def fastrcnn_training(self, image,
rcnn_labels, fg_rcnn_boxes, gt_boxes_per_fg,
rcnn_label_logits, fg_rcnn_box_logits):
"""
Args:
image (NCHW):
rcnn_labels (n): labels for each sampled targets
fg_rcnn_boxes (fg x 4): proposal boxes for each sampled foreground targets
gt_boxes_per_fg (fg x 4): matching gt boxes for each sampled foreground targets
rcnn_label_logits (n): label logits for each sampled targets
fg_rcnn_box_logits (fg x #class x 4): box logits for each sampled foreground targets
"""
with tf.name_scope('fg_sample_patch_viz'):
fg_sampled_patches = crop_and_resize(
image, fg_rcnn_boxes,
tf.zeros(tf.shape(fg_rcnn_boxes)[0], dtype=tf.int32), 300)
fg_sampled_patches = tf.transpose(fg_sampled_patches, [0, 2, 3, 1])
fg_sampled_patches = tf.reverse(fg_sampled_patches, axis=[-1]) # BGR->RGB
tf.summary.image('viz', fg_sampled_patches, max_outputs=30)
encoded_boxes = encode_bbox_target(
gt_boxes_per_fg, fg_rcnn_boxes) * tf.constant(cfg.FRCNN.BBOX_REG_WEIGHTS, dtype=tf.float32)
fastrcnn_label_loss, fastrcnn_box_loss = fastrcnn_losses(
rcnn_labels, rcnn_label_logits,
encoded_boxes,
fg_rcnn_box_logits)
return fastrcnn_label_loss, fastrcnn_box_loss
def fastrcnn_training(self, image,
rcnn_labels, fg_rcnn_boxes, gt_boxes_per_fg,
rcnn_label_logits, fg_rcnn_box_logits):
"""
Args:
image (NCHW):
rcnn_labels (n): labels for each sampled targets
fg_rcnn_boxes (fg x 4): proposal boxes for each sampled foreground targets
gt_boxes_per_fg (fg x 4): matching gt boxes for each sampled foreground targets
rcnn_label_logits (n): label logits for each sampled targets
fg_rcnn_box_logits (fg x #class x 4): box logits for each sampled foreground targets
"""
with tf.name_scope('fg_sample_patch_viz'):
fg_sampled_patches = crop_and_resize(
image, fg_rcnn_boxes,
tf.zeros([tf.shape(fg_rcnn_boxes)[0]], dtype=tf.int32), 300)
fg_sampled_patches = tf.transpose(fg_sampled_patches, [0, 2, 3, 1])
fg_sampled_patches = tf.reverse(fg_sampled_patches, axis=[-1]) # BGR->RGB
tf.summary.image('viz', fg_sampled_patches, max_outputs=30)
encoded_boxes = encode_bbox_target(
gt_boxes_per_fg, fg_rcnn_boxes) * tf.constant(cfg.FRCNN.BBOX_REG_WEIGHTS, dtype=tf.float32)
fastrcnn_label_loss, fastrcnn_box_loss = fastrcnn_losses(
rcnn_labels, rcnn_label_logits,
encoded_boxes,
fg_rcnn_box_logits)
return fastrcnn_label_loss, fastrcnn_box_loss
def roi_heads(self, image, features, proposals, targets):
image_shape2d = tf.shape(image)[2:] # h,w
featuremap = features[0]
gt_boxes, gt_labels, *_ = targets
if self.training:
# sample proposal boxes in training
proposals = sample_fast_rcnn_targets(proposals.boxes, gt_boxes, gt_labels)
# The boxes to be used to crop RoIs.
# Use all proposal boxes in inference
boxes_on_featuremap = proposals.boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE)
roi_resized = roi_align(featuremap, boxes_on_featuremap, 14)
feature_fastrcnn = resnet_conv5(roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCKS[-1]) # nxcx7x7
# Keep C5 feature to be shared with mask branch
feature_gap = GlobalAvgPooling('gap', feature_fastrcnn, data_format='channels_first')
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_outputs('fastrcnn', feature_gap, cfg.DATA.NUM_CLASS)
fastrcnn_head = FastRCNNHead(proposals, fastrcnn_box_logits, fastrcnn_label_logits, gt_boxes,
tf.constant(cfg.FRCNN.BBOX_REG_WEIGHTS, dtype=tf.float32))
if self.training:
all_losses = fastrcnn_head.losses()
if cfg.MODE_MASK:
gt_masks = targets[2]
# maskrcnn loss
# In training, mask branch shares the same C5 feature.
fg_feature = tf.gather(feature_fastrcnn, proposals.fg_inds())
mask_logits = maskrcnn_upXconv_head(
'maskrcnn', fg_feature, cfg.DATA.NUM_CATEGORY, num_convs=0) # #fg x #cat x 14x14
target_masks_for_fg = crop_and_resize(
tf.expand_dims(gt_masks, 1),
proposals.fg_boxes(),
proposals.fg_inds_wrt_gt, 14,
pad_border=False) # nfg x 1x14x14
target_masks_for_fg = tf.squeeze(target_masks_for_fg, 1, 'sampled_fg_mask_targets')
all_losses.append(maskrcnn_loss(mask_logits, proposals.fg_labels(), target_masks_for_fg))
return all_losses
else:
decoded_boxes = fastrcnn_head.decoded_output_boxes()
decoded_boxes = clip_boxes(decoded_boxes, image_shape2d, name='fastrcnn_all_boxes')
label_scores = fastrcnn_head.output_scores(name='fastrcnn_all_scores')
final_boxes, final_scores, final_labels = fastrcnn_predictions(
decoded_boxes, label_scores, name_scope='output')
if cfg.MODE_MASK:
roi_resized = roi_align(featuremap, final_boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE), 14)
feature_maskrcnn = resnet_conv5(roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCKS[-1])
mask_logits = maskrcnn_upXconv_head(
'maskrcnn', feature_maskrcnn, cfg.DATA.NUM_CATEGORY, 0) # #result x #cat x 14x14
indices = tf.stack([tf.range(tf.size(final_labels)), tf.cast(final_labels, tf.int32) - 1], axis=1)
final_mask_logits = tf.gather_nd(mask_logits, indices) # #resultx14x14
tf.sigmoid(final_mask_logits, name='output/masks')
return []
def roi_heads(self, image, features, proposals, targets):
image_shape2d = tf.shape(image)[2:] # h,w
assert len(features) == 5, "Features have to be P23456!"
gt_boxes, gt_labels, *_ = targets
if self.training:
proposals = sample_fast_rcnn_targets(proposals.boxes, gt_boxes, gt_labels)
fastrcnn_head_func = getattr(model_frcnn, cfg.FPN.FRCNN_HEAD_FUNC)
if not cfg.FPN.CASCADE:
roi_feature_fastrcnn = multilevel_roi_align(features[:4], proposals.boxes, 7)
head_feature = fastrcnn_head_func('fastrcnn', roi_feature_fastrcnn)
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_outputs(
'fastrcnn/outputs', head_feature, cfg.DATA.NUM_CLASS)
fastrcnn_head = FastRCNNHead(proposals, fastrcnn_box_logits, fastrcnn_label_logits,
gt_boxes, tf.constant(cfg.FRCNN.BBOX_REG_WEIGHTS, dtype=tf.float32))
else:
def roi_func(boxes):
return multilevel_roi_align(features[:4], boxes, 7)
fastrcnn_head = CascadeRCNNHead(
proposals, roi_func, fastrcnn_head_func,
(gt_boxes, gt_labels), image_shape2d, cfg.DATA.NUM_CLASS)
if self.training:
all_losses = fastrcnn_head.losses()
if cfg.MODE_MASK:
gt_masks = targets[2]
# maskrcnn loss
roi_feature_maskrcnn = multilevel_roi_align(
features[:4], proposals.fg_boxes(), 14,
name_scope='multilevel_roi_align_mask')
maskrcnn_head_func = getattr(model_mrcnn, cfg.FPN.MRCNN_HEAD_FUNC)
mask_logits = maskrcnn_head_func(
'maskrcnn', roi_feature_maskrcnn, cfg.DATA.NUM_CATEGORY) # #fg x #cat x 28 x 28
target_masks_for_fg = crop_and_resize(
tf.expand_dims(gt_masks, 1),
proposals.fg_boxes(),
proposals.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')
all_losses.append(maskrcnn_loss(mask_logits, proposals.fg_labels(), target_masks_for_fg))
return all_losses
else:
decoded_boxes = fastrcnn_head.decoded_output_boxes()
decoded_boxes = clip_boxes(decoded_boxes, image_shape2d, name='fastrcnn_all_boxes')
label_scores = fastrcnn_head.output_scores(name='fastrcnn_all_scores')
final_boxes, final_scores, final_labels = fastrcnn_predictions(
decoded_boxes, label_scores, name_scope='output')
if cfg.MODE_MASK:
# Cascade inference needs roi transform with refined boxes.
roi_feature_maskrcnn = multilevel_roi_align(features[:4], final_boxes, 14)
maskrcnn_head_func = getattr(model_mrcnn, cfg.FPN.MRCNN_HEAD_FUNC)
mask_logits = maskrcnn_head_func(
'maskrcnn', roi_feature_maskrcnn, cfg.DATA.NUM_CATEGORY) # #fg x #cat x 28 x 28
indices = tf.stack([tf.range(tf.size(final_labels)), tf.cast(final_labels, tf.int32) - 1], axis=1)
final_mask_logits = tf.gather_nd(mask_logits, indices) # #resultx28x28
tf.sigmoid(final_mask_logits, name='output/masks')
return []
def build_graph(self, *inputs):
inputs = dict(zip(self.input_names, inputs))
num_fpn_level = len(cfg.FPN.ANCHOR_STRIDES)
assert len(cfg.RPN.ANCHOR_SIZES) == num_fpn_level
is_training = get_current_tower_context().is_training
all_anchors_fpn = get_all_anchors_fpn()
multilevel_anchors = [
RPNAnchors(all_anchors_fpn[i],
inputs['anchor_labels_lvl{}'.format(i + 2)],
inputs['anchor_boxes_lvl{}'.format(i + 2)])
for i in range(len(all_anchors_fpn))
]
image = self.preprocess(inputs['image']) # 1CHW
image_shape2d = tf.shape(image)[2:] # h,w
c2345 = resnet_fpn_backbone(image, cfg.BACKBONE.RESNET_NUM_BLOCK)
p23456 = fpn_model('fpn', c2345)
self.slice_feature_and_anchors(image_shape2d, p23456,
multilevel_anchors)
# Multi-Level RPN Proposals
rpn_outputs = [
rpn_head('rpn', pi, cfg.FPN.NUM_CHANNEL,
len(cfg.RPN.ANCHOR_RATIOS)) for pi in p23456
]
multilevel_label_logits = [k[0] for k in rpn_outputs]
multilevel_box_logits = [k[1] for k in rpn_outputs]
proposal_boxes, proposal_scores = generate_fpn_proposals(
multilevel_anchors, multilevel_label_logits, multilevel_box_logits,
image_shape2d)
gt_boxes, gt_labels = inputs['gt_boxes'], inputs['gt_labels']
if is_training:
proposals = sample_fast_rcnn_targets(proposal_boxes, gt_boxes,
gt_labels)
else:
proposals = BoxProposals(proposal_boxes)
fastrcnn_head_func = getattr(model_frcnn, cfg.FPN.FRCNN_HEAD_FUNC)
if not cfg.FPN.CASCADE:
roi_feature_fastrcnn = multilevel_roi_align(
p23456[:4], proposals.boxes, 7)
head_feature = fastrcnn_head_func('fastrcnn', roi_feature_fastrcnn)
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_outputs(
'fastrcnn/outputs', head_feature, cfg.DATA.NUM_CLASS)
fastrcnn_head = FastRCNNHead(
proposals, fastrcnn_box_logits, fastrcnn_label_logits,
tf.constant(cfg.FRCNN.BBOX_REG_WEIGHTS, dtype=tf.float32))
else:
def roi_func(boxes):
return multilevel_roi_align(p23456[:4], boxes, 7)
fastrcnn_head = CascadeRCNNHead(proposals, roi_func,
fastrcnn_head_func, image_shape2d,
cfg.DATA.NUM_CLASS)
if is_training:
all_losses = []
all_losses.extend(
multilevel_rpn_losses(multilevel_anchors,
multilevel_label_logits,
multilevel_box_logits))
all_losses.extend(fastrcnn_head.losses())
if cfg.MODE_MASK:
# maskrcnn loss
roi_feature_maskrcnn = multilevel_roi_align(
p23456[:4],
proposals.fg_boxes(),
14,
name_scope='multilevel_roi_align_mask')
maskrcnn_head_func = getattr(model_mrcnn,
cfg.FPN.MRCNN_HEAD_FUNC)
mask_logits = maskrcnn_head_func(
'maskrcnn', roi_feature_maskrcnn,
cfg.DATA.NUM_CATEGORY) # #fg x #cat x 28 x 28
target_masks_for_fg = crop_and_resize(
tf.expand_dims(inputs['gt_masks'], 1),
proposals.fg_boxes(),
proposals.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')
all_losses.append(
maskrcnn_loss(mask_logits, proposals.fg_labels(),
target_masks_for_fg))
wd_cost = regularize_cost('.*/W',
l2_regularizer(cfg.TRAIN.WEIGHT_DECAY),
name='wd_cost')
all_losses.append(wd_cost)
total_cost = tf.add_n(all_losses, 'total_cost')
add_moving_summary(total_cost, wd_cost)
return total_cost
else:
decoded_boxes = fastrcnn_head.decoded_output_boxes()
decoded_boxes = clip_boxes(decoded_boxes,
image_shape2d,
name='fastrcnn_all_boxes')
label_scores = fastrcnn_head.output_scores(
name='fastrcnn_all_scores')
final_boxes, final_scores, final_labels = fastrcnn_predictions(
decoded_boxes, label_scores, name_scope='output')
if cfg.MODE_MASK:
# Cascade inference needs roi transform with refined boxes.
roi_feature_maskrcnn = multilevel_roi_align(
p23456[:4], final_boxes, 14)
maskrcnn_head_func = getattr(model_mrcnn,
cfg.FPN.MRCNN_HEAD_FUNC)
mask_logits = maskrcnn_head_func(
'maskrcnn', roi_feature_maskrcnn,
cfg.DATA.NUM_CATEGORY) # #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='output/masks')
def build_graph(self, *inputs):
# TODO need to make tensorpack handles dict better
inputs = dict(zip(self.input_names, inputs))
is_training = get_current_tower_context().is_training
image = self.preprocess(inputs['image']) # 1CHW
featuremap = resnet_c4_backbone(image,
cfg.BACKBONE.RESNET_NUM_BLOCK[:3])
rpn_label_logits, rpn_box_logits = rpn_head('rpn', featuremap,
cfg.RPN.HEAD_DIM,
cfg.RPN.NUM_ANCHOR)
anchors = RPNAnchors(get_all_anchors(), inputs['anchor_labels'],
inputs['anchor_boxes'])
anchors = anchors.narrow_to(featuremap)
image_shape2d = tf.shape(image)[2:] # h,w
pred_boxes_decoded = anchors.decode_logits(
rpn_box_logits) # fHxfWxNAx4, floatbox
proposal_boxes, proposal_scores = generate_rpn_proposals(
tf.reshape(pred_boxes_decoded, [-1, 4]),
tf.reshape(rpn_label_logits,
[-1]), image_shape2d, cfg.RPN.TRAIN_PRE_NMS_TOPK
if is_training else cfg.RPN.TEST_PRE_NMS_TOPK,
cfg.RPN.TRAIN_POST_NMS_TOPK
if is_training else cfg.RPN.TEST_POST_NMS_TOPK)
gt_boxes, gt_labels = inputs['gt_boxes'], inputs['gt_labels']
if is_training:
# sample proposal boxes in training
proposals = sample_fast_rcnn_targets(proposal_boxes, gt_boxes,
gt_labels)
else:
# The boxes to be used to crop RoIs.
# Use all proposal boxes in inference
proposals = BoxProposals(proposal_boxes)
boxes_on_featuremap = proposals.boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE)
roi_resized = roi_align(featuremap, boxes_on_featuremap, 14)
feature_fastrcnn = resnet_conv5(
roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCK[-1]) # nxcx7x7
# Keep C5 feature to be shared with mask branch
feature_gap = GlobalAvgPooling('gap',
feature_fastrcnn,
data_format='channels_first')
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_outputs(
'fastrcnn', feature_gap, cfg.DATA.NUM_CLASS)
fastrcnn_head = FastRCNNHead(
proposals, fastrcnn_box_logits, fastrcnn_label_logits,
tf.constant(cfg.FRCNN.BBOX_REG_WEIGHTS, dtype=tf.float32))
if is_training:
all_losses = []
# rpn loss
all_losses.extend(
rpn_losses(anchors.gt_labels, anchors.encoded_gt_boxes(),
rpn_label_logits, rpn_box_logits))
# fastrcnn loss
all_losses.extend(fastrcnn_head.losses())
if cfg.MODE_MASK:
# maskrcnn loss
# In training, mask branch shares the same C5 feature.
fg_feature = tf.gather(feature_fastrcnn, proposals.fg_inds())
mask_logits = maskrcnn_upXconv_head(
'maskrcnn', fg_feature, cfg.DATA.NUM_CATEGORY,
num_convs=0) # #fg x #cat x 14x14
target_masks_for_fg = crop_and_resize(
tf.expand_dims(inputs['gt_masks'], 1),
proposals.fg_boxes(),
proposals.fg_inds_wrt_gt,
14,
pad_border=False) # nfg x 1x14x14
target_masks_for_fg = tf.squeeze(target_masks_for_fg, 1,
'sampled_fg_mask_targets')
all_losses.append(
maskrcnn_loss(mask_logits, proposals.fg_labels(),
target_masks_for_fg))
wd_cost = regularize_cost('.*/W',
l2_regularizer(cfg.TRAIN.WEIGHT_DECAY),
name='wd_cost')
all_losses.append(wd_cost)
total_cost = tf.add_n(all_losses, 'total_cost')
add_moving_summary(total_cost, wd_cost)
return total_cost
else:
decoded_boxes = fastrcnn_head.decoded_output_boxes()
decoded_boxes = clip_boxes(decoded_boxes,
image_shape2d,
name='fastrcnn_all_boxes')
label_scores = fastrcnn_head.output_scores(
name='fastrcnn_all_scores')
final_boxes, final_scores, final_labels = fastrcnn_predictions(
decoded_boxes, label_scores, name_scope='output')
if cfg.MODE_MASK:
roi_resized = roi_align(
featuremap, final_boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE),
14)
feature_maskrcnn = resnet_conv5(
roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCK[-1])
mask_logits = maskrcnn_upXconv_head(
'maskrcnn', feature_maskrcnn, cfg.DATA.NUM_CATEGORY,
0) # #result x #cat x 14x14
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) # #resultx14x14
tf.sigmoid(final_mask_logits, name='output/masks')
def roi_heads(self, image, features, proposal_boxes, targets, inputs, seed_gen):
"""
Implement the RoI Align and construct the RoI head (box and mask branches) of the graph
Args:
image: BS x NumChannel x H_image x W_image
features: ([tf.Tensor]): A list of 5 FPN feature level P23456, each with BS X NumChannel X H_feature X W_feature
proposal_boxes(tf.Tensor): K x 5 boxes
targets: list of 'gt_boxes', 'gt_labels', 'gt_masks' from input
inputs: dict, contains all input information
Returns: all_losses: a list contains box loss and mask loss
"""
image_shape2d = inputs['orig_image_dims'][: ,:2] # BS x 2
assert len(features) == 5, "Features have to be P23456!"
gt_boxes, gt_labels, *_ = targets
prepadding_gt_counts = inputs['orig_gt_counts']
if self.training:
input_proposal_boxes = proposal_boxes # K x 5
input_gt_boxes = gt_boxes # BS x Num_gt_boxes x 4
input_gt_labels = gt_labels # BS x Num_gt_boxes
# Sample the input_proposal_boxes to make the foreground(fg) box and background(bg) boxes
# ratio close to configuration. proposal_boxes: Num_sampled_boxs x 5, proposal_labels: 1-D Num_sampled_boxes
# proposal_gt_id_for_each_fg contains indices for matching GT of each foreground box.
proposal_boxes, proposal_labels, proposal_gt_id_for_each_fg = sample_fast_rcnn_targets(
input_proposal_boxes,
input_gt_boxes,
input_gt_labels,
prepadding_gt_counts,
batch_size=cfg.TRAIN.BATCH_SIZE_PER_GPU, seed_gen=seed_gen)
# For the box/class branch
roi_feature_fastrcnn = multilevel_roi_align(features[:4], proposal_boxes, 7) # Num_sampled_boxes x NumChannel x H_roi_box x W_roi_box
fastrcnn_head_func = getattr(boxclass_head, cfg.FPN.BOXCLASS_HEAD_FUNC)
head_feature = fastrcnn_head_func('fastrcnn', roi_feature_fastrcnn, seed_gen=seed_gen, fp16=self.fp16) # Num_sampled_boxes x Num_features
# fastrcnn_label_logits: Num_sampled_boxes x Num_classes ,fastrcnn_box_logits: Num_sampled_boxes x Num_classes x 4
fastrcnn_label_logits, fastrcnn_box_logits = boxclass_outputs('fastrcnn/outputs', head_feature, cfg.DATA.NUM_CLASS, seed_gen=seed_gen)
regression_weights = tf.constant(cfg.FRCNN.BBOX_REG_WEIGHTS, dtype=tf.float32)
fastrcnn_head = BoxClassHead(fastrcnn_box_logits,
fastrcnn_label_logits,
regression_weights,
prepadding_gt_counts,
proposal_boxes)
if self.training:
# only calculate the losses for boxes if there is an object (foreground boxes)
proposal_fg_inds = tf.reshape(tf.where(proposal_labels > 0), [-1])
proposal_fg_boxes = tf.gather(proposal_boxes, proposal_fg_inds)
proposal_fg_labels = tf.gather(proposal_labels, proposal_fg_inds)
fastrcnn_head.add_training_info(input_gt_boxes,
proposal_labels,
proposal_fg_inds,
proposal_fg_boxes,
proposal_fg_labels,
proposal_gt_id_for_each_fg)
all_losses = fastrcnn_head.losses(cfg.TRAIN.BATCH_SIZE_PER_GPU)
if cfg.MODE_MASK:
gt_masks = targets[2]
maskrcnn_head_func = getattr(mask_head, cfg.FPN.MRCNN_HEAD_FUNC)
# For the mask branch. roi_feature_maskrcnn: Num_fg_boxes x NumChannel x H_roi_mask x W_roi_mask
roi_feature_maskrcnn = multilevel_roi_align(
features[:4], proposal_fg_boxes, 14,
name_scope='multilevel_roi_align_mask')
if not cfg.TRAIN.MASK_NCHW:
roi_feature_maskrcnn = nchw_to_nhwc_transform(roi_feature_maskrcnn)
mask_logits = maskrcnn_head_func(
'maskrcnn', roi_feature_maskrcnn, cfg.DATA.NUM_CATEGORY, seed_gen=seed_gen, fp16=self.fp16) # Num_fg_boxes x num_category x (H_roi_mask*2) x (W_roi_mask*2)
per_image_target_masks_for_fg = []
per_image_fg_labels = []
for i in range(cfg.TRAIN.BATCH_SIZE_PER_GPU):
single_image_gt_count = prepadding_gt_counts[i] # 1-D Num_gt_boxes_current_image
single_image_gt_masks = gt_masks[i, :single_image_gt_count, :, :] # Num_gt_boxes_current_image x H_gtmask x W_gtmask
single_image_fg_indices = tf.squeeze(tf.where(tf.equal(proposal_fg_boxes[:, 0], i)), axis=1) # 1-D Num_fg_boxes_current_image
single_image_fg_boxes = tf.gather(proposal_fg_boxes, single_image_fg_indices)[:, 1:] # Num_fg_boxes_current_image x 4
single_image_fg_labels = tf.gather(proposal_fg_labels, single_image_fg_indices) # 1-D Num_fg_boxes_current_image
single_image_fg_inds_wrt_gt = proposal_gt_id_for_each_fg[i] # 1-D Num_fg_boxes_current_image
assert isinstance(single_image_fg_inds_wrt_gt, tf.Tensor)
single_image_gt_masks = tf.expand_dims(single_image_gt_masks, axis=1) # Num_gt_boxes_current_image x 1 x H_gtmask x W_gtmask
# single_image_target_masks_for_fg: Num_fg_boxes_current_image x 1 x (H_roi_mask*2) x (W_roi_mask*2)
single_image_target_masks_for_fg = crop_and_resize(single_image_gt_masks,
single_image_fg_boxes,
single_image_fg_inds_wrt_gt,
28,
image_shape2d[i],
pad_border=False,
verbose_batch_index=i)
per_image_fg_labels.append(single_image_fg_labels)
per_image_target_masks_for_fg.append(single_image_target_masks_for_fg)
target_masks_for_fg = tf.concat(per_image_target_masks_for_fg, axis=0) # Num_fg_boxes x 1 x (H_roi_mask*2) x (W_roi_mask*2)
proposal_fg_labels = tf.concat(per_image_fg_labels, axis=0) # 1-D Num_fg_boxes
target_masks_for_fg = tf.squeeze(target_masks_for_fg, 1, 'sampled_fg_mask_targets') # Num_fg_boxes x (H_roi_mask*2) x (W_roi_mask*2)
mask_loss = maskrcnn_loss(mask_logits, proposal_fg_labels, target_masks_for_fg)
all_losses.append(mask_loss)
return all_losses
else:
decoded_boxes, batch_ids = fastrcnn_head.decoded_output_boxes_batch()
decoded_boxes = clip_boxes_batch(decoded_boxes, image_shape2d, tf.cast(batch_ids, dtype=tf.int32), name='fastrcnn_all_boxes')
label_scores = fastrcnn_head.output_scores(name='fastrcnn_all_scores')
final_boxes, final_scores, final_labels, box_ids = boxclass_predictions(decoded_boxes, label_scores, name_scope='output')
batch_indices = tf.gather(proposal_boxes[: ,0], box_ids, name='output/batch_indices')
if cfg.MODE_MASK:
batch_ind_boxes = tf.concat((tf.expand_dims(batch_indices, 1), final_boxes), axis=1)
roi_feature_maskrcnn = multilevel_roi_align(features[:4], batch_ind_boxes, 14)
if not cfg.TRAIN.MASK_NCHW:
roi_feature_maskrcnn = nchw_to_nhwc_transform(roi_feature_maskrcnn)
maskrcnn_head_func = getattr(mask_head, cfg.FPN.MRCNN_HEAD_FUNC)
mask_logits = maskrcnn_head_func(
'maskrcnn', roi_feature_maskrcnn, cfg.DATA.NUM_CATEGORY, seed_gen=seed_gen, fp16=self.fp16) # #fg x #cat x 28 x 28
indices = tf.stack([tf.range(tf.size(final_labels)), tf.cast(final_labels, tf.int32) - 1], axis=1)
final_mask_logits = tf.gather_nd(mask_logits, indices) # #resultx28x28
tf.sigmoid(final_mask_logits, name='output/masks')
return []
def build_graph(self, *inputs):
is_training = get_current_tower_context().is_training
if cfg.MODE_MASK:
image, anchor_labels, anchor_boxes, gt_boxes, gt_labels, gt_masks = inputs
else:
image, anchor_labels, anchor_boxes, gt_boxes, gt_labels = inputs
image = self.preprocess(image) # 1CHW
#with varreplace.freeze_variables(stop_gradient=True, skip_collection=True):
featuremap = resnet_c4_backbone(image, cfg.BACKBONE.RESNET_NUM_BLOCK[:3])
# freeze
# featuremap = tf.stop_gradient(featuremap)
rpn_label_logits, rpn_box_logits = rpn_head('rpn', featuremap, cfg.RPN.HEAD_DIM, cfg.RPN.NUM_ANCHOR)
anchors = RPNAnchors(get_all_anchors(), anchor_labels, anchor_boxes)
anchors = anchors.narrow_to(featuremap)
image_shape2d = tf.shape(image)[2:] # h,w
pred_boxes_decoded = anchors.decode_logits(rpn_box_logits) # fHxfWxNAx4, floatbox
proposal_boxes, proposal_scores = generate_rpn_proposals(
tf.reshape(pred_boxes_decoded, [-1, 4]),
tf.reshape(rpn_label_logits, [-1]),
image_shape2d,
cfg.RPN.TRAIN_PRE_NMS_TOPK if is_training else cfg.RPN.TEST_PRE_NMS_TOPK,
cfg.RPN.TRAIN_POST_NMS_TOPK if is_training else cfg.RPN.TEST_POST_NMS_TOPK)
if is_training:
# sample proposal boxes in 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.
# Use all proposal boxes in inference
rcnn_boxes = proposal_boxes
featuremap = resnet_conv5(featuremap, cfg.BACKBONE.RESNET_NUM_BLOCK[-1])
rfcn_cls = Conv2D('rfcn_cls', featuremap, cfg.DATA.NUM_CLASS*3*3, (1, 1), data_format='channels_first')
rfcn_reg = Conv2D('rfcn_reg', featuremap, cfg.DATA.NUM_CLASS*4*3*3, (1, 1), data_format='channels_first')
boxes_on_featuremap = rcnn_boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE)
classify_vote = VotePooling('votepooling_cls', rfcn_cls, boxes_on_featuremap, 3, 3)
classify_regr = VotePooling('votepooling_regr', rfcn_reg, boxes_on_featuremap, 3, 3, isCls=False)
classify_regr = tf.reshape(classify_regr, [-1, cfg.DATA.NUM_CLASS, 4])
if is_training:
# rpn loss
rpn_label_loss, rpn_box_loss = rpn_losses(
anchors.gt_labels, anchors.encoded_gt_boxes(), rpn_label_logits, rpn_box_logits)
# 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(classify_regr, fg_inds_wrt_sample)
fastrcnn_label_loss, fastrcnn_box_loss = self.fastrcnn_training(
image, rcnn_labels, fg_sampled_boxes,
matched_gt_boxes, classify_vote, fg_fastrcnn_box_logits)
if cfg.MODE_MASK:
# maskrcnn loss
fg_labels = tf.gather(rcnn_labels, fg_inds_wrt_sample)
# In training, mask branch shares the same C5 feature.
fg_feature = tf.gather(feature_fastrcnn, fg_inds_wrt_sample)
mask_logits = maskrcnn_upXconv_head(
'maskrcnn', fg_feature, cfg.DATA.NUM_CATEGORY, num_convs=0) # #fg x #cat x 14x14
target_masks_for_fg = crop_and_resize(
tf.expand_dims(gt_masks, 1),
fg_sampled_boxes,
fg_inds_wrt_gt, 14,
pad_border=False) # nfg x 1x14x14
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(
'.*/W', l2_regularizer(cfg.TRAIN.WEIGHT_DECAY), name='wd_cost')
total_cost = tf.add_n([
rpn_label_loss, rpn_box_loss,
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, classify_vote, classify_regr)
if cfg.MODE_MASK:
roi_resized = roi_align(featuremap, final_boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE), 14)
feature_maskrcnn = resnet_conv5(roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCK[-1])
mask_logits = maskrcnn_upXconv_head(
'maskrcnn', feature_maskrcnn, cfg.DATA.NUM_CATEGORY, 0) # #result x #cat x 14x14
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) # #resultx14x14
tf.sigmoid(final_mask_logits, name='final_masks')
def build_graph(self, *inputs):
num_fpn_level = len(cfg.FPN.ANCHOR_STRIDES)
assert len(cfg.RPN.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_anchors = [RPNAnchors(*args) for args in
zip(get_all_anchors_fpn(), input_anchors[0::2], input_anchors[1::2])]
gt_boxes, gt_labels = inputs[11], inputs[12]
if cfg.MODE_MASK:
gt_masks = inputs[-1]
image = self.preprocess(image) # 1CHW
image_shape2d = tf.shape(image)[2:] # h,w
c2345 = resnet_fpn_backbone(image, cfg.BACKBONE.RESNET_NUM_BLOCK)
p23456 = fpn_model('fpn', c2345)
self.slice_feature_and_anchors(image_shape2d, p23456, multilevel_anchors)
# Multi-Level RPN Proposals
rpn_outputs = [rpn_head('rpn', pi, cfg.FPN.NUM_CHANNEL, len(cfg.RPN.ANCHOR_RATIOS))
for pi in p23456]
multilevel_label_logits = [k[0] for k in rpn_outputs]
multilevel_box_logits = [k[1] for k in rpn_outputs]
proposal_boxes, proposal_scores = generate_fpn_proposals(
multilevel_anchors, multilevel_label_logits,
multilevel_box_logits, image_shape2d)
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_head_func = getattr(model_frcnn, cfg.FPN.FRCNN_HEAD_FUNC)
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_head_func(
'fastrcnn', roi_feature_fastrcnn, cfg.DATA.NUM_CLASS)
if is_training:
# rpn loss:
rpn_label_loss, rpn_box_loss = multilevel_rpn_losses(
multilevel_anchors, multilevel_label_logits, multilevel_box_logits)
# 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 cfg.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,
name_scope='multilevel_roi_align_mask')
maskrcnn_head_func = getattr(model_mrcnn, cfg.FPN.MRCNN_HEAD_FUNC)
mask_logits = maskrcnn_head_func(
'maskrcnn', roi_feature_maskrcnn, cfg.DATA.NUM_CATEGORY) # #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('fastrcnn/.*/W', l2_regularizer(cfg.TRAIN.WEIGHT_DECAY), name='wd_cost')
#wd_cost = regularize_cost(
# '.*/W', l2_regularizer(cfg.TRAIN.WEIGHT_DECAY), name='wd_cost')
total_cost = tf.add_n([rpn_label_loss, rpn_box_loss,
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 cfg.MODE_MASK:
# Cascade inference needs roi transform with refined boxes.
roi_feature_maskrcnn = multilevel_roi_align(p23456[:4], final_boxes, 14)
maskrcnn_head_func = getattr(model_mrcnn, cfg.FPN.MRCNN_HEAD_FUNC)
mask_logits = maskrcnn_head_func(
'maskrcnn', roi_feature_maskrcnn, cfg.DATA.NUM_CATEGORY) # #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 roi_heads(self,
image,
ref_features,
ref_box,
features,
proposals,
targets,
hard_negative_features=None,
hard_positive_features=None,
hard_positive_ious=None,
hard_positive_gt_boxes=None,
hard_positive_jitter_boxes=None,
precomputed_ref_features=None,
extra_feats=None):
image_shape2d = tf.shape(image)[2:] # h,w
assert len(features) == 5, "Features have to be P23456!"
gt_boxes, gt_labels, *_ = targets
if self.training:
proposals = sample_fast_rcnn_targets(proposals.boxes, gt_boxes,
gt_labels)
fastrcnn_head_func = getattr(model_frcnn, cfg.FPN.FRCNN_HEAD_FUNC)
if precomputed_ref_features is None:
roi_aligned_ref_features = multilevel_roi_align(
ref_features[:4], ref_box[tf.newaxis], 7)
else:
roi_aligned_ref_features = precomputed_ref_features[tf.newaxis]
# //////////
roi_aligned_extra_features = extra_feats[tf.newaxis]
# //////////
if cfg.MODE_SHARED_CONV_REDUCE:
scope = tf.get_variable_scope()
else:
scope = ""
assert cfg.FPN.CASCADE
def roi_func(boxes, already_aligned_features=None):
if already_aligned_features is None:
aligned_features = multilevel_roi_align(features[:4], boxes, 7)
else:
# for hard example mining
aligned_features = already_aligned_features
tiled = tf.tile(roi_aligned_ref_features,
[tf.shape(aligned_features)[0], 1, 1, 1])
concat_features = tf.concat((tiled, aligned_features), axis=1)
with argscope(Conv2D,
data_format='channels_first',
kernel_initializer=tf.variance_scaling_initializer(
scale=2.0,
mode='fan_out',
distribution='untruncated_normal'
if get_tf_version_tuple() >=
(1, 12) else 'normal')):
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
reduced_features = Conv2D('conv_reduce',
concat_features,
256,
1,
activation=None)
return reduced_features
def roi_func_extra(boxes, already_aligned_features=None):
if already_aligned_features is None:
aligned_features = multilevel_roi_align(features[:4], boxes, 7)
else:
# for hard example mining
aligned_features = already_aligned_features
tiled = tf.tile(roi_aligned_extra_features,
[tf.shape(aligned_features)[0], 1, 1, 1])
concat_features = tf.concat((tiled, aligned_features), axis=1)
with argscope(Conv2D,
data_format='channels_first',
kernel_initializer=tf.variance_scaling_initializer(
scale=2.0,
mode='fan_out',
distribution='untruncated_normal'
if get_tf_version_tuple() >=
(1, 12) else 'normal')):
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
reduced_features = Conv2D('conv_reduce',
concat_features,
256,
1,
activation=None)
return reduced_features
if cfg.MODE_HARD_MINING and self.training:
fastrcnn_head = CascadeRCNNHeadWithHardExamples(
proposals, roi_func, fastrcnn_head_func, (gt_boxes, gt_labels),
image_shape2d, cfg.DATA.NUM_CLASS, hard_negative_features,
hard_positive_features, cfg.HARD_NEGATIVE_LOSS_SCALING_FACTOR,
cfg.HARD_POSITIVE_LOSS_SCALING_FACTOR, hard_positive_ious,
hard_positive_gt_boxes, hard_positive_jitter_boxes)
else:
if cfg.MODE_EXTRA_FEATURES:
fastrcnn_head = CascadeRCNNHead(proposals, roi_func,
fastrcnn_head_func,
(gt_boxes, gt_labels),
image_shape2d,
cfg.DATA.NUM_CLASS,
roi_func_extra)
else:
fastrcnn_head = CascadeRCNNHead(proposals, roi_func,
fastrcnn_head_func,
(gt_boxes, gt_labels),
image_shape2d,
cfg.DATA.NUM_CLASS)
if cfg.EXTRACT_GT_FEATURES:
# get boxes and features for each of the three cascade stages!
b0 = proposals.boxes
b1, b2, _ = fastrcnn_head._cascade_boxes
f0 = multilevel_roi_align(features[:4], b0, 7)
f1 = multilevel_roi_align(features[:4], b1, 7)
f2 = multilevel_roi_align(features[:4], b2, 7)
tf.concat([b0, b1, b2], axis=0, name="boxes_for_extraction")
tf.concat([f0, f1, f2], axis=0, name="features_for_extraction")
if self.training:
all_losses = fastrcnn_head.losses()
if cfg.MODE_MASK:
gt_masks = targets[2]
# maskrcnn loss
roi_feature_maskrcnn = multilevel_roi_align(
features[:4],
proposals.fg_boxes(),
14,
name_scope='multilevel_roi_align_mask')
maskrcnn_head_func = getattr(model_mrcnn,
cfg.FPN.MRCNN_HEAD_FUNC)
mask_logits = maskrcnn_head_func(
'maskrcnn', roi_feature_maskrcnn,
cfg.DATA.NUM_CATEGORY) # #fg x #cat x 28 x 28
target_masks_for_fg = crop_and_resize(
tf.expand_dims(gt_masks, 1),
proposals.fg_boxes(),
proposals.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')
all_losses.append(
maskrcnn_loss(mask_logits, proposals.fg_labels(),
target_masks_for_fg))
if cfg.MEASURE_IOU_DURING_TRAINING:
decoded_boxes = fastrcnn_head.decoded_output_boxes()
decoded_boxes = clip_boxes(decoded_boxes,
image_shape2d,
name='fastrcnn_all_boxes')
label_scores = fastrcnn_head.output_scores(
name='fastrcnn_all_scores')
final_boxes, final_scores, final_labels = fastrcnn_predictions(
decoded_boxes, label_scores, name_scope='output_train')
# if predictions are empty, this might break...
# to prevent, stack dummy box
boxes_for_iou = tf.concat([
final_boxes[:1],
tf.constant([[0.0, 0.0, 1.0, 1.0]], dtype=tf.float32)
],
axis=0)
from examples.FasterRCNN.utils.box_ops import pairwise_iou
iou_at_1 = tf.identity(pairwise_iou(gt_boxes[:1],
boxes_for_iou)[0, 0],
name="train_iou_at_1")
add_moving_summary(iou_at_1)
return all_losses
else:
decoded_boxes = fastrcnn_head.decoded_output_boxes()
decoded_boxes = clip_boxes(decoded_boxes,
image_shape2d,
name='fastrcnn_all_boxes')
label_scores = fastrcnn_head.output_scores(
name='fastrcnn_all_scores')
final_boxes, final_scores, final_labels = fastrcnn_predictions(
decoded_boxes, label_scores, name_scope='output')
if cfg.MODE_MASK:
# Cascade inference needs roi transform with refined boxes.
roi_feature_maskrcnn = multilevel_roi_align(
features[:4], final_boxes, 14)
maskrcnn_head_func = getattr(model_mrcnn,
cfg.FPN.MRCNN_HEAD_FUNC)
mask_logits = maskrcnn_head_func(
'maskrcnn', roi_feature_maskrcnn,
cfg.DATA.NUM_CATEGORY) # #fg x #cat x 28 x 28
indices = tf.stack([
tf.range(tf.size(final_labels)),
tf.cast(final_labels, tf.int32) - 1
],
axis=1)
final_mask_logits = tf.gather_nd(mask_logits,
indices) # #resultx28x28
tf.sigmoid(final_mask_logits, name='output/masks')
return []
def build_graph(self, *inputs):
num_fpn_level = len(cfg.FPN.ANCHOR_STRIDES)
assert len(cfg.RPN.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 cfg.MODE_MASK:
gt_masks = inputs[-1]
image = self.preprocess(image) # 1CHW
image_shape2d = tf.shape(image)[2:] # h,w
c2345 = resnet_fpn_backbone(image, cfg.BACKBONE.RESNET_NUM_BLOCK)
p23456 = fpn_model('fpn', c2345)
# Images are padded for p5, which are too large for p2-p4.
# This seems to have no effect on mAP.
for i, stride in enumerate(cfg.FPN.ANCHOR_STRIDES[:3]):
pi = p23456[i]
target_shape = tf.to_int32(
tf.ceil(tf.to_float(image_shape2d) * (1.0 / stride)))
p23456[i] = tf.slice(pi, [0, 0, 0, 0],
tf.concat([[-1, -1], target_shape], axis=0))
p23456[i].set_shape([1, pi.shape[1], None, None])
# 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], cfg.FPN.NUM_CHANNEL,
len(cfg.RPN.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,
cfg.RPN.TRAIN_FPN_NMS_TOPK
if is_training else cfg.RPN.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), cfg.RPN.TRAIN_FPN_NMS_TOPK
if is_training else cfg.RPN.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_head_func = getattr(model, cfg.FPN.FRCNN_HEAD_FUNC)
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_head_func(
'fastrcnn', roi_feature_fastrcnn, cfg.DATA.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 cfg.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,
cfg.DATA.NUM_CATEGORY,
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|fpn|fastrcnn|maskrcnn)/.*W',
l2_regularizer(cfg.TRAIN.WEIGHT_DECAY),
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 cfg.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,
cfg.DATA.NUM_CATEGORY,
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):
is_training = get_current_tower_context().is_training
if cfg.MODE_MASK:
image, anchor_labels, anchor_boxes, gt_boxes, gt_labels, gt_masks = inputs
else:
image, anchor_labels, anchor_boxes, gt_boxes, gt_labels = inputs
image = self.preprocess(image) # 1CHW
featuremap = resnet_c4_backbone(image,
cfg.BACKBONE.RESNET_NUM_BLOCK[:3])
rpn_label_logits, rpn_box_logits = rpn_head('rpn', featuremap, 1024,
cfg.RPN.NUM_ANCHOR)
fm_anchors, anchor_labels, anchor_boxes = self.narrow_to_featuremap(
featuremap, get_all_anchors(), anchor_labels, anchor_boxes)
anchor_boxes_encoded = encode_bbox_target(anchor_boxes, fm_anchors)
image_shape2d = tf.shape(image)[2:] # h,w
pred_boxes_decoded = decode_bbox_target(
rpn_box_logits, fm_anchors) # fHxfWxNAx4, floatbox
proposal_boxes, proposal_scores = generate_rpn_proposals(
tf.reshape(pred_boxes_decoded, [-1, 4]),
tf.reshape(rpn_label_logits,
[-1]), image_shape2d, cfg.RPN.TRAIN_PRE_NMS_TOPK
if is_training else cfg.RPN.TEST_PRE_NMS_TOPK,
cfg.RPN.TRAIN_POST_NMS_TOPK
if is_training else cfg.RPN.TEST_POST_NMS_TOPK)
if is_training:
# sample proposal boxes in 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.
# Use all proposal boxes in inference
rcnn_boxes = proposal_boxes
boxes_on_featuremap = rcnn_boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE)
roi_resized = roi_align(featuremap, boxes_on_featuremap, 14)
feature_fastrcnn = resnet_conv5(
roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCK[-1]) # nxcx7x7
# Keep C5 feature to be shared with mask branch
feature_gap = GlobalAvgPooling('gap',
feature_fastrcnn,
data_format='channels_first')
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_outputs(
'fastrcnn', feature_gap, cfg.DATA.NUM_CLASS)
if is_training:
# rpn loss
rpn_label_loss, rpn_box_loss = rpn_losses(anchor_labels,
anchor_boxes_encoded,
rpn_label_logits,
rpn_box_logits)
# 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 cfg.MODE_MASK:
# maskrcnn loss
fg_labels = tf.gather(rcnn_labels, fg_inds_wrt_sample)
# In training, mask branch shares the same C5 feature.
fg_feature = tf.gather(feature_fastrcnn, fg_inds_wrt_sample)
mask_logits = maskrcnn_upXconv_head(
'maskrcnn', fg_feature, cfg.DATA.NUM_CATEGORY,
num_convs=0) # #fg x #cat x 14x14
target_masks_for_fg = crop_and_resize(
tf.expand_dims(gt_masks, 1),
fg_sampled_boxes,
fg_inds_wrt_gt,
14,
pad_border=False) # nfg x 1x14x14
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(cfg.TRAIN.WEIGHT_DECAY),
name='wd_cost')
total_cost = tf.add_n([
rpn_label_loss, rpn_box_loss, 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 cfg.MODE_MASK:
roi_resized = roi_align(
featuremap, final_boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE),
14)
feature_maskrcnn = resnet_conv5(
roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCK[-1])
mask_logits = maskrcnn_upXconv_head(
'maskrcnn', feature_maskrcnn, cfg.DATA.NUM_CATEGORY,
0) # #result x #cat x 14x14
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) # #resultx14x14
tf.sigmoid(final_mask_logits, name='final_masks')
def build_graph(self, *inputs):
num_fpn_level = len(cfg.FPN.ANCHOR_STRIDES)
assert len(cfg.RPN.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 cfg.MODE_MASK:
gt_masks = inputs[-1]
image = self.preprocess(image) # 1CHW
image_shape2d = tf.shape(image)[2:] # h,w
c2345 = resnet_fpn_backbone(image, cfg.BACKBONE.RESNET_NUM_BLOCK)
p23456 = fpn_model('fpn', c2345)
# Images are padded for p5, which are too large for p2-p4.
# This seems to have no effect on mAP.
for i, stride in enumerate(cfg.FPN.ANCHOR_STRIDES[:3]):
pi = p23456[i]
target_shape = tf.to_int32(tf.ceil(tf.to_float(image_shape2d) * (1.0 / stride)))
p23456[i] = tf.slice(pi, [0, 0, 0, 0],
tf.concat([[-1, -1], target_shape], axis=0))
p23456[i].set_shape([1, pi.shape[1], None, None])
# 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], cfg.FPN.NUM_CHANNEL, len(cfg.RPN.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,
cfg.RPN.TRAIN_FPN_NMS_TOPK if is_training else cfg.RPN.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),
cfg.RPN.TRAIN_FPN_NMS_TOPK if is_training else cfg.RPN.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_head_func = getattr(model, cfg.FPN.FRCNN_HEAD_FUNC)
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_head_func(
'fastrcnn', roi_feature_fastrcnn, cfg.DATA.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 cfg.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, cfg.DATA.NUM_CATEGORY, 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|fpn|fastrcnn|maskrcnn)/.*W',
l2_regularizer(cfg.TRAIN.WEIGHT_DECAY), 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 cfg.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, cfg.DATA.NUM_CATEGORY, 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):
is_training = get_current_tower_context().is_training
if cfg.MODE_MASK:
image, anchor_labels, anchor_boxes, gt_boxes, gt_labels, gt_masks = inputs
else:
image, anchor_labels, anchor_boxes, gt_boxes, gt_labels = inputs
image = self.preprocess(image) # 1CHW
featuremap = resnet_c4_backbone(image, cfg.BACKBONE.RESNET_NUM_BLOCK[:3])
rpn_label_logits, rpn_box_logits = rpn_head('rpn', featuremap, 1024, cfg.RPN.NUM_ANCHOR)
fm_anchors, anchor_labels, anchor_boxes = self.narrow_to_featuremap(
featuremap, get_all_anchors(), anchor_labels, anchor_boxes)
anchor_boxes_encoded = encode_bbox_target(anchor_boxes, fm_anchors)
image_shape2d = tf.shape(image)[2:] # h,w
pred_boxes_decoded = decode_bbox_target(rpn_box_logits, fm_anchors) # fHxfWxNAx4, floatbox
proposal_boxes, proposal_scores = generate_rpn_proposals(
tf.reshape(pred_boxes_decoded, [-1, 4]),
tf.reshape(rpn_label_logits, [-1]),
image_shape2d,
cfg.RPN.TRAIN_PRE_NMS_TOPK if is_training else cfg.RPN.TEST_PRE_NMS_TOPK,
cfg.RPN.TRAIN_POST_NMS_TOPK if is_training else cfg.RPN.TEST_POST_NMS_TOPK)
if is_training:
# sample proposal boxes in 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.
# Use all proposal boxes in inference
rcnn_boxes = proposal_boxes
boxes_on_featuremap = rcnn_boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE)
roi_resized = roi_align(featuremap, boxes_on_featuremap, 14)
feature_fastrcnn = resnet_conv5(roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCK[-1]) # nxcx7x7
# Keep C5 feature to be shared with mask branch
feature_gap = GlobalAvgPooling('gap', feature_fastrcnn, data_format='channels_first')
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_outputs('fastrcnn', feature_gap, cfg.DATA.NUM_CLASS)
if is_training:
# rpn loss
rpn_label_loss, rpn_box_loss = rpn_losses(
anchor_labels, anchor_boxes_encoded, rpn_label_logits, rpn_box_logits)
# 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 cfg.MODE_MASK:
# maskrcnn loss
fg_labels = tf.gather(rcnn_labels, fg_inds_wrt_sample)
# In training, mask branch shares the same C5 feature.
fg_feature = tf.gather(feature_fastrcnn, fg_inds_wrt_sample)
mask_logits = maskrcnn_upXconv_head(
'maskrcnn', fg_feature, cfg.DATA.NUM_CATEGORY, num_convs=0) # #fg x #cat x 14x14
target_masks_for_fg = crop_and_resize(
tf.expand_dims(gt_masks, 1),
fg_sampled_boxes,
fg_inds_wrt_gt, 14,
pad_border=False) # nfg x 1x14x14
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(cfg.TRAIN.WEIGHT_DECAY), name='wd_cost')
total_cost = tf.add_n([
rpn_label_loss, rpn_box_loss,
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 cfg.MODE_MASK:
roi_resized = roi_align(featuremap, final_boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE), 14)
feature_maskrcnn = resnet_conv5(roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCK[-1])
mask_logits = maskrcnn_upXconv_head(
'maskrcnn', feature_maskrcnn, cfg.DATA.NUM_CATEGORY, 0) # #result x #cat x 14x14
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) # #resultx14x14
tf.sigmoid(final_mask_logits, name='final_masks')
def build_graph(self, *inputs):
inputs = dict(zip(self.input_names, inputs))
num_fpn_level = len(cfg.FPN.ANCHOR_STRIDES)
assert len(cfg.RPN.ANCHOR_SIZES) == num_fpn_level
is_training = get_current_tower_context().is_training
all_anchors_fpn = get_all_anchors_fpn()
multilevel_anchors = [RPNAnchors(
all_anchors_fpn[i],
inputs['anchor_labels_lvl{}'.format(i + 2)],
inputs['anchor_boxes_lvl{}'.format(i + 2)]) for i in range(len(all_anchors_fpn))]
image = self.preprocess(inputs['image']) # 1CHW
image_shape2d = tf.shape(image)[2:] # h,w
c2345 = resnet_fpn_backbone(image, cfg.BACKBONE.RESNET_NUM_BLOCK)
p23456 = fpn_model('fpn', c2345)
self.slice_feature_and_anchors(image_shape2d, p23456, multilevel_anchors)
# Multi-Level RPN Proposals
rpn_outputs = [rpn_head('rpn', pi, cfg.FPN.NUM_CHANNEL, len(cfg.RPN.ANCHOR_RATIOS))
for pi in p23456]
multilevel_label_logits = [k[0] for k in rpn_outputs]
multilevel_box_logits = [k[1] for k in rpn_outputs]
proposal_boxes, proposal_scores = generate_fpn_proposals(
multilevel_anchors, multilevel_label_logits,
multilevel_box_logits, image_shape2d)
gt_boxes, gt_labels = inputs['gt_boxes'], inputs['gt_labels']
if is_training:
proposals = sample_fast_rcnn_targets(proposal_boxes, gt_boxes, gt_labels)
else:
proposals = BoxProposals(proposal_boxes)
fastrcnn_head_func = getattr(model_frcnn, cfg.FPN.FRCNN_HEAD_FUNC)
if not cfg.FPN.CASCADE:
roi_feature_fastrcnn = multilevel_roi_align(p23456[:4], proposals.boxes, 7)
head_feature = fastrcnn_head_func('fastrcnn', roi_feature_fastrcnn)
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_outputs(
'fastrcnn/outputs', head_feature, cfg.DATA.NUM_CLASS)
fastrcnn_head = FastRCNNHead(proposals, fastrcnn_box_logits, fastrcnn_label_logits,
tf.constant(cfg.FRCNN.BBOX_REG_WEIGHTS, dtype=tf.float32))
else:
def roi_func(boxes):
return multilevel_roi_align(p23456[:4], boxes, 7)
fastrcnn_head = CascadeRCNNHead(
proposals, roi_func, fastrcnn_head_func, image_shape2d, cfg.DATA.NUM_CLASS)
if is_training:
all_losses = []
all_losses.extend(multilevel_rpn_losses(
multilevel_anchors, multilevel_label_logits, multilevel_box_logits))
all_losses.extend(fastrcnn_head.losses())
if cfg.MODE_MASK:
# maskrcnn loss
roi_feature_maskrcnn = multilevel_roi_align(
p23456[:4], proposals.fg_boxes(), 14,
name_scope='multilevel_roi_align_mask')
maskrcnn_head_func = getattr(model_mrcnn, cfg.FPN.MRCNN_HEAD_FUNC)
mask_logits = maskrcnn_head_func(
'maskrcnn', roi_feature_maskrcnn, cfg.DATA.NUM_CATEGORY) # #fg x #cat x 28 x 28
target_masks_for_fg = crop_and_resize(
tf.expand_dims(inputs['gt_masks'], 1),
proposals.fg_boxes(),
proposals.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')
all_losses.append(maskrcnn_loss(mask_logits, proposals.fg_labels(), target_masks_for_fg))
wd_cost = regularize_cost(
'.*/W', l2_regularizer(cfg.TRAIN.WEIGHT_DECAY), name='wd_cost')
all_losses.append(wd_cost)
total_cost = tf.add_n(all_losses, 'total_cost')
add_moving_summary(total_cost, wd_cost)
return total_cost
else:
decoded_boxes = fastrcnn_head.decoded_output_boxes()
decoded_boxes = clip_boxes(decoded_boxes, image_shape2d, name='fastrcnn_all_boxes')
label_scores = fastrcnn_head.output_scores(name='fastrcnn_all_scores')
final_boxes, final_scores, final_labels = fastrcnn_predictions(
decoded_boxes, label_scores, name_scope='output')
if cfg.MODE_MASK:
# Cascade inference needs roi transform with refined boxes.
roi_feature_maskrcnn = multilevel_roi_align(p23456[:4], final_boxes, 14)
maskrcnn_head_func = getattr(model_mrcnn, cfg.FPN.MRCNN_HEAD_FUNC)
mask_logits = maskrcnn_head_func(
'maskrcnn', roi_feature_maskrcnn, cfg.DATA.NUM_CATEGORY) # #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='output/masks')
def build_graph(self, *inputs):
inputs = dict(zip(self.input_names, inputs))
is_training = get_current_tower_context().is_training
image = self.preprocess(inputs['image']) # 1CHW
featuremap = resnet_c4_backbone(image, cfg.BACKBONE.RESNET_NUM_BLOCK[:3])
rpn_label_logits, rpn_box_logits = rpn_head('rpn', featuremap, cfg.RPN.HEAD_DIM, cfg.RPN.NUM_ANCHOR)
anchors = RPNAnchors(get_all_anchors(), inputs['anchor_labels'], inputs['anchor_boxes'])
anchors = anchors.narrow_to(featuremap)
image_shape2d = tf.shape(image)[2:] # h,w
pred_boxes_decoded = anchors.decode_logits(rpn_box_logits) # fHxfWxNAx4, floatbox
proposal_boxes, proposal_scores = generate_rpn_proposals(
tf.reshape(pred_boxes_decoded, [-1, 4]),
tf.reshape(rpn_label_logits, [-1]),
image_shape2d,
cfg.RPN.TRAIN_PRE_NMS_TOPK if is_training else cfg.RPN.TEST_PRE_NMS_TOPK,
cfg.RPN.TRAIN_POST_NMS_TOPK if is_training else cfg.RPN.TEST_POST_NMS_TOPK)
gt_boxes, gt_labels = inputs['gt_boxes'], inputs['gt_labels']
if is_training:
# sample proposal boxes in training
proposals = sample_fast_rcnn_targets(proposal_boxes, gt_boxes, gt_labels)
else:
# The boxes to be used to crop RoIs.
# Use all proposal boxes in inference
proposals = BoxProposals(proposal_boxes)
boxes_on_featuremap = proposals.boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE)
roi_resized = roi_align(featuremap, boxes_on_featuremap, 14)
feature_fastrcnn = resnet_conv5(roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCK[-1]) # nxcx7x7
# Keep C5 feature to be shared with mask branch
feature_gap = GlobalAvgPooling('gap', feature_fastrcnn, data_format='channels_first')
fastrcnn_label_logits, fastrcnn_box_logits = fastrcnn_outputs('fastrcnn', feature_gap, cfg.DATA.NUM_CLASS)
fastrcnn_head = FastRCNNHead(proposals, fastrcnn_box_logits, fastrcnn_label_logits,
tf.constant(cfg.FRCNN.BBOX_REG_WEIGHTS, dtype=tf.float32))
if is_training:
all_losses = []
# rpn loss
all_losses.extend(rpn_losses(
anchors.gt_labels, anchors.encoded_gt_boxes(), rpn_label_logits, rpn_box_logits))
# fastrcnn loss
all_losses.extend(fastrcnn_head.losses())
if cfg.MODE_MASK:
# maskrcnn loss
# In training, mask branch shares the same C5 feature.
fg_feature = tf.gather(feature_fastrcnn, proposals.fg_inds())
mask_logits = maskrcnn_upXconv_head(
'maskrcnn', fg_feature, cfg.DATA.NUM_CATEGORY, num_convs=0) # #fg x #cat x 14x14
target_masks_for_fg = crop_and_resize(
tf.expand_dims(inputs['gt_masks'], 1),
proposals.fg_boxes(),
proposals.fg_inds_wrt_gt, 14,
pad_border=False) # nfg x 1x14x14
target_masks_for_fg = tf.squeeze(target_masks_for_fg, 1, 'sampled_fg_mask_targets')
all_losses.append(maskrcnn_loss(mask_logits, proposals.fg_labels(), target_masks_for_fg))
wd_cost = regularize_cost(
'.*/W', l2_regularizer(cfg.TRAIN.WEIGHT_DECAY), name='wd_cost')
all_losses.append(wd_cost)
total_cost = tf.add_n(all_losses, 'total_cost')
add_moving_summary(total_cost, wd_cost)
return total_cost
else:
decoded_boxes = fastrcnn_head.decoded_output_boxes()
decoded_boxes = clip_boxes(decoded_boxes, image_shape2d, name='fastrcnn_all_boxes')
label_scores = fastrcnn_head.output_scores(name='fastrcnn_all_scores')
final_boxes, final_scores, final_labels = fastrcnn_predictions(
decoded_boxes, label_scores, name_scope='output')
if cfg.MODE_MASK:
roi_resized = roi_align(featuremap, final_boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE), 14)
feature_maskrcnn = resnet_conv5(roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCK[-1])
mask_logits = maskrcnn_upXconv_head(
'maskrcnn', feature_maskrcnn, cfg.DATA.NUM_CATEGORY, 0) # #result x #cat x 14x14
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) # #resultx14x14
tf.sigmoid(final_mask_logits, name='output/masks')
