def _load_data(self, cfg):
"""
Load frame paths and annotations from files
Args:
cfg (CfgNode): config
"""
# Loading frame paths.
self._image_paths, self._video_idx_to_name = ava_helper.load_image_lists(
cfg, is_train=(self._split == "train"))
# Loading annotations for boxes and labels.
boxes_and_labels = ava_helper.load_boxes_and_labels(cfg,
mode=self._split)
assert len(boxes_and_labels) == len(self._image_paths)
boxes_and_labels = [
boxes_and_labels[self._video_idx_to_name[i]]
for i in range(len(self._image_paths))
]
# Get indices of keyframes and corresponding boxes and labels.
(
self._keyframe_indices,
self._keyframe_boxes_and_labels,
) = ava_helper.get_keyframe_data(boxes_and_labels)
# Calculate the number of used boxes.
self._num_boxes_used = ava_helper.get_num_boxes_used(
self._keyframe_indices, self._keyframe_boxes_and_labels)
self.print_summary()
def __init__(self, overall_iters, cfg, mode):
"""
overall_iters (int): the overall number of iterations of one epoch.
cfg (CfgNode): configs.
mode (str): `train`, `val`, or `test` mode.
"""
self.cfg = cfg
self.lr = None
self.loss = ScalarMeter(cfg.LOG_PERIOD)
self.full_ava_test = cfg.AVA.FULL_TEST_ON_VAL
self.mode = mode
self.iter_timer = Timer()
self.data_timer = Timer()
self.net_timer = Timer()
self.all_preds_train = []
self.all_ori_boxes_train = []
self.all_metadata_train = []
self.all_preds = []
self.all_ori_boxes = []
self.all_metadata = []
self.overall_iters = overall_iters
self.categories, self.class_whitelist = read_labelmap(
os.path.join(cfg.AVA.ANNOTATION_DIR, cfg.AVA.LABEL_MAP_FILE))
gt_filename = os.path.join(cfg.AVA.ANNOTATION_DIR,
cfg.AVA.GROUNDTRUTH_FILE)
self.full_groundtruth = read_csv(gt_filename, self.class_whitelist)
self.mini_groundtruth = get_ava_mini_groundtruth(self.full_groundtruth)
_, self.video_idx_to_name = ava_helper.load_image_lists(
cfg, mode == "train")
self.output_dir = cfg.OUTPUT_DIR
def __init__(self, overall_iters, cfg, mode):
"""
overall_iters (int): the overall number of iterations of one epoch.
cfg (CfgNode): configs.
mode (str): `train`, `val`, or `test` mode.
"""
is_custom_dataset = cfg.TRAIN.DATASET == "custom" and cfg.TEST.DATASET == "custom"
if is_custom_dataset:
logger.info("Creating AVA Meter for custom dataset in %s mode" %
mode)
else:
logger.info("Creating AVA Meter for AVA dataset in %s mode" % mode)
self.cfg = cfg
self.lr = None
self.loss = ScalarMeter(cfg.LOG_PERIOD)
self.full_ava_test = cfg.AVA.FULL_TEST_ON_VAL if not is_custom_dataset else cfg.CUSTOM_DATASET.FULL_TEST_ON_VAL
self.mode = mode
self.iter_timer = Timer()
self.all_preds = []
self.all_ori_boxes = []
self.all_metadata = []
self.overall_iters = overall_iters
self.excluded_keys = read_exclusions(
os.path.join(cfg.AVA.ANNOTATION_DIR, cfg.AVA.EXCLUSION_FILE)
) if not is_custom_dataset else read_exclusions(
os.path.join(cfg.CUSTOM_DATASET.ANNOTATION_DIR,
cfg.CUSTOM_DATASET.EXCLUSION_FILE))
self.categories, self.class_whitelist = read_labelmap(
os.path.join(cfg.AVA.ANNOTATION_DIR, cfg.AVA.LABEL_MAP_FILE)
) if not is_custom_dataset else read_labelmap(
os.path.join(cfg.CUSTOM_DATASET.ANNOTATION_DIR,
cfg.CUSTOM_DATASET.LABEL_MAP_FILE))
gt_filename = os.path.join(
cfg.AVA.ANNOTATION_DIR, cfg.AVA.GROUNDTRUTH_FILE
) if not is_custom_dataset else os.path.join(
cfg.CUSTOM_DATASET.ANNOTATION_DIR,
cfg.CUSTOM_DATASET.GROUNDTRUTH_FILE)
self.full_groundtruth = read_csv(gt_filename, self.class_whitelist)
self.mini_groundtruth = get_ava_mini_groundtruth(self.full_groundtruth)
_, self.video_idx_to_name = ava_helper.load_image_lists(
cfg, mode == "train"
) if not is_custom_dataset else custom_helper.load_image_lists(
cfg, mode == "train")
