init

backend/ppocr/postprocess/vqa_token_ser_layoutlm_postprocess.py

@@ -0,0 +1,93 @@
+# Copyright (c) 2021 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.
+import numpy as np
+import paddle
+from ppocr.utils.utility import load_vqa_bio_label_maps
+
+
+class VQASerTokenLayoutLMPostProcess(object):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, class_path, **kwargs):
+        super(VQASerTokenLayoutLMPostProcess, self).__init__()
+        label2id_map, self.id2label_map = load_vqa_bio_label_maps(class_path)
+
+        self.label2id_map_for_draw = dict()
+        for key in label2id_map:
+            if key.startswith("I-"):
+                self.label2id_map_for_draw[key] = label2id_map["B" + key[1:]]
+            else:
+                self.label2id_map_for_draw[key] = label2id_map[key]
+
+        self.id2label_map_for_show = dict()
+        for key in self.label2id_map_for_draw:
+            val = self.label2id_map_for_draw[key]
+            if key == "O":
+                self.id2label_map_for_show[val] = key
+            if key.startswith("B-") or key.startswith("I-"):
+                self.id2label_map_for_show[val] = key[2:]
+            else:
+                self.id2label_map_for_show[val] = key
+
+    def __call__(self, preds, batch=None, *args, **kwargs):
+        if isinstance(preds, paddle.Tensor):
+            preds = preds.numpy()
+
+        if batch is not None:
+            return self._metric(preds, batch[1])
+        else:
+            return self._infer(preds, **kwargs)
+
+    def _metric(self, preds, label):
+        pred_idxs = preds.argmax(axis=2)
+        decode_out_list = [[] for _ in range(pred_idxs.shape[0])]
+        label_decode_out_list = [[] for _ in range(pred_idxs.shape[0])]
+
+        for i in range(pred_idxs.shape[0]):
+            for j in range(pred_idxs.shape[1]):
+                if label[i, j] != -100:
+                    label_decode_out_list[i].append(self.id2label_map[label[i,
+                                                                            j]])
+                    decode_out_list[i].append(self.id2label_map[pred_idxs[i,
+                                                                          j]])
+        return decode_out_list, label_decode_out_list
+
+    def _infer(self, preds, attention_masks, segment_offset_ids, ocr_infos):
+        results = []
+
+        for pred, attention_mask, segment_offset_id, ocr_info in zip(
+                preds, attention_masks, segment_offset_ids, ocr_infos):
+            pred = np.argmax(pred, axis=1)
+            pred = [self.id2label_map[idx] for idx in pred]
+
+            for idx in range(len(segment_offset_id)):
+                if idx == 0:
+                    start_id = 0
+                else:
+                    start_id = segment_offset_id[idx - 1]
+
+                end_id = segment_offset_id[idx]
+
+                curr_pred = pred[start_id:end_id]
+                curr_pred = [self.label2id_map_for_draw[p] for p in curr_pred]
+
+                if len(curr_pred) <= 0:
+                    pred_id = 0
+                else:
+                    counts = np.bincount(curr_pred)
+                    pred_id = np.argmax(counts)
+                ocr_info[idx]["pred_id"] = int(pred_id)
+                ocr_info[idx]["pred"] = self.id2label_map_for_show[int(pred_id)]
+            results.append(ocr_info)
+        return results