From ada3440acad542bc23dc8a3d0134b2b536fe66c7 Mon Sep 17 00:00:00 2001
From: KakaruHayate <kakaru.hk@hotmail.com>
Date: Tue, 15 Oct 2024 19:07:01 +0800
Subject: [PATCH] delate lynxnet aux_decoder

---
 modules/aux_decoder/LYNXNetDecoder.py | 73 ---------------------------
 modules/aux_decoder/__init__.py       |  7 +--
 2 files changed, 2 insertions(+), 78 deletions(-)
 delete mode 100644 modules/aux_decoder/LYNXNetDecoder.py

diff --git a/modules/aux_decoder/LYNXNetDecoder.py b/modules/aux_decoder/LYNXNetDecoder.py
deleted file mode 100644
index 4ac5923ee..000000000
--- a/modules/aux_decoder/LYNXNetDecoder.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# refer to： 
-# https://github.com/CNChTu/Diffusion-SVC/blob/v2.0_dev/diffusion/naive_v2/model_conformer_naive.py
-# https://github.com/CNChTu/Diffusion-SVC/blob/v2.0_dev/diffusion/naive_v2/naive_v2_diff.py
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from modules.backbones.LYNXNet import LYNXConvModule
-
-
-class LYNXNetDecoderLayer(nn.Module):
-    """
-    LYNXNet Decoder Layer
-
-    Args:
-        dim (int): Dimension of model
-        expansion_factor (int): Expansion factor of conv module, default 2
-        kernel_size (int): Kernel size of conv module, default 31
-        in_norm (bool): Whether to use norm
-        activation (str): Activation Function for conv module
-    """
-
-    def __init__(self, dim, expansion_factor, kernel_size=31, in_norm=False, activation='SiLU', dropout=0.):
-        super().__init__()
-        self.convmodule = LYNXConvModule(dim=dim, expansion_factor=expansion_factor, kernel_size=kernel_size, in_norm=in_norm, activation=activation, dropout=dropout)
-
-    def forward(self, x) -> torch.Tensor:
-        residual = x
-        x = self.convmodule(x)
-        x = residual + x
-        
-        return x
-
-
-class LYNXNetDecoder(nn.Module):
-    def __init__(
-            self, in_dims, out_dims, /, *,
-            num_channels=512, num_layers=6, kernel_size=31, dropout_rate=0.
-    ):
-        super().__init__()
-        self.input_projection = nn.Conv1d(in_dims, num_channels, 1)
-        self.encoder_layers = nn.ModuleList(
-                LYNXNetDecoderLayer(
-                    dim=num_channels, 
-                    expansion_factor=2, 
-                    kernel_size=kernel_size, 
-                    in_norm=False, 
-                    activation='SiLU', 
-                    dropout=dropout_rate) for _ in range(num_layers)
-        )
-        self.norm = nn.LayerNorm(num_channels)
-        self.output_projection = nn.Conv1d(num_channels, out_dims, kernel_size=1)
-
-    def forward(self, x, infer=False):
-        """
-        Args:
-            x (torch.Tensor): Input tensor (#batch, length, in_dims)
-        return:
-            torch.Tensor: Output tensor (#batch, length, out_dims)
-        """
-        x = x.transpose(1, 2)
-        x = self.input_projection(x)
-        x = x.transpose(1, 2)
-        for layer in self.encoder_layers:
-            x = layer(x)
-        x = self.norm(x)
-        x = x.transpose(1, 2)
-        
-        x = self.output_projection(x)
-        x = x.transpose(1, 2)
-        
-        return x
diff --git a/modules/aux_decoder/__init__.py b/modules/aux_decoder/__init__.py
index 4801b1156..54ceb2113 100644
--- a/modules/aux_decoder/__init__.py
+++ b/modules/aux_decoder/__init__.py
@@ -2,16 +2,13 @@
 from torch import nn
 
 from .convnext import ConvNeXtDecoder
-from .LYNXNetDecoder import LYNXNetDecoder
 from utils import filter_kwargs
 
 AUX_DECODERS = {
-    'convnext': ConvNeXtDecoder, 
-    'lynxnet': LYNXNetDecoder
+    'convnext': ConvNeXtDecoder
 }
 AUX_LOSSES = {
-    'convnext': nn.L1Loss, 
-    'lynxnet': nn.L1Loss
+    'convnext': nn.L1Loss
 }