def __init__(self, cfgs, is_training):
self.cfgs = cfgs
self.base_network_name = cfgs.NET_NAME
self.is_training = is_training
if cfgs.METHOD == 'H':
self.num_anchors_per_location = len(cfgs.ANCHOR_SCALES) * len(cfgs.ANCHOR_RATIOS)
else:
self.num_anchors_per_location = len(cfgs.ANCHOR_SCALES) * len(cfgs.ANCHOR_RATIOS) * len(cfgs.ANCHOR_ANGLES)
self.method = cfgs.METHOD
self.losses_dict = {}
self.drawer = DrawBoxTensor(cfgs)
self.backbone = BuildBackbone(cfgs, is_training)
def __init__(self, cfgs, is_training):
self.cfgs = cfgs
self.base_network_name = cfgs.NET_NAME
self.is_training = is_training
if cfgs.ANCHOR_MODE == 'H':
self.num_anchors_per_location = len(cfgs.ANCHOR_SCALES) * len(
cfgs.ANCHOR_RATIOS)
else:
self.num_anchors_per_location = len(cfgs.ANCHOR_SCALES) * len(
cfgs.ANCHOR_RATIOS) * len(cfgs.ANCHOR_ANGLES)
self.anchor_mode = cfgs.ANCHOR_MODE
self.losses_dict = {}
self.drawer = DrawBoxTensor(cfgs)
self.backbone = BuildBackbone(cfgs, is_training)
self.pretrain_zoo = PretrainModelZoo()
ws, _ = tf.meshgrid(ws, anchor_angles)
hs, anchor_angles = tf.meshgrid(hs, anchor_angles)
anchor_angles = tf.reshape(anchor_angles, [-1, 1])
ws = tf.reshape(ws, [-1, 1])
hs = tf.reshape(hs, [-1, 1])
return ws, hs, anchor_angles
if __name__ == '__main__':
import os
from libs.configs import cfgs
# os.environ["CUDA_VISIBLE_DEVICES"] = '0'
from libs.utils.show_box_in_tensor import DrawBoxTensor
drawer = DrawBoxTensor(cfgs)
base_anchor_size = 256
anchor_scales = [1.]
anchor_ratios = [0.5, 2.0, 1 / 3, 3, 1 / 5, 5, 1 / 8, 8]
anchor_angles = [-90, -75, -60, -45, -30, -15]
base_anchor = tf.constant([0, 0, base_anchor_size, base_anchor_size],
tf.float32)
tmp1 = enum_ratios_and_thetas(enum_scales(base_anchor, anchor_scales),
anchor_ratios, anchor_angles)
anchors = make_anchors(32, [2.], [2.0, 1 / 2],
anchor_angles,
featuremap_height=800 // 8,
featuremap_width=800 // 8,
stride=8)
class DetectionNetworkBase(object):
def __init__(self, cfgs, is_training):
self.cfgs = cfgs
self.base_network_name = cfgs.NET_NAME
self.is_training = is_training
self.batch_size = cfgs.BATCH_SIZE if is_training else 1
if cfgs.METHOD == 'H':
self.num_anchors_per_location = len(cfgs.ANCHOR_SCALES) * len(
cfgs.ANCHOR_RATIOS)
else:
self.num_anchors_per_location = len(cfgs.ANCHOR_SCALES) * len(
cfgs.ANCHOR_RATIOS) * len(cfgs.ANCHOR_ANGLES)
self.method = cfgs.METHOD
self.losses_dict = {}
self.drawer = DrawBoxTensor(cfgs)
self.backbone = BuildBackbone(cfgs, is_training)
self.pretrain_zoo = PretrainModelZoo()
def build_backbone(self, input_img_batch):
return self.backbone.build_backbone(input_img_batch)
def rpn_cls_net(self, inputs, scope_list, reuse_flag, level):
rpn_conv2d_3x3 = inputs
for i in range(self.cfgs.NUM_SUBNET_CONV):
rpn_conv2d_3x3 = slim.conv2d(
inputs=rpn_conv2d_3x3,
num_outputs=self.cfgs.FPN_CHANNEL,
kernel_size=[3, 3],
stride=1,
activation_fn=None if self.cfgs.USE_GN else tf.nn.relu,
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
scope='{}_{}'.format(scope_list[0], i),
trainable=self.is_training,
reuse=reuse_flag)
if self.cfgs.USE_GN:
rpn_conv2d_3x3 = tf.contrib.layers.group_norm(rpn_conv2d_3x3)
rpn_conv2d_3x3 = tf.nn.relu(rpn_conv2d_3x3)
rpn_box_scores = slim.conv2d(
rpn_conv2d_3x3,
num_outputs=self.cfgs.CLASS_NUM * self.num_anchors_per_location,
kernel_size=[3, 3],
stride=1,
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.FINAL_CONV_BIAS_INITIALIZER,
scope=scope_list[2],
activation_fn=None,
trainable=self.is_training,
reuse=reuse_flag)
rpn_box_scores = tf.reshape(
rpn_box_scores, [self.batch_size, -1, self.cfgs.CLASS_NUM],
name='rpn_{}_classification_reshape'.format(level))
rpn_box_probs = tf.sigmoid(
rpn_box_scores, name='rpn_{}_classification_sigmoid'.format(level))
return rpn_box_scores, rpn_box_probs
def rpn_reg_net(self, inputs, scope_list, reuse_flag, level):
rpn_conv2d_3x3 = inputs
for i in range(self.cfgs.NUM_SUBNET_CONV):
rpn_conv2d_3x3 = slim.conv2d(
inputs=rpn_conv2d_3x3,
num_outputs=self.cfgs.FPN_CHANNEL,
kernel_size=[3, 3],
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
stride=1,
activation_fn=None if self.cfgs.USE_GN else tf.nn.relu,
scope='{}_{}'.format(scope_list[1], i),
trainable=self.is_training,
reuse=reuse_flag)
if self.cfgs.USE_GN:
rpn_conv2d_3x3 = tf.contrib.layers.group_norm(rpn_conv2d_3x3)
rpn_conv2d_3x3 = tf.nn.relu(rpn_conv2d_3x3)
rpn_delta_boxes = slim.conv2d(
rpn_conv2d_3x3,
num_outputs=5 * self.num_anchors_per_location,
kernel_size=[3, 3],
stride=1,
weights_initializer=self.cfgs.SUBNETS_WEIGHTS_INITIALIZER,
biases_initializer=self.cfgs.SUBNETS_BIAS_INITIALIZER,
scope=scope_list[3],
activation_fn=None,
trainable=self.is_training,
reuse=reuse_flag)
rpn_delta_boxes = tf.reshape(
rpn_delta_boxes, [self.batch_size, -1, 5],
name='rpn_{}_regression_reshape'.format(level))
return rpn_delta_boxes
def rpn_net(self, feature_pyramid, name):
rpn_delta_boxes_list = []
rpn_scores_list = []
rpn_probs_list = []
with tf.variable_scope(name):
with slim.arg_scope([slim.conv2d],
weights_regularizer=slim.l2_regularizer(
self.cfgs.WEIGHT_DECAY)):
for level in self.cfgs.LEVEL:
if self.cfgs.SHARE_NET:
reuse_flag = None if level == self.cfgs.LEVEL[
0] else True
scope_list = [
'conv2d_3x3_cls', 'conv2d_3x3_reg',
'rpn_classification', 'rpn_regression'
]
else:
reuse_flag = None
scope_list = [
'conv2d_3x3_cls_' + level,
'conv2d_3x3_reg_' + level,
'rpn_classification_' + level,
'rpn_regression_' + level
]
rpn_box_scores, rpn_box_probs = self.rpn_cls_net(
feature_pyramid[level], scope_list, reuse_flag, level)
rpn_delta_boxes = self.rpn_reg_net(feature_pyramid[level],
scope_list, reuse_flag,
level)
rpn_scores_list.append(rpn_box_scores)
rpn_probs_list.append(rpn_box_probs)
rpn_delta_boxes_list.append(rpn_delta_boxes)
return rpn_delta_boxes_list, rpn_scores_list, rpn_probs_list
def make_anchors(self, feature_pyramid, use_tf=False):
with tf.variable_scope('make_anchors'):
anchor = GenerateAnchors(self.cfgs, self.method)
if use_tf and self.method == 'H':
anchor_list = anchor.generate_all_anchor_tf(feature_pyramid)
else:
anchor_list = anchor.generate_all_anchor(feature_pyramid)
return anchor_list
def add_anchor_img_smry(self, img, anchors, labels, method):
positive_anchor_indices = tf.reshape(
tf.where(tf.greater_equal(labels, 1)), [-1])
# negative_anchor_indices = tf.reshape(tf.where(tf.equal(labels, 0)), [-1])
positive_anchor = tf.gather(anchors, positive_anchor_indices)
# negative_anchor = tf.gather(anchors, negative_anchor_indices)
pos_in_img = self.drawer.only_draw_boxes(img_batch=img,
boxes=positive_anchor,
method=method)
# neg_in_img = show_box_in_tensor.only_draw_boxes(img_batch=img,
# boxes=negative_anchor)
tf.summary.image('positive_anchor', pos_in_img)
# tf.summary.image('negative_anchors', neg_in_img)
def get_restorer(self):
checkpoint_path = tf.train.latest_checkpoint(
os.path.join(self.cfgs.TRAINED_CKPT, self.cfgs.VERSION))
if checkpoint_path is not None:
if self.cfgs.RESTORE_FROM_RPN:
print('___restore from rpn___')
model_variables = slim.get_model_variables()
restore_variables = [var for var in model_variables if not var.name.startswith('FastRCNN_Head')] + \
[slim.get_or_create_global_step()]
for var in restore_variables:
print(var.name)
restorer = tf.train.Saver(restore_variables)
else:
restorer = tf.train.Saver()
print("model restore from :", checkpoint_path)
else:
if self.cfgs.NET_NAME in self.pretrain_zoo.pth_zoo:
return None, None
checkpoint_path = self.cfgs.PRETRAINED_CKPT
print("model restore from pretrained mode, path is :",
checkpoint_path)
model_variables = slim.get_model_variables()
# for var in model_variables:
# print(var.name)
# print(20*"__++__++__")
def name_in_ckpt_rpn(var):
return var.op.name
def name_in_ckpt_fastrcnn_head(var):
'''
Fast-RCNN/resnet_v1_50/block4 -->resnet_v1_50/block4
Fast-RCNN/MobilenetV2/** -- > MobilenetV2 **
:param var:
:return:
'''
return '/'.join(var.op.name.split('/')[1:])
nameInCkpt_Var_dict = {}
for var in model_variables:
if var.name.startswith('Fast-RCNN/' +
self.base_network_name): # +'/block4'
var_name_in_ckpt = name_in_ckpt_fastrcnn_head(var)
nameInCkpt_Var_dict[var_name_in_ckpt] = var
else:
if var.name.startswith(self.base_network_name):
var_name_in_ckpt = name_in_ckpt_rpn(var)
nameInCkpt_Var_dict[var_name_in_ckpt] = var
else:
continue
restore_variables = nameInCkpt_Var_dict
for key, item in restore_variables.items():
print("var_in_graph: ", item.name)
print("var_in_ckpt: ", key)
print(20 * "___")
restorer = tf.train.Saver(restore_variables)
print(20 * "****")
print("restore from pretrained_weighs in IMAGE_NET")
return restorer, checkpoint_path
def __init__(self, cfgs):
self.cfgs = cfgs
self.reader = ReadTFRecord(cfgs)
self.drawer = DrawBoxTensor(cfgs)
class DetectionNetworkBase(object):
def __init__(self, cfgs, is_training):
self.cfgs = cfgs
self.base_network_name = cfgs.NET_NAME
self.is_training = is_training
if cfgs.ANCHOR_MODE == 'H':
self.num_anchors_per_location = len(cfgs.ANCHOR_SCALES) * len(
cfgs.ANCHOR_RATIOS)
else:
self.num_anchors_per_location = len(cfgs.ANCHOR_SCALES) * len(
cfgs.ANCHOR_RATIOS) * len(cfgs.ANCHOR_ANGLES)
self.anchor_mode = cfgs.ANCHOR_MODE
self.losses_dict = {}
self.drawer = DrawBoxTensor(cfgs)
self.backbone = BuildBackbone(cfgs, is_training)
self.pretrain_zoo = PretrainModelZoo()
def build_backbone(self, input_img_batch):
return self.backbone.build_backbone(input_img_batch)
def make_anchors(self, feature_pyramid):
with tf.variable_scope('make_anchors'):
anchor = GenerateAnchors(self.cfgs, self.anchor_mode)
anchor_list = anchor.generate_all_anchor(feature_pyramid)
return anchor_list
def rpn(self, feature_pyramid):
with tf.variable_scope('build_rpn',
regularizer=slim.l2_regularizer(
self.cfgs.WEIGHT_DECAY)):
fpn_cls_score = []
fpn_box_pred = []
for level_name in self.cfgs.LEVEL:
if self.cfgs.SHARE_HEADS:
reuse_flag = None if level_name == self.cfgs.LEVEL[
0] else True
scope_list = [
'rpn_conv/3x3', 'rpn_cls_score', 'rpn_bbox_pred'
]
else:
reuse_flag = None
scope_list = [
'rpn_conv/3x3_%s' % level_name,
'rpn_cls_score_%s' % level_name,
'rpn_bbox_pred_%s' % level_name
]
rpn_conv3x3 = slim.conv2d(
feature_pyramid[level_name],
self.cfgs.FPN_CHANNEL, [3, 3],
trainable=self.is_training,
weights_initializer=self.cfgs.INITIALIZER,
padding="SAME",
activation_fn=tf.nn.relu,
scope=scope_list[0],
reuse=reuse_flag)
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,
padding="VALID",
scope=scope_list[1],
reuse=reuse_flag)
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,
padding="VALID",
scope=scope_list[2],
reuse=reuse_flag)
rpn_box_pred = tf.reshape(rpn_box_pred, [-1, 4])
rpn_cls_score = tf.reshape(rpn_cls_score, [-1, 2])
fpn_cls_score.append(rpn_cls_score)
fpn_box_pred.append(rpn_box_pred)
fpn_cls_score = tf.concat(fpn_cls_score,
axis=0,
name='fpn_cls_score')
fpn_box_pred = tf.concat(fpn_box_pred, axis=0, name='fpn_box_pred')
fpn_cls_prob = slim.softmax(fpn_cls_score, scope='fpn_cls_prob')
return fpn_box_pred, fpn_cls_score, fpn_cls_prob
def postprocess_rpn_proposals(self, rpn_bbox_pred, rpn_cls_prob, img_shape,
anchors, is_training):
'''
:param rpn_bbox_pred: [-1, 4]
:param rpn_cls_prob: [-1, 2]
:param img_shape:
:param anchors:[-1, 4]
:param is_training:
:return:
'''
if is_training:
pre_nms_topN = self.cfgs.RPN_TOP_K_NMS_TRAIN
post_nms_topN = self.cfgs.RPN_MAXIMUM_PROPOSAL_TARIN
# pre_nms_topN = self.cfgs.FPN_TOP_K_PER_LEVEL_TRAIN
# post_nms_topN = pre_nms_topN
else:
pre_nms_topN = self.cfgs.RPN_TOP_K_NMS_TEST
post_nms_topN = self.cfgs.RPN_MAXIMUM_PROPOSAL_TEST
# pre_nms_topN = self.cfgs.FPN_TOP_K_PER_LEVEL_TEST
# post_nms_topN = pre_nms_topN
nms_thresh = self.cfgs.RPN_NMS_IOU_THRESHOLD
cls_prob = rpn_cls_prob[:, 1]
# 1. decode boxes
decode_boxes = bbox_transform.bbox_transform_inv(
boxes=anchors,
deltas=rpn_bbox_pred,
scale_factors=self.cfgs.ANCHOR_SCALE_FACTORS)
# 2. clip to img boundaries
decode_boxes = clip_boxes_to_img_boundaries(decode_boxes=decode_boxes,
img_shape=img_shape)
# 3. get top N to NMS
if pre_nms_topN > 0:
pre_nms_topN = tf.minimum(pre_nms_topN,
tf.shape(decode_boxes)[0],
name='avoid_unenough_boxes')
cls_prob, top_k_indices = tf.nn.top_k(cls_prob, k=pre_nms_topN)
decode_boxes = tf.gather(decode_boxes, top_k_indices)
# 4. NMS
keep = tf.image.non_max_suppression(boxes=decode_boxes,
scores=cls_prob,
max_output_size=post_nms_topN,
iou_threshold=nms_thresh)
final_boxes = tf.gather(decode_boxes, keep)
final_probs = tf.gather(cls_prob, keep)
return final_boxes, final_probs
def add_anchor_img_smry(self, img, anchors, labels, method):
positive_anchor_indices = tf.reshape(
tf.where(tf.greater_equal(labels, 1)), [-1])
negative_anchor_indices = tf.reshape(tf.where(tf.equal(labels, 0)),
[-1])
positive_anchor = tf.gather(anchors, positive_anchor_indices)
negative_anchor = tf.gather(anchors, negative_anchor_indices)
pos_in_img = self.drawer.only_draw_boxes(img_batch=img,
boxes=positive_anchor,
method=method)
neg_in_img = self.drawer.only_draw_boxes(img_batch=img,
boxes=negative_anchor,
method=method)
tf.summary.image('positive_anchor', pos_in_img)
tf.summary.image('negative_anchors', neg_in_img)
def add_roi_batch_img_smry(self, img, rois, labels, method):
positive_roi_indices = tf.reshape(
tf.where(tf.greater_equal(labels, 1)), [-1])
negative_roi_indices = tf.reshape(tf.where(tf.equal(labels, 0)), [-1])
pos_roi = tf.gather(rois, positive_roi_indices)
neg_roi = tf.gather(rois, negative_roi_indices)
pos_in_img = self.drawer.only_draw_boxes(img_batch=img,
boxes=pos_roi,
method=method)
neg_in_img = self.drawer.only_draw_boxes(img_batch=img,
boxes=neg_roi,
method=method)
tf.summary.image('pos_rois', pos_in_img)
tf.summary.image('neg_rois', neg_in_img)
def get_restorer(self):
checkpoint_path = tf.train.latest_checkpoint(
os.path.join(self.cfgs.TRAINED_CKPT, self.cfgs.VERSION))
if checkpoint_path is not None:
if self.cfgs.RESTORE_FROM_RPN:
print('___restore from rpn___')
model_variables = slim.get_model_variables()
restore_variables = [var for var in model_variables if not var.name.startswith('FastRCNN_Head')] + \
[slim.get_or_create_global_step()]
for var in restore_variables:
print(var.name)
restorer = tf.train.Saver(restore_variables)
else:
restorer = tf.train.Saver()
print("model restore from :", checkpoint_path)
else:
if self.cfgs.NET_NAME in self.pretrain_zoo.pth_zoo:
return None, None
checkpoint_path = self.cfgs.PRETRAINED_CKPT
print("model restore from pretrained mode, path is :",
checkpoint_path)
model_variables = slim.get_model_variables()
# for var in model_variables:
# print(var.name)
# print(20*"__++__++__")
def name_in_ckpt_rpn(var):
return var.op.name
def name_in_ckpt_fastrcnn_head(var):
'''
Fast-RCNN/resnet_v1_50/block4 -->resnet_v1_50/block4
Fast-RCNN/MobilenetV2/** -- > MobilenetV2 **
:param var:
:return:
'''
return '/'.join(var.op.name.split('/')[1:])
nameInCkpt_Var_dict = {}
for var in model_variables:
if var.name.startswith('Fast-RCNN/' +
self.base_network_name): # +'/block4'
var_name_in_ckpt = name_in_ckpt_fastrcnn_head(var)
nameInCkpt_Var_dict[var_name_in_ckpt] = var
else:
if var.name.startswith(self.base_network_name):
var_name_in_ckpt = name_in_ckpt_rpn(var)
nameInCkpt_Var_dict[var_name_in_ckpt] = var
else:
continue
restore_variables = nameInCkpt_Var_dict
for key, item in restore_variables.items():
print("var_in_graph: ", item.name)
print("var_in_ckpt: ", key)
print(20 * "___")
restorer = tf.train.Saver(restore_variables)
print(20 * "****")
print("restore from pretrained_weighs in IMAGE_NET")
return restorer, checkpoint_path
def assign_levels(self, all_rois, labels=None, bbox_targets=None):
'''
:param all_rois:
:param labels:
:param bbox_targets:
:return:
'''
with tf.name_scope('assign_levels'):
# all_rois = tf.Print(all_rois, [tf.shape(all_rois)], summarize=10, message='ALL_ROIS_SHAPE*****')
xmin, ymin, xmax, ymax = tf.unstack(all_rois, axis=1)
h = tf.maximum(0., ymax - ymin)
w = tf.maximum(0., xmax - xmin)
levels = tf.floor(4. + tf.log(tf.sqrt(w * h + 1e-8) / 224.0) /
tf.log(2.)) # 4 + log_2(***)
# use floor instead of round
min_level = int(self.cfgs.LEVEL[0][-1])
max_level = min(5, int(self.cfgs.LEVEL[-1][-1]))
levels = tf.maximum(levels,
tf.ones_like(levels) *
min_level) # level minimum is 2
levels = tf.minimum(levels,
tf.ones_like(levels) *
max_level) # level maximum is 5
levels = tf.stop_gradient(tf.reshape(levels, [-1]))
def get_rois(levels, level_i, rois, labels, bbox_targets):
level_i_indices = tf.reshape(
tf.where(tf.equal(levels, level_i)), [-1])
tf.summary.scalar('LEVEL/LEVEL_%d_rois_NUM' % level_i,
tf.shape(level_i_indices)[0])
level_i_rois = tf.gather(rois, level_i_indices)
if self.is_training:
if not self.cfgs.CUDA8:
# Note: for > cuda 9
level_i_rois = tf.stop_gradient(level_i_rois)
level_i_labels = tf.gather(labels, level_i_indices)
level_i_targets = tf.gather(bbox_targets,
level_i_indices)
else:
# Note: for cuda 8
level_i_rois = tf.stop_gradient(
tf.concat([level_i_rois, [[0, 0, 0., 0.]]],
axis=0))
# to avoid the num of level i rois is 0.0, which will broken the BP in tf
level_i_labels = tf.gather(labels, level_i_indices)
level_i_labels = tf.stop_gradient(
tf.concat([level_i_labels, [0]], axis=0))
level_i_targets = tf.gather(bbox_targets,
level_i_indices)
level_i_targets = tf.stop_gradient(
tf.concat([
level_i_targets,
tf.zeros(shape=(1, 5 *
(self.cfgs.CLASS_NUM + 1)),
dtype=tf.float32)
],
axis=0))
return level_i_rois, level_i_labels, level_i_targets
else:
if self.cfgs.CUDA8:
# Note: for cuda 8
level_i_rois = tf.concat(
[level_i_rois, [[0, 0, 0., 0.]]], axis=0)
return level_i_rois, None, None
rois_list = []
labels_list = []
targets_list = []
for i in range(min_level, max_level + 1):
P_i_rois, P_i_labels, P_i_targets = get_rois(
levels,
level_i=i,
rois=all_rois,
labels=labels,
bbox_targets=bbox_targets)
rois_list.append(P_i_rois)
labels_list.append(P_i_labels)
targets_list.append(P_i_targets)
if self.is_training:
all_labels = tf.concat(labels_list, axis=0)
all_targets = tf.concat(targets_list, axis=0)
return rois_list, all_labels, all_targets
else:
return rois_list # [P2_rois, P3_rois, P4_rois, P5_rois] Note: P6 do not assign rois
