Merge 86166b1 into 7d850df

mmpretrain/datasets/__init__.py

@@ -46,6 +46,7 @@
     from .refcoco import RefCOCO
     from .scienceqa import ScienceQA
     from .textvqa import TextVQA
+    from .visdial import VisDial
     from .visual_genome import VisualGenomeQA
     from .vizwiz import VizWiz
     from .vsr import VSR
@@ -54,5 +55,5 @@
         'COCOCaption', 'COCORetrieval', 'COCOVQA', 'FlamingoEvalCOCOCaption',
         'FlamingoEvalCOCOVQA', 'Flickr30kCaption', 'Flickr30kRetrieval',
         'RefCOCO', 'VisualGenomeQA', 'ScienceQA', 'NoCaps', 'GQA', 'TextVQA',
-        'VSR', 'VizWiz', 'OCRVQA'
+        'VSR', 'VizWiz', 'OCRVQA', 'VisDial'
     ])

mmpretrain/datasets/visdial.py

@@ -0,0 +1,96 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import List
+
+import mmengine
+from mmengine.dataset import BaseDataset
+from mmengine.fileio import get_file_backend
+
+from mmpretrain.registry import DATASETS
+
+
+@DATASETS.register_module()
+class VisDial(BaseDataset):
+    """VisDial dataset.
+
+    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 load_data_list(self) -> List[dict]:
+        """Load data list."""
+        annotations = mmengine.load(self.ann_file)['data']
+
+        dialogs = annotations['dialogs']
+        answers = annotations['answers']
+        questions = annotations['questions']
+
+        data_list = []
+
+        for dialog in dialogs:
+            image_id = dialog['image_id']
+            caption = dialog['caption']
+
+            historys = ['Caption:' + caption + '.']
+
+            for i in range(1, len(dialog['dialog'])):
+                historys.append('')
+
+                previous_idx = i - 1
+                # for j in range(i):
+                question_id = dialog['dialog'][previous_idx]['question']
+                answer_id = dialog['dialog'][previous_idx]['answer']
+
+                history = ' Question:{question}? Answer:{answer}.' \
+                    .format(question=questions[question_id],
+                            answer=answers[answer_id])
+
+                historys[i] = historys[previous_idx] + history
+
+            # get question and answer options for each dialog round
+            for dialog_id, dialog_round in enumerate(dialog['dialog']):
+                question_id = dialog_round['question']
+                answer_id = dialog_round['answer']
+                answer_options = [
+                    answers[answer_id]
+                    for answer_id in dialog_round['answer_options']
+                ]
+
+                data_info = dict(image_id=image_id)
+
+                img_prefix = self.data_prefix['img_path']
+                file_backend = get_file_backend(img_prefix)
+
+                data_info['img_path'] = file_backend.join_path(
+                    img_prefix,
+                    img_prefix.split('/')[-1] + '_' + str(image_id).zfill(12) +
+                    '.jpg')
+
+                data_info['dialog_history'] = historys[dialog_id]
+
+                data_info['question'] = questions[question_id] + '?'
+                data_info['answer'] = answers[answer_id]
+                data_info['answer_options'] = answer_options
+                data_info['gt_answer_index'] = data_info[
+                    'answer_options'].index(data_info['answer'])
+
+                data_list.append(data_info)
+
+        return data_list

mmpretrain/evaluation/metrics/__init__.py

@@ -7,6 +7,7 @@
 from .retrieval import RetrievalAveragePrecision, RetrievalRecall
 from .scienceqa import ScienceQAMetric
 from .single_label import Accuracy, ConfusionMatrix, SingleLabelMetric
+from .visual_dialog import SparseGTMetrics
 from .visual_grounding_eval import VisualGroundingMetric
 from .voc_multi_label import VOCAveragePrecision, VOCMultiLabelMetric
 from .vqa import ReportVQA, VQAAcc
@@ -16,5 +17,5 @@
     'MultiTasksMetric', 'VOCAveragePrecision', 'VOCMultiLabelMetric',
     'ConfusionMatrix', 'RetrievalRecall', 'VQAAcc', 'ReportVQA', 'COCOCaption',
     'VisualGroundingMetric', 'ScienceQAMetric', 'GQAAcc', 'NocapsSave',
-    'RetrievalAveragePrecision'
+    'RetrievalAveragePrecision', 'SparseGTMetrics'
 ]

mmpretrain/evaluation/metrics/visual_dialog.py

@@ -0,0 +1,92 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import List, Optional
+
+import torch
+from mmengine.evaluator import BaseMetric
+
+from mmpretrain.evaluation.metrics.vqa import (_process_digit_article,
+                                               _process_punctuation)
+from mmpretrain.registry import METRICS
+
+
+@METRICS.register_module()
+class SparseGTMetrics(BaseMetric):
+    """Visual Dialog Acc metric.
+
+    Compute Visual Dialogaccuracy.
+
+    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 = 'Visual Dialog'
+
+    def __init__(self,
+                 collect_device: str = 'cpu',
+                 prefix: Optional[str] = None) -> None:
+        super().__init__(collect_device=collect_device, prefix=prefix)
+
+    def process(self, data_batch, data_samples) -> None:
+        """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:
+            answer_options = sample.get('answer_options')
+
+            G = torch.Generator()
+            G.manual_seed(0)
+            rank = 1 + torch.randperm(len(answer_options), generator=G)
+
+            pred_answer = sample.get('pred_answer')
+
+            if pred_answer in answer_options:
+                answer_index = answer_options.index(pred_answer)
+                rank[answer_index] = 1
+
+            gt_index = sample.get('gt_answer_index')
+            gt_rank = rank[gt_index]
+
+            self.results.append(gt_rank)
+
+    def compute_metrics(self, results: List) -> dict:
+        """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.
+        """
+
+        R1 = (torch.tensor(results) <= 1).float().mean()
+        R5 = (torch.tensor(results) <= 5).float().mean()
+        R10 = (torch.tensor(results) <= 10).float().mean()
+        Mean = torch.tensor(results).float().mean()
+        MRR = torch.tensor(results).reciprocal().mean()
+
+        metrics = {
+            'R@1': R1.item(),
+            'R@5': R5.item(),
+            'R@10': R10.item(),
+            'Mean': Mean.item(),
+            'MRR': MRR.item()
+        }
+        return metrics
+
+    def _process_answer(self, answer) -> str:
+        answer = _process_punctuation(answer)
+        answer = _process_digit_article(answer)
+        return answer