def log_validation_results(engine):
data_list = [train_loader, val_loader]
name_list = ['train', 'val']
eval_list = [train_evaluator, val_evaluator]
for data, name, evl in zip(data_list, name_list, eval_list):
evl.run(data)
metrics_info = evl.state.metrics["multitask"]
for m, val in metrics_info['metrics'].items():
writer.add_scalar(name + '_metrics/{}'.format(m), val,
engine.state.epoch)
for m, val in metrics_info['summaries'].items():
writer.add_scalar(name + '_summary/{}'.format(m), val,
engine.state.epoch)
logger(
name +
": Validation Results - Epoch: {}".format(engine.state.epoch))
print_summar_table(logger, attr_name, metrics_info['logger'])
# Update Learning Rate
if name == 'train':
scheduler.step(metrics_info['logger']['attr']['ap'][-1])
def save(self, epoch, metrics_info):
ap = metrics_info['logger']['attr']['ap'][-1]
if ap > self.max_ap:
self.max_ap = ap
save_file_path = os.path.join(self.save_root, 'ap{}'.format(ap))
torch.save(s_net.module.state_dict(), save_file_path)
logger_file("val: Validation Results - Epoch: {} - LR: {}".format(epoch, optimizer.optimizer.param_groups[0]['lr']))
print_summar_table(logger_file, attr_name, metrics_info['logger'])
logger_file('AP:%0.3f' % metrics_info['logger']['attr']['ap'][-1])
def test(net, epoch):
net.eval()
data_list = [trainloader, testloader]
name_list = ['train', 'val']
eval_list = [train_evaluator, val_evaluator]
for data, name, evl in zip(data_list, name_list, eval_list):
evl.run(data)
metrics_info = evl.state.metrics["multitask"]
logger(name + ": Validation Results - Epoch: {}".format(epoch))
print_summar_table(logger, attr_name, metrics_info['logger'])
if args.scheduler == 'pleau':
optimizer.step(metrics_info['logger']['attr']['ap'][-1])
else:
optimizer.step()
return metrics_info
