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import numpy as np |
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from ppocr.metrics.det_metric import DetMetric |
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class TableStructureMetric(object): |
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def __init__(self, |
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main_indicator='acc', |
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eps=1e-6, |
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del_thead_tbody=False, |
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**kwargs): |
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self.main_indicator = main_indicator |
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self.eps = eps |
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self.del_thead_tbody = del_thead_tbody |
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self.reset() |
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def __call__(self, pred_label, batch=None, *args, **kwargs): |
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preds, labels = pred_label |
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pred_structure_batch_list = preds['structure_batch_list'] |
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gt_structure_batch_list = labels['structure_batch_list'] |
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correct_num = 0 |
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all_num = 0 |
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for (pred, pred_conf), target in zip(pred_structure_batch_list, |
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gt_structure_batch_list): |
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pred_str = ''.join(pred) |
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target_str = ''.join(target) |
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if self.del_thead_tbody: |
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pred_str = pred_str.replace('<thead>', '').replace( |
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'</thead>', '').replace('<tbody>', '').replace('</tbody>', |
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'') |
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target_str = target_str.replace('<thead>', '').replace( |
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'</thead>', '').replace('<tbody>', '').replace('</tbody>', |
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'') |
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if pred_str == target_str: |
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correct_num += 1 |
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all_num += 1 |
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self.correct_num += correct_num |
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self.all_num += all_num |
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def get_metric(self): |
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""" |
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return metrics { |
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'acc': 0, |
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} |
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""" |
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acc = 1.0 * self.correct_num / (self.all_num + self.eps) |
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self.reset() |
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return {'acc': acc} |
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def reset(self): |
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self.correct_num = 0 |
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self.all_num = 0 |
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self.len_acc_num = 0 |
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self.token_nums = 0 |
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self.anys_dict = dict() |
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class TableMetric(object): |
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def __init__(self, |
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main_indicator='acc', |
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compute_bbox_metric=False, |
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box_format='xyxy', |
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del_thead_tbody=False, |
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**kwargs): |
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""" |
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@param sub_metrics: configs of sub_metric |
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@param main_matric: main_matric for save best_model |
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@param kwargs: |
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""" |
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self.structure_metric = TableStructureMetric( |
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del_thead_tbody=del_thead_tbody) |
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self.bbox_metric = DetMetric() if compute_bbox_metric else None |
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self.main_indicator = main_indicator |
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self.box_format = box_format |
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self.reset() |
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def __call__(self, pred_label, batch=None, *args, **kwargs): |
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self.structure_metric(pred_label) |
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if self.bbox_metric is not None: |
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self.bbox_metric(*self.prepare_bbox_metric_input(pred_label)) |
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def prepare_bbox_metric_input(self, pred_label): |
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pred_bbox_batch_list = [] |
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gt_ignore_tags_batch_list = [] |
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gt_bbox_batch_list = [] |
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preds, labels = pred_label |
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batch_num = len(preds['bbox_batch_list']) |
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for batch_idx in range(batch_num): |
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pred_bbox_list = [ |
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self.format_box(pred_box) |
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for pred_box in preds['bbox_batch_list'][batch_idx] |
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] |
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pred_bbox_batch_list.append({'points': pred_bbox_list}) |
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gt_bbox_list = [] |
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gt_ignore_tags_list = [] |
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for gt_box in labels['bbox_batch_list'][batch_idx]: |
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gt_bbox_list.append(self.format_box(gt_box)) |
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gt_ignore_tags_list.append(0) |
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gt_bbox_batch_list.append(gt_bbox_list) |
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gt_ignore_tags_batch_list.append(gt_ignore_tags_list) |
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return [ |
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pred_bbox_batch_list, |
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[0, 0, gt_bbox_batch_list, gt_ignore_tags_batch_list] |
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] |
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def get_metric(self): |
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structure_metric = self.structure_metric.get_metric() |
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if self.bbox_metric is None: |
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return structure_metric |
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bbox_metric = self.bbox_metric.get_metric() |
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if self.main_indicator == self.bbox_metric.main_indicator: |
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output = bbox_metric |
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for sub_key in structure_metric: |
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output["structure_metric_{}".format( |
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sub_key)] = structure_metric[sub_key] |
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else: |
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output = structure_metric |
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for sub_key in bbox_metric: |
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output["bbox_metric_{}".format(sub_key)] = bbox_metric[sub_key] |
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return output |
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def reset(self): |
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self.structure_metric.reset() |
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if self.bbox_metric is not None: |
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self.bbox_metric.reset() |
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def format_box(self, box): |
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if self.box_format == 'xyxy': |
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x1, y1, x2, y2 = box |
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box = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]] |
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elif self.box_format == 'xywh': |
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x, y, w, h = box |
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x1, y1, x2, y2 = x - w // 2, y - h // 2, x + w // 2, y + h // 2 |
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box = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]] |
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elif self.box_format == 'xyxyxyxy': |
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x1, y1, x2, y2, x3, y3, x4, y4 = box |
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box = [[x1, y1], [x2, y2], [x3, y3], [x4, y4]] |
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return box |
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