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from collections import OrderedDict
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import torch
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from torch import nn
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import torch.nn.functional as F
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from modules.campplus.layers import DenseLayer, StatsPool, TDNNLayer, CAMDenseTDNNBlock, TransitLayer, BasicResBlock, get_nonlinear
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class FCM(nn.Module):
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def __init__(self,
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block=BasicResBlock,
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num_blocks=[2, 2],
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m_channels=32,
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feat_dim=80):
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super(FCM, self).__init__()
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self.in_planes = m_channels
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self.conv1 = nn.Conv2d(1, m_channels, kernel_size=3, stride=1, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(m_channels)
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self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
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self.layer2 = self._make_layer(block, m_channels, num_blocks[1], stride=2)
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self.conv2 = nn.Conv2d(m_channels, m_channels, kernel_size=3, stride=(2, 1), padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(m_channels)
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self.out_channels = m_channels * (feat_dim // 8)
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def _make_layer(self, block, planes, num_blocks, stride):
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strides = [stride] + [1] * (num_blocks - 1)
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layers = []
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for stride in strides:
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layers.append(block(self.in_planes, planes, stride))
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self.in_planes = planes * block.expansion
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return nn.Sequential(*layers)
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def forward(self, x):
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x = x.unsqueeze(1)
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out = F.relu(self.bn1(self.conv1(x)))
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out = self.layer1(out)
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out = self.layer2(out)
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out = F.relu(self.bn2(self.conv2(out)))
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shape = out.shape
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out = out.reshape(shape[0], shape[1]*shape[2], shape[3])
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return out
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class CAMPPlus(nn.Module):
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def __init__(self,
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feat_dim=80,
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embedding_size=512,
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growth_rate=32,
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bn_size=4,
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init_channels=128,
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config_str='batchnorm-relu',
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memory_efficient=True):
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super(CAMPPlus, self).__init__()
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self.head = FCM(feat_dim=feat_dim)
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channels = self.head.out_channels
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self.xvector = nn.Sequential(
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OrderedDict([
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('tdnn',
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TDNNLayer(channels,
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init_channels,
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5,
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stride=2,
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dilation=1,
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padding=-1,
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config_str=config_str)),
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]))
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channels = init_channels
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for i, (num_layers, kernel_size,
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dilation) in enumerate(zip((12, 24, 16), (3, 3, 3), (1, 2, 2))):
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block = CAMDenseTDNNBlock(num_layers=num_layers,
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in_channels=channels,
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out_channels=growth_rate,
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bn_channels=bn_size * growth_rate,
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kernel_size=kernel_size,
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dilation=dilation,
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config_str=config_str,
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memory_efficient=memory_efficient)
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self.xvector.add_module('block%d' % (i + 1), block)
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channels = channels + num_layers * growth_rate
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self.xvector.add_module(
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'transit%d' % (i + 1),
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TransitLayer(channels,
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channels // 2,
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bias=False,
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config_str=config_str))
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channels //= 2
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self.xvector.add_module(
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'out_nonlinear', get_nonlinear(config_str, channels))
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self.xvector.add_module('stats', StatsPool())
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self.xvector.add_module(
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'dense',
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DenseLayer(channels * 2, embedding_size, config_str='batchnorm_'))
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for m in self.modules():
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if isinstance(m, (nn.Conv1d, nn.Linear)):
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nn.init.kaiming_normal_(m.weight.data)
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if m.bias is not None:
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nn.init.zeros_(m.bias)
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def forward(self, x):
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x = x.permute(0, 2, 1)
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x = self.head(x)
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x = self.xvector(x)
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return x |
