import torch import torch.nn as nn import torch.nn.init as init import math class TwoStreamLipNet(torch.nn.Module): def __init__(self, dropout_p=0.5, coord_input_dim=40, coord_hidden_dim=128): super(TwoStreamLipNet, self).__init__() self.conv1 = nn.Conv3d(3, 32, (3, 5, 5), (1, 2, 2), (1, 2, 2)) self.pool1 = nn.MaxPool3d((1, 2, 2), (1, 2, 2)) self.conv2 = nn.Conv3d(32, 64, (3, 5, 5), (1, 1, 1), (1, 2, 2)) self.pool2 = nn.MaxPool3d((1, 2, 2), (1, 2, 2)) self.conv3 = nn.Conv3d(64, 96, (3, 3, 3), (1, 1, 1), (1, 1, 1)) self.pool3 = nn.MaxPool3d((1, 2, 2), (1, 2, 2)) self.gru1 = nn.GRU(96 * 4 * 8, 256, 1, bidirectional=True) self.gru2 = nn.GRU(512, 256, 1, bidirectional=True) self.FC = nn.Linear(512 + 2 * coord_hidden_dim, 27 + 1) self.dropout_p = dropout_p self.relu = nn.ReLU(inplace=True) self.dropout = nn.Dropout(self.dropout_p) self.dropout3d = nn.Dropout3d(self.dropout_p) # New GRU layer for lip coordinates self.coord_gru = nn.GRU( coord_input_dim, coord_hidden_dim, 1, bidirectional=True ) self._init() def _init(self): init.kaiming_normal_(self.conv1.weight, nonlinearity="relu") init.constant_(self.conv1.bias, 0) init.kaiming_normal_(self.conv2.weight, nonlinearity="relu") init.constant_(self.conv2.bias, 0) init.kaiming_normal_(self.conv3.weight, nonlinearity="relu") init.constant_(self.conv3.bias, 0) init.kaiming_normal_(self.FC.weight, nonlinearity="sigmoid") init.constant_(self.FC.bias, 0) for m in (self.gru1, self.gru2): stdv = math.sqrt(2 / (96 * 3 * 6 + 256)) for i in range(0, 256 * 3, 256): init.uniform_( m.weight_ih_l0[i : i + 256], -math.sqrt(3) * stdv, math.sqrt(3) * stdv, ) init.orthogonal_(m.weight_hh_l0[i : i + 256]) init.constant_(m.bias_ih_l0[i : i + 256], 0) init.uniform_( m.weight_ih_l0_reverse[i : i + 256], -math.sqrt(3) * stdv, math.sqrt(3) * stdv, ) init.orthogonal_(m.weight_hh_l0_reverse[i : i + 256]) init.constant_(m.bias_ih_l0_reverse[i : i + 256], 0) def forward(self, x, coords): # branch 1 x = self.conv1(x) x = self.relu(x) x = self.dropout3d(x) x = self.pool1(x) x = self.conv2(x) x = self.relu(x) x = self.dropout3d(x) x = self.pool2(x) x = self.conv3(x) x = self.relu(x) x = self.dropout3d(x) x = self.pool3(x) # (B, C, T, H, W)->(T, B, C, H, W) x = x.permute(2, 0, 1, 3, 4).contiguous() # (B, C, T, H, W)->(T, B, C*H*W) x = x.view(x.size(0), x.size(1), -1) self.gru1.flatten_parameters() self.gru2.flatten_parameters() x, h = self.gru1(x) x = self.dropout(x) x, h = self.gru2(x) x = self.dropout(x) # branch 2 # Process lip coordinates through GRU self.coord_gru.flatten_parameters() # (B, T, N, C)->(T, B, C, N, C) coords = coords.permute(1, 0, 2, 3).contiguous() # (T, B, C, N, C)->(T, B, C, N*C) coords = coords.view(coords.size(0), coords.size(1), -1) coords, _ = self.coord_gru(coords) coords = self.dropout(coords) # combine the two branches combined = torch.cat((x, coords), dim=2) x = self.FC(combined) x = x.permute(1, 0, 2).contiguous() return x