def build_loss(self, seg_layer, lab_holder):
lab_reform = tf.expand_dims(lab_holder, -1)
lab_reform = tf.image.resize_images(seg_layer,
tf.shape(lab_reform)[1:3])
lab_reform = tf.squeeze(lab_reform)
seg_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=seg_layer,
labels=lab_reform))
var_s = M.get_trainable_vars('SegLayer')
var_m = M.get_trainable_vars('MergingLayer')
train_step = tf.train.AdamOptimizer(0.0001).minimize(seg_loss,
var_list=var_s)
train_step2 = tf.train.AdamOptimizer(1e-5).minimize(
seg_loss, var_list=self.net_body.var)
train_step3 = tf.train.AdamOptimizer(0.0001).minimize(seg_loss,
var_list=var_m)
upds = M.get_update_ops()
with tf.control_dependencies(upds +
[train_step, train_step2, train_step3]):
train_op = tf.no_op()
self.loss = seg_loss
self.train_op = train_op
def __init__(self, class_num, is_training=True, mod_dir='./model/'):
self.mod_dir = mod_dir
with tf.variable_scope('Input'):
self.img_holder = tf.placeholder(tf.float32, [None, 128, 128, 3])
self.lab_holder = tf.placeholder(tf.float32, [None, class_num])
with tf.variable_scope('Res_101_cy'):
mod = M.Model(self.img_holder)
mod.set_bn_training(is_training)
# 64x64
mod.convLayer(7,
64,
stride=2,
activation=M.PARAM_LRELU,
batch_norm=True)
mod.res_block(256, stride=1, activation=M.PARAM_LRELU)
mod.res_block(256, stride=1, activation=M.PARAM_LRELU)
mod.res_block(256, stride=1, activation=M.PARAM_LRELU)
# 32x32
mod.res_block(512, stride=2, activation=M.PARAM_LRELU)
mod.res_block(512, stride=1, activation=M.PARAM_LRELU)
mod.res_block(512, stride=1, activation=M.PARAM_LRELU)
mod.res_block(512, stride=1, activation=M.PARAM_LRELU)
# 16x16
for i in range(14):
mod.res_block(1024, stride=2, activation=M.PARAM_LRELU)
# 8x8
mod.res_block(2048, stride=2, activation=M.PARAM_LRELU)
mod.res_block(2048, stride=1, activation=M.PARAM_LRELU)
mod.res_block(2048, stride=1, activation=M.PARAM_LRELU)
mod.avgpoolLayer(8)
mod.flatten()
#mod.fcLayer(256,nobias=True)
self.feat = mod.get_current_layer()
with tf.variable_scope('Classification'):
logit_layer, eval_layer = M.enforcedClassifier(self.feat,
self.lab_holder,
dropout=1,
multi=None,
L2norm=False)
self.accuracy = M.accuracy(eval_layer,
tf.argmax(self.lab_holder, -1))
if is_training:
print('Building optimizer...')
self.loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=logit_layer, labels=self.lab_holder))
with tf.control_dependencies(M.get_update_ops()):
self.train_op = tf.train.AdamOptimizer(0.0001).minimize(
self.loss)
self.sess = tf.Session()
self.saver = tf.train.Saver()
M.loadSess(mod_dir, self.sess, init=True)
def build_loss(self):
with tf.variable_scope('LS_optim'):
self.ls1 = tf.reduce_mean(
tf.square(self.recon - self.targetholder[:, -1]) * self.A)
self.ls = tf.reduce_mean(
tf.square(self.recon - self.targetholder[:, -1]))
self.A_length = tf.reduce_mean(tf.square(self.A))
train_step = tf.train.AdamOptimizer(0.0001).minimize(self.ls +
0.1 *
self.ls1)
with tf.control_dependencies(M.get_update_ops() + [train_step]):
self.train_step = tf.no_op()
def build_graph(train=True):
with tf.name_scope('inp'):
imgholder = tf.placeholder(tf.float32, [None, 256, 256, 3])
with tf.name_scope('gnd'):
gndholder = tf.placeholder(tf.float32, [None, 256, 256, 1])
x_fake = gen(imgholder, train=train)
d_fake = dis(x_fake, imgholder, train=train)
d_real = dis(gndholder, imgholder, reuse=True, train=train)
g_loss_L1 = tf.reduce_mean(tf.abs(x_fake - gndholder))
g_loss_lg = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake,
labels=tf.ones_like(d_fake)))
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_real,
labels=tf.ones_like(d_real)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake,
labels=tf.zeros_like(d_fake)))
varg = M.get_trainable_vars('gen')
vard = M.get_trainable_vars('dis')
updg = M.get_update_ops('gen')
updd = M.get_update_ops('dis')
with tf.name_scope('optimizer'):
train_d = tf.train.AdamOptimizer(0.001, beta1=0.5).minimize(
d_loss_real + d_loss_fake, var_list=vard)
train_g = tf.train.AdamOptimizer(0.001, beta1=0.5).minimize(
g_loss_lg + g_loss_L1 * 10, var_list=varg)
return [imgholder, gndholder], [
g_loss_L1 + g_loss_lg, d_loss_fake + d_loss_real
], [updg, updd], [train_g,
train_d], x_fake, [g_loss_lg, d_loss_fake + d_loss_real]
def build_loss(self,seg_layer,lab_holder):
lab_reform = tf.expand_dims(lab_holder, -1) # 460 x 460 x 1
seg_layer = tf.image.resize_images(seg_layer, tf.shape(lab_reform)[1:3]) # 460 x 460 x 2
lab_reform = tf.squeeze(lab_reform, axis=3) # 460 x 460 x 2
seg_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=seg_layer,labels=lab_reform))
var_s = M.get_trainable_vars('bg_fg/SegLayer')
var_net = M.get_trainable_vars('bg_fg/WideRes')
train_step = tf.train.AdamOptimizer(0.0001).minimize(seg_loss,var_list=var_s)
train_step2 = tf.train.AdamOptimizer(1e-6).minimize(seg_loss,var_list=var_net)
upds = M.get_update_ops()
with tf.control_dependencies(upds+[train_step,train_step2]):
train_op = tf.no_op()
self.loss = seg_loss
self.train_op = train_op
with tf.name_scope('lossD'):
lossD1 = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.ones(
[BSIZE], dtype=tf.int64),
logits=distrue))
lossD2 = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.zeros(
[BSIZE], dtype=tf.int64),
logits=disfalse))
lossD = 0.5 * (lossD1 + lossD2)
tf.summary.scalar('lossD', lossD)
#run update_ops for updating the batch_norm
VARD = M.get_trainable_vars('Discriminator')
VARG = M.get_trainable_vars('Generator')
UPDD = M.get_update_ops('Discriminator')
UPDG = M.get_update_ops('Generator')
with tf.name_scope('opti'):
with tf.name_scope('optiG'):
trainG = tf.train.AdamOptimizer(learning_rate=LR,
beta1=BETA).minimize(lossG,
var_list=VARG)
with tf.name_scope('optiD'):
trainD = tf.train.AdamOptimizer(learning_rate=LR,
beta1=BETA).minimize(lossD,
var_list=VARD)
with tf.control_dependencies([trainG, trainD]):
trainAll = tf.no_op(name='train')
def build_total_graph():
global totalLoss, dis_loss, train_d, train_g, train_e, updd, updg, upde, profHolder, gtHolder, leHolder, reHolder, mthHolder, nseHolder, domainHolder, zHolder, clsHolder, x_fake, losscollection
with tf.name_scope('ProfileImg'):
profHolder = tf.placeholder(tf.float32, [None, 128, 128, 3])
with tf.name_scope('GTIMG'):
gtHolder = tf.placeholder(tf.float32, [None, 128, 128, 3])
with tf.name_scope('LEIMG'):
leHolder = tf.placeholder(tf.float32, [None, 40, 40, 3])
with tf.name_scope('REIMG'):
reHolder = tf.placeholder(tf.float32, [None, 40, 40, 3])
with tf.name_scope('MTHIMG'):
mthHolder = tf.placeholder(tf.float32, [None, 32, 48, 3])
with tf.name_scope('NSEIMG'):
nseHolder = tf.placeholder(tf.float32, [None, 32, 40, 3])
with tf.name_scope('Z'):
zHolder = tf.placeholder(tf.float32, [None, ZDIM])
with tf.name_scope('domain'):
domainHolder = tf.placeholder(tf.int32, [None])
with tf.name_scope('CLASS'):
clsHolder = tf.placeholder(tf.int32, [None])
nse = localpath_nse(nseHolder)
mth = localpath_mth(mthHolder)
le = localpath_le(leHolder)
re = localpath_re(reHolder)
fusion = fusion_locals(le, re, nse, mth)
x_fake, domainlayer = globalpath(profHolder, zHolder, fusion)
d_fake, c_fake = discriminator(x_fake)
d_real, c_real = discriminator(gtHolder, reuse=True)
f_fake = lcnn(x_fake)
f_real = lcnn(gtHolder, reuse=True)
with tf.name_scope('pixel_loss'):
pix_loss = tf.reduce_mean(tf.abs(gtHolder - x_fake))
with tf.name_scope('sym_loss'):
x_left, x_right = tf.split(x_fake, 2, axis=2)
sym_loss = tf.reduce_mean(tf.abs(x_left - x_right))
with tf.name_scope('dis_loss'):
dis_true = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_real,
labels=tf.ones([BSIZE,
1])))
dis_false = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake,
labels=tf.zeros([BSIZE,
1])))
dis_loss = dis_true + dis_false
with tf.name_scope('gen_loss'):
gen_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake,
labels=tf.ones([BSIZE,
1])))
with tf.name_scope('ip_loss'):
ip_loss = tf.reduce_mean(tf.abs(f_real - f_fake))
with tf.name_scope('tv_loss'):
tv_loss = tf.reduce_mean(
tf.image.total_variation(x_fake)) / (128.0 * 128.0)
with tf.name_scope('domain_loss'):
domain_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=domainlayer, labels=domainHolder))
with tf.name_scope('cls_loss'):
cls_loss_real = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=c_real,
labels=clsHolder))
# cls_loss_fake = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=c_fake,labels=clsHolder))
vard = M.get_trainable_vars('dis')
varg = M.get_trainable_vars('local_le') \
+ M.get_trainable_vars('local_re') \
+ M.get_trainable_vars('local_mth') \
+ M.get_trainable_vars('local_nse')\
+ M.get_trainable_vars('global_path')\
+ M.get_trainable_vars('fusion_node')
updd = M.get_update_ops('dis')
updg = M.get_update_ops('local_le') \
+ M.get_update_ops('local_re') \
+ M.get_update_ops('local_mth') \
+ M.get_update_ops('local_nse')\
+ M.get_update_ops('global_path')\
+ M.get_update_ops('fusion_node')
upde = M.get_update_ops('global_path')[:18]
losscollection = [
pix_loss, sym_loss, gen_loss, ip_loss, tv_loss, domain_loss, dis_loss
]
with tf.name_scope('Optimizer'):
totalLoss = (pix_loss * 0.3 + sym_loss * 0.07 + 0.001 * gen_loss +
0.003 * ip_loss + 0.0001 * tv_loss + 0.1 * domain_loss)
train_d = tf.train.AdamOptimizer(0.0001).minimize(dis_loss +
cls_loss_real,
var_list=vard)
train_g = tf.train.AdamOptimizer(0.0001).minimize(totalLoss,
var_list=varg)
train_e = tf.train.AdamOptimizer(0.0001).minimize(domain_loss)
class network():
def __init__(self,class_num):
img_holder = tf.placeholder(tf.float32,[None,460,460,3])
with tf.variable_scope('bg_fg'):
net_bgfg = network_bg_fg(img_holder)
with tf.variable_scope('seg_part'):
net_seg = network_seg(class_num,img_holder,tf.nn.softmax(tf.image.resize_images(net_bgfg.seg_layer,[460,460]),1)[:,:,:,1])
with tf.variable_scope('inst_part'):
net_inst = network_inst(class_num,img_holder
tf.nn.softmax(tf.image.resize_images(net_bgfg.seg_layer,[460,460]),1)[:,:,:,1],
tf.image.resize_images(net_seg.seg_layer,[460,460]))
self.network_bg_fg = network_bg_fg
self.network_seg = network_seg
self.network_inst = network_inst
self.img_holder = img_holder
self.mask_holder = network_bg_fg.lab_holder
self.seg_holder = network_seg.lab_holder
self.coord_holder = network_inst.coord_holder
self.inst_holder = network_inst.inst_holder
self.mask_out = network_bg_fg.seg_layer
self.seg_out = network_seg.seg_layer
self.inst_num_out = network_inst.inst_layer
self.coord_out = network_inst.coord_layer
self.build_loss()
self.sess = tf.Session()
M.loadSess('./savings_bgfg/',sess=self.sess,init=True,var_list=M.get_all_vars('bg_fg'))
M.loadSess('./savings_seg/',sess=self.sess,var_list=M.get_all_vars('seg_part'))
M.loadSess('./savings_inst/',sess=self.sess,var_list=M.get_all_vars('inst_part'))
def build_loss(self):
with tf.variable_scope('mask_loss'):
lab_reform = tf.expand_dims(self.mask_holder,-1)
lab_reform = tf.image.resize_images(seg_layer,tf.shape(lab_reform)[1:3])
lab_reform = tf.squeeze(lab_reform)
mask_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.mask_out,labels=lab_reform))
train_mask0 = tf.train.AdamOptimizer(0.0001).minimize(mask_loss,var_list=M.get_trainable_vars('bg_fg/SegLayer'))
train_mask1 = tf.train.AdamOptimizer(1e-5).minimize(mask_loss,var_list=M.get_trainable_vars('bg_fg/WideRes'))
with tf.variable_scope('seg_loss'):
lab_reform = tf.expand_dims(self.seg_holder,-1)
lab_reform = tf.image.resize_images(self.seg_out,tf.shape(lab_reform)[1:3])
lab_reform = tf.squeeze(lab_reform)
seg_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.seg_out,labels=lab_reform))
train_seg0 = tf.train.AdamOptimizer(0.0001).minimize(seg_loss,var_list=M.get_trainable_vars('seg_part/SegLayer')+M.get_trainable_vars('seg_part/MergingLayer'))
train_seg1 = tf.train.AdamOptimizer(1e-5).minimize(seg_loss,var_list=M.get_trainable_vars('seg_part/WideRes'))
with tf.variable_scope('inst_loss'):
train_inst0 = tf.train.AdamOptimizer(0.0001).minimize(self.network_inst.overall_loss,var_list=M.get_trainable_vars('inst_part/MergingLayer')) # merging layer
train_inst1 = tf.train.AdamOptimizer(1e-5).minimize(self.network_inst.overall_loss,var_list=M.get_trainable_vars('inst_part/WideRes')) # main body
train_inst2 = tf.train.AdamOptimizer(0.0001).minimize(10*self.network_inst.coord_loss,var_list=M.get_trainable_vars('inst_part/stream')) # coord streams
train_inst3 = tf.train.AdamOptimizer(0.0001).minimize(self.network_inst.inst_loss,var_list=M.get_trainable_vars('inst_part/inst_layer')) # instant prediction
upd_ops = M.get_update_ops()
with tf.control_dependencies(upd_ops+[train_mask0,train_mask1,train_seg1,train_seg0,train_inst0,train_inst1,train_inst2,train_inst3]):
train_op = tf.no_op()
self.mask_loss = mask_loss
self.seg_loss = seg_loss
self.inst_loss = self.network_inst.inst_loss
self.coord_loss = self.network_inst.coord_loss
self.train_op = train_op
def update_ops(self):
with tf.control_dependencies(M.get_update_ops()):
self.update_bn = tf.no_op()
