<gh_stars>0
#! /usr/bin/python
# -*- encoding: utf-8 -*-

import torch
import torch.nn as nn
import pytorch_mfcc


class SyncNetModel(nn.Module):
    def __init__(self, nOut=1024, stride=1):
        super(SyncNetModel, self).__init__()

        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), stride=(1, stride)),
            nn.BatchNorm2d(512),
            nn.ReLU(),
        )

        self.netfcaud = nn.Sequential(
            nn.Conv1d(512, 512, kernel_size=1),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.Conv1d(512, nOut, kernel_size=1),
        )

        self.netfclip = nn.Sequential(
            nn.Conv1d(512, 512, kernel_size=1),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.Conv1d(512, nOut, kernel_size=1),
        )

        self.netfcspk = nn.Sequential(
            nn.Conv1d(512, 512, kernel_size=1),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.Conv1d(512, nOut, kernel_size=1),
        )

        self.netfcface = nn.Sequential(
            nn.Conv1d(512, 512, kernel_size=1),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.Conv1d(512, nOut, kernel_size=1),
        )

        self.netcnnlip = nn.Sequential(
            nn.Conv3d(3, 96, kernel_size=(5, 7, 7),
                      stride=(stride, 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),
        )

        self.mfcc_layer = pytorch_mfcc.MFCC(samplerate=16000)

    def forward_aud(self, x):

        x1, mfcc_lengths = self.mfcc_layer(
            x, [x.size()[1]]*x.size()[0])      # Do mfcc
        x1 = x1.unsqueeze(1).transpose(2, 3).detach()

        mid = self.netcnnaud(x1)  # N x ch x 24 x M
        mid = mid.view((mid.size()[0], mid.size()[1], -1))  # N x (ch x 24)

        out1 = self.netfcaud(mid)
        out2 = self.netfcspk(mid)

        return out1, out2

    def forward_vid(self, x):

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

        out1 = self.netfclip(mid)
        out2 = self.netfcface(mid)

        return out1, out2

    def forward(self, aud, vid):
        out_a1, out_a2 = self.forward_aud(aud)
        out_v1, out_v2 = self.forward_aud(vid)
        return {'audio1': out_a1, 'audio2': out_a2,
                'video1': out_v1, 'video2': out_v2}

#! /usr/bin/python
# -*- encoding: utf-8 -*-

import torch
import torch.nn as nn
import pytorch_mfcc


class SyncNetModel(nn.Module):
    def __init__(self, nOut=1024, stride=1):
        super(SyncNetModel, self).__init__()

        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), stride=(1, stride)),
            nn.BatchNorm2d(512),
            nn.ReLU(),
        )

        self.netfcaud = nn.Sequential(
            nn.Conv1d(512, 512, kernel_size=1),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.Conv1d(512, nOut, kernel_size=1),
        )

        self.netfclip = nn.Sequential(
            nn.Conv1d(512, 512, kernel_size=1),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.Conv1d(512, nOut, kernel_size=1),
        )

        self.netfcspk = nn.Sequential(
            nn.Conv1d(512, 512, kernel_size=1),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.Conv1d(512, nOut, kernel_size=1),
        )

        self.netfcface = nn.Sequential(
            nn.Conv1d(512, 512, kernel_size=1),
            nn.BatchNorm1d(512),
            nn.ReLU(),
            nn.Conv1d(512, nOut, kernel_size=1),
        )

        self.netcnnlip = nn.Sequential(
            nn.Conv3d(3, 96, kernel_size=(5, 7, 7),
                      stride=(stride, 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),
        )

        self.mfcc_layer = pytorch_mfcc.MFCC(samplerate=16000)

    def forward_aud(self, x):

        x1, mfcc_lengths = self.mfcc_layer(
            x, [x.size()[1]]*x.size()[0])      # Do mfcc
        x1 = x1.unsqueeze(1).transpose(2, 3).detach()

        mid = self.netcnnaud(x1)  # N x ch x 24 x M
        mid = mid.view((mid.size()[0], mid.size()[1], -1))  # N x (ch x 24)

        out1 = self.netfcaud(mid)
        out2 = self.netfcspk(mid)

        return out1, out2

    def forward_vid(self, x):

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

        out1 = self.netfclip(mid)
        out2 = self.netfcface(mid)

        return out1, out2

    def forward(self, aud, vid):
        out_a1, out_a2 = self.forward_aud(aud)
        out_v1, out_v2 = self.forward_aud(vid)
        return {'audio1': out_a1, 'audio2': out_a2,
                'video1': out_v1, 'video2': out_v2}
#! /usr/bin/python # -*- encoding: utf-8 -*- # Do mfcc # N x ch x 24 x M # N x (ch x 24) # N x (ch x 24)