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# ResNet18 Pretrained network to extract lip embedding
# This code is modified based on https://github.com/lordmartian/deep_avsr
##
import torch
import torch.nn as nn
import torch.nn.functional as F
class ResNetLayer(nn.Module):
"""
A ResNet layer used to build the ResNet network.
Architecture:
--> conv-bn-relu -> conv -> + -> bn-relu -> conv-bn-relu -> conv -> + -> bn-relu -->
| | | |
-----> downsample ------> ------------------------------------->
"""
def __init__(self, inplanes, outplanes, stride):
super(ResNetLayer, self).__init__()
self.conv1a = nn.Conv2d(inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1a = nn.BatchNorm2d(outplanes, momentum=0.01, eps=0.001)
self.conv2a = nn.Conv2d(outplanes, outplanes, kernel_size=3, stride=1, padding=1, bias=False)
self.stride = stride
self.downsample = nn.Conv2d(inplanes, outplanes, kernel_size=(1,1), stride=stride, bias=False)
self.outbna = nn.BatchNorm2d(outplanes, momentum=0.01, eps=0.001)
self.conv1b = nn.Conv2d(outplanes, outplanes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1b = nn.BatchNorm2d(outplanes, momentum=0.01, eps=0.001)
self.conv2b = nn.Conv2d(outplanes, outplanes, kernel_size=3, stride=1, padding=1, bias=False)
self.outbnb = nn.BatchNorm2d(outplanes, momentum=0.01, eps=0.001)
return
def forward(self, inputBatch):
batch = F.relu(self.bn1a(self.conv1a(inputBatch)))
batch = self.conv2a(batch)
if self.stride == 1:
residualBatch = inputBatch
else:
residualBatch = self.downsample(inputBatch)
batch = batch + residualBatch
intermediateBatch = batch
batch = F.relu(self.outbna(batch))
batch = F.relu(self.bn1b(self.conv1b(batch)))
batch = self.conv2b(batch)
residualBatch = intermediateBatch
batch = batch + residualBatch
outputBatch = F.relu(self.outbnb(batch))
return outputBatch
class ResNet(nn.Module):
"""
An 18-layer ResNet architecture.
"""
def __init__(self):
super(ResNet, self).__init__()
self.layer1 = ResNetLayer(64, 64, stride=1)
self.layer2 = ResNetLayer(64, 128, stride=2)
self.layer3 = ResNetLayer(128, 256, stride=2)
self.layer4 = ResNetLayer(256, 512, stride=2)
self.avgpool = nn.AvgPool2d(kernel_size=(4,4), stride=(1,1))
return
def forward(self, inputBatch):
batch = self.layer1(inputBatch)
batch = self.layer2(batch)
batch = self.layer3(batch)
batch = self.layer4(batch)
outputBatch = self.avgpool(batch)
return outputBatch
class GlobalLayerNorm(nn.Module):
def __init__(self, channel_size):
super(GlobalLayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1]
self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1]
self.reset_parameters()
def reset_parameters(self):
self.gamma.data.fill_(1)
self.beta.data.zero_()
def forward(self, y):
mean = y.mean(dim=1, keepdim=True).mean(dim=2, keepdim=True) #[M, 1, 1]
var = (torch.pow(y-mean, 2)).mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)
gLN_y = self.gamma * (y - mean) / torch.pow(var + 1e-8, 0.5) + self.beta
return gLN_y
class visualFrontend(nn.Module):
"""
A visual feature extraction module. Generates a 512-dim feature vector per video frame.
Architecture: A 3D convolution block followed by an 18-layer ResNet.
"""
def __init__(self):
super(visualFrontend, self).__init__()
self.frontend3D = nn.Sequential(
nn.Conv3d(1, 64, kernel_size=(5,7,7), stride=(1,2,2), padding=(2,3,3), bias=False),
nn.BatchNorm3d(64, momentum=0.01, eps=0.001),
nn.ReLU(),
nn.MaxPool3d(kernel_size=(1,3,3), stride=(1,2,2), padding=(0,1,1))
)
self.resnet = ResNet()
return
def forward(self, inputBatch):
inputBatch = inputBatch.transpose(0, 1).transpose(1, 2)
batchsize = inputBatch.shape[0]
batch = self.frontend3D(inputBatch)
batch = batch.transpose(1, 2)
batch = batch.reshape(batch.shape[0]*batch.shape[1], batch.shape[2], batch.shape[3], batch.shape[4])
outputBatch = self.resnet(batch)
outputBatch = outputBatch.reshape(batchsize, -1, 512)
outputBatch = outputBatch.transpose(1 ,2)
outputBatch = outputBatch.transpose(1, 2).transpose(0, 1)
return outputBatch
class DSConv1d(nn.Module):
def __init__(self):
super(DSConv1d, self).__init__()
self.net = nn.Sequential(
nn.ReLU(),
nn.BatchNorm1d(512),
nn.Conv1d(512, 512, 3, stride=1, padding=1,dilation=1, groups=512, bias=False),
nn.PReLU(),
GlobalLayerNorm(512),
nn.Conv1d(512, 512, 1, bias=False),
)
def forward(self, x):
out = self.net(x)
return out + x
class visualTCN(nn.Module):
def __init__(self):
super(visualTCN, self).__init__()
stacks = []
for x in range(5):
stacks += [DSConv1d()]
self.net = nn.Sequential(*stacks) # Visual Temporal Network V-TCN
def forward(self, x):
out = self.net(x)
return out
class visualConv1D(nn.Module):
def __init__(self):
super(visualConv1D, self).__init__()
self.net = nn.Sequential(
nn.Conv1d(512, 256, 5, stride=1, padding=2),
nn.BatchNorm1d(256),
nn.ReLU(),
nn.Conv1d(256, 128, 1),
)
def forward(self, x):
out = self.net(x)
return out |