From 8955e5efd399a2719f57adc23f43726c2c4cfb67 Mon Sep 17 00:00:00 2001
From: WongLaw <mailoflawrence@gmail.com>
Date: Mon, 9 Jan 2023 04:03:17 +0000
Subject: [PATCH] vits_init, test=tts

---
 paddlespeech/t2s/models/vits/vits.py | 53 ++--------------------------
 1 file changed, 3 insertions(+), 50 deletions(-)

diff --git a/paddlespeech/t2s/models/vits/vits.py b/paddlespeech/t2s/models/vits/vits.py
index 05ec93e80..afe1e8dd5 100644
--- a/paddlespeech/t2s/models/vits/vits.py
+++ b/paddlespeech/t2s/models/vits/vits.py
@@ -520,74 +520,27 @@ class VITS(nn.Layer):
                     if fan_in != 0:
                         bound = 1 / math.sqrt(fan_in)
                         uniform_(module.bias, -bound, bound)
-                """
-                init.kaiming_uniform_(self.weight, a=math.sqrt(5))
-                if self.bias is not None:
-                    fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
-                    if fan_in != 0:
-                        bound = 1 / math.sqrt(fan_in)
-                        init.uniform_(self.bias, -bound, bound)
-                """
                 
             if isinstance(module,
                           (nn.BatchNorm1D, nn.BatchNorm2D, nn.GroupNorm)):
                 ones_(module.weight)
                 zeros_(module.bias)
-                """
-                def reset_running_stats(self) -> None:
-                    if self.track_running_stats:
-                        # running_mean/running_var/num_batches... are registered at runtime depending
-                        # if self.track_running_stats is on
-                        self.running_mean.zero_()  # type: ignore[union-attr]
-                        self.running_var.fill_(1)  # type: ignore[union-attr]
-                        self.num_batches_tracked.zero_()  # type: ignore[union-attr,operator]
-                def reset_parameters(self) -> None:
-                    self.reset_running_stats()
-                    if self.affine:
-                        init.ones_(self.weight)
-                        init.zeros_(self.bias)
-                GroupNorm:
-                def reset_parameters(self) -> None:
-                    if self.affine:
-                        init.ones_(self.weight)
-                        init.zeros_(self.bias)
-                """
+
             if isinstance(module, nn.Linear):
                 kaiming_uniform_(module.weight, a=math.sqrt(5))
                 if module.bias is not None:
                     fan_in, _ = _calculate_fan_in_and_fan_out(module.weight)
                     bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
                     uniform_(module.bias, -bound, bound)
-                """
-                init.kaiming_uniform_(self.weight, a=math.sqrt(5))
-                if self.bias is not None:
-                    fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
-                    bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
-                    init.uniform_(self.bias, -bound, bound)
-                """
+
             if isinstance(module, nn.Embedding):
                 normal_(module.weight)
                 if module._padding_idx is not None:
                     with paddle.no_grad():
                         module.weight[module._padding_idx] = 0
-                """
-                def reset_parameters(self) -> None:
-                    init.normal_(self.weight)
-                    self._fill_padding_idx_with_zero()
-
-                def _fill_padding_idx_with_zero(self) -> None:
-                    if self.padding_idx is not None:
-                        with torch.no_grad():
-                            self.weight[self.padding_idx].fill_(0)
-                """
+
             if isinstance(module, nn.LayerNorm):
                 ones_(module.weight)
                 zeros_(module.bias)
-                """
-                    def reset_parameters(self) -> None:
-                    if self.elementwise_affine:
-                        init.ones_(self.weight)
-                        init.zeros_(self.bias)
-                """
 
         self.apply(_reset_parameters)