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import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
import math
import numpy as np
from helpers import *
class LipNet(torch.nn.Module):
def __init__(
self, output_classes, dropout_p=0.5, pre_gru_repeats=0
):
super(LipNet, self).__init__()
self.pre_gru_repeats = pre_gru_repeats
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.output_classes = output_classes
self.FC = nn.Linear(512, 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._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):
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)
x = self.FC(x)
x = x.permute(1, 0, 2).contiguous()
# assert not contains_nan_or_inf(x19)
return x
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