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tools/infer_kie_token_ser.py

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+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "..")))
+
+os.environ["FLAGS_allocator_strategy"] = "auto_growth"
+import cv2
+import json
+import paddle
+
+from ppocr.data import create_operators, transform
+from ppocr.modeling.architectures import build_model
+from ppocr.postprocess import build_post_process
+from ppocr.utils.save_load import load_model
+from ppocr.utils.visual import draw_ser_results
+from ppocr.utils.utility import get_image_file_list, load_vqa_bio_label_maps
+import tools.program as program
+
+
+def to_tensor(data):
+    import numbers
+    from collections import defaultdict
+
+    data_dict = defaultdict(list)
+    to_tensor_idxs = []
+
+    for idx, v in enumerate(data):
+        if isinstance(v, (np.ndarray, paddle.Tensor, numbers.Number)):
+            if idx not in to_tensor_idxs:
+                to_tensor_idxs.append(idx)
+        data_dict[idx].append(v)
+    for idx in to_tensor_idxs:
+        data_dict[idx] = paddle.to_tensor(data_dict[idx])
+    return list(data_dict.values())
+
+
+class SerPredictor(object):
+    def __init__(self, config):
+        global_config = config["Global"]
+        self.algorithm = config["Architecture"]["algorithm"]
+
+        # build post process
+        self.post_process_class = build_post_process(
+            config["PostProcess"], global_config
+        )
+
+        # build model
+        self.model = build_model(config["Architecture"])
+
+        load_model(config, self.model, model_type=config["Architecture"]["model_type"])
+
+        from paddleocr import PaddleOCR
+
+        self.ocr_engine = PaddleOCR(
+            use_angle_cls=False,
+            show_log=False,
+            rec_model_dir=global_config.get("kie_rec_model_dir", None),
+            det_model_dir=global_config.get("kie_det_model_dir", None),
+            use_gpu=global_config["use_gpu"],
+        )
+
+        # create data ops
+        transforms = []
+        for op in config["Eval"]["dataset"]["transforms"]:
+            op_name = list(op)[0]
+            if "Label" in op_name:
+                op[op_name]["ocr_engine"] = self.ocr_engine
+            elif op_name == "KeepKeys":
+                op[op_name]["keep_keys"] = [
+                    "input_ids",
+                    "bbox",
+                    "attention_mask",
+                    "token_type_ids",
+                    "image",
+                    "labels",
+                    "segment_offset_id",
+                    "ocr_info",
+                    "entities",
+                ]
+
+            transforms.append(op)
+        if config["Global"].get("infer_mode", None) is None:
+            global_config["infer_mode"] = True
+        self.ops = create_operators(
+            config["Eval"]["dataset"]["transforms"], global_config
+        )
+        self.model.eval()
+
+    def __call__(self, data):
+        with open(data["img_path"], "rb") as f:
+            img = f.read()
+        data["image"] = img
+        batch = transform(data, self.ops)
+        batch = to_tensor(batch)
+        preds = self.model(batch)
+
+        post_result = self.post_process_class(
+            preds, segment_offset_ids=batch[6], ocr_infos=batch[7]
+        )
+        return post_result, batch
+
+
+if __name__ == "__main__":
+    config, device, logger, vdl_writer = program.preprocess()
+    os.makedirs(config["Global"]["save_res_path"], exist_ok=True)
+
+    ser_engine = SerPredictor(config)
+
+    if config["Global"].get("infer_mode", None) is False:
+        data_dir = config["Eval"]["dataset"]["data_dir"]
+        with open(config["Global"]["infer_img"], "rb") as f:
+            infer_imgs = f.readlines()
+    else:
+        infer_imgs = get_image_file_list(config["Global"]["infer_img"])
+
+    with open(
+        os.path.join(config["Global"]["save_res_path"], "infer_results.txt"),
+        "w",
+        encoding="utf-8",
+    ) as fout:
+        for idx, info in enumerate(infer_imgs):
+            if config["Global"].get("infer_mode", None) is False:
+                data_line = info.decode("utf-8")
+                substr = data_line.strip("\n").split("\t")
+                img_path = os.path.join(data_dir, substr[0])
+                data = {"img_path": img_path, "label": substr[1]}
+            else:
+                img_path = info
+                data = {"img_path": img_path}
+
+            save_img_path = os.path.join(
+                config["Global"]["save_res_path"],
+                os.path.splitext(os.path.basename(img_path))[0] + "_ser.jpg",
+            )
+
+            result, _ = ser_engine(data)
+            result = result[0]
+            fout.write(
+                img_path
+                + "\t"
+                + json.dumps(
+                    {
+                        "ocr_info": result,
+                    },
+                    ensure_ascii=False,
+                )
+                + "\n"
+            )
+            img_res = draw_ser_results(img_path, result)
+            cv2.imwrite(save_img_path, img_res)
+
+            logger.info(
+                "process: [{}/{}], save result to {}".format(
+                    idx, len(infer_imgs), save_img_path
+                )
+            )