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datas | ||
ser | ||
marz | ||
manuel | ||
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--- | ||
authors: | ||
- Zhiyuan Chen | ||
date: 2024-05-04 | ||
--- | ||
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# MultiTask | ||
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::: multimolecule.data.multitask |
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--- | ||
authors: | ||
- Zhiyuan Chen | ||
date: 2024-05-04 | ||
--- | ||
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# MultiMoleculeConfig | ||
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::: multimolecule.runners.MultiMoleculeConfig |
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--- | ||
authors: | ||
- Zhiyuan Chen | ||
date: 2024-05-04 | ||
--- | ||
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# runners | ||
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--8<-- "multimolecule/runners/README.md:8:" |
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--- | ||
authors: | ||
- Zhiyuan Chen | ||
date: 2024-05-04 | ||
--- | ||
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# MultiMoleculeRunner | ||
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::: multimolecule.runners.MultiMoleculeRunner |
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# MultiMolecule | ||
# Copyright (C) 2024-Present MultiMolecule | ||
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# This program is free software: you can redistribute it and/or modify | ||
# it under the terms of the GNU Affero General Public License as published by | ||
# the Free Software Foundation, either version 3 of the License, or | ||
# any later version. | ||
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# This program is distributed in the hope that it will be useful, | ||
# but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
# GNU Affero General Public License for more details. | ||
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# You should have received a copy of the GNU Affero General Public License | ||
# along with this program. If not, see <http://www.gnu.org/licenses/>. | ||
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from .run import evaluate, infer, train | ||
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__all__ = ["train", "evaluate", "infer"] |
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# MultiMolecule | ||
# Copyright (C) 2024-Present MultiMolecule | ||
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# This program is free software: you can redistribute it and/or modify | ||
# it under the terms of the GNU Affero General Public License as published by | ||
# the Free Software Foundation, either version 3 of the License, or | ||
# any later version. | ||
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# This program is distributed in the hope that it will be useful, | ||
# but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
# GNU Affero General Public License for more details. | ||
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# You should have received a copy of the GNU Affero General Public License | ||
# along with this program. If not, see <http://www.gnu.org/licenses/>. | ||
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# mypy: disable-error-code="attr-defined" | ||
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import atexit | ||
import os | ||
import warnings | ||
from typing import Type | ||
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import danling as dl | ||
import torch | ||
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from multimolecule.runners import MultiMoleculeConfig, MultiMoleculeRunner | ||
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try: | ||
import nni | ||
except ImportError: | ||
nni = None | ||
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def train( | ||
config: MultiMoleculeConfig = None, # type: ignore | ||
runner_cls: Type[MultiMoleculeRunner] = MultiMoleculeRunner, | ||
): | ||
if config is None: | ||
config = MultiMoleculeConfig() | ||
config = config.parse(default_config="config", no_default_config_action="warn") | ||
config.interpolate(unsafe_eval=True) | ||
config.training = True | ||
if config.allow_tf32: | ||
torch.backends.cudnn.allow_tf32 = True | ||
torch.backends.cuda.matmul.allow_tf32 = True | ||
if config.reduced_precision_reduction: | ||
torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = True | ||
torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = True | ||
if config.get("nni", False): | ||
if nni is None: | ||
raise ValueError("Unable to retrieve nni parameters, since nni is not installed.") | ||
config.merge(nni.get_next_parameter()) | ||
with dl.debug(config.get("debug", False)): | ||
runner = runner_cls(config) | ||
atexit.register(runner.print_result) | ||
atexit.register(runner.save_result) | ||
atexit.register(runner.save_checkpoint) | ||
result = runner.train() | ||
return result | ||
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def evaluate( | ||
config: MultiMoleculeConfig = None, # type: ignore | ||
runner_cls: Type[MultiMoleculeRunner] = MultiMoleculeRunner, | ||
): | ||
if config is None: | ||
config = MultiMoleculeConfig.empty() | ||
config = config.parse(default_config="config", no_default_config_action="warn") | ||
config.interpolate(unsafe_eval=True) | ||
config.training = False | ||
if config.allow_tf32: | ||
torch.backends.cudnn.allow_tf32 = True | ||
torch.backends.cuda.matmul.allow_tf32 = True | ||
if config.reduced_precision_reduction: | ||
torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = True | ||
torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = True | ||
if "checkpoint" not in config or not isinstance(config.checkpoint, str): | ||
raise RuntimeError("Please specify `checkpoint` to run evaluate") | ||
for name, data in config.datas.items(): | ||
if "evaluation" not in data or not isinstance(data.evaluate, str): | ||
raise RuntimeError(f"Please specify `evaluation` to run evaluate in datas.{name}") | ||
runner = runner_cls(config) | ||
result = runner.evaluate_epoch("evaluation") | ||
print(result) | ||
return result | ||
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def infer( | ||
config: MultiMoleculeConfig = None, # type: ignore | ||
runner_cls: Type[MultiMoleculeRunner] = MultiMoleculeRunner, | ||
): | ||
if config is None: | ||
config = MultiMoleculeConfig.empty() | ||
config = config.parse(default_config="config", no_default_config_action="warn") | ||
config.interpolate(unsafe_eval=True) | ||
config.training = False | ||
if config.allow_tf32: | ||
torch.backends.cudnn.allow_tf32 = True | ||
torch.backends.cuda.matmul.allow_tf32 = True | ||
if config.reduced_precision_reduction: | ||
torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = True | ||
torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = True | ||
if "checkpoint" not in config or not isinstance(config.checkpoint, str): | ||
raise RuntimeError("Please specify `checkpoint` to run infer.") | ||
for name, data in config.datas.items(): | ||
if "inference" not in data or not isinstance(data.inference, str): | ||
raise RuntimeError(f"Please specify `inference` to run infer in datas.{name}") | ||
if "result_path" not in config or not isinstance(config.result_path, str): | ||
config.result_path = os.path.join(os.getcwd(), "result.json") | ||
warnings.warn("`result_path` is not specified, default to `result.json`.", RuntimeWarning, stacklevel=2) | ||
runner = runner_cls(config) | ||
result = runner.infer() | ||
runner.save(result, config.result_path) | ||
return result |
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# MultiMolecule | ||
# Copyright (C) 2024-Present MultiMolecule | ||
|
||
# This program is free software: you can redistribute it and/or modify | ||
# it under the terms of the GNU Affero General Public License as published by | ||
# the Free Software Foundation, either version 3 of the License, or | ||
# any later version. | ||
|
||
# This program is distributed in the hope that it will be useful, | ||
# but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
# GNU Affero General Public License for more details. | ||
|
||
# You should have received a copy of the GNU Affero General Public License | ||
# along with this program. If not, see <http://www.gnu.org/licenses/>. | ||
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import os | ||
import shutil | ||
from statistics import mean | ||
from typing import List | ||
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import chanfig | ||
import pandas as pd | ||
from chanfig import NestedDict | ||
from tqdm import tqdm | ||
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class Result(NestedDict): | ||
pretrained: str | ||
id: str | ||
seed: int | ||
epoch: int | ||
validation: NestedDict | ||
test: NestedDict | ||
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def get_result_stat(experiment_root: str, remove_empty: bool = True) -> List[Result]: | ||
results = [] | ||
for root, _, files in tqdm(os.walk(experiment_root)): | ||
if "run.log" in files: | ||
if "best.json" not in files: | ||
if remove_empty: | ||
shutil.rmtree(root) | ||
continue | ||
best = NestedDict.from_json(os.path.join(root, "best.json")) | ||
if "index" not in best: | ||
if remove_empty: | ||
shutil.rmtree(root) | ||
continue | ||
config = NestedDict.from_yaml(os.path.join(root, "trainer.yaml")) | ||
pretrained = config.pretrained.split("/")[-1] | ||
seed = config.seed | ||
pretrained, seed = "", 1 | ||
result = Result(id=best.id, pretrained=pretrained, seed=seed) | ||
result.validation = NestedDict( | ||
{k: format(mean(v) if isinstance(v, list) else v, ".8f") for k, v in best.validation.items()} | ||
) | ||
result.test = NestedDict( | ||
{k: format(mean(v) if isinstance(v, list) else v, ".8f") for k, v in best.test.items()} | ||
) | ||
result.epoch = best.index | ||
result.pop("validation.time", None) | ||
result.pop("test.time", None) | ||
result.pop("validation.loss", None) | ||
result.pop("test.loss", None) | ||
result.pop("validation.lr", None) | ||
result.pop("test.lr", None) | ||
results.append(result) | ||
# Remove empty directories, perform twice to remove all empty directories | ||
if remove_empty: | ||
for root, dirs, files in os.walk(experiment_root): | ||
if not files and not dirs: | ||
os.rmdir(root) | ||
for root, dirs, files in os.walk(experiment_root): | ||
if not files and not dirs: | ||
os.rmdir(root) | ||
results.sort(key=lambda x: (x.pretrained, x.seed, x.id)) | ||
return results | ||
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def write_result_stat(results: List[Result], path: str): | ||
results = [dict(result.all_items()) for result in results] # type: ignore[misc] | ||
df = pd.DataFrame.from_dict(results) | ||
df.insert(len(df.keys()) - 1, "comment", "") | ||
df.fillna("") | ||
df.to_csv(path, index=False) | ||
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class Config(chanfig.Config): | ||
experiment_root: str = "experiments" | ||
out_path: str = "result.csv" | ||
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if __name__ == "__main__": | ||
config = Config().parse() | ||
result_stat = get_result_stat(config.experiment_root) | ||
if not len(result_stat) > 0: | ||
raise ValueError("No results found") | ||
write_result_stat(result_stat, config.out_path) |
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