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| 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 | |