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import numpy as np
import paddle
def load_vqa_bio_label_maps(label_map_path):
with open(label_map_path, "r", encoding="utf-8") as fin:
lines = fin.readlines()
lines = [line.strip() for line in lines]
if "O" not in lines:
lines.insert(0, "O")
labels = []
for line in lines:
if line == "O":
labels.append("O")
else:
labels.append("B-" + line)
labels.append("I-" + line)
label2id_map = {label: idx for idx, label in enumerate(labels)}
id2label_map = {idx: label for idx, label in enumerate(labels)}
return label2id_map, id2label_map
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
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