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ppstructure/kie/predict_kie_token_ser.py
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191
ppstructure/kie/predict_kie_token_ser.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../..")))
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os.environ["FLAGS_allocator_strategy"] = "auto_growth"
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import cv2
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import json
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import numpy as np
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import time
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import tools.infer.utility as utility
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from ppocr.data import create_operators, transform
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from ppocr.postprocess import build_post_process
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from ppocr.utils.logging import get_logger
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from ppocr.utils.visual import draw_ser_results
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from ppocr.utils.utility import get_image_file_list, check_and_read
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from ppstructure.utility import parse_args
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from paddleocr import PaddleOCR
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logger = get_logger()
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class SerPredictor(object):
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def __init__(self, args):
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self.args = args
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self.ocr_engine = PaddleOCR(
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use_angle_cls=args.use_angle_cls,
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det_model_dir=args.det_model_dir,
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rec_model_dir=args.rec_model_dir,
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show_log=False,
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use_gpu=args.use_gpu,
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)
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pre_process_list = [
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{
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"VQATokenLabelEncode": {
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"algorithm": args.kie_algorithm,
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"class_path": args.ser_dict_path,
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"contains_re": False,
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"ocr_engine": self.ocr_engine,
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"order_method": args.ocr_order_method,
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}
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},
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{"VQATokenPad": {"max_seq_len": 512, "return_attention_mask": True}},
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{"VQASerTokenChunk": {"max_seq_len": 512, "return_attention_mask": True}},
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{"Resize": {"size": [224, 224]}},
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{
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"NormalizeImage": {
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"std": [58.395, 57.12, 57.375],
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"mean": [123.675, 116.28, 103.53],
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"scale": "1",
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"order": "hwc",
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}
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},
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{"ToCHWImage": None},
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{
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"KeepKeys": {
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"keep_keys": [
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"input_ids",
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"bbox",
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"attention_mask",
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"token_type_ids",
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"image",
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"labels",
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"segment_offset_id",
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"ocr_info",
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"entities",
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]
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}
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},
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]
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postprocess_params = {
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"name": "VQASerTokenLayoutLMPostProcess",
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"class_path": args.ser_dict_path,
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}
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self.preprocess_op = create_operators(pre_process_list, {"infer_mode": True})
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self.postprocess_op = build_post_process(postprocess_params)
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(
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self.predictor,
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self.input_tensor,
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self.output_tensors,
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self.config,
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) = utility.create_predictor(args, "ser", logger)
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def __call__(self, img):
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ori_im = img.copy()
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data = {"image": img}
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data = transform(data, self.preprocess_op)
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if data[0] is None:
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return None, 0
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starttime = time.time()
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for idx in range(len(data)):
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if isinstance(data[idx], np.ndarray):
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data[idx] = np.expand_dims(data[idx], axis=0)
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else:
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data[idx] = [data[idx]]
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if self.args.use_onnx:
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input_tensor = {
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name: data[idx] for idx, name in enumerate(self.input_tensor)
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}
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self.output_tensors = self.predictor.run(None, input_tensor)
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else:
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for idx in range(len(self.input_tensor)):
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self.input_tensor[idx].copy_from_cpu(data[idx])
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self.predictor.run()
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outputs = []
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for output_tensor in self.output_tensors:
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output = (
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output_tensor if self.args.use_onnx else output_tensor.copy_to_cpu()
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)
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outputs.append(output)
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preds = outputs[0]
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post_result = self.postprocess_op(
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preds, segment_offset_ids=data[6], ocr_infos=data[7]
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)
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elapse = time.time() - starttime
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return post_result, data, elapse
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def main(args):
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image_file_list = get_image_file_list(args.image_dir)
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ser_predictor = SerPredictor(args)
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count = 0
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total_time = 0
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os.makedirs(args.output, exist_ok=True)
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with open(
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os.path.join(args.output, "infer.txt"), mode="w", encoding="utf-8"
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) as f_w:
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for image_file in image_file_list:
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img, flag, _ = check_and_read(image_file)
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if not flag:
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img = cv2.imread(image_file)
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img = img[:, :, ::-1]
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if img is None:
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logger.info("error in loading image:{}".format(image_file))
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continue
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ser_res, _, elapse = ser_predictor(img)
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ser_res = ser_res[0]
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res_str = "{}\t{}\n".format(
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image_file,
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json.dumps(
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{
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"ocr_info": ser_res,
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},
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ensure_ascii=False,
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),
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)
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f_w.write(res_str)
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img_res = draw_ser_results(
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image_file,
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ser_res,
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font_path=args.vis_font_path,
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)
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img_save_path = os.path.join(args.output, os.path.basename(image_file))
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cv2.imwrite(img_save_path, img_res)
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logger.info("save vis result to {}".format(img_save_path))
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if count > 0:
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total_time += elapse
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count += 1
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logger.info("Predict time of {}: {}".format(image_file, elapse))
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if __name__ == "__main__":
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main(parse_args())
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