Merge pull request #3155 from opendatalab/dev

Dev

README.md

@@ -43,7 +43,7 @@
 </div>
 
 # Changelog
-- 2025/07/22 2.1.3 Released
+- 2025/07/23 2.1.4 Released
   - Bug Fixes
     - Fixed the issue of excessive memory consumption during the `MFR` step in the `pipeline` backend under certain scenarios #2771
     - Fixed the inaccurate matching between `image`/`table` and `caption`/`footnote` under certain conditions #3129

README_zh-CN.md

@@ -43,7 +43,7 @@
 </div>
 
 # 更新记录
-- 2025/07/22 2.1.3发布
+- 2025/07/23 2.1.4发布
   - bug修复
     - 修复`pipeline`后端中`MFR`步骤在某些情况下显存消耗过大的问题 #2771
     - 修复某些情况下`image`/`table`与`caption`/`footnote`匹配不准确的问题 #3129

mineru/backend/pipeline/pipeline_magic_model.py

@@ -1,5 +1,6 @@
-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, get_minbox_if_overlap_by_ratio
 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:
@@ -208,170 +209,39 @@ class MagicModel:
 
         return bbox_distance(bbox1, bbox2)
 
-    def __reduct_overlap(self, bboxes):
-        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(
-        self,
-        subject_category_id: int,
-        object_category_id: int,
-    ):
-        subjects = self.__reduct_overlap(
-            list(
-                map(
-                    lambda x: {'bbox': x['bbox'], 'score': x['score']},
-                    filter(
-                        lambda x: x['category_id'] == subject_category_id,
-                        self.__page_model_info['layout_dets'],
-                    ),
-                )
-            )
-        )
-        objects = self.__reduct_overlap(
-            list(
-                map(
-                    lambda x: {'bbox': x['bbox'], 'score': x['score']},
-                    filter(
-                        lambda x: x['category_id'] == object_category_id,
-                        self.__page_model_info['layout_dets'],
-                    ),
-                )
-            )
-        )
-
-        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': {
-                        '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,
-                        }
+    def __tie_up_category_by_distance_v3(self, subject_category_id, object_category_id):
+        # 定义获取主体和客体对象的函数
+        def get_subjects():
+            return reduct_overlap(
+                list(
+                    map(
+                        lambda x: {'bbox': x['bbox'], 'score': x['score']},
+                        filter(
+                            lambda x: x['category_id'] == subject_category_id,
+                            self.__page_model_info['layout_dets'],
+                        ),
                     )
-                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,
-                }
+                )
             )
 
+        def get_objects():
+            return reduct_overlap(
+                list(
+                    map(
+                        lambda x: {'bbox': x['bbox'], 'score': x['score']},
+                        filter(
+                            lambda x: x['category_id'] == object_category_id,
+                            self.__page_model_info['layout_dets'],
+                        ),
+                    )
+                )
+            )
 
-        return ret
+        # 调用通用方法
+        return tie_up_category_by_distance_v3(
+            get_subjects,
+            get_objects
+        )
 
     def get_imgs(self):
         with_captions = self.__tie_up_category_by_distance_v3(

mineru/backend/vlm/vlm_magic_model.py

@@ -3,10 +3,10 @@ from typing import Literal
 
 from loguru import logger
 
-from mineru.utils.boxbase import bbox_distance, is_in
 from mineru.utils.enum_class import ContentType, BlockType, SplitFlag
 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.magic_model_utils import reduct_overlap, tie_up_category_by_distance_v3
 
 
 class MagicModel:
@@ -251,175 +251,39 @@ def latex_fix(latex):
     return latex
 
 
-def __reduct_overlap(bboxes):
-    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(
-    blocks: list,
-    subject_block_type: str,
-    object_block_type: str,
-):
-    subjects = __reduct_overlap(
-        list(
-            map(
-                lambda x: {"bbox": x["bbox"], "lines": x["lines"], "index": x["index"]},
-                filter(
-                    lambda x: x["type"] == subject_block_type,
-                    blocks,
-                ),
-            )
-        )
-    )
-    objects = __reduct_overlap(
-        list(
-            map(
-                lambda x: {"bbox": x["bbox"], "lines": x["lines"], "index": x["index"]},
-                filter(
-                    lambda x: x["type"] == object_block_type,
-                    blocks,
-                ),
-            )
-        )
-    )
-
-    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]
-        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,
-                    }
+def __tie_up_category_by_distance_v3(blocks, subject_block_type, object_block_type):
+    # 定义获取主体和客体对象的函数
+    def get_subjects():
+        return reduct_overlap(
+            list(
+                map(
+                    lambda x: {"bbox": x["bbox"], "lines": x["lines"], "index": x["index"]},
+                    filter(
+                        lambda x: x["type"] == subject_block_type,
+                        blocks,
+                    ),
                 )
-            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_objects():
+        return reduct_overlap(
+            list(
+                map(
+                    lambda x: {"bbox": x["bbox"], "lines": x["lines"], "index": x["index"]},
+                    filter(
+                        lambda x: x["type"] == object_block_type,
+                        blocks,
+                    ),
+                )
+            )
+        )
+
+    # 调用通用方法
+    return tie_up_category_by_distance_v3(
+        get_subjects,
+        get_objects
+    )
 
 
 def get_type_blocks(blocks, block_type: Literal["image", "table"]):

mineru/model/mfr/unimernet/Unimernet.py

@@ -105,8 +105,8 @@ class UnimernetModel(object):
         # Create dataset with sorted images
         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
-        batch_size = min(batch_size, 2 ** (len(sorted_images).bit_length() - 1))
+        # 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, max(1, 2 ** (len(sorted_images).bit_length() - 1))) if sorted_images else 1
 
         dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=0)
 

mineru/utils/magic_model_utils.py

@@ -0,0 +1,168 @@
+"""
+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

mineru/version.py

@@ -1 +1 @@
-__version__ = "2.1.1"
+__version__ = "2.1.3"

signatures/version1/cla.json

@@ -391,6 +391,14 @@
       "created_at": "2025-07-16T08:53:24Z",
       "repoId": 765083837,
       "pullRequestNo": 3070
+    },
+    {
+      "name": "huazZeng",
+      "id": 125243371,
+      "comment_id": 3100630363,
+      "created_at": "2025-07-22T03:04:40Z",
+      "repoId": 765083837,
+      "pullRequestNo": 3129
     }
   ]
 }