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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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import math |
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import numpy as np |
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from helpers import * |
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class LipNet(torch.nn.Module): |
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def __init__( |
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self, output_classes, dropout_p=0.5, pre_gru_repeats=0 |
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): |
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super(LipNet, self).__init__() |
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self.pre_gru_repeats = pre_gru_repeats |
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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(96*4*8, 256, 1, bidirectional=True) |
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self.gru2 = nn.GRU(512, 256, 1, bidirectional=True) |
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self.output_classes = output_classes |
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self.FC = nn.Linear(512, 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._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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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(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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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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