def build_graph(self, *inputs):
num_fpn_level = len(config.ANCHOR_STRIDES_FPN)
assert len(config.ANCHOR_SIZES) == num_fpn_level
is_training = get_current_tower_context().is_training
image = inputs[0]
input_anchors = inputs[1:1 + 2 * num_fpn_level]
multilevel_anchor_labels = input_anchors[0::2]
multilevel_anchor_boxes = input_anchors[1::2]
gt_boxes, gt_labels = inputs[11], inputs[12]
if config.MODE_MASK:
gt_masks = inputs[-1]
image = self.preprocess(image) # 1CHW
image_shape2d = tf.shape(image)[2:] # h,w
c2345 = resnet_fpn_backbone(image, config.RESNET_NUM_BLOCK)
p23456 = fpn_model('fpn', c2345)
# Multi-Level RPN Proposals
multilevel_proposals = []
rpn_loss_collection = []
for lvl in range(num_fpn_level):
rpn_label_logits, rpn_box_logits = rpn_head(
'rpn', p23456[lvl], config.FPN_NUM_CHANNEL,
len(config.ANCHOR_RATIOS))
with tf.name_scope('FPN_lvl{}'.format(lvl + 2)):
anchors = tf.constant(get_all_anchors_fpn()[lvl],
name='rpn_anchor_lvl{}'.format(lvl + 2))
anchors, anchor_labels, anchor_boxes = \
self.narrow_to_featuremap(p23456[lvl], anchors,
multilevel_anchor_labels[lvl],
multilevel_anchor_boxes[lvl])
anchor_boxes_encoded = encode_bbox_target(
anchor_boxes, anchors)
pred_boxes_decoded = decode_bbox_target(
rpn_box_logits, anchors)
proposal_boxes, proposal_scores = generate_rpn_proposals(
tf.reshape(pred_boxes_decoded, [-1, 4]),
tf.reshape(rpn_label_logits, [-1]), image_shape2d,
config.TRAIN_FPN_NMS_TOPK
if is_training else config.TEST_FPN_NMS_TOPK)
multilevel_proposals.append((proposal_boxes, proposal_scores))
if is_training:
label_loss, box_loss = rpn_losses(anchor_labels,
anchor_boxes_encoded,
rpn_label_logits,
rpn_box_logits)
rpn_loss_collection.extend([label_loss, box_loss])
# Merge proposals from multi levels, pick top K
proposal_boxes = tf.concat([x[0] for x in multilevel_proposals],
axis=0) # nx4
proposal_scores = tf.concat([x[1] for x in multilevel_proposals],
axis=0) # n
proposal_topk = tf.minimum(
tf.size(proposal_scores), config.TRAIN_FPN_NMS_TOPK
if is_training else config.TEST_FPN_NMS_TOPK)
proposal_scores, topk_indices = tf.nn.top_k(proposal_scores,
k=proposal_topk,
sorted=False)
proposal_boxes = tf.gather(proposal_boxes, topk_indices)
if is_training:
rcnn_boxes, rcnn_labels, fg_inds_wrt_gt = sample_fast_rcnn_targets(
proposal_boxes, gt_boxes, gt_labels)
else:
# The boxes to be used to crop RoIs.
rcnn_boxes = proposal_boxes
roi_feature_fastrcnn = multilevel_roi_align(p23456[:4], rcnn_boxes, 7)
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_2fc_head(
'fastrcnn', roi_feature_fastrcnn, config.NUM_CLASS)
if is_training:
# rpn loss is already defined above
with tf.name_scope('rpn_losses'):
rpn_total_label_loss = tf.add_n(rpn_loss_collection[::2],
name='label_loss')
rpn_total_box_loss = tf.add_n(rpn_loss_collection[1::2],
name='box_loss')
add_moving_summary(rpn_total_box_loss, rpn_total_label_loss)
# fastrcnn loss:
matched_gt_boxes = tf.gather(gt_boxes, fg_inds_wrt_gt)
fg_inds_wrt_sample = tf.reshape(tf.where(rcnn_labels > 0),
[-1]) # fg inds w.r.t all samples
fg_sampled_boxes = tf.gather(rcnn_boxes, fg_inds_wrt_sample)
fg_fastrcnn_box_logits = tf.gather(fastrcnn_box_logits,
fg_inds_wrt_sample)
fastrcnn_label_loss, fastrcnn_box_loss = self.fastrcnn_training(
image, rcnn_labels, fg_sampled_boxes, matched_gt_boxes,
fastrcnn_label_logits, fg_fastrcnn_box_logits)
if config.MODE_MASK:
# maskrcnn loss
fg_labels = tf.gather(rcnn_labels, fg_inds_wrt_sample)
roi_feature_maskrcnn = multilevel_roi_align(
p23456[:4], fg_sampled_boxes, 14)
mask_logits = maskrcnn_upXconv_head('maskrcnn',
roi_feature_maskrcnn,
config.NUM_CLASS,
4) # #fg x #cat x 28 x 28
matched_gt_masks = tf.gather(gt_masks,
fg_inds_wrt_gt) # fg x H x W
target_masks_for_fg = crop_and_resize(
tf.expand_dims(matched_gt_masks, 1),
fg_sampled_boxes,
tf.range(tf.size(fg_inds_wrt_gt)),
28,
pad_border=False) # fg x 1x28x28
target_masks_for_fg = tf.squeeze(target_masks_for_fg, 1,
'sampled_fg_mask_targets')
mrcnn_loss = maskrcnn_loss(mask_logits, fg_labels,
target_masks_for_fg)
else:
mrcnn_loss = 0.0
wd_cost = regularize_cost(
'(?:group1|group2|group3|rpn|fastrcnn|maskrcnn)/.*W',
l2_regularizer(1e-4),
name='wd_cost')
total_cost = tf.add_n(
rpn_loss_collection +
[fastrcnn_label_loss, fastrcnn_box_loss, mrcnn_loss, wd_cost],
'total_cost')
add_moving_summary(total_cost, wd_cost)
return total_cost
else:
final_boxes, final_labels = self.fastrcnn_inference(
image_shape2d, rcnn_boxes, fastrcnn_label_logits,
fastrcnn_box_logits)
if config.MODE_MASK:
# Cascade inference needs roi transform with refined boxes.
roi_feature_maskrcnn = multilevel_roi_align(
p23456[:4], final_boxes, 14)
mask_logits = maskrcnn_upXconv_head('maskrcnn',
roi_feature_maskrcnn,
config.NUM_CLASS,
4) # #fg x #cat x 28 x 28
indices = tf.stack([
tf.range(tf.size(final_labels)),
tf.to_int32(final_labels) - 1
],
axis=1)
final_mask_logits = tf.gather_nd(mask_logits,
indices) # #resultx28x28
tf.sigmoid(final_mask_logits, name='final_masks')
def build_graph(self, *inputs):
num_fpn_level = len(config.ANCHOR_STRIDES_FPN)
assert len(config.ANCHOR_SIZES) == num_fpn_level
is_training = get_current_tower_context().is_training
image = inputs[0]
input_anchors = inputs[1: 1 + 2 * num_fpn_level]
multilevel_anchor_labels = input_anchors[0::2]
multilevel_anchor_boxes = input_anchors[1::2]
gt_boxes, gt_labels = inputs[11], inputs[12]
if config.MODE_MASK:
gt_masks = inputs[-1]
image = self.preprocess(image) # 1CHW
image_shape2d = tf.shape(image)[2:] # h,w
c2345 = resnet_fpn_backbone(image, config.RESNET_NUM_BLOCK)
p23456 = fpn_model('fpn', c2345)
# Multi-Level RPN Proposals
multilevel_proposals = []
rpn_loss_collection = []
for lvl in range(num_fpn_level):
rpn_label_logits, rpn_box_logits = rpn_head(
'rpn', p23456[lvl], config.FPN_NUM_CHANNEL, len(config.ANCHOR_RATIOS))
with tf.name_scope('FPN_lvl{}'.format(lvl + 2)):
anchors = tf.constant(get_all_anchors_fpn()[lvl], name='rpn_anchor_lvl{}'.format(lvl + 2))
anchors, anchor_labels, anchor_boxes = \
self.narrow_to_featuremap(p23456[lvl], anchors,
multilevel_anchor_labels[lvl],
multilevel_anchor_boxes[lvl])
anchor_boxes_encoded = encode_bbox_target(anchor_boxes, anchors)
pred_boxes_decoded = decode_bbox_target(rpn_box_logits, anchors)
proposal_boxes, proposal_scores = generate_rpn_proposals(
tf.reshape(pred_boxes_decoded, [-1, 4]),
tf.reshape(rpn_label_logits, [-1]),
image_shape2d,
config.TRAIN_FPN_NMS_TOPK if is_training else config.TEST_FPN_NMS_TOPK)
multilevel_proposals.append((proposal_boxes, proposal_scores))
if is_training:
label_loss, box_loss = rpn_losses(
anchor_labels, anchor_boxes_encoded,
rpn_label_logits, rpn_box_logits)
rpn_loss_collection.extend([label_loss, box_loss])
# Merge proposals from multi levels, pick top K
proposal_boxes = tf.concat([x[0] for x in multilevel_proposals], axis=0) # nx4
proposal_scores = tf.concat([x[1] for x in multilevel_proposals], axis=0) # n
proposal_topk = tf.minimum(tf.size(proposal_scores),
config.TRAIN_FPN_NMS_TOPK if is_training else config.TEST_FPN_NMS_TOPK)
proposal_scores, topk_indices = tf.nn.top_k(proposal_scores, k=proposal_topk, sorted=False)
proposal_boxes = tf.gather(proposal_boxes, topk_indices)
if is_training:
rcnn_boxes, rcnn_labels, fg_inds_wrt_gt = sample_fast_rcnn_targets(
proposal_boxes, gt_boxes, gt_labels)
else:
# The boxes to be used to crop RoIs.
rcnn_boxes = proposal_boxes
roi_feature_fastrcnn = multilevel_roi_align(p23456[:4], rcnn_boxes, 7)
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_2fc_head(
'fastrcnn', roi_feature_fastrcnn, config.NUM_CLASS)
if is_training:
# rpn loss is already defined above
with tf.name_scope('rpn_losses'):
rpn_total_label_loss = tf.add_n(rpn_loss_collection[::2], name='label_loss')
rpn_total_box_loss = tf.add_n(rpn_loss_collection[1::2], name='box_loss')
add_moving_summary(rpn_total_box_loss, rpn_total_label_loss)
# fastrcnn loss:
matched_gt_boxes = tf.gather(gt_boxes, fg_inds_wrt_gt)
fg_inds_wrt_sample = tf.reshape(tf.where(rcnn_labels > 0), [-1]) # fg inds w.r.t all samples
fg_sampled_boxes = tf.gather(rcnn_boxes, fg_inds_wrt_sample)
fg_fastrcnn_box_logits = tf.gather(fastrcnn_box_logits, fg_inds_wrt_sample)
fastrcnn_label_loss, fastrcnn_box_loss = self.fastrcnn_training(
image, rcnn_labels, fg_sampled_boxes,
matched_gt_boxes, fastrcnn_label_logits, fg_fastrcnn_box_logits)
if config.MODE_MASK:
# maskrcnn loss
fg_labels = tf.gather(rcnn_labels, fg_inds_wrt_sample)
roi_feature_maskrcnn = multilevel_roi_align(
p23456[:4], fg_sampled_boxes, 14)
mask_logits = maskrcnn_upXconv_head(
'maskrcnn', roi_feature_maskrcnn, config.NUM_CLASS, 4) # #fg x #cat x 28 x 28
target_masks_for_fg = crop_and_resize(
tf.expand_dims(gt_masks, 1),
fg_sampled_boxes,
fg_inds_wrt_gt, 28,
pad_border=False) # fg x 1x28x28
target_masks_for_fg = tf.squeeze(target_masks_for_fg, 1, 'sampled_fg_mask_targets')
mrcnn_loss = maskrcnn_loss(mask_logits, fg_labels, target_masks_for_fg)
else:
mrcnn_loss = 0.0
wd_cost = regularize_cost(
'(?:group1|group2|group3|rpn|fastrcnn|maskrcnn)/.*W',
l2_regularizer(1e-4), name='wd_cost')
total_cost = tf.add_n(rpn_loss_collection + [
fastrcnn_label_loss, fastrcnn_box_loss,
mrcnn_loss, wd_cost], 'total_cost')
add_moving_summary(total_cost, wd_cost)
return total_cost
else:
final_boxes, final_labels = self.fastrcnn_inference(
image_shape2d, rcnn_boxes, fastrcnn_label_logits, fastrcnn_box_logits)
if config.MODE_MASK:
# Cascade inference needs roi transform with refined boxes.
roi_feature_maskrcnn = multilevel_roi_align(
p23456[:4], final_boxes, 14)
mask_logits = maskrcnn_upXconv_head(
'maskrcnn', roi_feature_maskrcnn, config.NUM_CLASS, 4) # #fg x #cat x 28 x 28
indices = tf.stack([tf.range(tf.size(final_labels)), tf.to_int32(final_labels) - 1], axis=1)
final_mask_logits = tf.gather_nd(mask_logits, indices) # #resultx28x28
tf.sigmoid(final_mask_logits, name='final_masks')