def deconv_relu(x, kernal, scope = None):
with tf.name_scope(scope):
W = weight_xavier_init(shape = kernal, n_inputs = kernal[0] * kernal[1] * kernal[-1], n_outputs = kernal[-2],
activefunction = 'relu', variable_name = scope + 'W')
B = bias_variable([kernal[-2]], variable_name = scope + 'B')
deconv = deconv2d(x, W) + B
deconv = tf.nn.relu(deconv)
return deconv
def conv_bn_relu_drop(x, kernal, phase, drop, scope = None):
with tf.name_scope(scope):
W = weight_xavier_init(shape = kernal, n_inputs = kernal[0] * kernal[1] * kernal[2], n_outputs = kernal[-1],
activefunction = 'relu', variable_name = scope + 'conv_W')
B = bias_variable([kernal[-1]], variable_name = scope + 'conv_B')
conv = conv2d(x, W) + B
conv = normalizationlayer(conv, is_train=phase, norm_type='batch', scope = scope + 'normalization')
conv = tf.nn.dropout(tf.nn.relu(conv), drop, name = scope + 'conv_dropout')
return conv
def attention_layer(x, ratio=4, scope=None):
with tf.name_scope(scope):
_, width, height, channel = x.get_shape().as_list()
x_shape = x.get_shape().as_list()
recalibrate1 = conv_sigmoid(x,
kernal=(1, 1, channel, 1),
scope=scope + 'spatial_squeeze')
squeeze = tf.reduce_mean(x,
axis=(1, 2),
name=scope + 'channel_squeeze')
exciation = full_connected_relu(squeeze,
kernal=(channel, channel // ratio),
activefuncation='relu',
scope=scope + '_fully_connected1')
exciation = full_connected_relu(exciation,
kernal=(channel // ratio, channel),
activefuncation='sigmoid',
scope=scope + '_fully_connected2')
recalibrate2 = tf.reshape(exciation, [-1, 1, 1, channel])
recalibrate3 = conv_sigmoid(x,
kernal=(3, 3, channel, channel // 2),
scope=scope + 'conv1')
recalibrate3 = max_pooling_2x2(recalibrate3)
recalibrate3 = conv_sigmoid(recalibrate3,
kernal=(3, 3, channel // 2, channel // 4),
scope=scope + 'conv2')
recalibrate3 = max_pooling_2x2(recalibrate3)
kernal = [3, 3, channel, channel // 4]
W = weight_xavier_init(shape=kernal,
n_inputs=kernal[0] * kernal[1] * kernal[-1],
n_outputs=kernal[-2],
activefunction='relu',
variable_name=scope + 'W')
B = bias_variable([kernal[-2]], variable_name=scope + 'B')
output_shape = tf.stack([2, x_shape[1], x_shape[2], channel])
deconv = tf.nn.conv2d_transpose(recalibrate3,
W,
output_shape,
strides=[1, 4, 4, 1],
padding="SAME") + B
recalibrate3 = tf.nn.leaky_relu(deconv)
# recalibrate3 = tf.image.resize_images(images=recalibrate3, size=[width, height])
recalibrate4 = tf.multiply(recalibrate1, recalibrate2)
alpha_attention = tf.Variable(tf.constant(1.0))
beta_attention = tf.Variable(tf.constant(1.0))
attention = tf.multiply(alpha_attention * recalibrate4,
beta_attention * recalibrate3)
out = tf.multiply(attention, x)
return out
def full_connected_relu(x, kernal, activefuncation='relu', scope=None):
with tf.name_scope(scope):
W = weight_xavier_init(shape=kernal, n_inputs=kernal[0] * kernal[1],
n_outputs=kernal[-1], activefunction='relu', variable_name=scope + 'W')
B = bias_variable([kernal[-1]], variable_name=scope + 'B')
FC = tf.matmul(x, W) + B
if activefuncation == 'relu':
FC = tf.nn.relu(FC)
elif activefuncation == 'softmax':
FC = tf.nn.softmax(FC)
elif activefuncation == 'sigmoid':
FC = tf.nn.sigmoid(FC)
return FC