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