def __init__(self, dataset):
self._dataset = dataset
self._mini_batch_sampler = \
balanced_positive_negative_sampler.BalancedPositiveNegativeSampler()
##############################
# Parse KittiUtils config
##############################
self.kitti_utils_config = dataset.config.kitti_utils_config
self._area_extents = self.kitti_utils_config.area_extents
self._anchor_strides = np.reshape(
self.kitti_utils_config.anchor_strides, (-1, 2))
##############################
# Parse MiniBatchUtils config
##############################
self.config = self.kitti_utils_config.mini_batch_config
self._density_threshold = self.config.density_threshold
self.use_retinanet = self.config.use_retinanet
if not self.use_retinanet:
# RPN mini batches
rpn_config = self.config.rpn_config
rpn_iou_type = rpn_config.WhichOneof('iou_type')
if rpn_iou_type == 'iou_2d_thresholds':
self.rpn_iou_type = '2d'
self.rpn_iou_thresholds = rpn_config.iou_2d_thresholds
elif rpn_iou_type == 'iou_3d_thresholds':
self.rpn_iou_type = '3d'
self.rpn_iou_thresholds = rpn_config.iou_3d_thresholds
self.rpn_neg_iou_range = [
self.rpn_iou_thresholds.neg_iou_lo,
self.rpn_iou_thresholds.neg_iou_hi
]
self.rpn_pos_iou_range = [
self.rpn_iou_thresholds.pos_iou_lo,
self.rpn_iou_thresholds.pos_iou_hi
]
self.rpn_mini_batch_size = rpn_config.mini_batch_size
# AVOD mini batches
avod_config = self.config.avod_config
self.avod_iou_type = '2d'
self.avod_iou_thresholds = avod_config.iou_2d_thresholds
self.avod_neg_iou_range = [
self.avod_iou_thresholds.neg_iou_lo,
self.avod_iou_thresholds.neg_iou_hi
]
self.avod_pos_iou_range = [
self.avod_iou_thresholds.pos_iou_lo,
self.avod_iou_thresholds.pos_iou_hi
]
self.avod_mini_batch_size = avod_config.mini_batch_size
# Setup paths
self.mini_batch_dir = avod.root_dir() + '/data/mini_batches/' + \
'iou_{}/'.format(self.rpn_iou_type) + \
dataset.name + '/' + dataset.cluster_split + '/' + \
dataset.bev_source
# Array column indices for saving to files
self.col_length = 9
self.col_anchor_indices = 0
self.col_ious = 1
self.col_offsets_lo = 2
self.col_offsets_hi = 8
self.col_class_idx = 8
else:
#use retinanet
retinanet_config = self.config.retinanet_config
self.retinanet_pyramid_levels = retinanet_config.pyramid_levels
self.retinanet_iou_type = retinanet_config.iou_type
self.retinanet_iou_thresholds = retinanet_config.iou_2d_thresholds
self.retinanet_neg_iou_range = [
self.retinanet_iou_thresholds.neg_iou_lo,
self.retinanet_iou_thresholds.neg_iou_hi
]
self.retinanet_pos_iou_range = [
self.retinanet_iou_thresholds.pos_iou_lo,
self.retinanet_iou_thresholds.pos_iou_hi
]
self.mini_batch_dir = avod.root_dir() + '/data/mini_batches/' + \
'retinanet/' + 'iou_{}/'.format(self.retinanet_iou_type) + \
dataset.name + '/' + dataset.cluster_split
#[x_bev, y_bev, w_bev, h_bev, angle_bev, h_img, angle_cls]
#[2:9] is all offset cols.
self.col_length = 12 #11
self.col_anchor_indices = 0
self.col_ious = 1
self.col_offsets_lo = 2
self.col_offsets_hi = 10 #9
self.col_class_idx = 10 #9
anchor_strides = [2**(int(l[-1])) for l in \
self.retinanet_pyramid_levels]
input_bev_dims_h = retinanet_config.input_bev_dims_h
input_bev_dims_w = retinanet_config.input_bev_dims_w
self.retinanet_bev_shape = (input_bev_dims_h, input_bev_dims_w)
image_shapes = [(int(np.ceil(input_bev_dims_h/s)), int(np.ceil(input_bev_dims_w/s)))\
for s in anchor_strides]
anchor_base_sizes = [2**(int(l[-1])+2) for l in \
self.retinanet_pyramid_levels]
self.retinanet_anchor_params = {\
'anchor_strides': np.reshape(anchor_strides, (-1, 1)), \
'anchor_base_sizes': anchor_base_sizes, \
'anchor_scales': retinanet_config.anchor_scales,\
'anchor_ratios': retinanet_config.anchor_ratios,\
'anchor_init_ry_type': retinanet_config.anchor_init_ry_type,\
'image_shapes': image_shapes, }
def __init__(self, dataset):
self._dataset = dataset
self._mini_batch_sampler = \
balanced_positive_negative_sampler.BalancedPositiveNegativeSampler()
##############################
# Parse KittiUtils config
##############################
self.kitti_utils_config = dataset.config.kitti_utils_config
self._area_extents = self.kitti_utils_config.area_extents
self._anchor_strides = np.reshape(
self.kitti_utils_config.anchor_strides, (-1, 2))
##############################
# Parse MiniBatchUtils config
##############################
self.config = self.kitti_utils_config.mini_batch_config
self._density_threshold = self.config.density_threshold
# RPN mini batches
rpn_config = self.config.rpn_config
if (rpn_config.ByteSize() != 0):
rpn_iou_type = rpn_config.WhichOneof('iou_type')
if rpn_iou_type == 'iou_2d_thresholds':
self.rpn_iou_type = '2d'
self.rpn_iou_thresholds = rpn_config.iou_2d_thresholds
elif rpn_iou_type == 'iou_3d_thresholds':
self.rpn_iou_type = '3d'
self.rpn_iou_thresholds = rpn_config.iou_3d_thresholds
self.rpn_neg_iou_range = [self.rpn_iou_thresholds.neg_iou_lo,
self.rpn_iou_thresholds.neg_iou_hi]
self.rpn_pos_iou_range = [self.rpn_iou_thresholds.pos_iou_lo,
self.rpn_iou_thresholds.pos_iou_hi]
self.rpn_mini_batch_size = rpn_config.mini_batch_size
# AVOD mini batches
avod_config = self.config.avod_config
self.avod_iou_type = '2d'
self.avod_iou_thresholds = avod_config.iou_2d_thresholds
self.avod_neg_iou_range = [self.avod_iou_thresholds.neg_iou_lo,
self.avod_iou_thresholds.neg_iou_hi]
self.avod_pos_iou_range = [self.avod_iou_thresholds.pos_iou_lo,
self.avod_iou_thresholds.pos_iou_hi]
self.avod_mini_batch_size = avod_config.mini_batch_size
# Setup paths
if (rpn_config.ByteSize() != 0):
self.mini_batch_dir = avod.root_dir() + '/data/mini_batches/' + \
'iou_{}/'.format(self.rpn_iou_type) + \
dataset.name + '/' + dataset.cluster_split + '/' + \
dataset.bev_source
else:
self.mini_batch_dir = avod.root_dir() + '/data/mini_batches/' + \
'iou_{}/'.format(self.avod_iou_type) + \
dataset.name + '/' + dataset.cluster_split + '/' + \
dataset.bev_source
# Array column indices for saving to files
self.col_length = 9
self.col_anchor_indices = 0
self.col_ious = 1
self.col_offsets_lo = 2
self.col_offsets_hi = 8
self.col_class_idx = 8