fix loss info and typo (Closes GH-22).

lib/modeling/loss_aggregator.py

@@ -59,7 +59,7 @@ class LossAggregator():
                 loss, info = loss_func(**v)
                 for name, value in info.items():
                     loss_info['scalar/%s/%s' % (k, name)] = value
-                loss = loss.mean() * loss_func.loss_term_weights
+                loss = loss.mean() * loss_func.loss_term_weight
                 loss_sum += loss
 
             else:

lib/modeling/losses/base.py

@@ -29,14 +29,19 @@ class BaseLoss(nn.Module):
     """
     Base class for all losses.
 
-    Your loss should also subclass this class. 
-
-    Attribute:
-        loss_term_weights: the weight of the loss.
-        info: the loss info.
+    Your loss should also subclass this class.
     """
-    loss_term_weights = 1.0
-    info = Odict()
+
+    def __init__(self, loss_term_weight=1.0):
+        """
+        Initialize the base class.
+
+        Args:
+            loss_term_weight: the weight of the loss term.
+        """
+        super(BaseLoss, self).__init__()
+        self.loss_term_weight = loss_term_weight
+        self.info = Odict()
 
     def forward(self, logits, labels):
         """

lib/modeling/losses/softmax.py

@@ -5,15 +5,13 @@ from .base import BaseLoss
 
 
 class CrossEntropyLoss(BaseLoss):
-    def __init__(self, scale=2**4, label_smooth=True, eps=0.1, loss_term_weights=1.0, log_accuracy=False):
-        super(CrossEntropyLoss, self).__init__()
+    def __init__(self, scale=2**4, label_smooth=True, eps=0.1, loss_term_weight=1.0, log_accuracy=False):
+        super(CrossEntropyLoss, self).__init__(loss_term_weight)
         self.scale = scale
         self.label_smooth = label_smooth
         self.eps = eps
         self.log_accuracy = log_accuracy
 
-        self.loss_term_weights = loss_term_weights
-
     def forward(self, logits, labels):
         """
             logits: [n, p, c]

lib/modeling/losses/triplet.py

@@ -5,12 +5,10 @@ from .base import BaseLoss, gather_and_scale_wrapper
 
 
 class TripletLoss(BaseLoss):
-    def __init__(self, margin, loss_term_weights=1.0):
-        super(TripletLoss, self).__init__()
+    def __init__(self, margin, loss_term_weight=1.0):
+        super(TripletLoss, self).__init__(loss_term_weight)
         self.margin = margin
 
-        self.loss_term_weights = loss_term_weights
-
     @gather_and_scale_wrapper
     def forward(self, embeddings, labels):
         # embeddings: [n, p, c], label: [n]