[Feature] add chartqa

mmpretrain/datasets/__init__.py

@@ -34,6 +34,7 @@
 ]
 
 if WITH_MULTIMODAL:
+    from .chartqa import ChartQA
     from .coco_caption import COCOCaption
     from .coco_retrieval import COCORetrieval
     from .coco_vqa import COCOVQA
@@ -54,5 +55,5 @@
         'COCOCaption', 'COCORetrieval', 'COCOVQA', 'FlamingoEvalCOCOCaption',
         'FlamingoEvalCOCOVQA', 'Flickr30kCaption', 'Flickr30kRetrieval',
         'RefCOCO', 'VisualGenomeQA', 'ScienceQA', 'NoCaps', 'GQA', 'TextVQA',
-        'VSR', 'VizWiz', 'OCRVQA'
+        'VSR', 'VizWiz', 'OCRVQA', 'ChartQA'
     ])

mmpretrain/datasets/chartqa.py

@@ -0,0 +1,115 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+from typing import List
+
+import mmengine
+from mmengine.dataset import BaseDataset
+from mmengine.utils import is_abs
+
+from mmpretrain.registry import DATASETS
+
+
+@DATASETS.register_module()
+class ChartQA(BaseDataset):
+    """ChartQA dataset.
+
+    dataset:https://github.com/vis-nlp/ChartQA
+
+    folder structure:
+        data/chartqa
+        ├── test
+        │   ├── png
+        │   ├── tables
+        │   ├── test_human.json
+        │   └── test_augmented.json
+        ├── train
+        │   ├── png
+        │   ├── tables
+        │   ├── train_human.json
+        │   └── train_augmented.json
+        └── val
+            ├── png
+            ├── tables
+            ├── val_human.json
+            └── val_augmented.json
+    Args:
+        data_root (str): The root directory for ``data_prefix``, ``ann_file``
+            and ``question_file``.
+        data_prefix (str): The directory of images.
+        question_file (str): Question file path.
+        ann_file (str, optional): Annotation file path for training and
+            validation. Defaults to an empty string.
+        **kwargs: Other keyword arguments in :class:`BaseDataset`.
+    """
+
+    def __init__(self,
+                 data_root: str,
+                 data_prefix: str,
+                 ann_file: str = '',
+                 **kwarg):
+        super().__init__(
+            data_root=data_root,
+            data_prefix=dict(img_path=data_prefix),
+            ann_file=ann_file,
+            **kwarg,
+        )
+
+    def _join_prefix(self):
+        # Automatically join annotation file path with `self.root` if
+        # `self.ann_file` is not an absolute path.
+        if not any(is_abs(sub_ann_file)
+                   for sub_ann_file in self.ann_file) and self.ann_file:
+            self.ann_file = [
+                osp.join(self.data_root, sub_ann_file)
+                for sub_ann_file in self.ann_file
+            ]
+        # Automatically join data directory with `self.root` if path value in
+        # `self.data_prefix` is not an absolute path.
+        for data_key, prefix in self.data_prefix.items():
+            if isinstance(prefix, str):
+                if not is_abs(prefix):
+                    self.data_prefix[data_key] = osp.join(
+                        self.data_root, prefix)
+                else:
+                    self.data_prefix[data_key] = prefix
+            else:
+                raise TypeError('prefix should be a string, but got '
+                                f'{type(prefix)}')
+
+    def load_data_list(self) -> List[dict]:
+        """Load data list."""
+        data_list = []
+
+        for sub_ann_file in self.ann_file:
+
+            annotations = mmengine.load(sub_ann_file)
+
+            for ann in annotations:
+
+                # ann example
+                # {
+                #     'imgname': '41699051005347.png'
+                #     'query': 'How many food item i...bar graph?',
+                #       'label': '14'
+                # }
+
+                data_info = dict(question=ann['query'])
+                data_info['image_id'] = ann['imgname']
+
+                img_path = mmengine.join_path(self.data_prefix['img_path'],
+                                              ann['imgname'])
+
+                data_info['img_path'] = img_path
+                data_info['gt_answer'] = ann['label']
+
+                if 'human' in sub_ann_file:
+                    data_info['sub_set'] = 'ChartQA-H'
+                elif 'augmented' in sub_ann_file:
+                    data_info['sub_set'] = 'ChartQA-M'
+                else:
+                    raise ValueError(
+                        f'Do not support to subset {sub_ann_file}.')
+
+                data_list.append(data_info)
+
+        return data_list

mmpretrain/evaluation/metrics/__init__.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .caption import COCOCaption
+from .chartqa import ChartQARelaxACC
 from .gqa import GQAAcc
 from .multi_label import AveragePrecision, MultiLabelMetric
 from .multi_task import MultiTasksMetric
@@ -16,5 +17,5 @@
     'MultiTasksMetric', 'VOCAveragePrecision', 'VOCMultiLabelMetric',
     'ConfusionMatrix', 'RetrievalRecall', 'VQAAcc', 'ReportVQA', 'COCOCaption',
     'VisualGroundingMetric', 'ScienceQAMetric', 'GQAAcc', 'NocapsSave',
-    'RetrievalAveragePrecision'
+    'RetrievalAveragePrecision', 'ChartQARelaxACC'
 ]

mmpretrain/evaluation/metrics/chartqa.py

@@ -0,0 +1,130 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import re
+from typing import List, Optional
+
+from mmengine.evaluator import BaseMetric
+
+from mmpretrain.registry import METRICS
+from .vqa import _process_digit_article, _process_punctuation
+
+
+@METRICS.register_module()
+class ChartQARelaxACC(BaseMetric):
+    '''ChartQARelaxACC.
+    Args:
+
+        collect_device (str): Device name used for collecting results from
+            different ranks during distributed training. Must be 'cpu' or
+            'gpu'. Defaults to 'cpu'.
+        prefix (str, optional): The prefix that will be added in the metric
+            names to disambiguate homonymous metrics of different evaluators.
+            If prefix is not provided in the argument, self.default_prefix
+            will be used instead. Should be modified according to the
+            `retrieval_type` for unambiguous results. Defaults to TR.
+    '''
+    default_prefix = 'ChartQARelaxACC'
+
+    def __init__(self,
+                 full_score_weight: float = 0.3,
+                 collect_device: str = 'cpu',
+                 prefix: Optional[str] = None,
+                 relax_thresh: float = 0.05):
+        super().__init__(collect_device=collect_device, prefix=prefix)
+        self.full_score_weight = full_score_weight
+        self.relax_thresh = relax_thresh
+
+    def is_digit(self, x: str):
+        a = bool(re.match(r'^[+-]?\d+\.\d+$', x))
+        b = str(x).isnumeric()
+        return any([a, b])
+
+    def process(self, data_batch, data_samples):
+        """Process one batch of data samples.
+
+        The processed results should be stored in ``self.results``, which will
+        be used to computed the metrics when all batches have been processed.
+
+        Args:
+            data_batch: A batch of data from the dataloader.
+            data_samples (Sequence[dict]): A batch of outputs from the model.
+        """
+        for sample in data_samples:
+            gt_answer = sample.get('gt_answer')
+            sub_set = sample.get('sub_set')
+
+            is_digit = self.is_digit(gt_answer)
+
+            result = {
+                'pred_answer': sample.get('pred_answer'),
+                'gt_answer': gt_answer,
+                'is_digit': is_digit,
+                'sub_set': sub_set
+            }
+
+            self.results.append(result)
+
+    def compute_metrics(self, results: List):
+        """Compute the metrics from processed results.
+
+        Args:
+            results (dict): The processed results of each batch.
+
+        Returns:
+            Dict: The computed metrics. The keys are the names of the metrics,
+            and the values are corresponding results.
+        """
+        ChartQA_H_acc = []
+        ChartQA_M_acc = []
+        for result in results:
+            pred_answer = self._process_answer(result['pred_answer'])
+            gt_answer = result['gt_answer']
+            is_digit = result['is_digit']
+            sub_set = result['sub_set']
+
+            if is_digit:
+                if self.is_digit(pred_answer):
+                    pred_answer = float(pred_answer)
+                    gt_answer = float(gt_answer)
+                    upper_bound = \
+                        max(gt_answer - gt_answer * self.relax_thresh,
+                            gt_answer + gt_answer * self.relax_thresh)
+                    lower_bound = \
+                        min(gt_answer - gt_answer * self.relax_thresh,
+                            gt_answer + gt_answer * self.relax_thresh)
+                    chart_acc = float(
+                        all([
+                            pred_answer >= lower_bound,
+                            pred_answer <= upper_bound
+                        ]))
+                else:
+                    chart_acc = 0.0
+            else:
+                chart_acc = float(pred_answer == gt_answer)
+
+            if sub_set == 'ChartQA-H':
+                ChartQA_H_acc.append(chart_acc)
+            elif sub_set == 'ChartQA-M':
+                ChartQA_M_acc.append(chart_acc)
+            else:
+                raise ValueError(f'Do not support to subset {sub_set}.')
+
+        ChartQA_H_acc = sum(ChartQA_H_acc) / len(ChartQA_H_acc) * 100
+        ChartQA_M_acc = sum(ChartQA_M_acc) / len(ChartQA_M_acc) * 100
+
+        accuracy = (ChartQA_H_acc + ChartQA_M_acc) / 2
+
+        metrics = {
+            'ChartQA-H acc': ChartQA_H_acc,
+            'ChartQA-M acc': ChartQA_M_acc,
+            'Overall acc': accuracy
+        }
+
+        return metrics
+
+    def _process_answer(self, answer):
+        answer = answer.replace('\n', ' ')
+        answer = answer.replace('\t', ' ')
+        answer = answer.strip()
+        answer = _process_punctuation(answer)
+        answer = _process_digit_article(answer)
+        return answer