File size: 3,273 Bytes
f614769 | 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 | from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
import wandb
from matsciml.datasets.transforms import (
PeriodicPropertiesTransform,
PointCloudToGraphTransform,
)
from matsciml.lightning.data_utils import MatSciMLDataModule
from matsciml.models.base import ForceRegressionTask
"""
This script acts as an intermediate step for validating the trained
MACE model on LiPS.
We download the uploaded checkpoint with the lowest validation force
error, load the checkpoint into a `ForceRegressionTask`, then
run through the validation set with the same loading pipeline. The
saved checkpoint weights correspond to the exponential moving averaged
ones.
After going through the full validation set, we push the predicted
and ground truth values to the initialized `wandb` run.
"""
pl.seed_everything(215125)
torch.set_float32_matmul_precision("medium")
run = wandb.init(
project="matsciml-uip-eval", tags=["inference", "validation", "results"]
)
artifact = run.use_artifact(
"laserkelvin/matsciml-uip-eval/model-pfs05aqp:v74", type="model"
)
artifact_dir = Path(artifact.download())
task = ForceRegressionTask.load_from_checkpoint(artifact_dir.joinpath("model.ckpt"))
# move task to device
task = task.to("cuda")
ROOT_DIR = "/datasets-alt/molecular-data/lips"
dm = MatSciMLDataModule(
"LiPSDataset",
train_path=f"{ROOT_DIR}/train",
val_split=f"{ROOT_DIR}/val",
dset_kwargs={
"transforms": [
PeriodicPropertiesTransform(5.0, adaptive_cutoff=True),
PointCloudToGraphTransform(
"pyg",
node_keys=["pos", "atomic_numbers"],
),
],
},
batch_size=16,
num_workers=8,
)
# manual inference
dm.setup("fit")
val_loader = dm.val_dataloader()
def to(data, device):
"""Simple utility function to move things to correct device"""
new_dict = {}
for key, value in data.items():
if hasattr(value, "to"):
new_dict[key] = value.to(device)
else:
new_dict[key] = value
return new_dict
pred_energies = []
true_energies = []
pred_forces = []
true_forces = []
for index, batch in enumerate(val_loader):
# make sure we don't contaminate
task.zero_grad(True)
batch = to(batch, task.device)
# run forward pass
outputs = task(batch)
energies = outputs["energy"].detach().cpu().numpy()
forces = outputs["force"].detach().cpu().numpy()
pred_energies.append(energies)
pred_forces.append(forces)
# save the ground truth labels as well
true_energies.append(batch["targets"]["energy"].cpu().numpy())
true_forces.append(batch["targets"]["force"].cpu().numpy())
# create a wandb artifact object to stash the results to
infer_art = wandb.Artifact(name="mace-uip-validation", type="result")
for array, name in zip(
[pred_energies, pred_forces, true_energies, true_forces],
["pred_energies", "pred_forces", "true_energies", "true_forces"],
):
output_path = artifact_dir.joinpath(name).with_suffix(".npy")
array = np.vstack(array)
np.save(output_path, array)
# somewhat annoyingly, this omits the file extension when pushed
infer_art.add_file(local_path=output_path, name=name)
run.log_artifact(infer_art)
|