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