diff --git a/pdseg/lovasz_losses.py b/pdseg/lovasz_losses.py
index c6ffe66aaf4eaa2880ba2b8c6bfb70d200cb09c7..61d1876de2109b14dbfb3495621c1190b84e4df2 100755
--- a/pdseg/lovasz_losses.py
+++ b/pdseg/lovasz_losses.py
@@ -18,6 +18,7 @@ from __future__ import division
 from __future__ import print_function
 import paddle.fluid as fluid
 import numpy as np
+from utils.config import cfg
 
 
 def _cumsum(x):
@@ -203,3 +204,43 @@ def flatten_probas(probas, labels, ignore=None):
     vprobas = fluid.layers.gather(probas, indexs[:, 0])
     vlabels = fluid.layers.gather(labels, indexs[:, 0])
     return vprobas, vlabels
+
+
+def multi_lovasz_softmax_loss(logits, label, ignore_mask=None):
+    if isinstance(logits, tuple):
+        avg_loss = 0
+        for i, logit in enumerate(logits):
+            if label.shape[2] != logit.shape[2] or label.shape[
+                    3] != logit.shape[3]:
+                logit_label = fluid.layers.resize_nearest(
+                    label, logit.shape[2:])
+            else:
+                logit_label = label
+            logit_mask = (logit_label.astype('int32') !=
+                          cfg.DATASET.IGNORE_INDEX).astype('int32')
+            probas = fluid.layers.softmax(logit, axis=1)
+            loss = lovasz_softmax(probas, logit_label, ignore=logit_mask)
+            avg_loss += cfg.MODEL.MULTI_LOSS_WEIGHT[i] * loss
+    else:
+        probas = fluid.layers.softmax(logits, axis=1)
+        avg_loss = lovasz_softmax(probas, label, ignore=ignore_mask)
+    return avg_loss
+
+
+def multi_lovasz_hinge_loss(logits, label, ignore_mask=None):
+    if isinstance(logits, tuple):
+        avg_loss = 0
+        for i, logit in enumerate(logits):
+            if label.shape[2] != logit.shape[2] or label.shape[
+                    3] != logit.shape[3]:
+                logit_label = fluid.layers.resize_nearest(
+                    label, logit.shape[2:])
+            else:
+                logit_label = label
+            logit_mask = (logit_label.astype('int32') !=
+                          cfg.DATASET.IGNORE_INDEX).astype('int32')
+            loss = lovasz_hinge(logit, logit_label, ignore=logit_mask)
+            avg_loss += cfg.MODEL.MULTI_LOSS_WEIGHT[i] * loss
+    else:
+        avg_loss = lovasz_hinge(logits, label, ignore=ignore_mask)
+    return avg_loss
diff --git a/pdseg/models/model_builder.py b/pdseg/models/model_builder.py
index a15e351538871be005e135faef0a2c8bcb1d166a..44127436061b48c7b735fb2f85f6cc5b3f4970e3 100644
--- a/pdseg/models/model_builder.py
+++ b/pdseg/models/model_builder.py
@@ -24,8 +24,7 @@ from utils.config import cfg
 from loss import multi_softmax_with_loss
 from loss import multi_dice_loss
 from loss import multi_bce_loss
-from lovasz_losses import lovasz_hinge
-from lovasz_losses import lovasz_softmax
+from lovasz_losses import multi_lovasz_hinge_loss, multi_lovasz_softmax_loss
 from models.modeling import deeplab, unet, icnet, pspnet, hrnet, fast_scnn, ocrnet
 
 
@@ -189,13 +188,12 @@ def build_model(main_prog, start_prog, phase=ModelPhase.TRAIN):
                     valid_loss.append("bce_loss")
                 if "lovasz_hinge_loss" in loss_type:
                     avg_loss_list.append(
-                        lovasz_hinge(logits, label, ignore=mask))
+                        multi_lovasz_hinge_loss(logits, label, mask))
                     loss_valid = True
                     valid_loss.append("lovasz_hinge_loss")
                 if "lovasz_softmax_loss" in loss_type:
-                    probas = fluid.layers.softmax(logits, axis=1)
                     avg_loss_list.append(
-                        lovasz_softmax(probas, label, ignore=mask))
+                        multi_lovasz_softmax_loss(logits, label, mask))
                     loss_valid = True
                     valid_loss.append("lovasz_softmax_loss")
                 if not loss_valid: