forked from gcorso/DiffDock
/
train.py
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train.py
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import copy
import math
import os
from functools import partial
import wandb
import torch
torch.multiprocessing.set_sharing_strategy('file_system')
import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
resource.setrlimit(resource.RLIMIT_NOFILE, (64000, rlimit[1]))
import yaml
from utils.diffusion_utils import t_to_sigma as t_to_sigma_compl
from datasets.pdbbind import construct_loader
from utils.parsing import parse_train_args
from utils.training import train_epoch, test_epoch, loss_function, inference_epoch
from utils.utils import save_yaml_file, get_optimizer_and_scheduler, get_model, ExponentialMovingAverage
def train(args, model, optimizer, scheduler, ema_weights, train_loader, val_loader, t_to_sigma, run_dir):
best_val_loss = math.inf
best_val_inference_value = math.inf if args.inference_earlystop_goal == 'min' else 0
best_epoch = 0
best_val_inference_epoch = 0
loss_fn = partial(loss_function, tr_weight=args.tr_weight, rot_weight=args.rot_weight,
tor_weight=args.tor_weight, no_torsion=args.no_torsion)
print("Starting training...")
for epoch in range(args.n_epochs):
if epoch % 5 == 0: print("Run name: ", args.run_name)
logs = {}
train_losses = train_epoch(model, train_loader, optimizer, device, t_to_sigma, loss_fn, ema_weights)
print("Epoch {}: Training loss {:.4f} tr {:.4f} rot {:.4f} tor {:.4f}"
.format(epoch, train_losses['loss'], train_losses['tr_loss'], train_losses['rot_loss'],
train_losses['tor_loss']))
ema_weights.store(model.parameters())
if args.use_ema: ema_weights.copy_to(model.parameters()) # load ema parameters into model for running validation and inference
val_losses = test_epoch(model, val_loader, device, t_to_sigma, loss_fn, args.test_sigma_intervals)
print("Epoch {}: Validation loss {:.4f} tr {:.4f} rot {:.4f} tor {:.4f}"
.format(epoch, val_losses['loss'], val_losses['tr_loss'], val_losses['rot_loss'], val_losses['tor_loss']))
if args.val_inference_freq != None and (epoch + 1) % args.val_inference_freq == 0:
inf_metrics = inference_epoch(model, val_loader.dataset.complex_graphs[:args.num_inference_complexes], device, t_to_sigma, args)
print("Epoch {}: Val inference rmsds_lt2 {:.3f} rmsds_lt5 {:.3f}"
.format(epoch, inf_metrics['rmsds_lt2'], inf_metrics['rmsds_lt5']))
logs.update({'valinf_' + k: v for k, v in inf_metrics.items()}, step=epoch + 1)
if not args.use_ema: ema_weights.copy_to(model.parameters())
ema_state_dict = copy.deepcopy(model.module.state_dict() if device.type == 'cuda' else model.state_dict())
ema_weights.restore(model.parameters())
if args.wandb:
logs.update({'train_' + k: v for k, v in train_losses.items()})
logs.update({'val_' + k: v for k, v in val_losses.items()})
logs['current_lr'] = optimizer.param_groups[0]['lr']
wandb.log(logs, step=epoch + 1)
state_dict = model.module.state_dict() if device.type == 'cuda' else model.state_dict()
if args.inference_earlystop_metric in logs.keys() and \
(args.inference_earlystop_goal == 'min' and logs[args.inference_earlystop_metric] <= best_val_inference_value or
args.inference_earlystop_goal == 'max' and logs[args.inference_earlystop_metric] >= best_val_inference_value):
best_val_inference_value = logs[args.inference_earlystop_metric]
best_val_inference_epoch = epoch
torch.save(state_dict, os.path.join(run_dir, 'best_inference_epoch_model.pt'))
torch.save(ema_state_dict, os.path.join(run_dir, 'best_ema_inference_epoch_model.pt'))
if val_losses['loss'] <= best_val_loss:
best_val_loss = val_losses['loss']
best_epoch = epoch
torch.save(state_dict, os.path.join(run_dir, 'best_model.pt'))
torch.save(ema_state_dict, os.path.join(run_dir, 'best_ema_model.pt'))
if scheduler:
if args.val_inference_freq is not None:
scheduler.step(best_val_inference_value)
else:
scheduler.step(val_losses['loss'])
torch.save({
'epoch': epoch,
'model': state_dict,
'optimizer': optimizer.state_dict(),
'ema_weights': ema_weights.state_dict(),
}, os.path.join(run_dir, 'last_model.pt'))
print("Best Validation Loss {} on Epoch {}".format(best_val_loss, best_epoch))
print("Best inference metric {} on Epoch {}".format(best_val_inference_value, best_val_inference_epoch))
def main_function():
args = parse_train_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):
for v in value:
arg_dict[key].append(v)
else:
arg_dict[key] = value
args.config = args.config.name
assert (args.inference_earlystop_goal == 'max' or args.inference_earlystop_goal == 'min')
if args.val_inference_freq is not None and args.scheduler is not None:
assert (args.scheduler_patience > args.val_inference_freq) # otherwise we will just stop training after args.scheduler_patience epochs
if args.cudnn_benchmark:
torch.backends.cudnn.benchmark = True
# construct loader
t_to_sigma = partial(t_to_sigma_compl, args=args)
train_loader, val_loader = construct_loader(args, t_to_sigma)
model = get_model(args, device, t_to_sigma=t_to_sigma)
optimizer, scheduler = get_optimizer_and_scheduler(args, model, scheduler_mode=args.inference_earlystop_goal if args.val_inference_freq is not None else 'min')
ema_weights = ExponentialMovingAverage(model.parameters(),decay=args.ema_rate)
if args.restart_dir:
try:
dict = torch.load(f'{args.restart_dir}/last_model.pt', map_location=torch.device('cpu'))
if args.restart_lr is not None: dict['optimizer']['param_groups'][0]['lr'] = args.restart_lr
optimizer.load_state_dict(dict['optimizer'])
model.module.load_state_dict(dict['model'], strict=True)
if hasattr(args, 'ema_rate'):
ema_weights.load_state_dict(dict['ema_weights'], device=device)
print("Restarting from epoch", dict['epoch'])
except Exception as e:
print("Exception", e)
dict = torch.load(f'{args.restart_dir}/best_model.pt', map_location=torch.device('cpu'))
model.module.load_state_dict(dict, strict=True)
print("Due to exception had to take the best epoch and no optimiser")
numel = sum([p.numel() for p in model.parameters()])
print('Model with', numel, 'parameters')
if args.wandb:
wandb.init(
entity='entity',
settings=wandb.Settings(start_method="fork"),
project=args.project,
name=args.run_name,
config=args
)
wandb.log({'numel': numel})
# record parameters
run_dir = os.path.join(args.log_dir, args.run_name)
yaml_file_name = os.path.join(run_dir, 'model_parameters.yml')
save_yaml_file(yaml_file_name, args.__dict__)
args.device = device
train(args, model, optimizer, scheduler, ema_weights, train_loader, val_loader, t_to_sigma, run_dir)
if __name__ == '__main__':
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
main_function()