def denseASPP_block(input, train, keep_prob):
input_shape = input.get_shape().as_list()
c0 = input_shape[-1]
n = tf.cast(c0 / 8, tf.int32) # output feature maps of denseASPP layer
n0 = tf.cast(c0 / 4, tf.int32) # input feature maps of denseASPP layer
input_0 = input
for layer in range(1, denseASPP_layers_num + 1):
with tf.name_scope('denseASPP_layer_%d' % layer):
input_shape = input.get_shape().as_list()
####
input = densenet.bn_layer(input, train)
input = tf.nn.relu(input)
####
weight_1 = densenet.weight_variable([1, 1, input_shape[-1], n0])
input_compress = tf.nn.conv2d(input,
weight_1, [1, 1, 1, 1],
padding='SAME')
output = denseASPP_layer(input_compress,
denseASPP_rates[layer - 1], n, train,
keep_prob)
input_0 = tf.concat([input_0, output], axis=-1)
input = input_0
return input
def denseASPP_layer(input, rate, n, train, keep_prob):
'''
:param input: input feature map
:param rate: dilatation rate
:param n0: input channels
:param n: output channels
:return:
'''
with tf.name_scope("denseASPP_layer"):
input_shape = input.get_shape().as_list()
n = tf.cast(n, tf.int32)
weight_3 = densenet.weight_variable([3, 3, input_shape[-1], n])
####
input = densenet.bn_layer(input, train)
input = tf.nn.relu(input)
####
input = tf.nn.atrous_conv2d(input, weight_3, rate=rate, padding='SAME')
input = tf.nn.dropout(input, keep_prob=keep_prob)
return input
def denseASPP(input, keep_prob, train=True):
with tf.name_scope("densenet_121"):
input = densenet.densenet_121(input, keep_prob, train)
with tf.name_scope("denseASPP"):
input = densenet.bn_layer(input, train)
input = denseASPP_block(input, train, keep_prob)
with tf.name_scope("segmentation"):
input_shape = input.get_shape().as_list()
####
#input = densenet.bn_layer(input, train)
#input = tf.nn.relu(input)
####
weight_1 = densenet.weight_variable([1, 1, input_shape[-1], CLASSES])
input = tf.nn.conv2d(input, weight_1, [1, 1, 1, 1], padding='SAME')
with tf.name_scope("upsamling"):
input_shape = input.get_shape().as_list()
input = tf.image.resize_bilinear(
input, [8 * input_shape[1], 8 * input_shape[2]])
'''
with tf.name_scope("upsampling"):
input_shape = input.get_shape().as_list()
####
input = densenet.bn_layer(input, train)
#input = tf.nn.relu(input)
####
weight_2_1 = densenet.weight_variable([2, 2, CLASSES, CLASSES])
input = tf.nn.conv2d_transpose(input, weight_2_1, [input_shape[0], input_shape[1] * 2, input_shape[2] * 2, input_shape[3]], [1, 2, 2, 1], padding='SAME')
input_shape = input.get_shape().as_list()
####
input = densenet.bn_layer(input, train)
# input = tf.nn.relu(input)
####
weight_2_2 = densenet.weight_variable([2, 2, CLASSES, CLASSES])
input = tf.nn.conv2d_transpose(input, weight_2_2,
[input_shape[0], input_shape[1] * 2, input_shape[2] * 2, input_shape[3]],
[1, 2, 2, 1], padding='SAME')
input_shape = input.get_shape().as_list()
####
# input = densenet.bn_layer(input, train)
# input = tf.nn.relu(input)
####
weight_2_3 = densenet.weight_variable([2, 2, CLASSES, CLASSES])
input = tf.nn.conv2d_transpose(input, weight_2_3,
[input_shape[0], input_shape[1] * 2, input_shape[2] * 2, input_shape[3]],
[1, 2, 2, 1], padding='SAME')
'''
output = input
return output
