def evaluate(vocab: Vocabulary, corpus_filename: str, encoder: EncoderRNN,
decoder: AttnDecoderRNN, max_src_length: int,
max_tgt_length: int):
device: torch.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
encoder.to(device)
decoder.to(device)
encoder.eval()
decoder.eval()
with torch.no_grad():
corpus = Corpus(
filename=corpus_filename,
max_src_length=max_src_length, # decoder.max_src_length,
vocab=vocab,
device=device)
for batch in torch.utils.data.DataLoader(dataset=corpus, batch_size=1):
input_tensor: torch.Tensor = batch["data"].permute(1, 0)
encoder_outputs = encoder.encode_sequence(input_tensor)
decoder_output = decoder.decode_sequence(
encoder_outputs=encoder_outputs,
start_symbol=corpus.characters.start_of_sequence.integer,
max_length=max_tgt_length)
_, top_i = decoder_output.topk(k=1)
predictions = top_i.squeeze(dim=2).squeeze(dim=1).tolist()
predicted_string = "".join(
[corpus.characters[i].string for i in predictions])
print(predicted_string)
encoder_sd = checkpoint['encoder']
decoder_sd = checkpoint['decoder']
encoder_optimizer_sd = checkpoint['en_opt']
decoder_optimizer_sd = checkpoint['de_opt']
embedding_sd = checkpoint['embedding']
voc.__dict__ = checkpoint['voc_dict']
embedding = nn.Embedding(voc.num_words, Config.hidden_size)
embedding.load_state_dict(embedding_sd)
encoder = EncoderRNN(Config.hidden_size, embedding,
Config.encoder_n_layers, Config.dropout)
decoder = LuongAttnDecoderRNN(Config.attn_model, embedding,
Config.hidden_size, voc.num_words,
Config.decoder_n_layers, Config.dropout)
encoder.load_state_dict(encoder_sd)
decoder.load_state_dict(decoder_sd)
encoder = encoder.to(Config.device)
decoder = decoder.to(Config.device)
# Set dropout layers to eval mode
encoder.eval()
decoder.eval()
# Initialize search module
searcher = GreedySearchDecoder(encoder, decoder)
# Begin chatting (uncomment and run the following line to begin)
evaluateInput(encoder, decoder, searcher, voc)
voc.__dict__ = checkpoint['voc_dict']
print('Building encoder and decoder ...')
# 初始化word embedding
embedding = nn.Embedding(voc.num_words, hidden_size)
if model_checkpoint:
embedding.load_state_dict(embedding_sd)
# 初始化encoder和decoder模型
encoder = EncoderRNN(hidden_size, embedding, encoder_n_layers, dropout)
decoder = LuongAttnDecoderRNN(attn_model, embedding, hidden_size, voc.num_words, decoder_n_layers, dropout)
if model_checkpoint:
encoder.load_state_dict(encoder_sd)
decoder.load_state_dict(decoder_sd)
# 使用合适的设备
encoder = encoder.to(device)
decoder = decoder.to(device)
print('Models built and ready to go!')
class GreedySearchDecoder(nn.Module):
def __init__(self, encoder, decoder):
super(GreedySearchDecoder, self).__init__()
self.encoder = encoder
self.decoder = decoder
def forward(self, input_seq, input_length, max_length):
# Encoder的Forward计算
encoder_outputs, encoder_hidden = self.encoder(input_seq, input_length)
# 把Encoder最后时刻的隐状态作为Decoder的初始值
decoder_hidden = encoder_hidden[:decoder.n_layers]
vocab = build_vocab(cleaned_news, cleaned_summaries, min_freq=3) # 生成 dataset 是DataTensor 格式 news_dataset = build_dataset(vocab, cleaned_news, config['max_len_news'], type='news') summaries_dataset = build_dataset(vocab, cleaned_summaries, config['max_len_summaries'], type='summaries') # 合并在一起 dataset = TensorDataset(news_dataset, summaries_dataset) # 加载预训练的word2vec模型（使用搜狗新闻训练得到的word2vec），维度是300 pre_embeddings = get_pretrained_embedding(config['pretrained_vector_path'], vocab, vector_dim=300).to(device) # 构建模型，选择隐状态和词向量维度相同，都是300 vocab_size = len(vocab) # encoder 使用的是单层双向gru encoder = EncoderRNN(vocab_size, 300, 300, n_layers=1, pre_embeddings=pre_embeddings) # decoder 使用双层单项gru decoder = DecoderRNN(vocab_size, 300, 300, n_layers=2, pre_embeddings=pre_embeddings) # 迁移到cuda上，training 要用 encoder.to(device) decoder.to(device) # 训练模型 training(encoder, decoder, dataset, vocab, config['lr'], config['batch_size'], config['epochs'])
