DocumentAIKit/ppocr/data/imaug/vqa/token/vqa_token_pad.py

# 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