save_checkpoint(epoch, model, optimizer, prefix)
val_acc, val_preci, val_recall, mAP_scores = evaluate(
epoch, val_loader, model, bce_loss, log_writer)
avg_map = np.mean(mAP_scores)
if avg_map > best_mAP:
logger.info('mAP improved from {} to {}'.format(
best_mAP, avg_map))
best_mAP = avg_map
if last_model is not None:
os.remove(last_model)
fn = os.path.join(
c['model_dir'],
'{}_epoch{}_mAP{:.3}_preci{:.3}_recall{:.3}.pdparams'.
format(prefix, epoch, avg_map, val_preci, val_recall))
paddle.save(model.state_dict(), fn)
last_model = fn
else:
logger.info(
f'mAP {avg_map} did not improved from {best_mAP}')
if step % c['lr_dec_per_step'] == 0 and step != 0:
if optimizer.get_lr() <= 3e-6:
factor = 0.95
else:
factor = 0.1
optimizer.set_lr(optimizer.get_lr() * factor)
logger.info('decreased lr to {}'.format(optimizer.get_lr()))
step=epoch,
value=avg_map)
log_writer.add_scalar(tag="eval auc",
step=epoch,
value=auc_score)
log_writer.add_scalar(tag="eval dprime",
step=epoch,
value=dprime)
model.train()
model.clear_gradients()
if avg_map > best_mAP:
print('mAP improved from {} to {}'.format(
best_mAP, avg_map))
best_mAP = avg_map
fn = os.path.join(
c['model_dir'],
f'{prefix}_epoch{epoch}_mAP{avg_map:.3}.pdparams')
paddle.save(model.state_dict(), fn)
else:
print(f'mAP {avg_map} did not improved from {best_mAP}')
if step % c['lr_dec_per_step'] == 0 and step != 0:
if optimizer.get_lr() <= 1e-6:
factor = 0.95
else:
factor = 0.8
optimizer.set_lr(optimizer.get_lr() * factor)
print('decreased lr to {}'.format(optimizer.get_lr()))
msg += f', lr:{optimizer.get_lr():.2}'
msg += f', elapsed:{elapsed:.3}h'
msg += f', remained:{remain:.3}h'
if batch_id % config['log_step'] == 0 and local_rank == 0:
logger.info(msg)
if step % config['checkpoint_step'] == 0 and local_rank == 0:
fn = os.path.join(config['model_dir'],
f'{prefix}_checkpoint_epoch{epoch}.tar')
obj = {
'model': model.state_dict(),
'loss': loss_fn.state_dict(),
'opti': optimizer.state_dict(),
'lr': optimizer.get_lr()
}
paddle.save(obj, fn)
if step != 0 and step % config[
'eval_step'] == 0 and local_rank == 0:
result, min_dcf = compute_eer(config, model)
eer = result.eer
model.train()
model.clear_gradients()
if eer < best_eer:
logger.info('eer improved from {} to {}'.format(
best_eer, eer))
best_eer = eer