53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
import torch
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import torch.nn as nn
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import arguments
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from constants import EOS_ID, SOS_ID
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class LMModel(nn.Module):
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def __init__(self, hidden_size, vocab_size, num_layers=2):
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super(LMModel, self).__init__()
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self.args = arguments.parse_args()
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self.hidden_size = hidden_size
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self.num_layers = num_layers
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self.embedding = torch.nn.Embedding(
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vocab_size, self.args.embedding_dim)
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self.backbone = torch.nn.GRU(input_size=self.args.embedding_dim, hidden_size=hidden_size, num_layers=num_layers,
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batch_first=True, dropout=0.1)
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self.linear = torch.nn.Linear(
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in_features=hidden_size, out_features=vocab_size)
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def forward(self, input, hidden=None):
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x = self.embedding(input)
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x, h_n = self.backbone(x, hidden)
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x = self.linear(x)
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return x, h_n
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def initHidden(self, batch_size):
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return \
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torch.zeros((self.num_layers, batch_size,
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self.hidden_size), device=self.args.device)
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def generate(self, num_gen=1):
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input = torch.tensor([[SOS_ID]
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for _ in range(num_gen)]).to(self.args.device)
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ret = input.clone().detach()
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hidden = None
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for i in range(self.args.password_length):
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output, hidden = self.forward(input, hidden)
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output = output.view((output.size(0), output.size(2)))
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output = nn.functional.softmax(output, dim=1)
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choices = torch.multinomial(output, num_samples=1)
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ret = torch.cat((ret, choices), dim=1)
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input = choices
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eostensor = torch.tensor([[EOS_ID]
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for _ in range(num_gen)]).to(self.args.device)
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ret = torch.cat((ret, eostensor), dim=1)
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return ret
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