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Zero
File size: 6,390 Bytes
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import torch
import torch.nn as nn
class ResNetSE(nn.Module):
def __init__(self, block, layers, num_filters, nOut, encoder_type='SAP', n_mels=80, n_mel_T=1, log_input=True, **kwargs):
super(ResNetSE, self).__init__()
print('Embedding size is %d, encoder %s.' % (nOut, encoder_type))
self.inplanes = num_filters[0]
self.encoder_type = encoder_type
self.n_mels = n_mels
self.log_input = log_input
self.conv1 = nn.Conv2d(1, num_filters[0], kernel_size=3, stride=1, padding=1)
self.relu = nn.ReLU(inplace=True)
self.bn1 = nn.BatchNorm2d(num_filters[0])
self.layer1 = self._make_layer(block, num_filters[0], layers[0])
self.layer2 = self._make_layer(block, num_filters[1], layers[1], stride=(2, 2))
self.layer3 = self._make_layer(block, num_filters[2], layers[2], stride=(2, 2))
self.layer4 = self._make_layer(block, num_filters[3], layers[3], stride=(2, 2))
self.instancenorm = nn.InstanceNorm1d(n_mels)
outmap_size = int(self.n_mels * n_mel_T / 8)
self.attention = nn.Sequential(
nn.Conv1d(num_filters[3] * outmap_size, 128, kernel_size=1),
nn.ReLU(),
nn.BatchNorm1d(128),
nn.Conv1d(128, num_filters[3] * outmap_size, kernel_size=1),
nn.Softmax(dim=2),
)
if self.encoder_type == "SAP":
out_dim = num_filters[3] * outmap_size
elif self.encoder_type == "ASP":
out_dim = num_filters[3] * outmap_size * 2
else:
raise ValueError('Undefined encoder')
self.fc = nn.Linear(out_dim, nOut)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def new_parameter(self, *size):
out = nn.Parameter(torch.FloatTensor(*size))
nn.init.xavier_normal_(out)
return out
def forward(self, x):
# with torch.no_grad():
# x = self.torchfb(x) + 1e-6
# if self.log_input: x = x.log()
# x = self.instancenorm(x).unsqueeze(1)
x = self.conv1(x)
x = self.relu(x)
x = self.bn1(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = x.reshape(x.size()[0], -1, x.size()[-1])
w = self.attention(x)
if self.encoder_type == "SAP":
x = torch.sum(x * w, dim=2)
elif self.encoder_type == "ASP":
mu = torch.sum(x * w, dim=2)
sg = torch.sqrt((torch.sum((x ** 2) * w, dim=2) - mu ** 2).clamp(min=1e-5))
x = torch.cat((mu, sg), 1)
x = x.view(x.size()[0], -1)
x = self.fc(x)
return x
class SEBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=8):
super(SEBasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.se = SELayer(planes, reduction)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.relu(out)
out = self.bn1(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.se(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class SEBottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=8):
super(SEBottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.se = SELayer(planes * 4, reduction)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
out = self.se(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class SELayer(nn.Module):
def __init__(self, channel, reduction=8):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction),
nn.ReLU(inplace=True),
nn.Linear(channel // reduction, channel),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y
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