Merge branch 'fix' into 'master'

fix the bug 1/x=0 in python2

See merge request !41

parakeet/models/fastspeech/fastspeech.py

@@ -62,7 +62,7 @@ class FastSpeech(dg.Layer):
             dropout=0.1)
         self.weight = fluid.ParamAttr(
             initializer=fluid.initializer.XavierInitializer())
-        k = math.sqrt(1 / cfg['fs_hidden_size'])
+        k = math.sqrt(1.0 / cfg['fs_hidden_size'])
         self.bias = fluid.ParamAttr(initializer=fluid.initializer.Uniform(
             low=-k, high=k))
         self.mel_linear = dg.Linear(

parakeet/models/fastspeech/length_regulator.py

@@ -115,7 +115,7 @@ class DurationPredictor(dg.Layer):
         self.filter_size = filter_size
         self.dropout = dropout
 
-        k = math.sqrt(1 / self.input_size)
+        k = math.sqrt(1.0 / self.input_size)
         self.conv1 = Conv1D(
             num_channels=self.input_size,
             num_filters=self.out_channels,
@@ -126,7 +126,7 @@ class DurationPredictor(dg.Layer):
             bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
                 low=-k, high=k)))
         #data_format='NTC')
-        k = math.sqrt(1 / self.out_channels)
+        k = math.sqrt(1.0 / self.out_channels)
         self.conv2 = Conv1D(
             num_channels=self.out_channels,
             num_filters=self.out_channels,
@@ -142,7 +142,7 @@ class DurationPredictor(dg.Layer):
 
         self.weight = fluid.ParamAttr(
             initializer=fluid.initializer.XavierInitializer())
-        k = math.sqrt(1 / self.out_channels)
+        k = math.sqrt(1.0 / self.out_channels)
         self.bias = fluid.ParamAttr(initializer=fluid.initializer.Uniform(
             low=-k, high=k))
 

parakeet/models/transformer_tts/cbhg.py

@@ -46,7 +46,7 @@ class CBHG(dg.Layer):
         self.hidden_size = hidden_size
         self.projection_size = projection_size
         self.conv_list = []
-        k = math.sqrt(1 / projection_size)
+        k = math.sqrt(1.0 / projection_size)
         self.conv_list.append(
             Conv1D(
                 num_channels=projection_size,
@@ -58,7 +58,7 @@ class CBHG(dg.Layer):
                 bias_attr=fluid.ParamAttr(
                     initializer=fluid.initializer.Uniform(
                         low=-k, high=k))))
-        k = math.sqrt(1 / hidden_size)
+        k = math.sqrt(1.0 / hidden_size)
         for i in range(2, K + 1):
             self.conv_list.append(
                 Conv1D(
@@ -86,7 +86,7 @@ class CBHG(dg.Layer):
 
         conv_outdim = hidden_size * K
 
-        k = math.sqrt(1 / conv_outdim)
+        k = math.sqrt(1.0 / conv_outdim)
         self.conv_projection_1 = Conv1D(
             num_channels=conv_outdim,
             num_filters=hidden_size,
@@ -97,7 +97,7 @@ class CBHG(dg.Layer):
             bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
                 low=-k, high=k)))
 
-        k = math.sqrt(1 / hidden_size)
+        k = math.sqrt(1.0 / hidden_size)
         self.conv_projection_2 = Conv1D(
             num_channels=hidden_size,
             num_filters=projection_size,
@@ -121,7 +121,7 @@ class CBHG(dg.Layer):
 
         h_0 = np.zeros((batch_size, hidden_size // 2), dtype="float32")
         h_0 = dg.to_variable(h_0)
-        k = math.sqrt(1 / hidden_size)
+        k = math.sqrt(1.0 / hidden_size)
         self.fc_forward1 = dg.Linear(
             hidden_size,
             hidden_size // 2 * 3,
@@ -240,7 +240,7 @@ class Highwaynet(dg.Layer):
 
         self.gates = []
         self.linears = []
-        k = math.sqrt(1 / num_units)
+        k = math.sqrt(1.0 / num_units)
         for i in range(num_layers):
             self.linears.append(
                 dg.Linear(

parakeet/models/transformer_tts/decoder.py

@@ -55,7 +55,7 @@ class Decoder(dg.Layer):
             hidden_size=num_hidden * 2,
             output_size=num_hidden,
             dropout_rate=0.2)
-        k = math.sqrt(1 / num_hidden)
+        k = math.sqrt(1.0 / num_hidden)
         self.linear = dg.Linear(
             num_hidden,
             num_hidden,

parakeet/models/transformer_tts/encoderprenet.py

@@ -39,7 +39,7 @@ class EncoderPrenet(dg.Layer):
             param_attr=fluid.initializer.Normal(
                 loc=0.0, scale=1.0))
         self.conv_list = []
-        k = math.sqrt(1 / embedding_size)
+        k = math.sqrt(1.0 / embedding_size)
         self.conv_list.append(
             Conv1D(
                 num_channels=embedding_size,
@@ -52,7 +52,7 @@ class EncoderPrenet(dg.Layer):
                     initializer=fluid.initializer.Uniform(
                         low=-k, high=k)),
                 use_cudnn=use_cudnn))
-        k = math.sqrt(1 / num_hidden)
+        k = math.sqrt(1.0 / num_hidden)
         for _ in range(2):
             self.conv_list.append(
                 Conv1D(
@@ -78,7 +78,7 @@ class EncoderPrenet(dg.Layer):
         for i, layer in enumerate(self.batch_norm_list):
             self.add_sublayer("batch_norm_list_{}".format(i), layer)
 
-        k = math.sqrt(1 / num_hidden)
+        k = math.sqrt(1.0 / num_hidden)
         self.projection = dg.Linear(
             num_hidden,
             num_hidden,

parakeet/models/transformer_tts/post_convnet.py

@@ -48,7 +48,7 @@ class PostConvNet(dg.Layer):
         self.num_conv = num_conv
         self.batchnorm_last = batchnorm_last
         self.conv_list = []
-        k = math.sqrt(1 / (n_mels * outputs_per_step))
+        k = math.sqrt(1.0 / (n_mels * outputs_per_step))
         self.conv_list.append(
             Conv1D(
                 num_channels=n_mels * outputs_per_step,
@@ -62,7 +62,7 @@ class PostConvNet(dg.Layer):
                         low=-k, high=k)),
                 use_cudnn=use_cudnn))
 
-        k = math.sqrt(1 / num_hidden)
+        k = math.sqrt(1.0 / num_hidden)
         for _ in range(1, num_conv - 1):
             self.conv_list.append(
                 Conv1D(

parakeet/models/transformer_tts/prenet.py

@@ -33,7 +33,7 @@ class PreNet(dg.Layer):
         self.output_size = output_size
         self.dropout_rate = dropout_rate
 
-        k = math.sqrt(1 / input_size)
+        k = math.sqrt(1.0 / input_size)
         self.linear1 = dg.Linear(
             input_size,
             hidden_size,
@@ -41,7 +41,7 @@ class PreNet(dg.Layer):
                 initializer=fluid.initializer.XavierInitializer()),
             bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
                 low=-k, high=k)))
-        k = math.sqrt(1 / hidden_size)
+        k = math.sqrt(1.0 / hidden_size)
         self.linear2 = dg.Linear(
             hidden_size,
             output_size,

parakeet/modules/ffn.py

@@ -41,7 +41,7 @@ class PositionwiseFeedForward(dg.Layer):
         self.use_cudnn = use_cudnn
         self.dropout = dropout
 
-        k = math.sqrt(1 / d_in)
+        k = math.sqrt(1.0 / d_in)
         self.w_1 = Conv1D(
             num_channels=d_in,
             num_filters=num_hidden,
@@ -52,7 +52,7 @@ class PositionwiseFeedForward(dg.Layer):
             bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
                 low=-k, high=k)),
             use_cudnn=use_cudnn)
-        k = math.sqrt(1 / num_hidden)
+        k = math.sqrt(1.0 / num_hidden)
         self.w_2 = Conv1D(
             num_channels=num_hidden,
             num_filters=d_in,

parakeet/modules/multihead_attention.py

@@ -33,7 +33,7 @@ class Linear(dg.Layer):
         self.bias = is_bias
 
         if is_bias is not False:
-            k = math.sqrt(1 / in_features)
+            k = math.sqrt(1.0 / in_features)
             self.bias = fluid.ParamAttr(initializer=fluid.initializer.Uniform(
                 low=-k, high=k))