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model.py
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model.py
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import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import *
import numpy as np
from tensorlayer.layers import *
def u_net(x, is_train=False, reuse=False, n_out=1):
_, nx, ny, nz = x.get_shape().as_list()
with tf.variable_scope("u_net", reuse=reuse):
tl.layers.set_name_reuse(reuse)
inputs = InputLayer(x, name='inputs')
conv1 = Conv2d(inputs, 64, (3, 3), act=tf.nn.relu, name='conv1_1')
conv1 = Conv2d(conv1, 64, (3, 3), act=tf.nn.relu, name='conv1_2')
pool1 = MaxPool2d(conv1, (2, 2), name='pool1')
conv2 = Conv2d(pool1, 128, (3, 3), act=tf.nn.relu, name='conv2_1')
conv2 = Conv2d(conv2, 128, (3, 3), act=tf.nn.relu, name='conv2_2')
pool2 = MaxPool2d(conv2, (2, 2), name='pool2')
conv3 = Conv2d(pool2, 256, (3, 3), act=tf.nn.relu, name='conv3_1')
conv3 = Conv2d(conv3, 256, (3, 3), act=tf.nn.relu, name='conv3_2')
pool3 = MaxPool2d(conv3, (2, 2), name='pool3')
conv4 = Conv2d(pool3, 512, (3, 3), act=tf.nn.relu, name='conv4_1')
conv4 = Conv2d(conv4, 512, (3, 3), act=tf.nn.relu, name='conv4_2')
pool4 = MaxPool2d(conv4, (2, 2), name='pool4')
conv5 = Conv2d(pool4, 1024, (3, 3), act=tf.nn.relu, name='conv5_1')
conv5 = Conv2d(conv5, 1024, (3, 3), act=tf.nn.relu, name='conv5_2')
up4 = DeConv2d(conv5, 512, (3, 3), (nx/8, ny/8), (2, 2), name='deconv4')
up4 = ConcatLayer([up4, conv4], 3, name='concat4')
conv4 = Conv2d(up4, 512, (3, 3), act=tf.nn.relu, name='uconv4_1')
conv4 = Conv2d(conv4, 512, (3, 3), act=tf.nn.relu, name='uconv4_2')
up3 = DeConv2d(conv4, 256, (3, 3), (nx/4, ny/4), (2, 2), name='deconv3')
up3 = ConcatLayer([up3, conv3], 3, name='concat3')
conv3 = Conv2d(up3, 256, (3, 3), act=tf.nn.relu, name='uconv3_1')
conv3 = Conv2d(conv3, 256, (3, 3), act=tf.nn.relu, name='uconv3_2')
up2 = DeConv2d(conv3, 128, (3, 3), (nx/2, ny/2), (2, 2), name='deconv2')
up2 = ConcatLayer([up2, conv2], 3, name='concat2')
conv2 = Conv2d(up2, 128, (3, 3), act=tf.nn.relu, name='uconv2_1')
conv2 = Conv2d(conv2, 128, (3, 3), act=tf.nn.relu, name='uconv2_2')
up1 = DeConv2d(conv2, 64, (3, 3), (nx/1, ny/1), (2, 2), name='deconv1')
up1 = ConcatLayer([up1, conv1] , 3, name='concat1')
conv1 = Conv2d(up1, 64, (3, 3), act=tf.nn.relu, name='uconv1_1')
conv1 = Conv2d(conv1, 64, (3, 3), act=tf.nn.relu, name='uconv1_2')
conv1 = Conv2d(conv1, n_out, (1, 1), act=tf.nn.sigmoid, name='uconv1')
return conv1
# def u_net(x, is_train=False, reuse=False, pad='SAME', n_out=2):
# """ Original U-Net for cell segmentataion
# http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/
# Original x is [batch_size, 572, 572, ?], pad is VALID
# """
# from tensorlayer.layers import InputLayer, Conv2d, MaxPool2d, DeConv2d, ConcatLayer
# nx = int(x._shape[1])
# ny = int(x._shape[2])
# nz = int(x._shape[3])
# print(" * Input: size of image: %d %d %d" % (nx, ny, nz))
#
# w_init = tf.truncated_normal_initializer(stddev=0.01)
# b_init = tf.constant_initializer(value=0.0)
# with tf.variable_scope("u_net", reuse=reuse):
# tl.layers.set_name_reuse(reuse)
# inputs = InputLayer(x, name='inputs')
#
# conv1 = Conv2d(inputs, 64, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='conv1_1')
# conv1 = Conv2d(conv1, 64, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='conv1_2')
# pool1 = MaxPool2d(conv1, (2, 2), padding=pad, name='pool1')
#
# conv2 = Conv2d(pool1, 128, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='conv2_1')
# conv2 = Conv2d(conv2, 128, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='conv2_2')
# pool2 = MaxPool2d(conv2, (2, 2), padding=pad, name='pool2')
#
# conv3 = Conv2d(pool2, 256, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='conv3_1')
# conv3 = Conv2d(conv3, 256, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='conv3_2')
# pool3 = MaxPool2d(conv3, (2, 2), padding=pad, name='pool3')
#
# conv4 = Conv2d(pool3, 512, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='conv4_1')
# conv4 = Conv2d(conv4, 512, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='conv4_2')
# pool4 = MaxPool2d(conv4, (2, 2), padding=pad, name='pool4')
#
# conv5 = Conv2d(pool4, 1024, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='conv5_1')
# conv5 = Conv2d(conv5, 1024, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='conv5_2')
#
# print(" * After conv: %s" % conv5.outputs)
#
# up4 = DeConv2d(conv5, 512, (3, 3), out_size = (nx/8, ny/8),
# strides=(2, 2), padding=pad, act=None,
# W_init=w_init, b_init=b_init, name='deconv4')
# up4 = ConcatLayer([up4, conv4], concat_dim=3, name='concat4')
# conv4 = Conv2d(up4, 512, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='uconv4_1')
# conv4 = Conv2d(conv4, 512, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='uconv4_2')
#
# up3 = DeConv2d(conv4, 256, (3, 3), out_size = (nx/4, ny/4),
# strides=(2, 2), padding=pad, act=None,
# W_init=w_init, b_init=b_init, name='deconv3')
# up3 = ConcatLayer([up3, conv3], concat_dim=3, name='concat3')
# conv3 = Conv2d(up3, 256, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='uconv3_1')
# conv3 = Conv2d(conv3, 256, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='uconv3_2')
#
# up2 = DeConv2d(conv3, 128, (3, 3), out_size=(nx/2, ny/2),
# strides=(2, 2), padding=pad, act=None,
# W_init=w_init, b_init=b_init, name='deconv2')
# up2 = ConcatLayer([up2, conv2] ,concat_dim=3, name='concat2')
# conv2 = Conv2d(up2, 128, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='uconv2_1')
# conv2 = Conv2d(conv2, 128, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='uconv2_2')
#
# up1 = DeConv2d(conv2, 64, (3, 3), out_size=(nx/1, ny/1),
# strides=(2, 2), padding=pad, act=None,
# W_init=w_init, b_init=b_init, name='deconv1')
# up1 = ConcatLayer([up1, conv1] ,concat_dim=3, name='concat1')
# conv1 = Conv2d(up1, 64, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='uconv1_1')
# conv1 = Conv2d(conv1, 64, (3, 3), act=tf.nn.relu, padding=pad,
# W_init=w_init, b_init=b_init, name='uconv1_2')
#
# conv1 = Conv2d(conv1, n_out, (1, 1), act=tf.nn.sigmoid, name='uconv1')
# print(" * Output: %s" % conv1.outputs)
#
# # logits0 = conv1.outputs[:,:,:,0] # segmentataion
# # logits1 = conv1.outputs[:,:,:,1] # edge
# # logits0 = tf.expand_dims(logits0, axis=3)
# # logits1 = tf.expand_dims(logits1, axis=3)
# return conv1
def u_net_bn(x, is_train=False, reuse=False, batch_size=None, pad='SAME', n_out=1):
"""image to image translation via conditional adversarial learning"""
nx = int(x._shape[1])
ny = int(x._shape[2])
nz = int(x._shape[3])
print(" * Input: size of image: %d %d %d" % (nx, ny, nz))
w_init = tf.truncated_normal_initializer(stddev=0.01)
b_init = tf.constant_initializer(value=0.0)
gamma_init=tf.random_normal_initializer(1., 0.02)
with tf.variable_scope("u_net", reuse=reuse):
tl.layers.set_name_reuse(reuse)
inputs = InputLayer(x, name='inputs')
conv1 = Conv2d(inputs, 64, (4, 4), (2, 2), act=None, padding=pad, W_init=w_init, b_init=b_init, name='conv1')
conv2 = Conv2d(conv1, 128, (4, 4), (2, 2), act=None, padding=pad, W_init=w_init, b_init=b_init, name='conv2')
conv2 = BatchNormLayer(conv2, act=lambda x: tl.act.lrelu(x, 0.2), is_train=is_train, gamma_init=gamma_init, name='bn2')
conv3 = Conv2d(conv2, 256, (4, 4), (2, 2), act=None, padding=pad, W_init=w_init, b_init=b_init, name='conv3')
conv3 = BatchNormLayer(conv3, act=lambda x: tl.act.lrelu(x, 0.2), is_train=is_train, gamma_init=gamma_init, name='bn3')
conv4 = Conv2d(conv3, 512, (4, 4), (2, 2), act=None, padding=pad, W_init=w_init, b_init=b_init, name='conv4')
conv4 = BatchNormLayer(conv4, act=lambda x: tl.act.lrelu(x, 0.2), is_train=is_train, gamma_init=gamma_init, name='bn4')
conv5 = Conv2d(conv4, 512, (4, 4), (2, 2), act=None, padding=pad, W_init=w_init, b_init=b_init, name='conv5')
conv5 = BatchNormLayer(conv5, act=lambda x: tl.act.lrelu(x, 0.2), is_train=is_train, gamma_init=gamma_init, name='bn5')
conv6 = Conv2d(conv5, 512, (4, 4), (2, 2), act=None, padding=pad, W_init=w_init, b_init=b_init, name='conv6')
conv6 = BatchNormLayer(conv6, act=lambda x: tl.act.lrelu(x, 0.2), is_train=is_train, gamma_init=gamma_init, name='bn6')
conv7 = Conv2d(conv6, 512, (4, 4), (2, 2), act=None, padding=pad, W_init=w_init, b_init=b_init, name='conv7')
conv7 = BatchNormLayer(conv7, act=lambda x: tl.act.lrelu(x, 0.2), is_train=is_train, gamma_init=gamma_init, name='bn7')
conv8 = Conv2d(conv7, 512, (4, 4), (2, 2), act=lambda x: tl.act.lrelu(x, 0.2), padding=pad, W_init=w_init, b_init=b_init, name='conv8')
print(" * After conv: %s" % conv8.outputs)
# exit()
# print(nx/8)
up7 = DeConv2d(conv8, 512, (4, 4), out_size=(2, 2), strides=(2, 2),
padding=pad, act=None, batch_size=batch_size, W_init=w_init, b_init=b_init, name='deconv7')
up7 = BatchNormLayer(up7, act=tf.nn.relu, is_train=is_train, gamma_init=gamma_init, name='dbn7')
# print(up6.outputs)
up6 = ConcatLayer([up7, conv7], concat_dim=3, name='concat6')
up6 = DeConv2d(up6, 1024, (4, 4), out_size=(4, 4), strides=(2, 2),
padding=pad, act=None, batch_size=batch_size, W_init=w_init, b_init=b_init, name='deconv6')
up6 = BatchNormLayer(up6, act=tf.nn.relu, is_train=is_train, gamma_init=gamma_init, name='dbn6')
# print(up6.outputs)
# exit()
up5 = ConcatLayer([up6, conv6], concat_dim=3, name='concat5')
up5 = DeConv2d(up5, 1024, (4, 4), out_size=(8, 8), strides=(2, 2),
padding=pad, act=None, batch_size=batch_size, W_init=w_init, b_init=b_init, name='deconv5')
up5 = BatchNormLayer(up5, act=tf.nn.relu, is_train=is_train, gamma_init=gamma_init, name='dbn5')
# print(up5.outputs)
# exit()
up4 = ConcatLayer([up5, conv5] ,concat_dim=3, name='concat4')
up4 = DeConv2d(up4, 1024, (4, 4), out_size=(15, 15), strides=(2, 2),
padding=pad, act=None, batch_size=batch_size, W_init=w_init, b_init=b_init, name='deconv4')
up4 = BatchNormLayer(up4, act=tf.nn.relu, is_train=is_train, gamma_init=gamma_init, name='dbn4')
up3 = ConcatLayer([up4, conv4] ,concat_dim=3, name='concat3')
up3 = DeConv2d(up3, 256, (4, 4), out_size=(30, 30), strides=(2, 2),
padding=pad, act=None, batch_size=batch_size, W_init=w_init, b_init=b_init, name='deconv3')
up3 = BatchNormLayer(up3, act=tf.nn.relu, is_train=is_train, gamma_init=gamma_init, name='dbn3')
up2 = ConcatLayer([up3, conv3] ,concat_dim=3, name='concat2')
up2 = DeConv2d(up2, 128, (4, 4), out_size=(60, 60), strides=(2, 2),
padding=pad, act=None, batch_size=batch_size, W_init=w_init, b_init=b_init, name='deconv2')
up2 = BatchNormLayer(up2, act=tf.nn.relu, is_train=is_train, gamma_init=gamma_init, name='dbn2')
up1 = ConcatLayer([up2, conv2] ,concat_dim=3, name='concat1')
up1 = DeConv2d(up1, 64, (4, 4), out_size=(120, 120), strides=(2, 2),
padding=pad, act=None, batch_size=batch_size, W_init=w_init, b_init=b_init, name='deconv1')
up1 = BatchNormLayer(up1, act=tf.nn.relu, is_train=is_train, gamma_init=gamma_init, name='dbn1')
up0 = ConcatLayer([up1, conv1] ,concat_dim=3, name='concat0')
up0 = DeConv2d(up0, 64, (4, 4), out_size=(240, 240), strides=(2, 2),
padding=pad, act=None, batch_size=batch_size, W_init=w_init, b_init=b_init, name='deconv0')
up0 = BatchNormLayer(up0, act=tf.nn.relu, is_train=is_train, gamma_init=gamma_init, name='dbn0')
# print(up0.outputs)
# exit()
out = Conv2d(up0, n_out, (1, 1), act=tf.nn.sigmoid, name='out')
print(" * Output: %s" % out.outputs)
# exit()
return out
## old implementation
# def u_net_2d_64_1024_deconv(x, n_out=2):
# from tensorlayer.layers import InputLayer, Conv2d, MaxPool2d, DeConv2d, ConcatLayer
# nx = int(x._shape[1])
# ny = int(x._shape[2])
# nz = int(x._shape[3])
# print(" * Input: size of image: %d %d %d" % (nx, ny, nz))
#
# w_init = tf.truncated_normal_initializer(stddev=0.01)
# b_init = tf.constant_initializer(value=0.0)
# inputs = InputLayer(x, name='inputs')
#
# conv1 = Conv2d(inputs, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv1_1')
# conv1 = Conv2d(conv1, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv1_2')
# pool1 = MaxPool2d(conv1, (2, 2), padding='SAME', name='pool1')
#
# conv2 = Conv2d(pool1, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv2_1')
# conv2 = Conv2d(conv2, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv2_2')
# pool2 = MaxPool2d(conv2, (2, 2), padding='SAME', name='pool2')
#
# conv3 = Conv2d(pool2, 256, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv3_1')
# conv3 = Conv2d(conv3, 256, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv3_2')
# pool3 = MaxPool2d(conv3, (2, 2), padding='SAME', name='pool3')
#
# conv4 = Conv2d(pool3, 512, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv4_1')
# conv4 = Conv2d(conv4, 512, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv4_2')
# pool4 = MaxPool2d(conv4, (2, 2), padding='SAME', name='pool4')
#
# conv5 = Conv2d(pool4, 1024, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv5_1')
# conv5 = Conv2d(conv5, 1024, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv5_2')
#
# print(" * After conv: %s" % conv5.outputs)
#
# up4 = DeConv2d(conv5, 512, (3, 3), out_size = (nx/8, ny/8), strides = (2, 2),
# padding = 'SAME', act=None, W_init=w_init, b_init=b_init, name='deconv4')
# up4 = ConcatLayer([up4, conv4], concat_dim=3, name='concat4')
# conv4 = Conv2d(up4, 512, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='uconv4_1')
# conv4 = Conv2d(conv4, 512, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='uconv4_2')
#
# up3 = DeConv2d(conv4, 256, (3, 3), out_size = (nx/4, ny/4), strides = (2, 2),
# padding = 'SAME', act=None, W_init=w_init, b_init=b_init, name='deconv3')
# up3 = ConcatLayer([up3, conv3], concat_dim=3, name='concat3')
# conv3 = Conv2d(up3, 256, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='uconv3_1')
# conv3 = Conv2d(conv3, 256, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='uconv3_2')
#
# up2 = DeConv2d(conv3, 128, (3, 3), out_size = (nx/2, ny/2), strides = (2, 2),
# padding = 'SAME', act=None, W_init=w_init, b_init=b_init, name='deconv2')
# up2 = ConcatLayer([up2, conv2] ,concat_dim=3, name='concat2')
# conv2 = Conv2d(up2, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='uconv2_1')
# conv2 = Conv2d(conv2, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='uconv2_2')
#
# up1 = DeConv2d(conv2, 64, (3, 3), out_size = (nx/1, ny/1), strides = (2, 2),
# padding = 'SAME', act=None, W_init=w_init, b_init=b_init, name='deconv1')
# up1 = ConcatLayer([up1, conv1] ,concat_dim=3, name='concat1')
# conv1 = Conv2d(up1, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='uconv1_1')
# conv1 = Conv2d(conv1, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='uconv1_2')
#
# conv1 = Conv2d(conv1, n_out, (1, 1), act=None, name='uconv1')
# print(" * Output: %s" % conv1.outputs)
# outputs = tl.act.pixel_wise_softmax(conv1.outputs)
# return conv1, outputs
#
#
# def u_net_2d_32_1024_upsam(x, n_out=2):
# """
# https://github.com/jocicmarko/ultrasound-nerve-segmentation
# """
# from tensorlayer.layers import InputLayer, Conv2d, MaxPool2d, DeConv2d, ConcatLayer
# batch_size = int(x._shape[0])
# nx = int(x._shape[1])
# ny = int(x._shape[2])
# nz = int(x._shape[3])
# print(" * Input: size of image: %d %d %d" % (nx, ny, nz))
# ## define initializer
# w_init = tf.truncated_normal_initializer(stddev=0.01)
# b_init = tf.constant_initializer(value=0.0)
# inputs = InputLayer(x, name='inputs')
#
# conv1 = Conv2d(inputs, 32, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv1_1')
# conv1 = Conv2d(conv1, 32, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv1_2')
# pool1 = MaxPool2d(conv1, (2, 2), padding='SAME', name='pool1')
#
# conv2 = Conv2d(pool1, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv2_1')
# conv2 = Conv2d(conv2, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv2_2')
# pool2 = MaxPool2d(conv2, (2,2), padding='SAME', name='pool2')
#
# conv3 = Conv2d(pool2, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv3_1')
# conv3 = Conv2d(conv3, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv3_2')
# pool3 = MaxPool2d(conv3, (2, 2), padding='SAME', name='pool3')
#
# conv4 = Conv2d(pool3, 256, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv4_1')
# conv4 = Conv2d(conv4, 256, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv4_2')
# pool4 = MaxPool2d(conv4, (2, 2), padding='SAME', name='pool4')
#
# conv5 = Conv2d(pool4, 512, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv5_1')
# conv5 = Conv2d(conv5, 512, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv5_2')
# pool5 = MaxPool2d(conv5, (2, 2), padding='SAME', name='pool6')
#
# # hao add
# conv6 = Conv2d(pool5, 1024, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv6_1')
# conv6 = Conv2d(conv6, 1024, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv6_2')
#
# print(" * After conv: %s" % conv6.outputs)
#
# # hao add
# up7 = UpSampling2dLayer(conv6, (15, 15), is_scale=False, method=1, name='up7')
# up7 = ConcatLayer([up7, conv5], concat_dim=3, name='concat7')
# conv7 = Conv2d(up7, 512, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv7_1')
# conv7 = Conv2d(conv7, 512, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv7_2')
#
# # print(nx/8,ny/8) # 30 30
# up8 = UpSampling2dLayer(conv7, (2, 2), method=1, name='up8')
# up8 = ConcatLayer([up8, conv4], concat_dim=3, name='concat8')
# conv8 = Conv2d(up8, 256, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv8_1')
# conv8 = Conv2d(conv8, 256, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv8_2')
#
# up9 = UpSampling2dLayer(conv8, (2, 2), method=1, name='up9')
# up9 = ConcatLayer([up9, conv3] ,concat_dim=3, name='concat9')
# conv9 = Conv2d(up9, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv9_1')
# conv9 = Conv2d(conv9, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv9_2')
#
# up10 = UpSampling2dLayer(conv9, (2, 2), method=1, name='up10')
# up10 = ConcatLayer([up10, conv2] ,concat_dim=3, name='concat10')
# conv10 = Conv2d(up10, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv10_1')
# conv10 = Conv2d(conv10, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv10_2')
#
# up11 = UpSampling2dLayer(conv10, (2, 2), method=1, name='up11')
# up11 = ConcatLayer([up11, conv1] ,concat_dim=3, name='concat11')
# conv11 = Conv2d(up11, 32, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv11_1')
# conv11 = Conv2d(conv11, 32, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv11_2')
#
# conv12 = Conv2d(conv11, n_out, (1, 1), act=None, name='conv12')
# print(" * Output: %s" % conv12.outputs)
# outputs = tl.act.pixel_wise_softmax(conv12.outputs)
# return conv10, outputs
#
#
# def u_net_2d_32_512_upsam(x, n_out=2):
# """
# https://github.com/jocicmarko/ultrasound-nerve-segmentation
# """
# from tensorlayer.layers import InputLayer, Conv2d, MaxPool2d, DeConv2d, ConcatLayer
# batch_size = int(x._shape[0])
# nx = int(x._shape[1])
# ny = int(x._shape[2])
# nz = int(x._shape[3])
# print(" * Input: size of image: %d %d %d" % (nx, ny, nz))
# ## define initializer
# w_init = tf.truncated_normal_initializer(stddev=0.01)
# b_init = tf.constant_initializer(value=0.0)
# inputs = InputLayer(x, name='inputs')
# # inputs = Input((1, img_rows, img_cols))
# conv1 = Conv2d(inputs, 32, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv1_1')
# # print(conv1.outputs) # (10, 240, 240, 32)
# # conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(inputs)
# conv1 = Conv2d(conv1, 32, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv1_2')
# # print(conv1.outputs) # (10, 240, 240, 32)
# # conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv1)
# pool1 = MaxPool2d(conv1, (2, 2), padding='SAME', name='pool1')
# # pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
# # print(pool1.outputs) # (10, 120, 120, 32)
# # exit()
# conv2 = Conv2d(pool1, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv2_1')
# # conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(pool1)
# conv2 = Conv2d(conv2, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv2_2')
# # conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv2)
# pool2 = MaxPool2d(conv2, (2,2), padding='SAME', name='pool2')
# # pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
#
# conv3 = Conv2d(pool2, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv3_1')
# # conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(pool2)
# conv3 = Conv2d(conv3, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv3_2')
# # conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(conv3)
# pool3 = MaxPool2d(conv3, (2, 2), padding='SAME', name='pool3')
# # pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
# # print(pool3.outputs) # (10, 30, 30, 64)
#
# conv4 = Conv2d(pool3, 256, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv4_1')
# # print(conv4.outputs) # (10, 30, 30, 256)
# # conv4 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(pool3)
# conv4 = Conv2d(conv4, 256, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv4_2')
# # print(conv4.outputs) # (10, 30, 30, 256) != (10, 30, 30, 512)
# # conv4 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(conv4)
# pool4 = MaxPool2d(conv4, (2, 2), padding='SAME', name='pool4')
# # pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
#
# conv5 = Conv2d(pool4, 512, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv5_1')
# # conv5 = Convolution2D(512, 3, 3, activation='relu', border_mode='same')(pool4)
# conv5 = Conv2d(conv5, 512, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv5_2')
# # conv5 = Convolution2D(512, 3, 3, activation='relu', border_mode='same')(conv5)
# # print(conv5.outputs) # (10, 15, 15, 512)
# print(" * After conv: %s" % conv5.outputs)
# # print(nx/8,ny/8) # 30 30
# up6 = UpSampling2dLayer(conv5, (2, 2), name='up6')
# # print(up6.outputs) # (10, 30, 30, 512) == (10, 30, 30, 512)
# up6 = ConcatLayer([up6, conv4], concat_dim=3, name='concat6')
# # print(up6.outputs) # (10, 30, 30, 768)
# # up6 = merge([UpSampling2D(size=(2, 2))(conv5), conv4], mode='concat', concat_axis=1)
# conv6 = Conv2d(up6, 256, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv6_1')
# # conv6 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(up6)
# conv6 = Conv2d(conv6, 256, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv6_2')
# # conv6 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(conv6)
#
# up7 = UpSampling2dLayer(conv6, (2, 2), name='up7')
# up7 = ConcatLayer([up7, conv3] ,concat_dim=3, name='concat7')
# # up7 = merge([UpSampling2D(size=(2, 2))(conv6), conv3], mode='concat', concat_axis=1)
# conv7 = Conv2d(up7, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv7_1')
# # conv7 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(up7)
# conv7 = Conv2d(conv7, 128, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv7_2')
# # conv7 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(conv7)
#
# up8 = UpSampling2dLayer(conv7, (2, 2), name='up8')
# up8 = ConcatLayer([up8, conv2] ,concat_dim=3, name='concat8')
# # up8 = merge([UpSampling2D(size=(2, 2))(conv7), conv2], mode='concat', concat_axis=1)
# conv8 = Conv2d(up8, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv8_1')
# # conv8 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(up8)
# conv8 = Conv2d(conv8, 64, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv8_2')
# # conv8 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv8)
#
# up9 = UpSampling2dLayer(conv8, (2, 2), name='up9')
# up9 = ConcatLayer([up9, conv1] ,concat_dim=3, name='concat9')
# # up9 = merge([UpSampling2D(size=(2, 2))(conv8), conv1], mode='concat', concat_axis=1)
# conv9 = Conv2d(up9, 32, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv9_1')
# # conv9 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(up9)
# conv9 = Conv2d(conv9, 32, (3, 3), act=tf.nn.relu, padding='SAME', W_init=w_init, b_init=b_init, name='conv9_2')
# # conv9 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv9)
#
# conv10 = Conv2d(conv9, n_out, (1, 1), act=None, name='conv9')
# # conv10 = Convolution2D(1, 1, 1, activation='sigmoid')(conv9)
# print(" * Output: %s" % conv10.outputs)
# outputs = tl.act.pixel_wise_softmax(conv10.outputs)
# return conv10, outputs
if __name__ == "__main__":
pass
# main()
#