def setup(self, bottom, top):
layer_params = yaml.load(self.param_str_)
self._feat_stride = layer_params['feat_stride']
self._anchors = generate_anchors()
self._num_anchors = self._anchors.shape[0]
self._use_clip = layer_params.get('use_clip', 0)
self._clip_denominator = float(layer_params.get('clip_base', 256))
self._clip_thresh = 1.0 / self._clip_denominator
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
self._top_name_map = {}
top[0].reshape(1, 5)
self._top_name_map['rois'] = 0
if str(self.phase) == 'TEST':
top[1].reshape(1, 441)
self._top_name_map['init_states'] = 1
# For MNC, we force the output proposals will also be used to train RPN
# this is achieved by passing proposal_index to anchor_target_layer
if str(self.phase) == 'TRAIN':
if cfg.TRAIN.MIX_INDEX:
top[1].reshape(1, 1)
self._top_name_map['proposal_index'] = 1
def setup(self, bottom, top):
layer_params = yaml.load(self.param_str_)
self._anchors = generate_anchors()
self._num_anchors = self._anchors.shape[0]
self._num_classes = layer_params['num_classes']
self._bp_all = layer_params.get('bp_all', True)
self._top_name_map = {}
top[0].reshape(1, 5)
self._top_name_map['rois'] = 0
top[1].reshape(1, 1)
self._top_name_map['labels'] = 1
top[2].reshape(1, self._num_classes * 4)
self._top_name_map['bbox_targets'] = 2
top[3].reshape(1, self._num_classes * 4)
self._top_name_map['bbox_inside_weights'] = 3
top[4].reshape(1, self._num_classes * 4)
self._top_name_map['bbox_outside_weights'] = 4
# Add mask-related information
if cfg.MNC_MODE:
top[5].reshape(1, 1, cfg.MASK_SIZE, cfg.MASK_SIZE)
self._top_name_map['mask_targets'] = 5
top[6].reshape(1, 1, cfg.MASK_SIZE, cfg.MASK_SIZE)
self._top_name_map['mask_weight'] = 6
top[7].reshape(1, 4)
self._top_name_map['gt_masks_info'] = 7
if cfg.TRAIN.MIX_INDEX:
top[8].reshape(1, 4)
self._top_name_map['fg_inds'] = 8
top[9].reshape(1, 4)
self._top_name_map['bg_inds'] = 9
def setup(self, bottom, top):
layer_params = yaml.load(self.param_str_)
self._anchors = generate_anchors()
self._num_anchors = self._anchors.shape[0]
self._num_classes = layer_params['num_classes']
self._bp_all = layer_params.get('bp_all', True)
self._top_name_map = {}
top[0].reshape(1, 5)
self._top_name_map['rois'] = 0
top[1].reshape(1, 1)
self._top_name_map['labels'] = 1
top[2].reshape(1, self._num_classes * 4)
self._top_name_map['bbox_targets'] = 2
top[3].reshape(1, self._num_classes * 4)
self._top_name_map['bbox_inside_weights'] = 3
top[4].reshape(1, self._num_classes * 4)
self._top_name_map['bbox_outside_weights'] = 4
# Add mask-related information
if cfg.MNC_MODE:
top[5].reshape(1, 1, cfg.MASK_SIZE, cfg.MASK_SIZE)
self._top_name_map['mask_targets'] = 5
top[6].reshape(1, 1, cfg.MASK_SIZE, cfg.MASK_SIZE)
self._top_name_map['mask_weight'] = 6
top[7].reshape(1, 4)
self._top_name_map['gt_masks_info'] = 7
if cfg.TRAIN.MIX_INDEX:
top[8].reshape(1, 4)
self._top_name_map['fg_inds'] = 8
top[9].reshape(1, 4)
self._top_name_map['bg_inds'] = 9
def setup(self, bottom, top):
layer_params = yaml.load(self.param_str_)
self._feat_stride = layer_params['feat_stride']
self._anchors = generate_anchors()
self._num_anchors = self._anchors.shape[0]
self._use_clip = layer_params.get('use_clip', 0)
self._clip_denominator = float(layer_params.get('clip_base', 256))
self._clip_thresh = 1.0 / self._clip_denominator
# rois blob: holds R regions of interest, each is a 5-tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle (x1, y1, x2, y2)
self._top_name_map = {}
top[0].reshape(1, 5)
self._top_name_map['rois'] = 0
# For MNC, we force the output proposals will also be used to train RPN
# this is achieved by passing proposal_index to anchor_target_layer
if str(self.phase) == 'TRAIN':
if cfg.TRAIN.MIX_INDEX:
top[1].reshape(1, 1)
self._top_name_map['proposal_index'] = 1
def setup(self, bottom, top):
layer_params = yaml.load(self.param_str_)
self._anchors = generate_anchors()
self._num_anchors = self._anchors.shape[0]
self._num_classes = layer_params['num_classes']
self._bp_all = layer_params.get('bp_all', True)
self._top_name_map = {}
top[0].reshape(1, 5)
self._top_name_map['rois'] = 0
top[1].reshape(1, 1, cfg.MASK_SIZE, cfg.MASK_SIZE)
self._top_name_map['mask_targets'] = 1
top[2].reshape(1, 1, cfg.MASK_SIZE, cfg.MASK_SIZE)
self._top_name_map['mask_weight'] = 2
top[3].reshape(1, 4)
self._top_name_map['gt_masks_info'] = 3
if cfg.TRAIN.MIX_INDEX:
top[4].reshape(1, 4)
self._top_name_map['fg_inds'] = 4
top[5].reshape(1, 4)
self._top_name_map['bg_inds'] = 5
def setup(self, bottom, top):
self._anchors = generate_anchors()
self._num_anchors = self._anchors.shape[0]
layer_params = yaml.load(self.param_str_)
self._feat_stride = layer_params['feat_stride']
# allow boxes to sit over the edge by a small amount
self._allowed_border = layer_params.get('allowed_border', 0)
height, width = bottom[0].data.shape[-2:]
A = self._num_anchors
# labels
top[0].reshape(1, 1, A * height, width)
# bbox_targets
top[1].reshape(1, A * 4, height, width)
# bbox_inside_weights
top[2].reshape(1, A * 4, height, width)
# bbox_outside_weights
top[3].reshape(1, A * 4, height, width)
def setup(self, bottom, top):
self._anchors = generate_anchors()
self._num_anchors = self._anchors.shape[0]
layer_params = yaml.load(self.param_str_)
self._feat_stride = layer_params['feat_stride']
# allow boxes to sit over the edge by a small amount
self._allowed_border = layer_params.get('allowed_border', 0)
height, width = bottom[0].data.shape[-2:]
A = self._num_anchors
# labels
top[0].reshape(1, 1, A * height, width)
# bbox_targets
top[1].reshape(1, A * 4, height, width)
# bbox_inside_weights
top[2].reshape(1, A * 4, height, width)
# bbox_outside_weights
top[3].reshape(1, A * 4, height, width)