118 lines
4.7 KiB
Python
118 lines
4.7 KiB
Python
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import paddle
|
|
import numpy as np
|
|
|
|
|
|
class VQATokenPad(object):
|
|
def __init__(
|
|
self,
|
|
max_seq_len=512,
|
|
pad_to_max_seq_len=True,
|
|
return_attention_mask=True,
|
|
return_token_type_ids=True,
|
|
truncation_strategy="longest_first",
|
|
return_overflowing_tokens=False,
|
|
return_special_tokens_mask=False,
|
|
infer_mode=False,
|
|
**kwargs,
|
|
):
|
|
self.max_seq_len = max_seq_len
|
|
self.pad_to_max_seq_len = max_seq_len
|
|
self.return_attention_mask = return_attention_mask
|
|
self.return_token_type_ids = return_token_type_ids
|
|
self.truncation_strategy = truncation_strategy
|
|
self.return_overflowing_tokens = return_overflowing_tokens
|
|
self.return_special_tokens_mask = return_special_tokens_mask
|
|
self.pad_token_label_id = paddle.nn.CrossEntropyLoss().ignore_index
|
|
self.infer_mode = infer_mode
|
|
|
|
def __call__(self, data):
|
|
needs_to_be_padded = (
|
|
self.pad_to_max_seq_len and len(data["input_ids"]) < self.max_seq_len
|
|
)
|
|
|
|
if needs_to_be_padded:
|
|
if "tokenizer_params" in data:
|
|
tokenizer_params = data.pop("tokenizer_params")
|
|
else:
|
|
tokenizer_params = dict(
|
|
padding_side="right", pad_token_type_id=0, pad_token_id=1
|
|
)
|
|
|
|
difference = self.max_seq_len - len(data["input_ids"])
|
|
if tokenizer_params["padding_side"] == "right":
|
|
if self.return_attention_mask:
|
|
data["attention_mask"] = [1] * len(data["input_ids"]) + [
|
|
0
|
|
] * difference
|
|
if self.return_token_type_ids:
|
|
data["token_type_ids"] = (
|
|
data["token_type_ids"]
|
|
+ [tokenizer_params["pad_token_type_id"]] * difference
|
|
)
|
|
if self.return_special_tokens_mask:
|
|
data["special_tokens_mask"] = (
|
|
data["special_tokens_mask"] + [1] * difference
|
|
)
|
|
data["input_ids"] = (
|
|
data["input_ids"] + [tokenizer_params["pad_token_id"]] * difference
|
|
)
|
|
if not self.infer_mode:
|
|
data["labels"] = (
|
|
data["labels"] + [self.pad_token_label_id] * difference
|
|
)
|
|
data["bbox"] = data["bbox"] + [[0, 0, 0, 0]] * difference
|
|
elif tokenizer_params["padding_side"] == "left":
|
|
if self.return_attention_mask:
|
|
data["attention_mask"] = [0] * difference + [1] * len(
|
|
data["input_ids"]
|
|
)
|
|
if self.return_token_type_ids:
|
|
data["token_type_ids"] = [
|
|
tokenizer_params["pad_token_type_id"]
|
|
] * difference + data["token_type_ids"]
|
|
if self.return_special_tokens_mask:
|
|
data["special_tokens_mask"] = [1] * difference + data[
|
|
"special_tokens_mask"
|
|
]
|
|
data["input_ids"] = [
|
|
tokenizer_params["pad_token_id"]
|
|
] * difference + data["input_ids"]
|
|
if not self.infer_mode:
|
|
data["labels"] = [self.pad_token_label_id] * difference + data[
|
|
"labels"
|
|
]
|
|
data["bbox"] = [[0, 0, 0, 0]] * difference + data["bbox"]
|
|
else:
|
|
if self.return_attention_mask:
|
|
data["attention_mask"] = [1] * len(data["input_ids"])
|
|
|
|
for key in data:
|
|
if key in [
|
|
"input_ids",
|
|
"labels",
|
|
"token_type_ids",
|
|
"bbox",
|
|
"attention_mask",
|
|
]:
|
|
if self.infer_mode:
|
|
if key != "labels":
|
|
length = min(len(data[key]), self.max_seq_len)
|
|
data[key] = data[key][:length]
|
|
else:
|
|
continue
|
|
data[key] = np.array(data[key], dtype="int64")
|
|
return data
|