def __init__(self, class_num):
inp_holder = tf.placeholder(tf.float32, [None, 460, 460, 3])
lab_holder = tf.placeholder(tf.int32, [None, 460, 460])
mask_holder = tf.placeholder(tf.float32, [None, 460, 460])
mask = tf.expand_dims(mask_holder, -1)
c_ = tf.concat([inp_holder, mask], -1)
merged_layer = self.merging_layer(c_)
self.net_body = seg_main_body(merged_layer)
seg_layer = self.segmentation_layer(self.net_body.feature_layer, 12,
class_num)
self.build_loss(seg_layer, lab_holder)
self.saver = tf.train.Saver()
self.sess = tf.Session()
M.loadSess('./model/',
self.sess,
init=True,
var_list=self.net_body.var)
self.inp_holder = inp_holder
self.lab_holder = lab_holder
self.seg_layer = seg_layer
self.mask_holder = mask_holder
def __init__(self,img_holder, class_num, mask_layer, seg_layer):
self.size = 460
self.class_num = class_num
# build placeholders
inp_holder = img_holder
seg_holder = seg_layer
mask_holder = mask_layer
coord_holder = tf.placeholder(tf.float32,[None,size,size,6],name='coordinate_holder')
inst_holder = tf.placeholder(tf.float32,[None,class_num],name='instance_holder')
# construct input (4 -> 3 with 1x1 conv)
merged_layer = self.merging_layer(inp_holder,seg_holder,mask_holder)
# build network
self.get_coord(size)
self.net_body = seg_main_body(merged_layer)
stream_list = self.get_stream_list(self.net_body.feature_maps)
inst_pred = self.inst_layer(self.net_body.feature_layer,stream_list[-1],class_num)
self.build_loss(seg_layer,stream_list,inst_pred,lab_holder,mask_holder,coord_holder,inst_holder)
# set class variables
# holders
self.inp_holder = inp_holder
self.lab_holder = lab_holder
self.mask_holder = mask_holder
self.coord_holder = coord_holder
self.inst_holder = inst_holder
# layers
self.coord_layer = stream_list[-1]
self.inst_layer = inst_pred
def __init__(self, img_holder):
inp_holder = img_holder
self.net_body = seg_main_body(inp_holder)
seg_layer = self.segmentation_layer(self.net_body.feature_layer, 12)
self.inp_holder = inp_holder
self.seg_layer = seg_layer
def __init__(self,img_holder):
inp_holder = img_holder
lab_holder = tf.placeholder(tf.int32,[None,460,460])
self.net_body = seg_main_body(inp_holder)
seg_layer = self.segmentation_layer(self.net_body.feature_layer,12)
self.build_loss(seg_layer,lab_holder)
self.inp_holder = inp_holder
self.lab_holder = lab_holder
self.seg_layer = seg_layer
def __init__(self, img_holder, class_num, mask_layer):
inp_holder = img_holder
mask = tf.expand_dims(mask_layer, -1)
c_ = tf.concat([inp_holder, mask], -1)
merged_layer = self.merging_layer(c_)
self.net_body = seg_main_body(merged_layer)
seg_layer = self.segmentation_layer(self.net_body.feature_layer, 12,
class_num)
self.inp_holder = inp_holder
self.seg_layer = seg_layer
self.mask_layer = mask_layer
def __init__(self):
inp_holder = tf.placeholder(tf.float32,[None,460,460,3])
lab_holder = tf.placeholder(tf.int32,[None,460,460])
self.net_body = seg_main_body(inp_holder)
seg_layer = self.segmentation_layer(self.net_body.feature_layer,12)
self.build_loss(seg_layer,lab_holder)
self.saver = tf.train.Saver()
self.sess = tf.Session()
M.loadSess('./savings_bgfg/',self.sess,init=True,var_list=M.get_trainable_vars('bg_fg/WideRes'))
self.inp_holder = inp_holder
self.lab_holder = lab_holder
self.seg_layer = seg_layer
def __init__(self, class_num):
self.size = 460
self.class_num = 20
# build placeholders
inp_holder = tf.placeholder(tf.float32, [None, size, size, 3],
name='image_holder')
seg_holder = tf.placeholder(tf.float32, [None, size, size, class_num],
name='segment_holder')
mask_holder = tf.placeholder(tf.float32, [None, size, size],
name='mask_holder')
coord_holder = tf.placeholder(tf.float32, [None, size, size, 6],
name='coordinate_holder')
inst_holder = tf.placeholder(tf.float32, [None, class_num],
name='instance_holder')
# construct input (4 -> 3 with 1x1 conv)
merged_layer = self.merging_layer(inp_holder, seg_holder, mask_holder)
# build network
self.get_coord(size)
self.net_body = seg_main_body(merged_layer)
stream_list = self.get_stream_list(self.net_body.feature_maps)
inst_pred = self.inst_layer(self.net_body.feature_layer,
stream_list[-1], class_num)
self.build_loss(seg_layer, stream_list, inst_pred, lab_holder,
mask_holder, coord_holder, inst_holder)
# build saver and session
self.saver = tf.train.Saver()
self.sess = tf.Session()
# self.writer = tf.summary.FileWriter('./logs/',self.sess.graph)
M.loadSess('./model/',
self.sess,
init=True,
var_list=self.net_body.var)
# set class variables
# holders
self.inp_holder = inp_holder
self.lab_holder = lab_holder
self.mask_holder = mask_holder
self.coord_holder = coord_holder
self.inst_holder = inst_holder
# layers
self.coord_layer = stream_list[-1]
self.inst_layer = inst_pred
def __init__(self,img_holder,class_num,mask_layer):
inp_holder = img_holder
lab_holder = tf.placeholder(tf.int32,[None,460,460])
mask = tf.expand_dims(mask_layer,-1)
c_ = tf.concat([inp_holder,mask],-1)
merged_layer = self.merging_layer(c_)
self.net_body = seg_main_body(merged_layer)
seg_layer = self.segmentation_layer(self.net_body.feature_layer,12,class_num)
self.build_loss(seg_layer,lab_holder)
self.inp_holder = inp_holder
self.lab_holder = lab_holder
self.seg_layer = seg_layer
self.mask_layer = mask_layer
def __init__(self):
inp_holder = tf.placeholder(tf.float32, [None, 460, 460, 3])
lab_holder = tf.placeholder(tf.int32, [None, 460, 460])
self.net_body = seg_main_body(inp_holder)
seg_layer = self.segmentation_layer(self.net_body.feature_layer, 12)
self.build_loss(seg_layer, lab_holder)
self.saver = tf.train.Saver()
self.sess = tf.Session()
M.loadSess('./model/',
self.sess,
init=True,
var_list=self.net_body.var)
self.inp_holder = inp_holder
self.lab_holder = lab_holder
self.seg_layer = seg_layer
def __init__(self,img_holder, class_num, mask_layer, seg_layer):
self.class_num = class_num
# build placeholders
inp_holder = img_holder
# construct input (4 -> 3 with 1x1 conv)
merged_layer = self.merging_layer(inp_holder,seg_holder,mask_holder)
# build network
self.get_coord(size)
self.net_body = seg_main_body(merged_layer)
stream_list = self.get_stream_list(self.net_body.feature_maps)
inst_pred = self.inst_layer(self.net_body.feature_layer,stream_list[-1],class_num)
# set class variables
# holders
self.inp_holder = inp_holder
# layers
self.coord_layer = stream_list[-1]
self.inst_layer = inst_pred
