120 lines
4.9 KiB
Python
120 lines
4.9 KiB
Python
|
# coding=utf-8
|
||
|
|
# Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and HuggingFace Inc. team. All rights reserved.
|
||
|
|
#
|
||
|
|
# 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.
|
||
|
|
"""
|
||
|
|
Processor class for BridgeTower.
|
||
|
|
"""
|
||
|
|
|
||
|
|
from typing import List, Optional, Union
|
||
|
|
|
||
|
|
from ...processing_utils import ProcessorMixin
|
||
|
|
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
|
||
|
|
from ...utils import TensorType
|
||
|
|
|
||
|
|
|
||
|
|
class BridgeTowerProcessor(ProcessorMixin):
|
||
|
|
r"""
|
||
|
|
Constructs a BridgeTower processor which wraps a Roberta tokenizer and BridgeTower image processor into a single
|
||
|
|
processor.
|
||
|
|
|
||
|
|
[`BridgeTowerProcessor`] offers all the functionalities of [`BridgeTowerImageProcessor`] and
|
||
|
|
[`RobertaTokenizerFast`]. See the docstring of [`~BridgeTowerProcessor.__call__`] and
|
||
|
|
[`~BridgeTowerProcessor.decode`] for more information.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
image_processor (`BridgeTowerImageProcessor`):
|
||
|
|
An instance of [`BridgeTowerImageProcessor`]. The image processor is a required input.
|
||
|
|
tokenizer (`RobertaTokenizerFast`):
|
||
|
|
An instance of ['RobertaTokenizerFast`]. The tokenizer is a required input.
|
||
|
|
"""
|
||
|
|
|
||
|
|
attributes = ["image_processor", "tokenizer"]
|
||
|
|
image_processor_class = "BridgeTowerImageProcessor"
|
||
|
|
tokenizer_class = ("RobertaTokenizer", "RobertaTokenizerFast")
|
||
|
|
|
||
|
|
def __init__(self, image_processor, tokenizer):
|
||
|
|
super().__init__(image_processor, tokenizer)
|
||
|
|
|
||
|
|
def __call__(
|
||
|
|
self,
|
||
|
|
images,
|
||
|
|
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
|
||
|
|
add_special_tokens: bool = True,
|
||
|
|
padding: Union[bool, str, PaddingStrategy] = False,
|
||
|
|
truncation: Union[bool, str, TruncationStrategy] = None,
|
||
|
|
max_length: Optional[int] = None,
|
||
|
|
stride: int = 0,
|
||
|
|
pad_to_multiple_of: Optional[int] = None,
|
||
|
|
return_token_type_ids: Optional[bool] = None,
|
||
|
|
return_attention_mask: Optional[bool] = None,
|
||
|
|
return_overflowing_tokens: bool = False,
|
||
|
|
return_special_tokens_mask: bool = False,
|
||
|
|
return_offsets_mapping: bool = False,
|
||
|
|
return_length: bool = False,
|
||
|
|
verbose: bool = True,
|
||
|
|
return_tensors: Optional[Union[str, TensorType]] = None,
|
||
|
|
**kwargs,
|
||
|
|
) -> BatchEncoding:
|
||
|
|
"""
|
||
|
|
This method uses [`BridgeTowerImageProcessor.__call__`] method to prepare image(s) for the model, and
|
||
|
|
[`RobertaTokenizerFast.__call__`] to prepare text for the model.
|
||
|
|
|
||
|
|
Please refer to the docstring of the above two methods for more information.
|
||
|
|
"""
|
||
|
|
encoding = self.tokenizer(
|
||
|
|
text=text,
|
||
|
|
add_special_tokens=add_special_tokens,
|
||
|
|
padding=padding,
|
||
|
|
truncation=truncation,
|
||
|
|
max_length=max_length,
|
||
|
|
stride=stride,
|
||
|
|
pad_to_multiple_of=pad_to_multiple_of,
|
||
|
|
return_token_type_ids=return_token_type_ids,
|
||
|
|
return_attention_mask=return_attention_mask,
|
||
|
|
return_overflowing_tokens=return_overflowing_tokens,
|
||
|
|
return_special_tokens_mask=return_special_tokens_mask,
|
||
|
|
return_offsets_mapping=return_offsets_mapping,
|
||
|
|
return_length=return_length,
|
||
|
|
verbose=verbose,
|
||
|
|
return_tensors=return_tensors,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
# add pixel_values + pixel_mask
|
||
|
|
encoding_image_processor = self.image_processor(
|
||
|
|
images, return_tensors=return_tensors, do_normalize=True, do_center_crop=True, **kwargs
|
||
|
|
)
|
||
|
|
encoding.update(encoding_image_processor)
|
||
|
|
|
||
|
|
return encoding
|
||
|
|
|
||
|
|
def batch_decode(self, *args, **kwargs):
|
||
|
|
"""
|
||
|
|
This method forwards all its arguments to RobertaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
|
||
|
|
refer to the docstring of this method for more information.
|
||
|
|
"""
|
||
|
|
return self.tokenizer.batch_decode(*args, **kwargs)
|
||
|
|
|
||
|
|
def decode(self, *args, **kwargs):
|
||
|
|
"""
|
||
|
|
This method forwards all its arguments to RobertaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer
|
||
|
|
to the docstring of this method for more information.
|
||
|
|
"""
|
||
|
|
return self.tokenizer.decode(*args, **kwargs)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def model_input_names(self):
|
||
|
|
tokenizer_input_names = self.tokenizer.model_input_names
|
||
|
|
image_processor_input_names = self.image_processor.model_input_names
|
||
|
|
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
|