--- a
+++ b/mmaction/datasets/pose_dataset.py
@@ -0,0 +1,113 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+
+import mmcv
+import numpy as np
+
+from ..utils import get_root_logger
+from .base import BaseDataset
+from .builder import DATASETS
+
+
+@DATASETS.register_module()
+class PoseDataset(BaseDataset):
+    """Pose dataset for action recognition.
+
+    The dataset loads pose and apply specified transforms to return a
+    dict containing pose information.
+
+    The ann_file is a pickle file, the json file contains a list of
+    annotations, the fields of an annotation include frame_dir(video_id),
+    total_frames, label, kp, kpscore.
+
+    Args:
+        ann_file (str): Path to the annotation file.
+        pipeline (list[dict | callable]): A sequence of data transforms.
+        split (str | None): The dataset split used. Only applicable to UCF or
+            HMDB. Allowed choiced are 'train1', 'test1', 'train2', 'test2',
+            'train3', 'test3'. Default: None.
+        valid_ratio (float | None): The valid_ratio for videos in KineticsPose.
+            For a video with n frames, it is a valid training sample only if
+            n * valid_ratio frames have human pose. None means not applicable
+            (only applicable to Kinetics Pose). Default: None.
+        box_thr (str | None): The threshold for human proposals. Only boxes
+            with confidence score larger than `box_thr` is kept. None means
+            not applicable (only applicable to Kinetics Pose [ours]). Allowed
+            choices are '0.5', '0.6', '0.7', '0.8', '0.9'. Default: None.
+        class_prob (dict | None): The per class sampling probability. If not
+            None, it will override the class_prob calculated in
+            BaseDataset.__init__(). Default: None.
+        **kwargs: Keyword arguments for ``BaseDataset``.
+    """
+
+    def __init__(self,
+                 ann_file,
+                 pipeline,
+                 split=None,
+                 valid_ratio=None,
+                 box_thr=None,
+                 class_prob=None,
+                 **kwargs):
+        modality = 'Pose'
+        # split, applicable to ucf or hmdb
+        self.split = split
+
+        super().__init__(
+            ann_file, pipeline, start_index=0, modality=modality, **kwargs)
+
+        # box_thr, which should be a string
+        self.box_thr = box_thr
+        if self.box_thr is not None:
+            assert box_thr in ['0.5', '0.6', '0.7', '0.8', '0.9']
+
+        # Thresholding Training Examples
+        self.valid_ratio = valid_ratio
+        if self.valid_ratio is not None:
+            assert isinstance(self.valid_ratio, float)
+            if self.box_thr is None:
+                self.video_infos = self.video_infos = [
+                    x for x in self.video_infos
+                    if x['valid_frames'] / x['total_frames'] >= valid_ratio
+                ]
+            else:
+                key = f'valid@{self.box_thr}'
+                self.video_infos = [
+                    x for x in self.video_infos
+                    if x[key] / x['total_frames'] >= valid_ratio
+                ]
+                if self.box_thr != '0.5':
+                    box_thr = float(self.box_thr)
+                    for item in self.video_infos:
+                        inds = [
+                            i for i, score in enumerate(item['box_score'])
+                            if score >= box_thr
+                        ]
+                        item['anno_inds'] = np.array(inds)
+
+        if class_prob is not None:
+            self.class_prob = class_prob
+
+        logger = get_root_logger()
+        logger.info(f'{len(self)} videos remain after valid thresholding')
+
+    def load_annotations(self):
+        """Load annotation file to get video information."""
+        assert self.ann_file.endswith('.pkl')
+        return self.load_pkl_annotations()
+
+    def load_pkl_annotations(self):
+        data = mmcv.load(self.ann_file)
+
+        if self.split:
+            split, data = data['split'], data['annotations']
+            identifier = 'filename' if 'filename' in data[0] else 'frame_dir'
+            data = [x for x in data if x[identifier] in split[self.split]]
+
+        for item in data:
+            # Sometimes we may need to load anno from the file
+            if 'filename' in item:
+                item['filename'] = osp.join(self.data_prefix, item['filename'])
+            if 'frame_dir' in item:
+                item['frame_dir'] = osp.join(self.data_prefix,
+                                             item['frame_dir'])
+        return data