def make_model(self, inputs, is_training):
layer1_1 = relu(conv2d(inputs, 64, [3, 3], name='layer1_1'))
layer1_2 = relu(conv2d(layer1_1, 64, [3, 3], name='layer1_2'))
layer1_3 = max_pool(layer1_2, name='layer1_3') # original image 1/2
layer2_1 = relu(bn(conv2d(layer1_3, 128, [3, 3], name='layer2_1'), is_training))
layer2_2 = relu(bn(conv2d(layer2_1, 128, [3, 3], name='layer2_2'), is_training))
layer2_3 = max_pool(layer2_2, name='layer2_3') # original image 1/4
layer3_1 = relu(bn(conv2d(layer2_3, 256, [3, 3], name='layer3_1'), is_training))
layer3_2 = relu(bn(conv2d(layer3_1, 256, [3, 3], name='layer3_2'), is_training))
layer3_3 = max_pool(layer3_2, name='layer3_3') # original image 1/8
layer4_1 = relu(bn(conv2d(layer3_3, 512, [3, 3], name='layer4_1'), is_training))
layer4_2 = relu(bn(conv2d(layer4_1, 512, [3, 3], name='layer4_2'), is_training))
layer4_3 = max_pool(layer4_2, name='layer4_3') # original image 1/16
layer5_1 = relu(bn(conv2d(layer4_3, 512, [3, 3], name='layer5_1'), is_training))
layer5_2 = relu(bn(conv2d(layer5_1, 512, [3, 3], name='layer5_2'), is_training))
layer5_3 = max_pool(layer5_2, name='layer5_3') # original image 1/32
layer6_1 = relu(bn(conv2d(layer5_3, 2048, [7, 7], name='layer6_1'), is_training))
layer6_2 = relu(bn(conv2d(layer6_1, 2048, [1, 1], name='layer6_2'), is_training))
layer6_3 = relu(bn(conv2d(layer6_2, self.N_CLASS, [1, 1], name='layer6_3'), is_training))
layer7_1 = conv2d_t(layer6_3, [None, 14, 14, 512], [4, 4], name='layer7_1')
layer7_2 = tf.add(layer7_1, layer4_3, name='layer7_3')
layer7_3 = conv2d_t(layer7_2, [None, 28, 28, 256], [4, 4], name='layer7_3')
layer7_4 = tf.add(layer7_3, layer3_3, name='layer7_4')
layer7_5 = conv2d_t(layer7_4, [None, self.RESIZE, self.RESIZE, self.N_CLASS], [16, 16], strides=[1, 8, 8, 1], name='layer7_5')
annot_pred = tf.argmax(layer7_5, axis=3)
expand_pred = tf.expand_dims(annot_pred, dim=3)
return layer7_5, expand_pred
def generator_AB(self, inputs, is_training, reuse=False):
with tf.variable_scope('Generator_AB', reuse=reuse):
with tf.variable_scope('g_ab_hidden1'):
layer1_1 = lrelu(
bn(
conv2d(inputs,
64, [3, 3],
initializer='random',
name='conv_1'), is_training))
with tf.variable_scope('g_ab_hidden2'):
layer2_1 = max_pool(layer1_1, name='pool1')
layer2_2 = lrelu(
bn(
conv2d(layer2_1,
128, [3, 3],
initializer='random',
name='conv_2'), is_training))
with tf.variable_scope('g_ab_hidden3'):
layer3_1 = max_pool(layer2_2, name='pool2')
layer3_2 = lrelu(
bn(
conv2d(layer3_1,
256, [3, 3],
initializer='random',
name='conv_3'), is_training))
with tf.variable_scope('g_ab_hidden4'):
layer4_1 = conv2d_t(layer3_2, [None, 14, 14, 128], [2, 2],
initializer='random',
name='convT_4')
layer4_2 = tf.concat([layer4_1, layer2_2], axis=3)
layer4_3 = lrelu(
bn(
conv2d(layer4_2,
128, [3, 3],
initializer='random',
name='conv_4'), is_training))
with tf.variable_scope('g_ab_hidden5'):
layer5_1 = conv2d_t(layer4_3, [None, 28, 28, 64], [2, 2],
initializer='random',
name='convT5')
layer5_2 = tf.concat([layer1_1, layer5_1], axis=3)
layer5_3 = conv2d(layer5_2,
1, [3, 3],
initializer='random',
name='conv5')
layer5_4 = conv2d(layer5_3,
1, [1, 1],
initializer='random',
name='conv6')
gen_ab = tf.nn.sigmoid(layer5_4)
return gen_ab, layer5_4
def generator(self, inputs, is_training, reuse=False):
with tf.variable_scope('generator', reuse=reuse):
g_hidden1 = fully_connect(inputs, 1024, name='g_hidden1')
g_hidden2 = lrelu(bn(fully_connect(g_hidden1, 7*7*128), is_training))
g_reshape = tf.reshape(g_hidden2, [-1, 7, 7, 128])
g_hidden3 = lrelu(bn(conv2d_t(g_reshape, [None, 14, 14, 64], [4, 4], name='g_hidden3'), is_training))
g_hidden4 = conv2d_t(g_hidden3, [None, 28, 28, 1], [4, 4], name='g_hidden4')
g_logtis = sigmoid(g_hidden4)
return g_hidden4, g_logtis
def make_model(self, inputs, is_training):
with tf.variable_scope('ENCODER'):
layer1_1 = conv2d(inputs, 32, [3, 3], name='layer1_1')
layer1_2 = conv2d(layer1_1, 32, [3, 3], name='layer1_2')
layer1_3 = max_pool(layer1_2, name='layer1_3') # original image 1/2, (112, 112)
layer2_1 = relu(bn(conv2d(layer1_3, 64, [3, 3], name='layer2_1'), is_training))
layer2_2 = relu(bn(conv2d(layer2_1, 64, [3, 3], name='layer2_2'), is_training))
layer2_3 = max_pool(layer2_2, name='layer2_3') # original image 1/4, (56, 56)
layer3_1 = relu(bn(conv2d(layer2_3, 128, [3, 3], name='layer3_1'), is_training))
layer3_2 = relu(bn(conv2d(layer3_1, 128, [3, 3], name='layer3_2'), is_training))
layer3_3 = max_pool(layer3_2, name='layer3_3') # original image 1/8, (28, 28)
layer4_1 = relu(bn(conv2d(layer3_3, 256, [3, 3], name='layer4_1'), is_training))
layer4_2 = relu(bn(conv2d(layer4_1, 256, [3, 3], name='layer4_2'), is_training))
layer4_3 = max_pool(layer4_2, name='layer4_3') # original image 1/16, (14, 14)
layer5_1 = relu(bn(conv2d(layer4_3, 512, [3, 3], name='layer5_1'), is_training))
layer5_2 = relu(bn(conv2d(layer5_1, 512, [3, 3], name='layer5_2'), is_training))
with tf.variable_scope('DECODER'):
layer6_1 = relu(bn(conv2d_t(layer5_2, [None, 28, 28, 256], [2, 2], name='layer6_1'), is_training))
layer6_2 = tf.concat([layer4_2, layer6_1], axis=3, name='layer6_2')
layer6_3 = relu(bn(conv2d(layer6_2, 256, [3, 3], name='layer6_3'), is_training))
layer6_4 = relu(bn(conv2d(layer6_3, 256, [3, 3], name='layer6_4'), is_training))
l6_4_shape = layer6_4.get_shape()
layer7_1 = relu(bn(conv2d_t(layer6_4, [None, 56, 56, 128], [2, 2], name='layer7_1'), is_training))
layer7_2 = tf.concat([layer3_2, layer7_1], axis=3, name='layer7_2')
layer7_3 = relu(bn(conv2d(layer7_2, 128, [3, 3], name='layer7_2'), is_training))
layer7_4 = relu(bn(conv2d(layer7_3, 128, [3, 3], name='layer7_3'), is_training))
l7_4_shape = layer7_4.get_shape()
layer8_1 = relu(bn(conv2d_t(layer7_4, [None, 112, 112, 64], [2, 2], name='layer8_1'), is_training))
layer8_2 = tf.concat([layer2_2, layer8_1], axis=3, name='layer8_2')
layer8_3 = relu(bn(conv2d(layer8_2, 64, [3, 3], name='layer8_3'), is_training))
layer8_4 = relu(bn(conv2d(layer8_3, 64, [3, 3], name='layer8_4'), is_training))
l8_4_shape = layer8_4.get_shape()
layer9_1 = relu(bn(conv2d_t(layer8_4, [None, 224, 224, 32], [2, 2], name='layer9_1'), is_training))
layer9_2 = tf.concat([layer1_2, layer9_1], axis=3, name='layer9_2')
layer9_3 = relu(bn(conv2d(layer9_2, self.N_CLASS, [3, 3], name='layer9_3'), is_training))
layer9_4 = relu(bn(conv2d(layer9_3, self.N_CLASS, [3, 3], name='layer9_4'), is_training))
logits = conv2d(layer9_4, self.N_CLASS, [1, 1], name='logits')
annot_pred = tf.argmax(logits, axis=3)
expand_pred = tf.expand_dims(annot_pred, dim=3)
return logits, expand_pred, layer5_2
def make_model(self, inputs, is_training):
layer1_1 = relu(conv2d(inputs, 64, [3, 3], name='layer1_1'))
layer1_2 = relu(conv2d(layer1_1, 64, [3, 3], name='layer1_2'))
layer1_3 = max_pool(layer1_2, name='layer1_3') # original image 1/2
layer2_1 = relu(
bn(conv2d(layer1_3, 128, [3, 3], name='layer2_1'), is_training))
layer2_2 = relu(
bn(conv2d(layer2_1, 128, [3, 3], name='layer2_2'), is_training))
layer2_3 = max_pool(layer2_2, name='layer2_3') # original image 1/4
layer3_1 = relu(
bn(conv2d(layer2_3, 256, [3, 3], name='layer3_1'), is_training))
layer3_2 = relu(
bn(conv2d(layer3_1, 256, [3, 3], name='layer3_2'), is_training))
layer3_3 = max_pool(layer3_2, name='layer3_3') # original image 1/8
layer4_1 = relu(
bn(conv2d(layer3_3, 512, [3, 3], name='layer4_1'), is_training))
layer4_2 = relu(
bn(conv2d(layer4_1, 512, [3, 3], name='layer4_2'), is_training))
layer4_3 = max_pool(layer4_2, name='layer4_3') # original image 1/16
layer5_1 = relu(
bn(conv2d(layer4_3, 512, [3, 3], name='layer5_1'), is_training))
layer5_2 = relu(
bn(conv2d(layer5_1, 512, [3, 3], name='layer5_2'), is_training))
layer5_3 = max_pool(layer5_2, name='layer5_3') # original image 1/32
# make [batch, 1, 1, 2048] similary flatten in fully connected layer
layer6_1 = relu(
bn(conv2d(layer5_3, 2048, [7, 7], name='layer6_1'), is_training))
layer6_2 = relu(
bn(conv2d(layer6_1, 2048, [1, 1], name='layer6_2'), is_training))
layer6_3 = relu(
bn(conv2d(layer6_2, self.N_CLASS, [1, 1], name='layer6_3'),
is_training))
# FCN32 is not use previous pooling information
# just last layer size up(x32)
# conv2d_transpose로 upscaling 할때, strides 크기로 결정됨.
# 만약, 32배로 사이즈를 늘리려면 strides=[1, 32, 32, 1], 16배로 늘리려면 strides=[1, 16, 16, 1]로 하면 되는 듯하다.
layer7_1 = conv2d_t(layer6_3, [None, 224, 224, self.N_CLASS], [4, 4],
strides=[1, 32, 32, 1],
name='layer7_1')
annot_pred = tf.argmax(layer7_1, axis=3)
expand_pred = tf.expand_dims(annot_pred, dim=3)
return layer7_1, expand_pred
def make_model(self, inputs, is_training):
with tf.variable_scope('ResNet50'):
x = conv2d(inputs, 64, [7, 7], strides=[1, 2, 2, 1],
name='conv1') # size 1/2
x = bn(x, is_training)
x = relu(x)
x = max_pool(x, ksize=[1, 3, 3, 1], name='max_pool1') # size 1/4
x = self.conv_block(x, [64, 64, 256], is_training, '2_1', s=1)
x = self.identity_block(x, [64, 64, 256], is_training, '2_2')
x = self.identity_block(x, [64, 64, 256], is_training, '2_3')
x = self.conv_block(x, [128, 128, 512], is_training, '3_1')
x = self.identity_block(x, [128, 128, 512], is_training, '3_2')
x = self.identity_block(x, [128, 128, 512], is_training, '3_3')
x = self.conv_block(x, [256, 256, 1024], is_training, '4_1')
x = self.identity_block(x, [256, 256, 1024], is_training, '4_2')
x = self.identity_block(x, [256, 256, 1024], is_training, '4_3')
x = self.identity_block(x, [256, 256, 1024], is_training, '4_4')
x = self.identity_block(x, [256, 256, 1024], is_training, '4_5')
x = self.identity_block(x, [256, 256, 1024], is_training, '4_6')
x = self.conv_block(x, [512, 512, 2048], is_training, '5_1')
x = self.identity_block(x, [512, 512, 2048], is_training, '5_2')
feature_map = self.identity_block(x, [512, 512, 2048], is_training,
'5_3') # size: (6, 6)
with tf.variable_scope('Pyramid_Pool'):
pool_1x1 = max_pool(feature_map,
ksize=[1, 6, 6, 1],
strides=[1, 6, 6, 1],
name='pool_1x1')
pool_2x2 = max_pool(feature_map,
ksize=[1, 3, 3, 1],
strides=[1, 3, 3, 1],
name='pool_2x2')
pool_3x3 = max_pool(feature_map,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
name='pool_3x3')
pool_6x6 = max_pool(feature_map,
ksize=[1, 1, 1, 1],
strides=[1, 1, 1, 1],
name='pool_6x6')
conv_1x1 = relu(
bn(conv2d(pool_1x1, 512, [3, 3], name='conv_1x1'),
is_training)) # reduce dimension
conv_2x2 = relu(
bn(conv2d(pool_2x2, 512, [3, 3], name='conv_2x2'),
is_training)) # reduce dimension
conv_3x3 = relu(
bn(conv2d(pool_3x3, 512, [3, 3], name='conv_3x3'),
is_training)) # reduce dimension
conv_6x6 = relu(
bn(conv2d(pool_6x6, 512, [3, 3], name='conv_6x6'),
is_training)) # reduce dimension
upconv_1x1 = tf.image.resize_bilinear(conv_1x1, [6, 6])
upconv_2x2 = tf.image.resize_bilinear(conv_2x2, [6, 6])
upconv_3x3 = tf.image.resize_bilinear(conv_3x3, [6, 6])
upconv_6x6 = tf.image.resize_bilinear(conv_6x6, [6, 6])
concated = tf.concat(
[feature_map, upconv_1x1, upconv_2x2, upconv_3x3, upconv_6x6],
axis=3)
out = relu(
bn(conv2d(concated, 512, [3, 3], name='out1'), is_training))
out = conv2d_t(out, [None, 12, 12, 256], [3, 3],
name='out2') # (12, 12)
out = conv2d_t(out, [None, 24, 24, self.N_CLASS], [3, 3],
name='out3') # (24, 24)
out = conv2d_t(out, [None, 48, 48, self.N_CLASS], [3, 3],
name='out4') # (24, 24)
out = conv2d_t(out, [None, self.RESIZE, self.RESIZE, self.N_CLASS],
[3, 3],
name='out5',
strides=[1, 4, 4, 1]) # (24, 24)
pred = tf.argmax(out, axis=3)
pred = tf.expand_dims(pred, dim=3)
return out, pred
def bottleneck(self,
inputs,
out_depth,
f_h,
f_w,
is_training,
keep_prob,
dilated_rate=None,
mode=None,
scope=None):
reduce_depth = int(inputs.get_shape().as_list()[3] / 4)
with tf.variable_scope(scope):
if mode == 'downsampling':
main_branch = max_pool(inputs, name='_pool')
depth_to_pad = abs(inputs.get_shape().as_list()[3] - out_depth)
paddings = tf.convert_to_tensor([[0, 0], [0, 0], [0, 0],
[0, depth_to_pad]])
main_branch = tf.pad(main_branch,
paddings=paddings,
name='_main_padding')
sub_branch = prelu(bn(
conv2d(inputs,
reduce_depth, [2, 2],
name='_conv1',
strides=[1, 2, 2, 1]), is_training),
name='prelu_conv1')
sub_branch = prelu(bn(
conv2d(sub_branch,
reduce_depth, [f_h, f_w],
name='_conv2',
strides=[1, 1, 1, 1]), is_training),
name='prelu_conv2')
sub_branch = prelu(bn(
conv2d(sub_branch,
out_depth, [1, 1],
name='_conv3',
strides=[1, 1, 1, 1]), is_training),
name='prelu_conv3')
sub_branch = prelu(spatial_dropout(sub_branch, keep_prob),
name='prelu_dropout')
out = prelu(tf.add(main_branch, sub_branch), name='prelu_add')
return out
elif mode == 'dilated':
main_branch = inputs
sub_branch = prelu(bn(
conv2d(
inputs,
reduce_depth,
[1, 1],
name='_conv1',
), is_training),
name='prelu_conv1')
sub_branch = prelu(bn(
atrous_conv2d(sub_branch,
reduce_depth, [f_h, f_w],
dilated_rate,
name='_conv2'), is_training),
name='prelu_conv2')
sub_branch = prelu(bn(
conv2d(inputs, out_depth, [1, 1], name='_conv3'),
is_training),
name='prelu_conv3')
sub_branch = prelu(spatial_dropout(sub_branch, keep_prob),
name='prelu_dropout')
out = prelu(tf.add(main_branch, sub_branch), name='prelu_add')
return out
elif mode == 'asymmetric':
main_branch = inputs
sub_branch = prelu(bn(
conv2d(inputs, reduce_depth, [1, 1], name='_conv1'),
is_training),
name='prelu_conv1')
sub_branch = prelu(bn(
conv2d(sub_branch, reduce_depth, [f_h, 1], name='_conv2'),
is_training),
name='prelu_conv2')
sub_branch = prelu(bn(
conv2d(sub_branch, reduce_depth, [1, f_w], name='_conv3'),
is_training),
name='prelu_conv3')
sub_branch = prelu(bn(
conv2d(sub_branch, out_depth, [1, 1], name='_conv4'),
is_training),
name='prelu_conv4')
sub_branch = prelu(spatial_dropout(sub_branch, keep_prob),
name='prelu_dropout')
out = prelu(tf.add(main_branch, sub_branch), name='prelu_add')
return out
elif mode == 'upsampling':
# 논문에서 나오는 unpool 대신 bilinear interpolation 사용
in_shape = inputs.get_shape().as_list()
main_branch = tf.image.resize_bilinear(
inputs, size=[in_shape[1] * 2, in_shape[2] * 2])
main_branch = prelu(bn(
conv2d(main_branch, out_depth, [3, 3], name='_conv0'),
is_training),
name='prelu_conv1')
sub_branch = prelu(bn(
conv2d(inputs, reduce_depth, [1, 1], name='_conv1'),
is_training),
name='prelu_conv2')
sub_branch = prelu(bn(
conv2d_t(sub_branch, [
in_shape[0], in_shape[1] * 2, in_shape[2] * 2,
reduce_depth
], [3, 3],
name='_conv2'), is_training),
name='prelu_conv3')
sub_branch = prelu(bn(
conv2d(sub_branch, out_depth, [1, 1], name='_conv3'),
is_training),
name='prelu_conv4')
sub_branch = prelu(spatial_dropout(sub_branch, keep_prob),
name='prelu_dropout')
out = prelu(tf.add(main_branch, sub_branch), name='prelu_add')
return out
elif mode == 'normal':
main_branch = inputs
sub_branch = prelu(bn(
conv2d(inputs,
reduce_depth, [1, 1],
name='_conv1',
strides=[1, 1, 1, 1]), is_training),
name='prelu_conv1')
sub_branch = prelu(bn(
conv2d(sub_branch,
reduce_depth, [f_h, f_w],
name='_conv2',
strides=[1, 1, 1, 1]), is_training),
name='prelu_conv2')
sub_branch = prelu(bn(
conv2d(sub_branch,
out_depth, [1, 1],
name='_conv3',
strides=[1, 1, 1, 1]), is_training),
name='prelu_conv3')
sub_branch = prelu(spatial_dropout(sub_branch, keep_prob),
name='prelu_dropout')
out = prelu(tf.add(main_branch, sub_branch), name='prelu_add')
return out
def make_model(self, inputs, is_training, keep_prob):
in_shape = inputs.get_shape().as_list()
with tf.variable_scope('STAGE_INIT'):
net = self.initial_block(inputs, is_training)
with tf.variable_scope('STAGE_1'):
net = self.bottleneck(net,
64,
3,
3,
is_training,
keep_prob,
mode='downsampling',
scope='bottleneck1.0')
net = self.bottleneck(net,
64,
3,
3,
is_training,
keep_prob,
mode='normal',
scope='bottleneck1.1')
net = self.bottleneck(net,
64,
3,
3,
is_training,
keep_prob,
mode='normal',
scope='bottleneck1.2')
net = self.bottleneck(net,
64,
3,
3,
is_training,
keep_prob,
mode='normal',
scope='bottleneck1.3')
net = self.bottleneck(net,
64,
3,
3,
is_training,
keep_prob,
mode='normal',
scope='bottleneck1.4')
with tf.variable_scope('STAGE_2'):
net = self.bottleneck(net,
128,
3,
3,
is_training,
keep_prob,
mode='downsampling',
scope='bottleneck2.0')
net = self.bottleneck(net,
128,
3,
3,
is_training,
keep_prob,
mode='normal',
scope='bottleneck2.1')
net = self.bottleneck(net,
128,
3,
3,
is_training,
keep_prob,
dilated_rate=2,
mode='dilated',
scope='bottleneck2.2')
net = self.bottleneck(net,
128,
5,
5,
is_training,
keep_prob,
mode='asymmetric',
scope='bottleneck2.3')
net = self.bottleneck(net,
128,
3,
3,
is_training,
keep_prob,
dilated_rate=4,
mode='dilated',
scope='bottleneck2.4')
net = self.bottleneck(net,
128,
3,
3,
is_training,
keep_prob,
mode='normal',
scope='bottleneck2.5')
net = self.bottleneck(net,
128,
3,
3,
is_training,
keep_prob,
dilated_rate=8,
mode='dilated',
scope='bottleneck2.6')
net = self.bottleneck(net,
128,
5,
5,
is_training,
keep_prob,
mode='asymmetric',
scope='bottleneck2.7')
net = self.bottleneck(net,
128,
3,
3,
is_training,
keep_prob,
dilated_rate=16,
mode='dilated',
scope='bottleneck2.8')
with tf.variable_scope('STAGE_3'):
net = self.bottleneck(net,
128,
3,
3,
is_training,
keep_prob,
mode='normal',
scope='bottleneck3.0')
net = self.bottleneck(net,
128,
3,
3,
is_training,
keep_prob,
dilated_rate=2,
mode='dilated',
scope='bottleneck3.1')
net = self.bottleneck(net,
128,
5,
5,
is_training,
keep_prob,
mode='asymmetric',
scope='bottleneck3.2')
net = self.bottleneck(net,
128,
3,
3,
is_training,
keep_prob,
dilated_rate=4,
mode='dilated',
scope='bottleneck3.3')
net = self.bottleneck(net,
128,
3,
3,
is_training,
keep_prob,
mode='normal',
scope='bottleneck3.4')
net = self.bottleneck(net,
128,
3,
3,
is_training,
keep_prob,
dilated_rate=8,
mode='dilated',
scope='bottleneck3.5')
net = self.bottleneck(net,
128,
5,
5,
is_training,
keep_prob,
mode='asymmetric',
scope='bottleneck3.6')
net = self.bottleneck(net,
128,
3,
3,
is_training,
keep_prob,
dilated_rate=16,
mode='dilated',
scope='bottleneck3.7')
with tf.variable_scope('STAGE_4'):
net = self.bottleneck(net,
64,
3,
3,
is_training,
keep_prob,
mode='upsampling',
scope='bottleneck4.0')
net = self.bottleneck(net,
64,
3,
3,
is_training,
keep_prob,
mode='normal',
scope='bottleneck4.1')
net = self.bottleneck(net,
64,
3,
3,
is_training,
keep_prob,
mode='normal',
scope='bottleneck4.2')
with tf.variable_scope('STAGE_5'):
net = self.bottleneck(net,
16,
3,
3,
is_training,
keep_prob,
mode='upsampling',
scope='bottleneck5.0')
net = self.bottleneck(net,
16,
3,
3,
is_training,
keep_prob,
mode='normal',
scope='bottleneck5.1')
with tf.variable_scope('STAGE_FULLCONV'):
net = conv2d_t(net,
in_shape[:3] + [64], [2, 2],
name='final_conv_t')
net = conv2d(net, self.N_CLASS, [3, 3], name='pred')
pred = tf.argmax(net, axis=3)
pred = tf.expand_dims(pred, dim=3)
return net, pred
