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import torch |
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import torch.nn as nn |
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import torch.nn.init as init |
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import torch.nn.functional as F |
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from torch.nn import Transformer |
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import math |
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import numpy as np |
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from helpers import * |
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from torch import Tensor |
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from models.PhonemeTransformer import ( |
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PositionalEncoding, TokenEmbedding |
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) |
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class LipNetPlus(torch.nn.Module): |
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def __init__( |
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self, output_classes, dropout_p=0.0, pre_gru_repeats=0, |
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gru_output_size=512, embeds_size=256, |
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output_vocab_size=512, dropout_t=0.1, |
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src_vocab_size=4, num_encoder_layers: int = 3, |
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num_decoder_layers: int = 3, nhead: int = 8, |
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dim_feedforward: int = 512, |
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): |
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super(LipNetPlus, self).__init__() |
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assert gru_output_size % 2 == 0 |
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self.pre_gru_repeats = pre_gru_repeats |
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self.gru_out_size = gru_output_size |
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self.gru_hidden_size = gru_output_size // 2 |
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self.embeds_size = embeds_size |
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self.output_vocab_size = output_vocab_size |
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self.gru_output_size = gru_output_size |
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self.dropout_t = dropout_t |
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self.conv1 = nn.Conv3d(3, 32, (3, 5, 5), (1, 2, 2), (1, 2, 2)) |
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self.pool1 = nn.MaxPool3d((1, 2, 2), (1, 2, 2)) |
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self.conv2 = nn.Conv3d(32, 64, (3, 5, 5), (1, 1, 1), (1, 2, 2)) |
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self.pool2 = nn.MaxPool3d((1, 2, 2), (1, 2, 2)) |
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self.conv3 = nn.Conv3d(64, 96, (3, 3, 3), (1, 1, 1), (1, 1, 1)) |
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self.pool3 = nn.MaxPool3d((1, 2, 2), (1, 2, 2)) |
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self.gru1 = nn.GRU( |
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96 * 4 * 8, self.gru_hidden_size, 1, bidirectional=True |
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) |
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self.gru2 = nn.GRU( |
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self.gru_output_size, self.gru_hidden_size, 1, bidirectional=True |
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) |
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self.output_classes = output_classes |
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self.FC = nn.Linear(self.gru_output_size, output_classes + 1) |
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self.dropout_p = dropout_p |
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self.relu = nn.ReLU(inplace=True) |
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self.dropout = nn.Dropout(self.dropout_p) |
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self.dropout3d = nn.Dropout3d(self.dropout_p) |
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self.src_tok_emb = TokenEmbedding( |
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src_vocab_size, self.embeds_size |
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) |
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self.tgt_tok_emb = TokenEmbedding( |
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output_vocab_size, self.embeds_size |
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) |
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self.embeds_layer = nn.Linear( |
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self.gru_output_size, self.embeds_size |
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) |
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self.transformer = Transformer( |
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d_model=self.embeds_size, nhead=nhead, |
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num_encoder_layers=num_encoder_layers, |
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num_decoder_layers=num_decoder_layers, |
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dim_feedforward=dim_feedforward, |
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dropout=dropout_t |
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) |
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self.positional_encoding = PositionalEncoding( |
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self.embeds_size, dropout=self.dropout_t |
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) |
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self.generator = nn.Linear( |
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self.embeds_size, self.output_vocab_size |
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) |
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self._init() |
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def _init(self): |
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init.kaiming_normal_(self.conv1.weight, nonlinearity='relu') |
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init.constant_(self.conv1.bias, 0) |
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init.kaiming_normal_(self.conv2.weight, nonlinearity='relu') |
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init.constant_(self.conv2.bias, 0) |
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init.kaiming_normal_(self.conv3.weight, nonlinearity='relu') |
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init.constant_(self.conv3.bias, 0) |
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init.kaiming_normal_(self.FC.weight, nonlinearity='sigmoid') |
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init.constant_(self.FC.bias, 0) |
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transformer_components = [ |
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self.transformer, self.generator, |
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self.positional_encoding |
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] |
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for component in transformer_components: |
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for p in component.parameters(): |
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if p.dim() > 1: |
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nn.init.xavier_uniform_(p) |
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for m in (self.gru1, self.gru2): |
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stdv = math.sqrt(2 / (96 * 3 * 6 + 256)) |
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for i in range(0, 256 * 3, 256): |
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init.uniform_(m.weight_ih_l0[i: i + 256], |
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-math.sqrt(3) * stdv, math.sqrt(3) * stdv) |
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init.orthogonal_(m.weight_hh_l0[i: i + 256]) |
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init.constant_(m.bias_ih_l0[i: i + 256], 0) |
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init.uniform_(m.weight_ih_l0_reverse[i: i + 256], |
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-math.sqrt(3) * stdv, math.sqrt(3) * stdv) |
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init.orthogonal_(m.weight_hh_l0_reverse[i: i + 256]) |
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init.constant_(m.bias_ih_l0_reverse[i: i + 256], 0) |
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def forward_gru(self, x): |
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x = self.conv1(x) |
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x = self.relu(x) |
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x = self.dropout3d(x) |
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x = self.pool1(x) |
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x = self.conv2(x) |
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x = self.relu(x) |
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x = self.dropout3d(x) |
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x = self.pool2(x) |
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x = self.conv3(x) |
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x = self.relu(x) |
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x = self.dropout3d(x) |
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x = self.pool3(x) |
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x = x.permute(2, 0, 1, 3, 4).contiguous() |
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x = x.view(x.size(0), x.size(1), -1) |
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self.gru1.flatten_parameters() |
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self.gru2.flatten_parameters() |
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if self.pre_gru_repeats > 1: |
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x = torch.repeat_interleave( |
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x, dim=0, repeats=self.pre_gru_repeats |
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) |
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x, h = self.gru1(x) |
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x = self.dropout(x) |
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x, h = self.gru2(x) |
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x = self.dropout(x) |
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return x |
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def predict_from_gru_out(self, x): |
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x = self.FC(x) |
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x = x.permute(1, 0, 2).contiguous() |
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return x |
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def forward(self, x): |
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x = self.forward_gru(x) |
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x = self.predict_from_gru_out(x) |
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return x |
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def make_src_embeds(self, x): |
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x = self.embeds_layer(x) |
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x = self.relu(x) |
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return x |
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def seq_forward( |
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self, src_embeds: Tensor, trg: Tensor, |
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src_mask: Tensor, tgt_mask: Tensor, src_padding_mask: Tensor, |
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tgt_padding_mask: Tensor, memory_key_padding_mask: Tensor |
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): |
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src_emb = self.positional_encoding(src_embeds) |
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tgt_emb = self.positional_encoding(self.tgt_tok_emb(trg)) |
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outs = self.transformer( |
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src_emb, tgt_emb, src_mask, tgt_mask, None, |
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src_padding_mask, tgt_padding_mask, memory_key_padding_mask |
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) |
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return self.generator(outs) |
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