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train_diffpp.py
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train_diffpp.py
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import os
import shutil
import argparse
import torch
from torch.nn.utils import clip_grad_norm_
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
from ppflow.utils.misc import *
from ppflow.utils.train import *
from ppflow.utils.data import *
from ppflow.datasets import get_dataset
from ppflow.models import get_model
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='./configs/train/diffpp.yml')
parser.add_argument('--logdir', type=str, default='./logs')
parser.add_argument('--debug', action='store_true', default=False)
parser.add_argument('--device', type=str, default='cuda')
parser.add_argument('--num_workers', type=int, default=8)
parser.add_argument('--tag', type=str, default='')
parser.add_argument('--resume', type=str, default=None)
parser.add_argument('--finetune', type=str, default=None)
args = parser.parse_args()
# Load configs
config, config_name = load_config(args)
seed_all(config.train.seed)
# Logging
if args.debug:
logger = get_logger('train', None)
writer = BlackHole()
else:
if args.resume:
log_dir = os.path.dirname(os.path.dirname(args.resume))
else:
log_dir = get_new_log_dir(args.logdir, prefix=config_name, tag=args.tag)
ckpt_dir = os.path.join(log_dir, 'checkpoints')
if not os.path.exists(ckpt_dir): os.makedirs(ckpt_dir)
logger = get_logger('train', log_dir)
writer = BlackHole()
if not os.path.exists(os.path.join(log_dir, os.path.basename(args.config))):
shutil.copyfile(args.config, os.path.join(log_dir, os.path.basename(args.config)))
logger.info(args)
logger.info(config)
# Data
logger.info('Loading dataset...')
train_dataset = get_dataset(config.dataset, split='train')
val_dataset = get_dataset(config.dataset, split='val')
train_iterator = inf_iterator(DataLoader(
train_dataset,
batch_size=config.train.batch_size,
collate_fn=PaddingCollate(),
shuffle=True,
num_workers=args.num_workers
))
val_loader = DataLoader(val_dataset,
batch_size=config.train.batch_size,
collate_fn=PaddingCollate(),
shuffle=False,
num_workers=args.num_workers)
logger.info('Train %d | Val %d' % (len(train_dataset), len(val_dataset)))
# Model
logger.info('Building model...')
model = get_model(config.model).to(args.device)
logger.info('Number of parameters: %d' % count_parameters(model))
# Optimizer & scheduler
optimizer = get_optimizer(config.train.optimizer, model)
scheduler = get_scheduler(config.train.scheduler, optimizer)
optimizer.zero_grad()
it_first = 1
# Resume
if args.resume is not None or args.finetune is not None:
ckpt_path = args.resume if args.resume is not None else args.finetune
logger.info('Resuming from checkpoint: %s' % ckpt_path)
ckpt = torch.load(ckpt_path, map_location=args.device)
it_first = ckpt['iteration'] # + 1
model.load_state_dict(ckpt['model'])
logger.info('Resuming optimizer states...')
optimizer.load_state_dict(ckpt['optimizer'])
logger.info('Resuming scheduler states...')
scheduler.load_state_dict(ckpt['scheduler'])
# Train
def train(it):
time_start = current_milli_time()
model.train()
# Prepare data
batch = recursive_to(next(train_iterator), args.device)
# print(it)
# Forward
if args.debug: torch.set_anomaly_enabled(True)
loss_dict = model(batch)
loss = sum_weighted_losses(loss_dict, config.train.loss_weights)
loss_dict['overall'] = loss
time_forward_end = current_milli_time()
# Backward
loss.backward()
orig_grad_norm = clip_grad_norm_(model.parameters(), config.train.max_grad_norm)
optimizer.step()
optimizer.zero_grad()
time_backward_end = current_milli_time()
# Logging
log_losses(loss_dict, it, 'train', logger, writer, others={
'grad': orig_grad_norm,
'lr': optimizer.param_groups[0]['lr'],
'time_forward': (time_forward_end - time_start) / 1000,
'time_backward': (time_backward_end - time_forward_end) / 1000,
})
if not torch.isfinite(loss):
logger.error('NaN or Inf detected.')
torch.save({
'config': config,
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'iteration': it,
'batch': recursive_to(batch, 'cpu'),
}, os.path.join(log_dir, 'checkpoint_nan_%d.pt' % it))
raise KeyboardInterrupt()
# Validate
def validate(it):
loss_tape = ValidationLossTape()
with torch.no_grad():
model.eval()
for i, batch in enumerate(tqdm(val_loader, desc='Validate', dynamic_ncols=True)):
# Prepare data
batch = recursive_to(batch, args.device)
# Forward
loss_dict = model(batch)
loss = sum_weighted_losses(loss_dict, config.train.loss_weights)
loss_dict['overall'] = loss
loss_tape.update(loss_dict, 1)
avg_loss = loss_tape.log(it, logger, writer, 'val')
# Trigger scheduler
if config.train.scheduler.type == 'plateau':
scheduler.step(avg_loss)
else:
scheduler.step()
return avg_loss
try:
for it in range(it_first, config.train.max_iters + 1):
train(it)
if it % config.train.val_freq == 0:
avg_val_loss = validate(it)
if not args.debug:
ckpt_path = os.path.join(ckpt_dir, '%d.pt' % it)
torch.save({
'config': config,
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'iteration': it,
'avg_val_loss': avg_val_loss,
}, ckpt_path)
except KeyboardInterrupt:
logger.info('Terminating...')