opensource/MinerU
3
0
Fork 0
mirror of https://github.com/opendatalab/MinerU.git synced 2026-09-24 23:10:23 +08:00
Code Issues Packages Projects Releases Wiki Activity

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

Browse Source
This commit is contained in:
Suven
2024-12-13 08:25:23 +08:00
parent 4fd1e41e0e
commit 49bfdf07c8
1 changed files with 56 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files
magic_pdf/model/batch_analyze.py
+56 -8
View File
@@ -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)