Update deephashloss.py

ppcls/loss/deephashloss.py

@@ -24,30 +24,28 @@ class DSHSDLoss(nn.Layer):
     # [DSHSD] epoch:135, bit:48,  dataset:imagenet,   MAP:0.647, Best MAP: 0.647
     """
     def __init__(self, n_class, bit, alpha, multi_label=False):
-        super(DSHSDLoss, self).__init__()
-        self.m = 2 * bit     
+        super(DSHSDLoss, self).__init__()   
         self.alpha = alpha
         self.multi_label = multi_label
-        self.n_class = n_class
-        self.fc = paddle.nn.Linear(bit, n_class, bias_attr=False)
-
+        
     def forward(self, input, label):        
         feature = input["features"]
-        feature = feature.tanh().astype("float32")
-
+        logits  = input["logits"]
+        
         dist = paddle.sum(
                     paddle.square((paddle.unsqueeze(feature, 1) - paddle.unsqueeze(feature, 0))), 
                     axis=2)
         
         # label to ont-hot
         label = paddle.flatten(label)
-        label = paddle.nn.functional.one_hot(label,  self.n_class).astype("float32")
+        n_class = logits.shape[1]
+        label = paddle.nn.functional.one_hot(label,  n_class).astype("float32")
 
         s = (paddle.matmul(label, label, transpose_y=True) == 0).astype("float32")
-        Ld = (1 - s) / 2 * dist + s / 2 * (self.m - dist).clip(min=0)
+        margin = 2 * feature.shape[1]
+        Ld = (1 - s) / 2 * dist + s / 2 * (margin - dist).clip(min=0)
         Ld = Ld.mean()
         
-        logits = self.fc(feature)
         if self.multi_label:
             # multiple labels classification loss
             Lc = (logits - label * logits + ((1 + (-logits).exp()).log())).sum(axis=1).mean()