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)
all_tokens = torch.zeros([0], device=device, dtype=torch.long)
all_scores = torch.zeros([0], device=device)
# 循环，这里只使用长度限制，后面处理的时候把EOS去掉了。
for _ in range(max_length):
# Decoder forward一步
decoder_output, decoder_hidden = self.decoder(decoder_input, decoder_hidden, encoder_outputs)
# decoder_outputs是(batch=1, vob_size)
# 使用max返回概率最大的词和得分
decoder_scores, decoder_input = torch.max(decoder_output, dim=1)
# 把解码结果保存到all_tokens和all_scores里
all_tokens = torch.cat((all_tokens, decoder_input), dim=0)
all_scores = torch.cat((all_scores, decoder_scores), dim=0)
# decoder_input是当前时刻输出的词的ID，这是个一维的向量，因为max会减少一维。
# 但是decoder要求有一个batch维度，因此用unsqueeze增加batch维度。
decoder_input = torch.unsqueeze(decoder_input, 0)
# 返回所有的词和得分。
return all_tokens, all_scores
######################################################################
# Run Evaluation
#
# 进入eval模式，从而去掉dropout。
encoder.eval()
decoder.eval()
# 构造searcher对象
searcher = GreedySearchDecoder(encoder, decoder)
# 测试
evaluateInput(encoder, decoder, searcher, voc)