ppocr/metrics/vqa_token_re_metric.py

# 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import paddle

__all__ = ['KIEMetric']


class VQAReTokenMetric(object):
    def __init__(self, main_indicator='hmean', **kwargs):
        self.main_indicator = main_indicator
        self.reset()

    def __call__(self, preds, batch, **kwargs):
        pred_relations, relations, entities = preds
        self.pred_relations_list.extend(pred_relations)
        self.relations_list.extend(relations)
        self.entities_list.extend(entities)

    def get_metric(self):
        gt_relations = []
        for b in range(len(self.relations_list)):
            rel_sent = []
            relation_list = self.relations_list[b]
            entitie_list = self.entities_list[b]
            head_len = relation_list[0, 0]
            if head_len > 0:
                entitie_start_list = entitie_list[1:entitie_list[0, 0] + 1, 0]
                entitie_end_list = entitie_list[1:entitie_list[0, 1] + 1, 1]
                entitie_label_list = entitie_list[1:entitie_list[0, 2] + 1, 2]
                for head, tail in zip(relation_list[1:head_len + 1, 0],
                                      relation_list[1:head_len + 1, 1]):
                    rel = {}
                    rel["head_id"] = head
                    rel["head"] = (entitie_start_list[head],
                                   entitie_end_list[head])
                    rel["head_type"] = entitie_label_list[head]

                    rel["tail_id"] = tail
                    rel["tail"] = (entitie_start_list[tail],
                                   entitie_end_list[tail])
                    rel["tail_type"] = entitie_label_list[tail]

                    rel["type"] = 1
                    rel_sent.append(rel)
            gt_relations.append(rel_sent)
        re_metrics = self.re_score(
            self.pred_relations_list, gt_relations, mode="boundaries")
        metrics = {
            "precision": re_metrics["ALL"]["p"],
            "recall": re_metrics["ALL"]["r"],
            "hmean": re_metrics["ALL"]["f1"],
        }
        self.reset()
        return metrics

    def reset(self):
        self.pred_relations_list = []
        self.relations_list = []
        self.entities_list = []

    def re_score(self, pred_relations, gt_relations, mode="strict"):
        """Evaluate RE predictions

        Args:
            pred_relations (list) :  list of list of predicted relations (several relations in each sentence)
            gt_relations (list) :    list of list of ground truth relations

                rel = { "head": (start_idx (inclusive), end_idx (exclusive)),
                        "tail": (start_idx (inclusive), end_idx (exclusive)),
                        "head_type": ent_type,
                        "tail_type": ent_type,
                        "type": rel_type}

            vocab (Vocab) :         dataset vocabulary
            mode (str) :            in 'strict' or 'boundaries'"""

        assert mode in ["strict", "boundaries"]

        relation_types = [v for v in [0, 1] if not v == 0]
        scores = {
            rel: {
                "tp": 0,
                "fp": 0,
                "fn": 0
            }
            for rel in relation_types + ["ALL"]
        }

        # Count GT relations and Predicted relations
        n_sents = len(gt_relations)
        n_rels = sum([len([rel for rel in sent]) for sent in gt_relations])
        n_found = sum([len([rel for rel in sent]) for sent in pred_relations])

        # Count TP, FP and FN per type
        for pred_sent, gt_sent in zip(pred_relations, gt_relations):
            for rel_type in relation_types:
                # strict mode takes argument types into account
                if mode == "strict":
                    pred_rels = {(rel["head"], rel["head_type"], rel["tail"],
                                  rel["tail_type"])
                                 for rel in pred_sent
                                 if rel["type"] == rel_type}
                    gt_rels = {(rel["head"], rel["head_type"], rel["tail"],
                                rel["tail_type"])
                               for rel in gt_sent if rel["type"] == rel_type}

                # boundaries mode only takes argument spans into account
                elif mode == "boundaries":
                    pred_rels = {(rel["head"], rel["tail"])
                                 for rel in pred_sent
                                 if rel["type"] == rel_type}
                    gt_rels = {(rel["head"], rel["tail"])
                               for rel in gt_sent if rel["type"] == rel_type}

                scores[rel_type]["tp"] += len(pred_rels & gt_rels)
                scores[rel_type]["fp"] += len(pred_rels - gt_rels)
                scores[rel_type]["fn"] += len(gt_rels - pred_rels)

        # Compute per entity Precision / Recall / F1
        for rel_type in scores.keys():
            if scores[rel_type]["tp"]:
                scores[rel_type]["p"] = scores[rel_type]["tp"] / (
                    scores[rel_type]["fp"] + scores[rel_type]["tp"])
                scores[rel_type]["r"] = scores[rel_type]["tp"] / (
                    scores[rel_type]["fn"] + scores[rel_type]["tp"])
            else:
                scores[rel_type]["p"], scores[rel_type]["r"] = 0, 0

            if not scores[rel_type]["p"] + scores[rel_type]["r"] == 0:
                scores[rel_type]["f1"] = (
                    2 * scores[rel_type]["p"] * scores[rel_type]["r"] /
                    (scores[rel_type]["p"] + scores[rel_type]["r"]))
            else:
                scores[rel_type]["f1"] = 0

        # Compute micro F1 Scores
        tp = sum([scores[rel_type]["tp"] for rel_type in relation_types])
        fp = sum([scores[rel_type]["fp"] for rel_type in relation_types])
        fn = sum([scores[rel_type]["fn"] for rel_type in relation_types])

        if tp:
            precision = tp / (tp + fp)
            recall = tp / (tp + fn)
            f1 = 2 * precision * recall / (precision + recall)

        else:
            precision, recall, f1 = 0, 0, 0

        scores["ALL"]["p"] = precision
        scores["ALL"]["r"] = recall
        scores["ALL"]["f1"] = f1
        scores["ALL"]["tp"] = tp
        scores["ALL"]["fp"] = fp
        scores["ALL"]["fn"] = fn

        # Compute Macro F1 Scores
        scores["ALL"]["Macro_f1"] = np.mean(
            [scores[ent_type]["f1"] for ent_type in relation_types])
        scores["ALL"]["Macro_p"] = np.mean(
            [scores[ent_type]["p"] for ent_type in relation_types])
        scores["ALL"]["Macro_r"] = np.mean(
            [scores[ent_type]["r"] for ent_type in relation_types])

        return scores