<gh_stars>0
#!/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, return_feats=False):

        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)
        if not return_feats:
            return out
        else:
            return out, mid

    def forward_lip(self, x, return_feats=False):

        mid = self.netcnnlip(x)
        mid = mid.view((mid.size()[0], -1))  # N x (ch x 24)
        out = self.netfclip(mid)
        if not return_feats:
            return out
        else:
            return out, mid

    def forward_lipfeat(self, x):

        mid = self.netcnnlip(x)
        out = mid.view((mid.size()[0], -1))  # N x (ch x 24)

        return out

    def forward_audfeat(self, x):

        mid = self.netcnnaud(x)  # N x ch x 24 x M
        out = mid.view((mid.size()[0], -1))  # N x (ch x 24)
        return out

#!/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, return_feats=False):

        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)
        if not return_feats:
            return out
        else:
            return out, mid

    def forward_lip(self, x, return_feats=False):

        mid = self.netcnnlip(x)
        mid = mid.view((mid.size()[0], -1))  # N x (ch x 24)
        out = self.netfclip(mid)
        if not return_feats:
            return out
        else:
            return out, mid

    def forward_lipfeat(self, x):

        mid = self.netcnnlip(x)
        out = mid.view((mid.size()[0], -1))  # N x (ch x 24)

        return out

    def forward_audfeat(self, x):

        mid = self.netcnnaud(x)  # N x ch x 24 x M
        out = mid.view((mid.size()[0], -1))  # N x (ch x 24)
        return out
#!/usr/bin/python # -*- coding: utf-8 -*- # N x ch x 24 x M # N x (ch x 24) # N x (ch x 24) # N x (ch x 24) # N x ch x 24 x M # N x (ch x 24)