Logging improvements

core/test.py

@@ -39,6 +39,6 @@ def test(model, data_loader, device, loggi, flag):
     acc = acc_ / n_total
     acc_domain = acc_domain_ / n_total
 
-    loggi.info("Avg Loss = {:.6f}, Avg Accuracy = {:.2%}, {}/{}, Avg Domain Accuracy = {:2%}".format(loss, acc, acc_, n_total, acc_domain))
+    loggi.info("{}: Avg Loss = {:.6f}, Avg Accuracy = {:.2%}, {}/{}, Avg Domain Accuracy = {:2%}".format(flag, loss, acc, acc_, n_total, acc_domain))
 
     return loss, acc, acc_domain

core/train.py

@@ -217,6 +217,7 @@ def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_
         if ((epoch + 1) % params.eval_step == 0):
             tgt_test_loss, tgt_acc, tgt_acc_domain = test(model, tgt_data_loader_eval, device, loggi, flag='target')
             src_test_loss, src_acc, src_acc_domain = test(model, src_data_loader, device, loggi, flag='source')
+            loggi.info('\n')
             if tgt_acc > bestAcc:
                 bestAcc = tgt_acc
                 bestAccS = src_acc
@@ -232,7 +233,7 @@ def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_
 
     # save final model
     save_model(model, params.model_root, params.src_dataset + '-' + params.tgt_dataset + "-dann-final.pt")
-    loggi.info('============ Summary ============= \n')
+    loggi.info('\n============ Summary ============= \n')
     loggi.info('Accuracy of the %s dataset: %f' % (params.src_dataset, bestAccS))
     loggi.info('Accuracy of the %s dataset: %f' % (params.tgtc_dataset, bestAcc))