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| from torchtext.data.metrics import bleu_score | |
| def bleu(valid_src_data, valid_trg_data, model, device, k, max_strlen): | |
| pred_sents = [] | |
| for sentence in valid_src_data: | |
| pred_trg = model.translate_sentence(sentence, device, k, max_strlen) | |
| pred_sents.append(pred_trg) | |
| pred_sents = [self.TRG.preprocess(sent) for sent in pred_sents] | |
| trg_sents = [[sent.split()] for sent in valid_trg_data] | |
| return bleu_score(pred_sents, trg_sents) | |
| def bleu_single(model, valid_dataset, debug=False): | |
| """Perform single sentence translation, then calculate bleu score. Update when batch beam search is online""" | |
| # need to join the sentence back per sample (the iterator is the one that had already been split to tokens) | |
| # THIS METRIC USE 2D vs 3D! AAAAAAHHHHHHH!!!! | |
| translate_pair = ( ([pair.trg], model.translate_sentence(pair.src, debug=debug)) for pair in valid_dataset) | |
| # raise Exception(next(translate_pair)) | |
| labels, predictions = [list(l) for l in zip(*translate_pair)] # zip( *((l, p.split()) for l, p in translate_pair) ) | |
| return bleu_score(predictions, labels) | |
| def bleu_batch(model, valid_dataset, batch_size, debug=False): | |
| """Perform batch sentence translation in the same vein.""" | |
| predictions = model.translate_batch_sentence([s.src for s in valid_dataset], output_tokens=True, batch_size=batch_size) | |
| labels = [[s.trg] for s in valid_dataset] | |
| return bleu_score(predictions, labels) | |
| def _revert_trg(sent, eos): # revert batching process on sentence | |
| try: | |
| endloc = sent.index(eos) | |
| return sent[1:endloc] | |
| except ValueError: | |
| return sent[1:] | |
| def bleu_batch_iter(model, valid_iter, src_lang=None, trg_lang=None, eos_token="<eos>", debug=False): | |
| """Perform batched translations; other metrics are the same. Note that the inputs/outputs had been preprocessed, but have [length, batch_size] shape as per BucketIterator""" | |
| # raise NotImplementedError("Error during calculation, currently unusable.") | |
| # raise Exception([[model.SRC.vocab.itos[t] for t in batch] for batch in next(iter(valid_iter)).src.transpose(0, 1)]) | |
| translated_batched_pair = ( | |
| ( | |
| pair.trg.transpose(0, 1), # transpose due to timestep-first batches | |
| model.decode_strategy.translate_batch_sentence( | |
| pair.src.transpose(0, 1), | |
| src_lang=src_lang, | |
| trg_lang=trg_lang, | |
| output_tokens=True, | |
| field_processed=True, | |
| replace_unk=False, # do not replace in this version | |
| debug=debug | |
| ) | |
| ) | |
| for pair in valid_iter | |
| ) | |
| flattened_pair = ( ([model.TRG.vocab.itos[i] for i in trg], pred) for batch_trg, batch_pred in translated_batched_pair for trg, pred in zip(batch_trg, batch_pred) ) | |
| flat_labels, predictions = [list(l) for l in zip(*flattened_pair)] | |
| labels = [[_revert_trg(l, eos_token)] for l in flat_labels] # remove <sos> and <eos> also updim the trg for 3D requirements. | |
| return bleu_score(predictions, labels) | |