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'''
Author: Qiguang Chen
Date: 2023-01-11 10:39:26
LastEditors: Qiguang Chen
LastEditTime: 2023-02-19 15:39:48
Description: all class for load data.
'''
import os
import torch
import json
from datasets import load_dataset, Dataset
from torch.utils.data import DataLoader
from common.utils import InputData
ABS_PATH=os.path.join(os.path.abspath(os.path.dirname(__file__)), "../")
class DataFactory(object):
def __init__(self, tokenizer,use_multi_intent=False, to_lower_case=True):
"""_summary_
Args:
tokenizer (Tokenizer): _description_
use_multi_intent (bool, optional): _description_. Defaults to False.
"""
self.tokenizer = tokenizer
self.slot_label_list = []
self.intent_label_list = []
self.use_multi = use_multi_intent
self.to_lower_case = to_lower_case
self.slot_label_dict = None
self.intent_label_dict = None
def __is_supported_datasets(self, dataset_name:str)->bool:
return dataset_name.lower() in ["atis", "snips", "mix-atis", "mix-atis"]
def load_dataset(self, dataset_config, split="train"):
dataset_name = None
if split not in dataset_config:
dataset_name = dataset_config.get("dataset_name")
elif self.__is_supported_datasets(dataset_config[split]):
dataset_name = dataset_config[split].lower()
if dataset_name is not None:
return load_dataset("LightChen2333/OpenSLU", dataset_name, split=split)
else:
data_file = dataset_config[split]
data_dict = {"text": [], "slot": [], "intent":[]}
with open(data_file, encoding="utf-8") as f:
for line in f:
row = json.loads(line)
data_dict["text"].append(row["text"])
data_dict["slot"].append(row["slot"])
data_dict["intent"].append(row["intent"])
return Dataset.from_dict(data_dict)
def update_label_names(self, dataset):
for intent_labels in dataset["intent"]:
if self.use_multi:
intent_label = intent_labels.split("#")
else:
intent_label = [intent_labels]
for x in intent_label:
if x not in self.intent_label_list:
self.intent_label_list.append(x)
for slot_label in dataset["slot"]:
for x in slot_label:
if x not in self.slot_label_list:
self.slot_label_list.append(x)
self.intent_label_dict = {key: index for index,
key in enumerate(self.intent_label_list)}
self.slot_label_dict = {key: index for index,
key in enumerate(self.slot_label_list)}
def update_vocabulary(self, dataset):
if self.tokenizer.name_or_path in ["word_tokenizer"]:
for data in dataset:
self.tokenizer.add_instance(data["text"])
@staticmethod
def fast_align_data(text, padding_side="right"):
for i in range(len(text.input_ids)):
desired_output = []
for word_id in text.word_ids(i):
if word_id is not None:
start, end = text.word_to_tokens(
i, word_id, sequence_index=0 if padding_side == "right" else 1)
if start == end - 1:
tokens = [start]
else:
tokens = [start, end - 1]
if len(desired_output) == 0 or desired_output[-1] != tokens:
desired_output.append(tokens)
yield desired_output
def fast_align(self,
batch,
ignore_index=-100,
device="cuda",
config=None,
enable_label=True,
label2tensor=True):
if self.to_lower_case:
input_list = [[t.lower() for t in x["text"]] for x in batch]
else:
input_list = [x["text"] for x in batch]
text = self.tokenizer(input_list,
return_tensors="pt",
padding=True,
is_split_into_words=True,
truncation=True,
**config).to(device)
if enable_label:
if label2tensor:
slot_mask = torch.ones_like(text.input_ids) * ignore_index
for i, offsets in enumerate(
DataFactory.fast_align_data(text, padding_side=self.tokenizer.padding_side)):
num = 0
assert len(offsets) == len(batch[i]["text"])
assert len(offsets) == len(batch[i]["slot"])
for off in offsets:
slot_mask[i][off[0]
] = self.slot_label_dict[batch[i]["slot"][num]]
num += 1
slot = slot_mask.clone()
attentin_id = 0 if self.tokenizer.padding_side == "right" else 1
for i, slot_batch in enumerate(slot):
for j, x in enumerate(slot_batch):
if x == ignore_index and text.attention_mask[i][j] == attentin_id and (text.input_ids[i][
j] not in self.tokenizer.all_special_ids or text.input_ids[i][j] == self.tokenizer.unk_token_id):
slot[i][j] = slot[i][j - 1]
slot = slot.to(device)
if not self.use_multi:
intent = torch.tensor(
[self.intent_label_dict[x["intent"]] for x in batch]).to(device)
else:
one_hot = torch.zeros(
(len(batch), len(self.intent_label_list)), dtype=torch.float)
for index, b in enumerate(batch):
for x in b["intent"].split("#"):
one_hot[index][self.intent_label_dict[x]] = 1.
intent = one_hot.to(device)
else:
slot_mask = None
slot = [['#' for _ in range(text.input_ids.shape[1])]
for _ in range(text.input_ids.shape[0])]
for i, offsets in enumerate(DataFactory.fast_align_data(text)):
num = 0
for off in offsets:
slot[i][off[0]] = batch[i]["slot"][num]
num += 1
if not self.use_multi:
intent = [x["intent"] for x in batch]
else:
intent = [
[x for x in b["intent"].split("#")] for b in batch]
return InputData((text, slot, intent))
else:
return InputData((text, None, None))
def general_align_data(self, split_text_list, raw_text_list, encoded_text):
for i in range(len(split_text_list)):
desired_output = []
jdx = 0
offset = encoded_text.offset_mapping[i].tolist()
split_texts = split_text_list[i]
raw_text = raw_text_list[i]
last = 0
temp_offset = []
for off in offset:
s, e = off
if len(temp_offset) > 0 and (e != 0 and last == s):
len_1 = off[1] - off[0]
len_2 = temp_offset[-1][1] - temp_offset[-1][0]
if len_1 > len_2:
temp_offset.pop(-1)
temp_offset.append([0, 0])
temp_offset.append(off)
continue
temp_offset.append(off)
last = s
offset = temp_offset
for split_text in split_texts:
while jdx < len(offset) and offset[jdx][0] == 0 and offset[jdx][1] == 0:
jdx += 1
if jdx == len(offset):
continue
start_, end_ = offset[jdx]
tokens = None
if split_text == raw_text[start_:end_].strip():
tokens = [jdx]
else:
# Compute "xxx" -> "xx" "#x"
temp_jdx = jdx
last_str = raw_text[start_:end_].strip()
while last_str != split_text and temp_jdx < len(offset) - 1:
temp_jdx += 1
last_str += raw_text[offset[temp_jdx]
[0]:offset[temp_jdx][1]].strip()
if temp_jdx == jdx:
raise ValueError("Illegal Input data")
elif last_str == split_text:
tokens = [jdx, temp_jdx]
jdx = temp_jdx
else:
jdx -= 1
jdx += 1
if tokens is not None:
desired_output.append(tokens)
yield desired_output
def general_align(self,
batch,
ignore_index=-100,
device="cuda",
config=None,
enable_label=True,
label2tensor=True,
locale="en-US"):
if self.to_lower_case:
raw_data = [" ".join(x["text"]).lower() if locale not in ['ja-JP', 'zh-CN', 'zh-TW'] else "".join(x["text"]) for x in
batch]
input_list = [[t.lower() for t in x["text"]] for x in batch]
else:
input_list = [x["text"] for x in batch]
raw_data = [" ".join(x["text"]) if locale not in ['ja-JP', 'zh-CN', 'zh-TW'] else "".join(x["text"]) for x in
batch]
text = self.tokenizer(raw_data,
return_tensors="pt",
padding=True,
truncation=True,
return_offsets_mapping=True,
**config).to(device)
if enable_label:
if label2tensor:
slot_mask = torch.ones_like(text.input_ids) * ignore_index
for i, offsets in enumerate(
self.general_align_data(input_list, raw_data, encoded_text=text)):
num = 0
# if len(offsets) != len(batch[i]["text"]) or len(offsets) != len(batch[i]["slot"]):
# if
for off in offsets:
slot_mask[i][off[0]
] = self.slot_label_dict[batch[i]["slot"][num]]
num += 1
# slot = slot_mask.clone()
# attentin_id = 0 if self.tokenizer.padding_side == "right" else 1
# for i, slot_batch in enumerate(slot):
# for j, x in enumerate(slot_batch):
# if x == ignore_index and text.attention_mask[i][j] == attentin_id and text.input_ids[i][
# j] not in self.tokenizer.all_special_ids:
# slot[i][j] = slot[i][j - 1]
slot = slot_mask.to(device)
if not self.use_multi:
intent = torch.tensor(
[self.intent_label_dict[x["intent"]] for x in batch]).to(device)
else:
one_hot = torch.zeros(
(len(batch), len(self.intent_label_list)), dtype=torch.float)
for index, b in enumerate(batch):
for x in b["intent"].split("#"):
one_hot[index][self.intent_label_dict[x]] = 1.
intent = one_hot.to(device)
else:
slot_mask = None
slot = [['#' for _ in range(text.input_ids.shape[1])]
for _ in range(text.input_ids.shape[0])]
for i, offsets in enumerate(self.general_align_data(input_list, raw_data, encoded_text=text)):
num = 0
for off in offsets:
slot[i][off[0]] = batch[i]["slot"][num]
num += 1
if not self.use_multi:
intent = [x["intent"] for x in batch]
else:
intent = [
[x for x in b["intent"].split("#")] for b in batch]
return InputData((text, slot, intent))
else:
return InputData((text, None, None))
def batch_fn(self,
batch,
ignore_index=-100,
device="cuda",
config=None,
align_mode="fast",
enable_label=True,
label2tensor=True):
if align_mode == "fast":
# try:
return self.fast_align(batch,
ignore_index=ignore_index,
device=device,
config=config,
enable_label=enable_label,
label2tensor=label2tensor)
# except:
# return self.general_align(batch,
# ignore_index=ignore_index,
# device=device,
# config=config,
# enable_label=enable_label,
# label2tensor=label2tensor)
else:
return self.general_align(batch,
ignore_index=ignore_index,
device=device,
config=config,
enable_label=enable_label,
label2tensor=label2tensor)
def get_data_loader(self,
dataset,
batch_size,
shuffle=False,
device="cuda",
enable_label=True,
align_mode="fast",
label2tensor=True, **config):
data_loader = DataLoader(dataset,
shuffle=shuffle,
batch_size=batch_size,
collate_fn=lambda x: self.batch_fn(x,
device=device,
config=config,
enable_label=enable_label,
align_mode=align_mode,
label2tensor=label2tensor))
return data_loader
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