def forward(self, inputs, truth_boxes=None, truth_labels=None, truth_instances=None):
cfg = self.cfg
mode = self.mode
batch_size = len(inputs)
print('Input size: {}'.format(inputs.size()))
# Features
features = data_parallel(self.feature_net, inputs)
print('Features size: {}'.format(len(features)))
# RPN proposals
self._rpn_logits_flat, self._rpn_deltas_flat = data_parallel(self.rpn_head, features)
print('RPN logits flat size: {}'.format(self._rpn_logits_flat.size()))
print('RPN deltas flat size: {}'.format(self._rpn_deltas_flat.size()))
rpn_window = make_rpn_windows(cfg, features)
rpn_proposals = rpn_nms(cfg, mode, inputs, rpn_window, self._rpn_logits_flat,
self._rpn_deltas_flat)
print('RPN proposals size: {}'.format(rpn_proposals.size()))
if mode in ['train', 'valid']:
self._rpn_labels, _, self._rpn_label_weights, self._rpn_targets, self._rpn_target_weights = make_rpn_target(
cfg, mode, inputs, rpn_window, truth_boxes, truth_labels)
rpn_proposals, self._rcnn_labels, _, self._rcnn_targets = \
make_rcnn_target(cfg, mode, inputs, rpn_proposals, truth_boxes, truth_labels )
print('RPN proposals size after `if mode`: {}'.format(rpn_proposals.size()))
# RCNN proposals
rcnn_proposals = rpn_proposals
if len(rpn_proposals) > 0:
rcnn_crops = self.rcnn_crop(features, rpn_proposals)
self._rcnn_logits, self._rcnn_deltas = data_parallel(self.rcnn_head, rcnn_crops)
rcnn_proposals = rcnn_nms(cfg, mode, inputs, rpn_proposals, self._rcnn_logits,
self._rcnn_deltas)
if mode in ['train', 'valid']:
rcnn_proposals, self._mask_labels, _, self._mask_instances, = make_mask_target(
cfg, mode, inputs, rcnn_proposals, truth_boxes, truth_labels, truth_instances)
# Segmentation
self._detections = rcnn_proposals
if len(self._detections) > 0:
# ROI crop
mask_crops = self.mask_crop(features, self._detections)
# Mask head
self._mask_logits = data_parallel(self.mask_head, mask_crops)
self.results = self._construct_results(cfg, inputs, self._detections, self._mask_logits)
def forward(self,
inputs,
truth_boxes=None,
truth_labels=None,
truth_instances=None):
cfg = self.cfg
mode = self.mode
batch_size = len(inputs)
#features
# print ('input', inputs.shape)
features_rpn, features_rcnn = data_parallel(self.feature_net, inputs)
#rpn proposals -------------------------------------------
self.rpn_logits_flat, self.rpn_probs_flat, self.rpn_deltas_flat = data_parallel(
self.rpn_head, features_rpn)
self.rpn_window = make_rpn_windows(cfg, features_rpn)
self.rpn_proposals = rpn_nms(cfg, mode, inputs, self.rpn_window,
self.rpn_logits_flat,
self.rpn_deltas_flat)
if mode in ['train', 'valid']:
self.rpn_labels, self.rpn_label_assigns, self.rpn_label_weights, self.rpn_targets, self.rpn_target_weights = \
make_rpn_target(cfg, mode, inputs, self.rpn_window, truth_boxes, truth_labels )
self.rpn_proposals, self.rcnn_labels, self.rcnn_assigns, self.rcnn_targets = \
make_rcnn_target(cfg, mode, inputs, self.rpn_proposals, truth_boxes, truth_labels )
#rcnn proposals ------------------------------------------------
self.rcnn_proposals = self.rpn_proposals
if len(self.rpn_proposals) > 0:
rcnn_crops = self.rcnn_crop(features_rcnn, self.rpn_proposals)
self.rcnn_logits, self.rcnn_deltas = data_parallel(
self.rcnn_head, rcnn_crops)
self.rcnn_proposals = rcnn_nms(cfg, mode, inputs,
self.rpn_proposals,
self.rcnn_logits, self.rcnn_deltas)
if mode in ['train', 'valid']:
self.rcnn_proposals, self.mask_labels, self.mask_assigns, self.mask_instances, = \
make_mask_target(cfg, mode, inputs, self.rcnn_proposals, truth_boxes, truth_labels, truth_instances)
#segmentation -------------------------------------------
self.detections = self.rcnn_proposals
self.masks = make_empty_masks(cfg, mode, inputs)
self.keeps = [[]]
self.category_labels = [[]]
self.label_sorted = [[]]
if len(self.detections) > 0:
mask_crops = self.mask_crop(features_rcnn, self.detections)
self.mask_logits = data_parallel(self.mask_head, mask_crops)
self.masks, self.keeps, self.category_labels, self.label_sorted = mask_nms(
cfg, mode, inputs, self.detections,
self.mask_logits) #<todo> better nms for mask
