Add HMDB51 and UCF101 datasets (#1156)

* Add HMDB51 and UCF101

* Remove debug code

torchvision/datasets/__init__.py

@@ -20,6 +20,8 @@ from .sbd import SBDataset
 from .vision import VisionDataset
 from .usps import USPS
 from .kinetics import KineticsVideo
+from .hmdb51 import HMDB51
+from .ucf101 import UCF101
 
 __all__ = ('LSUN', 'LSUNClass',
            'ImageFolder', 'DatasetFolder', 'FakeData',
@@ -29,4 +31,4 @@ __all__ = ('LSUN', 'LSUNClass',
            'Omniglot', 'SBU', 'Flickr8k', 'Flickr30k',
            'VOCSegmentation', 'VOCDetection', 'Cityscapes', 'ImageNet',
            'Caltech101', 'Caltech256', 'CelebA', 'SBDataset', 'VisionDataset',
-           'USPS', 'KineticsVideo')
+           'USPS', 'KineticsVideo', 'HMDB51', 'UCF101')

torchvision/datasets/hmdb51.py

+import glob
+import os
+
+from .video_utils import VideoClips
+from .utils import list_dir
+from .folder import make_dataset
+from .vision import VisionDataset
+
+
+class HMDB51(VisionDataset):
+
+    data_url = "http://serre-lab.clps.brown.edu/wp-content/uploads/2013/10/hmdb51_org.rar"
+    splits = {
+        "url": "http://serre-lab.clps.brown.edu/wp-content/uploads/2013/10/test_train_splits.rar",
+        "md5": "15e67781e70dcfbdce2d7dbb9b3344b5"
+    }
+
+    def __init__(self, root, annotation_path, frames_per_clip, step_between_clips=1,
+                 fold=1, train=True):
+        super(HMDB51, self).__init__(root)
+        extensions = ('avi',)
+        self.fold = fold
+        self.train = train
+
+        classes = list(sorted(list_dir(root)))
+        class_to_idx = {classes[i]: i for i in range(len(classes))}
+        self.samples = make_dataset(self.root, class_to_idx, extensions, is_valid_file=None)
+        self.classes = classes
+        video_list = [x[0] for x in self.samples]
+        video_clips = VideoClips(video_list, frames_per_clip, step_between_clips)
+        indices = self._select_fold(video_list, annotation_path, fold, train)
+        self.video_clips = video_clips.subset(indices)
+
+    def _select_fold(self, video_list, annotation_path, fold, train):
+        target_tag = 1 if train else 2
+        name = "*test_split{}.txt".format(fold)
+        files = glob.glob(os.path.join(annotation_path, name))
+        selected_files = []
+        for f in files:
+            with open(f, "r") as fid:
+                data = fid.readlines()
+                data = [x.strip().split(" ") for x in data]
+                data = [x[0] for x in data if int(x[1]) == target_tag]
+                selected_files.extend(data)
+        selected_files = set(selected_files)
+        indices = [i for i in range(len(video_list)) if os.path.basename(video_list[i]) in selected_files]
+        return indices
+
+    def __len__(self):
+        return self.video_clips.num_clips()
+
+    def __getitem__(self, idx):
+        video, audio, info, video_idx = self.video_clips.get_clip(idx)
+        label = self.samples[video_idx][1]
+
+        return video, audio, label

torchvision/datasets/ucf101.py

+import glob
+import os
+
+from .video_utils import VideoClips
+from .utils import list_dir
+from .folder import make_dataset
+from .vision import VisionDataset
+
+
+class UCF101(VisionDataset):
+
+    def __init__(self, root, annotation_path, frames_per_clip, step_between_clips=1,
+                 fold=1, train=True):
+        super(UCF101, self).__init__(root)
+        extensions = ('avi',)
+        self.fold = fold
+        self.train = train
+
+        classes = list(sorted(list_dir(root)))
+        class_to_idx = {classes[i]: i for i in range(len(classes))}
+        self.samples = make_dataset(self.root, class_to_idx, extensions, is_valid_file=None)
+        self.classes = classes
+        video_list = [x[0] for x in self.samples]
+        video_clips = VideoClips(video_list, frames_per_clip, step_between_clips)
+        indices = self._select_fold(video_list, annotation_path, fold, train)
+        self.video_clips = video_clips.subset(indices)
+
+    def _select_fold(self, video_list, annotation_path, fold, train):
+        name = "train" if train else "test"
+        name = "{}list{:02d}.txt".format(name, fold)
+        f = os.path.join(annotation_path, name)
+        selected_files = []
+        with open(f, "r") as fid:
+            data = fid.readlines()
+            data = [x.strip().split(" ") for x in data]
+            data = [x[0] for x in data]
+            selected_files.extend(data)
+        selected_files = set(selected_files)
+        indices = [i for i in range(len(video_list)) if video_list[i][len(self.root) + 1:] in selected_files]
+        return indices
+
+    def __len__(self):
+        return self.video_clips.num_clips()
+
+    def __getitem__(self, idx):
+        video, audio, info, video_idx = self.video_clips.get_clip(idx)
+        label = self.samples[video_idx][1]
+
+        return video, audio, label