def dense_blk(name, l, ch, ksize, count, split=1, padding='valid'):
with tf.variable_scope(name):
for i in range(0, count):
with tf.variable_scope('blk/' + str(i)):
x = BNReLU('preact_bna', l)
x = Conv2D('conv1',
x,
ch[0],
ksize[0],
padding=padding,
activation=BNReLU)
x = Conv2D('conv2',
x,
ch[1],
ksize[1],
padding=padding,
split=split)
##
if padding == 'valid':
x_shape = x.get_shape().as_list()
l_shape = l.get_shape().as_list()
l = crop_op(
l, (l_shape[2] - x_shape[2], l_shape[3] - x_shape[3]))
l = tf.concat([l, x], axis=1)
l = BNReLU('blk_bna', l)
return l
def dense_blk(name, l, ch, ksize, count, split=1, padding='valid'):
with tf.variable_scope(name):
for i in range(0, count):
with tf.variable_scope('blk/' + str(i)):
x = BNReLU('preact_bna', l)
x = Conv2D('conv1',
x,
ch[0],
ksize[0],
padding=padding,
activation=BNReLU)
x = Conv2D('conv2',
x,
ch[1],
ksize[1],
padding=padding,
split=split)
##
if padding == 'valid':
x_shape = x.get_shape().as_list()
l_shape = l.get_shape().as_list()
l = crop_op(
l, (l_shape[2] - x_shape[2], l_shape[3] - x_shape[3]))
l = Conv2D('conv2forl',
l,
ch[1],
ksize[0],
padding='same',
split=split)
with tf.variable_scope('CBAM', reuse=tf.AUTO_REUSE):
residual = x
ratio = 8
###CBAM module
kernel_initializer = tf.contrib.layers.variance_scaling_initializer(
)
bias_initializer = tf.constant_initializer(value=0.0)
channel = x.shape[1]
print(channel)
# plt.pause(3.0)
avg_pool = tf.reduce_mean(x, axis=[2, 3], keepdims=True)
# avg_pool = GlobalAvgPooling('globalAveragepooling',x,data_format='channels_first')
# avg_pool = tf.reshape(avg_pool,[-1,channel,1,1],name='reshape_avgpool')
print(avg_pool)
# plt.pause(3.0)
assert avg_pool.shape[1:] == (channel, 1, 1)
# avg_pool = tf.compat.v1.layers.Dense(
# units=channel//ratio,
# activation=tf.nn.relu,
# kernel_initializer=kernel_initializer,
# bias_initializer=bias_initializer,
# name='mlp_0')(avg_pool)
avg_pool = FullyConnected(
'fullyconnected1',
avg_pool,
units=channel // ratio,
activation=tf.nn.relu,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer)
print(avg_pool)
# plt.pause(3.0)
avg_pool = tf.reshape(avg_pool,
[-1, channel // ratio, 1, 1],
name='reshape_avgpoolB')
assert avg_pool.shape[1:] == (channel // ratio, 1, 1)
print(avg_pool)
# plt.pause(3.0)
avg_pool = FullyConnected(
'fullyconnectedB',
avg_pool,
units=channel,
activation=None,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer)
avg_pool = tf.reshape(avg_pool, [-1, channel, 1, 1],
name='reshape_avgpoolC')
assert avg_pool.get_shape()[1:] == (channel, 1, 1)
print(avg_pool)
# plt.pause(3.0)
max_pool = tf.reduce_max(x, axis=[2, 3], keepdims=True)
assert max_pool.get_shape()[1:] == (channel, 1, 1)
max_pool = FullyConnected('fullyconnected1',
max_pool,
units=channel // ratio,
activation=tf.nn.relu)
max_pool = tf.reshape(max_pool,
[-1, channel // ratio, 1, 1],
name='reshape_maxpoolA')
assert max_pool.get_shape()[1:] == (channel // ratio, 1, 1)
max_pool = FullyConnected('fullyconnectedB',
max_pool,
units=channel,
activation=None)
max_pool = tf.reshape(max_pool, [-1, channel, 1, 1],
name='reshape_maxpoolB')
assert max_pool.get_shape()[1:] == (channel, 1, 1)
scale = tf.sigmoid(avg_pool + max_pool, 'sigmoid')
channel_map = tf.math.multiply(residual,
scale,
name='merge_Channel')
#spatial_attention
kernel_size = 7
avg_pool_spatial = tf.reduce_mean(channel_map,
axis=[1],
keepdims=True)
print("Spatial averge pooling : ", avg_pool_spatial)
# plt.pause(3.0)
assert avg_pool_spatial.get_shape()[1] == 1
max_pool_spatial = tf.reduce_max(channel_map,
axis=[1],
keepdims=True)
assert max_pool_spatial.get_shape()[1] == 1
print("Spatial max pooling : ", max_pool_spatial)
# plt.pause(3.0)
concat = tf.concat([avg_pool_spatial, max_pool_spatial], 1)
assert concat.get_shape()[1] == 2
print(concat.get_shape())
concat = tp.models.Conv2D(
'spatialconvolution',
concat,
1,
kernel_size,
kernel_initializer=kernel_initializer,
use_bias=False)
assert concat.get_shape()[1] == 1
print("concat convolution shape : ", concat.get_shape())
concat = tf.sigmoid(concat, 'sigmoid')
concat = concat * channel_map
l = l + concat
# l = tf.concat([l, concat], axis=1)
l = BNReLU('blk_bna', l)
return l