feat(model): add tqdm progress bar to model prediction loops

- Add tqdm progress bar to batch prediction loops in multiple model modules
- Improve logging and error handling in batch analysis script
- Update table model initialization to use default sub-model if none specified
- Add tqdm dependency to requirements.txt

magic_pdf/model/batch_analyze.py

@@ -3,13 +3,16 @@ import time
 import cv2
 import torch
 from loguru import logger
+from tqdm import tqdm
 
 from magic_pdf.config.constants import MODEL_NAME
+from magic_pdf.libs.config_reader import get_table_recog_config
 from magic_pdf.model.sub_modules.model_init import AtomModelSingleton
 from magic_pdf.model.sub_modules.model_utils import (
     clean_vram, crop_img, get_res_list_from_layout_res)
 from magic_pdf.model.sub_modules.ocr.paddleocr2pytorch.ocr_utils import (
     get_adjusted_mfdetrec_res, get_ocr_result_list)
+from magic_pdf.model.sub_modules.table.rapidtable.rapid_table import RapidTableModel
 
 YOLO_LAYOUT_BASE_BATCH_SIZE = 1
 MFD_BASE_BATCH_SIZE = 1
@@ -52,9 +55,9 @@ class BatchAnalyze:
                 layout_images, YOLO_LAYOUT_BASE_BATCH_SIZE
             )
 
-        logger.info(
-            f'layout time: {round(time.time() - layout_start_time, 2)}, image num: {len(images)}'
-        )
+        # logger.info(
+        #     f'layout time: {round(time.time() - layout_start_time, 2)}, image num: {len(images)}'
+        # )
 
         if self.model.apply_formula:
             # 公式检测
@@ -63,9 +66,9 @@ class BatchAnalyze:
                 # images, self.batch_ratio * MFD_BASE_BATCH_SIZE
                 images, MFD_BASE_BATCH_SIZE
             )
-            logger.info(
-                f'mfd time: {round(time.time() - mfd_start_time, 2)}, image num: {len(images)}'
-            )
+            # logger.info(
+            #     f'mfd time: {round(time.time() - mfd_start_time, 2)}, image num: {len(images)}'
+            # )
 
             # 公式识别
             mfr_start_time = time.time()
@@ -78,82 +81,100 @@ class BatchAnalyze:
             for image_index in range(len(images)):
                 images_layout_res[image_index] += images_formula_list[image_index]
                 mfr_count += len(images_formula_list[image_index])
-            logger.info(
-                f'mfr time: {round(time.time() - mfr_start_time, 2)}, image num: {mfr_count}'
-            )
+            # logger.info(
+            #     f'mfr time: {round(time.time() - mfr_start_time, 2)}, image num: {mfr_count}'
+            # )
 
         # 清理显存
         clean_vram(self.model.device, vram_threshold=8)
 
-        det_time = 0
-        det_count = 0
-        table_time = 0
-        table_count = 0
-        # reference: magic_pdf/model/doc_analyze_by_custom_model.py:doc_analyze
+        ocr_res_list_all_page = []
+        table_res_list_all_page = []
         for index in range(len(images)):
             _, ocr_enable, _lang = images_with_extra_info[index]
-            self.model = self.model_manager.get_model(ocr_enable, self.show_log, _lang, self.layout_model, self.formula_enable, self.table_enable)
             layout_res = images_layout_res[index]
             np_array_img = images[index]
 
             ocr_res_list, table_res_list, single_page_mfdetrec_res = (
                 get_res_list_from_layout_res(layout_res)
             )
-            # ocr识别
-            det_start = time.time()
+
+            ocr_res_list_all_page.append({'ocr_res_list':ocr_res_list,
+                                          'lang':_lang,
+                                          'ocr_enable':ocr_enable,
+                                          'np_array_img':np_array_img,
+                                          'single_page_mfdetrec_res':single_page_mfdetrec_res,
+                                          'layout_res':layout_res,
+                                          })
+            table_res_list_all_page.append({'table_res_list':table_res_list,
+                                            'lang':_lang,
+                                            'np_array_img':np_array_img,
+                                          })
+
+        # 文本框检测
+        det_start = time.time()
+        det_count = 0
+        # for ocr_res_list_dict in ocr_res_list_all_page:
+        for ocr_res_list_dict in tqdm(ocr_res_list_all_page, desc="OCR-det Predict"):
             # Process each area that requires OCR processing
-            for res in ocr_res_list:
+            _lang = ocr_res_list_dict['lang']
+            # Get OCR results for this language's images
+            atom_model_manager = AtomModelSingleton()
+            ocr_model = atom_model_manager.get_atom_model(
+                atom_model_name='ocr',
+                ocr_show_log=False,
+                det_db_box_thresh=0.3,
+                lang=_lang
+            )
+            for res in ocr_res_list_dict['ocr_res_list']:
                 new_image, useful_list = crop_img(
-                    res, np_array_img, crop_paste_x=50, crop_paste_y=50
+                    res, ocr_res_list_dict['np_array_img'], crop_paste_x=50, crop_paste_y=50
                 )
                 adjusted_mfdetrec_res = get_adjusted_mfdetrec_res(
-                    single_page_mfdetrec_res, useful_list
+                    ocr_res_list_dict['single_page_mfdetrec_res'], useful_list
                 )
 
-                # OCR recognition
+                # OCR-det
                 new_image = cv2.cvtColor(new_image, cv2.COLOR_RGB2BGR)
-
-                # if ocr_enable:
-                #     ocr_res = self.model.ocr_model.ocr(
-                #         new_image, mfd_res=adjusted_mfdetrec_res
-                #     )[0]
-                # else:
-                ocr_res = self.model.ocr_model.ocr(
+                ocr_res = ocr_model.ocr(
                     new_image, mfd_res=adjusted_mfdetrec_res, rec=False
                 )[0]
 
                 # Integration results
                 if ocr_res:
-                    ocr_result_list = get_ocr_result_list(ocr_res, useful_list, ocr_enable, new_image, _lang)
-                    layout_res.extend(ocr_result_list)
-            det_time += time.time() - det_start
-            det_count += len(ocr_res_list)
-
-            # 表格识别 table recognition
-            if self.model.apply_table:
-                table_start = time.time()
-                for res in table_res_list:
-                    new_image, _ = crop_img(res, np_array_img)
-                    single_table_start_time = time.time()
-                    html_code = None
-                    if self.model.table_model_name == MODEL_NAME.STRUCT_EQTABLE:
-                        with torch.no_grad():
-                            table_result = self.model.table_model.predict(
-                                new_image, 'html'
-                            )
-                            if len(table_result) > 0:
-                                html_code = table_result[0]
-                    elif self.model.table_model_name == MODEL_NAME.TABLE_MASTER:
-                        html_code = self.model.table_model.img2html(new_image)
-                    elif self.model.table_model_name == MODEL_NAME.RAPID_TABLE:
-                        html_code, table_cell_bboxes, logic_points, elapse = (
-                            self.model.table_model.predict(new_image)
-                        )
-                    run_time = time.time() - single_table_start_time
-                    if run_time > self.model.table_max_time:
-                        logger.warning(
-                            f'table recognition processing exceeds max time {self.model.table_max_time}s'
-                        )
+                    ocr_result_list = get_ocr_result_list(ocr_res, useful_list, ocr_res_list_dict['ocr_enable'], new_image, _lang)
+                    ocr_res_list_dict['layout_res'].extend(ocr_result_list)
+            det_count += len(ocr_res_list_dict['ocr_res_list'])
+        # logger.info(f'ocr-det time: {round(time.time()-det_start, 2)}, image num: {det_count}')
+
+
+        # 表格识别 table recognition
+        if self.model.apply_table:
+            table_start = time.time()
+            table_count = 0
+            # for table_res_list_dict in table_res_list_all_page:
+            for table_res_list_dict in tqdm(table_res_list_all_page, desc="Table Predict"):
+                _lang = table_res_list_dict['lang']
+                atom_model_manager = AtomModelSingleton()
+                ocr_engine = atom_model_manager.get_atom_model(
+                    atom_model_name='ocr',
+                    ocr_show_log=False,
+                    det_db_box_thresh=0.5,
+                    det_db_unclip_ratio=1.6,
+                    lang=_lang
+                )
+                table_model = atom_model_manager.get_atom_model(
+                    atom_model_name='table',
+                    table_model_name='rapid_table',
+                    table_model_path='',
+                    table_max_time=400,
+                    device='cpu',
+                    ocr_engine=ocr_engine,
+                    table_sub_model_name='slanet_plus'
+                )
+                for res in table_res_list_dict['table_res_list']:
+                    new_image, _ = crop_img(res, table_res_list_dict['np_array_img'])
+                    html_code, table_cell_bboxes, logic_points, elapse = table_model.predict(new_image)
                     # 判断是否返回正常
                     if html_code:
                         expected_ending = html_code.strip().endswith(
@@ -169,13 +190,8 @@ class BatchAnalyze:
                         logger.warning(
                             'table recognition processing fails, not get html return'
                         )
-                table_time += time.time() - table_start
-                table_count += len(table_res_list)
-
-
-        logger.info(f'ocr-det time: {round(det_time, 2)}, image num: {det_count}')
-        if self.model.apply_table:
-            logger.info(f'table time: {round(table_time, 2)}, image num: {table_count}')
+                table_count += len(table_res_list_dict['table_res_list'])
+            # logger.info(f'table time: {round(time.time() - table_start, 2)}, image num: {table_count}')
 
         # Create dictionaries to store items by language
         need_ocr_lists_by_lang = {}  # Dict of lists for each language
@@ -219,7 +235,7 @@ class BatchAnalyze:
                         det_db_box_thresh=0.3,
                         lang=lang
                     )
-                    ocr_res_list = ocr_model.ocr(img_crop_list, det=False)[0]
+                    ocr_res_list = ocr_model.ocr(img_crop_list, det=False, tqdm_enable=True)[0]
 
                     # Verify we have matching counts
                     assert len(ocr_res_list) == len(
@@ -234,7 +250,7 @@ class BatchAnalyze:
                     total_processed += len(img_crop_list)
 
             rec_time += time.time() - rec_start
-            logger.info(f'ocr-rec time: {round(rec_time, 2)}, total images processed: {total_processed}')
+            # logger.info(f'ocr-rec time: {round(rec_time, 2)}, total images processed: {total_processed}')
 
 
 

magic_pdf/model/sub_modules/layout/doclayout_yolo/DocLayoutYOLO.py

@@ -1,4 +1,5 @@
 from doclayout_yolo import YOLOv10
+from tqdm import tqdm
 
 
 class DocLayoutYOLOModel(object):
@@ -31,7 +32,8 @@ class DocLayoutYOLOModel(object):
 
     def batch_predict(self, images: list, batch_size: int) -> list:
         images_layout_res = []
-        for index in range(0, len(images), batch_size):
+        # for index in range(0, len(images), batch_size):
+        for index in tqdm(range(0, len(images), batch_size), total=len(images) // batch_size + (1 if len(images) % batch_size != 0 else 0), desc="Layout Predict"):
             doclayout_yolo_res = [
                 image_res.cpu()
                 for image_res in self.model.predict(

magic_pdf/model/sub_modules/mfd/yolov8/YOLOv8.py

@@ -1,3 +1,4 @@
+from tqdm import tqdm
 from ultralytics import YOLO
 
 
@@ -14,7 +15,10 @@ class YOLOv8MFDModel(object):
 
     def batch_predict(self, images: list, batch_size: int) -> list:
         images_mfd_res = []
-        for index in range(0, len(images), batch_size):
+        # for index in range(0, len(images), batch_size):
+        for index in tqdm(range(0, len(images), batch_size),
+                          total=len(images) // batch_size + (1 if len(images) % batch_size != 0 else 0),
+                          desc="MFD Predict"):
             mfd_res = [
                 image_res.cpu()
                 for image_res in self.mfd_model.predict(

magic_pdf/model/sub_modules/mfr/unimernet/Unimernet.py

@@ -1,5 +1,6 @@
 import torch
 from torch.utils.data import DataLoader, Dataset
+from tqdm import tqdm
 
 
 class MathDataset(Dataset):
@@ -107,7 +108,8 @@ class UnimernetModel(object):
 
         # Process batches and store results
         mfr_res = []
-        for mf_img in dataloader:
+        # for mf_img in dataloader:
+        for mf_img in tqdm(dataloader, desc="MFR Predict"):
             mf_img = mf_img.to(dtype=self.model.dtype)
             mf_img = mf_img.to(self.device)
             with torch.no_grad():

magic_pdf/model/sub_modules/ocr/paddleocr2pytorch/pytorch_paddle.py

@@ -86,6 +86,7 @@ class PytorchPaddleOCR(TextSystem):
             det=True,
             rec=True,
             mfd_res=None,
+            tqdm_enable=False,
             ):
         assert isinstance(img, (np.ndarray, list, str, bytes))
         if isinstance(img, list) and det == True:
@@ -129,7 +130,7 @@ class PytorchPaddleOCR(TextSystem):
                     if not isinstance(img, list):
                         img = preprocess_image(img)
                         img = [img]
-                    rec_res, elapse = self.text_recognizer(img)
+                    rec_res, elapse = self.text_recognizer(img, tqdm_enable=tqdm_enable)
                     # logger.debug("rec_res num  : {}, elapsed : {}".format(len(rec_res), elapse))
                     ocr_res.append(rec_res)
                 return ocr_res

magic_pdf/model/sub_modules/ocr/paddleocr2pytorch/tools/infer/predict_rec.py

@@ -4,6 +4,8 @@ import numpy as np
 import math
 import time
 import torch
+from tqdm import tqdm
+
 from ...pytorchocr.base_ocr_v20 import BaseOCRV20
 from . import pytorchocr_utility as utility
 from ...pytorchocr.postprocess import build_post_process
@@ -286,7 +288,7 @@ class TextRecognizer(BaseOCRV20):
 
         return img
 
-    def __call__(self, img_list):
+    def __call__(self, img_list, tqdm_enable=False):
         img_num = len(img_list)
         # Calculate the aspect ratio of all text bars
         width_list = []
@@ -299,7 +301,8 @@ class TextRecognizer(BaseOCRV20):
         rec_res = [['', 0.0]] * img_num
         batch_num = self.rec_batch_num
         elapse = 0
-        for beg_img_no in range(0, img_num, batch_num):
+        # for beg_img_no in range(0, img_num, batch_num):
+        for beg_img_no in tqdm(range(0, img_num, batch_num), desc='OCR-rec Predict', disable=not tqdm_enable):
             end_img_no = min(img_num, beg_img_no + batch_num)
             norm_img_batch = []
             max_wh_ratio = 0

magic_pdf/model/sub_modules/table/rapidtable/rapid_table.py

@@ -9,7 +9,7 @@ from magic_pdf.libs.config_reader import get_device
 
 
 class RapidTableModel(object):
-    def __init__(self, ocr_engine, table_sub_model_name):
+    def __init__(self, ocr_engine, table_sub_model_name='slanet_plus'):
         sub_model_list = [model.value for model in ModelType]
         if table_sub_model_name is None:
             input_args = RapidTableInput()

requirements.txt

@@ -11,4 +11,5 @@ torch>=2.2.2,!=2.5.0,!=2.5.1,<=2.6.0
 torchvision
 transformers>=4.49.0,<5.0.0
 pdfminer.six==20231228
+tqdm>=4.67.1
 # The requirements.txt must ensure that only necessary external dependencies are introduced. If there are new dependencies to add, please contact the project administrator.