Update doc

kale/loaddata/video_datasets.py

@@ -21,7 +21,7 @@ class BasicVideoDataset(VideoFrameDataset):
  num_segments (int): The number of segments the video should be divided into to sample frames from.
  frames_per_segment (int): The number of frames that should be loaded per segment.
  imagefile_template (string): The image filename template.
- transform (Compose): Video transform.
+ transform (torchvision.transforms.Compose, optional): Video transform.
  random_shift (bool): Whether the frames from each segment should be taken consecutively starting from
  the center(False) of the segment, or consecutively starting from
  a random(True) location inside the segment range.

kale/loaddata/videos.py

@@ -17,7 +17,7 @@ class VideoRecord(object):
  represents a video sample's metadata.
 
  Args:
- root_datapath (Path, optional): the system path to the root folder of the videos.
+ root_datapath (pathlib.Path, optional): the system path to the root folder of the videos.
  row (tuple, optional): A list with four or more elements where
  1) The first element is the path to the video sample's frames excluding the root_datapath prefix.
  2) The second element is the starting frame id of the video.
@@ -126,27 +126,19 @@ class VideoFrameDataset(torch.utils.data.Dataset):
  might be ``jumping\0052\`` or ``sample1\`` or ``00053\``.
 
  Args:
- root_path (str, Path): root path in which video folders lie.
- this is ROOT_DATA from the description above.
- annotationfile_path (str, Path): .txt annotation file containing
- one row per video sample as described above.
+ root_path (str, pathlib.Path): root path in which video folders lie. This is ROOT_DATA from the above.
+ annotationfile_path (str, pathlib.Path): .txt annotation file containing one row per video sample as described above.
  image_modality (str): image modality (RGB or Optical Flow).
  num_segments (int): number of segments the video should be divided into to sample frames from.
  Default is 1 in image mode and 5 in feature vector mode.
- frames_per_segment (int): number of frames that should
- be loaded per segment. For each segment's
- frame-range, a random start index or the
- center is chosen, from which frames_per_segment
- consecutive frames are loaded.
- imagefile_template (str): image filename template that video frame files
- have inside of their video folders as described above.
- transform (Compose, optional): transform pipeline that receives a list of PIL images/frames.
- random_shift (bool): whether the frames from each segment should be taken
- consecutively starting from the center of the segment, or
- consecutively starting from a random location inside the
- segment range.
- test_mode (bool): whether this is a test dataset. If so, chooses
- frames from segments with random_shift=False.
+ frames_per_segment (int): number of frames that should be loaded per segment. For each segment's frame-range,
+ a random start index or the center is chosen,
+ from which frames_per_segment consecutive frames are loaded.
+ imagefile_template (str): image filename template that video frame files have inside the video folders as described above.
+ transform (torchvision.transforms.Compose, optional): transform pipeline that receives a list of PIL images/frames.
+ random_shift (bool): whether the frames from each segment should be taken consecutively starting from the center
+ of the segment, or consecutively starting from a random location inside the segment range.
+ test_mode (bool): whether this is a test dataset. If so, chooses frames from segments with random_shift=False.
  input_type (str): type of input. (options: 'image' or 'feature')
  num_data_load (int): number of the data to load. (only used in feature vector mode)
  total_segments (int): total number of segments a video is divided into. (only used in feature vector mode)