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import os |
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from pathlib import Path |
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from typing import Literal |
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import fire |
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from mattergen.common.data.types import TargetProperty |
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from mattergen.common.utils.eval_utils import MatterGenCheckpointInfo |
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from mattergen.generator import CrystalGenerator |
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def main( |
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output_path: str, |
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model_path: str, |
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batch_size: int = 64, |
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num_batches: int = 1, |
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config_overrides: list[str] | None = None, |
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checkpoint_epoch: Literal["best", "last"] | int = "last", |
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properties_to_condition_on: TargetProperty | None = None, |
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sampling_config_path: str | None = None, |
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sampling_config_name: str = "default", |
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sampling_config_overrides: list[str] | None = None, |
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record_trajectories: bool = True, |
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diffusion_guidance_factor: float | None = None, |
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strict_checkpoint_loading: bool = True, |
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target_compositions: list[dict[str, int]] | None = None, |
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): |
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""" |
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Evaluate diffusion model against molecular metrics. |
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Args: |
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model_path: Path to DiffusionLightningModule checkpoint directory. |
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output_path: Path to output directory. |
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config_overrides: Overrides for the model config, e.g., `model.num_layers=3 model.hidden_dim=128`. |
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properties_to_condition_on: Property value to draw conditional sampling with respect to. When this value is an empty dictionary (default), unconditional samples are drawn. |
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sampling_config_path: Path to the sampling config file. (default: None, in which case we use `DEFAULT_SAMPLING_CONFIG_PATH` from explorers.common.utils.utils.py) |
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sampling_config_name: Name of the sampling config (corresponds to `{sampling_config_path}/{sampling_config_name}.yaml` on disk). (default: default) |
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sampling_config_overrides: Overrides for the sampling config, e.g., `condition_loader_partial.batch_size=32`. |
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load_epoch: Epoch to load from the checkpoint. If None, the best epoch is loaded. (default: None) |
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record: Whether to record the trajectories of the generated structures. (default: True) |
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strict_checkpoint_loading: Whether to raise an exception when not all parameters from the checkpoint can be matched to the model. |
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target_compositions: List of dictionaries with target compositions to condition on. Each dictionary should have the form `{element: number_of_atoms}`. If None, the target compositions are not conditioned on. |
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Only supported for models trained for crystal structure prediction (CSP) (default: None) |
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NOTE: When specifying dictionary values via the CLI, make sure there is no whitespace between the key and value, e.g., `--properties_to_condition_on={key1:value1}`. |
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""" |
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if not os.path.exists(output_path): |
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os.makedirs(output_path) |
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sampling_config_overrides = sampling_config_overrides or [] |
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config_overrides = config_overrides or [] |
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properties_to_condition_on = properties_to_condition_on or {} |
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target_compositions = target_compositions or [] |
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checkpoint_info = MatterGenCheckpointInfo( |
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model_path=Path(model_path).resolve(), |
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load_epoch=checkpoint_epoch, |
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config_overrides=config_overrides, |
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strict_checkpoint_loading=strict_checkpoint_loading, |
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) |
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_sampling_config_path = Path(sampling_config_path) if sampling_config_path is not None else None |
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generator = CrystalGenerator( |
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checkpoint_info=checkpoint_info, |
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properties_to_condition_on=properties_to_condition_on, |
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batch_size=batch_size, |
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num_batches=num_batches, |
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sampling_config_name=sampling_config_name, |
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sampling_config_path=_sampling_config_path, |
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sampling_config_overrides=sampling_config_overrides, |
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record_trajectories=record_trajectories, |
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diffusion_guidance_factor=( |
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diffusion_guidance_factor if diffusion_guidance_factor is not None else 0.0 |
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), |
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target_compositions_dict=target_compositions, |
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) |
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generator.generate(output_dir=Path(output_path)) |
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if __name__ == "__main__": |
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fire.Fire(main) |
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