def multilevel_roi_align(features, rcnn_boxes, resolution):
"""
Args:
features ([tf.Tensor]): 4 FPN feature level 2-5
rcnn_boxes (tf.Tensor): nx4 boxes
resolution (int): output spatial resolution
Returns:
NxC x res x res
"""
assert len(features) == 4, features
# Reassign rcnn_boxes to levels
level_ids, level_boxes = fpn_map_rois_to_levels(rcnn_boxes)
all_rois = []
# Crop patches from corresponding levels
for i, boxes, featuremap in zip(itertools.count(), level_boxes, features):
with tf.name_scope('roi_level{}'.format(i + 2)):
boxes_on_featuremap = boxes * (1.0 / cfg.FPN.ANCHOR_STRIDES[i])
all_rois.append(
roi_align(featuremap, boxes_on_featuremap, resolution))
# this can fail if using TF<=1.8 with MKL build
all_rois = tf.concat(all_rois, axis=0) # NCHW
# Unshuffle to the original order, to match the original samples
level_id_perm = tf.concat(level_ids, axis=0) # A permutation of 1~N
level_id_invert_perm = tf.invert_permutation(level_id_perm)
all_rois = tf.gather(all_rois, level_id_invert_perm)
return all_rois
def build_graph(self, *inputs):
inputs = dict(zip(self.input_names, inputs))
image = self.preprocess(inputs['image']) # 1CHW
# build resnet c4
featuremap = resnet_c4_backbone(image, cfg.BACKBONE.RESNET_NUM_BLOCK[:3])
# ROI_align
x, y, w, h = tf.split(inputs['gt_boxes'], 4, axis=1)
gt_boxes = tf.concat([x, y, x + w, y + h], axis=1)
boxes_on_featuremap = gt_boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE) # ANCHOR_STRIDE = 16
roi_resized = roi_align(featuremap, boxes_on_featuremap, 14) # 14x14 for each roi
person_labels = tf.ones_like(inputs['male'])
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(person_labels)), tf.to_int32(person_labels) - 1], axis=1)
final_mask_logits = tf.gather_nd(mask_logits, indices) # #resultx14x14
final_mask_logits = tf.sigmoid(final_mask_logits, name='output/masks')
final_mask_logits_expand = tf.expand_dims(final_mask_logits, axis=1)
final_mask_logits_tile = tf.tile(final_mask_logits_expand, multiples=[1, 1024, 1, 1])
fg_mask_roi_resized = tf.where(final_mask_logits_tile >= 0.5, roi_resized,
roi_resized * 1.0)
feature_attrs = resnet_conv5_attr(fg_mask_roi_resized,
cfg.BACKBONE.RESNET_NUM_BLOCK[-1])
feature_gap = GlobalAvgPooling('gap', feature_attrs, data_format='channels_first') # ??
# attrs_logits = attrs_head('attrs', feature_gap)
attrs_labels = attrs_predict(feature_gap)
def build_graph(self, *inputs):
inputs = dict(zip(self.input_names, inputs))
image = self.preprocess(inputs['image']) # 1CHW
# build resnet c4
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)
# HEAD_DIM = 1024, NUM_ANCHOR = 15
# rpn_label_logits: fHxfWxNA
# rpn_box_logits: fHxfWxNAx4
anchors = RPNAnchors(get_all_anchors(), inputs['anchor_labels'],
inputs['anchor_boxes'])
# anchor_boxes is Groundtruth boxes corresponding to each anchor
anchors = anchors.narrow_to(featuremap) # ??
image_shape2d = tf.shape(image)[2:] # h,w
pred_boxes_decoded = anchors.decode_logits(
rpn_box_logits) # fHxfWxNAx4, floatbox
# ProposalCreator (get the topk proposals)
proposal_boxes, proposal_scores = generate_rpn_proposals(
tf.reshape(pred_boxes_decoded, [-1, 4]),
tf.reshape(rpn_label_logits, [-1]),
image_shape2d,
cfg.RPN.TEST_PRE_NMS_TOPK, # 2000
cfg.RPN.TEST_POST_NMS_TOPK) # 1000
x, y, w, h = tf.split(inputs['gt_boxes'], 4, axis=1)
gt_boxes = tf.concat([x, y, x + w, y + h], axis=1)
boxes_on_featuremap = gt_boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE
) # ANCHOR_STRIDE = 16
roi_resized = roi_align(featuremap, boxes_on_featuremap, 14)
feature_fastrcnn = resnet_conv5(
roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCK[-1]
) # nxcx7x7 # RESNET_NUM_BLOCK = [3, 4, 6, 3]
# 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) # ??
# Returns:
# cls_logits: Tensor("fastrcnn/class/output:0", shape=(n, 81), dtype=float32)
# reg_logits: Tensor("fastrcnn/output_box:0", shape=(n, 81, 4), dtype=float32)
# ------------------Fastrcnn_Head------------------------
fastrcnn_head = FastRCNNHead(
proposal_boxes,
fastrcnn_box_logits,
fastrcnn_label_logits, #
tf.constant(cfg.FRCNN.BBOX_REG_WEIGHTS,
dtype=tf.float32)) # [10., 10., 5., 5.]
decoded_boxes = fastrcnn_head.decoded_output_boxes(
) # pre_boxes_on_images
decoded_boxes = clip_boxes(decoded_boxes,
image_shape2d,
name='fastrcnn_all_boxes')
label_scores = tf.nn.softmax(fastrcnn_label_logits,
name='fastrcnn_all_scores')
# class scores, summed to one for each box.
final_boxes, final_scores, final_labels = fastrcnn_predictions(
decoded_boxes, label_scores, name_scope='output')
feature_maskrcnn = resnet_conv5(
roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCK[-1]
) # nxcx7x7 # RESNET_NUM_BLOCK = [3, 4, 6, 3]
# Keep C5 feature to be shared with mask branch
mask_logits = maskrcnn_upXconv_head('maskrcnn', feature_maskrcnn,
cfg.DATA.NUM_CATEGORY,
0) # #result x #cat x 14x14
# Assume only person here
person_labels = tf.ones_like(inputs['male'])
indices = tf.stack(
[tf.range(tf.size(person_labels)),
tf.to_int32(person_labels) - 1],
axis=1)
final_mask_logits = tf.gather_nd(mask_logits, indices) # #resultx14x14
final_mask_logits = tf.sigmoid(final_mask_logits, name='output/masks')
mask = False
if mask:
final_mask_logits_expand = tf.expand_dims(final_mask_logits,
axis=1)
final_mask_logits_tile = tf.tile(final_mask_logits_expand,
multiples=[1, 1024, 1, 1])
fg_roi_resized = tf.where(final_mask_logits_tile >= 0.5,
roi_resized, roi_resized * 1.0)
feature_attrs = resnet_conv5_attr(
fg_roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCK[-1])
else:
feature_attrs = resnet_conv5_attr(
roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCK[-1])
feature_attrs_gap = GlobalAvgPooling('gap',
feature_attrs,
data_format='channels_first')
attrs_logits = attrs_head('attrs', feature_attrs_gap)
attrs_loss = all_attrs_losses(inputs, attrs_logits, attr_losses_v2)
all_losses = [attrs_loss]
# male loss
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(wd_cost, total_cost)
return total_cost
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')
def build_graph(self, *inputs):
inputs = dict(zip(self.input_names, inputs))
image = self.preprocess(inputs['image']) # 1CHW
# build resnet c4
featuremap = resnet_c4_backbone(image,
cfg.BACKBONE.RESNET_NUM_BLOCK[:3])
# build rpn
rpn_label_logits, rpn_box_logits = rpn_head('rpn', featuremap,
cfg.RPN.HEAD_DIM,
cfg.RPN.NUM_ANCHOR)
# HEAD_DIM = 1024, NUM_ANCHOR = 15
# rpn_label_logits: fHxfWxNA
# rpn_box_logits: fHxfWxNAx4
anchors = RPNAnchors(get_all_anchors(), inputs['anchor_labels'],
inputs['anchor_boxes'])
# anchor_boxes is Groundtruth boxes corresponding to each anchor
anchors = anchors.narrow_to(featuremap)
image_shape2d = tf.shape(image)[2:] # h,w
pred_boxes_decoded = anchors.decode_logits(
rpn_box_logits) # fHxfWxNAx4, floatbox
# ProposalCreator (get the topk proposals)
proposal_boxes, proposal_scores = generate_rpn_proposals(
tf.reshape(pred_boxes_decoded, [-1, 4]),
tf.reshape(rpn_label_logits, [-1]),
image_shape2d,
cfg.RPN.TEST_PRE_NMS_TOPK, # 6000
cfg.RPN.TEST_POST_NMS_TOPK) # 1000
boxes_on_featuremap = proposal_boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE
) # ANCHOR_STRIDE = 16
# ROI_align
roi_resized = roi_align(featuremap, boxes_on_featuremap,
14) # 14x14 for each roi
feature_fastrcnn = resnet_conv5(
roi_resized, cfg.BACKBONE.RESNET_NUM_BLOCK[-1]
) # nxcx7x7 # RESNET_NUM_BLOCK = [3, 4, 6, 3]
# 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)
# Returns:
# cls_logits: Tensor("fastrcnn/class/output:0", shape=(n, 81), dtype=float32)
# reg_logits: Tensor("fastrcnn/output_box:0", shape=(n, 81, 4), dtype=float32)
# ------------------Fastrcnn_Head------------------------
proposals = BoxProposals(proposal_boxes)
fastrcnn_head = FastRCNNHead(
proposals,
fastrcnn_box_logits,
fastrcnn_label_logits, #
tf.constant(cfg.FRCNN.BBOX_REG_WEIGHTS,
dtype=tf.float32)) # [10., 10., 5., 5.]
decoded_boxes = fastrcnn_head.decoded_output_boxes(
) # pre_boxes_on_images
decoded_boxes = clip_boxes(decoded_boxes,
image_shape2d,
name='fastrcnn_all_boxes')
label_scores = tf.nn.softmax(fastrcnn_label_logits,
name='fastrcnn_all_scores')
# class scores, summed to one for each box.
final_boxes, final_scores, final_labels = fastrcnn_predictions(
decoded_boxes, label_scores, name_scope='output')
def build_graph(self, *inputs):
inputs = dict(zip(self.input_names, inputs))
image = self.preprocess(inputs['image']) # 1CHW
# build resnet c4
featuremap = resnet_c4_backbone(image,
cfg.BACKBONE.RESNET_NUM_BLOCK[:3])
#
# # build rpn
# rpn_label_logits, rpn_box_logits = rpn_head('rpn', featuremap, cfg.RPN.HEAD_DIM, cfg.RPN.NUM_ANCHOR)
# # HEAD_DIM = 1024, NUM_ANCHOR = 15
# # rpn_label_logits: fHxfWxNA
# # rpn_box_logits: fHxfWxNAx4
# anchors = RPNAnchors(get_all_anchors(), inputs['anchor_labels'], inputs['anchor_boxes'])
# # anchor_boxes is Groundtruth boxes corresponding to each anchor
# anchors = anchors.narrow_to(featuremap)
# image_shape2d = tf.shape(image)[2:] # h,w
# pred_boxes_decoded = anchors.decode_logits(rpn_box_logits) # fHxfWxNAx4, floatbox
#
# # ProposalCreator (get the topk proposals)
# proposal_boxes, proposal_scores = generate_rpn_proposals(
# tf.reshape(pred_boxes_decoded, [-1, 4]),
# tf.reshape(rpn_label_logits, [-1]),
# image_shape2d,
# cfg.RPN.TEST_PRE_NMS_TOPK, # 2000
# cfg.RPN.TEST_POST_NMS_TOPK) # 1000
#
# boxes_on_featuremap = proposal_boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE) # ANCHOR_STRIDE = 16
#
# # ROI_align
# roi_resized = roi_align(featuremap, boxes_on_featuremap, 14) # 14x14 for each roi
#
# feature_fastrcnn = resnet_conv5(roi_resized,
# cfg.BACKBONE.RESNET_NUM_BLOCK[-1]) # nxcx7x7 # RESNET_NUM_BLOCK = [3, 4, 6, 3]
# # 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)
# # Returns:
# # cls_logits: Tensor("fastrcnn/class/output:0", shape=(n, 81), dtype=float32)
# # reg_logits: Tensor("fastrcnn/output_box:0", shape=(n, 81, 4), dtype=float32)
#
# # ------------------Fastrcnn_Head------------------------
# proposals = BoxProposals(proposal_boxes)
# fastrcnn_head = FastRCNNHead(proposals, fastrcnn_box_logits, fastrcnn_label_logits, #
# tf.constant(cfg.FRCNN.BBOX_REG_WEIGHTS, dtype=tf.float32)) # [10., 10., 5., 5.]
#
# decoded_boxes = fastrcnn_head.decoded_output_boxes() # pre_boxes_on_images
# decoded_boxes = clip_boxes(decoded_boxes, image_shape2d, name='fastrcnn_all_boxes')
#
# label_scores = tf.nn.softmax(fastrcnn_label_logits, name='fastrcnn_all_scores')
# # class scores, summed to one for each box.
#
# final_boxes, final_scores, final_labels = fastrcnn_predictions(
# decoded_boxes, label_scores, name_scope='output')
#
# person_slice = tf.where(final_labels <= 1)
# person_labels = tf.gather(final_labels, person_slice)
# final_person_labels = tf.reshape(person_labels, (-1,), name='person_labels')
#
# person_boxes = tf.gather(final_boxes, person_slice)
# final_person_boxes = tf.reshape(person_boxes, (-1, 4), name='person_boxes')
#
# person_scores = tf.gather(final_scores, person_slice)
# tf.reshape(person_scores, (-1,), name='person_scores')
#
# # Attributes branch
x1, y1, w, h = tf.split(inputs['gt_boxes'], 4, axis=1)
gt_boxes = tf.concat([x1, y1, x1 + w, y1 + h], axis=1)
person_roi_resized = roi_align(
featuremap, gt_boxes * (1.0 / cfg.RPN.ANCHOR_STRIDE), 14)
feature_attrs = resnet_conv5_attr(person_roi_resized,
cfg.BACKBONE.RESNET_NUM_BLOCK[-1])
feature_attrs_gap = GlobalAvgPooling('gap',
feature_attrs,
data_format='channels_first') #
attrs_predict(feature_attrs_gap)
