Add cache switch, allowing load all data into RAM before sampling

2 files changed, 108 insertions, 23 deletions

diff --git a/requirements.txt b/requirements.txt
index 46fb677..a0eaf1a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,4 @@
 torch~=1.7.1
 torchvision~=0.8.0a0+ecf4e9c
-numpy~=1.19.4
\ No newline at end of file
+numpy~=1.19.4
+tqdm~=4.54.1
\ No newline at end of file
diff --git a/utils/dataset.py b/utils/dataset.py
index 7518127..fad94c0 100644
--- a/utils/dataset.py
+++ b/utils/dataset.py
@@ -8,6 +8,7 @@ import torch
 from torch.utils import data
 from torchvision.io import read_image
 import torchvision.transforms as transforms
+from tqdm import tqdm
 
 ClipLabels = NewType('ClipLabels', Set[str])
 ClipConditions = NewType('ClipConditions', Set[str])
@@ -27,36 +28,48 @@ class CASIAB(data.Dataset):
             is_train: bool = True,
             train_size: int = 74,
             num_sampled_frames: int = 30,
+            discard_threshold: int = 15,
             selector: Optional[Dict[
                 str, Union[ClipLabels, ClipConditions, ClipLabels]
             ]] = None,
             num_input_channels: int = 3,
             frame_height: int = 64,
             frame_width: int = 32,
-            device: torch.device = torch.device('cpu')
+            device: torch.device = torch.device('cpu'),
+            cache_on: bool = False
     ):
         """
         :param root_dir: Directory to dataset root.
         :param is_train: Train or test, True for train, False for test.
-        :param train_size: Number of subjects in train, when `is_train`
-            is False, test size will be inferred.
-        :param num_sampled_frames: Number of sampled frames for train
-        :param selector: Restrict data labels, conditions and views
-        :param num_input_channels Number of input channel, RBG image
-            has 3 channel, grayscale image has 1 channel
-        :param frame_height Frame height after transforms
-        :param frame_width Frame width after transforms
-        :param device Device be used for transforms
+        :param train_size: The number of subjects used for training,
+            when `is_train` is False, test size will be inferred.
+        :param num_sampled_frames: The number of sampled frames.
+            (Training Only)
+        :param discard_threshold: Discard the sample if its number of
+            frames is less than this threshold.
+        :param selector: Restrict output data labels, conditions and
+            views.
+        :param num_input_channels: The number of input channel(s),
+            RBG image has 3 channels, grayscale image has 1 channel.
+        :param frame_height: Frame height after transforming.
+        :param frame_width: Frame width after transforming.
+        :param device: Device used in transforms.
+        :param cache_on: Preload all clips in memory or not, this will
+            increase loading speed, but will add a preload process and
+            cost a lot of RAM. Loading the entire dataset need about
+            7 GB of RAM.
         """
         super(CASIAB, self).__init__()
         self.root_dir = root_dir
         self.is_train = is_train
         self.train_size = train_size
         self.num_sampled_frames = num_sampled_frames
+        self.discard_threshold = discard_threshold
         self.num_input_channels = num_input_channels
         self.frame_height = frame_height
         self.frame_width = frame_width
         self.device = device
+        self.cache_on = cache_on
 
         self.frame_transform: transforms.Compose
         transform_compose_list = [
@@ -84,10 +97,14 @@ class CASIAB(data.Dataset):
             clip_names = clip_names[self.train_size * 10 * 11:]
 
         # Remove empty clips
+        discard_clips_names = []
         for clip_name in clip_names.copy():
-            if len(os.listdir(os.path.join(self.root_dir, clip_name))) == 0:
-                print("Clip '{}' is empty.".format(clip_name))
+            clip_path = os.path.join(self.root_dir, clip_name)
+            if len(os.listdir(clip_path)) < self.discard_threshold:
+                discard_clips_names.append(clip_name)
                 clip_names.remove(clip_name)
+        print(', '.join(discard_clips_names[:-1]),
+              'and', discard_clips_names[-1], 'will be discarded.')
 
         # clip name constructed by label, condition and view
         # e.g 002-bg-02-090 means clip from Subject #2
@@ -109,12 +126,12 @@ class CASIAB(data.Dataset):
                 condition_regex = '|'.join(selected_conditions)
             if selected_views:
                 view_regex = '|'.join(selected_views)
-            clip_regex = '(' + ')-('.join([
+            clip_re = re.compile('(' + ')-('.join((
                 label_regex, condition_regex, view_regex
-            ]) + ')'
+            )) + ')')
 
             for clip_name in clip_names:
-                match = re.fullmatch(clip_regex, clip_name)
+                match = clip_re.fullmatch(clip_name)
                 if match:
                     labels.append(match.group(1))
                     conditions.append(match.group(2))
@@ -148,6 +165,13 @@ class CASIAB(data.Dataset):
                 'views': set(self.views.tolist())
             }
 
+        self._cached_clips_frame_names: Optional[Dict[str, List[str]]] = None
+        self._cached_clips: Optional[Dict[str, torch.Tensor]] = None
+        if self.cache_on:
+            self._cached_clips_frame_names = dict()
+            self._cached_clips = dict()
+            self._preload_all_video()
+
     def __len__(self) -> int:
         return len(self.labels)
 
@@ -157,7 +181,6 @@ class CASIAB(data.Dataset):
         view = self.views[index]
         clip_name = self._clip_names[index]
         clip = self._read_video(clip_name)
-
         sample = {
             'label': label,
             'condition': condition,
@@ -167,23 +190,84 @@ class CASIAB(data.Dataset):
 
         return sample
 
-    def _read_video(self, clip_name: str) -> torch.Tensor:
-        frames = []
+    def _preload_all_video(self):
+        for clip_name in tqdm(self._clip_names,
+                              desc='Preloading dataset', unit='clips'):
+            self._read_video(clip_name, is_caching=True)
+
+    def _read_video(self, clip_name: str,
+                    is_caching: bool = False) -> torch.Tensor:
         clip_path = os.path.join(self.root_dir, clip_name)
-        sampled_frame_names = self._sample_frames(clip_path)
-        for frame_name in sampled_frame_names:
+        sampled_frame_names = self._sample_frames(clip_path, is_caching)
+
+        if self.cache_on:
+            if is_caching:
+                clip = self._read_frames(clip_path, sampled_frame_names)
+                self._cached_clips[clip_name] = clip
+            else:  # Load cache
+                cached_clip = self._cached_clips[clip_name]
+                cached_clip_frame_names \
+                    = self._cached_clips_frame_names[clip_path]
+                # Index the original clip via sampled frame names
+                clip = self._load_cached_video(cached_clip,
+                                               cached_clip_frame_names,
+                                               sampled_frame_names)
+        else:  # Cache off
+            clip = self._read_frames(clip_path, sampled_frame_names)
+
+        return clip
+
+    def _load_cached_video(
+            self,
+            clip: torch.Tensor,
+            frame_names: List[str],
+            sampled_frame_names: List[str]
+    ) -> torch.Tensor:
+        # Mask the original clip when it is long enough
+        if len(frame_names) >= self.num_sampled_frames:
+            sampled_frame_mask = np.isin(frame_names,
+                                         sampled_frame_names)
+            sampled_clip = clip[sampled_frame_mask]
+        else:  # Create a indexing filter from the beginning of clip
+            sampled_index = frame_names.index(sampled_frame_names[0])
+            sampled_frame_filter = [sampled_index]
+            for i in range(1, self.num_sampled_frames):
+                if sampled_frame_names[i] != sampled_frame_names[i - 1]:
+                    sampled_index += 1
+                sampled_frame_filter.append(sampled_index)
+            sampled_clip = clip[sampled_frame_filter]
+
+        return sampled_clip
+
+    def _read_frames(self, clip_path, frame_names):
+        frames = []
+        for frame_name in frame_names:
             frame_path = os.path.join(clip_path, frame_name)
             frame = read_image(frame_path)
+            # Transforming using CPU is not efficient
             frame = self.frame_transform(frame.to(self.device))
             frames.append(frame.cpu())
         clip = torch.stack(frames)
 
         return clip
 
-    def _sample_frames(self, clip_path: str) -> List[str]:
-        frame_names = os.listdir(clip_path)
+    def _sample_frames(self, clip_path: str,
+                       is_caching: bool = False) -> List[str]:
+        if self.cache_on:
+            if is_caching:
+                # Sort frame in advance for loading convenience
+                frame_names = sorted(os.listdir(clip_path))
+                self._cached_clips_frame_names[clip_path] = frame_names
+                # Load all without sampling
+                return frame_names
+            else:  # Load cache
+                frame_names = self._cached_clips_frame_names[clip_path]
+        else:  # Cache off
+            frame_names = os.listdir(clip_path)
+
         if self.is_train:
             num_frames = len(frame_names)
+            # Sample frames without replace if have enough frames
             if num_frames < self.num_sampled_frames:
                 frame_names = random.choices(frame_names,
                                              k=self.num_sampled_frames)