add confidence scores for `od` and `description_with_bboxes` tasks

processing_florence2.py

@@ -20,6 +20,7 @@ import re
 import logging
 from typing import List, Optional, Union
 import numpy as np
+import math
 
 import torch
 
@@ -32,6 +33,7 @@ from transformers.tokenization_utils_base import (
     TextInput,
     TruncationStrategy,
 )
+from transformers import BartTokenizer, BartTokenizerFast
 from transformers.utils import TensorType
 
 
@@ -304,7 +306,7 @@ class Florence2Processor(ProcessorMixin):
         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):
+    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.
 
@@ -317,6 +319,8 @@ class Florence2Processor(ProcessorMixin):
         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]
@@ -330,6 +334,9 @@ class Florence2Processor(ProcessorMixin):
             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]
@@ -591,7 +598,8 @@ class Florence2PostProcesser(object):
             'PARSE_TASKS': [
                 {
                     'TASK_NAME': 'od',
-                    'PATTERN': r'([a-zA-Z0-9 ]+)<loc_(\\d+)><loc_(\\d+)><loc_(\\d+)><loc_(\\d+)>'
+                    'PATTERN': r'([a-zA-Z0-9 ]+)<loc_(\\d+)><loc_(\\d+)><loc_(\\d+)><loc_(\\d+)>',
+                    'SCORE_MODE': 'avg_loc_scores'
                 },
                 {
                     'TASK_NAME': 'ocr',
@@ -607,6 +615,7 @@ class Florence2PostProcesser(object):
                 },
                 {
                     'TASK_NAME': 'description_with_bboxes',
+                    'SCORE_MODE': 'avg_loc_scores'
                 },
                 {
                     'TASK_NAME': 'description_with_polygons',
@@ -648,9 +657,6 @@ class Florence2PostProcesser(object):
             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:
@@ -658,10 +664,6 @@ class Florence2PostProcesser(object):
             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)
@@ -673,13 +675,6 @@ class Florence2PostProcesser(object):
             text += sub_text
             spans.append(span)
 
-        # Text format:
-        # 1. T5Tokenizer/T5TokenizerFast: 
-        #      "<loc_1><loc_2><loc_3><loc_4> transplanting dog<loc_1><loc_2><loc_3><loc_4> cat</s>"
-        #    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):
-        #      "<s><loc_1><loc_2><loc_3><loc_4>transplanting dog<loc_1><loc_2><loc_3><loc_4>cat</s>"
-        #    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(
@@ -714,7 +709,7 @@ class Florence2PostProcesser(object):
         return instances
 
     def parse_ocr_from_text_and_spans(self, 
-                                    text, 
+                                     text, 
                                      pattern, 
                                      image_size,
                                      area_threshold=-1.0,
@@ -818,9 +813,26 @@ class Florence2PostProcesser(object):
 
         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 <s> </s> and <pad>
+    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('<s>'):   
+            cur_span += 3
 
         text = text.replace('<s>', '')
         text = text.replace('</s>', '')
@@ -842,13 +854,16 @@ class Florence2PostProcesser(object):
             phrase_text_strip = pharse_text.replace('<obj>', '', 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()
@@ -856,6 +871,7 @@ class Florence2PostProcesser(object):
             # 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 
@@ -866,14 +882,42 @@ class Florence2PostProcesser(object):
                 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 _bboxes in bboxes:
+            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
 
@@ -991,6 +1035,8 @@ class Florence2PostProcesser(object):
     def __call__(
         self,
         text=None,
+        sequence=None,
+        transition_beam_score=None,
         image_size=None,
         parse_tasks=None,
     ):
@@ -999,7 +1045,6 @@ class Florence2PostProcesser(object):
             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):
@@ -1008,7 +1053,18 @@ class Florence2PostProcesser(object):
                 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'
+        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
@@ -1019,6 +1075,7 @@ class Florence2PostProcesser(object):
                 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(
@@ -1040,6 +1097,9 @@ class Florence2PostProcesser(object):
             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,
                 )