def subtract_refine(subtract, layer7, layer8, layer9, phase, drop_conv, n_class=1):
_, y_width, y_height, y_channel = subtract.get_shape().as_list() # 拿出 输入 tensor 的 最后一维:也就是通道数
seed = tf.reshape(subtract, [-1, y_width, y_height, y_channel]) # 将图片转换成tf识别格式
refine1 = conv_bn_relu_drop(x=layer7, kernal=[1, 1, 128, 64], phase=phase, drop=drop_conv, scope='refine1_1')
seed1 = tf.image.resize_images(images=seed, size=[int(y_width / 4), int(y_height / 4)], method=0)
refine1 = crop_and_concat(refine1, seed1)
refine1 = conv_bn_relu_drop(x=refine1, kernal=[3, 3, 65, 65], phase=phase, drop=drop_conv, scope='refine1_2')
refine1 = squeeze_excitation_model(refine1, out_dim=65, scope='sem2_1')
refine1 = deconv_relu(x=refine1, kernal=[3, 3, 32, 65], scope='refine1_deconv')
refine2 = conv_bn_relu_drop(x=layer8, kernal=[1, 1, 64, 32], phase=phase, drop=drop_conv, scope='refine2_1')
seed2 = tf.image.resize_images(images=seed, size=[int(y_width / 2), int(y_height / 2)], method=0)
refine2 = crop_and_concat(refine2, seed2)
refine2 = conv_bn_relu_drop(x=refine2, kernal=[3, 3, 33, 32], phase=phase, drop=drop_conv, scope='refine2_2')
refine2 = crop_and_concat(refine1, refine2)
refine2 = conv_bn_relu_drop(x=refine2, kernal=[3, 3, 64, 64], phase=phase, drop=drop_conv, scope='refine1_3')
refine2 = squeeze_excitation_model(refine2, out_dim=64, scope='sem2_2')
refine2 = deconv_relu(x=refine2, kernal=[3, 3, 32, 64], scope='refine2_deconv')
refine3 = conv_bn_relu_drop(x=layer9, kernal=[1, 1, 32, 1], phase=phase, drop=drop_conv, scope='refine3_1')
seed3 = tf.image.resize_images(images=seed, size=[int(y_width), int(y_height)], method=0)
refine3 = crop_and_concat(refine3, seed3)
refine3 = conv_bn_relu_drop(x=refine3, kernal=[3, 3, 2, 1], phase=phase, drop=drop_conv, scope='refine3_2')
refine3 = crop_and_concat(refine2, refine3)
refine3 = conv_bn_relu_drop(x=refine3, kernal=[3, 3, 33, 33], phase=phase, drop=drop_conv, scope='refine3_3')
refine3 = squeeze_excitation_model(refine3, out_dim=33, scope='sem2_3')
out1 = conv_sigmoid(x=refine3, kernal=[1, 1, 33, n_class], scope='out1')
return out1
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)
# UNet model
# layer1->convolution
layer0 = conv_bn_relu_drop(x=inputX,
kernal=[3, 3, image_channel, 32],
phase=phase,
drop=drop_conv,
scope='layer0')
layer1 = conv_bn_relu_drop(x=layer0,
kernal=[3, 3, 32, 32],
phase=phase,
drop=drop_conv,
scope='layer1')
# print(layer1.get_shape().as_list())
layer1 = squeeze_excitation_model(layer1, out_dim=32, scope='sem1')
pool1 = max_pooling_2x2(layer1)
# layer2->convolution
layer2 = conv_bn_relu_drop(x=pool1,
kernal=[3, 3, 32, 64],
phase=phase,
drop=drop_conv,
scope='layer2_1')
layer2 = conv_bn_relu_drop(x=layer2,
kernal=[3, 3, 64, 64],
phase=phase,
drop=drop_conv,
scope='layer2_2')
layer2 = squeeze_excitation_model(layer2, out_dim=64, scope='sem2')
pool2 = max_pooling_2x2(layer2)
# layer3->convolution
layer3 = conv_bn_relu_drop(x=pool2,
kernal=[3, 3, 64, 128],
phase=phase,
drop=drop_conv,
scope='layer3_1')
layer3 = conv_bn_relu_drop(x=layer3,
kernal=[3, 3, 128, 128],
phase=phase,
drop=drop_conv,
scope='layer3_2')
layer3 = squeeze_excitation_model(layer3, out_dim=128, scope='sem3')
pool3 = max_pooling_2x2(layer3)
# layer4->convolution
layer4 = conv_bn_relu_drop(x=pool3,
kernal=[3, 3, 128, 256],
phase=phase,
drop=drop_conv,
scope='layer4_1')
layer4 = conv_bn_relu_drop(x=layer4,
kernal=[3, 3, 256, 256],
phase=phase,
drop=drop_conv,
scope='layer4_2')
layer4 = squeeze_excitation_model(layer4, out_dim=256, scope='sem4')
pool4 = max_pooling_2x2(layer4)
# layer5->convolution
layer5 = conv_bn_relu_drop(x=pool4,
kernal=[3, 3, 256, 512],
phase=phase,
drop=drop_conv,
scope='layer5_1')
layer5 = conv_bn_relu_drop(x=layer5,
kernal=[3, 3, 512, 512],
phase=phase,
drop=drop_conv,
scope='layer5_2')
# layer6->deconvolution
deconv1 = deconv_relu(x=layer5, kernal=[3, 3, 256, 512], scope='deconv1')
layer6 = crop_and_concat(layer4, deconv1)
# layer7->convolution
layer6 = conv_bn_relu_drop(x=layer6,
kernal=[3, 3, 512, 256],
phase=phase,
drop=drop_conv,
scope='layer6_1')
layer6 = conv_bn_relu_drop(x=layer6,
kernal=[3, 3, 256, 256],
phase=phase,
drop=drop_conv,
scope='layer6_2')
layer6 = squeeze_excitation_model(layer6, out_dim=256, scope='sem5')
deconv2 = deconv_relu(layer6, kernal=[3, 3, 128, 256], scope='deconv2')
layer7 = crop_and_concat(layer3, deconv2)
# layer9->convolution
layer7 = conv_bn_relu_drop(x=layer7,
kernal=[3, 3, 256, 128],
phase=phase,
drop=drop_conv,
scope='layer7_1')
layer7 = conv_bn_relu_drop(x=layer7,
kernal=[3, 3, 128, 128],
phase=phase,
drop=drop_conv,
scope='layer7_2')
layer7 = squeeze_excitation_model(layer7, out_dim=128, scope='sem6')
# layer10->deconvolution
deconv3 = deconv_relu(x=layer7, kernal=[3, 3, 64, 128], scope='deconv3')
layer8 = crop_and_concat(layer2, deconv3)
# layer11->convolution
layer8 = conv_bn_relu_drop(x=layer8,
kernal=[3, 3, 128, 64],
phase=phase,
drop=drop_conv,
scope='layer8_1')
layer8 = conv_bn_relu_drop(x=layer8,
kernal=[3, 3, 64, 64],
phase=phase,
drop=drop_conv,
scope='layer8_2')
layer8 = squeeze_excitation_model(layer8, out_dim=64, scope='sem7')
deconv4 = deconv_relu(layer8, kernal=[3, 3, 32, 64], scope='deconv4')
layer9 = crop_and_concat(layer1, deconv4)
# layer 13->convolution
layer9 = conv_bn_relu_drop(x=layer9,
kernal=[3, 3, 64, 32],
phase=phase,
drop=drop_conv,
scope='layer9_1')
layer9 = conv_bn_relu_drop(x=layer9,
kernal=[3, 3, 32, 32],
phase=phase,
drop=drop_conv,
scope='layer9_2')
layer9 = squeeze_excitation_model(layer9, out_dim=32, scope='sem8')
output = conv_sigmoid(x=layer9, kernal=[1, 1, 32, n_class], scope='output')
return layer7, layer8, layer9, output