# coding=utf-8
# Copyright 2024 Microsoft and The HuggingFace Inc. team.
#
# 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 Florence-2.
"""
import re
import logging
from typing import List, Optional, Union
import numpy as np
import torch
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput, is_valid_image
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils_base import (
PaddingStrategy,
PreTokenizedInput,
TextInput,
TruncationStrategy,
)
from transformers.utils import TensorType
logger = logging.getLogger(__name__)
# Copied from transformers.models.idefics2.processing_idefics2.is_url
def is_url(val) -> bool:
return isinstance(val, str) and val.startswith("http")
# Copied from transformers.models.idefics2.processing_idefics2.is_image_or_image_url
def is_image_or_image_url(elem):
return is_url(elem) or is_valid_image(elem)
def _is_str_or_image(elem):
return isinstance(elem, (str)) or is_image_or_image_url(elem)
class Florence2Processor(ProcessorMixin):
r"""
Constructs a Florence2 processor which wraps a Florence2 image processor and a Florence2 tokenizer into a single processor.
[`Florence2Processor`] offers all the functionalities of [`CLIPImageProcessor`] and [`BartTokenizerFast`]. See the
[`~Florence2Processor.__call__`] and [`~Florence2Processor.decode`] for more information.
Args:
image_processor ([`CLIPImageProcessor`], *optional*):
The image processor is a required input.
tokenizer ([`BartTokenizerFast`], *optional*):
The tokenizer is a required input.
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "CLIPImageProcessor"
tokenizer_class = ("BartTokenizer", "BartTokenizerFast")
def __init__(
self,
image_processor=None,
tokenizer=None,
):
if image_processor is None:
raise ValueError("You need to specify an `image_processor`.")
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`.")
if not hasattr(image_processor, "image_seq_length"):
raise ValueError("Image processor is missing an `image_seq_length` attribute.")
self.image_seq_length = image_processor.image_seq_length
tokens_to_add = {
'additional_special_tokens': \
tokenizer.additional_special_tokens + \
['', '', '', ''] + \
[f'' for x in range(1000)] + \
['', '', '', '','', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']
}
tokenizer.add_special_tokens(tokens_to_add)
self.tasks_answer_post_processing_type = {
'': 'pure_text',
'': 'pure_text',
'': 'ocr',
'': 'pure_text',
'': 'pure_text',
'': 'pure_text',
'': 'pure_text',
'': 'pure_text',
'': 'pure_text',
'': 'description_with_bboxes',
'': 'description_with_bboxes',
'': "phrase_grounding",
'': 'polygons',
'': 'polygons',
'': 'description_with_bboxes_or_polygons',
'': 'pure_text',
'': 'pure_text',
'': 'pure_text',
'': 'bboxes'
}
self.task_prompts_without_inputs = {
'': 'What is the text in the image?',
'': 'What is the text in the image, with regions?',
'': 'Convert the image into markdown style reconstructed table in text.',
'': 'What does the image describe?',
'': '图中描述了什么？',
'': 'Describe in detail what is shown in the image.',
'': '详细描述图像中所展示的内容。',
'': 'Describe with a paragraph what is shown in the image.',
'': '用一段话描述图像中所展示的内容。',
'': 'Locate the objects with category name in the image.',
'': 'Locate the objects in the image, with their descriptions.',
'': 'Locate the region proposals in the image.'
}
self.task_prompts_with_input = {
'': "Locate the phrases in the caption: {input}",
'': 'Locate {input} in the image with mask',
'': 'What is the polygon mask of region {input}',
'': 'Locate {input} in the image.',
'': 'What is the region {input}?',
'': 'What does the region {input} describe?',
'': 'What text is in the region {input}?',
}
self.post_processor = Florence2PostProcesser(tokenizer=tokenizer)
super().__init__(image_processor, tokenizer)
def _construct_prompts(self, text):
# replace the task tokens with the task prompts if task token is in the text
prompts = []
for _text in text:
# 1. fixed task prompts without additional inputs
for task_token, task_prompt in self.task_prompts_without_inputs.items():
if task_token in _text:
assert _text == task_token, f"Task token {task_token} should be the only token in the text."
_text = task_prompt
break
# 2. task prompts with additional inputs
for task_token, task_prompt in self.task_prompts_with_input.items():
if task_token in _text:
_text = task_prompt.format(input=_text.replace(task_token, ''))
break
prompts.append(_text)
return prompts
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
images: ImageInput = None,
tokenize_newline_separately: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length=None,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
do_resize: bool = None,
do_normalize: bool = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
data_format: Optional["ChannelDimension"] = "channels_first", # noqa: F821
input_data_format: Optional[
Union[str, "ChannelDimension"] # noqa: F821
] = None,
resample: "PILImageResampling" = None, # noqa: F821
do_convert_rgb: bool = None,
do_thumbnail: bool = None,
do_align_long_axis: bool = None,
do_rescale: bool = None,
) -> BatchFeature:
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to BartTokenizerFast's [`~BartTokenizerFast.__call__`] if `text` is not `None` to encode
the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
of the above two methods for more information.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
number of channels, H and W are image height and width.
tokenize_newline_separately (`bool`, defaults to `True`):
Adds a separately tokenized '\n' at the end of the prompt.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
truncation (`bool`, *optional*):
Activates truncation to cut input sequences longer than `max_length` to `max_length`.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. If `suffix`
is provided, the `input_ids` will also contain the suffix input ids.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
`None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
- **labels** -- Labels compatible with training if `suffix` is not None
"""
return_token_type_ids = False
if images is None:
raise ValueError("`images` are expected as arguments to a `Florence2Processor` instance.")
if text is None:
logger.warning_once(
"You are using Florence-2 without a text prompt."
)
text = ""
if isinstance(text, List) and isinstance(images, List):
if len(images) < len(text):
raise ValueError(
f"Received {len(images)} images for {len(text)} prompts. Each prompt should be associated with an image."
)
if _is_str_or_image(text):
text = [text]
elif isinstance(text, list) and _is_str_or_image(text[0]):
pass
pixel_values = self.image_processor(
images,
do_resize=do_resize,
do_normalize=do_normalize,
return_tensors=return_tensors,
image_mean=image_mean,
image_std=image_std,
input_data_format=input_data_format,
data_format=data_format,
resample=resample,
do_convert_rgb=do_convert_rgb,
)["pixel_values"]
if max_length is not None:
max_length -= self.image_seq_length # max_length has to account for the image tokens
text = self._construct_prompts(text)
inputs = self.tokenizer(
text,
return_tensors=return_tensors,
padding=padding,
max_length=max_length,
truncation=truncation,
return_token_type_ids=return_token_type_ids,
)
return_data = {**inputs, "pixel_values": pixel_values}
if return_token_type_ids:
labels = inputs["input_ids"].masked_fill(inputs["token_type_ids"] == 0, -100)
return_data.update({"labels": labels})
return BatchFeature(data=return_data)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Florence2
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to BartTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Florence2
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to BartTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names with CLIP->Florence2
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))
def post_process_generation(self, text, task, image_size):
"""
Post-process the output of the model to each of the task outputs.
Args:
text (`str`): The text to post-process.
task (`str`): The task to post-process the text for.
image_size (`Tuple[int, int]`): The size of the image. height x width.
"""
task_answer_post_processing_type = self.tasks_answer_post_processing_type.get(task, 'pure_text')
task_answer = self.post_processor(
text=text,
image_size=image_size,
parse_tasks=task_answer_post_processing_type,
)[task_answer_post_processing_type]
if task_answer_post_processing_type == 'pure_text':
final_answer = task_answer
# remove the special tokens
final_answer = final_answer.replace('', '').replace('', '\n')
elif task_answer_post_processing_type in ['od', 'description_with_bboxes', 'bboxes']:
od_instances = task_answer
bboxes_od = [_od_instance['bbox'] for _od_instance in od_instances]
labels_od = [str(_od_instance['cat_name']) for _od_instance in od_instances]
final_answer = {'bboxes': bboxes_od, 'labels': labels_od}
elif task_answer_post_processing_type in ['ocr']:
bboxes = [_od_instance['quad_box'] for _od_instance in task_answer]
labels = [str(_od_instance['text']) for _od_instance in task_answer]
final_answer = {'quad_boxes': bboxes, 'labels': labels}
elif task_answer_post_processing_type in ['phrase_grounding']:
bboxes = []
labels = []
for _grounded_phrase in task_answer:
for _bbox in _grounded_phrase['bbox']:
bboxes.append(_bbox)
labels.append(_grounded_phrase['cat_name'])
final_answer = {'bboxes': bboxes, 'labels': labels}
elif task_answer_post_processing_type in ['description_with_polygons', 'polygons']:
labels = []
polygons = []
for result in task_answer:
label = result['cat_name']
_polygons = result['polygons']
labels.append(label)
polygons.append(_polygons)
final_answer = {'polygons': polygons, 'labels': labels}
elif task_answer_post_processing_type in ['description_with_bboxes_or_polygons']:
bboxes = []
bboxes_labels = []
polygons = []
polygons_labels = []
for result in task_answer:
label = result['cat_name']
if 'polygons' in result:
_polygons = result['polygons']
polygons.append(_polygons)
polygons_labels.append(label)
else:
_bbox = result['bbox']
bboxes.append(_bbox)
bboxes_labels.append(label)
final_answer = {'bboxes': bboxes, 'bboxes_labels': bboxes_labels, 'polygons': polygons, 'polygons_labels': polygons_labels}
else:
raise ValueError('Unknown task answer post processing type: {}'.format(task_answer_post_processing_type))
final_answer = {
task: final_answer}
return final_answer
class BoxQuantizer(object):
def __init__(self, mode, bins):
self.mode = mode
self.bins = bins
def quantize(self, boxes: torch.Tensor, size):
bins_w, bins_h = self.bins # Quantization bins.
size_w, size_h = size # Original image size.
size_per_bin_w = size_w / bins_w
size_per_bin_h = size_h / bins_h
xmin, ymin, xmax, ymax = boxes.split(1, dim=-1) # Shape: 4 * [N, 1].
if self.mode == 'floor':
quantized_xmin = (
xmin / size_per_bin_w).floor().clamp(0, bins_w - 1)
quantized_ymin = (
ymin / size_per_bin_h).floor().clamp(0, bins_h - 1)
quantized_xmax = (
xmax / size_per_bin_w).floor().clamp(0, bins_w - 1)
quantized_ymax = (
ymax / size_per_bin_h).floor().clamp(0, bins_h - 1)
elif self.mode == 'round':
raise NotImplementedError()
else:
raise ValueError('Incorrect quantization type.')
quantized_boxes = torch.cat(
(quantized_xmin, quantized_ymin, quantized_xmax, quantized_ymax), dim=-1
).int()
return quantized_boxes
def dequantize(self, boxes: torch.Tensor, size):
bins_w, bins_h = self.bins # Quantization bins.
size_w, size_h = size # Original image size.
size_per_bin_w = size_w / bins_w
size_per_bin_h = size_h / bins_h
xmin, ymin, xmax, ymax = boxes.split(1, dim=-1) # Shape: 4 * [N, 1].
if self.mode == 'floor':
# Add 0.5 to use the center position of the bin as the coordinate.
dequantized_xmin = (xmin + 0.5) * size_per_bin_w
dequantized_ymin = (ymin + 0.5) * size_per_bin_h
dequantized_xmax = (xmax + 0.5) * size_per_bin_w
dequantized_ymax = (ymax + 0.5) * size_per_bin_h
elif self.mode == 'round':
raise NotImplementedError()
else:
raise ValueError('Incorrect quantization type.')
dequantized_boxes = torch.cat(
(dequantized_xmin, dequantized_ymin,
dequantized_xmax, dequantized_ymax), dim=-1
)
return dequantized_boxes
class CoordinatesQuantizer(object):
"""
Quantize coornidates (Nx2)
"""
def __init__(self, mode, bins):
self.mode = mode
self.bins = bins
def quantize(self, coordinates: torch.Tensor, size):
bins_w, bins_h = self.bins # Quantization bins.
size_w, size_h = size # Original image size.
size_per_bin_w = size_w / bins_w
size_per_bin_h = size_h / bins_h
assert coordinates.shape[-1] == 2, 'coordinates should be shape (N, 2)'
x, y = coordinates.split(1, dim=-1) # Shape: 4 * [N, 1].
if self.mode == 'floor':
quantized_x = (x / size_per_bin_w).floor().clamp(0, bins_w - 1)
quantized_y = (y / size_per_bin_h).floor().clamp(0, bins_h - 1)
elif self.mode == 'round':
raise NotImplementedError()
else:
raise ValueError('Incorrect quantization type.')
quantized_coordinates = torch.cat(
(quantized_x, quantized_y), dim=-1
).int()
return quantized_coordinates
def dequantize(self, coordinates: torch.Tensor, size):
bins_w, bins_h = self.bins # Quantization bins.
size_w, size_h = size # Original image size.
size_per_bin_w = size_w / bins_w
size_per_bin_h = size_h / bins_h
assert coordinates.shape[-1] == 2, 'coordinates should be shape (N, 2)'
x, y = coordinates.split(1, dim=-1) # Shape: 4 * [N, 1].
if self.mode == 'floor':
# Add 0.5 to use the center position of the bin as the coordinate.
dequantized_x = (x + 0.5) * size_per_bin_w
dequantized_y = (y + 0.5) * size_per_bin_h
elif self.mode == 'round':
raise NotImplementedError()
else:
raise ValueError('Incorrect quantization type.')
dequantized_coordinates = torch.cat(
(dequantized_x, dequantized_y), dim=-1
)
return dequantized_coordinates
class Florence2PostProcesser(object):
"""
Florence-2 post process for converting text prediction to various tasks results.
Args:
config: A dict of configs.
tokenizer: A tokenizer for decoding text to spans.
sample config:
UNIFIED_POST_PROCESS:
# commom configs
NUM_BBOX_HEIGHT_BINS: 1000
NUM_BBOX_WIDTH_BINS: 1000
COORDINATES_HEIGHT_BINS: 1000
COORDINATES_WIDTH_BINS: 1000
# task specific configs, override the common configs
PRASE_TASKS:
- TASK_NAME: 'video_dense_caption'
PATTERN: 'r([a-zA-Z0-9 ]+)'
SCORE_MODE: 'avg_cat_name_scores'
NUM_BINS: 100
- TASK_NAME: 'od'
PATTERN: 'r([a-zA-Z0-9 ]+)'
SCORE_MODE: 'avg_cat_name_scores'
Returns:
parsed_dict (dict): A dict of parsed results.
"""
def __init__(
self,
tokenizer=None
):
parse_tasks = []
parse_task_configs = {}
config = self._create_default_config()
for task in config['PARSE_TASKS']:
parse_tasks.append(task['TASK_NAME'])
parse_task_configs[task['TASK_NAME']] = task
self.config = config
self.parse_tasks = parse_tasks
self.parse_tasks_configs = parse_task_configs
self.tokenizer = tokenizer
if self.tokenizer is not None:
self.all_special_tokens = set(self.tokenizer.all_special_tokens)
self.init_quantizers()
self.black_list_of_phrase_grounding = self._create_black_list_of_phrase_grounding()
def _create_black_list_of_phrase_grounding(self):
black_list = {}
if 'phrase_grounding' in self.parse_tasks and self.parse_tasks_configs['phrase_grounding']['FILTER_BY_BLACK_LIST']:
black_list = set(
['it', 'I', 'me', 'mine',
'you', 'your', 'yours',
'he', 'him', 'his',
'she', 'her', 'hers',
'they', 'them', 'their', 'theirs',
'one', 'oneself',
'we', 'us', 'our', 'ours',
'you', 'your', 'yours',
'they', 'them', 'their', 'theirs',
'mine', 'yours', 'his', 'hers', 'its',
'ours', 'yours', 'theirs',
'myself', 'yourself', 'himself', 'herself', 'itself',
'ourselves', 'yourselves', 'themselves',
'this', 'that',
'these', 'those',
'who', 'whom', 'whose', 'which', 'what',
'who', 'whom', 'whose', 'which', 'that',
'all', 'another', 'any', 'anybody', 'anyone', 'anything',
'each', 'everybody', 'everyone', 'everything',
'few', 'many', 'nobody', 'none', 'one', 'several',
'some', 'somebody', 'someone', 'something',
'each other', 'one another',
'myself', 'yourself', 'himself', 'herself', 'itself',
'ourselves', 'yourselves', 'themselves',
'the image', 'image', 'images', 'the', 'a', 'an', 'a group',
'other objects', 'lots', 'a set',
]
)
return black_list
def _create_default_config(self):
config = {
'NUM_BBOX_HEIGHT_BINS': 1000,
'NUM_BBOX_WIDTH_BINS': 1000,
'BOX_QUANTIZATION_MODE': 'floor',
'COORDINATES_HEIGHT_BINS': 1000,
'COORDINATES_WIDTH_BINS': 1000,
'COORDINATES_QUANTIZATION_MODE': 'floor',
'PARSE_TASKS': [
{
'TASK_NAME': 'od',
'PATTERN': r'([a-zA-Z0-9 ]+)'
},
{
'TASK_NAME': 'ocr',
'PATTERN': r'(.+?)',
'AREA_THRESHOLD': 0.01
},
{
'TASK_NAME': 'phrase_grounding',
'FILTER_BY_BLACK_LIST': True
},
{
'TASK_NAME': 'pure_text',
},
{
'TASK_NAME': 'description_with_bboxes',
},
{
'TASK_NAME': 'description_with_polygons',
},
{
'TASK_NAME': 'polygons',
},
{
'TASK_NAME': 'bboxes',
},
{
'TASK_NAME': 'description_with_bboxes_or_polygons',
}
]
}
return config
def init_quantizers(self):
# we have box_quantizer (od, grounding) and coordinates_quantizer (ocr, referring_segmentation)
num_bbox_height_bins = self.config.get('NUM_BBOX_HEIGHT_BINS', 1000)
num_bbox_width_bins = self.config.get('NUM_BBOX_WIDTH_BINS', 1000)
box_quantization_mode = self.config.get('BOX_QUANTIZATION_MODE', 'floor')
self.box_quantizer = BoxQuantizer(
box_quantization_mode,
(num_bbox_width_bins, num_bbox_height_bins),
)
num_bbox_height_bins = self.config['COORDINATES_HEIGHT_BINS'] if 'COORDINATES_HEIGHT_BINS' in self.config else self.config.get('NUM_BBOX_HEIGHT_BINS', 1000)
num_bbox_width_bins = self.config['COORDINATES_WIDTH_BINS'] if 'COORDINATES_WIDTH_BINS' in self.config else self.config.get('NUM_BBOX_WIDTH_BINS', 1000)
box_quantization_mode = self.config.get('COORDINATES_QUANTIZATION_MODE') if 'COORDINATES_QUANTIZATION_MODE' in self.config else self.config.get('BOX_QUANTIZATION_MODE', 'floor')
self.coordinates_quantizer = CoordinatesQuantizer(
box_quantization_mode,
(num_bbox_width_bins, num_bbox_height_bins),
)
def decode_with_spans(self, tokenizer, token_ids):
filtered_tokens = tokenizer.convert_ids_to_tokens(
token_ids, skip_special_tokens=False)
assert len(filtered_tokens) == len(token_ids)
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
sub_texts = []
for token in filtered_tokens:
if token in self.all_special_tokens:
sub_texts.append(token)
else:
if isinstance(tokenizer, (BartTokenizer, BartTokenizerFast)):
sub_text = tokenizer.convert_tokens_to_string([token])
elif isinstance(tokenizer, (T5Tokenizer, T5TokenizerFast)):
# Ref: https://github.com/google/sentencepiece#whitespace-is-treated-as-a-basic-symbol
# Note: Do not strip sub_text as it may have functional whitespace
sub_text = token.replace('▁', ' ')
else:
raise ValueError(f'type {type(tokenizer)} not supported')
sub_texts.append(sub_text)
text = ''
spans = []
for sub_text in sub_texts:
span = (len(text), len(text) + len(sub_text)) # [start index, end index).
text += sub_text
spans.append(span)
# Text format:
# 1. T5Tokenizer/T5TokenizerFast:
# " transplanting dog cat"
# Equivalent to t5_tokenizer.decode(input_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False, spaces_between_special_tokens=False)
# 2. BartTokenizer (need to double check):
# "transplanting dogcat"
# Equivalent to bart_tokenizer.decode(input_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False, spaces_between_special_tokens=False)
return text, spans
def parse_od_from_text_and_spans(
self,
text,
pattern,
image_size,
phrase_centric=False
):
parsed = list(re.finditer(pattern, text))
instances = []
for i in range(len(parsed)):
# Prepare instance.
instance = {}
if phrase_centric:
bbox_bins = [int(parsed[i].group(j)) for j in range(2, 6)]
else:
bbox_bins = [int(parsed[i].group(j)) for j in range(1, 5)]
instance['bbox'] = self.box_quantizer.dequantize(
boxes=torch.tensor(bbox_bins),
size=image_size
).tolist()
if phrase_centric:
instance['cat_name'] = parsed[i].group(1).lower().strip()
else:
instance['cat_name'] = parsed[i].group(5).lower().strip()
instances.append(instance)
return instances
def parse_ocr_from_text_and_spans(self,
text,
pattern,
image_size,
area_threshold=-1.0,
):
bboxes = []
labels = []
text = text.replace('', '')
# ocr with regions
parsed = re.findall(pattern, text)
instances = []
image_width, image_height = image_size
for ocr_line in parsed:
ocr_content = ocr_line[0]
quad_box = ocr_line[1:]
quad_box = [int(i) for i in quad_box]
quad_box = self.coordinates_quantizer.dequantize(
torch.tensor(np.array(quad_box).reshape(-1, 2)),
size=image_size
).reshape(-1).tolist()
if area_threshold > 0:
x_coords = [i for i in quad_box[0::2]]
y_coords = [i for i in quad_box[1::2]]
# apply the Shoelace formula
area = 0.5 * abs(sum(x_coords[i] * y_coords[i + 1] - x_coords[i + 1] * y_coords[i] for i in range(4 - 1)))
if area < (image_width * image_height) * area_threshold:
continue
bboxes.append(quad_box)
labels.append(ocr_content)
instances.append({
'quad_box': quad_box,
'text': ocr_content,
})
return instances
def parse_phrase_grounding_from_text_and_spans(self, text, pattern, image_size):
# ignore and
cur_span = 0
if text.startswith(''):
cur_span += 3
text = text.replace('', '')
text = text.replace('', '')
text = text.replace('', '')
pattern = r"([^<]+(?:){4,})"
phrases = re.findall(pattern, text)
# pattern should be text pattern and od pattern
pattern = r'^\s*(.*?)(?=||||||'
instances = []
for pharse_text in phrases:
phrase_text_strip = pharse_text.replace('', '', 1)
phrase_text_strip = pharse_text.replace('', '', 1)
if phrase_text_strip == '':
cur_span += len(pharse_text)
continue
# Prepare instance.
instance = {}
# parse phrase, get string
phrase = re.search(pattern, phrase_text_strip)
if phrase is None:
cur_span += len(pharse_text)
continue
# parse bboxes by box_pattern
bboxes_parsed = list(re.finditer(box_pattern, pharse_text))
if len(bboxes_parsed) == 0:
cur_span += len(pharse_text)
continue
phrase = phrase.group()
# remove leading and trailing spaces
phrase = phrase.strip()
if phrase in self.black_list_of_phrase_grounding:
cur_span += len(pharse_text)
continue
# a list of list
bbox_bins = [[int(_bboxes_parsed.group(j)) for j in range(1, 5)] for _bboxes_parsed in bboxes_parsed]
instance['bbox'] = self.box_quantizer.dequantize(
boxes=torch.tensor(bbox_bins),
size=image_size
).tolist()
# exclude non-ascii characters
phrase = phrase.encode('ascii',errors='ignore').decode('ascii')
instance['cat_name'] = phrase
instances.append(instance)
return instances
def parse_description_with_bboxes_from_text_and_spans(self, text, pattern, image_size, allow_empty_phrase=False):
# temporary parse solution, split by '.'
# ignore and
text = text.replace('', '')
text = text.replace('', '')
text = text.replace('', '')
if allow_empty_phrase:
pattern = rf"(?:(?:){{4,}})"
else:
pattern = r"([^<]+(?:){4,})"
phrases = re.findall(pattern, text)
# pattern should be text pattern and od pattern
pattern = r'^\s*(.*?)(?=||||||'
instances = []
for pharse_text in phrases:
phrase_text_strip = pharse_text.replace('', '', 1)
phrase_text_strip = pharse_text.replace('', '', 1)
if phrase_text_strip == '' and not allow_empty_phrase:
continue
# parse phrase, get string
phrase = re.search(pattern, phrase_text_strip)
if phrase is None:
continue
phrase = phrase.group()
# remove leading and trailing spaces
phrase = phrase.strip()
# parse bboxes by box_pattern
bboxes_parsed = list(re.finditer(box_pattern, pharse_text))
if len(bboxes_parsed) == 0:
continue
# a list of list
bbox_bins = [[int(_bboxes_parsed.group(j)) for j in range(1, 5)] for _bboxes_parsed in bboxes_parsed]
bboxes = self.box_quantizer.dequantize(
boxes=torch.tensor(bbox_bins),
size=image_size
).tolist()
phrase = phrase.encode('ascii',errors='ignore').decode('ascii')
for _bboxes in bboxes:
# Prepare instance.
instance = {}
instance['bbox'] = _bboxes
# exclude non-ascii characters
instance['cat_name'] = phrase
instances.append(instance)
return instances
def parse_description_with_polygons_from_text_and_spans(self, text, pattern, image_size,
allow_empty_phrase=False,
polygon_sep_token='',
polygon_start_token='',
polygon_end_token='',
with_box_at_start=False,
):
# ref_seg format: '<><><><><><>'
# ignore and
text = text.replace('', '')
text = text.replace('', '')
text = text.replace('', '')
if allow_empty_phrase:
pattern = rf"(?:(?:|{re.escape(polygon_sep_token)}|{re.escape(polygon_start_token)}|{re.escape(polygon_end_token)}){{4,}})"
else:
# [^<]+: This part matches one or more characters that are not the < symbol.
# The ^ inside the square brackets [] is a negation, meaning it matches anything except <.
#
pattern = rf"([^<]+(?:|{re.escape(polygon_sep_token)}|{re.escape(polygon_start_token)}|{re.escape(polygon_end_token)}){{4,}})"
phrases = re.findall(pattern, text)
phrase_string_pattern = r'^\s*(.*?)(?=||||||)'
box_pattern = rf'((?:)+)(?:{re.escape(polygon_sep_token)}|$)'
# one polygons instance is separated by polygon_start_token and polygon_end_token
polygons_instance_pattern = rf'{re.escape(polygon_start_token)}(.*?){re.escape(polygon_end_token)}'
instances = []
for phrase_text in phrases:
# exclude loc_\d+>
# need to get span if want to include category score
phrase_text_strip = re.sub(r'^loc_\d+>', '', phrase_text, count=1)
# phrase = phrase.replace('', '')
# phrase = phrase.replace('poly>', '')
if phrase_text_strip == '' and not allow_empty_phrase:
continue
# parse phrase, get string
phrase = re.search(phrase_string_pattern, phrase_text_strip)
if phrase is None:
continue
phrase = phrase.group()
# remove leading and trailing spaces
phrase = phrase.strip()
# parse bboxes by box_pattern
# split by polygon_start_token and polygon_end_token first using polygons_instance_pattern
if polygon_start_token in phrase_text and polygon_end_token in phrase_text:
polygons_instances_parsed = list(re.finditer(polygons_instance_pattern, phrase_text))
else:
polygons_instances_parsed = [phrase_text]
for _polygons_instances_parsed in polygons_instances_parsed:
# Prepare instance.
instance = {}
# polygons_parsed= list(re.finditer(box_pattern, phrase_text))
if isinstance(_polygons_instances_parsed, str):
polygons_parsed= list(re.finditer(box_pattern, _polygons_instances_parsed))
else:
polygons_parsed= list(re.finditer(box_pattern, _polygons_instances_parsed.group(1)))
if len(polygons_parsed) == 0:
continue
# a list of list (polygon)
bbox = []
polygons = []
for _polygon_parsed in polygons_parsed:
# group 1: whole ...
_polygon = _polygon_parsed.group(1)
# parse into list of int
_polygon = [int(_loc_parsed.group(1)) for _loc_parsed in re.finditer(r'', _polygon)]
if with_box_at_start and len(bbox) == 0:
if len(_polygon) > 4:
# no valid bbox prediction
bbox = _polygon[:4]
_polygon = _polygon[4:]
else:
bbox = [0, 0, 0, 0]
# abandon last element if is not paired
if len(_polygon) % 2 == 1:
_polygon = _polygon[:-1]
# reshape into (n, 2)
_polygon = self.coordinates_quantizer.dequantize(
torch.tensor(np.array(_polygon).reshape(-1, 2)),
size=image_size
).reshape(-1).tolist()
# reshape back
polygons.append(_polygon)
instance['cat_name'] = phrase
instance['polygons'] = polygons
if len(bbox) != 0:
instance['bbox'] = self.box_quantizer.dequantize(
boxes=torch.tensor([bbox]),
size=image_size
).tolist()[0]
instances.append(instance)
return instances
def __call__(
self,
text=None,
image_size=None,
parse_tasks=None,
):
"""
Args:
text: model outputs
image_size: (width, height)
parse_tasks: a list of tasks to parse, if None, parse all tasks.
"""
if parse_tasks is not None:
if isinstance(parse_tasks, str):
parse_tasks = [parse_tasks]
for _parse_task in parse_tasks:
assert _parse_task in self.parse_tasks, f'parse task {_parse_task} not supported'
# sequence or text should be provided
assert text is not None, 'text should be provided'
parsed_dict = {
'text': text
}
for task in self.parse_tasks:
if parse_tasks is not None and task not in parse_tasks:
continue
pattern = self.parse_tasks_configs[task].get('PATTERN', None)
if task == 'ocr':
instances = self.parse_ocr_from_text_and_spans(
text,
pattern=pattern,
image_size=image_size,
area_threshold=self.parse_tasks_configs[task].get('AREA_THRESHOLD', 0.01),
)
parsed_dict['ocr'] = instances
elif task == 'phrase_grounding':
instances = self.parse_phrase_grounding_from_text_and_spans(
text,
pattern=pattern,
image_size=image_size,
)
parsed_dict['phrase_grounding'] = instances
elif task == 'pure_text':
parsed_dict['pure_text'] = text
elif task == 'description_with_bboxes':
instances = self.parse_description_with_bboxes_from_text_and_spans(
text,
pattern=pattern,
image_size=image_size,
)
parsed_dict['description_with_bboxes'] = instances
elif task == 'description_with_polygons':
instances = self.parse_description_with_polygons_from_text_and_spans(
text,
pattern=pattern,
image_size=image_size,
)
parsed_dict['description_with_polygons'] = instances
elif task == 'polygons':
instances = self.parse_description_with_polygons_from_text_and_spans(
text,
pattern=pattern,
image_size=image_size,
allow_empty_phrase=True,
)
parsed_dict['polygons'] = instances
elif task == 'bboxes':
instances = self.parse_description_with_bboxes_from_text_and_spans(
text,
pattern=pattern,
image_size=image_size,
allow_empty_phrase=True,
)
parsed_dict['bboxes'] = instances
elif task == 'description_with_bboxes_or_polygons':
if '' in text:
# only support either polygons or bboxes, not both at the same time
instances = self.parse_description_with_polygons_from_text_and_spans(
text,
pattern=pattern,
image_size=image_size,
)
else:
instances = self.parse_description_with_bboxes_from_text_and_spans(
text,
pattern=pattern,
image_size=image_size,
)
parsed_dict['description_with_bboxes_or_polygons'] = instances
else:
raise ValueError("task {} is not supported".format(task))
return parsed_dict