feat: enhance batch processing in BatchAnalyze with layout and OCR timing logs

magic_pdf/model/batch_analyze.py

@@ -35,6 +35,8 @@ class BatchAnalyze:
 
     def __call__(self, images: list) -> list:
         images_layout_res = []
+
+        layout_start_time = time.time()
         if self.model.layout_model_name == MODEL_NAME.LAYOUTLMv3:
             # layoutlmv3
             for image in images:
@@ -42,17 +44,52 @@ class BatchAnalyze:
                 images_layout_res.append(layout_res)
         elif self.model.layout_model_name == MODEL_NAME.DocLayout_YOLO:
             # doclayout_yolo
+            layout_images = []
+            modified_images = []
+            for image_index, image in enumerate(images):
+                pil_img = Image.fromarray(image)
+                width, height = pil_img.size
+                if height > width:
+                    input_res = {"poly": [0, 0, width, 0, width, height, 0, height]}
+                    new_image, useful_list = crop_img(
+                        input_res, pil_img, crop_paste_x=width // 2, crop_paste_y=0
+                    )
+                    layout_images.append(new_image)
+                    modified_images.append([image_index, useful_list])
+                else:
+                    layout_images.append(pil_img)
+
             images_layout_res += self.model.layout_model.batch_predict(
-                images, self.batch_ratio * YOLO_LAYOUT_BASE_BATCH_SIZE
+                layout_images, self.batch_ratio * YOLO_LAYOUT_BASE_BATCH_SIZE
             )
 
+            for image_index, useful_list in modified_images:
+                for res in images_layout_res[image_index]:
+                    for i in range(len(res["poly"])):
+                        if i % 2 == 0:
+                            res["poly"][i] = (
+                                res["poly"][i] - useful_list[0] + useful_list[2]
+                            )
+                        else:
+                            res["poly"][i] = (
+                                res["poly"][i] - useful_list[1] + useful_list[3]
+                            )
+        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
+            mfd_start_time = time.time()
             images_mfd_res = self.model.mfd_model.batch_predict(
                 images, self.batch_ratio * MFD_BASE_BATCH_SIZE
             )
+            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()
             images_formula_list = self.model.mfr_model.batch_predict(
                 images_mfd_res,
                 images,
@@ -60,10 +97,17 @@ class BatchAnalyze:
             )
             for image_index in range(len(images)):
                 images_layout_res[image_index] += images_formula_list[image_index]
+            logger.info(
+                f"mfr time: {round(time.time() - mfr_start_time, 2)}, image num: {len(images)}"
+            )
 
         # 清理显存
         clean_vram(self.model.device, vram_threshold=8)
 
+        ocr_time = 0
+        ocr_count = 0
+        table_time = 0
+        table_count = 0
         # reference: magic_pdf/model/doc_analyze_by_custom_model.py:doc_analyze
         for index in range(len(images)):
             layout_res = images_layout_res[index]
@@ -99,12 +143,8 @@ class BatchAnalyze:
                 if ocr_res:
                     ocr_result_list = get_ocr_result_list(ocr_res, useful_list)
                     layout_res.extend(ocr_result_list)
-
-            ocr_cost = round(time.time() - ocr_start, 2)
-            if self.model.apply_ocr:
-                logger.info(f"ocr time: {ocr_cost}")
-            else:
-                logger.info(f"det time: {ocr_cost}")
+            ocr_time += time.time() - ocr_start
+            ocr_count += len(ocr_res_list)
 
             # 表格识别 table recognition
             if self.model.apply_table:
@@ -146,7 +186,13 @@ class BatchAnalyze:
                         logger.warning(
                             "table recognition processing fails, not get html return"
                         )
-                logger.info(f"table time: {round(time.time() - table_start, 2)}")
+                table_time += time.time() - table_start
+                table_count += len(table_res_list)
+
+        if self.model.apply_ocr:
+            logger.info(f"ocr time: {round(ocr_time, 2)}, image num: {ocr_count}")
+        if self.model.apply_table:
+            logger.info(f"table time: {round(table_time, 2)}, image num: {table_count}")
 
         return images_layout_res
 
@@ -225,6 +271,8 @@ def doc_batch_analyze(
         model_json.append(page_dict)
 
     # TODO: clean memory when gpu memory is not enough
+    clean_memory_start_time = time.time()
     clean_memory()
+    logger.info(f"clean memory time: {round(time.time() - clean_memory_start_time, 2)}")
 
     return InferenceResult(model_json, dataset)