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72c09ec0b7* indextts2 * update lfs for audio files --------- Co-authored-by: wangyining02 <wangyining02@bilibili.com>
70 lines
No EOL
1.8 KiB
Python
70 lines
No EOL
1.8 KiB
Python
# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from modules.campplus.layers import DenseLayer
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class CosineClassifier(nn.Module):
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def __init__(
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self,
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input_dim,
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num_blocks=0,
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inter_dim=512,
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out_neurons=1000,
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):
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super().__init__()
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self.blocks = nn.ModuleList()
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for index in range(num_blocks):
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self.blocks.append(
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DenseLayer(input_dim, inter_dim, config_str='batchnorm')
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)
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input_dim = inter_dim
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self.weight = nn.Parameter(
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torch.FloatTensor(out_neurons, input_dim)
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)
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nn.init.xavier_uniform_(self.weight)
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def forward(self, x):
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# x: [B, dim]
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for layer in self.blocks:
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x = layer(x)
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# normalized
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x = F.linear(F.normalize(x), F.normalize(self.weight))
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return x
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class LinearClassifier(nn.Module):
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def __init__(
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self,
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input_dim,
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num_blocks=0,
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inter_dim=512,
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out_neurons=1000,
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):
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super().__init__()
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self.blocks = nn.ModuleList()
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self.nonlinear = nn.ReLU(inplace=True)
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for index in range(num_blocks):
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self.blocks.append(
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DenseLayer(input_dim, inter_dim, bias=True)
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)
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input_dim = inter_dim
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self.linear = nn.Linear(input_dim, out_neurons, bias=True)
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def forward(self, x):
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# x: [B, dim]
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x = self.nonlinear(x)
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for layer in self.blocks:
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x = layer(x)
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x = self.linear(x)
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return x |