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Unverified Commit a5583ff4 authored by Xiaomeng Zhao's avatar Xiaomeng Zhao Committed by GitHub
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fix: improve candidate sorting logic in vlm_magic_model.py 

fix: improve candidate sorting logic in vlm_magic_model.py
parents beacccb6 1906643c
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Showing with 226 additions and 324 deletions
mineru/backend/pipeline/pipeline_magic_model.py
View file @ a5583ff4
from mineru.utils.boxbase import bbox_relative_pos, calculate_iou, bbox_distance, is_in, get_minbox_if_overlap_by_ratio from mineru.utils.boxbase import bbox_relative_pos, calculate_iou, bbox_distance, is_in, get_minbox_if_overlap_by_ratio
from mineru.utils.enum_class import CategoryId, ContentType from mineru.utils.enum_class import CategoryId, ContentType
from mineru.utils.magic_model_utils import tie_up_category_by_distance_v3, reduct_overlap
class MagicModel: class MagicModel:
...@@ -208,23 +209,10 @@ class MagicModel: ...@@ -208,23 +209,10 @@ class MagicModel:
return bbox_distance(bbox1, bbox2) return bbox_distance(bbox1, bbox2)
def __reduct_overlap(self, bboxes): def __tie_up_category_by_distance_v3(self, subject_category_id, object_category_id):
N = len(bboxes) # 定义获取主体和客体对象的函数
keep = [True] * N def get_subjects():
for i in range(N): return reduct_overlap(
for j in range(N):
if i == j:
continue
if is_in(bboxes[i]['bbox'], bboxes[j]['bbox']):
keep[i] = False
return [bboxes[i] for i in range(N) if keep[i]]
def __tie_up_category_by_distance_v3(
self,
subject_category_id: int,
object_category_id: int,
):
subjects = self.__reduct_overlap(
list( list(
map( map(
lambda x: {'bbox': x['bbox'], 'score': x['score']}, lambda x: {'bbox': x['bbox'], 'score': x['score']},
...@@ -235,7 +223,9 @@ class MagicModel: ...@@ -235,7 +223,9 @@ class MagicModel:
) )
) )
) )
objects = self.__reduct_overlap(
def get_objects():
return reduct_overlap(
list( list(
map( map(
lambda x: {'bbox': x['bbox'], 'score': x['score']}, lambda x: {'bbox': x['bbox'], 'score': x['score']},
...@@ -247,131 +237,11 @@ class MagicModel: ...@@ -247,131 +237,11 @@ class MagicModel:
) )
) )
ret = [] # 调用通用方法
N, M = len(subjects), len(objects) return tie_up_category_by_distance_v3(
subjects.sort(key=lambda x: x['bbox'][0] ** 2 + x['bbox'][1] ** 2) get_subjects,
objects.sort(key=lambda x: x['bbox'][0] ** 2 + x['bbox'][1] ** 2) get_objects
OBJ_IDX_OFFSET = 10000
SUB_BIT_KIND, OBJ_BIT_KIND = 0, 1
all_boxes_with_idx = [(i, SUB_BIT_KIND, sub['bbox'][0], sub['bbox'][1]) for i, sub in enumerate(subjects)] + [(i + OBJ_IDX_OFFSET , OBJ_BIT_KIND, obj['bbox'][0], obj['bbox'][1]) for i, obj in enumerate(objects)]
seen_idx = set()
seen_sub_idx = set()
while N > len(seen_sub_idx):
candidates = []
for idx, kind, x0, y0 in all_boxes_with_idx:
if idx in seen_idx:
continue
candidates.append((idx, kind, x0, y0))
if len(candidates) == 0:
break
left_x = min([v[2] for v in candidates])
top_y = min([v[3] for v in candidates])
candidates.sort(key=lambda x: (x[2]-left_x) ** 2 + (x[3] - top_y) ** 2)
fst_idx, fst_kind, left_x, top_y = candidates[0]
fst_bbox = subjects[fst_idx]['bbox'] if fst_kind == SUB_BIT_KIND else objects[fst_idx - OBJ_IDX_OFFSET]['bbox']
candidates.sort(key=lambda x: bbox_distance(fst_bbox, subjects[x[0]]['bbox']) if x[1] == SUB_BIT_KIND else bbox_distance(fst_bbox, objects[x[0] - OBJ_IDX_OFFSET]['bbox']))
nxt = None
for i in range(1, len(candidates)):
if candidates[i][1] ^ fst_kind == 1:
nxt = candidates[i]
break
if nxt is None:
break
if fst_kind == SUB_BIT_KIND:
sub_idx, obj_idx = fst_idx, nxt[0] - OBJ_IDX_OFFSET
else:
sub_idx, obj_idx = nxt[0], fst_idx - OBJ_IDX_OFFSET
pair_dis = bbox_distance(subjects[sub_idx]['bbox'], objects[obj_idx]['bbox'])
nearest_dis = float('inf')
for i in range(N):
# 取消原先算法中 1对1 匹配的偏置
# if i in seen_idx or i == sub_idx:continue
nearest_dis = min(nearest_dis, bbox_distance(subjects[i]['bbox'], objects[obj_idx]['bbox']))
if pair_dis >= 3*nearest_dis:
seen_idx.add(sub_idx)
continue
seen_idx.add(sub_idx)
seen_idx.add(obj_idx + OBJ_IDX_OFFSET)
seen_sub_idx.add(sub_idx)
ret.append(
{
'sub_bbox': {
'bbox': subjects[sub_idx]['bbox'],
'score': subjects[sub_idx]['score'],
},
'obj_bboxes': [
{'score': objects[obj_idx]['score'], 'bbox': objects[obj_idx]['bbox']}
],
'sub_idx': sub_idx,
}
)
for i in range(len(objects)):
j = i + OBJ_IDX_OFFSET
if j in seen_idx:
continue
seen_idx.add(j)
nearest_dis, nearest_sub_idx = float('inf'), -1
for k in range(len(subjects)):
dis = bbox_distance(objects[i]['bbox'], subjects[k]['bbox'])
if dis < nearest_dis:
nearest_dis = dis
nearest_sub_idx = k
for k in range(len(subjects)):
if k != nearest_sub_idx: continue
if k in seen_sub_idx:
for kk in range(len(ret)):
if ret[kk]['sub_idx'] == k:
ret[kk]['obj_bboxes'].append({'score': objects[i]['score'], 'bbox': objects[i]['bbox']})
break
else:
ret.append(
{
'sub_bbox': {
'bbox': subjects[k]['bbox'],
'score': subjects[k]['score'],
},
'obj_bboxes': [
{'score': objects[i]['score'], 'bbox': objects[i]['bbox']}
],
'sub_idx': k,
}
) )
seen_sub_idx.add(k)
seen_idx.add(k)
for i in range(len(subjects)):
if i in seen_sub_idx:
continue
ret.append(
{
'sub_bbox': {
'bbox': subjects[i]['bbox'],
'score': subjects[i]['score'],
},
'obj_bboxes': [],
'sub_idx': i,
}
)
return ret
def get_imgs(self): def get_imgs(self):
with_captions = self.__tie_up_category_by_distance_v3( with_captions = self.__tie_up_category_by_distance_v3(
......
mineru/backend/vlm/vlm_magic_model.py
View file @ a5583ff4
...@@ -3,10 +3,10 @@ from typing import Literal ...@@ -3,10 +3,10 @@ from typing import Literal
from loguru import logger from loguru import logger
from mineru.utils.boxbase import bbox_distance, is_in
from mineru.utils.enum_class import ContentType, BlockType, SplitFlag from mineru.utils.enum_class import ContentType, BlockType, SplitFlag
from mineru.backend.vlm.vlm_middle_json_mkcontent import merge_para_with_text from mineru.backend.vlm.vlm_middle_json_mkcontent import merge_para_with_text
from mineru.utils.format_utils import convert_otsl_to_html from mineru.utils.format_utils import convert_otsl_to_html
from mineru.utils.magic_model_utils import reduct_overlap, tie_up_category_by_distance_v3
class MagicModel: class MagicModel:
...@@ -251,24 +251,10 @@ def latex_fix(latex): ...@@ -251,24 +251,10 @@ def latex_fix(latex):
return latex return latex
def __reduct_overlap(bboxes): def __tie_up_category_by_distance_v3(blocks, subject_block_type, object_block_type):
N = len(bboxes) # 定义获取主体和客体对象的函数
keep = [True] * N def get_subjects():
for i in range(N): return reduct_overlap(
for j in range(N):
if i == j:
continue
if is_in(bboxes[i]["bbox"], bboxes[j]["bbox"]):
keep[i] = False
return [bboxes[i] for i in range(N) if keep[i]]
def __tie_up_category_by_distance_v3(
blocks: list,
subject_block_type: str,
object_block_type: str,
):
subjects = __reduct_overlap(
list( list(
map( map(
lambda x: {"bbox": x["bbox"], "lines": x["lines"], "index": x["index"]}, lambda x: {"bbox": x["bbox"], "lines": x["lines"], "index": x["index"]},
...@@ -279,7 +265,9 @@ def __tie_up_category_by_distance_v3( ...@@ -279,7 +265,9 @@ def __tie_up_category_by_distance_v3(
) )
) )
) )
objects = __reduct_overlap(
def get_objects():
return reduct_overlap(
list( list(
map( map(
lambda x: {"bbox": x["bbox"], "lines": x["lines"], "index": x["index"]}, lambda x: {"bbox": x["bbox"], "lines": x["lines"], "index": x["index"]},
...@@ -291,136 +279,12 @@ def __tie_up_category_by_distance_v3( ...@@ -291,136 +279,12 @@ def __tie_up_category_by_distance_v3(
) )
) )
ret = [] # 调用通用方法
N, M = len(subjects), len(objects) return tie_up_category_by_distance_v3(
subjects.sort(key=lambda x: x["bbox"][0] ** 2 + x["bbox"][1] ** 2) get_subjects,
objects.sort(key=lambda x: x["bbox"][0] ** 2 + x["bbox"][1] ** 2) get_objects
OBJ_IDX_OFFSET = 10000
SUB_BIT_KIND, OBJ_BIT_KIND = 0, 1
all_boxes_with_idx = [(i, SUB_BIT_KIND, sub["bbox"][0], sub["bbox"][1]) for i, sub in enumerate(subjects)] + [
(i + OBJ_IDX_OFFSET, OBJ_BIT_KIND, obj["bbox"][0], obj["bbox"][1]) for i, obj in enumerate(objects)
]
seen_idx = set()
seen_sub_idx = set()
while N > len(seen_sub_idx):
candidates = []
for idx, kind, x0, y0 in all_boxes_with_idx:
if idx in seen_idx:
continue
candidates.append((idx, kind, x0, y0))
if len(candidates) == 0:
break
left_x = min([v[2] for v in candidates])
top_y = min([v[3] for v in candidates])
candidates.sort(key=lambda x: (x[2] - left_x) ** 2 + (x[3] - top_y) ** 2)
fst_idx, fst_kind, left_x, top_y = candidates[0]
candidates.sort(key=lambda x: (x[2] - left_x) ** 2 + (x[3] - top_y) ** 2)
nxt = None
for i in range(1, len(candidates)):
if candidates[i][1] ^ fst_kind == 1:
nxt = candidates[i]
break
if nxt is None:
break
if fst_kind == SUB_BIT_KIND:
sub_idx, obj_idx = fst_idx, nxt[0] - OBJ_IDX_OFFSET
else:
sub_idx, obj_idx = nxt[0], fst_idx - OBJ_IDX_OFFSET
pair_dis = bbox_distance(subjects[sub_idx]["bbox"], objects[obj_idx]["bbox"])
nearest_dis = float("inf")
for i in range(N):
if i in seen_idx or i == sub_idx:
continue
nearest_dis = min(nearest_dis, bbox_distance(subjects[i]["bbox"], objects[obj_idx]["bbox"]))
if pair_dis >= 3 * nearest_dis:
seen_idx.add(sub_idx)
continue
seen_idx.add(sub_idx)
seen_idx.add(obj_idx + OBJ_IDX_OFFSET)
seen_sub_idx.add(sub_idx)
ret.append(
{
"sub_bbox": {
"bbox": subjects[sub_idx]["bbox"],
"lines": subjects[sub_idx]["lines"],
"index": subjects[sub_idx]["index"],
},
"obj_bboxes": [
{"bbox": objects[obj_idx]["bbox"], "lines": objects[obj_idx]["lines"], "index": objects[obj_idx]["index"]}
],
"sub_idx": sub_idx,
}
)
for i in range(len(objects)):
j = i + OBJ_IDX_OFFSET
if j in seen_idx:
continue
seen_idx.add(j)
nearest_dis, nearest_sub_idx = float("inf"), -1
for k in range(len(subjects)):
dis = bbox_distance(objects[i]["bbox"], subjects[k]["bbox"])
if dis < nearest_dis:
nearest_dis = dis
nearest_sub_idx = k
for k in range(len(subjects)):
if k != nearest_sub_idx:
continue
if k in seen_sub_idx:
for kk in range(len(ret)):
if ret[kk]["sub_idx"] == k:
ret[kk]["obj_bboxes"].append(
{"bbox": objects[i]["bbox"], "lines": objects[i]["lines"], "index": objects[i]["index"]}
)
break
else:
ret.append(
{
"sub_bbox": {
"bbox": subjects[k]["bbox"],
"lines": subjects[k]["lines"],
"index": subjects[k]["index"],
},
"obj_bboxes": [
{"bbox": objects[i]["bbox"], "lines": objects[i]["lines"], "index": objects[i]["index"]}
],
"sub_idx": k,
}
)
seen_sub_idx.add(k)
seen_idx.add(k)
for i in range(len(subjects)):
if i in seen_sub_idx:
continue
ret.append(
{
"sub_bbox": {
"bbox": subjects[i]["bbox"],
"lines": subjects[i]["lines"],
"index": subjects[i]["index"],
},
"obj_bboxes": [],
"sub_idx": i,
}
) )
return ret
def get_type_blocks(blocks, block_type: Literal["image", "table"]): def get_type_blocks(blocks, block_type: Literal["image", "table"]):
with_captions = __tie_up_category_by_distance_v3(blocks, f"{block_type}_body", f"{block_type}_caption") with_captions = __tie_up_category_by_distance_v3(blocks, f"{block_type}_body", f"{block_type}_caption")
......
mineru/model/mfr/unimernet/Unimernet.py
View file @ a5583ff4
...@@ -105,8 +105,8 @@ class UnimernetModel(object): ...@@ -105,8 +105,8 @@ class UnimernetModel(object):
# Create dataset with sorted images # Create dataset with sorted images
dataset = MathDataset(sorted_images, transform=self.model.transform) dataset = MathDataset(sorted_images, transform=self.model.transform)
# 如果batch_size> len(sorted_images)，则设置为不超过len(sorted_images)的2的幂 # 如果batch_size > len(sorted_images)，则设置为不超过len(sorted_images)的2的幂
batch_size = min(batch_size, 2 ** (len(sorted_images).bit_length() - 1)) batch_size = min(batch_size, max(1, 2 ** (len(sorted_images).bit_length() - 1))) if sorted_images else 1
dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=0) dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=0)
......
mineru/utils/magic_model_utils.py 0 → 100644
View file @ a5583ff4
"""
包含两个MagicModel类中重复使用的方法和逻辑
"""
from typing import List, Dict, Any, Callable
from mineru.utils.boxbase import bbox_distance, is_in
def reduct_overlap(bboxes: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""
去除重叠的bbox，保留不被其他bbox包含的bbox
Args:
bboxes: 包含bbox信息的字典列表
Returns:
去重后的bbox列表
"""
N = len(bboxes)
keep = [True] * N
for i in range(N):
for j in range(N):
if i == j:
continue
if is_in(bboxes[i]['bbox'], bboxes[j]['bbox']):
keep[i] = False
return [bboxes[i] for i in range(N) if keep[i]]
def tie_up_category_by_distance_v3(
get_subjects_func: Callable,
get_objects_func: Callable,
extract_subject_func: Callable = None,
extract_object_func: Callable = None
):
"""
通用的类别关联方法，用于将主体对象与客体对象进行关联
参数:
get_subjects_func: 函数，提取主体对象
get_objects_func: 函数，提取客体对象
extract_subject_func: 函数，自定义提取主体属性（默认使用bbox和其他属性）
extract_object_func: 函数，自定义提取客体属性（默认使用bbox和其他属性）
返回:
关联后的对象列表
"""
subjects = get_subjects_func()
objects = get_objects_func()
# 如果没有提供自定义提取函数，使用默认函数
if extract_subject_func is None:
extract_subject_func = lambda x: x
if extract_object_func is None:
extract_object_func = lambda x: x
ret = []
N, M = len(subjects), len(objects)
subjects.sort(key=lambda x: x["bbox"][0] ** 2 + x["bbox"][1] ** 2)
objects.sort(key=lambda x: x["bbox"][0] ** 2 + x["bbox"][1] ** 2)
OBJ_IDX_OFFSET = 10000
SUB_BIT_KIND, OBJ_BIT_KIND = 0, 1
all_boxes_with_idx = [(i, SUB_BIT_KIND, sub["bbox"][0], sub["bbox"][1]) for i, sub in enumerate(subjects)] + [
(i + OBJ_IDX_OFFSET, OBJ_BIT_KIND, obj["bbox"][0], obj["bbox"][1]) for i, obj in enumerate(objects)
]
seen_idx = set()
seen_sub_idx = set()
while N > len(seen_sub_idx):
candidates = []
for idx, kind, x0, y0 in all_boxes_with_idx:
if idx in seen_idx:
continue
candidates.append((idx, kind, x0, y0))
if len(candidates) == 0:
break
left_x = min([v[2] for v in candidates])
top_y = min([v[3] for v in candidates])
candidates.sort(key=lambda x: (x[2] - left_x) ** 2 + (x[3] - top_y) ** 2)
fst_idx, fst_kind, left_x, top_y = candidates[0]
fst_bbox = subjects[fst_idx]['bbox'] if fst_kind == SUB_BIT_KIND else objects[fst_idx - OBJ_IDX_OFFSET]['bbox']
candidates.sort(
key=lambda x: bbox_distance(fst_bbox, subjects[x[0]]['bbox']) if x[1] == SUB_BIT_KIND else bbox_distance(
fst_bbox, objects[x[0] - OBJ_IDX_OFFSET]['bbox']))
nxt = None
for i in range(1, len(candidates)):
if candidates[i][1] ^ fst_kind == 1:
nxt = candidates[i]
break
if nxt is None:
break
if fst_kind == SUB_BIT_KIND:
sub_idx, obj_idx = fst_idx, nxt[0] - OBJ_IDX_OFFSET
else:
sub_idx, obj_idx = nxt[0], fst_idx - OBJ_IDX_OFFSET
pair_dis = bbox_distance(subjects[sub_idx]["bbox"], objects[obj_idx]["bbox"])
nearest_dis = float("inf")
for i in range(N):
# 取消原先算法中 1对1 匹配的偏置
# if i in seen_idx or i == sub_idx:continue
nearest_dis = min(nearest_dis, bbox_distance(subjects[i]["bbox"], objects[obj_idx]["bbox"]))
if pair_dis >= 3 * nearest_dis:
seen_idx.add(sub_idx)
continue
seen_idx.add(sub_idx)
seen_idx.add(obj_idx + OBJ_IDX_OFFSET)
seen_sub_idx.add(sub_idx)
ret.append(
{
"sub_bbox": extract_subject_func(subjects[sub_idx]),
"obj_bboxes": [extract_object_func(objects[obj_idx])],
"sub_idx": sub_idx,
}
)
for i in range(len(objects)):
j = i + OBJ_IDX_OFFSET
if j in seen_idx:
continue
seen_idx.add(j)
nearest_dis, nearest_sub_idx = float("inf"), -1
for k in range(len(subjects)):
dis = bbox_distance(objects[i]["bbox"], subjects[k]["bbox"])
if dis < nearest_dis:
nearest_dis = dis
nearest_sub_idx = k
for k in range(len(subjects)):
if k != nearest_sub_idx:
continue
if k in seen_sub_idx:
for kk in range(len(ret)):
if ret[kk]["sub_idx"] == k:
ret[kk]["obj_bboxes"].append(extract_object_func(objects[i]))
break
else:
ret.append(
{
"sub_bbox": extract_subject_func(subjects[k]),
"obj_bboxes": [extract_object_func(objects[i])],
"sub_idx": k,
}
)
seen_sub_idx.add(k)
seen_idx.add(k)
for i in range(len(subjects)):
if i in seen_sub_idx:
continue
ret.append(
{
"sub_bbox": extract_subject_func(subjects[i]),
"obj_bboxes": [],
"sub_idx": i,
}
)
return ret
\ No newline at end of file
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