'''
'''
from Vnet.layer import (conv_bn_relu_drop, down_sampling, deconv_relu, crop_and_concat, resnet_Add, conv_sigmod,
save_images)
import tensorflow as tf
import numpy as np
import os
def _create_conv_net(X, image_z, image_width, image_height, image_channel, phase, drop, n_class=1):
inputX = tf.reshape(X, [-1, image_z, image_width, image_height, image_channel]) # shape=(?, 32, 32, 1)
# Vnet model
# layer1->convolution
layer0 = conv_bn_relu_drop(x=inputX, kernal=(3, 3, 3, image_channel, 16), phase=phase, drop=drop,
scope='layer0')
layer1 = conv_bn_relu_drop(x=layer0, kernal=(3, 3, 3, 16, 16), phase=phase, drop=drop,
scope='layer1')
layer1 = resnet_Add(x1=layer0, x2=layer1)
# down sampling1
down1 = down_sampling(x=layer1, kernal=(3, 3, 3, 16, 32), phase=phase, drop=drop, scope='down1')
# layer2->convolution
layer2 = conv_bn_relu_drop(x=down1, kernal=(3, 3, 3, 32, 32), phase=phase, drop=drop,
scope='layer2_1')
layer2 = conv_bn_relu_drop(x=layer2, kernal=(3, 3, 3, 32, 32), phase=phase, drop=drop,
scope='layer2_2')
layer2 = resnet_Add(x1=down1, x2=layer2)
# down sampling2
down2 = down_sampling(x=layer2, kernal=(3, 3, 3, 32, 64), phase=phase, drop=drop, scope='down2')
# layer3->convolution
layer3 = conv_bn_relu_drop(x=down2, kernal=(3, 3, 3, 64, 64), phase=phase, drop=drop,
scope='layer3_1')
layer3 = conv_bn_relu_drop(x=layer3, kernal=(3, 3, 3, 64, 64), phase=phase, drop=drop,
scope='layer3_2')
layer3 = conv_bn_relu_drop(x=layer3, kernal=(3, 3, 3, 64, 64), phase=phase, drop=drop,
scope='layer3_3')
layer3 = resnet_Add(x1=down2, x2=layer3)
# down sampling3
down3 = down_sampling(x=layer3, kernal=(3, 3, 3, 64, 128), phase=phase, drop=drop, scope='down3')
# layer4->convolution
layer4 = conv_bn_relu_drop(x=down3, kernal=(3, 3, 3, 128, 128), phase=phase, drop=drop,
scope='layer4_1')
layer4 = conv_bn_relu_drop(x=layer4, kernal=(3, 3, 3, 128, 128), phase=phase, drop=drop,
scope='layer4_2')
layer4 = conv_bn_relu_drop(x=layer4, kernal=(3, 3, 3, 128, 128), phase=phase, drop=drop,
scope='layer4_3')
layer4 = resnet_Add(x1=down3, x2=layer4)
# down sampling4
down4 = down_sampling(x=layer4, kernal=(3, 3, 3, 128, 256), phase=phase, drop=drop, scope='down4')
# layer5->convolution
layer5 = conv_bn_relu_drop(x=down4, kernal=(3, 3, 3, 256, 256), phase=phase, drop=drop,
scope='layer5_1')
layer5 = conv_bn_relu_drop(x=layer5, kernal=(3, 3, 3, 256, 256), phase=phase, drop=drop,
scope='layer5_2')
layer5 = conv_bn_relu_drop(x=layer5, kernal=(3, 3, 3, 256, 256), phase=phase, drop=drop,
scope='layer5_3')
layer5 = resnet_Add(x1=down4, x2=layer5)
# layer9->deconvolution
deconv1 = deconv_relu(x=layer5, kernal=(3, 3, 3, 128, 256), scope='deconv1')
# layer8->convolution
layer6 = crop_and_concat(layer4, deconv1)
_, Z, H, W, _ = layer4.get_shape().as_list()
layer6 = conv_bn_relu_drop(x=layer6, kernal=(3, 3, 3, 256, 128), image_z=Z, height=H, width=W, phase=phase,
drop=drop, scope='layer6_1')
layer6 = conv_bn_relu_drop(x=layer6, kernal=(3, 3, 3, 128, 128), image_z=Z, height=H, width=W, phase=phase,
drop=drop, scope='layer6_2')
layer6 = conv_bn_relu_drop(x=layer6, kernal=(3, 3, 3, 128, 128), image_z=Z, height=H, width=W, phase=phase,
drop=drop, scope='layer6_3')
layer6 = resnet_Add(x1=deconv1, x2=layer6)
# layer9->deconvolution
deconv2 = deconv_relu(x=layer6, kernal=(3, 3, 3, 64, 128), scope='deconv2')
# layer8->convolution
layer7 = crop_and_concat(layer3, deconv2)
_, Z, H, W, _ = layer3.get_shape().as_list()
layer7 = conv_bn_relu_drop(x=layer7, kernal=(3, 3, 3, 128, 64), image_z=Z, height=H, width=W, phase=phase,
drop=drop, scope='layer7_1')
layer7 = conv_bn_relu_drop(x=layer7, kernal=(3, 3, 3, 64, 64), image_z=Z, height=H, width=W, phase=phase,
drop=drop, scope='layer7_2')
layer7 = conv_bn_relu_drop(x=layer7, kernal=(3, 3, 3, 64, 64), image_z=Z, height=H, width=W, phase=phase,
drop=drop, scope='layer7_3')
layer7 = resnet_Add(x1=deconv2, x2=layer7)
# layer9->deconvolution
deconv3 = deconv_relu(x=layer7, kernal=(3, 3, 3, 32, 64), scope='deconv3')
# layer8->convolution
layer8 = crop_and_concat(layer2, deconv3)
_, Z, H, W, _ = layer2.get_shape().as_list()
layer8 = conv_bn_relu_drop(x=layer8, kernal=(3, 3, 3, 64, 32), image_z=Z, height=H, width=W, phase=phase,
drop=drop, scope='layer8_1')
layer8 = conv_bn_relu_drop(x=layer8, kernal=(3, 3, 3, 32, 32), image_z=Z, height=H, width=W, phase=phase,
drop=drop, scope='layer8_2')
layer8 = conv_bn_relu_drop(x=layer8, kernal=(3, 3, 3, 32, 32), image_z=Z, height=H, width=W, phase=phase,
drop=drop, scope='layer8_3')
layer8 = resnet_Add(x1=deconv3, x2=layer8)
# layer9->deconvolution
deconv4 = deconv_relu(x=layer8, kernal=(3, 3, 3, 16, 32), scope='deconv4')
# layer8->convolution
layer9 = crop_and_concat(layer1, deconv4)
_, Z, H, W, _ = layer1.get_shape().as_list()
layer9 = conv_bn_relu_drop(x=layer9, kernal=(3, 3, 3, 32, 16), image_z=Z, height=H, width=W, phase=phase,
drop=drop, scope='layer9_1')
layer9 = conv_bn_relu_drop(x=layer9, kernal=(3, 3, 3, 16, 16), image_z=Z, height=H, width=W, phase=phase,
drop=drop, scope='layer9_2')
layer9 = conv_bn_relu_drop(x=layer9, kernal=(3, 3, 3, 16, 16), image_z=Z, height=H, width=W, phase=phase,
drop=drop, scope='layer9_3')
layer9 = resnet_Add(x1=deconv4, x2=layer9)
# layer14->output
output_map = conv_sigmod(x=layer9, kernal=(1, 1, 1, 16, n_class), scope='output')
return output_map
# Serve data by batches
def _next_batch(train_images, train_labels, batch_size, index_in_epoch):
start = index_in_epoch
index_in_epoch += batch_size
num_examples = train_images.shape[0]
# when all trainig data have been already used, it is reorder randomly
if index_in_epoch > num_examples:
# shuffle the data
perm = np.arange(num_examples)
np.random.shuffle(perm)
train_images = train_images[perm]
train_labels = train_labels[perm]
# start next epoch
start = 0
index_in_epoch = batch_size
assert batch_size <= num_examples
end = index_in_epoch
return train_images[start:end], train_labels[start:end], index_in_epoch
class Vnet3dModule(object):
"""
A VNet3d implementation
:param image_height: number of height in the input image
:param image_width: number of width in the input image
:param image_depth: number of depth in the input image
:param channels: number of channels in the input image
:param costname: name of the cost function.Default is "dice coefficient"
"""
def __init__(self, image_height, image_width, image_depth, channels=1, numclass=1, costname=("dice coefficient",),
inference=False, model_path=None):
self.image_width = image_width
self.image_height = image_height
self.image_depth = image_depth
self.channels = channels
self.numclass = numclass
self.X = tf.placeholder("float", shape=[None, self.image_depth, self.image_height, self.image_width,
self.channels])
self.Y_gt = tf.placeholder("float", shape=[None, self.image_depth, self.image_height, self.image_width,
self.numclass])
self.lr = tf.placeholder('float')
self.phase = tf.placeholder(tf.bool)
self.drop = tf.placeholder('float')
self.Y_pred = _create_conv_net(self.X, self.image_depth, self.image_width, self.image_height, self.channels,
self.phase, self.drop, self.numclass)
self.cost = self.__get_cost(self.Y_pred, self.Y_gt, costname[0])
self.accuracy = -self.cost
if inference:
init = tf.global_variables_initializer()
saver = tf.train.Saver()
self.sess = tf.InteractiveSession()
self.sess.run(init)
saver.restore(self.sess, model_path)
def __get_cost(self, Y_pred, Y_gt, cost_name):
Z, H, W, C = Y_gt.get_shape().as_list()[1:]
if cost_name == "dice coefficient":
smooth = 1e-5
pred_flat = tf.reshape(Y_pred, [-1, H * W * C * Z])
true_flat = tf.reshape(Y_gt, [-1, H * W * C * Z])
intersection = 2 * tf.reduce_sum(pred_flat * true_flat, axis=1) + smooth
denominator = tf.reduce_sum(pred_flat, axis=1) + tf.reduce_sum(true_flat, axis=1) + smooth
loss = -tf.reduce_mean(intersection / denominator)
return loss
def train(self, train_images, train_lanbels, model_path, logs_path, learning_rate,
dropout_conv=0.8, train_epochs=5, batch_size=1, showwindow=[8, 8]):
num_sample = 100
if not os.path.exists(logs_path):
os.makedirs(logs_path)
if not os.path.exists(logs_path + "model\\"):
os.makedirs(logs_path + "model\\")
model_path = logs_path + "model\\" + model_path
train_op = tf.train.AdamOptimizer(self.lr).minimize(self.cost)
init = tf.global_variables_initializer()
saver = tf.train.Saver(tf.all_variables(), max_to_keep=10)
tf.summary.scalar("loss", self.cost)
tf.summary.scalar("accuracy", self.accuracy)
merged_summary_op = tf.summary.merge_all()
sess = tf.InteractiveSession(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False))
summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())
sess.run(init)
if os.path.exists(model_path):
saver.restore(sess, model_path)
# load data and show result param
DISPLAY_STEP = 1
num_sample_index_in_epoch = 0
index_in_epoch = 0
train_epochs = train_images.shape[0] * train_epochs
subbatch_xs = np.empty((num_sample, self.image_depth, self.image_height, self.image_width, self.channels))
subbatch_ys = np.empty((num_sample, self.image_depth, self.image_height, self.image_width, self.numclass))
for i in range(train_epochs):
# Extracting num_sample images and labels from given data
if i % num_sample == 0 or i == 0:
batch_xs_path, batch_ys_path, num_sample_index_in_epoch = _next_batch(train_images, train_lanbels,
num_sample,
num_sample_index_in_epoch)
for num in range(len(batch_xs_path)):
image = np.load(batch_xs_path[num])
label = np.load(batch_ys_path[num])
# prepare 3 model output
batch_ys1 = label.copy()
batch_ys1[label == 1.] = 1.
batch_ys1[label != 1.] = 0.
batch_ys2 = label.copy()
batch_ys2[label == 2.] = 1.
batch_ys2[label != 2.] = 0.
batch_ys3 = label.copy()
batch_ys3[label == 4.] = 1.
batch_ys3[label != 4.] = 0.
subbatch_xs[num, :, :, :, :] = np.reshape(image,
(self.image_depth, self.image_height, self.image_width,
self.channels))
label_ys = np.empty((self.image_depth, self.image_height, self.image_width, self.numclass))
label_ys[:, :, :, 0] = batch_ys1
label_ys[:, :, :, 1] = batch_ys2
label_ys[:, :, :, 2] = batch_ys3
subbatch_ys[num, :, :, :, :] = np.reshape(label_ys,
(self.image_depth, self.image_height, self.image_width,
self.numclass))
subbatch_xs = subbatch_xs.astype(np.float)
subbatch_ys = subbatch_ys.astype(np.float)
# get new batch
batch_xs, batch_ys, index_in_epoch = _next_batch(subbatch_xs, subbatch_ys, batch_size, index_in_epoch)
# check progress on every 1st,2nd,...,10th,20th,...,100th... step
if i % DISPLAY_STEP == 0 or (i + 1) == train_epochs:
train_loss, train_accuracy = sess.run(
[self.cost, self.accuracy], feed_dict={self.X: batch_xs,
self.Y_gt: batch_ys,
self.lr: learning_rate,
self.phase: 1,
self.drop: dropout_conv})
print('epochs %d training_loss ,training_accuracy ''=> %.5f,%.5f ' % (i, train_loss, train_accuracy))
pred = sess.run(self.Y_pred, feed_dict={self.X: batch_xs,
self.Y_gt: batch_ys,
self.phase: 1,
self.drop: 1})
gt = np.reshape(batch_ys[0], (self.image_depth, self.image_height, self.image_width, self.numclass))
gt1 = gt[:, :, :, 0]
gt1 = np.reshape(gt1, (self.image_depth, self.image_height, self.image_width))
gt1 = gt1.astype(np.float)
save_images(gt1, showwindow, path=logs_path + 'gt1_%d_epoch.png' % i)
gt2 = gt[:, :, :, 1]
gt2 = np.reshape(gt2, (self.image_depth, self.image_height, self.image_width))
gt2 = gt2.astype(np.float)
save_images(gt2, showwindow, path=logs_path + 'gt2_%d_epoch.png' % i)
gt3 = gt[:, :, :, 2]
gt3 = np.reshape(gt3, (self.image_depth, self.image_height, self.image_width))
gt3 = gt3.astype(np.float)
save_images(gt3, showwindow, path=logs_path + 'gt3_%d_epoch.png' % i)
result = np.reshape(pred[0], (self.image_depth, self.image_height, self.image_width, self.numclass))
result1 = result[:, :, :, 0]
result1 = np.reshape(result1, (self.image_depth, self.image_height, self.image_width))
result1 = result1.astype(np.float)
save_images(result1, showwindow, path=logs_path + 'predict1_%d_epoch.png' % i)
result2 = result[:, :, :, 1]
result2 = np.reshape(result2, (self.image_depth, self.image_height, self.image_width))
result2 = result2.astype(np.float)
save_images(result2, showwindow, path=logs_path + 'predict2_%d_epoch.png' % i)
result3 = result[:, :, :, 2]
result3 = np.reshape(result3, (self.image_depth, self.image_height, self.image_width))
result3 = result3.astype(np.float)
save_images(result3, showwindow, path=logs_path + 'predict3_%d_epoch.png' % i)
save_path = saver.save(sess, model_path, global_step=i)
print("Model saved in file:", save_path)
if i % (DISPLAY_STEP * 10) == 0 and i:
DISPLAY_STEP *= 10
# train on batch
_, summary = sess.run([train_op, merged_summary_op], feed_dict={self.X: batch_xs,
self.Y_gt: batch_ys,
self.lr: learning_rate,
self.phase: 1,
self.drop: dropout_conv})
summary_writer.add_summary(summary, i)
summary_writer.close()
save_path = saver.save(sess, model_path)
print("Model saved in file:", save_path)
def prediction(self, test_images):
test_images = np.reshape(test_images,
(test_images.shape[0], test_images.shape[1], test_images.shape[2], self.channels))
test_images = test_images.astype(np.float)
y_dummy = np.zeros((test_images.shape[0], test_images.shape[1], test_images.shape[2], 3))
pred = self.sess.run(self.Y_pred, feed_dict={self.X: [test_images], self.Y_gt: [y_dummy], self.phase: 1,
self.drop: 1})
result = pred.astype(np.float) * 255.
result = np.clip(result, 0, 255).astype('uint8')
result = np.reshape(result, (test_images.shape[0], test_images.shape[1], test_images.shape[2], self.numclass))
return result
