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()))
else:
best_mAP = 0.0
step = 0
for epoch in range(start_epoch, epoch_num):
avg_loss = 0.0
avg_preci = 0.0
avg_recall = 0.0
model.train()
model.clear_gradients()
t0 = time.time()
for batch_id, (x, y) in enumerate(train_loader()):
if step < warm_steps:
optimizer.set_lr(lrs[step])
x.stop_gradient = False
if c['balanced_sampling']:
x = x.squeeze()
y = y.squeeze()
x = x.unsqueeze((1))
if c['mixup']:
mixed_x, mixed_y = mixup_data(x, y, c['mixup_alpha'])
logits = model(mixed_x)
loss_val = loss_fn(logits, mixed_y)
loss_val.backward()
else:
logits = model(x)
loss_val = bce_loss(logits, y)
loss_val.backward()
optimizer.step()
p.stop_gradient = True
if not isinstance(p, nn.BatchNorm1D):
p.stop_gradient = True
for epoch in range(start_epoch, epoch_num):
avg_loss = 0.0
avg_acc = 0.0
model.train()
model.clear_gradients()
t0 = time.time()
if config['max_lr'] > config['base_lr']:
lr = get_lr(epoch - start_epoch, config['base_lr'],
config['max_lr'], config['half_cycle'],
config['reverse_lr'])
optimizer.set_lr(lr)
logger.info(f'Setting lr to {lr}')
for batch_id, (x, y) in enumerate(train_loader()):
x_mel = transforms(x)
logits = model(x_mel)
loss, pred = loss_fn(logits, y)
loss.backward()
optimizer.step()
model.clear_gradients()
acc = np.mean(np.argmax(pred.numpy(), axis=1) == y.numpy())
if batch_id < 100:
avg_acc = acc
avg_loss = loss.numpy()[0]
