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import torch
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import torch.nn as nn
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def save(model, filename):
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with open(filename, "wb") as f:
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torch.save(model, f)
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print("%s saved." % filename)
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def load(filename):
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net = torch.load(filename)
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return net
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class S(nn.Module):
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def __init__(self, num_layers_in_fc_layers=1024):
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super(S, self).__init__()
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self.__nFeatures__ = 24
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self.__nChs__ = 32
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self.__midChs__ = 32
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self.netcnnaud = nn.Sequential(
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nn.Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
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nn.BatchNorm2d(64),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=(1, 1), stride=(1, 1)),
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nn.Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
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nn.BatchNorm2d(192),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=(3, 3), stride=(1, 2)),
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nn.Conv2d(192, 384, kernel_size=(3, 3), padding=(1, 1)),
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nn.BatchNorm2d(384),
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nn.ReLU(inplace=True),
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nn.Conv2d(384, 256, kernel_size=(3, 3), padding=(1, 1)),
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nn.BatchNorm2d(256),
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nn.ReLU(inplace=True),
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nn.Conv2d(256, 256, kernel_size=(3, 3), padding=(1, 1)),
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nn.BatchNorm2d(256),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)),
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nn.Conv2d(256, 512, kernel_size=(5, 4), padding=(0, 0)),
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nn.BatchNorm2d(512),
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nn.ReLU(),
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)
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self.netfcaud = nn.Sequential(
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nn.Linear(512, 512),
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nn.BatchNorm1d(512),
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nn.ReLU(),
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nn.Linear(512, num_layers_in_fc_layers),
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)
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self.netfclip = nn.Sequential(
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nn.Linear(512, 512),
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nn.BatchNorm1d(512),
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nn.ReLU(),
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nn.Linear(512, num_layers_in_fc_layers),
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)
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self.netcnnlip = nn.Sequential(
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nn.Conv3d(3, 96, kernel_size=(5, 7, 7), stride=(1, 2, 2), padding=0),
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nn.BatchNorm3d(96),
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nn.ReLU(inplace=True),
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nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2)),
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nn.Conv3d(96, 256, kernel_size=(1, 5, 5), stride=(1, 2, 2), padding=(0, 1, 1)),
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nn.BatchNorm3d(256),
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nn.ReLU(inplace=True),
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nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)),
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nn.Conv3d(256, 256, kernel_size=(1, 3, 3), padding=(0, 1, 1)),
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nn.BatchNorm3d(256),
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nn.ReLU(inplace=True),
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nn.Conv3d(256, 256, kernel_size=(1, 3, 3), padding=(0, 1, 1)),
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nn.BatchNorm3d(256),
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nn.ReLU(inplace=True),
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nn.Conv3d(256, 256, kernel_size=(1, 3, 3), padding=(0, 1, 1)),
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nn.BatchNorm3d(256),
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nn.ReLU(inplace=True),
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nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2)),
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nn.Conv3d(256, 512, kernel_size=(1, 6, 6), padding=0),
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nn.BatchNorm3d(512),
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nn.ReLU(inplace=True),
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)
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def forward_aud(self, x):
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mid = self.netcnnaud(x)
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mid = mid.view((mid.size()[0], -1))
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out = self.netfcaud(mid)
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return out
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def forward_lip(self, x):
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mid = self.netcnnlip(x)
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mid = mid.view((mid.size()[0], -1))
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out = self.netfclip(mid)
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return out
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def forward_lipfeat(self, x):
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mid = self.netcnnlip(x)
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out = mid.view((mid.size()[0], -1))
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return out
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