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import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.nn import Transformer
import math
import numpy as np
from helpers import *
from torch import Tensor
from models.PhonemeTransformer import (
PositionalEncoding, TokenEmbedding
)
class LipNetPlus(torch.nn.Module):
def __init__(
self, output_classes, dropout_p=0.0, pre_gru_repeats=0,
gru_output_size=512, embeds_size=256,
output_vocab_size=512, dropout_t=0.1,
src_vocab_size=4, num_encoder_layers: int = 3,
num_decoder_layers: int = 3, nhead: int = 8,
dim_feedforward: int = 512,
):
super(LipNetPlus, self).__init__()
assert gru_output_size % 2 == 0
self.pre_gru_repeats = pre_gru_repeats
self.gru_out_size = gru_output_size
self.gru_hidden_size = gru_output_size // 2
self.embeds_size = embeds_size
self.output_vocab_size = output_vocab_size
self.gru_output_size = gru_output_size
self.dropout_t = dropout_t
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, self.gru_hidden_size, 1, bidirectional=True
)
self.gru2 = nn.GRU(
self.gru_output_size, self.gru_hidden_size, 1, bidirectional=True
)
self.output_classes = output_classes
self.FC = nn.Linear(self.gru_output_size, output_classes + 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)
self.src_tok_emb = TokenEmbedding(
src_vocab_size, self.embeds_size
)
self.tgt_tok_emb = TokenEmbedding(
output_vocab_size, self.embeds_size
)
self.embeds_layer = nn.Linear(
self.gru_output_size, self.embeds_size
)
self.transformer = Transformer(
d_model=self.embeds_size, nhead=nhead,
num_encoder_layers=num_encoder_layers,
num_decoder_layers=num_decoder_layers,
dim_feedforward=dim_feedforward,
dropout=dropout_t
)
self.positional_encoding = PositionalEncoding(
self.embeds_size, dropout=self.dropout_t
)
self.generator = nn.Linear(
self.embeds_size, self.output_vocab_size
)
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)
transformer_components = [
self.transformer, self.generator,
self.positional_encoding
]
for component in transformer_components:
for p in component.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
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_gru(self, x):
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()
if self.pre_gru_repeats > 1:
x = torch.repeat_interleave(
x, dim=0, repeats=self.pre_gru_repeats
)
x, h = self.gru1(x)
x = self.dropout(x)
x, h = self.gru2(x)
x = self.dropout(x)
return x
def predict_from_gru_out(self, x):
x = self.FC(x)
x = x.permute(1, 0, 2).contiguous()
# assert not contains_nan_or_inf(x19)
return x
def forward(self, x):
x = self.forward_gru(x)
x = self.predict_from_gru_out(x)
return x
def make_src_embeds(self, x):
x = self.embeds_layer(x)
x = self.relu(x)
return x
def seq_forward(
self, src_embeds: Tensor, trg: Tensor,
src_mask: Tensor, tgt_mask: Tensor, src_padding_mask: Tensor,
tgt_padding_mask: Tensor, memory_key_padding_mask: Tensor
):
src_emb = self.positional_encoding(src_embeds)
tgt_emb = self.positional_encoding(self.tgt_tok_emb(trg))
outs = self.transformer(
src_emb, tgt_emb, src_mask, tgt_mask, None,
src_padding_mask, tgt_padding_mask, memory_key_padding_mask
)
return self.generator(outs)
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