def _create_conv_net(X, image_width, image_height, image_channel, phase, drop_conv, n_class=1):
inputX = tf.reshape(X, [-1, image_width, image_height, image_channel]) # shape=(?, 32, 32, 1)
# VNet model
# layer0->convolution
layer0 = conv_relu(x=inputX, kernalshape=(3, 3, image_channel, 16), scope='layer0')
# layer1->convolution
layer1 = conv_bn_relu_drop(x=layer0, kernalshape=(3, 3, 16, 16), phase=phase, drop_conv=drop_conv,
scope='layer1-1')
layer1 = conv_bn_relu_drop(x=layer1, kernalshape=(3, 3, 16, 16), phase=phase, drop_conv=drop_conv,
scope='layer1-2')
layer1 = resnet_Add(x1=layer0, x2=layer1)
# down sampling1
down1 = down_sampling(x=layer1, kernalshape=(3, 3, 16, 32), phase=phase, drop_conv=drop_conv, scope='down1')
# layer2->convolution
layer2 = conv_bn_relu_drop(x=down1, kernalshape=(3, 3, 32, 32), phase=phase, drop_conv=drop_conv,
scope='layer2_1')
layer2 = conv_bn_relu_drop(x=layer2, kernalshape=(3, 3, 32, 32), phase=phase, drop_conv=drop_conv,
scope='layer2_2')
layer2 = resnet_Add(x1=down1, x2=layer2)
# down sampling2
down2 = down_sampling(x=layer2, kernalshape=(3, 3, 32, 64), phase=phase, drop_conv=drop_conv, scope='down2')
# layer3->convolution
layer3 = conv_bn_relu_drop(x=down2, kernalshape=(3, 3, 64, 64), phase=phase, drop_conv=drop_conv,
scope='layer3_1')
layer3 = conv_bn_relu_drop(x=layer3, kernalshape=(3, 3, 64, 64), phase=phase, drop_conv=drop_conv,
scope='layer3_2')
layer3 = conv_bn_relu_drop(x=layer3, kernalshape=(3, 3, 64, 64), phase=phase, drop_conv=drop_conv,
scope='layer3_3')
layer3 = resnet_Add(x1=down2, x2=layer3)
# down sampling3
down3 = down_sampling(x=layer3, kernalshape=(3, 3, 64, 128), phase=phase, drop_conv=drop_conv, scope='down3')
# layer4->convolution
layer4 = conv_bn_relu_drop(x=down3, kernalshape=(3, 3, 128, 128), phase=phase, drop_conv=drop_conv,
scope='layer4_1')
layer4 = conv_bn_relu_drop(x=layer4, kernalshape=(3, 3, 128, 128), phase=phase, drop_conv=drop_conv,
scope='layer4_2')
layer4 = conv_bn_relu_drop(x=layer4, kernalshape=(3, 3, 128, 128), phase=phase, drop_conv=drop_conv,
scope='layer4_3')
layer4 = resnet_Add(x1=down3, x2=layer4)
# down sampling4
down4 = down_sampling(x=layer4, kernalshape=(3, 3, 128, 256), phase=phase, drop_conv=drop_conv, scope='down4')
# layer5->convolution
layer5 = conv_bn_relu_drop(x=down4, kernalshape=(3, 3, 256, 256), phase=phase, drop_conv=drop_conv,
scope='layer5_1')
layer5 = conv_bn_relu_drop(x=layer5, kernalshape=(3, 3, 256, 256), phase=phase, drop_conv=drop_conv,
scope='layer5_2')
layer5 = conv_bn_relu_drop(x=layer5, kernalshape=(3, 3, 256, 256), phase=phase, drop_conv=drop_conv,
scope='layer5_3')
layer5 = resnet_Add(x1=down4, x2=layer5)
# down sampling5
down5 = down_sampling(x=layer5, kernalshape=(3, 3, 256, 512), phase=phase, drop_conv=drop_conv, scope='down5')
# layer6->convolution
layer6 = conv_bn_relu_drop(x=down5, kernalshape=(3, 3, 512, 512), phase=phase, drop_conv=drop_conv,
scope='layer6_1')
layer6 = conv_bn_relu_drop(x=layer6, kernalshape=(3, 3, 512, 512), phase=phase, drop_conv=drop_conv,
scope='layer6_2')
layer6 = conv_bn_relu_drop(x=layer6, kernalshape=(3, 3, 512, 512), phase=phase, drop_conv=drop_conv,
scope='layer6_3')
layer6 = resnet_Add(x1=down5, x2=layer6)
# layer7->deconvolution
deconv1 = deconv_relu_drop(x=layer6, kernalshape=(3, 3, 256, 512), scope='deconv1')
# layer8->convolution
layer7 = crop_and_concat(layer5, deconv1)
_, H, W, _ = layer5.get_shape().as_list()
layer7 = conv_bn_relu_drop(x=layer7, kernalshape=(3, 3, 512, 256), height=H, width=W, phase=phase,
drop_conv=drop_conv, scope='layer7_1')
layer7 = conv_bn_relu_drop(x=layer7, kernalshape=(3, 3, 256, 256), height=H, width=W, phase=phase,
drop_conv=drop_conv, scope='layer7_2')
layer7 = conv_bn_relu_drop(x=layer7, kernalshape=(3, 3, 256, 256), height=H, width=W, phase=phase,
drop_conv=drop_conv, scope='layer7_3')
layer7 = resnet_Add(x1=deconv1, x2=layer7)
# deepspu1
output_map1 = upsample2d(x=layer7, scale_factor=16, scope="upsample3d_1")
output_map1 = conv_sigmod(x=output_map1, kernalshape=(1, 1, 256, n_class), scope='output_map1')
# layer9->deconvolution
deconv2 = deconv_relu_drop(x=layer7, kernalshape=(3, 3, 128, 256), scope='deconv2')
# layer8->convolution
layer8 = crop_and_concat(layer4, deconv2)
_, H, W, _ = layer4.get_shape().as_list()
layer8 = conv_bn_relu_drop(x=layer8, kernalshape=(3, 3, 256, 128), height=H, width=W, phase=phase,
drop_conv=drop_conv, scope='layer8_1')
layer8 = conv_bn_relu_drop(x=layer8, kernalshape=(3, 3, 128, 128), height=H, width=W, phase=phase,
drop_conv=drop_conv, scope='layer8_2')
layer8 = conv_bn_relu_drop(x=layer8, kernalshape=(3, 3, 128, 128), height=H, width=W, phase=phase,
drop_conv=drop_conv, scope='layer8_3')
layer8 = resnet_Add(x1=deconv2, x2=layer8)
# deepspu2
output_map2 = upsample2d(x=layer8, scale_factor=8, scope="upsample3d_2")
output_map2 = conv_sigmod(x=output_map2, kernalshape=(1, 1, 128, n_class), scope='output_map2')
# layer9->deconvolution
deconv3 = deconv_relu_drop(x=layer8, kernalshape=(3, 3, 64, 128), scope='deconv3')
# layer8->convolution
layer9 = crop_and_concat(layer3, deconv3)
_, H, W, _ = layer3.get_shape().as_list()
layer9 = conv_bn_relu_drop(x=layer9, kernalshape=(3, 3, 128, 64), height=H, width=W, phase=phase,
drop_conv=drop_conv, scope='layer9_1')
layer9 = conv_bn_relu_drop(x=layer9, kernalshape=(3, 3, 64, 64), height=H, width=W, phase=phase,
drop_conv=drop_conv, scope='layer9_2')
layer9 = conv_bn_relu_drop(x=layer9, kernalshape=(3, 3, 64, 64), height=H, width=W, phase=phase,
drop_conv=drop_conv, scope='layer9_3')
layer9 = resnet_Add(x1=deconv3, x2=layer9)
# deepspu3
output_map3 = upsample2d(x=layer9, scale_factor=4, scope="upsample3d_3")
output_map3 = conv_sigmod(x=output_map3, kernalshape=(1, 1, 64, n_class), scope='output_map3')
# layer9->deconvolution
deconv4 = deconv_relu_drop(x=layer9, kernalshape=(3, 3, 32, 64), scope='deconv4')
# layer8->convolution
layer10 = crop_and_concat(layer2, deconv4)
_, H, W, _ = layer2.get_shape().as_list()
layer10 = conv_bn_relu_drop(x=layer10, kernalshape=(3, 3, 64, 32), height=H, width=W, phase=phase,
drop_conv=drop_conv, scope='layer10_1')
layer10 = conv_bn_relu_drop(x=layer10, kernalshape=(3, 3, 32, 32), height=H, width=W, phase=phase,
drop_conv=drop_conv, scope='layer10_2')
layer10 = resnet_Add(x1=deconv4, x2=layer10)
# deepspu4
output_map4 = upsample2d(x=layer10, scale_factor=2, scope="upsample3d_4")
output_map4 = conv_sigmod(x=output_map4, kernalshape=(1, 1, 32, n_class), scope='output_map4')
# layer9->deconvolution
deconv5 = deconv_relu_drop(x=layer10, kernalshape=(3, 3, 16, 32), scope='deconv5')
# layer8->convolution
layer11 = crop_and_concat(layer1, deconv5)
_, H, W, _ = layer1.get_shape().as_list()
layer11 = conv_bn_relu_drop(x=layer11, kernalshape=(3, 3, 32, 16), height=H, width=W, phase=phase,
drop_conv=drop_conv, scope='layer11_1')
layer11 = conv_bn_relu_drop(x=layer11, kernalshape=(3, 3, 16, 16), height=H, width=W, phase=phase,
drop_conv=drop_conv, scope='layer11_2')
layer11 = resnet_Add(x1=deconv5, x2=layer11)
# layer14->output
output_map = conv_sigmod(x=layer11, kernalshape=(1, 1, 16, n_class), scope='output')
return output_map, output_map1, output_map2, output_map3, output_map4
def _create_conv_net(X,
image_width,
image_height,
image_channel,
phase,
drop_conv,
n_class=1,
n_classify=2):
inputX = tf.reshape(
X,
[-1, image_width, image_height, image_channel]) # shape=(?, 32, 32, 1)
# VNet model
# layer0->convolution
layer0 = conv_relu(x=inputX,
kernalshape=(3, 3, image_channel, 16),
scope='layer0')
# layer1->convolution
layer1 = conv_bn_relu_drop(x=layer0,
kernalshape=(3, 3, 16, 16),
phase=phase,
drop_conv=drop_conv,
scope='layer1-1')
layer1 = conv_bn_relu_drop(x=layer1,
kernalshape=(3, 3, 16, 16),
phase=phase,
drop_conv=drop_conv,
scope='layer1-2')
layer1 = resnet_Add(x1=layer0, x2=layer1)
# down sampling1
down1 = down_sampling(x=layer1,
kernalshape=(3, 3, 16, 32),
phase=phase,
drop_conv=drop_conv,
scope='down1')
# layer2->convolution
layer2 = conv_bn_relu_drop(x=down1,
kernalshape=(3, 3, 32, 32),
phase=phase,
drop_conv=drop_conv,
scope='layer2_1')
layer2 = conv_bn_relu_drop(x=layer2,
kernalshape=(3, 3, 32, 32),
phase=phase,
drop_conv=drop_conv,
scope='layer2_2')
layer2 = resnet_Add(x1=down1, x2=layer2)
# down sampling2
down2 = down_sampling(x=layer2,
kernalshape=(3, 3, 32, 64),
phase=phase,
drop_conv=drop_conv,
scope='down2')
# layer3->convolution
layer3 = conv_bn_relu_drop(x=down2,
kernalshape=(3, 3, 64, 64),
phase=phase,
drop_conv=drop_conv,
scope='layer3_1')
layer3 = conv_bn_relu_drop(x=layer3,
kernalshape=(3, 3, 64, 64),
phase=phase,
drop_conv=drop_conv,
scope='layer3_2')
layer3 = conv_bn_relu_drop(x=layer3,
kernalshape=(3, 3, 64, 64),
phase=phase,
drop_conv=drop_conv,
scope='layer3_3')
layer3 = resnet_Add(x1=down2, x2=layer3)
# down sampling3
down3 = down_sampling(x=layer3,
kernalshape=(3, 3, 64, 128),
phase=phase,
drop_conv=drop_conv,
scope='down3')
# layer4->convolution
layer4 = conv_bn_relu_drop(x=down3,
kernalshape=(3, 3, 128, 128),
phase=phase,
drop_conv=drop_conv,
scope='layer4_1')
layer4 = conv_bn_relu_drop(x=layer4,
kernalshape=(3, 3, 128, 128),
phase=phase,
drop_conv=drop_conv,
scope='layer4_2')
layer4 = conv_bn_relu_drop(x=layer4,
kernalshape=(3, 3, 128, 128),
phase=phase,
drop_conv=drop_conv,
scope='layer4_3')
layer4 = resnet_Add(x1=down3, x2=layer4)
# down sampling4
down4 = down_sampling(x=layer4,
kernalshape=(3, 3, 128, 256),
phase=phase,
drop_conv=drop_conv,
scope='down4')
# layer5->convolution
layer5 = conv_bn_relu_drop(x=down4,
kernalshape=(3, 3, 256, 256),
phase=phase,
drop_conv=drop_conv,
scope='layer5_1')
layer5 = conv_bn_relu_drop(x=layer5,
kernalshape=(3, 3, 256, 256),
phase=phase,
drop_conv=drop_conv,
scope='layer5_2')
layer5 = conv_bn_relu_drop(x=layer5,
kernalshape=(3, 3, 256, 256),
phase=phase,
drop_conv=drop_conv,
scope='layer5_3')
layer5 = resnet_Add(x1=down4, x2=layer5)
# down sampling5
down5 = down_sampling(x=layer5,
kernalshape=(3, 3, 256, 512),
phase=phase,
drop_conv=drop_conv,
scope='down5')
# layer6->convolution
layer6 = conv_bn_relu_drop(x=down5,
kernalshape=(3, 3, 512, 512),
phase=phase,
drop_conv=drop_conv,
scope='layer6_1')
layer6 = conv_bn_relu_drop(x=layer6,
kernalshape=(3, 3, 512, 512),
phase=phase,
drop_conv=drop_conv,
scope='layer6_2')
layer6 = conv_bn_relu_drop(x=layer6,
kernalshape=(3, 3, 512, 512),
phase=phase,
drop_conv=drop_conv,
scope='layer6_3')
layer6 = resnet_Add(x1=down5, x2=layer6)
gap = tf.reduce_mean(layer6, axis=(1, 2))
# layer7->FC1
layerfc1 = tf.reshape(gap, [-1, 512]) # shape=(?, 512)
layerfc2 = full_connected_relu_drop(x=layerfc1,
kernal=(512, 512),
drop=drop_conv,
activefunction='relu',
scope='fc1')
# layer7->output
classify_output = full_connected_relu_drop(x=layerfc2,
kernal=(512, n_classify),
drop=drop_conv,
activefunction='regression',
scope='classify_output')
# layer7->deconvolution
deconv1 = deconv_relu_drop(x=layer6,
kernalshape=(3, 3, 256, 512),
scope='deconv1')
# layer8->convolution
layer7 = crop_and_concat(layer5, deconv1)
_, H, W, _ = layer5.get_shape().as_list()
layer7 = conv_bn_relu_drop(x=layer7,
kernalshape=(3, 3, 512, 256),
height=H,
width=W,
phase=phase,
drop_conv=drop_conv,
scope='layer7_1')
layer7 = conv_bn_relu_drop(x=layer7,
kernalshape=(3, 3, 256, 256),
height=H,
width=W,
phase=phase,
drop_conv=drop_conv,
scope='layer7_2')
layer7 = conv_bn_relu_drop(x=layer7,
kernalshape=(3, 3, 256, 256),
height=H,
width=W,
phase=phase,
drop_conv=drop_conv,
scope='layer7_3')
layer7 = resnet_Add(x1=deconv1, x2=layer7)
# layer9->deconvolution
deconv2 = deconv_relu_drop(x=layer7,
kernalshape=(3, 3, 128, 256),
scope='deconv2')
# layer8->convolution
layer8 = crop_and_concat(layer4, deconv2)
_, H, W, _ = layer4.get_shape().as_list()
layer8 = conv_bn_relu_drop(x=layer8,
kernalshape=(3, 3, 256, 128),
height=H,
width=W,
phase=phase,
drop_conv=drop_conv,
scope='layer8_1')
layer8 = conv_bn_relu_drop(x=layer8,
kernalshape=(3, 3, 128, 128),
height=H,
width=W,
phase=phase,
drop_conv=drop_conv,
scope='layer8_2')
layer8 = conv_bn_relu_drop(x=layer8,
kernalshape=(3, 3, 128, 128),
height=H,
width=W,
phase=phase,
drop_conv=drop_conv,
scope='layer8_3')
layer8 = resnet_Add(x1=deconv2, x2=layer8)
# layer9->deconvolution
deconv3 = deconv_relu_drop(x=layer8,
kernalshape=(3, 3, 64, 128),
scope='deconv3')
# layer8->convolution
layer9 = crop_and_concat(layer3, deconv3)
_, H, W, _ = layer3.get_shape().as_list()
layer9 = conv_bn_relu_drop(x=layer9,
kernalshape=(3, 3, 128, 64),
height=H,
width=W,
phase=phase,
drop_conv=drop_conv,
scope='layer9_1')
layer9 = conv_bn_relu_drop(x=layer9,
kernalshape=(3, 3, 64, 64),
height=H,
width=W,
phase=phase,
drop_conv=drop_conv,
scope='layer9_2')
layer9 = conv_bn_relu_drop(x=layer9,
kernalshape=(3, 3, 64, 64),
height=H,
width=W,
phase=phase,
drop_conv=drop_conv,
scope='layer9_3')
layer9 = resnet_Add(x1=deconv3, x2=layer9)
# layer9->deconvolution
deconv4 = deconv_relu_drop(x=layer9,
kernalshape=(3, 3, 32, 64),
scope='deconv4')
# layer8->convolution
layer10 = crop_and_concat(layer2, deconv4)
_, H, W, _ = layer2.get_shape().as_list()
layer10 = conv_bn_relu_drop(x=layer10,
kernalshape=(3, 3, 64, 32),
height=H,
width=W,
phase=phase,
drop_conv=drop_conv,
scope='layer10_1')
layer10 = conv_bn_relu_drop(x=layer10,
kernalshape=(3, 3, 32, 32),
height=H,
width=W,
phase=phase,
drop_conv=drop_conv,
scope='layer10_2')
layer10 = resnet_Add(x1=deconv4, x2=layer10)
# layer9->deconvolution
deconv5 = deconv_relu_drop(x=layer10,
kernalshape=(3, 3, 16, 32),
scope='deconv5')
# layer8->convolution
layer11 = crop_and_concat(layer1, deconv5)
_, H, W, _ = layer1.get_shape().as_list()
layer11 = conv_bn_relu_drop(x=layer11,
kernalshape=(3, 3, 32, 16),
height=H,
width=W,
phase=phase,
drop_conv=drop_conv,
scope='layer11_1')
layer11 = conv_bn_relu_drop(x=layer11,
kernalshape=(3, 3, 16, 16),
height=H,
width=W,
phase=phase,
drop_conv=drop_conv,
scope='layer11_2')
layer11 = resnet_Add(x1=deconv5, x2=layer11)
# layer14->output
output_map = conv_sigmod(x=layer11,
kernalshape=(1, 1, 16, n_class),
scope='output')
return output_map, classify_output