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train.py
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train.py
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import torch
import torch.optim as optim
import numpy as np
import os
import argparse
import time
from im2mesh import config, data
from im2mesh.checkpoints import CheckpointIO
from im2mesh.utils.logs import create_logger
# Arguments
parser = argparse.ArgumentParser(
description='Train a 3D reconstruction model.'
)
parser.add_argument('config', type=str, help='Path to config file.')
parser.add_argument('--no-cuda', action='store_true', help='Do not use cuda.')
parser.add_argument('--exit-after', type=int, default=-1,
help='Checkpoint and exit after specified number of seconds'
'with exit code 2.')
parser.add_argument('--num-workers', type=int, default=4,
help='Number of workers to use for train and val loaders.')
args = parser.parse_args()
cfg = config.load_config(args.config, 'configs/default.yaml')
is_cuda = (torch.cuda.is_available() and not args.no_cuda)
device = torch.device("cuda" if is_cuda else "cpu")
num_workers = args.num_workers
gpus = cfg['model']['gpus']
# Set t0
t0 = time.time()
# Shorthands
out_dir = cfg['training']['out_dir']
batch_size = cfg['training']['batch_size']
backup_every = cfg['training']['backup_every']
exit_after = args.exit_after
model_selection_metric = cfg['training']['model_selection_metric']
if cfg['training']['model_selection_mode'] == 'maximize':
model_selection_sign = 1
elif cfg['training']['model_selection_mode'] == 'minimize':
model_selection_sign = -1
else:
raise ValueError('model_selection_mode must be '
'either maximize or minimize.')
# Output directory
if not os.path.exists(out_dir):
os.makedirs(out_dir)
model = config.get_model(cfg, device='cuda')
model = torch.nn.DataParallel(model, device_ids=gpus).to(device)
# Intialize training
optimizer = optim.Adam(model.parameters(), lr=1e-4)
trainer = config.get_trainer(model, optimizer, cfg, device=device)
checkpoint_io = CheckpointIO(out_dir, model=model, optimizer=optimizer)
try:
load_dict = checkpoint_io.load('model.pt')
except FileExistsError:
load_dict = dict()
epoch_it = load_dict.get('epoch_it', -1)
it = load_dict.get('it', -1)
metric_val_best = load_dict.get(
'loss_val_best', -model_selection_sign * np.inf)
# Hack because of previous bug in code
# TODO: remove, because shouldn't be necessary
if metric_val_best == np.inf or metric_val_best == -np.inf:
metric_val_best = -model_selection_sign * np.inf
print('Current best validation metric (%s): %.8f'
% (model_selection_metric, metric_val_best))
# Dataset
train_dataset = config.get_dataset('train', cfg)
val_dataset = config.get_dataset('val', cfg)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=batch_size*len(gpus),
num_workers=num_workers,
collate_fn=data.collate_remove_none,
worker_init_fn=data.worker_init_fn)
val_loader = torch.utils.data.DataLoader(
val_dataset, batch_size=1, num_workers=1, shuffle=False,
collate_fn=data.collate_remove_none,
worker_init_fn=data.worker_init_fn)
# Create logger
logger, writter = create_logger(out_dir)
# Shorthands
print_every = cfg['training']['print_every']
checkpoint_every = cfg['training']['checkpoint_every']
validate_every = cfg['training']['validate_every']
visualize_every = cfg['training']['visualize_every']
max_iterations = cfg['training']['max_iterations']
max_epochs = cfg['training']['max_epochs']
# Print model
nparameters = sum(p.numel() for p in model.parameters())
logger.info(model)
logger.info('Total number of parameters: %d' % nparameters)
logger.info (len(train_loader))
while True:
epoch_it += 1
for batch in train_loader:
it += 1
loss_dict = trainer.train_step(batch)
loss = loss_dict['total_loss']
for k, v in loss_dict.items():
if k == 'iou':
continue
writter.add_scalar('train/{}'.format(k), v, it)
# Print output
if print_every > 0 and (it % print_every) == 0:
logger.info('[Epoch %02d] it=%03d, loss=%.4f'
% (epoch_it, it, loss))
# Save checkpoint
if (checkpoint_every > 0 and it > 0 and (it % checkpoint_every) == 0):
logger.info('Saving checkpoint')
checkpoint_io.save('model.pt', epoch_it=epoch_it, it=it,
loss_val_best=metric_val_best)
# Backup if necessary
if (backup_every > 0 and it > 0 and (it % backup_every) == 0):
logger.info('Backup checkpoint')
checkpoint_io.save('model_%d.pt' % it, epoch_it=epoch_it, it=it,
loss_val_best=metric_val_best)
# Run validation
if validate_every > 0 and it > 0 and (it % validate_every) == 0:
eval_dict = trainer.evaluate(val_loader)
metric_val = eval_dict[model_selection_metric]
logger.info('Validation metric (%s): %.4f'
% (model_selection_metric, metric_val))
for k, v in eval_dict.items():
writter.add_scalar('val/%s' % k, v, it)
if model_selection_sign * (metric_val - metric_val_best) > 0:
metric_val_best = metric_val
logger.info('New best model (loss %.4f)' % metric_val_best)
checkpoint_io.save('model_best.pt', epoch_it=epoch_it, it=it,
loss_val_best=metric_val_best)
# Exit if necessary
if exit_after > 0 and (time.time() - t0) >= exit_after:
logger.info('Time limit reached. Exiting.')
checkpoint_io.save('model.pt', epoch_it=epoch_it, it=it,
loss_val_best=metric_val_best)
exit(3)
if max_iterations > 0 and it >= max_iterations:
logger.info('Maximum iteration reached. Exiting.')
checkpoint_io.save('model.pt', epoch_it=epoch_it, it=it,
loss_val_best=metric_val_best)
exit(3)
if max_epochs > 0 and epoch_it >= max_epochs:
logger.info('Maximum epoch reached. Exiting.')
checkpoint_io.save('model.pt', epoch_it=epoch_it, it=it,
loss_val_best=metric_val_best)
exit(3)