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#!/usr/bin/python
#-*- coding: utf-8 -*-
import torch
import torch.nn as nn
def save(model, filename):
with open(filename, "wb") as f:
torch.save(model, f);
print("%s saved."%filename);
def load(filename):
net = torch.load(filename)
return net;
class S(nn.Module):
def __init__(self, num_layers_in_fc_layers = 1024):
super(S, self).__init__();
self.__nFeatures__ = 24;
self.__nChs__ = 32;
self.__midChs__ = 32;
self.netcnnaud = nn.Sequential(
nn.Conv2d(1, 64, kernel_size=(3,3), stride=(1,1), padding=(1,1)),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=(1,1), stride=(1,1)),
nn.Conv2d(64, 192, kernel_size=(3,3), stride=(1,1), padding=(1,1)),
nn.BatchNorm2d(192),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=(3,3), stride=(1,2)),
nn.Conv2d(192, 384, kernel_size=(3,3), padding=(1,1)),
nn.BatchNorm2d(384),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=(3,3), padding=(1,1)),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=(3,3), padding=(1,1)),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=(3,3), stride=(2,2)),
nn.Conv2d(256, 512, kernel_size=(5,4), padding=(0,0)),
nn.BatchNorm2d(512),
nn.ReLU(),
);
self.netfcaud = nn.Sequential(
nn.Linear(512, 512),
nn.BatchNorm1d(512),
nn.ReLU(),
nn.Linear(512, num_layers_in_fc_layers),
);
self.netfclip = nn.Sequential(
nn.Linear(512, 512),
nn.BatchNorm1d(512),
nn.ReLU(),
nn.Linear(512, num_layers_in_fc_layers),
);
self.netcnnlip = nn.Sequential(
nn.Conv3d(3, 96, kernel_size=(5,7,7), stride=(1,2,2), padding=0),
nn.BatchNorm3d(96),
nn.ReLU(inplace=True),
nn.MaxPool3d(kernel_size=(1,3,3), stride=(1,2,2)),
nn.Conv3d(96, 256, kernel_size=(1,5,5), stride=(1,2,2), padding=(0,1,1)),
nn.BatchNorm3d(256),
nn.ReLU(inplace=True),
nn.MaxPool3d(kernel_size=(1,3,3), stride=(1,2,2), padding=(0,1,1)),
nn.Conv3d(256, 256, kernel_size=(1,3,3), padding=(0,1,1)),
nn.BatchNorm3d(256),
nn.ReLU(inplace=True),
nn.Conv3d(256, 256, kernel_size=(1,3,3), padding=(0,1,1)),
nn.BatchNorm3d(256),
nn.ReLU(inplace=True),
nn.Conv3d(256, 256, kernel_size=(1,3,3), padding=(0,1,1)),
nn.BatchNorm3d(256),
nn.ReLU(inplace=True),
nn.MaxPool3d(kernel_size=(1,3,3), stride=(1,2,2)),
nn.Conv3d(256, 512, kernel_size=(1,6,6), padding=0),
nn.BatchNorm3d(512),
nn.ReLU(inplace=True),
);
def forward_aud(self, x):
mid = self.netcnnaud(x); # N x ch x 24 x M
mid = mid.view((mid.size()[0], -1)); # N x (ch x 24)
out = self.netfcaud(mid);
return out;
def forward_lip(self, x):
mid = self.netcnnlip(x);
mid = mid.view((mid.size()[0], -1)); # N x (ch x 24)
out = self.netfclip(mid);
return out;
def forward_lipfeat(self, x):
mid = self.netcnnlip(x);
out = mid.view((mid.size()[0], -1)); # N x (ch x 24)
return out; |