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# Copyright (c) 2022 GT4SD team
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"""PaccMann training utilities."""
from dataclasses import dataclass, field
from typing import Any, Dict, Optional
from ..core import TrainingPipeline, TrainingPipelineArguments
[docs]class PaccMannTrainingPipeline(TrainingPipeline):
"""PyTorch lightining training pipelines."""
[docs] def train( # type: ignore
self,
training_args: Dict[str, Any],
model_args: Dict[str, Any],
dataset_args: Dict[str, Any],
) -> None:
"""Generic training function for PaccMann training.
Args:
training_args: training arguments passed to the configuration.
model_args: model arguments passed to the configuration.
dataset_args: dataset arguments passed to the configuration.
Raises:
NotImplementedError: the generic trainer does not implement the pipeline.
"""
raise NotImplementedError
[docs]@dataclass
class PaccMannTrainingArguments(TrainingPipelineArguments):
"""Arguments related to PaccMann trainer."""
__name__ = "training_args"
model_path: str = field(
metadata={"help": "Path where the model artifacts are stored."}
)
training_name: str = field(metadata={"help": "Name used to identify the training."})
checkpoint_path: Optional[str] = field(
default=None,
metadata={
"help": "Path to model checkpoint for weights initialization. Leave None if you want to train a model from scratch"
},
)
epochs: int = field(default=50, metadata={"help": "Number of epochs."})
batch_size: int = field(default=256, metadata={"help": "Size of the batch."})
learning_rate: float = field(
default=0.0005, metadata={"help": "Learning rate used in training."}
)
optimizer: str = field(
default="adam", metadata={"help": "Optimizer used during training."}
)
log_interval: int = field(
default=100, metadata={"help": "Number of steps between log intervals."}
)
save_interval: int = field(
default=1000, metadata={"help": "Number of steps between model save intervals."}
)
eval_interval: int = field(
default=500, metadata={"help": "Number of steps between evaluation intervals."}
)
[docs]@dataclass
class PaccMannDataArguments(TrainingPipelineArguments):
"""Arguments related to PaccMann data loading."""
__name__ = "dataset_args"
train_smiles_filepath: str = field(
metadata={"help": "Training file containing SMILES in .smi format."}
)
test_smiles_filepath: str = field(
metadata={"help": "Testing file containing SMILES in .smi format."}
)
smiles_language_filepath: str = field(
default="none", metadata={"help": "Optional SMILES language file."}
)
add_start_stop_token: bool = field(
default=True, metadata={"help": "Whether start and stop token should be added."}
)
selfies: bool = field(
default=True, metadata={"help": "Whether SELFIES representations are used."}
)
num_workers: int = field(
default=0, metadata={"help": "Number of workers used in data loading."}
)
pin_memory: bool = field(
default=False, metadata={"help": "Whether memory in the data loader is pinned."}
)
augment_smiles: bool = field(
default=False, metadata={"help": "Whether SMILES augumentation is used."}
)
canonical: bool = field(
default=False, metadata={"help": "Whether SMILES canonicalization is used."}
)
kekulize: bool = field(
default=False, metadata={"help": "Whether SMILES kekulization is used."}
)
all_bonds_explicit: bool = field(
default=False, metadata={"help": "Whether all bonds are explicit."}
)
all_hs_explicit: bool = field(
default=False, metadata={"help": "Whether all hydrogens are explicit."}
)
remove_bonddir: bool = field(
default=False, metadata={"help": "Remove bond directionality."}
)
remove_chirality: bool = field(
default=False, metadata={"help": "Remove chirality."}
)
[docs]@dataclass
class PaccMannSavingArguments(TrainingPipelineArguments):
"""Saving arguments related to PaccMann trainer."""
__name__ = "saving_args"
model_path: str = field(
metadata={"help": "Path where the model artifacts are stored."}
)
training_name: str = field(metadata={"help": "Name used to identify the training."})