class RCNN(object):
def __init__(self, batch_size, num_point, num_channel=133, bn_decay=None, is_training=True):
self.batch_size = batch_size
self.num_point = num_point
self.num_channel = num_channel
self.bn_decay = bn_decay
self.is_training = is_training
self.end_points = {}
self.placeholders = self.get_placeholders()
self.box_encoder = BoxEncoder(CENTER_SEARCH_RANGE, NUM_CENTER_BIN, HEADING_SEARCH_RANGE, NUM_HEADING_BIN)
self.build()
def get_placeholders(self):
batch_size = self.batch_size
num_point = self.num_point
num_channel = self.num_channel
return {
'pointclouds': tf.placeholder(tf.float32, shape=(batch_size, num_point, num_channel)),
'proposal_boxes': tf.placeholder(tf.float32, shape=(batch_size, 7)),
'class_labels': tf.placeholder(tf.int32, shape=(batch_size,)),
'center_bin_x_labels': tf.placeholder(tf.int32, shape=(batch_size,)),
'center_bin_z_labels': tf.placeholder(tf.int32, shape=(batch_size,)),
'center_x_res_labels': tf.placeholder(tf.float32, shape=(batch_size,)),
'center_z_res_labels': tf.placeholder(tf.float32, shape=(batch_size,)),
'center_y_res_labels': tf.placeholder(tf.float32, shape=(batch_size,)),
'heading_bin_labels': tf.placeholder(tf.int32, shape=(batch_size,)),
'heading_res_labels': tf.placeholder(tf.float32, shape=(batch_size,)),
'size_class_labels': tf.placeholder(tf.int32, shape=(batch_size,)),
'size_res_labels': tf.placeholder(tf.float32, shape=(batch_size, 3)),
'gt_box_of_prop': tf.placeholder(tf.float32, shape=(batch_size, 8, 3)),
'img_inputs': tf.placeholder(tf.float32, shape=(batch_size, 360, 1200, 3)),
'calib': tf.placeholder(tf.float32, shape=(batch_size, 3, 4)),
'train_regression': tf.placeholder(tf.bool, shape=(batch_size,)),
'img_seg_map': tf.placeholder(tf.float32, shape=(batch_size, 360, 1200, 4)),
'is_training_pl': tf.placeholder(tf.bool, shape=())
}
def build_img_extractor(self):
self._img_pixel_size = np.asarray([360, 1200])
VGG_config = namedtuple('VGG_config', 'vgg_conv1 vgg_conv2 vgg_conv3 vgg_conv4 l2_weight_decay')
self._img_feature_extractor = ImgVggPyr(VGG_config(**{
'vgg_conv1': [2, 32],
'vgg_conv2': [2, 64],
'vgg_conv3': [3, 128],
'vgg_conv4': [3, 256],
'l2_weight_decay': 0.0005
}))
self._img_preprocessed = \
self._img_feature_extractor.preprocess_input(
self.placeholders['img_inputs'], self._img_pixel_size)
self.img_feature_maps, self.img_end_points = \
self._img_feature_extractor.build(
self._img_preprocessed,
self._img_pixel_size,
self.is_training)
#return self.img_feature_maps
self.img_bottleneck = slim.conv2d(
self.img_feature_maps,
1, [1, 1],
scope='bottleneck',
normalizer_fn=slim.batch_norm,
normalizer_params={
'is_training': self.is_training})
#tf.summary.image('img_feature', tf.reduce_max(self.img_bottleneck,axis=-1,keepdims=True),max_outputs=3)
return self.img_bottleneck
def build(self):
point_cloud = self.placeholders['pointclouds']
is_training = self.placeholders['is_training_pl']
batch_size = self.batch_size
# image
'''
img_bottleneck = self.build_img_extractor()
box2d_corners, box2d_corners_norm = projection.tf_project_to_image_space(
self.placeholders['proposal_boxes'],
self.placeholders['calib'], self._img_pixel_size)
img_rois = tf.image.crop_and_resize(
img_bottleneck,
box2d_corners_norm,
tf.range(0, batch_size),
[16,16])
'''
seg_softmax = self.placeholders['img_seg_map']
seg_pred = tf.expand_dims(tf.argmax(seg_softmax, axis=-1), axis=-1)
self._img_pixel_size = np.asarray([360, 1200])
box2d_corners, box2d_corners_norm = projection.tf_project_to_image_space(
self.placeholders['proposal_boxes'],
self.placeholders['calib'], self._img_pixel_size)
# y1, x1, y2, x2
box2d_corners_norm_reorder = tf.stack([
tf.gather(box2d_corners_norm, 1, axis=-1),
tf.gather(box2d_corners_norm, 0, axis=-1),
tf.gather(box2d_corners_norm, 3, axis=-1),
tf.gather(box2d_corners_norm, 2, axis=-1),
], axis=-1)
img_rois = tf.image.crop_and_resize(
seg_softmax,
#seg_pred,
box2d_corners_norm_reorder,
tf.range(0, batch_size),
[16,16])
self.end_points['img_rois'] = img_rois
self.end_points['box2d_corners_norm_reorder'] = box2d_corners_norm_reorder
l0_xyz = tf.slice(point_cloud, [0,0,0], [-1,-1,3])
l0_points = tf.slice(point_cloud, [0,0,3], [-1,-1,self.num_channel-3])
# Set abstraction layers
l1_xyz, l1_points, _ = pointnet_sa_module(l0_xyz, l0_points,
npoint=128, radius=0.2, nsample=64, mlp=[128,128,128],
mlp2=None, group_all=False, is_training=is_training, bn_decay=self.bn_decay,
scope='rcnn-sa1', bn=True)
l2_xyz, l2_points, _ = pointnet_sa_module(l1_xyz, l1_points,
npoint=64, radius=0.4, nsample=64, mlp=[128,128,256],
mlp2=None, group_all=False, is_training=is_training, bn_decay=self.bn_decay,
scope='rcnn-sa2', bn=True)
l3_xyz, l3_points, _ = pointnet_sa_module(l2_xyz, l2_points,
npoint=64, radius=0.4, nsample=64, mlp=[256,256,512],
mlp2=None, group_all=True, is_training=is_training, bn_decay=self.bn_decay,
scope='rcnn-sa3', bn=True)
point_feats = tf.reshape(l3_points, [batch_size, -1])
img_feats = tf.reshape(img_rois, [batch_size, -1])
feats = tf.concat([point_feats, img_feats], axis=-1)
#tf.summary.scalar('img_features', tf.reduce_mean(img_feats))
#tf.summary.scalar('point_features', tf.reduce_mean(point_feats))
# Classification
cls_net = tf_util.fully_connected(img_feats, 256, bn=True, is_training=is_training, scope='rcnn-cls-fc1', bn_decay=self.bn_decay)
#cls_net = tf_util.fully_connected(point_feats, 256, bn=True, is_training=is_training, scope='rcnn-cls-fc1', bn_decay=self.bn_decay)
#cls_net = tf_util.fully_connected(feats, 256, bn=True, is_training=is_training, scope='rcnn-cls-fc1', bn_decay=self.bn_decay)
cls_net = tf_util.dropout(cls_net, keep_prob=0.5, is_training=is_training, scope='rcnn-cls-dp1')
cls_net = tf_util.fully_connected(cls_net, 256, bn=True, is_training=is_training, scope='rcnn-cls-fc2', bn_decay=self.bn_decay)
cls_net = tf_util.dropout(cls_net, keep_prob=0.5, is_training=is_training, scope='rcnn-cls-dp2')
cls_net = tf_util.fully_connected(cls_net, NUM_OBJ_CLASSES, activation_fn=None, scope='rcnn-cls-fc3')
self.end_points['cls_logits'] = cls_net
# Box estimation
cls_label_pred = tf.argmax(tf.nn.softmax(cls_net), axis=1)
one_hot_pred = tf.one_hot(cls_label_pred, NUM_OBJ_CLASSES)
one_hot_gt = tf.one_hot(self.placeholders['class_labels'], NUM_OBJ_CLASSES)
one_hot_vec = tf.cond(is_training, lambda: one_hot_gt, lambda: one_hot_pred)
est_intput = tf.concat([point_feats, one_hot_vec], axis=1)
net = tf_util.fully_connected(est_intput, 512, bn=True,
is_training=is_training, scope='rcnn-est-fc1', bn_decay=self.bn_decay)
net = tf_util.fully_connected(net, 256, bn=True,
is_training=is_training, scope='rcnn-est-fc2', bn_decay=self.bn_decay)
net = tf_util.fully_connected(net, 512, bn=True,
is_training=is_training, scope='rcnn-est-fc3', bn_decay=self.bn_decay)
# The first NUM_CENTER_BIN*2*2: CENTER_BIN class scores and bin residuals for (x,z)
# next 1: center residual for y
# next NUM_HEADING_BIN*2: heading bin class scores and residuals
# next NUM_SIZE_CLUSTER*4: size cluster class scores and residuals(l,w,h)
output = tf_util.fully_connected(net,
NUM_CENTER_BIN*2*2+1+NUM_HEADING_BIN*2+NUM_SIZE_CLUSTER*4,
activation_fn=None, scope='rcnn-est-out')
self.parse_output_to_tensors(output)
self.get_output_boxes()
def parse_output_to_tensors(self, output):
''' Parse batch output to separate tensors (added to end_points)'''
batch_size = self.batch_size
# objectness and center
#end_points['objectness'] = tf.slice(output, [0,0,0], [-1,-1,2])
center_x_scores = tf.slice(output, [0,0], [-1,NUM_CENTER_BIN])
center_x_residuals_normalized = tf.slice(output, [0,NUM_CENTER_BIN],
[-1,NUM_CENTER_BIN])
self.end_points['center_x_scores'] = center_x_scores # (B,NUM_CENTER_BIN)
self.end_points['center_x_residuals_normalized'] = \
center_x_residuals_normalized # (B,NUM_CENTER_BIN)
center_z_scores = tf.slice(output, [0,NUM_CENTER_BIN*2], [-1,NUM_CENTER_BIN])
center_z_residuals_normalized = tf.slice(output, [0,NUM_CENTER_BIN*3],
[-1,NUM_CENTER_BIN])
self.end_points['center_z_scores'] = center_z_scores # (B,NUM_CENTER_BIN)
self.end_points['center_z_residuals_normalized'] = \
center_z_residuals_normalized # (B,NUM_CENTER_BIN)
self.end_points['center_y_residuals'] = tf.slice(output, [0,NUM_CENTER_BIN*4], [-1,1])
# heading
heading_scores = tf.slice(output, [0,NUM_CENTER_BIN*4+1], [-1,NUM_HEADING_BIN])
heading_residuals_normalized = tf.slice(output, [0,NUM_CENTER_BIN*4+1+NUM_HEADING_BIN],
[-1,NUM_HEADING_BIN])
self.end_points['heading_scores'] = heading_scores # (B,NUM_HEADING_BIN)
self.end_points['heading_residuals_normalized'] = heading_residuals_normalized # (B,NUM_HEADING_BIN)
# end_points['heading_residuals'] = \
# heading_residuals_normalized * (np.pi/NUM_HEADING_BIN) # BxNUM_HEADING_BIN
# size
size_scores = tf.slice(output, [0,NUM_CENTER_BIN*4+1+NUM_HEADING_BIN*2],
[-1,NUM_SIZE_CLUSTER]) # BxNUM_SIZE_CLUSTER
size_residuals_normalized = tf.slice(output,
[0,NUM_CENTER_BIN*4+1+NUM_HEADING_BIN*2+NUM_SIZE_CLUSTER], [-1,NUM_SIZE_CLUSTER*3])
size_residuals_normalized = tf.reshape(size_residuals_normalized,
[batch_size, NUM_SIZE_CLUSTER, 3])
self.end_points['size_scores'] = size_scores
self.end_points['size_residuals_normalized'] = size_residuals_normalized
# end_points['size_residuals'] = size_residuals_normalized * \
# tf.expand_dims(tf.constant(type_mean_size, dtype=tf.float32), 0)
return self.end_points
def get_output_boxes(self):
end_points = {}
# adapt the dimension
for k in ['center_x_scores', 'center_x_residuals_normalized',
'center_z_scores', 'center_z_residuals_normalized',
'center_y_residuals', 'heading_scores', 'heading_residuals_normalized',
'size_scores', 'size_residuals_normalized']:
end_points[k] = tf.expand_dims(self.end_points[k], axis=1)
box_center, box_angle, box_size = self.box_encoder.tf_decode(end_points)
box_center = tf.squeeze(box_center, axis=1)
box_center = box_center + tf.slice(self.placeholders['proposal_boxes'], [0,0], [-1,3])
box_angle = tf.squeeze(box_angle, axis=1)
box_angle += tf.gather(self.placeholders['proposal_boxes'], 6, axis=-1) # resotre absoluate angle
box_size = tf.squeeze(box_size, axis=1)
self.end_points['box_center'] = box_center
self.end_points['box_angle'] = box_angle
self.end_points['box_size'] = box_size
corners_3d = get_box3d_corners_helper(box_center, box_angle, box_size)
self.end_points['box_corners'] = corners_3d
# box score
seg_scores = tf.reduce_max(tf.nn.softmax(self.end_points['cls_logits']), axis=-1) # (B,)
bin_x_scores = tf.reduce_max(tf.nn.softmax(self.end_points['center_x_scores']), axis=-1) # (B,M)
bin_z_scores = tf.reduce_max(tf.nn.softmax(self.end_points['center_z_scores']), axis=-1) # (B,M)
heading_scores = tf.reduce_max(tf.nn.softmax(self.end_points['heading_scores']), axis=-1) # (B,M)
size_scores = tf.reduce_max(tf.nn.softmax(self.end_points['size_scores']), axis=-1) # (B,M)
# confidence = seg_scores + bin_x_scores + bin_z_scores + heading_scores + size_scores
confidence = seg_scores * bin_x_scores * bin_z_scores * heading_scores * size_scores
self.end_points['box_score'] = confidence
return corners_3d
def get_loss(self):
end_points = self.end_points
cls_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\
logits=end_points['cls_logits'], labels=self.placeholders['class_labels']))
tf.summary.scalar('classification loss', cls_loss)
# is_obj_mask = tf.to_float(tf.not_equal(self.placeholders['class_labels'], 0))
train_reg_mask = tf.to_float(self.placeholders['train_regression'])
center_x_cls_loss = tf.reduce_mean(train_reg_mask*tf.nn.sparse_softmax_cross_entropy_with_logits(\
logits=end_points['center_x_scores'], labels=self.placeholders['center_bin_x_labels']))
center_z_cls_loss = tf.reduce_mean(train_reg_mask*tf.nn.sparse_softmax_cross_entropy_with_logits(\
logits=end_points['center_z_scores'], labels=self.placeholders['center_bin_z_labels']))
bin_x_onehot = tf.one_hot(self.placeholders['center_bin_x_labels'],
depth=NUM_CENTER_BIN,
on_value=1, off_value=0, axis=-1) # BxNUM_CENTER_BIN
# NOTICE: labels['center_x_residuals'] is already normalized
center_x_residuals_normalized = tf.reduce_sum(end_points['center_x_residuals_normalized']*tf.to_float(bin_x_onehot), axis=-1) # B
center_x_residuals_dist = tf.norm(self.placeholders['center_x_res_labels'] - center_x_residuals_normalized, axis=-1)
center_x_res_loss = huber_loss(train_reg_mask*center_x_residuals_dist, delta=1.0)
bin_z_onehot = tf.one_hot(self.placeholders['center_bin_z_labels'],
depth=NUM_CENTER_BIN,
on_value=1, off_value=0, axis=-1) # BxNUM_CENTER_BIN
center_z_residuals_normalized = tf.reduce_sum(end_points['center_z_residuals_normalized']*tf.to_float(bin_z_onehot), axis=-1) # B
center_z_residuals_dist = tf.norm(self.placeholders['center_z_res_labels'] - center_z_residuals_normalized, axis=-1)
center_z_res_loss = huber_loss(train_reg_mask*center_z_residuals_dist, delta=1.0)
# y is directly regressed
center_y_residuals_dist = tf.norm(self.placeholders['center_y_res_labels'] - tf.gather(end_points['center_y_residuals'], 0, axis=-1), axis=-1)
center_y_res_loss = huber_loss(train_reg_mask*center_y_residuals_dist, delta=1.0)
tf.summary.scalar('center_x class loss', center_x_cls_loss)
tf.summary.scalar('center_z class loss', center_z_cls_loss)
tf.summary.scalar('center_x residual loss', center_x_res_loss)
tf.summary.scalar('center_y residual loss', center_y_res_loss)
tf.summary.scalar('center_z residual loss', center_z_res_loss)
# Heading loss
heading_class_loss = tf.reduce_mean( \
train_reg_mask*tf.nn.sparse_softmax_cross_entropy_with_logits( \
logits=end_points['heading_scores'], labels=self.placeholders['heading_bin_labels']))
hcls_onehot = tf.one_hot(self.placeholders['heading_bin_labels'],
depth=NUM_HEADING_BIN,
on_value=1, off_value=0, axis=-1) # BxNxNUM_HEADING_BIN
heading_residual_normalized_label = self.placeholders['heading_res_labels']
heading_res_dist = tf.norm(tf.reduce_sum( \
end_points['heading_residuals_normalized']*tf.to_float(hcls_onehot), axis=-1) - \
heading_residual_normalized_label)
heading_res_loss = huber_loss(train_reg_mask*heading_res_dist, delta=1.0)
tf.summary.scalar('heading class loss', heading_class_loss)
tf.summary.scalar('heading residual loss', heading_res_loss)
# Size loss
size_class_loss = tf.reduce_mean( \
train_reg_mask*tf.nn.sparse_softmax_cross_entropy_with_logits( \
logits=end_points['size_scores'], labels=self.placeholders['size_class_labels']))
scls_onehot = tf.one_hot(self.placeholders['size_class_labels'],
depth=NUM_SIZE_CLUSTER,
on_value=1, off_value=0, axis=-1) # BxNUM_SIZE_CLUSTER
scls_onehot_tiled = tf.tile(tf.expand_dims( \
tf.to_float(scls_onehot), -1), [1,1,3]) # BxNUM_SIZE_CLUSTERx3
predicted_size_residual_normalized = tf.reduce_sum( \
end_points['size_residuals_normalized']*scls_onehot_tiled, axis=1) # Bx3
size_residual_label_normalized = self.placeholders['size_res_labels'] # Bx3
size_dist = tf.norm(size_residual_label_normalized - predicted_size_residual_normalized, axis=-1)
size_res_loss = huber_loss(train_reg_mask*size_dist, delta=1.0)
tf.summary.scalar('size class loss', size_class_loss)
tf.summary.scalar('size residual loss', size_res_loss)
obj_cls_weight = 1
cls_weight = 1
res_weight = 1
total_loss = obj_cls_weight * cls_loss + \
cls_weight * (center_x_cls_loss + center_z_cls_loss + heading_class_loss + size_class_loss) + \
res_weight * (center_x_res_loss + center_z_res_loss + center_y_res_loss + heading_res_loss + size_res_loss)
loss_endpoints = {
#'size_class_loss': size_class_loss,
'size_res_loss': size_res_loss,
#'heading_class_loss': heading_class_loss,
#'heading_res_loss': heading_res_loss,
#'center_x_cls_loss': center_x_cls_loss,
#'center_z_cls_loss': center_z_cls_loss,
#'center_x_res_loss': center_x_res_loss,
#'center_z_res_loss': center_z_res_loss,
#'center_y_res_loss': center_y_res_loss,
#'mask_loss': cls_loss
#'mean_size_label': mean_size_label,
'size_residuals_normalized': end_points['size_residuals_normalized']
}
return total_loss, loss_endpoints
class RPN(object):
"""docstring for RPN."""
def __init__(self,
batch_size,
num_point,
num_channel=4,
bn_decay=None,
is_training=True):
self.batch_size = batch_size
self.num_point = num_point
self.num_channel = num_channel
self.bn_decay = bn_decay
self.is_training = is_training
self.end_points = {}
self.box_encoder = BoxEncoder(CENTER_SEARCH_RANGE, NUM_CENTER_BIN,
HEADING_SEARCH_RANGE, NUM_HEADING_BIN)
self.placeholders = self.get_placeholders()
self.build()
def get_placeholders(self):
batch_size = self.batch_size
num_point = self.num_point
return {
'pointclouds':
tf.placeholder(tf.float32,
shape=(batch_size, num_point, self.num_channel)),
'img_inputs':
tf.placeholder(tf.float32, shape=(batch_size, 360, 1200, 3)),
'calib':
tf.placeholder(tf.float32, shape=(batch_size, 3, 4)),
'seg_labels':
tf.placeholder(tf.int32, shape=(batch_size, num_point)),
'center_bin_x_labels':
tf.placeholder(tf.int32, shape=(batch_size, num_point)),
'center_bin_z_labels':
tf.placeholder(tf.int32, shape=(batch_size, num_point)),
'center_x_residuals_labels':
tf.placeholder(tf.float32, shape=(batch_size, num_point)),
'center_z_residuals_labels':
tf.placeholder(tf.float32, shape=(batch_size, num_point)),
'center_y_residuals_labels':
tf.placeholder(tf.float32, shape=(batch_size, num_point)),
'heading_bin_labels':
tf.placeholder(tf.int32, shape=(batch_size, num_point)),
'heading_residuals_labels':
tf.placeholder(tf.float32, shape=(batch_size, num_point)),
'size_class_labels':
tf.placeholder(tf.int32, shape=(batch_size, num_point)),
'size_residuals_labels':
tf.placeholder(tf.float32, shape=(batch_size, num_point, 3)),
'gt_boxes':
tf.placeholder(tf.float32, shape=(batch_size, None, 8, 3)),
'gt_box_of_point':
tf.placeholder(tf.float32, shape=(batch_size, num_point, 8, 3)),
'img_seg_softmax':
tf.placeholder(tf.float32,
shape=(batch_size, num_point, NUM_SEG_CLASSES)),
'is_training_pl':
tf.placeholder(tf.bool, shape=())
}
def parse_output_to_tensors(self, output, end_points):
''' Parse batch output to separate tensors (added to end_points)
Input:
output: TF tensor in shape (B,N,NUM_CENTER_BIN*2*2+1+NUM_HEADING_BIN*2+NUM_SIZE_CLUSTER*4)
end_points: dict
Output:
end_points: dict (updated)
'''
batch_size = output.get_shape()[0].value
npoints = output.get_shape()[1].value
# objectness and center
#end_points['objectness'] = tf.slice(output, [0,0,0], [-1,-1,2])
center_x_scores = tf.slice(output, [0, 0, 0], [-1, -1, NUM_CENTER_BIN])
center_x_residuals_normalized = tf.slice(output,
[0, 0, NUM_CENTER_BIN],
[-1, -1, NUM_CENTER_BIN])
end_points['center_x_scores'] = center_x_scores # (B,N,NUM_CENTER_BIN)
end_points['center_x_residuals_normalized'] = \
center_x_residuals_normalized # (B,N,NUM_CENTER_BIN)
center_z_scores = tf.slice(output, [0, 0, NUM_CENTER_BIN * 2],
[-1, -1, NUM_CENTER_BIN])
center_z_residuals_normalized = tf.slice(output,
[0, 0, NUM_CENTER_BIN * 3],
[-1, -1, NUM_CENTER_BIN])
end_points['center_z_scores'] = center_z_scores # (B,N,NUM_CENTER_BIN)
end_points['center_z_residuals_normalized'] = \
center_z_residuals_normalized # (B,N,NUM_CENTER_BIN)
end_points['center_y_residuals'] = tf.slice(output,
[0, 0, NUM_CENTER_BIN * 4],
[-1, -1, 1])
# heading
heading_scores = tf.slice(output, [0, 0, NUM_CENTER_BIN * 4 + 1],
[-1, -1, NUM_HEADING_BIN])
heading_residuals_normalized = tf.slice(
output, [0, 0, NUM_CENTER_BIN * 4 + 1 + NUM_HEADING_BIN],
[-1, -1, NUM_HEADING_BIN])
end_points['heading_scores'] = heading_scores # (B,N,NUM_HEADING_BIN)
end_points[
'heading_residuals_normalized'] = heading_residuals_normalized # (B,N,NUM_HEADING_BIN)
# end_points['heading_residuals'] = \
# heading_residuals_normalized * (np.pi/NUM_HEADING_BIN) # BxNUM_HEADING_BIN
# size
size_scores = tf.slice(
output, [0, 0, NUM_CENTER_BIN * 4 + 1 + NUM_HEADING_BIN * 2],
[-1, -1, NUM_SIZE_CLUSTER]) # BxNUM_SIZE_CLUSTER
size_residuals_normalized = tf.slice(output, [
0, 0,
NUM_CENTER_BIN * 4 + 1 + NUM_HEADING_BIN * 2 + NUM_SIZE_CLUSTER
], [-1, -1, NUM_SIZE_CLUSTER * 3])
size_residuals_normalized = tf.reshape(
size_residuals_normalized,
[batch_size, npoints, NUM_SIZE_CLUSTER, 3])
end_points['size_scores'] = size_scores
end_points['size_residuals_normalized'] = size_residuals_normalized
# end_points['size_residuals'] = size_residuals_normalized * \
# tf.expand_dims(tf.constant(type_mean_size, dtype=tf.float32), 0)
box_center, box_angle, box_size = self.box_encoder.tf_decode(
end_points)
box_center = box_center + end_points['fg_points_xyz']
box_num = batch_size * npoints
corners_3d = get_box3d_corners_helper(
tf.reshape(box_center, [box_num, 3]),
tf.reshape(box_angle, [box_num]),
tf.reshape(box_size, [box_num, 3]))
end_points['proposal_boxes'] = tf.reshape(corners_3d,
[batch_size, npoints, 8, 3])
return end_points
def build_img_extractor(self):
self._img_pixel_size = np.asarray([360, 1200])
VGG_config = namedtuple(
'VGG_config',
'vgg_conv1 vgg_conv2 vgg_conv3 vgg_conv4 l2_weight_decay')
self._img_feature_extractor = ImgVggPyr(
VGG_config(
**{
'vgg_conv1': [2, 32],
'vgg_conv2': [2, 64],
'vgg_conv3': [3, 128],
'vgg_conv4': [3, 256],
'l2_weight_decay': 0.0005
}))
self._img_preprocessed = \
self._img_feature_extractor.preprocess_input(
self.placeholders['img_inputs'], self._img_pixel_size)
self.img_feature_maps, self.img_end_points = \
self._img_feature_extractor.build(
self._img_preprocessed,
self._img_pixel_size,
self.is_training)
#return self.img_feature_maps
self.img_bottleneck = slim.conv2d(
self.img_feature_maps,
128,
[1, 1],
#2, [1, 1],
scope='bottleneck',
normalizer_fn=slim.batch_norm,
#normalizer_fn=None,
normalizer_params={'is_training': self.is_training})
return self.img_bottleneck
def get_segmentation_net(self, point_cloud, is_training, bn_decay,
end_points):
''' 3D instance segmentation PointNet v2 network.
Input:
point_cloud: TF tensor in shape (B,N,4)
frustum point clouds with XYZ and intensity in point channels
XYZs are in frustum coordinate
is_training: TF boolean scalar
bn_decay: TF float scalar
end_points: dict
Output:
logits: TF tensor in shape (B,N,2), scores for bkg/clutter and object
end_points: dict
'''
l0_xyz = tf.slice(point_cloud, [0, 0, 0], [-1, -1, 3])
l0_points = tf.slice(point_cloud, [0, 0, 3], [-1, -1, NUM_CHANNEL - 3])
# Set abstraction layers
l1_xyz, l1_points = pointnet_sa_module_msg(
l0_xyz,
l0_points,
4096, [0.1, 0.5], [16, 32], [[16, 16, 32], [32, 32, 64]],
is_training,
bn_decay,
scope='layer1',
bn=True)
l2_xyz, l2_points = pointnet_sa_module_msg(
l1_xyz,
l1_points,
1024, [0.5, 1.0], [16, 32], [[64, 64, 128], [64, 96, 128]],
is_training,
bn_decay,
scope='layer2',
bn=True)
l3_xyz, l3_points = pointnet_sa_module_msg(
l2_xyz,
l2_points,
256, [1.0, 2.0], [16, 32], [[128, 196, 256], [128, 196, 256]],
is_training,
bn_decay,
scope='layer3',
bn=True)
l4_xyz, l4_points = pointnet_sa_module_msg(
l3_xyz,
l3_points,
64, [2.0, 4.0], [16, 32], [[256, 256, 512], [256, 384, 512]],
is_training,
bn_decay,
scope='layer4',
bn=True)
# Feature Propagation layers
l3_points = pointnet_fp_module(l3_xyz,
l4_xyz,
l3_points,
l4_points, [512, 512],
is_training,
bn_decay,
scope='fa_layer2',
bn=True)
l2_points = pointnet_fp_module(l2_xyz,
l3_xyz,
l2_points,
l3_points, [512, 512],
is_training,
bn_decay,
scope='fa_layer3',
bn=True)
l1_points = pointnet_fp_module(l1_xyz,
l2_xyz,
l1_points,
l2_points, [256, 256],
is_training,
bn_decay,
scope='fa_layer4',
bn=True)
l0_points = pointnet_fp_module(l0_xyz,
l1_xyz,
tf.concat([l0_xyz, l0_points], axis=-1),
l1_points, [128, 128],
is_training,
bn_decay,
scope='fa_layer5',
bn=True)
end_points['point_feats'] = tf.concat([l0_xyz, l0_points],
axis=-1) # (B, N, 3+C1)
end_points['point_feats_fuse'] = tf.concat(
[end_points['point_feats'], end_points['point_img_feats']],
axis=-1) # (B, N, 3+C1+C2)
semantic_features = tf.concat(
[l0_points, end_points['point_img_feats']],
axis=-1) # (B, N, C1+C2)
#end_points['point_feats_fuse'] = end_points['point_feats']
#semantic_features = l0_points
# FC layers
net = tf_util.dropout(semantic_features,
keep_prob=0.5,
is_training=is_training,
scope='dp0')
net = tf_util.conv1d(net,
128,
1,
padding='VALID',
bn=True,
is_training=is_training,
scope='conv1d-fc1',
bn_decay=bn_decay)
net = tf_util.dropout(net,
keep_prob=0.7,
is_training=is_training,
scope='dp1')
logits = tf_util.conv1d(net,
NUM_SEG_CLASSES,
1,
padding='VALID',
activation_fn=None,
scope='conv1d-fc2')
end_points['foreground_logits'] = logits
return end_points
def reduce_proposals(self, end_points):
'''Use NMS to reduce the number of proposals'''
batch_size = end_points['fg_points_xyz'].shape[0]
# confidence
fg_logits = tf.gather_nd(end_points['foreground_logits'],
end_points['fg_point_indices']) # (B,M)
seg_scores = tf.reduce_max(tf.nn.softmax(fg_logits), axis=-1) # (B,M)
bin_x_scores = tf.reduce_max(tf.nn.softmax(
end_points['center_x_scores']),
axis=-1) # (B,M)
bin_z_scores = tf.reduce_max(tf.nn.softmax(
end_points['center_z_scores']),
axis=-1) # (B,M)
heading_scores = tf.reduce_max(tf.nn.softmax(
end_points['heading_scores']),
axis=-1) # (B,M)
size_scores = tf.reduce_max(tf.nn.softmax(end_points['size_scores']),
axis=-1) # (B,M)
# confidence = seg_scores + bin_x_scores + bin_z_scores + heading_scores + size_scores
confidence = seg_scores * bin_x_scores * bin_z_scores * heading_scores * size_scores
confidence.set_shape([batch_size, NUM_FG_POINT])
end_points['proposal_scores'] = confidence
# BEV boxes
boxes_3d = end_points['proposal_boxes'] # (B,M,8,3)
corners_min = tf.gather(tf.reduce_min(boxes_3d, axis=2), [0, 2],
axis=-1)
corners_max = tf.gather(tf.reduce_max(boxes_3d, axis=2), [0, 2],
axis=-1) # (B,M,2) x,z
boxes_bev = tf.concat([corners_min, corners_max], axis=-1) # (B,M,4)
boxes_bev.set_shape([batch_size, NUM_FG_POINT, 4])
confidence_unpack = tf.unstack(confidence, axis=0)
boxes_bev_unpack = tf.unstack(boxes_bev, axis=0)
#boxes_3d_unpack = tf.unstack(end_points['proposal_boxes'], axis=0)
#boxes_3d_list = []
batch_nms_indices = []
for i in range(len(confidence_unpack)):
nms_indices = tf.image.non_max_suppression(boxes_bev_unpack[i],
confidence_unpack[i],
300) # at most 300
#boxes_3d_list.append(tf.gather(boxes_3d_unpack[i], nms_indices))
nms_indices = tf.pad(
nms_indices, [[0, NUM_FG_POINT - tf.shape(nms_indices)[0]]],
mode='CONSTANT',
constant_values=-1)
batch_nms_indices.append(nms_indices)
end_points['nms_indices'] = tf.stack(batch_nms_indices, axis=0)
return end_points
def get_region_proposal_net(self, point_feats, is_training, bn_decay,
end_points):
batch_size = point_feats.get_shape()[0].value
npoints = point_feats.get_shape()[1].value
point_feats = tf.slice(point_feats, [0, 0, 3],
[-1, -1, -1]) # (B, N, D)
net = tf.reshape(point_feats, [batch_size * npoints, -1])
# Fully connected layers
net = tf_util.fully_connected(net,
256,
bn=True,
is_training=is_training,
scope='rp-fc0',
bn_decay=bn_decay)
#net = tf_util.dropout(net, keep_prob=0.7,
# is_training=is_training, scope='rp-dp0')
net = tf_util.fully_connected(net,
256,
bn=True,
is_training=is_training,
scope='rp-fc1',
bn_decay=bn_decay)
#net = tf_util.dropout(net, keep_prob=0.7,
# is_training=is_training, scope='rp-dp1')
net = tf_util.fully_connected(net,
512,
bn=True,
is_training=is_training,
scope='rp-fc2',
bn_decay=bn_decay)
#net = tf_util.dropout(net, keep_prob=0.7,
# is_training=is_training, scope='rp-dp2')
# The first NUM_CENTER_BIN*2*2: CENTER_BIN class scores and bin residuals for (x,z)
# next 1: center residual for y
# next NUM_HEADING_BIN*2: heading bin class scores and residuals
# next NUM_SIZE_CLUSTER*4: size cluster class scores and residuals(l,w,h)
output = tf_util.fully_connected(net,
NUM_CENTER_BIN * 2 * 2 + 1 +
NUM_HEADING_BIN * 2 +
NUM_SIZE_CLUSTER * 4,
activation_fn=None,
scope='rp-fc3')
end_points['proposals'] = output
return output
def build(self):
point_cloud = self.placeholders['pointclouds']
is_training = self.placeholders['is_training_pl']
mask_label = self.placeholders['seg_labels']
bn_decay = self.bn_decay
end_points = self.end_points
#with tf.device('/gpu:0'):
img_feature_maps = self.build_img_extractor() # (B,360,1200,C)
pts2d = projection.tf_rect_to_image(
tf.slice(point_cloud, [0, 0, 0], [-1, -1, 3]),
self.placeholders['calib'])
pts2d = tf.cast(pts2d, tf.int32) #(B,N,2)
indices = tf.concat(
[
tf.expand_dims(tf.tile(tf.range(0, self.batch_size),
[self.num_point]),
axis=-1), # (B*N, 1)
tf.reshape(pts2d, [self.batch_size * self.num_point, 2])
],
axis=-1) # (B*N,3)
indices = tf.gather(indices, [0, 2, 1],
axis=-1) # image's shape is (y,x)
end_points['point_img_feats'] = tf.reshape(
tf.gather_nd(img_feature_maps, indices), # (B*N,C)
[self.batch_size, self.num_point, -1]) # (B,N,C)
end_points = self.get_segmentation_net(point_cloud, is_training,
bn_decay, end_points)
#with tf.device('/gpu:1'):
#seg_softmax = tf.nn.softmax(end_points['foreground_logits'], axis=-1) + self.placeholders['img_seg_softmax']
seg_softmax = tf.nn.softmax(end_points['foreground_logits'], axis=-1)
seg_logits = tf.cond(is_training,
lambda: tf.one_hot(mask_label, NUM_SEG_CLASSES),
lambda: seg_softmax)
#end_points['point_feats_fuse'] = tf.concat([end_points['point_feats_fuse'], seg_logits], axis=-1)
# fg_point_feats include xyz
fg_point_feats, end_points = point_cloud_masking(
end_points['point_feats'], seg_logits, end_points,
xyz_only=False) # BxNUM_FG_POINTxD
proposals = self.get_region_proposal_net(fg_point_feats, is_training,
bn_decay, end_points)
proposals_reshaped = tf.reshape(proposals,
[self.batch_size, NUM_FG_POINT, -1])
# Parse output to 3D box parameters
end_points = self.parse_output_to_tensors(proposals_reshaped,
end_points)
end_points = self.reduce_proposals(end_points)
# for iou eval
end_points['gt_box_of_point'] = tf.gather_nd(
self.placeholders['gt_box_of_point'],
end_points['fg_point_indices'])
end_points['gt_box_of_point'].set_shape(
[self.batch_size, NUM_FG_POINT, 8, 3])
return end_points
def get_seg_loss(self):
pls = self.placeholders
end_points = self.end_points
batch_size = self.batch_size
# 3D Segmentation loss
mask_loss = focal_loss(
end_points['foreground_logits'],
tf.one_hot(pls['seg_labels'], NUM_SEG_CLASSES, axis=-1))
tf.summary.scalar('mask loss', mask_loss)
return mask_loss, {}
def get_loss(self):
pls = self.placeholders
end_points = self.end_points
batch_size = self.batch_size
# 3D Segmentation loss
mask_loss = focal_loss(
end_points['foreground_logits'],
tf.one_hot(pls['seg_labels'], NUM_SEG_CLASSES, axis=-1))
tf.summary.scalar('mask loss', mask_loss)
#return mask_loss, {}
# gather box estimation labels of foreground points
labels_fg = {}
for k in pls.keys():
if k not in [
'center_bin_x_labels',
'center_bin_z_labels',
'center_x_residuals_labels',
'center_z_residuals_labels',
'center_y_residuals_labels',
'heading_bin_labels',
'heading_residuals_labels',
'size_class_labels',
'size_residuals_labels',
]:
continue
labels_fg[k] = tf.gather_nd(pls[k], end_points['fg_point_indices'])
if k == 'size_residuals_labels':
labels_fg[k].set_shape([batch_size, NUM_FG_POINT, 3])
else:
labels_fg[k].set_shape([batch_size, NUM_FG_POINT])
# Center loss
center_x_cls_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\
logits=end_points['center_x_scores'], labels=labels_fg['center_bin_x_labels']))
center_z_cls_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\
logits=end_points['center_z_scores'], labels=labels_fg['center_bin_z_labels']))
bin_x_onehot = tf.one_hot(labels_fg['center_bin_x_labels'],
depth=NUM_CENTER_BIN,
on_value=1,
off_value=0,
axis=-1) # BxNxNUM_CENTER_BIN
# NOTICE: labels['center_x_residuals'] is already normalized
center_x_residuals_normalized = tf.reduce_sum(
end_points['center_x_residuals_normalized'] *
tf.to_float(bin_x_onehot),
axis=2) # BxN
center_x_residuals_dist = tf.norm(
labels_fg['center_x_residuals_labels'] -
center_x_residuals_normalized,
axis=-1)
center_x_res_loss = huber_loss(center_x_residuals_dist, delta=2.0)
bin_z_onehot = tf.one_hot(labels_fg['center_bin_z_labels'],
depth=NUM_CENTER_BIN,
on_value=1,
off_value=0,
axis=-1) # BxNxNUM_CENTER_BIN
center_z_residuals_normalized = tf.reduce_sum(
end_points['center_z_residuals_normalized'] *
tf.to_float(bin_z_onehot),
axis=2) # BxN
center_z_residuals_dist = tf.norm(
labels_fg['center_z_residuals_labels'] -
center_z_residuals_normalized,
axis=-1)
center_z_res_loss = huber_loss(center_z_residuals_dist, delta=2.0)
# y is directly regressed
center_y_residuals_dist = tf.norm(
labels_fg['center_y_residuals_labels'] -
tf.gather(end_points['center_y_residuals'], 0, axis=-1),
axis=-1)
center_y_res_loss = huber_loss(center_y_residuals_dist, delta=2.0)
tf.summary.scalar('center_x class loss', center_x_cls_loss)
tf.summary.scalar('center_z class loss', center_z_cls_loss)
tf.summary.scalar('center_x residual loss', center_x_res_loss)
tf.summary.scalar('center_y residual loss', center_y_res_loss)
tf.summary.scalar('center_z residual loss', center_z_res_loss)
# Heading loss
heading_class_loss = tf.reduce_mean( \
tf.nn.sparse_softmax_cross_entropy_with_logits( \
logits=end_points['heading_scores'], labels=labels_fg['heading_bin_labels']))
hcls_onehot = tf.one_hot(labels_fg['heading_bin_labels'],
depth=NUM_HEADING_BIN,
on_value=1,
off_value=0,
axis=-1) # BxNxNUM_HEADING_BIN
heading_residual_normalized_label = labels_fg[
'heading_residuals_labels']
heading_res_dist = tf.norm(heading_residual_normalized_label - tf.reduce_sum( \
end_points['heading_residuals_normalized']*tf.to_float(hcls_onehot), axis=2))
heading_res_loss = huber_loss(heading_res_dist, delta=1.0)
tf.summary.scalar('heading class loss', heading_class_loss)
tf.summary.scalar('heading residual loss', heading_res_loss)
# Size loss
size_class_loss = tf.reduce_mean( \
tf.nn.sparse_softmax_cross_entropy_with_logits( \
logits=end_points['size_scores'], labels=labels_fg['size_class_labels']))
scls_onehot = tf.one_hot(labels_fg['size_class_labels'],
depth=NUM_SIZE_CLUSTER,
on_value=1,
off_value=0,
axis=-1) # BxNxNUM_SIZE_CLUSTER
scls_onehot_tiled = tf.tile(tf.expand_dims( \
tf.to_float(scls_onehot), -1), [1,1,1,3]) # BxNxNUM_SIZE_CLUSTERx3
predicted_size_residual_normalized = tf.reduce_sum( \
end_points['size_residuals_normalized']*scls_onehot_tiled, axis=2) # BxNx3
size_residual_label_normalized = labels_fg[
'size_residuals_labels'] # BxNx3
size_dist = tf.norm(size_residual_label_normalized -
predicted_size_residual_normalized,
axis=-1)
size_res_loss = huber_loss(size_dist, delta=1.0)
tf.summary.scalar('size class loss', size_class_loss)
tf.summary.scalar('size residual loss', size_res_loss)
seg_weight = 0.1
cls_weight = 10
res_weight = 10
total_loss = seg_weight * mask_loss + \
cls_weight * (center_x_cls_loss + center_z_cls_loss + heading_class_loss + size_class_loss) + \
res_weight * (center_x_res_loss + center_z_res_loss + center_y_res_loss + heading_res_loss + size_res_loss)
loss_endpoints = {
'size_class_loss': size_class_loss,
'size_res_loss': size_res_loss,
'heading_class_loss': heading_class_loss,
'heading_res_loss': heading_res_loss,
'center_x_cls_loss': center_x_cls_loss,
'center_z_cls_loss': center_z_cls_loss,
'center_x_res_loss': center_x_res_loss,
'center_z_res_loss': center_z_res_loss,
'center_y_res_loss': center_y_res_loss,
'mask_loss': mask_loss
}
return total_loss, loss_endpoints
class SingleStageDetector:
def __init__(self, batch_size, is_training):
self.batch_size = batch_size
self.is_training = is_training
# placeholders
self.placeholders_builder = PlaceHolders(self.batch_size)
self.placeholders_builder.get_placeholders()
self.placeholders = self.placeholders_builder.placeholders
self.cls_list = cfg.DATASET.KITTI.CLS_LIST
self.cls2idx = dict([(cls, i + 1) for i, cls in enumerate(self.cls_list)])
self.idx2cls = dict([(i + 1, cls) for i, cls in enumerate(self.cls_list)])
# anchor_builder
self.anchor_builder = Anchors(0, self.cls_list)
# encoder_decoder
self.encoder_decoder = EncoderDecoder(0)
# postprocessor
self.postprocessor = PostProcessor(0, len(self.cls_list))
# loss builder
self.loss_builder = LossBuilder(0)
self.corner_loss = cfg.MODEL.FIRST_STAGE.CORNER_LOSS
# head builder
self.iou_loss = False
self.heads = []
head_cfg = cfg.MODEL.NETWORK.FIRST_STAGE.HEAD
for i in range(len(head_cfg)):
self.heads.append(HeadBuilder(self.batch_size,
self.anchor_builder.anchors_num, 0, head_cfg[i], is_training))
if self.heads[-1].layer_type == 'IoU': self.iou_loss = True
# target assigner
self.target_assigner = TargetAssigner(0) # first stage
self.vote_loss = False
# layer builder
layer_cfg = cfg.MODEL.NETWORK.FIRST_STAGE.ARCHITECTURE
layers = []
for i in range(len(layer_cfg)):
layers.append(LayerBuilder(i, self.is_training, layer_cfg))
if layers[-1].layer_type == 'Vote_Layer': self.vote_loss = True
self.layers = layers
self.attr_velo_loss = cfg.MODEL.FIRST_STAGE.PREDICT_ATTRIBUTE_AND_VELOCITY
self.__init_dict()
def __init_dict(self):
self.output = dict()
# sampled xyz/feature
self.output[maps_dict.KEY_OUTPUT_XYZ] = []
self.output[maps_dict.KEY_OUTPUT_FEATURE] = []
# generated anchors
self.output[maps_dict.KEY_ANCHORS_3D] = [] # generated anchors
# vote output
self.output[maps_dict.PRED_VOTE_OFFSET] = []
self.output[maps_dict.PRED_VOTE_BASE] = []
# det output
self.output[maps_dict.PRED_CLS] = []
self.output[maps_dict.PRED_OFFSET] = []
self.output[maps_dict.PRED_ANGLE_CLS] = []
self.output[maps_dict.PRED_ANGLE_RES] = []
self.output[maps_dict.CORNER_LOSS_PRED_BOXES_CORNERS] = []
self.output[maps_dict.PRED_ATTRIBUTE] = []
self.output[maps_dict.PRED_VELOCITY] = []
# iou output
self.output[maps_dict.PRED_IOU_3D_VALUE] = []
# final result
self.output[maps_dict.PRED_3D_BBOX] = []
self.output[maps_dict.PRED_3D_SCORE] = []
self.output[maps_dict.PRED_3D_CLS_CATEGORY] = []
self.output[maps_dict.PRED_3D_ATTRIBUTE] = []
self.output[maps_dict.PRED_3D_VELOCITY] = []
self.prediction_keys = self.output.keys()
self.labels = dict()
self.labels[maps_dict.GT_CLS] = []
self.labels[maps_dict.GT_OFFSET] = []
self.labels[maps_dict.GT_ANGLE_CLS] = []
self.labels[maps_dict.GT_ANGLE_RES] = []
self.labels[maps_dict.GT_ATTRIBUTE] = []
self.labels[maps_dict.GT_VELOCITY] = []
self.labels[maps_dict.GT_BOXES_ANCHORS_3D] = []
self.labels[maps_dict.GT_IOU_3D_VALUE] = []
self.labels[maps_dict.GT_PMASK] = []
self.labels[maps_dict.GT_NMASK] = []
self.labels[maps_dict.CORNER_LOSS_GT_BOXES_CORNERS] = []
def build_img_extractor(self, img_input):
self._img_pixel_size = np.asarray([360, 1200])
VGG_config = namedtuple('VGG_config', 'vgg_conv1 vgg_conv2 vgg_conv3 vgg_conv4 l2_weight_decay')
self._img_feature_extractor = ImgVggPyr(VGG_config(**{
'vgg_conv1': [2, 32],
'vgg_conv2': [2, 64],
'vgg_conv3': [3, 128],
'vgg_conv4': [3, 256],
'l2_weight_decay': 0.0005
}))
self._img_preprocessed = \
self._img_feature_extractor.preprocess_input(img_input, self._img_pixel_size)
# self._img_preprocessed = img_input
self.img_feature_maps, self.img_end_points = \
self._img_feature_extractor.build(
self._img_preprocessed,
self._img_pixel_size,
self.is_training)
#return self.img_feature_maps
self.img_bottleneck = slim.conv2d(
self.img_feature_maps,
128, [1, 1],
#2, [1, 1],
scope='bottleneck',
normalizer_fn=slim.batch_norm,
#normalizer_fn=None,
normalizer_params={
'is_training': self.is_training})
return self.img_bottleneck
def network_forward(self, point_cloud, bn_decay, img_input):
l0_xyz = tf.slice(point_cloud, [0,0,0], [-1,-1,3])
l0_points = tf.slice(point_cloud, [0,0,3], [-1,-1,-1])
num_point = l0_xyz.get_shape().as_list()[1]
img_feature_maps = self.build_img_extractor(img_input)
pts2d = projection.tf_rect_to_image(tf.slice(point_cloud, [0, 0, 0], [-1, -1, 3]),
self.placeholders[maps_dict.PL_CALIB_P2])
pts2d = tf.cast(pts2d, tf.int32) # (B,N,2)
indices = tf.concat([
tf.expand_dims(tf.tile(tf.range(0, self.batch_size), [num_point]), axis=-1), # (B*N, 1)
tf.reshape(pts2d, [self.batch_size * num_point, 2])
], axis=-1) # (B*N,3)
indices = tf.gather(indices, [0, 2, 1], axis=-1) # image's shape is (y,x)
point_img_feats = tf.reshape(tf.gather_nd(img_feature_maps, indices), # (B*N,C)
[self.batch_size, num_point, -1]) # (B,N,C)
xyz_list, feature_list, fps_idx_list, point_img_feats_list = [l0_xyz], [l0_points], [None], [point_img_feats]
for layer in self.layers:
xyz_list, feature_list, fps_idx_list, point_img_feats_list = layer.build_layer(xyz_list, feature_list, fps_idx_list, bn_decay, self.output, point_img_feats_list)
cur_head_start_idx = len(self.output[maps_dict.KEY_OUTPUT_XYZ])
for head in self.heads:
head.build_layer(xyz_list, feature_list, bn_decay, self.output)
merge_head_prediction(cur_head_start_idx, self.output, self.prediction_keys)
def model_forward(self, bn_decay=None):
points_input_det = self.placeholders[maps_dict.PL_POINTS_INPUT]
img_input_det = self.placeholders[maps_dict.PL_IMG_INPUT]
# forward the point cloud
self.network_forward(points_input_det, bn_decay, img_input_det)
# generate anchors
base_xyz = self.output[maps_dict.KEY_OUTPUT_XYZ][-1]
anchors = self.anchor_builder.generate(base_xyz) # [bs, pts_num, 1/cls_num, 7]
self.output[maps_dict.KEY_ANCHORS_3D].append(anchors)
if self.is_training: # training mode
self.train_forward(-1, anchors)
else: # testing mode
self.test_forward(-1, anchors)
def train_forward(self, index, anchors):
"""
Calculating loss
"""
base_xyz = self.output[maps_dict.KEY_OUTPUT_XYZ][index]
pred_offset = self.output[maps_dict.PRED_OFFSET][index]
pred_angle_cls = self.output[maps_dict.PRED_ANGLE_CLS][index]
pred_angle_res = self.output[maps_dict.PRED_ANGLE_RES][index]
gt_boxes_3d = self.placeholders[maps_dict.PL_LABEL_BOXES_3D]
gt_classes = self.placeholders[maps_dict.PL_LABEL_CLASSES]
gt_angle_cls = self.placeholders[maps_dict.PL_ANGLE_CLS]
gt_angle_res = self.placeholders[maps_dict.PL_ANGLE_RESIDUAL]
if maps_dict.PL_LABEL_ATTRIBUTES in self.placeholders.keys():
gt_attributes = self.placeholders[maps_dict.PL_LABEL_ATTRIBUTES]
else: gt_attributes = None
if maps_dict.PL_LABEL_VELOCITY in self.placeholders.keys():
gt_velocity = self.placeholders[maps_dict.PL_LABEL_VELOCITY]
else: gt_velocity = None
returned_list = self.target_assigner.assign(base_xyz, anchors, gt_boxes_3d, gt_classes, gt_angle_cls, gt_angle_res, gt_velocity, gt_attributes)
assigned_idx, assigned_pmask, assigned_nmask, assigned_gt_boxes_3d, assigned_gt_labels, assigned_gt_angle_cls, assigned_gt_angle_res, assigned_gt_velocity, assigned_gt_attribute = returned_list
# encode offset
assigned_gt_offset, assigned_gt_angle_cls, assigned_gt_angle_res = self.encoder_decoder.encode(base_xyz, assigned_gt_boxes_3d, anchors)
# corner_loss
corner_loss_angle_cls = tf.cast(tf.one_hot(assigned_gt_angle_cls, depth=cfg.MODEL.ANGLE_CLS_NUM, on_value=1, off_value=0, axis=-1), tf.float32) # bs, pts_num, cls_num, -1
pred_anchors_3d = self.encoder_decoder.decode(base_xyz, pred_offset, corner_loss_angle_cls, pred_angle_res, self.is_training, anchors) # [bs, points_num, cls_num, 7]
pred_corners = transfer_box3d_to_corners(pred_anchors_3d) # [bs, points_num, cls_num, 8, 3]
gt_corners = transfer_box3d_to_corners(assigned_gt_boxes_3d) # [bs, points_num, cls_num,8,3]
self.output[maps_dict.CORNER_LOSS_PRED_BOXES_CORNERS].append(pred_corners)
self.labels[maps_dict.CORNER_LOSS_GT_BOXES_CORNERS].append(gt_corners)
self.labels[maps_dict.GT_CLS].append(assigned_gt_labels)
self.labels[maps_dict.GT_BOXES_ANCHORS_3D].append(assigned_gt_boxes_3d)
self.labels[maps_dict.GT_OFFSET].append(assigned_gt_offset)
self.labels[maps_dict.GT_ANGLE_CLS].append(assigned_gt_angle_cls)
self.labels[maps_dict.GT_ANGLE_RES].append(assigned_gt_angle_res)
self.labels[maps_dict.GT_ATTRIBUTE].append(assigned_gt_attribute)
self.labels[maps_dict.GT_VELOCITY].append(assigned_gt_velocity)
self.labels[maps_dict.GT_PMASK].append(assigned_pmask)
self.labels[maps_dict.GT_NMASK].append(assigned_nmask)
self.loss_builder.forward(index, self.labels, self.output, self.placeholders, self.corner_loss, self.vote_loss, self.attr_velo_loss, self.iou_loss)
def test_forward(self, index, anchors):
base_xyz = self.output[maps_dict.KEY_OUTPUT_XYZ][index]
pred_cls = self.output[maps_dict.PRED_CLS][index] # [bs, points_num, cls_num + 1/0]
pred_offset = self.output[maps_dict.PRED_OFFSET][index]
pred_angle_cls = self.output[maps_dict.PRED_ANGLE_CLS][index]
pred_angle_res = self.output[maps_dict.PRED_ANGLE_RES][index]
# decode predictions
pred_anchors_3d = self.encoder_decoder.decode(base_xyz, pred_offset, pred_angle_cls, pred_angle_res, self.is_training, anchors) # [bs, points_num, cls_num, 7]
# decode classification
if cfg.MODEL.FIRST_STAGE.CLS_ACTIVATION == 'Softmax':
# softmax
pred_score = tf.nn.softmax(pred_cls)
pred_score = tf.slice(pred_score, [0, 0, 1], [-1, -1, -1])
else: # sigmoid
pred_score = tf.nn.sigmoid(pred_cls)
# using IoU branch proposed by sparse-to-dense
if self.iou_loss:
pred_iou = self.output[maps_dict.PRED_IOU_3D_VALUE][index]
pred_score = pred_score * pred_iou
if len(self.output[maps_dict.PRED_ATTRIBUTE]) <= 0:
pred_attribute = None
else: pred_attribute = self.output[maps_dict.PRED_ATTRIBUTE][index]
if len(self.output[maps_dict.PRED_VELOCITY]) <= 0:
pred_velocity = None
else: pred_velocity = self.output[maps_dict.PRED_VELOCITY][index]
self.postprocessor.forward(pred_anchors_3d, pred_score, self.output, pred_attribute, pred_velocity)
class ImgSegNet(object):
"""docstring for ImgSegNet."""
def __init__(self,
batch_size,
num_point,
num_channel=4,
bn_decay=None,
is_training=True):
self.batch_size = batch_size
self.num_point = num_point
self.num_channel = num_channel
self.bn_decay = bn_decay
self.is_training = is_training
self.end_points = {}
self.placeholders = self.get_placeholders()
self.build()
def get_placeholders(self):
batch_size = self.batch_size
num_point = self.num_point
return {
'pointclouds':
tf.placeholder(tf.float32,
shape=(batch_size, num_point, self.num_channel)),
'img_inputs':
tf.placeholder(tf.float32, shape=(batch_size, 360, 1200, 3)),
'calib':
tf.placeholder(tf.float32, shape=(batch_size, 3, 4)),
'seg_labels':
tf.placeholder(tf.int32, shape=(batch_size, num_point)),
'is_training_pl':
tf.placeholder(tf.bool, shape=())
}
def build(self):
point_cloud = self.placeholders['pointclouds']
self._img_pixel_size = np.asarray([360, 1200])
bn_decay = self.bn_decay
is_training = self.placeholders['is_training_pl']
VGG_config = namedtuple(
'VGG_config',
'vgg_conv1 vgg_conv2 vgg_conv3 vgg_conv4 l2_weight_decay')
self._img_feature_extractor = ImgVggPyr(
VGG_config(
**{
'vgg_conv1': [2, 32],
'vgg_conv2': [2, 64],
'vgg_conv3': [3, 128],
'vgg_conv4': [3, 256],
'l2_weight_decay': 0.0005
}))
self._img_preprocessed = \
self._img_feature_extractor.preprocess_input(
self.placeholders['img_inputs'], self._img_pixel_size)
self.img_feature_maps, self.img_end_points = \
self._img_feature_extractor.build(
self._img_preprocessed,
self._img_pixel_size,
self.is_training)
'''
self.seg_logits = slim.conv2d(
self.img_feature_maps,
NUM_SEG_CLASSES, [1, 1],
scope='bottleneck',
normalizer_fn=slim.batch_norm,
#normalizer_fn=None,
normalizer_params={
'is_training': self.is_training})
'''
pts2d = projection.tf_rect_to_image(
tf.slice(point_cloud, [0, 0, 0], [-1, -1, 3]),
self.placeholders['calib'])
pts2d = tf.cast(pts2d, tf.int32) #(B,N,2)
indices = tf.concat(
[
tf.expand_dims(tf.tile(tf.range(0, self.batch_size),
[self.num_point]),
axis=-1), # (B*N, 1)
tf.reshape(pts2d, [self.batch_size * self.num_point, 2])
],
axis=-1) # (B*N,3)
indices = tf.gather(indices, [0, 2, 1],
axis=-1) # image's shape is (y,x)
self.end_points['point_img_feats'] = tf.reshape(
tf.gather_nd(self.img_feature_maps, indices), # (B*N,C)
[self.batch_size, self.num_point, -1]) # (B,N,C)
net = tf_util.conv1d(self.end_points['point_img_feats'],
128,
1,
padding='VALID',
bn=True,
is_training=is_training,
scope='img-seg-conv1d-fc1',
bn_decay=bn_decay)
net = tf_util.dropout(net,
keep_prob=0.7,
is_training=is_training,
scope='img-seg-dp1')
logits = tf_util.conv1d(net,
NUM_SEG_CLASSES,
1,
padding='VALID',
activation_fn=None,
scope='img-seg-conv1d-fc2')
self.end_points['foreground_logits'] = logits
def get_seg_softmax(self):
img_seg_softmax = tf.nn.softmax(self.end_points['foreground_logits'],
axis=-1)
return img_seg_softmax
def get_loss(self):
pls = self.placeholders
end_points = self.end_points
batch_size = self.batch_size
# 3D Segmentation loss
mask_loss = focal_loss(
end_points['foreground_logits'],
tf.one_hot(pls['seg_labels'], NUM_SEG_CLASSES, axis=-1))
tf.summary.scalar('mask loss', mask_loss)
return mask_loss