diff --git a/config.py b/config.py
index d6817bd..7fb9fc4 100755
--- a/config.py
+++ b/config.py
@@ -24,6 +24,7 @@ def get_seg_config():
 
         parser.add_argument('--augmentation_flag', type=bool, default=True, help='if true, use augmentation method in train set')
         parser.add_argument('--n_splits', type=int, default=5, help='n_splits_fold')
+        parser.add_argument('--mask_only_flag', type=bool, default=False, help='if true, use masked data only.')
 
         # model set 
         parser.add_argument('--model_name', type=str, default='unet_resnet34', \
diff --git a/datasets/steel_dataset.py b/datasets/steel_dataset.py
index ebcb2ea..5b453cf 100644
--- a/datasets/steel_dataset.py
+++ b/datasets/steel_dataset.py
@@ -9,6 +9,7 @@
 from sklearn.model_selection import train_test_split, StratifiedKFold
 import torch
 from torch.utils.data import DataLoader, Dataset, sampler
+from torch.utils.data.dataloader import default_collate
 from torchvision import transforms
 from albumentations.pytorch import ToTensor
 import sys
@@ -127,6 +128,22 @@ def get_transforms(phase, image, mask, mean, std):
     return image, mask
 
 
+def mask_only_collate_fun(batch):
+    """自定义collate_fn函数，用于从一个batch中去除没有掩膜的样本
+    """
+    batch_scale = list()
+    for image, mask in batch:
+        pair = list()
+        mask_pixel_num = torch.sum(mask)
+        if mask_pixel_num > 0:
+            pair.append(image)
+            pair.append(mask)
+            batch_scale.append(pair)
+    batch_scale = default_collate(batch_scale)
+
+    return batch_scale
+
+
 def provider(
     data_folder,
     df_path,
@@ -135,6 +152,7 @@ def provider(
     batch_size=8,
     num_workers=4,
     n_splits=0,
+    mask_only=False
 ):
     """返回数据加载器，用于分割模型
 
@@ -146,6 +164,7 @@ def provider(
         batch_size
         num_workers
         n_split: 交叉验证折数，为1时不使用交叉验证
+        mask_only: 是否只在有掩膜的样本上训练分割模型
     
     Return:
         dataloadrs: list，该list中的每一个元素为list，元素list中保存训练集和验证集
@@ -175,20 +194,41 @@ def provider(
     for df_index, (train_df, val_df) in enumerate(zip(train_dfs, val_dfs)):
         train_dataset = SteelDataset(train_df, data_folder, mean, std, 'train')
         val_dataset = SteelDataset(val_df, data_folder, mean, std, 'val')
-        train_dataloader = DataLoader(
-            train_dataset, 
-            batch_size=batch_size, 
-            num_workers=num_workers, 
-            pin_memory=True, 
-            shuffle=True
+        if mask_only:
+            # 只在有掩膜的样本上训练
+            print('Segmentation modle: only masked data.')
+            train_dataloader = DataLoader(
+                train_dataset, 
+                batch_size=batch_size, 
+                num_workers=num_workers, 
+                collate_fn=mask_only_collate_fun,
+                pin_memory=True, 
+                shuffle=True
+                )
+            val_dataloader = DataLoader(
+                val_dataset,
+                batch_size=batch_size, 
+                num_workers=num_workers,
+                collate_fn=mask_only_collate_fun, 
+                pin_memory=True, 
+                shuffle=True
+            )
+        else:    
+            print('Segmentation model: all data.')
+            train_dataloader = DataLoader(
+                train_dataset, 
+                batch_size=batch_size, 
+                num_workers=num_workers, 
+                pin_memory=True, 
+                shuffle=True
+                )
+            val_dataloader = DataLoader(
+                val_dataset,
+                batch_size=batch_size, 
+                num_workers=num_workers, 
+                pin_memory=True, 
+                shuffle=True
             )
-        val_dataloader = DataLoader(
-            val_dataset,
-            batch_size=batch_size, 
-            num_workers=num_workers, 
-            pin_memory=True, 
-            shuffle=True
-        )
         dataloaders.append([train_dataloader, val_dataloader])
 
     return dataloaders
diff --git a/train_segment.py b/train_segment.py
index 34baa0d..ec76da2 100644
--- a/train_segment.py
+++ b/train_segment.py
@@ -14,6 +14,8 @@
 from utils.set_seed import seed_torch
 from config import get_seg_config
 from solver import Solver
+from utils.loss import MultiClassesSoftBCEDiceLoss
+
 
 class TrainVal():
     def __init__(self, config, fold):
@@ -36,7 +38,8 @@ def __init__(self, config, fold):
         self.solver = Solver(self.model)
 
         # 加载损失函数
-        self.criterion = torch.nn.BCEWithLogitsLoss()
+        # self.criterion = torch.nn.BCEWithLogitsLoss()
+        self.criterion = MultiClassesSoftBCEDiceLoss(classes_num=4, size_average=True, weight=[0.75, 0.25])
 
         # 创建保存权重的路径
         self.model_path = os.path.join(config.save_path, config.model_name)
@@ -145,9 +148,18 @@ def validation(self, valid_loader):
 
 if __name__ == "__main__":
     config = get_seg_config()
-    mean=(0.485, 0.456, 0.406)
-    std=(0.229, 0.224, 0.225)
-    dataloaders = provider(config.dataset_root, os.path.join(config.dataset_root, 'train.csv'), mean, std, config.batch_size, config.num_workers, config.n_splits)
+    mean = (0.485, 0.456, 0.406)
+    std = (0.229, 0.224, 0.225)
+    dataloaders = provider(
+        config.dataset_root, 
+        os.path.join(config.dataset_root, 'train.csv'),
+        mean, 
+        std,
+        config.batch_size, 
+        config.num_workers, 
+        config.n_splits, 
+        mask_only=config.mask_only_flag
+        )
     for fold_index, [train_loader, valid_loader] in enumerate(dataloaders):
         if fold_index != 1:
             continue
diff --git a/utils/loss.py b/utils/loss.py
index 8df7817..209bba7 100644
--- a/utils/loss.py
+++ b/utils/loss.py
@@ -32,3 +32,131 @@ def forward(self, logit, truth):
             # raise NotImplementedError
 
         return loss
+
+
+# reference https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation/discussion/101429#latest-588288
+class SoftDiceLoss(nn.Module):
+    """二分类加权dice损失
+    """
+    def __init__(self, size_average=True, weight=[0.2, 0.8]):
+        """
+        weight: 各类别权重
+        """
+        super(SoftDiceLoss, self).__init__()
+        self.size_average = size_average
+        self.weight = torch.FloatTensor(weight)
+    
+    def forward(self, logit_pixel, truth_pixel):
+        batch_size = len(logit_pixel)
+        logit = logit_pixel.view(batch_size, -1)
+        truth = truth_pixel.view(batch_size, -1)
+        assert(logit.shape == truth.shape)
+
+        loss = self.soft_dice_criterion(logit, truth)
+
+        if self.size_average:
+            loss = loss.mean()
+        return loss
+
+    def soft_dice_criterion(self, logit, truth):
+        batch_size = len(logit)
+        probability = torch.sigmoid(logit)
+
+        p = probability.view(batch_size, -1)
+        t = truth.view(batch_size, -1)
+        # 向各样本分配所属类别的权重
+        w = truth.detach()
+        self.weight = self.weight.type_as(logit)
+        w = w * (self.weight[1] - self.weight[0]) + self.weight[0]
+
+        p = w * (p*2 - 1)  #convert to [0,1] --> [-1, 1]
+        t = w * (t*2 - 1)
+
+        intersection = (p * t).sum(-1)
+        union =  (p * p).sum(-1) + (t * t).sum(-1)
+        dice  = 1 - 2 * intersection/union
+
+        loss = dice
+        return loss
+
+
+# reference https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation/discussion/101429#latest-588288
+class SoftBceLoss(nn.Module):
+    """二分类交叉熵加权损失
+    """
+    def __init__(self, weight=[0.25, 0.75]):
+        super(SoftBceLoss, self).__init__()
+        self.weight = weight
+
+    def forward(self, logit_pixel, truth_pixel):
+        logit = logit_pixel.view(-1)
+        truth = truth_pixel.view(-1)
+        assert(logit.shape==truth.shape)
+
+        loss = F.binary_cross_entropy_with_logits(logit, truth, reduction='none')
+        if self.weight:
+            pos = (truth>0.5).float()
+            neg = (truth<0.5).float()
+            # pos_weight = pos.sum().item() + 1e-12
+            # neg_weight = neg.sum().item() + 1e-12
+            # loss = (self.weight[0]*pos*loss/pos_weight + self.weight[1]*neg*loss/neg_weight).sum()
+            loss = (self.weight[1]*pos*loss + self.weight[0]*neg*loss).mean()
+        else:
+            loss = loss.mean()
+        return loss
+
+
+class SoftBCEDiceLoss(nn.Module):
+    """加权BCE+DiceLoss
+    """
+    def __init__(self, size_average=True, weight=[0.2, 0.8]):
+        """
+        weight: weight[0]为负类的权重，weight[1]为正类的权重
+        """
+        super(SoftBCEDiceLoss, self).__init__()
+        self.size_average = size_average
+        self.weight = weight
+        self.bce_loss = nn.BCEWithLogitsLoss(size_average=self.size_average, pos_weight=torch.tensor(self.weight[1]))
+        # self.bce_loss = SoftBceLoss(weight=weight)
+        self.softdiceloss = SoftDiceLoss(size_average=self.size_average, weight=weight)
+    
+    def forward(self, input, target):
+        soft_bce_loss = self.bce_loss(input, target)
+        soft_dice_loss = self.softdiceloss(input, target)
+        loss = soft_bce_loss + soft_dice_loss
+
+        return loss
+
+
+class MultiClassesSoftBCEDiceLoss(nn.Module):
+    def __init__(self, classes_num=4, size_average=True, weight=[0.2, 0.8]):
+        super(MultiClassesSoftBCEDiceLoss, self).__init__()
+        self.classes_num = classes_num
+        self.size_average = size_average
+        self.weight = weight
+        self.soft_bce_dice_loss = SoftBCEDiceLoss(size_average=self.size_average, weight=self.weight)
+    
+    def forward(self, input, target):
+        """
+        Args:
+            input: tensor, [batch_size, classes_num, height, width]
+            target: tensor, [batch_size, classes_num, height, width]
+        """
+        loss = 0
+        for class_index in range(self.classes_num):
+            input_single_class = input[:, class_index, :, :]
+            target_singlt_class = target[:, class_index, :, :]
+            single_class_loss = self.soft_bce_dice_loss(input_single_class, target_singlt_class)
+            loss += single_class_loss
+        
+        loss /= self.classes_num
+
+        return loss
+
+
+if __name__ == "__main__":
+    input = torch.Tensor(4, 4, 256, 1600)
+    target = torch.Tensor(4, 4, 256, 1600)
+    criterion = MultiClassesSoftBCEDiceLoss(4, True)
+    loss = criterion(input, target)
+