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train.py
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train.py
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import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
import numpy as np
import os
import argparse
import time
from im2mesh import config, data
from im2mesh.checkpoints import CheckpointIO
import logging
if __name__ == '__main__':
logger_py = logging.getLogger(__name__)
# Arguments
parser = argparse.ArgumentParser(
description='Train implicit representations without 3D supervision.'
)
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.')
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")
# Shorthands
out_dir = cfg['training']['out_dir']
backup_every = cfg['training']['backup_every']
exit_after = args.exit_after
lr = cfg['training']['learning_rate']
batch_size = cfg['training']['batch_size']
batch_size_val = cfg['training']['batch_size_val']
n_workers = cfg['training']['n_workers']
t0 = time.time()
# Set mesh extraction to low resolution for fast visuliation
# during training
cfg['generation']['upsampling_steps'] = 2
cfg['generation']['refinement_step'] = 0
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)
train_dataset = config.get_dataset(cfg, mode='train')
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=batch_size, num_workers=n_workers, shuffle=True,
collate_fn=data.collate_remove_none,
)
val_dataset = config.get_dataset(cfg, mode='val')
val_loader = torch.utils.data.DataLoader(
val_dataset, batch_size=batch_size_val, num_workers=int(n_workers/2),
shuffle=True, collate_fn=data.collate_remove_none,
)
data_viz = next(iter(val_loader))
model = config.get_model(cfg, device=device, len_dataset=len(train_dataset))
# Initialize training
optimizer = optim.Adam(model.parameters(), lr=lr)
generator = config.get_generator(model, cfg, device=device)
trainer = config.get_trainer(model, optimizer, cfg, device=device,
generator=generator)
checkpoint_io = CheckpointIO(out_dir, model=model, optimizer=optimizer)
try:
load_dict = checkpoint_io.load('model.pt', device=device)
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)
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))
scheduler = optim.lr_scheduler.MultiStepLR(
optimizer, cfg['training']['scheduler_milestones'],
gamma=cfg['training']['scheduler_gamma'], last_epoch=epoch_it)
logger = SummaryWriter(os.path.join(out_dir, 'logs'))
# 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']
# Print model
nparameters = sum(p.numel() for p in model.parameters())
logger_py.info(model)
logger_py.info('Total number of parameters: %d' % nparameters)
t0b = time.time()
while True:
epoch_it += 1
# for batch in train_loader:
for batch in train_loader:
it += 1
loss = trainer.train_step(batch, it)
logger.add_scalar('train/loss', loss, it)
# Print output
if print_every > 0 and (it % print_every) == 0:
logger_py.info('[Epoch %02d] it=%03d, loss=%.4f, time=%.4f'
% (epoch_it, it, loss, time.time() - t0b))
t0b = time.time()
# Visualize output
if visualize_every > 0 and (it % visualize_every) == 0:
logger_py.info('Visualizing')
trainer.visualize(data_viz, it=it)
# Save checkpoint
if (checkpoint_every > 0 and (it % checkpoint_every) == 0):
logger_py.info('Saving checkpoint')
print('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 % backup_every) == 0):
logger_py.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 % validate_every) == 0:
eval_dict = trainer.evaluate(val_loader)
metric_val = eval_dict[model_selection_metric]
logger_py.info('Validation metric (%s): %.4f'
% (model_selection_metric, metric_val))
for k, v in eval_dict.items():
logger.add_scalar('val/%s' % k, v, it)
if model_selection_sign * (metric_val - metric_val_best) > 0:
metric_val_best = metric_val
logger_py.info('New best model (loss %.4f)' % metric_val_best)
checkpoint_io.backup_model_best('model_best.pt')
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_py.info('Time limit reached. Exiting.')
checkpoint_io.save('model.pt', epoch_it=epoch_it, it=it,
loss_val_best=metric_val_best)
exit(3)
# Make scheduler step after full epoch
scheduler.step()