mask_all = np.append(mask_all, to_np_array(cell_state_full))
grid_all = np.append(grid_all, to_np_array(full_grid.unsqueeze(0)))
metrics = {
"mean_l2": float(np.mean(analysis_record["L2"])),
"std_l2": float(np.std(analysis_record["L2"])),
"mean_mae": float(np.mean(analysis_record["MAE"])),
"num_samples": int(len(analysis_record["L2"])),
}
metrics_path = Path(cfg.inference.metrics_path)
metrics_path.parent.mkdir(parents=True, exist_ok=True)
metrics_path.write_text(json.dumps(metrics, indent=2), encoding="utf-8")
LOGGER.info("Metrics: %s", metrics)
plot_samples = min(int(cfg.inference.num_visualize), metrics["num_samples"])
if plot_samples >= 2:
plot_path = picture_dir / "forcing_solution_comparison.png"
plot_data(
predict_term=out_all,
true_term=label_all,
forcing_term=a_ori_all,
forcing_mask=mask_all,
grid_info=grid_all,
resolution=resolution,
num_samples=plot_samples,
interpolation="bilinear",
save_path=str(plot_path),
)
LOGGER.info("Plot saved to %s", plot_path)
elif plot_samples == 1:
LOGGER.info("Skipping plot_data for one sample because the runtime helper expects a 2D axes grid.")
dist.cleanup()
if __name__ == "__main__":
main()
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