partial model initialization

layers/tacotron.py

@@ -339,11 +339,10 @@ class Decoder(nn.Module):
 
     def _reshape_memory(self, memory):
         B = memory.shape[0]
-        if memory is not None:
-            # Grouping multiple frames if necessary
-            if memory.size(-1) == self.memory_dim:
-                memory = memory.contiguous()
-                memory = memory.view(B, memory.size(1) // self.r, -1)
+        # Grouping multiple frames if necessary
+        if memory.size(-1) == self.memory_dim:
+            memory = memory.contiguous()
+            memory = memory.view(B, memory.size(1) // self.r, -1)
         # Time first (T_decoder, B, memory_dim)
         memory = memory.transpose(0, 1)
         return memory
@@ -370,7 +369,8 @@ class Decoder(nn.Module):
         T = inputs.size(1)
         # Run greedy decoding if memory is None
         greedy = not self.training
-        memory = self._reshape_memory(memory)
+        if memory is not None:
+            memory = self._reshape_memory(memory)
         T_decoder = memory.size(0)
         # go frame as zeros matrix
         initial_memory = inputs.data.new(B, self.memory_dim * self.r).zero_()
@@ -461,4 +461,4 @@ class StopNet(nn.Module):
         outputs = self.dropout(inputs)
         outputs = self.linear(outputs)
         outputs = self.sigmoid(outputs)
-        return outputs
+        return outputs

train.py

@@ -401,6 +401,16 @@ def main(args):
     if args.restore_path:
         checkpoint = torch.load(args.restore_path)
         model.load_state_dict(checkpoint['model'])
+        # Partial initialization: if there is a mismatch with new and old layer, it is skipped.
+        # 1. filter out unnecessary keys
+        pretrained_dict = {
+            k: v
+            for k, v in checkpoint['model'].items() if k in model_dict
+        }
+        # 2. overwrite entries in the existing state dict
+        model_dict.update(pretrained_dict)
+        # 3. load the new state dict
+        model.load_state_dict(model_dict)
         if use_cuda:
             model = model.cuda()
             criterion.cuda()