File size: 6,923 Bytes
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import sys
from pathlib import Path
from typing import Any
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import torch
PROJECT_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PROJECT_ROOT))
sys.path.insert(0, str(Path(__file__).resolve().parent))
LOCAL_WORKSPACE = PROJECT_ROOT.parents[1]
if (LOCAL_WORKSPACE / "onescience" / "src" / "onescience" / "datapipes").is_dir():
sys.path.insert(0, str(LOCAL_WORKSPACE))
from common import ( # noqa: E402
load_config,
project_path,
relative_l2,
resolve_device,
rollout,
to_attr_dict,
)
from model.uno import UNO # noqa: E402
from onescience.datapipes.cfd import NavierStokesDatapipe # noqa: E402
from onescience.distributed.manager import DistributedManager # noqa: E402
DEFAULT_CONFIG = PROJECT_ROOT / "conf" / "config.yaml"
def prepare_config(config: dict[str, Any]) -> None:
datapipe = config["datapipe"]
datapipe["source"]["data_dir"] = str(
project_path(datapipe["source"]["data_dir"], PROJECT_ROOT).resolve()
)
def validate_checkpoint_config(
datapipe_config: dict[str, Any],
checkpoint: dict[str, Any],
) -> None:
checkpoint_data = checkpoint["datapipe_config"]["data"]
current_data = datapipe_config["data"]
for key in ("t_in", "t_out", "out_dim", "downsamplex", "downsampley", "normalize"):
if current_data[key] != checkpoint_data[key]:
raise ValueError(
f"Config value datapipe.data.{key}={current_data[key]} does not match "
f"checkpoint value {checkpoint_data[key]}"
)
def build_model(model_config: dict[str, Any], spatial_shape: tuple[int, int]) -> UNO:
return UNO(
in_dim=int(model_config["in_dim"]),
out_dim=int(model_config["out_dim"]),
spatial_shape=spatial_shape,
hidden_dim=int(model_config["hidden_dim"]),
modes=int(model_config["modes"]),
space_dim=int(model_config["space_dim"]),
include_pos=bool(model_config["include_pos"]),
normtype=str(model_config["normtype"]),
bilinear=bool(model_config["bilinear"]),
activation=str(model_config["activation"]),
pad_to_multiple=int(model_config["pad_to_multiple"]),
)
def save_visualization(
prediction: torch.Tensor,
target: torch.Tensor,
spatial_shape: tuple[int, int],
output_path: Path,
) -> None:
prediction = prediction.detach().cpu()
target = target.detach().cpu()
height, width = spatial_shape
frame_count = prediction.shape[-1]
frames = sorted({0, frame_count // 2, frame_count - 1})
sample_prediction = prediction[0].reshape(height, width, frame_count)
sample_target = target[0].reshape(height, width, frame_count)
rows = (
("Target", sample_target),
("Prediction", sample_prediction),
("Abs Error", (sample_prediction - sample_target).abs()),
)
figure, axes = plt.subplots(3, len(frames), figsize=(4 * len(frames), 9), squeeze=False)
for row_index, (title, values) in enumerate(rows):
for column_index, frame in enumerate(frames):
axis = axes[row_index][column_index]
image = axis.imshow(values[..., frame], origin="lower", cmap="viridis")
axis.set_title(f"{title} t+{frame + 1}")
axis.set_xticks([])
axis.set_yticks([])
figure.colorbar(image, ax=axis, fraction=0.046, pad=0.04)
figure.tight_layout()
figure.savefig(output_path, dpi=150)
plt.close(figure)
def main() -> None:
config_path = DEFAULT_CONFIG.resolve()
config = load_config(config_path)
prepare_config(config)
training_config = config["training"]
inference_config = config["inference"]
datapipe_config = config["datapipe"]
num_samples = int(inference_config["num_samples"])
if num_samples < 1:
raise ValueError("num_samples must be positive")
checkpoint_path = (
project_path(training_config["weight_dir"], PROJECT_ROOT).resolve()
/ str(training_config["checkpoint_name"])
)
if not checkpoint_path.exists():
raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}")
checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
model_config = dict(checkpoint["model_config"])
validate_checkpoint_config(datapipe_config, checkpoint)
data_file = (
Path(datapipe_config["source"]["data_dir"])
/ datapipe_config["source"]["file_name"]
)
device = resolve_device(str(config["common"]["device"]))
result_dir = project_path(
inference_config["result_dir"], PROJECT_ROOT
).resolve()
print(f"Checkpoint: {checkpoint_path}")
print(f"Data: {data_file}")
print(f"Device: {device}")
DistributedManager.initialize()
try:
datapipe = NavierStokesDatapipe(
to_attr_dict(datapipe_config),
distributed=False,
normalizer_state=checkpoint.get("normalizer"),
)
test_loader, _ = datapipe.test_dataloader()
spatial_shape = tuple(checkpoint["spatial_shape"])
if tuple(datapipe.spatial_shape) != spatial_shape:
raise ValueError(
f"Checkpoint grid {spatial_shape} does not match data grid {datapipe.spatial_shape}"
)
model = build_model(model_config, spatial_shape).to(device)
model.load_state_dict(checkpoint["model_state"])
model.eval()
batch = next(iter(test_loader))
pos = batch["pos"].to(device)
state = batch["x"].to(device)
target = batch["y"].to(device)
with torch.no_grad():
prediction = rollout(
model,
pos,
state,
int(datapipe_config["data"]["t_out"]),
int(datapipe_config["data"]["out_dim"]),
)
prediction = datapipe.decode_solution(prediction)
target = datapipe.decode_solution(target)
error = relative_l2(prediction, target).mean().item()
sample_count = min(num_samples, prediction.shape[0])
result_dir.mkdir(parents=True, exist_ok=True)
tensor_path = result_dir / "prediction_sample.pt"
image_path = result_dir / "prediction_sample.png"
torch.save(
{
"prediction": prediction[:sample_count].cpu(),
"target": target[:sample_count].cpu(),
"spatial_shape": spatial_shape,
"relative_l2": error,
},
tensor_path,
)
save_visualization(prediction[:1], target[:1], spatial_shape, image_path)
print(f"Relative L2: {error:.6e}")
print(f"Saved tensor: {tensor_path}")
print(f"Saved figure: {image_path}")
finally:
DistributedManager.cleanup()
if __name__ == "__main__":
main()
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