get_scores() without batch

nnet/sad_rnn.py

@@ -184,20 +184,17 @@ class SAD_RNN():
 
         features, _ = features_server.load(show)
         
-        x = []
-        X = torch.tensor([]).to(device)
+        X = []
         for i in range(0, len(features) - self.duration, self.step):
-            x.append(features[i:i + self.duration])
+            X.append(features[i:i + self.duration])
             if i + self.step > len(features) - self.duration:
-                pad_size = self.batch_size - len(x)
+                pad_size = self.batch_size - len(X)
                 pad = [[[0] * self.input_size] * self.duration] * pad_size
-                x += pad
-            if len(x) == self.batch_size:
-                x = torch.Tensor(x)
-                x = x.to(device)
-                self.model.hidden = None
-                X = torch.cat((X, self.model(x)))
-                x = []
+                X += pad
+
+        X = torch.Tensor(X).to(device)
+        self.model.hidden = None
+        X = self.model(X)
 
         o = numpy.asarray(X.squeeze(2).tolist())
         scores = numpy.zeros((len(o) * self.step + self.duration - self.step))