def fusion2(self,
conv5_feature,
conv4_feature,
upconv3,
name="fusion2"): #(1/16, 1/8, 1)
with tf.variable_scope(name) as scope:
output_channel = np.shape(upconv3)[3]
conv5_feature = conv_bn_relu(
conv5_feature,
[3, 3, np.shape(conv5_feature)[3], output_channel], 1)
conv5_feature = UpSampling2D((16, 16))(conv5_feature)
conv4_feature = conv_bn_relu(
conv4_feature,
[3, 3, np.shape(conv4_feature)[3], output_channel], 1)
conv4_feature = UpSampling2D((8, 8))(conv4_feature)
upconv3 = conv_bn_relu(
upconv3, [3, 3, np.shape(upconv3)[3], output_channel], 1)
output = tf.add(conv5_feature, conv4_feature)
output = tf.add(output, upconv3)
output = conv_bn_relu(output,
[3, 3, output_channel, output_channel], 1)
return output
def generation_stage(self, inputs):
D = np.shape(inputs)[3]
x = conv_bn_relu(inputs, [1, 1, D, D // 2], 1)
x = conv_bn_relu(x, [3, 3, D // 2, D // 2], 1)
x = conv_bn_relu(x, [1, 1, D // 2, D], 1)
outputs = tf.add(inputs, x)
return outputs
def generation_stage(self, inputs, name):
with tf.variable_scope(name) as scope:
D = np.shape(inputs)[3]
x = conv_bn_relu(inputs, [1, 1, D, D // 2], 1, "c1")
x = conv_bn_relu(x, [3, 3, D // 2, D // 2], 1, "c2")
x = conv_bn_relu(x, [1, 1, D // 2, D], 1, "c3")
outputs = tf.add(inputs, x)
return outputs
def Conv_Pool_Conv(self, inputs):
D = np.shape(inputs)[3]
outputs = conv_bn_relu(inputs, [3, 3, D, D], stride=1)
outputs = tf.nn.max_pool(outputs,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pooling')
outputs = conv_bn_relu(outputs, [3, 3, D, D // 4], stride=1)
return outputs
def Dense_decoder_feature_generation(self, inputs):
branch1 = self.Conv_Pool_Conv(inputs)
branch2 = self.Conv_Pool_Conv(inputs)
branch3 = self.Conv_Pool_Conv(inputs)
branch4 = self.Conv_Pool_Conv(inputs)
features = tf.concat([branch1, branch2, branch3, branch4], axis=3)
D = np.shape(inputs)[3]
features = conv_bn_relu(features, [3, 3, D, D], stride=1)
for i in range(4):
features = self.generation_stage(features)
features = UpSampling2D((2, 2))(features)
features = conv_bn_relu(features, [3, 3, D, D], 1)
return features
def fusion1(self, conv5_feature, upconv4): #(1/16, 1/8)
output_channel = np.shape(upconv4)[3]
conv5_feature = conv_bn_relu(
conv5_feature,
[3, 3, np.shape(conv5_feature)[3], output_channel], 1)
conv5_feature = UpSampling2D((2, 2))(conv5_feature)
upconv4 = conv_bn_relu(
upconv4, [3, 3, np.shape(upconv4)[3], output_channel], 1)
output = tf.add(conv5_feature, upconv4)
output = conv_bn_relu(output, [3, 3, output_channel, output_channel],
1)
return output
def fusion1(self, conv5_feature, upconv4, name="fusion1"): #(1/16, 1/8)
with tf.variable_scope(name) as scope:
output_channel = np.shape(upconv4)[3]
conv5_feature = conv_bn_relu(
conv5_feature,
[3, 3, np.shape(conv5_feature)[3], output_channel], 1)
conv5_feature = UpSampling2D((2, 2))(conv5_feature)
upconv4 = conv_bn_relu(
upconv4, [3, 3, np.shape(upconv4)[3], output_channel], 1)
output = tf.add(conv5_feature, upconv4)
output = conv_bn_relu(output,
[3, 3, output_channel, output_channel], 1)
return output
def Dense_decoder_feature_generation(self, inputs, name):
with tf.variable_scope(name) as scope:
branch1 = self.Conv_Pool_Conv(inputs, "b1")
branch2 = self.Conv_Pool_Conv(inputs, "b2")
branch3 = self.Conv_Pool_Conv(inputs, "b3")
branch4 = self.Conv_Pool_Conv(inputs, "b4")
features = tf.concat([branch1, branch2, branch3, branch4], axis=3)
D = np.shape(inputs)[3]
features = conv_bn_relu(features, [3, 3, D, D], stride=1)
for i in range(4):
features = self.generation_stage(features, "stage" + str(i))
features = UpSampling2D((2, 2))(features)
features = conv_bn_relu(features, [3, 3, D, D], 1)
return features
def build(self, x, class_num, channel, wd, train=True):
self.conv1_1 = _conv_layer(x, [3, 3, channel, 64], 'conv1_1')
self.conv1_2 = _conv_layer(self.conv1_1, [3, 3, 64, 64], 'conv1_2')
self.pooling1 = _pooling_layer(self.conv1_2, 'pooling1')
self.conv2_1 = _conv_layer(self.pooling1, [3, 3, 64, 128], 'conv2_1')
self.conv2_2 = _conv_layer(self.conv2_1, [3, 3, 128, 128], 'conv2_2')
self.pooling2 = _pooling_layer(self.conv2_2, 'pooling2')
self.conv3_1 = _conv_layer(self.pooling2, [3, 3, 128, 256], 'conv3_1')
self.conv3_2 = _conv_layer(self.conv3_1, [3, 3, 256, 256], 'conv3_2')
self.conv3_3 = _conv_layer(self.conv3_2, [3, 3, 256, 256], 'conv3_3')
self.pooling3 = _pooling_layer(self.conv3_3, 'pooling3')
self.conv4_1 = _conv_layer(self.pooling3, [3, 3, 256, 512], 'conv4_1')
self.conv4_2 = _conv_layer(self.conv4_1, [3, 3, 512, 512], 'conv4_2')
self.conv4_3 = _conv_layer(self.conv4_2, [3, 3, 512, 512], 'conv4_3')
self.pooling4 = _pooling_layer(self.conv4_3, 'pooling4')
self.conv5_1 = _conv_layer(self.pooling4, [3, 3, 512, 512], 'conv5_1')
self.conv5_2 = _conv_layer(self.conv5_1, [3, 3, 512, 512], 'conv5_2')
self.conv5_3 = _conv_layer(self.conv5_2, [3, 3, 512, 512], 'conv5_3')
self.pooling5 = _pooling_layer(self.conv5_3, 'pooling5')
self.conv6 = _conv_layer(self.pooling5, [7, 7, 512, 4096], 'conv6')
if train:
self.conv6 = tf.nn.dropout(self.conv6, 0.5)
self.conv7 = _conv_layer(self.conv6, [1, 1, 4096, 4096], 'conv7')
if train:
self.conv7 = tf.nn.dropout(self.conv7, 0.5)
self.score_fr = _conv_layer_without_relu(self.conv7, [1, 1, 4096, 64],
'conv7_1x1conv',
wd=wd)
self.pooling4_conv = _conv_layer_without_relu(self.pooling4,
[1, 1, 512, 64],
'pool4_1x1conv',
wd=wd)
self.pooling3_conv = _conv_layer_without_relu(self.pooling3,
[1, 1, 256, 64],
'pool3_1x1conv',
wd=wd)
self.upconv5 = UpSampling2D((2, 2))(self.score_fr) #1/16
self.upconv4 = UpSampling2D((2, 2))(self.pooling4_conv) #1/8
self.upconv3 = UpSampling2D((8, 8))(self.pooling3_conv) #1
self.conv5_feature = self.Dense_decoder_feature_generation(
self.upconv5)
self.upconv4 = self.fusion1(self.conv5_feature, self.upconv4) #1/8
self.conv4_feature = self.Dense_decoder_feature_generation(
self.upconv4)
self.upconv3 = self.fusion2(self.conv5_feature, self.conv4_feature,
self.upconv3)
self.conv3_feature = self.Dense_decoder_feature_generation(
self.upconv3)
self.upscore = conv_bn_relu(
self.conv3_feature,
[3, 3, np.shape(self.conv3_feature)[3], 64], 1)
self.upscore = conv_bn_relu(self.upscore, [3, 3, 64, class_num], 1)
return self.upscore
def build(self, x, class_num, channel, wd, train=True):
self.conv1_1 = _conv_layer(x, [3, 3, channel, 64], 'conv1_1')
self.conv1_2 = _conv_layer(self.conv1_1, [3, 3, 64, 64], 'conv1_2')
self.pooling1 = _pooling_layer(self.conv1_2, 'pooling1')
self.conv2_1 = _conv_layer(self.pooling1, [3, 3, 64, 128], 'conv2_1')
self.conv2_2 = _conv_layer(self.conv2_1, [3, 3, 128, 128], 'conv2_2')
self.pooling2 = _pooling_layer(self.conv2_2, 'pooling2')
# self.inception_block2_1 = _inception_layer(self.pooling1, in_c=64, out_1=32, out_21=32, out_22=32, out_31=32, out_32=64, out_33=64)
# self.inception_block2_2 = _inception_layer(self.inception_block2_1, in_c=128, out_1=32, out_21=32, out_22=32, out_31=32,
# out_32=64, out_33=64)
# self.pooling2 = _pooling_layer(self.inception_block2_2, 'pooling2')
# self.conv3_1 = _conv_layer(self.pooling2, [3, 3, 128, 256], 'conv3_1')
# self.conv3_2 = _conv_layer(self.conv3_1, [3, 3, 256, 256], 'conv3_2')
# self.conv3_3 = _conv_layer(self.conv3_2, [3, 3, 256, 256], 'conv3_3')
# self.pooling3 = _pooling_layer(self.conv3_3, 'pooling3')
self.inception_block3_1 = _inception_layer(
'inception3_1',
self.pooling2,
in_c=128,
out_1=64,
out_21=64,
out_22=64,
out_31=64,
out_32=128,
out_33=64,
out_41=64,
out_42=128,
out_43=64) # output channel 256
self.inception_block3_2 = _inception_layer(
'inception3_2',
self.inception_block3_1,
in_c=256,
out_1=64,
out_21=64,
out_22=64,
out_31=64,
out_32=128,
out_33=64,
out_41=64,
out_42=128,
out_43=64) # output channel 256
self.inception_block3_3 = _inception_layer(
'inception3_3',
self.inception_block3_2,
in_c=256,
out_1=64,
out_21=64,
out_22=64,
out_31=64,
out_32=128,
out_33=64,
out_41=64,
out_42=128,
out_43=64) # output channel 256
self.pooling3 = _pooling_layer(self.inception_block3_3, 'pooling3')
self.inception_block4_1 = _inception_layer('inception4_1',
self.pooling3,
in_c=256,
out_1=128,
out_21=128,
out_22=128,
out_31=128,
out_32=256,
out_33=128,
out_41=128,
out_42=256,
out_43=128) # output 512
self.inception_block4_2 = _inception_layer('inception4_2',
self.inception_block4_1,
in_c=512,
out_1=128,
out_21=128,
out_22=128,
out_31=128,
out_32=256,
out_33=128,
out_41=128,
out_42=256,
out_43=128) # output 512
self.inception_block4_3 = _inception_layer('inception4_3',
self.inception_block4_2,
in_c=512,
out_1=128,
out_21=128,
out_22=128,
out_31=128,
out_32=256,
out_33=128,
out_41=128,
out_42=256,
out_43=128) # output 512
self.pooling4 = _pooling_layer(self.inception_block4_3, 'pooling4')
self.inception_block5_1 = _inception_layer('inception5_1',
self.pooling4,
in_c=512,
out_1=128,
out_21=128,
out_22=128,
out_31=128,
out_32=256,
out_33=128,
out_41=128,
out_42=256,
out_43=128) # output 512
self.inception_block5_2 = _inception_layer('inception5_2',
self.inception_block5_1,
in_c=512,
out_1=128,
out_21=128,
out_22=128,
out_31=128,
out_32=256,
out_33=128,
out_41=128,
out_42=256,
out_43=128) # output 512
self.inception_block5_3 = _inception_layer('inception5_3',
self.inception_block5_2,
in_c=512,
out_1=128,
out_21=128,
out_22=128,
out_31=128,
out_32=256,
out_33=128,
out_41=128,
out_42=256,
out_43=128) # output 512
self.pooling5 = _pooling_layer(self.inception_block5_3, 'pooling5')
self.conv6 = _conv_layer(self.pooling5, [7, 7, 512, 4096], 'conv6')
if train:
self.conv6 = tf.nn.dropout(self.conv6, 0.5)
self.conv7 = _conv_layer(self.conv6, [1, 1, 4096, 4096], 'conv7')
if train:
self.conv7 = tf.nn.dropout(self.conv7, 0.5)
self.score_fr = _conv_layer_without_relu(self.conv7, [1, 1, 4096, 64],
'conv7_1x1conv',
wd=wd)
self.pooling4_conv = _conv_layer_without_relu(self.pooling4,
[1, 1, 512, 64],
'pool4_1x1conv',
wd=wd)
self.pooling3_conv = _conv_layer_without_relu(self.pooling3,
[1, 1, 256, 64],
'pool3_1x1conv',
wd=wd)
self.upconv5 = UpSampling2D((2, 2))(self.score_fr) #1/16
self.upconv4 = UpSampling2D((2, 2))(self.pooling4_conv) #1/8
self.upconv3 = UpSampling2D((8, 8))(self.pooling3_conv) #1
self.conv5_feature = self.Dense_decoder_feature_generation(
self.upconv5, name="gen1")
self.upconv4 = self.fusion1(self.conv5_feature, self.upconv4) #1/8
self.conv4_feature = self.Dense_decoder_feature_generation(
self.upconv4, name="gen2")
self.upconv3 = self.fusion2(self.conv5_feature, self.conv4_feature,
self.upconv3)
self.conv3_feature = self.Dense_decoder_feature_generation(
self.upconv3, name="gen3")
self.upscore = conv_bn_relu(
self.conv3_feature,
[3, 3, np.shape(self.conv3_feature)[3], 64],
1,
name="final1")
self.upscore = conv_bn_relu(self.upscore, [3, 3, 64, class_num],
1,
name="final2")
return self.upscore
