class DetectionNetworkR2CNN(DetectionNetworkBase):
def __init__(self, cfgs, is_training):
super(DetectionNetworkR2CNN, self).__init__(cfgs, is_training)
self.anchor_sampler_r2cnn = AnchorSamplerR2CNN(cfgs)
self.proposal_sampler_r2cnn = ProposalSamplerR2CNN(cfgs)
self.losses = Loss(cfgs)
self.roi_extractor = RoIExtractor(cfgs)
self.box_head = BoxHead(cfgs)
def build_loss(self, rpn_box_pred, rpn_bbox_targets, rpn_cls_score,
rpn_labels, bbox_pred, bbox_targets, cls_score, labels):
with tf.variable_scope('build_loss'):
with tf.variable_scope('rpn_loss'):
rpn_reg_loss = self.losses.smooth_l1_loss_rpn(
bbox_pred=rpn_box_pred,
bbox_targets=rpn_bbox_targets,
label=rpn_labels,
sigma=self.cfgs.RPN_SIGMA)
rpn_select = tf.reshape(tf.where(tf.not_equal(rpn_labels, -1)),
[-1])
rpn_cls_score = tf.reshape(
tf.gather(rpn_cls_score, rpn_select), [-1, 2])
rpn_labels = tf.reshape(tf.gather(rpn_labels, rpn_select),
[-1])
rpn_cls_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=rpn_cls_score, labels=rpn_labels))
self.losses_dict[
'rpn_cls_loss'] = rpn_cls_loss * self.cfgs.RPN_CLASSIFICATION_LOSS_WEIGHT
self.losses_dict[
'rpn_reg_loss'] = rpn_reg_loss * self.cfgs.RPN_LOCATION_LOSS_WEIGHT
with tf.variable_scope('FastRCNN_loss'):
reg_loss = self.losses.smooth_l1_loss_rcnn_r(
bbox_pred=bbox_pred,
bbox_targets=bbox_targets,
label=labels,
num_classes=self.cfgs.CLASS_NUM + 1,
sigma=self.cfgs.FASTRCNN_SIGMA)
# cls_score = tf.reshape(cls_score, [-1, cfgs.CLASS_NUM + 1])
# labels = tf.reshape(labels, [-1])
cls_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=cls_score,
labels=labels)) # beacause already sample before
self.losses_dict[
'fast_cls_loss'] = cls_loss * self.cfgs.FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT
self.losses_dict[
'fast_reg_loss'] = reg_loss * self.cfgs.FAST_RCNN_LOCATION_LOSS_WEIGHT
def build_whole_detection_network(self,
input_img_batch,
gtboxes_batch_h=None,
gtboxes_batch_r=None,
gpu_id=0):
if self.is_training:
gtboxes_batch_h = tf.reshape(gtboxes_batch_h, [-1, 5])
gtboxes_batch_h = tf.cast(gtboxes_batch_h, tf.float32)
gtboxes_batch_r = tf.reshape(gtboxes_batch_r, [-1, 6])
gtboxes_batch_r = tf.cast(gtboxes_batch_r, tf.float32)
img_shape = tf.shape(input_img_batch)
# 1. build backbone
feature_pyramid = self.build_backbone(input_img_batch)
# 2. build rpn
fpn_box_pred, fpn_cls_score, fpn_cls_prob = self.rpn(feature_pyramid)
# 3. generate anchors
anchor_list = self.make_anchors(feature_pyramid)
anchors = tf.concat(anchor_list, axis=0)
# 4. postprocess rpn proposals. such as: decode, clip, NMS
with tf.variable_scope('postprocess_FPN'):
rois, roi_scores = self.postprocess_rpn_proposals(
rpn_bbox_pred=fpn_box_pred,
rpn_cls_prob=fpn_cls_prob,
img_shape=img_shape,
anchors=anchors,
is_training=self.is_training)
# 5. sample minibatch
if self.is_training:
with tf.variable_scope('sample_anchors_minibatch'):
fpn_labels, fpn_bbox_targets = \
tf.py_func(
self.anchor_sampler_r2cnn.anchor_target_layer,
[gtboxes_batch_h, img_shape, anchors],
[tf.float32, tf.float32])
fpn_bbox_targets = tf.reshape(fpn_bbox_targets, [-1, 4])
fpn_labels = tf.to_int32(fpn_labels, name="to_int32")
fpn_labels = tf.reshape(fpn_labels, [-1])
self.add_anchor_img_smry(input_img_batch,
anchors,
fpn_labels,
method=0)
fpn_cls_category = tf.argmax(fpn_cls_prob, axis=1)
kept_rpppn = tf.reshape(tf.where(tf.not_equal(fpn_labels, -1)),
[-1])
fpn_cls_category = tf.gather(fpn_cls_category, kept_rpppn)
acc = tf.reduce_mean(
tf.to_float(
tf.equal(fpn_cls_category,
tf.to_int64(tf.gather(fpn_labels, kept_rpppn)))))
tf.summary.scalar('ACC/fpn_accuracy', acc)
with tf.control_dependencies([fpn_labels]):
with tf.variable_scope('sample_RCNN_minibatch'):
rois, labels, _, bbox_targets, _, _ = \
tf.py_func(self.proposal_sampler_r2cnn.proposal_target_layer,
[rois, gtboxes_batch_h, gtboxes_batch_r],
[tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32])
rois = tf.reshape(rois, [-1, 4])
labels = tf.to_int32(labels)
labels = tf.reshape(labels, [-1])
bbox_targets = tf.reshape(
bbox_targets, [-1, 5 * (self.cfgs.CLASS_NUM + 1)])
self.add_roi_batch_img_smry(input_img_batch,
rois,
labels,
method=0)
# 6. assign level
if self.is_training:
rois_list, labels, bbox_targets = self.assign_levels(
all_rois=rois, labels=labels, bbox_targets=bbox_targets)
else:
rois_list = self.assign_levels(all_rois=rois)
# 7. build Fast-RCNN, include roi align/pooling, box head
bbox_pred, cls_score = self.box_head.fpn_fc_head(
self.roi_extractor, rois_list, feature_pyramid, img_shape,
self.is_training)
rois = tf.concat(rois_list, axis=0, name='concat_rois')
cls_prob = slim.softmax(cls_score, 'cls_prob')
if self.is_training:
cls_category = tf.argmax(cls_prob, axis=1)
fast_acc = tf.reduce_mean(
tf.to_float(tf.equal(cls_category, tf.to_int64(labels))))
tf.summary.scalar('ACC/fast_acc', fast_acc)
# 8. build loss
if self.is_training:
self.build_loss(rpn_box_pred=fpn_box_pred,
rpn_bbox_targets=fpn_bbox_targets,
rpn_cls_score=fpn_cls_score,
rpn_labels=fpn_labels,
bbox_pred=bbox_pred,
bbox_targets=bbox_targets,
cls_score=cls_score,
labels=labels)
# 9. postprocess_fastrcnn
final_bbox, final_scores, final_category = self.postprocess_fastrcnn(
rois=rois, bbox_ppred=bbox_pred, scores=cls_prob, gpu_id=gpu_id)
if self.is_training:
return final_bbox, final_scores, final_category, self.losses_dict
else:
return final_bbox, final_scores, final_category
def postprocess_fastrcnn(self, rois, bbox_ppred, scores, gpu_id):
'''
:param rois:[-1, 4]
:param bbox_ppred: [-1, (cfgs.Class_num+1) * 5]
:param scores: [-1, cfgs.Class_num + 1]
:return:
'''
with tf.name_scope('postprocess_fastrcnn'):
rois = tf.stop_gradient(rois)
scores = tf.stop_gradient(scores)
bbox_ppred = tf.reshape(bbox_ppred,
[-1, self.cfgs.CLASS_NUM + 1, 5])
bbox_ppred = tf.stop_gradient(bbox_ppred)
bbox_pred_list = tf.unstack(bbox_ppred, axis=1)
score_list = tf.unstack(scores, axis=1)
allclasses_boxes = []
allclasses_scores = []
categories = []
x_c = (rois[:, 2] + rois[:, 0]) / 2
y_c = (rois[:, 3] + rois[:, 1]) / 2
h = rois[:, 2] - rois[:, 0] + 1
w = rois[:, 3] - rois[:, 1] + 1
theta = -90 * tf.ones_like(x_c)
rois = tf.transpose(tf.stack([x_c, y_c, w, h, theta]))
for i in range(1, self.cfgs.CLASS_NUM + 1):
# 1. decode boxes in each class
tmp_encoded_box = bbox_pred_list[i]
tmp_score = score_list[i]
tmp_decoded_boxes = bbox_transform.rbbox_transform_inv(
boxes=rois,
deltas=tmp_encoded_box,
scale_factors=self.cfgs.ROI_SCALE_FACTORS)
# 2. clip to img boundaries
# tmp_decoded_boxes = boxes_utils.clip_boxes_to_img_boundaries(decode_boxes=tmp_decoded_boxes,
# img_shape=img_shape)
# 3. NMS
if self.cfgs.SOFT_NMS:
print("Using Soft NMS.......")
raise NotImplementedError(
"soft NMS for rotate has not implemented")
else:
max_output_size = 4000 if 'DOTA' in self.cfgs.NET_NAME else 200
keep = nms_rotate.nms_rotate(
decode_boxes=tmp_decoded_boxes,
scores=tmp_score,
iou_threshold=self.cfgs.FAST_RCNN_NMS_IOU_THRESHOLD,
max_output_size=100
if self.is_training else max_output_size,
use_gpu=self.cfgs.ROTATE_NMS_USE_GPU,
gpu_id=gpu_id)
perclass_boxes = tf.gather(tmp_decoded_boxes, keep)
perclass_scores = tf.gather(tmp_score, keep)
allclasses_boxes.append(perclass_boxes)
allclasses_scores.append(perclass_scores)
categories.append(tf.ones_like(perclass_scores) * i)
final_boxes = tf.concat(allclasses_boxes, axis=0)
final_scores = tf.concat(allclasses_scores, axis=0)
final_category = tf.concat(categories, axis=0)
if self.is_training:
'''
in training. We should show the detecitons in the tensorboard. So we add this.
'''
kept_indices = tf.reshape(
tf.where(
tf.greater_equal(final_scores, self.cfgs.VIS_SCORE)),
[-1])
else:
kept_indices = tf.reshape(
tf.where(
tf.greater_equal(final_scores,
self.cfgs.FILTERED_SCORE)), [-1])
final_boxes = tf.gather(final_boxes, kept_indices)
final_scores = tf.gather(final_scores, kept_indices)
final_category = tf.gather(final_category, kept_indices)
return final_boxes, final_scores, final_category
class DetectionNetworkSCRDet(DetectionNetworkBase):
def __init__(self, cfgs, is_training):
super(DetectionNetworkSCRDet, self).__init__(cfgs, is_training)
self.proposal_sampler_r2cnn = ProposalSamplerR2CNN(cfgs)
self.anchor_sampler_r2cnn = AnchorSamplerR2CNN(cfgs)
self.losses = Loss(cfgs)
self.roi_extractor = RoIExtractor(cfgs)
self.box_head = BoxHead(cfgs)
def rpn(self, inputs):
rpn_conv3x3 = slim.conv2d(inputs,
512, [3, 3],
trainable=self.is_training,
weights_initializer=self.cfgs.INITIALIZER,
activation_fn=tf.nn.relu,
scope='rpn_conv/3x3')
rpn_cls_score = slim.conv2d(rpn_conv3x3,
self.num_anchors_per_location * 2, [1, 1],
stride=1,
trainable=self.is_training,
weights_initializer=self.cfgs.INITIALIZER,
activation_fn=None,
scope='rpn_cls_score')
rpn_box_pred = slim.conv2d(
rpn_conv3x3,
self.num_anchors_per_location * 4, [1, 1],
stride=1,
trainable=self.is_training,
weights_initializer=self.cfgs.BBOX_INITIALIZER,
activation_fn=None,
scope='rpn_bbox_pred')
rpn_cls_prob = slim.softmax(rpn_cls_score, scope='rpn_cls_prob')
return rpn_box_pred, rpn_cls_score, rpn_cls_prob
def make_anchors(self, feature_to_cropped):
featuremap_height, featuremap_width = tf.shape(
feature_to_cropped)[1], tf.shape(feature_to_cropped)[2]
featuremap_height = tf.cast(featuremap_height, tf.float32)
featuremap_width = tf.cast(featuremap_width, tf.float32)
anchors = anchor_utils.make_anchors(
base_anchor_size=self.cfgs.BASE_ANCHOR_SIZE_LIST,
anchor_scales=self.cfgs.ANCHOR_SCALES,
anchor_ratios=self.cfgs.ANCHOR_RATIOS,
featuremap_height=featuremap_height,
featuremap_width=featuremap_width,
stride=self.cfgs.ANCHOR_STRIDE,
name="make_anchors_forRPN")
return anchors
def build_loss(self, rpn_box_pred, rpn_bbox_targets, rpn_cls_score,
rpn_labels, bbox_pred_h, bbox_targets_h, cls_score_h,
bbox_pred_r, bbox_targets_r, rois, target_gt_r, cls_score_r,
labels, mask_gt, pa_mask_pred):
'''
:param rpn_box_pred: [-1, 4]
:param rpn_bbox_targets: [-1, 4]
:param rpn_cls_score: [-1]
:param rpn_labels: [-1]
:param bbox_pred_h: [-1, 4*(cls_num+1)]
:param bbox_targets_h: [-1, 4*(cls_num+1)]
:param cls_score_h: [-1, cls_num+1]
:param bbox_pred_r: [-1, 5*(cls_num+1)]
:param bbox_targets_r: [-1, 5*(cls_num+1)]
:param cls_score_r: [-1, cls_num+1]
:param labels: [-1]
:return:
'''
with tf.variable_scope('build_loss'):
with tf.variable_scope('rpn_loss'):
rpn_reg_loss = self.losses.smooth_l1_loss_rpn(
bbox_pred=rpn_box_pred,
bbox_targets=rpn_bbox_targets,
label=rpn_labels,
sigma=self.cfgs.RPN_SIGMA)
rpn_select = tf.reshape(tf.where(tf.not_equal(rpn_labels, -1)),
[-1])
rpn_cls_score = tf.reshape(
tf.gather(rpn_cls_score, rpn_select), [-1, 2])
rpn_labels = tf.reshape(tf.gather(rpn_labels, rpn_select),
[-1])
rpn_cls_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=rpn_cls_score, labels=rpn_labels))
self.losses_dict[
'rpn_cls_loss'] = rpn_cls_loss * self.cfgs.RPN_CLASSIFICATION_LOSS_WEIGHT
self.losses_dict[
'rpn_reg_loss'] = rpn_reg_loss * self.cfgs.RPN_LOCATION_LOSS_WEIGHT
with tf.variable_scope('FastRCNN_loss'):
reg_loss_h = self.losses.smooth_l1_loss_rcnn_h(
bbox_pred=bbox_pred_h,
bbox_targets=bbox_targets_h,
label=labels,
num_classes=self.cfgs.CLASS_NUM + 1,
sigma=self.cfgs.FASTRCNN_SIGMA)
if self.cfgs.USE_IOU_FACTOR:
reg_loss_r = self.losses.iou_smooth_l1_loss_rcnn_r(
bbox_pred=bbox_pred_r,
bbox_targets=bbox_targets_r,
label=labels,
rois=rois,
target_gt_r=target_gt_r,
num_classes=self.cfgs.CLASS_NUM + 1,
sigma=self.cfgs.FASTRCNN_SIGMA)
else:
reg_loss_r = self.losses.smooth_l1_loss_rcnn_r(
bbox_pred=bbox_pred_r,
bbox_targets=bbox_targets_r,
label=labels,
num_classes=self.cfgs.CLASS_NUM + 1,
sigma=self.cfgs.FASTRCNN_SIGMA)
cls_loss_h = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=cls_score_h,
labels=labels)) # beacause already sample before
cls_loss_r = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=cls_score_r, labels=labels))
self.losses_dict[
'fast_cls_loss_h'] = cls_loss_h * self.cfgs.FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT
self.losses_dict[
'fast_reg_loss_h'] = reg_loss_h * self.cfgs.FAST_RCNN_LOCATION_LOSS_WEIGHT
self.losses_dict[
'fast_cls_loss_r'] = cls_loss_r * self.cfgs.FAST_RCNN_CLASSIFICATION_LOSS_WEIGHT
self.losses_dict[
'fast_reg_loss_r'] = reg_loss_r * self.cfgs.FAST_RCNN_LOCATION_LOSS_WEIGHT
with tf.variable_scope('build_attention_loss',
regularizer=slim.l2_regularizer(
self.cfgs.WEIGHT_DECAY)):
attention_loss = self.losses.build_attention_loss(
mask_gt, pa_mask_pred)
self.losses_dict['attention_loss'] = attention_loss
def build_whole_detection_network(self,
input_img_batch,
gtboxes_batch_h=None,
gtboxes_batch_r=None,
mask_batch=None,
gpu_id=0):
if self.is_training:
gtboxes_batch_h = tf.reshape(gtboxes_batch_h, [-1, 5])
gtboxes_batch_h = tf.cast(gtboxes_batch_h, tf.float32)
gtboxes_batch_r = tf.reshape(gtboxes_batch_r, [-1, 6])
gtboxes_batch_r = tf.cast(gtboxes_batch_r, tf.float32)
img_shape = tf.shape(input_img_batch)
# 1. build backbone
feature, pa_mask = self.build_backbone(input_img_batch)
# 2. build rpn
rpn_box_pred, rpn_cls_score, rpn_cls_prob = self.rpn(feature)
rpn_box_pred = tf.reshape(rpn_box_pred, [-1, 4])
rpn_cls_score = tf.reshape(rpn_cls_score, [-1, 2])
rpn_cls_prob = slim.softmax(rpn_cls_score, scope='rpn_cls_prob')
# 3. generate anchors
anchors = self.make_anchors(feature)
# 4. postprocess rpn proposals. such as: decode, clip, NMS
with tf.variable_scope('postprocess_RPN'):
rois, roi_scores = self.postprocess_rpn_proposals(
rpn_bbox_pred=rpn_box_pred,
rpn_cls_prob=rpn_cls_prob,
img_shape=img_shape,
anchors=anchors,
is_training=self.is_training)
# 5. sample minibatch
if self.is_training:
with tf.variable_scope('sample_anchors_minibatch'):
rpn_labels, rpn_bbox_targets = \
tf.py_func(
self.anchor_sampler_r2cnn.anchor_target_layer,
[gtboxes_batch_h, img_shape, anchors],
[tf.float32, tf.float32])
rpn_bbox_targets = tf.reshape(rpn_bbox_targets, [-1, 4])
rpn_labels = tf.to_int32(rpn_labels, name="to_int32")
rpn_labels = tf.reshape(rpn_labels, [-1])
self.add_anchor_img_smry(input_img_batch,
anchors,
rpn_labels,
method=0)
rpn_cls_category = tf.argmax(rpn_cls_prob, axis=1)
kept_rpppn = tf.reshape(tf.where(tf.not_equal(rpn_labels, -1)),
[-1])
rpn_cls_category = tf.gather(rpn_cls_category, kept_rpppn)
acc = tf.reduce_mean(
tf.to_float(
tf.equal(rpn_cls_category,
tf.to_int64(tf.gather(rpn_labels, kept_rpppn)))))
tf.summary.scalar('ACC/fpn_accuracy', acc)
with tf.control_dependencies([rpn_labels]):
with tf.variable_scope('sample_RCNN_minibatch'):
rois, labels, bbox_targets_h, bbox_targets_r, target_gt_h, target_gt_r = \
tf.py_func(self.proposal_sampler_r2cnn.proposal_target_layer,
[rois, gtboxes_batch_h, gtboxes_batch_r],
[tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32])
rois = tf.reshape(rois, [-1, 4])
labels = tf.to_int32(labels)
labels = tf.reshape(labels, [-1])
bbox_targets_h = tf.reshape(
bbox_targets_h, [-1, 4 * (self.cfgs.CLASS_NUM + 1)])
bbox_targets_r = tf.reshape(
bbox_targets_r, [-1, 5 * (self.cfgs.CLASS_NUM + 1)])
self.add_roi_batch_img_smry(input_img_batch,
rois,
labels,
method=0)
# 6. build Fast-RCNN, include roi align/pooling, box head
bbox_pred_h, cls_score_h, bbox_pred_r, cls_score_r = self.box_head.fc_head(
self.roi_extractor,
rois,
feature,
img_shape,
self.is_training,
mode=0)
cls_prob_h = slim.softmax(cls_score_h, 'cls_prob_h')
cls_prob_r = slim.softmax(cls_score_r, 'cls_prob_r')
if self.is_training:
cls_category_h = tf.argmax(cls_prob_h, axis=1)
fast_acc_h = tf.reduce_mean(
tf.to_float(tf.equal(cls_category_h, tf.to_int64(labels))))
tf.summary.scalar('ACC/fast_acc_h', fast_acc_h)
cls_category_r = tf.argmax(cls_prob_r, axis=1)
fast_acc_r = tf.reduce_mean(
tf.to_float(tf.equal(cls_category_r, tf.to_int64(labels))))
tf.summary.scalar('ACC/fast_acc_r', fast_acc_r)
# 8. build loss
if self.is_training:
self.build_loss(rpn_box_pred=rpn_box_pred,
rpn_bbox_targets=rpn_bbox_targets,
rpn_cls_score=rpn_cls_score,
rpn_labels=rpn_labels,
bbox_pred_h=bbox_pred_h,
bbox_targets_h=bbox_targets_h,
cls_score_h=cls_score_h,
bbox_pred_r=bbox_pred_r,
bbox_targets_r=bbox_targets_r,
rois=rois,
target_gt_r=target_gt_r,
cls_score_r=cls_score_r,
labels=labels,
mask_gt=mask_batch,
pa_mask_pred=pa_mask)
# 9. postprocess_fastrcnn
final_boxes_h, final_scores_h, final_category_h = self.postprocess_fastrcnn_h(
rois=rois,
bbox_ppred=bbox_pred_h,
scores=cls_prob_h,
img_shape=img_shape)
final_boxes_r, final_scores_r, final_category_r = self.postprocess_fastrcnn_r(
rois=rois,
bbox_ppred=bbox_pred_r,
scores=cls_prob_r,
gpu_id=gpu_id)
if self.is_training:
return final_boxes_h, final_scores_h, final_category_h, \
final_boxes_r, final_scores_r, final_category_r, self.losses_dict
else:
return final_boxes_h, final_scores_h, final_category_h, \
final_boxes_r, final_scores_r, final_category_r,
def postprocess_fastrcnn_r(self, rois, bbox_ppred, scores, gpu_id):
'''
:param rois:[-1, 4]
:param bbox_ppred: [-1, (cfgs.Class_num+1) * 5]
:param scores: [-1, cfgs.Class_num + 1]
:return:
'''
with tf.name_scope('postprocess_fastrcnn'):
rois = tf.stop_gradient(rois)
scores = tf.stop_gradient(scores)
bbox_ppred = tf.reshape(bbox_ppred,
[-1, self.cfgs.CLASS_NUM + 1, 5])
bbox_ppred = tf.stop_gradient(bbox_ppred)
bbox_pred_list = tf.unstack(bbox_ppred, axis=1)
score_list = tf.unstack(scores, axis=1)
allclasses_boxes = []
allclasses_scores = []
categories = []
x_c = (rois[:, 2] + rois[:, 0]) / 2
y_c = (rois[:, 3] + rois[:, 1]) / 2
h = rois[:, 2] - rois[:, 0] + 1
w = rois[:, 3] - rois[:, 1] + 1
theta = -90 * tf.ones_like(x_c)
rois = tf.transpose(tf.stack([x_c, y_c, w, h, theta]))
for i in range(1, self.cfgs.CLASS_NUM + 1):
# 1. decode boxes in each class
tmp_encoded_box = bbox_pred_list[i]
tmp_score = score_list[i]
tmp_decoded_boxes = bbox_transform.rbbox_transform_inv(
boxes=rois,
deltas=tmp_encoded_box,
scale_factors=self.cfgs.ROI_SCALE_FACTORS)
# 2. clip to img boundaries
# tmp_decoded_boxes = boxes_utils.clip_boxes_to_img_boundaries(decode_boxes=tmp_decoded_boxes,
# img_shape=img_shape)
# 3. NMS
if self.cfgs.SOFT_NMS:
print("Using Soft NMS.......")
raise NotImplementedError(
"soft NMS for rotate has not implemented")
else:
keep = nms_rotate.nms_rotate(
decode_boxes=tmp_decoded_boxes,
scores=tmp_score,
iou_threshold=self.cfgs.FAST_RCNN_R_NMS_IOU_THRESHOLD,
max_output_size=self.cfgs.
FAST_RCNN_NMS_MAX_BOXES_PER_CLASS,
use_gpu=self.cfgs.ROTATE_NMS_USE_GPU,
gpu_id=gpu_id)
perclass_boxes = tf.gather(tmp_decoded_boxes, keep)
perclass_scores = tf.gather(tmp_score, keep)
allclasses_boxes.append(perclass_boxes)
allclasses_scores.append(perclass_scores)
categories.append(tf.ones_like(perclass_scores) * i)
final_boxes = tf.concat(allclasses_boxes, axis=0)
final_scores = tf.concat(allclasses_scores, axis=0)
final_category = tf.concat(categories, axis=0)
if self.is_training:
'''
in training. We should show the detecitons in the tensorboard. So we add this.
'''
kept_indices = tf.reshape(
tf.where(
tf.greater_equal(final_scores, self.cfgs.VIS_SCORE)),
[-1])
else:
kept_indices = tf.reshape(
tf.where(
tf.greater_equal(final_scores,
self.cfgs.FILTERED_SCORE)), [-1])
final_boxes = tf.gather(final_boxes, kept_indices)
final_scores = tf.gather(final_scores, kept_indices)
final_category = tf.gather(final_category, kept_indices)
return final_boxes, final_scores, final_category
def postprocess_fastrcnn_h(self, rois, bbox_ppred, scores, img_shape):
'''
:param rois:[-1, 4]
:param bbox_ppred: [-1, (cfgs.Class_num+1) * 4]
:param scores: [-1, cfgs.Class_num + 1]
:return:
'''
with tf.name_scope('postprocess_fastrcnn_h'):
rois = tf.stop_gradient(rois)
scores = tf.stop_gradient(scores)
bbox_ppred = tf.reshape(bbox_ppred,
[-1, self.cfgs.CLASS_NUM + 1, 4])
bbox_ppred = tf.stop_gradient(bbox_ppred)
bbox_pred_list = tf.unstack(bbox_ppred, axis=1)
score_list = tf.unstack(scores, axis=1)
allclasses_boxes = []
allclasses_scores = []
categories = []
for i in range(1, self.cfgs.CLASS_NUM + 1):
# 1. decode boxes in each class
tmp_encoded_box = bbox_pred_list[i]
tmp_score = score_list[i]
tmp_decoded_boxes = bbox_transform.bbox_transform_inv(
boxes=rois,
deltas=tmp_encoded_box,
scale_factors=self.cfgs.ROI_SCALE_FACTORS)
# 2. clip to img boundaries
tmp_decoded_boxes = clip_boxes_to_img_boundaries(
decode_boxes=tmp_decoded_boxes, img_shape=img_shape)
# 3. NMS
keep = tf.image.non_max_suppression(
boxes=tmp_decoded_boxes,
scores=tmp_score,
max_output_size=self.cfgs.
FAST_RCNN_NMS_MAX_BOXES_PER_CLASS,
iou_threshold=self.cfgs.FAST_RCNN_H_NMS_IOU_THRESHOLD)
perclass_boxes = tf.gather(tmp_decoded_boxes, keep)
perclass_scores = tf.gather(tmp_score, keep)
allclasses_boxes.append(perclass_boxes)
allclasses_scores.append(perclass_scores)
categories.append(tf.ones_like(perclass_scores) * i)
final_boxes = tf.concat(allclasses_boxes, axis=0)
final_scores = tf.concat(allclasses_scores, axis=0)
final_category = tf.concat(categories, axis=0)
# if self.is_training:
'''
in training. We should show the detecitons in the tensorboard. So we add this.
'''
if self.is_training:
'''
in training. We should show the detecitons in the tensorboard. So we add this.
'''
kept_indices = tf.reshape(
tf.where(
tf.greater_equal(final_scores, self.cfgs.VIS_SCORE)),
[-1])
else:
kept_indices = tf.reshape(
tf.where(
tf.greater_equal(final_scores,
self.cfgs.FILTERED_SCORE)), [-1])
final_boxes = tf.gather(final_boxes, kept_indices)
final_scores = tf.gather(final_scores, kept_indices)
final_category = tf.gather(final_category, kept_indices)
return final_boxes, final_scores, final_category
