Spaces:
Running
Running
File size: 1,867 Bytes
9a83644 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import torch
import torch.nn as nn
import torch.nn.functional as F
from modules.campplus.layers import DenseLayer
class CosineClassifier(nn.Module):
def __init__(
self,
input_dim,
num_blocks=0,
inter_dim=512,
out_neurons=1000,
):
super().__init__()
self.blocks = nn.ModuleList()
for index in range(num_blocks):
self.blocks.append(
DenseLayer(input_dim, inter_dim, config_str='batchnorm')
)
input_dim = inter_dim
self.weight = nn.Parameter(
torch.FloatTensor(out_neurons, input_dim)
)
nn.init.xavier_uniform_(self.weight)
def forward(self, x):
# x: [B, dim]
for layer in self.blocks:
x = layer(x)
# normalized
x = F.linear(F.normalize(x), F.normalize(self.weight))
return x
class LinearClassifier(nn.Module):
def __init__(
self,
input_dim,
num_blocks=0,
inter_dim=512,
out_neurons=1000,
):
super().__init__()
self.blocks = nn.ModuleList()
self.nonlinear = nn.ReLU(inplace=True)
for index in range(num_blocks):
self.blocks.append(
DenseLayer(input_dim, inter_dim, bias=True)
)
input_dim = inter_dim
self.linear = nn.Linear(input_dim, out_neurons, bias=True)
def forward(self, x):
# x: [B, dim]
x = self.nonlinear(x)
for layer in self.blocks:
x = layer(x)
x = self.linear(x)
return x |