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resnet.py
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resnet.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import os
import random
import numpy as np
## Adapted from https://github.com/joaomonteirof/e2e_antispoofing
class SelfAttention(nn.Module):
def __init__(self, hidden_size, mean_only=False):
super(SelfAttention, self).__init__()
#self.output_size = output_size
self.hidden_size = hidden_size
self.att_weights = nn.Parameter(torch.Tensor(1, hidden_size),requires_grad=True)
self.mean_only = mean_only
init.kaiming_uniform_(self.att_weights)
def forward(self, inputs):
batch_size = inputs.size(0)
weights = torch.bmm(inputs, self.att_weights.permute(1, 0).unsqueeze(0).repeat(batch_size, 1, 1))
if inputs.size(0)==1:
attentions = F.softmax(torch.tanh(weights),dim=1)
weighted = torch.mul(inputs, attentions.expand_as(inputs))
else:
attentions = F.softmax(torch.tanh(weights.squeeze()),dim=1)
weighted = torch.mul(inputs, attentions.unsqueeze(2).expand_as(inputs))
if self.mean_only:
return weighted.sum(1)
else:
noise = 1e-5*torch.randn(weighted.size())
if inputs.is_cuda:
noise = noise.to(inputs.device)
avg_repr, std_repr = weighted.sum(1), (weighted+noise).std(1)
representations = torch.cat((avg_repr,std_repr),1)
return representations
class PreActBlock(nn.Module):
'''Pre-activation version of the BasicBlock.'''
expansion = 1
def __init__(self, in_planes, planes, stride, *args, **kwargs):
super(PreActBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False))
def forward(self, x):
out = F.relu(self.bn1(x))
shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
out = self.conv1(out)
out = self.conv2(F.relu(self.bn2(out)))
out += shortcut
return out
class PreActBottleneck(nn.Module):
'''Pre-activation version of the original Bottleneck module.'''
expansion = 4
def __init__(self, in_planes, planes, stride, *args, **kwargs):
super(PreActBottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False))
def forward(self, x):
out = F.relu(self.bn1(x))
shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
out = self.conv1(out)
out = self.conv2(F.relu(self.bn2(out)))
out = self.conv3(F.relu(self.bn3(out)))
out += shortcut
return out
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
def conv1x1(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
RESNET_CONFIGS = {'18': [[2, 2, 2, 2], PreActBlock],
'28': [[3, 4, 6, 3], PreActBlock],
'34': [[3, 4, 6, 3], PreActBlock],
'50': [[3, 4, 6, 3], PreActBottleneck],
'101': [[3, 4, 23, 3], PreActBottleneck]
}
def setup_seed(random_seed, cudnn_deterministic=True):
# initialization
torch.manual_seed(random_seed)
random.seed(random_seed)
np.random.seed(random_seed)
os.environ['PYTHONHASHSEED'] = str(random_seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(random_seed)
torch.backends.cudnn.deterministic = cudnn_deterministic
torch.backends.cudnn.benchmark = False
class ResNet(nn.Module):
def __init__(self, num_nodes, enc_dim, resnet_type='18', nclasses=2):
self.in_planes = 16
super(ResNet, self).__init__()
layers, block = RESNET_CONFIGS[resnet_type]
self._norm_layer = nn.BatchNorm2d
self.conv1 = nn.Conv2d(1, 16, kernel_size=(9, 3), stride=(3, 1), padding=(1, 1), bias=False)
self.bn1 = nn.BatchNorm2d(16)
self.activation = nn.ReLU()
self.layer1 = self._make_layer(block, 64, layers[0], stride=1)
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.conv5 = nn.Conv2d(512 * block.expansion, 256, kernel_size=(num_nodes, 3), stride=(1, 1), padding=(0, 1),
bias=False)
self.bn5 = nn.BatchNorm2d(256)
self.fc = nn.Linear(256 * 2, enc_dim)
self.fc_mu = nn.Linear(enc_dim, nclasses) if nclasses >= 2 else nn.Linear(enc_dim, 1)
self.initialize_params()
self.attention = SelfAttention(256)
def initialize_params(self):
for layer in self.modules():
if isinstance(layer, torch.nn.Conv2d):
init.kaiming_normal_(layer.weight, a=0, mode='fan_out')
elif isinstance(layer, torch.nn.Linear):
init.kaiming_uniform_(layer.weight)
elif isinstance(layer, torch.nn.BatchNorm2d) or isinstance(layer, torch.nn.BatchNorm1d):
layer.weight.data.fill_(1)
layer.bias.data.zero_()
def _make_layer(self, block, planes, num_blocks, stride=1):
norm_layer = self._norm_layer
downsample = None
if stride != 1 or self.in_planes != planes * block.expansion:
downsample = nn.Sequential(conv1x1(self.in_planes, planes * block.expansion, stride),
norm_layer(planes * block.expansion))
layers = []
layers.append(block(self.in_planes, planes, stride, downsample, 1, 64, 1, norm_layer))
self.in_planes = planes * block.expansion
for _ in range(1, num_blocks):
layers.append(
block(self.in_planes, planes, 1, groups=1, base_width=64, dilation=False, norm_layer=norm_layer))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.activation(self.bn1(x))
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.conv5(x)
x = self.activation(self.bn5(x)).squeeze(2)
stats = self.attention(x.permute(0, 2, 1).contiguous())
feat = self.fc(stats)
mu = self.fc_mu(feat)
return feat, mu