def fast_rcnn_net(self):
with tf.variable_scope('fast_rcnn_net'):
with slim.arg_scope([slim.fully_connected],
weights_regularizer=slim.l2_regularizer(
self.weight_decay)):
flatten_rois_features = slim.flatten(
self.fast_rcnn_all_level_rois)
net = slim.fully_connected(flatten_rois_features,
1024,
scope='fc_1')
if self.use_dropout:
net = slim.dropout(net,
keep_prob=0.5,
is_training=self.is_training,
scope='dropout')
net = slim.fully_connected(net, 1024, scope='fc_2')
fast_rcnn_scores = slim.fully_connected(net,
self.num_classes + 1,
activation_fn=None,
scope='classifier')
fast_rcnn_encode_boxes = slim.fully_connected(
net,
self.num_classes * 4,
activation_fn=None,
scope='regressor')
if DEBUG:
print_tensors(fast_rcnn_encode_boxes, 'fast_rcnn_encode_bxes')
return fast_rcnn_encode_boxes, fast_rcnn_scores
def get_rois(self):
'''
1)get roi from feature map
2)roi align or roi pooling. Here is roi align
:return:
all_level_rois: [N, 7, 7, C]
all_level_proposals : [N, 4]
all_level_proposals is matched with all_level_rois
'''
levels = self.assign_level()
all_level_roi_list = []
all_level_proposal_list = []
if DEBUG:
print_tensors(levels, 'levels')
with tf.variable_scope('fast_rcnn_roi'):
# P6 is not used by the Fast R-CNN detector.
for i in range(self.min_level, self.max_level + 1):
level_i_proposal_indices = tf.reshape(
tf.where(tf.equal(levels, i)), [-1])
level_i_proposals = tf.gather(self.rpn_proposals_boxes,
level_i_proposal_indices)
level_i_proposals = tf.cond(
tf.equal(tf.shape(level_i_proposals)[0], 0),
lambda: tf.constant([[0, 0, 0, 0]], dtype=tf.float32),
lambda: level_i_proposals
) # to avoid level_i_proposals batch is 0, or it will broken when gradient BP
all_level_proposal_list.append(level_i_proposals)
ymin, xmin, ymax, xmax = tf.unstack(level_i_proposals, axis=1)
img_h, img_w = tf.cast(self.img_shape[1], tf.float32), tf.cast(
self.img_shape[2], tf.float32)
normalize_ymin = ymin / img_h
normalize_xmin = xmin / img_w
normalize_ymax = ymax / img_h
normalize_xmax = xmax / img_w
level_i_cropped_rois = tf.image.crop_and_resize(
self.feature_pyramid['P%d' % i],
boxes=tf.transpose(
tf.stack([
normalize_ymin, normalize_xmin, normalize_ymax,
normalize_xmax
])),
box_ind=tf.zeros(shape=[
tf.shape(level_i_proposals)[0],
],
dtype=tf.int32),
crop_size=[self.roi_size, self.roi_size])
level_i_rois = slim.max_pool2d(
level_i_cropped_rois,
[self.roi_pool_kernel_size, self.roi_pool_kernel_size],
stride=self.roi_pool_kernel_size)
all_level_roi_list.append(level_i_rois)
all_level_rois = tf.concat(all_level_roi_list, axis=0)
all_level_proposals = tf.concat(all_level_proposal_list, axis=0)
return all_level_rois, all_level_proposals
def batch_slice_rcnn_proposals(rpn_proposal_bbox, encode_boxes,
categories, scores, image_window,
config):
"""
mutilclass NMS
:param rpn_proposal_bbox: (N, 4)
:param encode_boxes: (N, 4)
:param categories:(N, )
:param scores: (N, )
:param image_window:(y1, x1, y2, x2) the boundary of image
:return:
detection_boxes_scores_labels : (-1, 6)[y1, x1, y2, x2, scores, labels]
"""
with tf.variable_scope('fast_rcnn_proposals'):
# trim the zero graph
rpn_proposal_bbox, non_zeros = boxes_utils.trim_zeros_graph(
rpn_proposal_bbox, name="trim_proposals_detection")
encode_boxes = tf.boolean_mask(encode_boxes, non_zeros)
categories = tf.boolean_mask(categories, non_zeros)
scores = tf.boolean_mask(scores, non_zeros)
fast_rcnn_decode_boxes = encode_and_decode.decode_boxes(
encode_boxes=encode_boxes,
reference_boxes=rpn_proposal_bbox,
dev_factors=config.BBOX_STD_DEV)
fast_rcnn_decode_boxes = boxes_utils.clip_boxes_to_img_boundaries(
fast_rcnn_decode_boxes, image_window)
# remove the background
keep = tf.cast(tf.where(categories > 0)[:, 0], tf.int32)
if DEBUG:
print_categories = tf.gather(categories, keep)
print_scores = tf.gather(scores, keep)
num_item = tf.minimum(tf.shape(print_scores)[0], 50)
print_scores_vision, print_index = tf.nn.top_k(
print_scores, k=num_item)
print_categories_vision = tf.gather(
print_categories, print_index)
print_tensors(print_categories_vision, "categories")
print_tensors(print_scores_vision, "scores")
# Filter out low confidence boxes
if config.FINAL_SCORE_THRESHOLD:
conf_keep = tf.cast(
tf.where(scores >= config.FINAL_SCORE_THRESHOLD)[:, 0],
tf.int32)
keep = tf.sets.set_intersection(
tf.expand_dims(keep, 0), tf.expand_dims(conf_keep, 0))
keep = tf.sparse_tensor_to_dense(keep)[0]
pre_nms_class_ids = tf.gather(categories, keep)
pre_nms_scores = tf.gather(scores, keep)
pre_nms_rois = tf.gather(fast_rcnn_decode_boxes, keep)
unique_pre_nms_class_ids = tf.unique(pre_nms_class_ids)[0]
def nms_keep_map(class_id):
"""Apply Non-Maximum Suppression on ROIs of the given class."""
# Indices of ROIs of the given class
ixs = tf.where(tf.equal(pre_nms_class_ids, class_id))[:, 0]
# Apply NMS
class_keep = tf.image.non_max_suppression(
tf.gather(pre_nms_rois, ixs),
tf.gather(pre_nms_scores, ixs),
max_output_size=config.DETECTION_MAX_INSTANCES,
iou_threshold=config.FAST_RCNN_NMS_IOU_THRESHOLD)
# Map indicies
class_keep = tf.gather(keep, tf.gather(ixs, class_keep))
# Pad with -1 so returned tensors have the same shape
gap = config.DETECTION_MAX_INSTANCES - tf.shape(
class_keep)[0]
class_keep = tf.pad(class_keep, [(0, gap)],
mode='CONSTANT',
constant_values=-1)
# Set shape so map_fn() can infer result shape
class_keep.set_shape([config.DETECTION_MAX_INSTANCES])
return class_keep
# 2. Map over class IDs
nms_keep = tf.map_fn(nms_keep_map,
unique_pre_nms_class_ids,
dtype=tf.int32)
# 3. Merge results into one list, and remove -1 padding
nms_keep = tf.reshape(nms_keep, [-1])
nms_keep = tf.gather(nms_keep, tf.where(nms_keep > -1)[:, 0])
# 4. Compute intersection between keep and nms_keep
keep = tf.sets.set_intersection(tf.expand_dims(keep, 0),
tf.expand_dims(nms_keep, 0))
keep = tf.sparse_tensor_to_dense(keep)[0]
# Keep top detections
roi_count = config.DETECTION_MAX_INSTANCES
class_scores_keep = tf.gather(scores, keep)
num_keep = tf.minimum(
tf.shape(class_scores_keep)[0], roi_count)
top_ids = tf.nn.top_k(class_scores_keep,
k=num_keep,
sorted=True)[1]
keep = tf.gather(keep, top_ids)
# Arrange output as [N, (y1, x1, y2, x2, class_id, score)]
# Coordinates are normalized.
detections = tf.concat([
tf.gather(fast_rcnn_decode_boxes, keep),
tf.to_float(tf.gather(categories, keep))[..., tf.newaxis],
tf.gather(scores, keep)[..., tf.newaxis]
],
axis=1)
# Pad with zeros if detections < DETECTION_MAX_INSTANCES
gap = config.DETECTION_MAX_INSTANCES - tf.shape(detections)[0]
detections = tf.pad(detections, [(0, gap), (0, 0)], "CONSTANT")
return detections
def rpn_losses(self):
with tf.variable_scope('rpn_losses'):
minibatch_indices, minibatch_anchor_matched_gtboxes, object_mask, minibatch_labels_one_hot = \
self.make_minibatch(self.anchors)
minibatch_anchors = tf.gather(self.anchors, minibatch_indices)
minibatch_encode_boxes = tf.gather(self.rpn_encode_boxes,
minibatch_indices)
minibatch_boxes_scores = tf.gather(self.rpn_scores,
minibatch_indices)
# encode gtboxes
minibatch_encode_gtboxes = encode_and_decode.encode_boxes(
unencode_boxes=minibatch_anchor_matched_gtboxes,
reference_boxes=minibatch_anchors,
scale_factors=self.scale_factors)
positive_anchors_in_img = draw_box_with_color(
self.img_batch,
minibatch_anchors * tf.expand_dims(object_mask, 1),
text=tf.shape(tf.where(tf.equal(object_mask, 1.0)))[0])
negative_mask = tf.cast(
tf.logical_not(tf.cast(object_mask, tf.bool)), tf.float32)
negative_anchors_in_img = draw_box_with_color(
self.img_batch,
minibatch_anchors * tf.expand_dims(negative_mask, 1),
text=tf.shape(tf.where(tf.equal(object_mask, 0.0)))[0])
minibatch_decode_boxes = encode_and_decode.decode_boxes(
encode_boxes=minibatch_encode_boxes,
reference_boxes=minibatch_anchors,
scale_factors=self.scale_factors)
tf.summary.image('/positive_anchors', positive_anchors_in_img)
tf.summary.image('/negative_anchors', negative_anchors_in_img)
# print_tensors(minibatch_boxes_softmax_scores, "minibatch_boxes_softmax_scores")
last_dim_num = tf.shape(minibatch_boxes_scores)[-2]
k_shown = tf.minimum(30, last_dim_num)
# print(k_shown, last_dim_num)
top_k_scores, top_k_indices = tf.nn.top_k(
minibatch_boxes_scores[:, 1], k=k_shown)
print_tensors(minibatch_boxes_scores, "minibatch_boxes_scores")
top_detections_in_img = draw_box_with_color(
self.img_batch,
tf.gather(minibatch_decode_boxes, top_k_indices),
text=tf.shape(top_k_scores)[0])
tf.summary.image('/top_5', top_detections_in_img)
# losses
with tf.variable_scope('rpn_location_loss'):
location_loss = losses.l1_smooth_losses(
predict_boxes=minibatch_encode_boxes,
gtboxes=minibatch_encode_gtboxes,
object_weights=object_mask)
slim.losses.add_loss(
location_loss) # add smooth l1 loss to losses collection
with tf.variable_scope('rpn_classification_loss'):
classification_loss = slim.losses.softmax_cross_entropy(
logits=minibatch_boxes_scores,
onehot_labels=minibatch_labels_one_hot)
return location_loss, classification_loss
