UCF101 dataset more *loading* efficient

torchvision/datasets/ucf101.py

@@ -1,4 +1,3 @@
-import glob
 import os
 
 from .utils import list_dir
@@ -50,17 +49,28 @@ def __init__(self, root, annotation_path, frames_per_clip, step_between_clips=1,
         if not 1 <= fold <= 3:
             raise ValueError("fold should be between 1 and 3, got {}".format(fold))
 
-        extensions = ('avi',)
         self.fold = fold
         self.train = train
+        self.transform = transform
+
+        # Create class to index mapping with sorted class names
+        self.classes = list(sorted(list_dir(root)))
+        class_to_idx = {c: i for i, c in enumerate(self.classes)}
+
+        # Iterate through root directory to retrieve the path and the labels
+        # for each dataset example
+        self.samples = make_dataset(
+            self.root, class_to_idx, ('avi',), is_valid_file=None)
 
-        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,
+        # Get the video paths that belong to the selected fold and split
+        _video_paths_in_fold = self._fold_paths(annotation_path, fold, train)
+        # Filter the dataset samples so only the video paths belonging to the
+        # selected fold are processed
+        self.samples = [o for o in self.samples if o[0] in _video_paths_in_fold]
+
+        # At this point, only the needed videos' path are selected
+        self.video_clips = VideoClips(
+            [x[0] for x in self.samples],
             frames_per_clip,
             step_between_clips,
             frame_rate,
@@ -71,35 +81,30 @@ def __init__(self, root, annotation_path, frames_per_clip, step_between_clips=1,
             _video_min_dimension=_video_min_dimension,
             _audio_samples=_audio_samples,
         )
-        self.video_clips_metadata = video_clips.metadata
-        self.indices = self._select_fold(video_list, annotation_path, fold, train)
-        self.video_clips = video_clips.subset(self.indices)
-        self.transform = transform
+        self.video_clips_metadata = self.video_clips.metadata
 
     @property
     def metadata(self):
         return self.video_clips_metadata
 
-    def _select_fold(self, video_list, annotation_path, fold, train):
-        name = "train" if train else "test"
-        name = "{}list{:02d}.txt".format(name, fold)
+    def _fold_paths(self, annotation_path, fold, train):
+        split = 'train' if train else 'test'
+        name = f'{split}list{fold:02d}.txt'
         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 = [os.path.join(self.root, 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] in selected_files]
-        return indices
+            video_files = fid.readlines()
+            video_files = [o.strip().split(" ")[0] for o in video_files]
+            video_files = [os.path.join(self.root, o) for o in video_files]
+            video_files = set(video_files)
+        return video_files
 
     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[self.indices[video_idx]][1]
+        label = self.samples[video_idx][1]
 
         if self.transform is not None:
             video = self.transform(video)