Update metrics

exps/basic/xmain.py

@@ -69,10 +69,13 @@ def main(args):
         weight_decay=args.weight_decay,
     )
     objective = xmisc.nested_call_by_yaml(args.loss_config)
+    metric = xmisc.nested_call_by_yaml(args.metric_config)
 
     logger.log("The optimizer is:\n{:}".format(optimizer))
     logger.log("The objective is {:}".format(objective))
-    logger.log("The iters_per_epoch={:}".format(iters_per_epoch))
+    logger.log("The metric is {:}".format(metric))
+    logger.log("The iters_per_epoch = {:}, estimated epochs = {:}".format(
+      iters_per_epoch, args.steps // iters_per_epoch))
 
     model, objective = torch.nn.DataParallel(model).cuda(), objective.cuda()
     scheduler = xmisc.LRMultiplier(
@@ -99,6 +102,7 @@ def main(args):
         loss.backward()
         optimizer.step()
         scheduler.step()
+
         if xiter % iters_per_epoch == 0:
             logger.log("TRAIN [{:}] loss = {:.6f}".format(iter_str, loss.item()))
 
@@ -123,6 +127,7 @@ if __name__ == "__main__":
     parser.add_argument("--model_config", type=str, help="The path to the model config")
     parser.add_argument("--optim_config", type=str, help="The optimizer config file.")
     parser.add_argument("--loss_config", type=str, help="The loss config file.")
+    parser.add_argument("--metric_config", type=str, help="The metric config file.")
     parser.add_argument(
         "--train_data_config", type=str, help="The training dataset config path."
     )