# -*- coding: utf-8 -*-
# file: data_utils_for_inference.py
# time: 02/11/2022 15:39
# author: YANG, HENG <hy345@exeter.ac.uk> (杨恒)
# github: https://github.com/yangheng95
# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en
# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research
# Copyright (C) 2022. All Rights Reserved.
import tqdm
from torch.utils.data import Dataset
from pyabsa import LabelPaddingOption
from pyabsa.framework.dataset_class.dataset_template import PyABSADataset
from pyabsa.utils.file_utils.file_utils import load_dataset_from_file
from pyabsa.utils.pyabsa_utils import fprint
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class GloVeCDDInferenceDataset(Dataset):
def __init__(self, config, tokenizer):
self.tokenizer = tokenizer
self.config = config
self.data = []
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def parse_sample(self, text):
return [text]
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def prepare_infer_sample(self, text: str, ignore_error):
if isinstance(text, list):
self.process_data(text, ignore_error=ignore_error)
else:
self.process_data(self.parse_sample(text), ignore_error=ignore_error)
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def prepare_infer_dataset(self, infer_file, ignore_error):
lines = load_dataset_from_file(infer_file, config=self.config)
samples = []
for sample in lines:
if sample:
samples.extend(self.parse_sample(sample))
self.process_data(samples, ignore_error)
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def process_data(self, samples, ignore_error=True):
all_data = []
if len(samples) > 100:
it = tqdm.tqdm(samples, desc="building word indices")
else:
it = samples
for ex_id, text in enumerate(it):
try:
# handle for empty lines in inference dataset
if text is None or "" == text.strip():
raise RuntimeError("Invalid Input!")
if "$LABEL$" in text:
text, label = (
text.split("$LABEL$")[0].strip(),
text.split("$LABEL$")[1].strip(),
)
text = text.replace("[PADDING]", "")
else:
label = LabelPaddingOption.LABEL_PADDING
text_indices = self.tokenizer.text_to_sequence(text)
data = {
"ex_id": ex_id,
"text_indices": text_indices
if "text_indices" in self.config.model.inputs
else 0,
"text_raw": text,
"label": label,
}
all_data.append(data)
except Exception as e:
if ignore_error:
fprint("Ignore error while processing:", text)
else:
raise e
self.data = all_data
self.data = PyABSADataset.covert_to_tensor(self.data)
return self.data
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def __getitem__(self, index):
return self.data[index]
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def __len__(self):
return len(self.data)