Merge branch 'master' into 'master'

fixes for wavenet and modules

See merge request !47

examples/deepvoice3/synthesis.py

@@ -101,6 +101,8 @@ if __name__ == "__main__":
         state, _ = dg.load_dygraph(args.checkpoint)
         dv3.set_dict(state)
 
+        # WARNING: don't forget to remove weight norm to re-compute each wrapped layer's weight
+        # removing weight norm also speeds up computation
         for layer in dv3.sublayers():
             if isinstance(layer, WeightNormWrapper):
                 layer.remove_weight_norm()

examples/wavenet/synthesis.py

@@ -21,6 +21,7 @@ from tensorboardX import SummaryWriter
 from paddle import fluid
 import paddle.fluid.dygraph as dg
 
+from parakeet.modules.weight_norm import WeightNormWrapper
 from parakeet.data import SliceDataset, TransformDataset, DataCargo, SequentialSampler, RandomSampler
 from parakeet.models.wavenet import UpsampleNet, WaveNet, ConditionalWavenet
 from parakeet.utils.layer_tools import summary
@@ -114,6 +115,12 @@ if __name__ == "__main__":
         print("Loading from {}.pdparams".format(args.checkpoint))
         model.set_dict(model_dict)
 
+        # WARNING: don't forget to remove weight norm to re-compute each wrapped layer's weight
+        # removing weight norm also speeds up computation
+        for layer in model.sublayers():
+            if isinstance(layer, WeightNormWrapper):
+                layer.remove_weight_norm()
+
         train_loader = fluid.io.DataLoader.from_generator(
             capacity=10, return_list=True)
         train_loader.set_batch_generator(train_cargo, place)

parakeet/models/wavenet/wavenet.py

@@ -313,6 +313,7 @@ class WaveNet(dg.Layer):
         """
         # Causal Conv
         if self.loss_type == "softmax":
+            x = F.clip(x, min=-1., max=0.99999)
             x = quantize(x, self.output_dim)
             x = self.embed(x)  # (B, T, C), T=1
         else:

parakeet/modules/customized.py

@@ -86,7 +86,7 @@ class Conv1D(dg.Conv2D):
                  stride=1,
                  padding=0,
                  dilation=1,
-                 groups=None,
+                 groups=1,
                  param_attr=None,
                  bias_attr=None,
                  use_cudnn=True,
@@ -128,7 +128,7 @@ class Conv1DTranspose(dg.Conv2DTranspose):
                  padding=0,
                  stride=1,
                  dilation=1,
-                 groups=None,
+                 groups=1,
                  param_attr=None,
                  bias_attr=None,
                  use_cudnn=True,
@@ -179,7 +179,7 @@ class Conv1DCell(Conv1D):
                  filter_size,
                  dilation=1,
                  causal=False,
-                 groups=None,
+                 groups=1,
                  param_attr=None,
                  bias_attr=None,
                  use_cudnn=True,
@@ -225,6 +225,12 @@ class Conv1DCell(Conv1D):
 
     def start_sequence(self):
         """Prepare the Conv1DCell to generate a new sequence, this method should be called before calling add_input multiple times.
+
+        WARNING: 
+            This method accesses `self.weight` directly. If a `Conv1DCell` object is wrapped in a `WeightNormWrapper`, make sure this method is called only after the `WeightNormWrapper`'s hook is called. 
+            `WeightNormWrapper` removes the wrapped layer's `weight`, add has a `weight_v` and `weight_g` to re-compute the wrapped layer's weight as $weight = weight_g * weight_v / ||weight_v||$. (Recomputing the `weight` is a hook before calling the wrapped layer's `forward` method.)
+            Whenever a `WeightNormWrapper`'s `forward` method is called, the wrapped layer's weight is updated. But when loading from a checkpoint, `weight_v` and `weight_g` are updated but the wrapped layer's weight is not, since it is no longer a `Parameter`. You should manually call `remove_weight_norm` or `hook` to re-compute the wrapped layer's weight before calling this method if you don't call `forward` first.
+            So when loading a model which uses `Conv1DCell` objects wrapped in `WeightNormWrapper`s, remember to call `remove_weight_norm` for all `WeightNormWrapper`s before synthesizing. Also, removing weight norm speeds up computation.
         """
         if not self.causal:
             raise ValueError(

parakeet/modules/weight_norm.py

@@ -151,7 +151,7 @@ def Conv1D(num_channels,
            stride=1,
            padding=0,
            dilation=1,
-           groups=None,
+           groups=1,
            param_attr=None,
            bias_attr=None,
            use_cudnn=True,
@@ -170,7 +170,7 @@ def Conv1DTranspose(num_channels,
                     padding=0,
                     stride=1,
                     dilation=1,
-                    groups=None,
+                    groups=1,
                     param_attr=None,
                     bias_attr=None,
                     use_cudnn=True,
@@ -188,7 +188,7 @@ def Conv1DCell(num_channels,
                filter_size,
                dilation=1,
                causal=False,
-               groups=None,
+               groups=1,
                param_attr=None,
                bias_attr=None,
                use_cudnn=True,
@@ -207,7 +207,7 @@ def Conv2D(num_channels,
            stride=1,
            padding=0,
            dilation=1,
-           groups=None,
+           groups=1,
            param_attr=None,
            bias_attr=None,
            use_cudnn=True,
@@ -228,7 +228,7 @@ def Conv2DTranspose(num_channels,
                     padding=0,
                     stride=1,
                     dilation=1,
-                    groups=None,
+                    groups=1,
                     param_attr=None,
                     bias_attr=None,
                     use_cudnn=True,