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model.py
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model.py
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import torch
import torch.nn as nn
import math
from torch.autograd import Function
class PairwiseDistance(Function):
def __init__(self, p):
super(PairwiseDistance, self).__init__()
self.norm = p
def forward(self, x1, x2):
assert x1.size() == x2.size()
eps = 1e-4 / x1.size(1)
diff = torch.abs(x1 - x2)
out = torch.pow(diff, self.norm).sum(dim=1)
return torch.pow(out + eps, 1. / self.norm)
class TripletMarginLoss(Function):
"""Triplet loss function.
"""
def __init__(self, margin):
super(TripletMarginLoss, self).__init__()
self.margin = margin
self.pdist = PairwiseDistance(2) # norm 2
def forward(self, anchor, positive, negative):
d_p = self.pdist.forward(anchor, positive)
d_n = self.pdist.forward(anchor, negative)
dist_hinge = torch.clamp(self.margin + d_p - d_n, min=0.0)
loss = torch.mean(dist_hinge)
return loss
class ReLU(nn.Hardtanh):
def __init__(self, inplace=False):
super(ReLU, self).__init__(0, 20, inplace)
def __repr__(self):
inplace_str = 'inplace' if self.inplace else ''
return self.__class__.__name__ + ' (' \
+ inplace_str + ')'
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
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)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class myResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
super(myResNet, self).__init__()
self.relu = ReLU(inplace=True)
self.inplanes = 64
self.conv1 = nn.Conv2d(1, 64, kernel_size=5, stride=2, padding=2,bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, layers[0])
self.inplanes = 128
self.conv2 = nn.Conv2d(64, 128, kernel_size=5, stride=2, padding=2,bias=False)
self.bn2 = nn.BatchNorm2d(128)
self.layer2 = self._make_layer(block, 128, layers[1])
self.inplanes = 256
self.conv3 = nn.Conv2d(128, 256, kernel_size=5, stride=2, padding=2,bias=False)
self.bn3 = nn.BatchNorm2d(256)
self.layer3 = self._make_layer(block, 256, layers[2])
self.inplanes = 512
self.conv4 = nn.Conv2d(256, 512, kernel_size=5, stride=2, padding=2,bias=False)
self.bn4 = nn.BatchNorm2d(512)
self.layer4 = self._make_layer(block, 512, layers[3])
self.avgpool = nn.AdaptiveAvgPool2d((1,None))
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1):
layers = []
layers.append(block(self.inplanes, planes, stride))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
class DeepSpeakerModel(nn.Module):
def __init__(self,embedding_size,num_classes,feature_dim = 64):
super(DeepSpeakerModel, self).__init__()
self.embedding_size = embedding_size
self.model = myResNet(BasicBlock, [1, 1, 1, 1])
if feature_dim == 64:
self.model.fc = nn.Linear(512*4, self.embedding_size)
elif feature_dim == 40:
self.model.fc = nn.Linear(256 * 5, self.embedding_size)
self.model.classifier = nn.Linear(self.embedding_size, num_classes)
def l2_norm(self,input):
input_size = input.size()
buffer = torch.pow(input, 2)
normp = torch.sum(buffer, 1).add_(1e-10)
norm = torch.sqrt(normp)
_output = torch.div(input, norm.view(-1, 1).expand_as(input))
output = _output.view(input_size)
return output
def forward(self, x):
x = self.model.conv1(x)
x = self.model.bn1(x)
x = self.model.relu(x)
x = self.model.layer1(x)
x = self.model.conv2(x)
x = self.model.bn2(x)
x = self.model.relu(x)
x = self.model.layer2(x)
x = self.model.conv3(x)
x = self.model.bn3(x)
x = self.model.relu(x)
x = self.model.layer3(x)
x = self.model.conv4(x)
x = self.model.bn4(x)
x = self.model.relu(x)
x = self.model.layer4(x)
x = self.model.avgpool(x)
x = x.view(x.size(0), -1)
x = self.model.fc(x)
self.features = self.l2_norm(x)
# Multiply by alpha = 10 as suggested in https://arxiv.org/pdf/1703.09507.pdf
alpha=10
self.features = self.features*alpha
#x = x.resize(int(x.size(0) / 17),17 , 512)
#self.features =torch.mean(x,dim=1)
#x = self.model.classifier(self.features)
return self.features
def forward_classifier(self, x):
features = self.forward(x)
res = self.model.classifier(features)
return res