def evaluate(model, data_loader, tokenizer, rouge1, rouge2, attn_id,
tgt_type_id, args):
model.eval()
vocab = tokenizer.vocab
eos_id = vocab[tokenizer.sep_token]
sos_id = vocab[tokenizer.cls_token]
pad_id = vocab[tokenizer.pad_token]
unk_id = vocab[tokenizer.unk_token]
vocab_size = len(vocab)
evaluated_sentences_ids = []
reference_sentences_ids = []
logger.info("Evaluating...")
for data in tqdm(data_loader):
(src_ids, src_tids, src_pids, _, _, _, _, _, _, _, _,
raw_tgt_labels) = data # never use target when infer
# Use greedy_search_infilling or beam_search_infilling to get predictions
output_ids = beam_search_infilling(
model,
src_ids,
src_tids,
eos_id=eos_id,
sos_id=sos_id,
attn_id=attn_id,
pad_id=pad_id,
unk_id=unk_id,
vocab_size=vocab_size,
max_decode_len=args.max_decode_len,
max_encode_len=args.max_encode_len,
beam_width=args.beam_width,
length_penalty=args.length_penalty,
tgt_type_id=tgt_type_id)
for ids in output_ids.tolist():
if eos_id in ids:
ids = ids[:ids.index(eos_id)]
evaluated_sentences_ids.append(ids)
for ids in raw_tgt_labels.numpy().tolist():
ids = ids[:ids.index(eos_id)]
reference_sentences_ids.append(ids)
score1 = rouge1.score(evaluated_sentences_ids, reference_sentences_ids)
score2 = rouge2.score(evaluated_sentences_ids, reference_sentences_ids)
logger.info("Rouge-1: %.5f ,Rouge-2: %.5f" % (score1 * 100, score2 * 100))
evaluated_sentences = []
reference_sentences = []
for ids in reference_sentences_ids[:5]:
reference_sentences.append(''.join(
map(post_process, vocab.to_tokens(ids))))
for ids in evaluated_sentences_ids[:5]:
evaluated_sentences.append(''.join(
map(post_process, vocab.to_tokens(ids))))
logger.debug(reference_sentences)
logger.debug(evaluated_sentences)
model.train()
def predict():
paddle.set_device("gpu" if args.use_gpu else "cpu")
model = ErnieForGeneration.from_pretrained(args.model_name_or_path)
if "ernie-tiny" in args.model_name_or_path:
tokenizer = ErnieTinyTokenizer.from_pretrained(args.model_name_or_path)
elif "ernie" in args.model_name_or_path:
tokenizer = ErnieTokenizer.from_pretrained(args.model_name_or_path)
elif "roberta" in args.model_name_or_path or "rbt" in args.model_name_or_path:
tokenizer = RobertaTokenizer.from_pretrained(args.model_name_or_path)
elif "electra" in args.model_name_or_path:
tokenizer = ElectraTokenizer.from_pretrained(args.model_name_or_path)
else:
tokenizer = BertTokenizer.from_pretrained(args.model_name_or_path)
dev_dataset = Poetry.get_datasets(['dev'])
attn_id = tokenizer.vocab[
'[ATTN]'] if '[ATTN]' in tokenizer.vocab else tokenizer.vocab['[MASK]']
tgt_type_id = model.sent_emb.weight.shape[0] - 1
trans_func = convert_example(tokenizer=tokenizer,
attn_id=attn_id,
tgt_type_id=tgt_type_id,
max_encode_len=args.max_encode_len,
max_decode_len=args.max_decode_len)
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id), # src_ids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # src_pids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # src_sids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # tgt_ids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # tgt_pids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # tgt_sids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # attn_ids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # tgt_labels
): after_padding(fn(samples))
dev_dataset = dev_dataset.apply(trans_func, lazy=True)
test_batch_sampler = paddle.io.BatchSampler(dev_dataset,
batch_size=args.batch_size,
shuffle=False)
data_loader = DataLoader(dataset=dev_dataset,
batch_sampler=test_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
if args.init_checkpoint:
model_state = paddle.load(args.init_checkpoint)
model.set_state_dict(model_state)
model.eval()
vocab = tokenizer.vocab
eos_id = vocab[tokenizer.sep_token]
sos_id = vocab[tokenizer.cls_token]
pad_id = vocab[tokenizer.pad_token]
unk_id = vocab[tokenizer.unk_token]
vocab_size = len(vocab)
evaluated_sentences = []
evaluated_sentences_ids = []
logger.info("Predicting...")
for data in data_loader:
(src_ids, src_sids, src_pids, _, _, _, _, _, _, _, _,
raw_tgt_labels) = data # never use target when infer
# Use greedy_search_infilling or beam_search_infilling to get predictions
output_ids = beam_search_infilling(model,
src_ids,
src_sids,
eos_id=eos_id,
sos_id=sos_id,
attn_id=attn_id,
pad_id=pad_id,
unk_id=unk_id,
vocab_size=vocab_size,
max_decode_len=args.max_decode_len,
max_encode_len=args.max_encode_len,
beam_width=args.beam_width,
length_penalty=args.length_penalty,
tgt_type_id=tgt_type_id)
for source_ids, target_ids, predict_ids in zip(
src_ids.numpy().tolist(),
raw_tgt_labels.numpy().tolist(), output_ids.tolist()):
if eos_id in predict_ids:
predict_ids = predict_ids[:predict_ids.index(eos_id)]
source_sentence = ''.join(
map(post_process,
vocab.to_tokens(source_ids[1:source_ids.index(eos_id)])))
tgt_sentence = ''.join(
map(post_process,
vocab.to_tokens(target_ids[1:target_ids.index(eos_id)])))
predict_ids = ''.join(
map(post_process, vocab.to_tokens(predict_ids)))
print("source :%s\ntarget :%s\npredict:%s\n" %
(source_sentence, tgt_sentence, predict_ids))
def evaluate():
paddle.set_device("gpu" if args.use_gpu else "cpu")
model = ErnieForGeneration.from_pretrained(args.model_name_or_path)
if "ernie-tiny" in args.model_name_or_path:
tokenizer = ErnieTinyTokenizer.from_pretrained(args.model_name_or_path)
elif "ernie" in args.model_name_or_path:
tokenizer = ErnieTokenizer.from_pretrained(args.model_name_or_path)
elif "roberta" in args.model_name_or_path or "rbt" in args.model_name_or_path:
tokenizer = RobertaTokenizer.from_pretrained(args.model_name_or_path)
elif "electra" in args.model_name_or_path:
tokenizer = ElectraTokenizer.from_pretrained(args.model_name_or_path)
else:
tokenizer = BertTokenizer.from_pretrained(args.model_name_or_path)
dev_dataset = Poetry.get_datasets(['dev'])
attn_id = tokenizer.vocab[
'[ATTN]'] if '[ATTN]' in tokenizer.vocab else tokenizer.vocab['[MASK]']
tgt_type_id = model.sent_emb.weight.shape[0] - 1
trans_func = convert_example(tokenizer=tokenizer,
attn_id=attn_id,
tgt_type_id=tgt_type_id,
max_encode_len=args.max_encode_len,
max_decode_len=args.max_decode_len)
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id), # src_ids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # src_pids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # src_sids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # tgt_ids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # tgt_pids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # tgt_sids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # attn_ids
Pad(axis=0, pad_val=tokenizer.pad_token_id), # tgt_labels
): after_padding(fn(samples))
dev_dataset = dev_dataset.apply(trans_func, lazy=True)
dev_batch_sampler = paddle.io.BatchSampler(dev_dataset,
batch_size=args.batch_size,
shuffle=False)
data_loader = DataLoader(dataset=dev_dataset,
batch_sampler=dev_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
rouge1 = Rouge1()
rouge2 = Rouge2()
if args.init_checkpoint:
model_state = paddle.load(args.init_checkpoint)
model.set_state_dict(model_state)
model.eval()
vocab = tokenizer.vocab
eos_id = vocab[tokenizer.sep_token]
sos_id = vocab[tokenizer.cls_token]
pad_id = vocab[tokenizer.pad_token]
unk_id = vocab[tokenizer.unk_token]
vocab_size = len(vocab)
evaluated_sentences_ids = []
reference_sentences_ids = []
logger.info("Evaluating...")
for data in tqdm(data_loader):
(src_ids, src_sids, src_pids, _, _, _, _, _, _, _, _,
raw_tgt_labels) = data # never use target when infer
# Use greedy_search_infilling or beam_search_infilling to get predictions
output_ids = beam_search_infilling(model,
src_ids,
src_sids,
eos_id=eos_id,
sos_id=sos_id,
attn_id=attn_id,
pad_id=pad_id,
unk_id=unk_id,
vocab_size=vocab_size,
max_decode_len=args.max_decode_len,
max_encode_len=args.max_encode_len,
beam_width=args.beam_width,
length_penalty=args.length_penalty,
tgt_type_id=tgt_type_id)
for ids in output_ids.tolist():
if eos_id in ids:
ids = ids[:ids.index(eos_id)]
evaluated_sentences_ids.append(ids)
for ids in raw_tgt_labels.numpy().tolist():
ids = ids[:ids.index(eos_id)]
reference_sentences_ids.append(ids)
score1 = rouge1.score(evaluated_sentences_ids, reference_sentences_ids)
score2 = rouge2.score(evaluated_sentences_ids, reference_sentences_ids)
logger.info("Rouge-1: %.5f ,Rouge-2: %.5f" % (score1 * 100, score2 * 100))
