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data:
data_dir: "data/MOSES/shapemol/MOSES2_training_val_dataset.pkl" # Path to the raw data (pickle file)
save_dir: "./data/MOSES" # Directory to save the LMDB database
num_samples: 1024 # Number of point cloud samples generated per molecule
num_workers: 20 # Number of parallel workers for data processing
aug_mode: 1
aug_times: 2
map_size: 500 # LMDB map_size in bytes (100GB)
batch_size: 20000 # Batch size for processing data
seed: 42 # Random seed for reproducibility
use_smiles: False # Whether to use SMILES strings for data processing
model:
surf:
n: 2
normal_channel: True
mlp:
hidden_dim: 256 # Hidden dimension for MLP
output_dim: 768 # Output dimension for MLP
num_layers: 2 # Number of layers in MLP
dropout_rate: 0.1 # Dropout rate in MLP
activation_function: "relu" # Activation function for MLP
bart:
max_position_embeddings: 512
d_model: 768
encoder_layers: 0
decoder_layers: 6
encoder_attention_heads: 0
decoder_attention_heads: 8
encoder_ffn_dim: 0
decoder_ffn_dim: 3072
activation_function: 'gelu'
generation_config:
do_sample: true
max_length: 512
top_k: 50
top_p: 1.0
temperature: 1.0
num_return_sequences: 50
train:
output_dir: "./checkpoints/conditional/surfbartv2-sample1024-merge-angles-0421" # TODO: Directory to save model and checkpoints
resume_path: null
overwrite_output_dir: true # Whether to overwrite existing outputs
num_train_epochs: 50 # Total number of training epochs
per_device_train_batch_size: 150 # Training batch size per device
per_device_eval_batch_size: 4 # Evaluation batch size per device
dataloader_num_workers: 2 # Number of workers for data loading
save_total_limit: 6 # Maximum number of checkpoints to keep
logging_steps: 50 # Steps interval for logging
eval_strategy: "steps" # Evaluation strategy (e.g., "steps", "epoch")
eval_steps: 5000 # Evaluation frequency (in steps)
do_eval: true # Whether to perform evaluation
learning_rate: 1e-4 # Initial learning rate
warmup_ratio: 0.1 # Warm-up ratio for learning rate scheduler
save_strategy: "steps" # Save strategy (e.g., "steps", "epoch")
save_steps: 5000 # Save frequency (in steps)
load_best_model_at_end: true # Load the best model at the end of training
logging_first_step: true # Log the first training step
bf16: True # Whether to use bf16 precision
early_stopping_patience: 10 # Patience for early stopping callback
early_stopping_threshold: 0 # Threshold for early stopping
seed: 42
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