fix style

egs/librispeech/ASR/lstm_transducer_stateless/lstm.py

@@ -15,7 +15,7 @@
 # limitations under the License.
 
 import copy
-from typing import Optional, Tuple
+from typing import Tuple
 
 import torch
 from encoder_interface import EncoderInterface

egs/librispeech/ASR/pruned_transducer_stateless2/scaling.py

@@ -19,10 +19,10 @@ import collections
 from itertools import repeat
 from typing import Optional, Tuple
 
-from torch import Tensor, _VF
 import torch
 import torch.backends.cudnn.rnn as rnn
 import torch.nn as nn
+from torch import _VF, Tensor
 
 
 def _ntuple(n):
@@ -155,7 +155,7 @@ class BasicNorm(torch.nn.Module):
     def forward(self, x: Tensor) -> Tensor:
         assert x.shape[self.channel_dim] == self.num_channels
         scales = (
-            torch.mean(x**2, dim=self.channel_dim, keepdim=True)
+            torch.mean(x ** 2, dim=self.channel_dim, keepdim=True)
             + self.eps.exp()
         ) ** -0.5
         return x * scales
@@ -208,12 +208,12 @@ class ScaledLinear(nn.Linear):
 
     def _reset_parameters(self, initial_speed: float):
         std = 0.1 / initial_speed
-        a = (3**0.5) * std
+        a = (3 ** 0.5) * std
         nn.init.uniform_(self.weight, -a, a)
         if self.bias is not None:
             nn.init.constant_(self.bias, 0.0)
         fan_in = self.weight.shape[1] * self.weight[0][0].numel()
-        scale = fan_in**-0.5  # 1/sqrt(fan_in)
+        scale = fan_in ** -0.5  # 1/sqrt(fan_in)
         with torch.no_grad():
             self.weight_scale += torch.tensor(scale / std).log()
 
@@ -257,12 +257,12 @@ class ScaledConv1d(nn.Conv1d):
 
     def _reset_parameters(self, initial_speed: float):
         std = 0.1 / initial_speed
-        a = (3**0.5) * std
+        a = (3 ** 0.5) * std
         nn.init.uniform_(self.weight, -a, a)
         if self.bias is not None:
             nn.init.constant_(self.bias, 0.0)
         fan_in = self.weight.shape[1] * self.weight[0][0].numel()
-        scale = fan_in**-0.5  # 1/sqrt(fan_in)
+        scale = fan_in ** -0.5  # 1/sqrt(fan_in)
         with torch.no_grad():
             self.weight_scale += torch.tensor(scale / std).log()
 
@@ -326,12 +326,12 @@ class ScaledConv2d(nn.Conv2d):
 
     def _reset_parameters(self, initial_speed: float):
         std = 0.1 / initial_speed
-        a = (3**0.5) * std
+        a = (3 ** 0.5) * std
         nn.init.uniform_(self.weight, -a, a)
         if self.bias is not None:
             nn.init.constant_(self.bias, 0.0)
         fan_in = self.weight.shape[1] * self.weight[0][0].numel()
-        scale = fan_in**-0.5  # 1/sqrt(fan_in)
+        scale = fan_in ** -0.5  # 1/sqrt(fan_in)
         with torch.no_grad():
             self.weight_scale += torch.tensor(scale / std).log()
 
@@ -408,9 +408,9 @@ class ScaledLSTM(nn.LSTM):
 
     def _reset_parameters(self, initial_speed: float):
         std = 0.1 / initial_speed
-        a = (3**0.5) * std
+        a = (3 ** 0.5) * std
         fan_in = self.input_size
-        scale = fan_in**-0.5
+        scale = fan_in ** -0.5
         v = scale / std
         for idx, name in enumerate(self._flat_weights_names):
             if "weight" in name:
@@ -864,8 +864,8 @@ def _test_basic_norm():
     y = m(x)
 
     assert y.shape == x.shape
-    x_rms = (x**2).mean().sqrt()
-    y_rms = (y**2).mean().sqrt()
+    x_rms = (x ** 2).mean().sqrt()
+    y_rms = (y ** 2).mean().sqrt()
     print("x rms = ", x_rms)
     print("y rms = ", y_rms)
     assert y_rms < x_rms