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train_multi.py
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train_multi.py
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import argparse
import os
import pwd
import sys
import yaml
from datetime import datetime
from pytorch_lightning import Trainer, callbacks, loggers
from src.const import NUMBER_OF_ATOM_TYPES
from src.model_multi import DDPM
from src.utils import disable_rdkit_logging, Logger
def find_last_checkpoint(checkpoints_dir):
epoch2fname = [
(int(fname.split('=')[1].split('.')[0]), fname)
for fname in os.listdir(checkpoints_dir)
if fname.endswith('.ckpt')
]
latest_fname = max(epoch2fname, key=lambda t: t[0])[1]
return os.path.join(checkpoints_dir, latest_fname)
def main(args):
start_time = datetime.now().strftime('date%d-%m_time%H-%M-%S.%f')
run_name = f'{os.path.splitext(os.path.basename(args.config))[0]}_{pwd.getpwuid(os.getuid())[0]}_{args.exp_name}_bs{args.batch_size}_{start_time}'
experiment = run_name if args.resume is None else args.resume
checkpoints_dir = os.path.join(args.checkpoints, experiment)
os.makedirs(os.path.join(args.logs, "general_logs", experiment),exist_ok=True)
sys.stdout = Logger(logpath=os.path.join(args.logs, "general_logs", experiment, f'log.log'), syspart=sys.stdout)
sys.stderr = Logger(logpath=os.path.join(args.logs, "general_logs", experiment, f'log.log'), syspart=sys.stderr)
os.makedirs(checkpoints_dir, exist_ok=True)
os.makedirs(args.logs, exist_ok=True)
samples_dir = os.path.join(args.logs, 'samples', experiment)
torch_device = 'cuda:0' if args.device == 'gpu' else 'cpu'
wandb_logger = loggers.WandbLogger(
save_dir=args.logs,
project='diffdec_multi',
name=experiment,
id=experiment,
resume='must' if args.resume is not None else 'allow',
entity=args.wandb_entity,
)
number_of_atoms = NUMBER_OF_ATOM_TYPES
in_node_nf = number_of_atoms + args.include_charges
anchors_context = not args.remove_anchors_context
context_node_nf = 2 if anchors_context else 1
if '.' in args.train_data_prefix:
context_node_nf += 1
ddpm = DDPM(
data_path=args.data,
train_data_prefix=args.train_data_prefix,
val_data_prefix=args.val_data_prefix,
in_node_nf=in_node_nf,
n_dims=3,
context_node_nf=context_node_nf,
hidden_nf=args.nf,
activation=args.activation,
n_layers=args.n_layers,
attention=args.attention,
tanh=args.tanh,
norm_constant=args.norm_constant,
inv_sublayers=args.inv_sublayers,
sin_embedding=args.sin_embedding,
normalization_factor=args.normalization_factor,
aggregation_method=args.aggregation_method,
diffusion_steps=args.diffusion_steps,
diffusion_noise_schedule=args.diffusion_noise_schedule,
diffusion_noise_precision=args.diffusion_noise_precision,
diffusion_loss_type=args.diffusion_loss_type,
normalize_factors=args.normalize_factors,
include_charges=args.include_charges,
lr=args.lr,
batch_size=args.batch_size,
torch_device=torch_device,
model=args.model,
test_epochs=args.test_epochs,
n_stability_samples=args.n_stability_samples,
normalization=args.normalization,
log_iterations=args.log_iterations,
samples_dir=samples_dir,
data_augmentation=args.data_augmentation,
center_of_mass=args.center_of_mass,
inpainting=args.inpainting,
anchors_context=anchors_context,
)
checkpoint_callback = callbacks.ModelCheckpoint(
dirpath=checkpoints_dir,
filename=experiment + '_{epoch:02d}',
monitor='loss/val',
save_top_k=-1,
every_n_epochs=20
)
trainer = Trainer(
max_epochs=args.n_epochs,
logger=wandb_logger,
callbacks=checkpoint_callback,
accelerator=args.device,
devices=1,
num_sanity_val_steps=0,
enable_progress_bar=args.enable_progress_bar,
)
if args.resume is None:
last_checkpoint = None
else:
last_checkpoint = find_last_checkpoint(checkpoints_dir)
print(f'Training will be resumed from the latest checkpoint {last_checkpoint}')
print('Start training')
trainer.fit(model=ddpm, ckpt_path=last_checkpoint)
if __name__ == '__main__':
p = argparse.ArgumentParser(description='E3Diffusion')
p.add_argument('--config', type=argparse.FileType(mode='r'), default='configs/single.yml')
p.add_argument('--data', action='store', type=str, default="datasets")
p.add_argument('--train_data_prefix', action='store', type=str, default='train')
p.add_argument('--val_data_prefix', action='store', type=str, default='val')
p.add_argument('--checkpoints', action='store', type=str, default='checkpoints')
p.add_argument('--logs', action='store', type=str, default='logs')
p.add_argument('--device', action='store', type=str, default='cpu')
p.add_argument('--trainer_params', type=dict, help='parameters with keywords of the lightning trainer')
p.add_argument('--log_iterations', action='store', type=str, default=20)
p.add_argument('--exp_name', type=str, default='YourName')
p.add_argument('--model', type=str, default='egnn_dynamics',help='our_dynamics | schnet | simple_dynamics | kernel_dynamics | egnn_dynamics |gnn_dynamics')
p.add_argument('--probabilistic_model', type=str, default='diffusion', help='diffusion')
# Training complexity is O(1) (unaffected), but sampling complexity is O(steps).
p.add_argument('--diffusion_steps', type=int, default=500)
p.add_argument('--diffusion_noise_schedule', type=str, default='polynomial_2', help='learned, cosine')
p.add_argument('--diffusion_noise_precision', type=float, default=1e-5, )
p.add_argument('--diffusion_loss_type', type=str, default='l2', help='vlb, l2')
p.add_argument('--n_epochs', type=int, default=200)
p.add_argument('--batch_size', type=int, default=128)
p.add_argument('--lr', type=float, default=2e-4)
p.add_argument('--brute_force', type=eval, default=False,help='True | False')
p.add_argument('--actnorm', type=eval, default=True,help='True | False')
p.add_argument('--break_train_epoch', type=eval, default=False,help='True | False')
p.add_argument('--dp', type=eval, default=True,help='True | False')
p.add_argument('--condition_time', type=eval, default=True,help='True | False')
p.add_argument('--clip_grad', type=eval, default=True,help='True | False')
p.add_argument('--trace', type=str, default='hutch',help='hutch | exact')
# EGNN args -->
p.add_argument('--n_layers', type=int, default=6, help='number of layers')
p.add_argument('--inv_sublayers', type=int, default=1, help='number of layers')
p.add_argument('--nf', type=int, default=128, help='number of layers')
p.add_argument('--tanh', type=eval, default=True, help='use tanh in the coord_mlp')
p.add_argument('--attention', type=eval, default=True, help='use attention in the EGNN')
p.add_argument('--norm_constant', type=float, default=1,help='diff/(|diff| + norm_constant)')
p.add_argument('--sin_embedding', type=eval, default=False, help='whether using or not the sin embedding')
p.add_argument('--ode_regularization', type=float, default=1e-3)
p.add_argument('--dataset', type=str, default='crossdock', help='crossdock')
p.add_argument('--datadir', type=str, default='/crossdock/', help='crossdock directory')
p.add_argument('--filter_n_atoms', type=int, default=None, help='')
p.add_argument('--dequantization', type=str, default='argmax_variational', help='uniform | variational | argmax_variational | deterministic')
p.add_argument('--n_report_steps', type=int, default=1)
p.add_argument('--wandb_usr', type=str)
p.add_argument('--no_wandb', action='store_true', help='Disable wandb')
p.add_argument('--enable_progress_bar', action='store_true', help='Disable wandb')
p.add_argument('--online', type=bool, default=True, help='True = wandb online -- False = wandb offline')
p.add_argument('--no-cuda', action='store_true', default=False, help='enables CUDA training')
p.add_argument('--save_model', type=eval, default=True, help='save model')
p.add_argument('--generate_epochs', type=int, default=1,help='save model')
p.add_argument('--num_workers', type=int, default=0, help='Number of worker for the dataloader')
p.add_argument('--test_epochs', type=int, default=1)
p.add_argument('--data_augmentation', type=eval, default=False, help='use attention in the EGNN')
p.add_argument("--conditioning", nargs='+', default=[], help='arguments : homo | lumo | alpha | gap | mu | Cv')
p.add_argument('--resume', type=str, default=None, help='')
p.add_argument('--start_epoch', type=int, default=0, help='')
p.add_argument('--ema_decay', type=float, default=0.999, help='Amount of EMA decay, 0 means off. A reasonable value is 0.999.')
p.add_argument('--augment_noise', type=float, default=0)
p.add_argument('--n_stability_samples', type=int, default=500,help='Number of samples to compute the stability')
p.add_argument('--normalize_factors', type=eval, default=[1, 4, 1], help='normalize factors for [x, categorical, integer]')
p.add_argument('--remove_h', action='store_true')
p.add_argument('--include_charges', type=eval, default=True,help='include atom charge or not')
p.add_argument('--visualize_every_batch', type=int, default=1e8,help="Can be used to visualize multiple times per epoch")
p.add_argument('--normalization_factor', type=float, default=1,help="Normalize the sum aggregation of EGNN")
p.add_argument('--aggregation_method', type=str, default='sum',help='"sum" or "mean"')
p.add_argument('--normalization', type=str, default='batch_norm', help='batch_norm')
p.add_argument('--wandb_entity', type=str, default='geometric', help='Entity (project) name')
p.add_argument('--center_of_mass', type=str, default='scaffold', help='Where to center the data: scaffold | anchors')
p.add_argument('--inpainting', action='store_true', default=False, help='Inpainting mode (full generation)')
p.add_argument('--remove_anchors_context', action='store_true', default=False, help='Remove anchors context')
disable_rdkit_logging()
args = p.parse_args()
if args.config:
config_dict = yaml.load(args.config, Loader=yaml.FullLoader)
arg_dict = args.__dict__
for key, value in config_dict.items():
if isinstance(value, list) and key != 'normalize_factors':
for v in value:
arg_dict[key].append(v)
else:
arg_dict[key] = value
args.config = args.config.name
else:
config_dict = {}
main(args=args)