# 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 math 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 import BartTokenizer, BartTokenizerFast 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', '': 'ocr', '': '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?', '': 'What does the image describe?', '': 'Describe in detail what is shown in the image.', '': 'Describe with a paragraph what is shown in the image.', '': '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=None, sequence=None, transition_beam_score=None, task=None, image_size=None): """ 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, sequence=sequence, transition_beam_score=transition_beam_score, 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('', '') 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} if len(od_instances) and 'score' in od_instances[0]: scores_od = [_od_instance['score'] for _od_instance in od_instances] final_answer['scores'] = scores_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): r""" 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 ]+)', 'SCORE_MODE': 'avg_loc_scores' }, { 'TASK_NAME': 'ocr', 'PATTERN': r'(.+?)', 'AREA_THRESHOLD': 0.00 }, { 'TASK_NAME': 'phrase_grounding', 'FILTER_BY_BLACK_LIST': True }, { 'TASK_NAME': 'pure_text', }, { 'TASK_NAME': 'description_with_bboxes', 'SCORE_MODE': 'avg_loc_scores' }, { '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) 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]) 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) 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, spans=None, scores=None, score_mode=None, pattern=None, image_size=None, allow_empty_phrase=False ): def find_matched_token_indices(cur_span, token_spans): inds = [] for i, token_span in enumerate(token_spans): if not (token_span[1] <= cur_span[0] or token_span[0] >= cur_span[1]): inds.append(i) return inds cur_span = 0 if text.startswith(''): cur_span += 3 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: cur_span += len(pharse_text) continue # parse phrase, get string phrase = re.search(pattern, phrase_text_strip) if phrase is None: cur_span += len(pharse_text) continue phrase_span = phrase.span() 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: 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] bboxes = self.box_quantizer.dequantize( boxes=torch.tensor(bbox_bins), size=image_size ).tolist() if score_mode == 'avg_loc_scores': if spans is None or scores is None: all_scores = None else: bbox_end_spans = [_bboxes_parsed.span(0) for _bboxes_parsed in bboxes_parsed] all_scores = [] for _spans in bbox_end_spans: token_inds = find_matched_token_indices((_spans[0] + cur_span, _spans[1]+ cur_span), spans) loc_scores = [scores[token_i] for token_i in token_inds] score = sum(loc_scores) / len(loc_scores) all_scores.append(score) elif score_mode == 'avg_cat_name_scores': if spans is None or scores is None: all_scores = None else: cat_name_token_inds = find_matched_token_indices((phrase_span[0] + cur_span, phrase_span[1]+cur_span), spans) cat_name_scores = [scores[token_i] for token_i in cat_name_token_inds] score = sum(cat_name_scores) / len(cat_name_scores) all_scores = [score] * len(bboxes) elif score_mode is None: all_scores = None else: raise ValueError('Unknown score mode: {}'.format(score_mode)) phrase = phrase.encode('ascii',errors='ignore').decode('ascii') for _idx, _bboxes in enumerate(bboxes): # Prepare instance. instance = {} instance['bbox'] = _bboxes # exclude non-ascii characters instance['cat_name'] = phrase if all_scores is not None: instance['score'] = math.exp(all_scores[_idx]) instances.append(instance) cur_span += len(pharse_text) 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, sequence=None, transition_beam_score=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 sequence is not None or text is not None, 'sequence or text should be provided' assert sequence is None or text is None, 'only one of sequence and text should be provided' if sequence is not None: sequence = sequence.tolist()[1:] text, spans = self.decode_with_spans(self.tokenizer, sequence) if transition_beam_score is not None: transition_beam_score = transition_beam_score.tolist() assert len(sequence) == len(transition_beam_score) else: spans = None transition_beam_score = None 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) score_mode = self.parse_tasks_configs[task].get('SCORE_MODE', 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', 0.0), ) 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, spans=spans, scores=transition_beam_score, score_mode=score_mode, 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