def _get_data(self):
"""Load frame paths and annotations. """
# Loading frame paths.
list_filenames = [
os.path.join(cfg.CHARADES.FRAME_LIST_DIR, filename)
for filename in (
cfg.CHARADES.TRAIN_LISTS
if (self._split == 'train' or cfg.GET_TRAIN_LFB)
else cfg.CHARADES.TEST_LISTS)
]
(self._image_paths,
self._image_labels,
self._video_idx_to_name, _) = dataset_helper.load_image_lists(
list_filenames)
if self._split != 'train':
# Charades is a video-level task.
self._convert_to_video_level_labels()
self._num_videos = len(self._image_paths)
if self._lfb_infer_only:
self._lfb_frames = get_lfb_frames(self._image_paths)
logger.info(
'Inferring LFB from %d clips in %d videos.' % (
len(self._lfb_frames), len(self._image_paths)))
self.print_summary()
def _get_data(self):
"""Load frame paths and annotations. """
# Load frame paths.
list_filenames = [
os.path.join(cfg.EPIC.FRAME_LIST_DIR, filename)
for filename in (
cfg.EPIC.TRAIN_LISTS
if (self._is_train or cfg.GET_TRAIN_LFB)
else cfg.EPIC.TEST_LISTS)
]
(self._image_paths,
self._image_labels,
self._video_idx_to_name,
self._video_name_to_idx) = dataset_helper.load_image_lists(
list_filenames, return_dict=True)
# Load annotations.
if self._lfb_infer_only:
self._annotations = get_annotations_for_lfb_frames(
self._image_paths)
logger.info(
'Inferring LFB from %d clips in %d videos.' % (
len(self._annotations), len(self._image_paths)))
else:
self._annotations = load_annotations(is_train=self._is_train)
self.print_summary()
def _get_data(self):
"""Load frame paths and annotations. """
# Loading frame paths.
list_filenames = [
os.path.join(cfg.AVA.FRAME_LIST_DIR, filename)
for filename in (cfg.AVA.TRAIN_LISTS if (
self._split == 'train' or cfg.GET_TRAIN_LFB
) else cfg.AVA.TEST_LISTS)
]
(self._image_paths, _, self._video_idx_to_name,
_) = dataset_helper.load_image_lists(list_filenames)
# Loading annotations.
if self._lfb_infer_only:
ann_filenames = [
os.path.join(cfg.AVA.ANNOTATION_DIR, filename)
for filename in (cfg.AVA.TRAIN_LFB_BOX_LISTS if cfg.
GET_TRAIN_LFB else cfg.AVA.TEST_LFB_BOX_LISTS)
]
else:
ann_filenames = [
os.path.join(cfg.AVA.ANNOTATION_DIR, filename)
for filename in (cfg.AVA.TRAIN_BOX_LISTS if self._split ==
'train' else cfg.AVA.TEST_BOX_LISTS)
]
self._boxes_and_labels = load_boxes_and_labels(
ann_filenames,
is_train=(self._split == 'train'),
detect_thresh=self._detect_thresh,
full_eval=self._full_eval)
assert len(self._boxes_and_labels) == len(self._image_paths), \
(len(self._boxes_and_labels), len(self._image_paths))
self._boxes_and_labels = [
self._boxes_and_labels[self._video_idx_to_name[i]]
for i in range(len(self._image_paths))
]
self._keyframe_indices = get_keyframe_indices(self._boxes_and_labels)
self._num_boxes_used = self.get_db_size()
self.print_summary()
def _get_data(self):
"""Load frame paths and annotations. """
if cfg.JHMDB.SPLIT_NUM == 0:
if self._split == 'train':
list_filenames = [os.path.join(cfg.JHMDB.FRAME_LIST_DIR,
'frame_list_train.csv')]
else:
list_filenames = [os.path.join(cfg.JHMDB.FRAME_LIST_DIR,
'frame_list_test.csv')]
else:
if self._split == 'train':
list_filenames = [os.path.join(cfg.JHMDB.FRAME_LIST_DIR,
'frame_list_train_split%d.csv'%cfg.JHMDB.SPLIT_NUM)]
else:
list_filenames = [os.path.join(cfg.JHMDB.FRAME_LIST_DIR,
'frame_list_test_split%d.csv'%cfg.JHMDB.SPLIT_NUM)]
(self._image_paths, _,
self._video_idx_to_name, _) = dataset_helper.load_image_lists(
list_filenames)
if cfg.JHMDB.SPLIT_NUM == 0:
if self._split == 'train':
ann_filenames = [os.path.join(cfg.JHMDB.ANNOTATION_DIR,
'train.csv')]
if cfg.JHMDB.USE_PRED_BOX_TRAIN:
ann_filenames.append(os.path.join(cfg.JHMDB.ANNOTATION_DIR,
'predicted_train.csv'))
else:
if cfg.JHMDB.USE_GT_BOX_TEST:
ann_filenames = [os.path.join(cfg.JHMDB.ANNOTATION_DIR,
'test.csv')]
else:
ann_filenames = [os.path.join(cfg.JHMDB.ANNOTATION_DIR,
'max_predicted_test.csv')]
cfg.JHMDB.EVAL_GT_FILENAME = 'test.csv'
else:
if self._split == 'train':
ann_filenames = [os.path.join(cfg.JHMDB.ANNOTATION_DIR,
'train_split%d.csv'%cfg.JHMDB.SPLIT_NUM)]
if cfg.JHMDB.USE_PRED_BOX_TRAIN:
ann_filenames.append(os.path.join(cfg.JHMDB.ANNOTATION_DIR,
'ft_ep12_res50_pred/predicted_train_split%d.csv'%cfg.JHMDB.SPLIT_NUM))
else:
if cfg.JHMDB.USE_GT_BOX_TEST:
ann_filenames = [os.path.join(cfg.JHMDB.ANNOTATION_DIR,
'test_split%d.csv'%cfg.JHMDB.SPLIT_NUM)]
else:
ann_filenames = [os.path.join(cfg.JHMDB.ANNOTATION_DIR,
'ft_ep12_res50_pred/predicted_test_split%d.csv'%cfg.JHMDB.SPLIT_NUM)]
cfg.JHMDB.EVAL_GT_FILENAME = 'test_split%d.csv'%cfg.JHMDB.SPLIT_NUM
self._boxes_and_labels = load_boxes_and_labels(
ann_filenames,
is_train=(self._split == 'train'),
detect_thresh=self._detect_thresh,
full_eval=self._full_eval)
self.new_boxes_and_labels = []
for i in range(len(self._image_paths)):
vname = self._video_idx_to_name[i]
if vname in self._boxes_and_labels:
self.new_boxes_and_labels.append(self._boxes_and_labels[vname])
else:
self.new_boxes_and_labels.append({})
self._boxes_and_labels = self.new_boxes_and_labels
self._keyframe_indices = get_keyframe_indices(self._boxes_and_labels)
self._box_indices = get_box_indices(self._boxes_and_labels)
self._num_boxes_used = get_num_boxes_used(
self._keyframe_indices, self._boxes_and_labels)
self.print_summary()