def self(x_train, y_train, x_test, y_test):
int_put = input_data(shape=[None, 224, 5, 5, 1], )
conv1 = conv_3d(
int_put,
24,
[24, 3, 3],
padding='VALID',
strides=[1, 1, 1, 1, 1],
activation='prelu',
)
print('conv1', conv1.get_shape().as_list())
batch_norm = batch_normalization(conv1)
conv2 = conv_3d(
batch_norm,
12,
[24, 3, 3],
padding='VALID',
strides=[1, 1, 1, 1, 1],
activation='prelu',
)
print('conv2', conv2.get_shape().as_list())
batch_norm_con = batch_normalization(conv2)
decon2 = conv_3d_transpose(batch_norm_con,
24, [24, 3, 3],
padding='VALID',
output_shape=[201, 3, 3, 24])
batch_norm = batch_normalization(decon2)
print('a')
decon2 = conv_3d_transpose(batch_norm,
1, [24, 3, 3],
padding='VALID',
output_shape=[224, 5, 5, 1])
batch_norm = batch_normalization(decon2)
network = regression(batch_norm,
optimizer='Adagrad',
loss='mean_square',
learning_rate=0.01,
metric='R2')
feature_model = tflearn.DNN(network)
feature_model.load('my_model_self.tflearn')
x_feature = feature_model.predict(x_train)
save_hdf5(x_feature)
print('asd')
def conv_transpose_layer(input, n_filters, stride, output_shape):
return conv.conv_3d_transpose(input,
n_filters,
3,
output_shape=output_shape,
strides=stride,
padding='same',
activation='elu',
bias_init='zeros',
scope=None,
name='Conv3D')
def up_block(input_tensor, concat, reuse, scope, init):
n_channels = int(input_tensor.get_shape()[-1])
x = conv_3d_transpose(input_tensor,
n_channels,
filter_size=2,
strides=2,
output_shape=[concat.get_shape().as_list()[1]] * 3,
activation='linear',
padding='same',
weights_init=init,
scope=scope + "_1",
reuse=reuse)
# x = tflearn.layers.normalization.batch_normalization(x, reuse=reuse, scope=scope + "bn")
x_out = tflearn.activation(x, "prelu")
return x_out
def arch_fusionnet_translator_3d_iso_tflearn(img,
feats=[None, None, None],
last_dim=1,
nl=INLReLU3D,
nb_filters=32):
# Add decorator to tflearn source code
# sudo nano /usr/local/lib/python2.7/dist-packages/tflearn/layers/conv.py
# @tf.contrib.framework.add_arg_scope
with tf.contrib.framework.arg_scope([conv_3d],
filter_size=4,
strides=[1, 2, 2, 2, 1],
activation='leaky_relu'):
with tf.contrib.framework.arg_scope([conv_3d_transpose],
filter_size=4,
strides=[1, 2, 2, 2, 1],
activation='leaky_relu'):
shape = img.get_shape().as_list()
dimb, dimz, dimy, dimx, dimc = shape
e1a = conv_3d(incoming=img,
name="e1a",
nb_filter=nb_filters * 1,
bias=False)
r1a = tf_bottleneck(e1a, name="r1a", nb_filter=nb_filters * 1)
r1a = tf.nn.dropout(r1a, keep_prob=0.5)
e2a = conv_3d(incoming=r1a,
name="e2a",
nb_filter=nb_filters * 1,
bias=False)
r2a = tf_bottleneck(e2a, name="r2a", nb_filter=nb_filters * 1)
r2a = tf.nn.dropout(r2a, keep_prob=0.5)
e3a = conv_3d(incoming=r2a,
name="e3a",
nb_filter=nb_filters * 2,
bias=False)
r3a = tf_bottleneck(e3a, name="r3a", nb_filter=nb_filters * 2)
r3a = tf.nn.dropout(r3a, keep_prob=0.5)
e4a = conv_3d(incoming=r3a,
name="e4a",
nb_filter=nb_filters * 2,
bias=False)
r4a = tf_bottleneck(e4a, name="r4a", nb_filter=nb_filters * 2)
r4a = tf.nn.dropout(r4a, keep_prob=0.5)
e5a = conv_3d(incoming=r4a,
name="e5a",
nb_filter=nb_filters * 4,
bias=False)
r5a = tf_bottleneck(e5a, name="r5a", nb_filter=nb_filters * 4)
r5a = tf.nn.dropout(r5a, keep_prob=0.5)
# e6a = conv_3d(incoming=r5a, name="e6a", nb_filter=nb_filters*4, bias=False)
# r6a = tf_bottleneck(e6a, name="r6a", nb_filter=nb_filters*4)
# e7a = conv_3d(incoming=r6a, name="e7a", nb_filter=nb_filters*8) , bias=False
# r7a = tf_bottleneck(e7a, name="r7a", nb_filter=nb_filters*8)
# r7a = dropout(incoming=r7a, keep_prob=0.5)
print "In1 :", img.get_shape().as_list()
print "E1a :", e1a.get_shape().as_list()
print "R1a :", r1a.get_shape().as_list()
print "E2a :", e2a.get_shape().as_list()
print "R2a :", r2a.get_shape().as_list()
print "E3a :", e3a.get_shape().as_list()
print "R3a :", r3a.get_shape().as_list()
print "E4a :", e4a.get_shape().as_list()
print "R4a :", r4a.get_shape().as_list()
print "E5a :", e5a.get_shape().as_list()
print "R5a :", r5a.get_shape().as_list()
r5b = tf_bottleneck(r5a, name="r5b", nb_filter=nb_filters * 4)
d4b = conv_3d_transpose(incoming=r5b,
name="d4b",
nb_filter=nb_filters * 2,
output_shape=[
-(-dimz // (2**4)), -(-dimy // (2**4)),
-(-dimx / (2**4))
],
bias=False)
a4b = tf.add(d4b, r4a, name="a4b")
r4b = tf_bottleneck(a4b, name="r4b", nb_filter=nb_filters * 2)
d3b = conv_3d_transpose(incoming=r4b,
name="d3b",
nb_filter=nb_filters * 2,
output_shape=[
-(-dimz // (2**3)), -(-dimy // (2**3)),
-(-dimx / (2**3))
],
bias=False)
a3b = tf.add(d3b, r3a, name="a3b")
r3b = tf_bottleneck(a3b, name="r3b", nb_filter=nb_filters * 2)
d2b = conv_3d_transpose(incoming=r3b,
name="d2b",
nb_filter=nb_filters * 1,
output_shape=[
-(-dimz // (2**2)), -(-dimy // (2**2)),
-(-dimx / (2**2))
],
bias=False)
a2b = tf.add(d2b, r2a, name="a2b")
r2b = tf_bottleneck(a2b, name="r2b", nb_filter=nb_filters * 1)
d1b = conv_3d_transpose(incoming=r2b,
name="d1b",
nb_filter=nb_filters * 1,
output_shape=[
-(-dimz // (2**1)), -(-dimy // (2**1)),
-(-dimx / (2**1))
],
bias=False)
a1b = tf.add(d1b, r1a, name="a1b")
out = conv_3d_transpose(incoming=a1b,
name="out",
nb_filter=last_dim,
activation='tanh',
output_shape=[
-(-dimz // (2**0)), -(-dimy // (2**0)),
-(-dimx / (2**0))
])
# print "R7b :", r7b.get_shape().as_list()
# print "D6b :", d6b.get_shape().as_list()
# print "A6b :", a6b.get_shape().as_list()
# print "R6b :", r6b.get_shape().as_list()
# print "D5b :", d5b.get_shape().as_list()
# print "A5b :", a5b.get_shape().as_list()
print "R5b :", r5b.get_shape().as_list()
print "D4b :", d4b.get_shape().as_list()
print "A4b :", a4b.get_shape().as_list()
print "R4b :", r4b.get_shape().as_list()
print "D3b :", d3b.get_shape().as_list()
print "A3b :", a3b.get_shape().as_list()
print "R3b :", r3b.get_shape().as_list()
print "D2b :", d2b.get_shape().as_list()
print "A2b :", a2b.get_shape().as_list()
print "R2b :", r2b.get_shape().as_list()
print "D1b :", d1b.get_shape().as_list()
print "A1b :", a1b.get_shape().as_list()
print "Out :", out.get_shape().as_list()
return out
def train(X=None,
gpu_id=0,
sparsity=False,
latent=64,
num_filters=32,
filter_size=5,
sparsity_level=DEFAULT_SPARSITY_LEVEL,
sparsity_weight=DEFAULT_SPARSITY_WEIGHT,
epochs=10,
conv=False,
checkpoint=None,
is_training=True):
assert checkpoint is not None or X is not None,\
'Either data to train on or model to restore is required.'
print(' * [INFO] Using GPU %s' % gpu_id)
print(' * [INFO]', 'Using' if sparsity else 'Not using', 'sparsity')
print(' * [INFO] Latent dimensions: %d' % latent)
print(' * [INFO]', 'Using' if conv else 'Not using',
'convolutional layers.')
with tf.device(None if gpu_id is None else '/gpu:%s' % gpu_id):
# Building the encoder
if conv:
encoder = tflearn.input_data(shape=[None, 30, 30, 30, 1])
encoder = conv_3d(encoder,
num_filters,
filter_size,
activation=tf.nn.sigmoid)
encoder = tflearn.fully_connected(encoder,
latent,
activation=tf.nn.sigmoid)
else:
encoder = tflearn.input_data(shape=[None, 27000])
encoder = tflearn.fully_connected(encoder,
256,
activation=tf.nn.relu)
encoder = tflearn.fully_connected(encoder,
64,
activation=tf.nn.relu)
encoder = tflearn.fully_connected(encoder,
latent,
activation=tf.nn.relu)
if sparsity:
avg_activations = tf.reduce_mean(encoder, axis=1)
div = tf.reduce_mean(kl_divergence(avg_activations,
sparsity_level))
# Building the decoder
if conv:
decoder = tflearn.fully_connected(encoder, (30**3) * num_filters,
activation=tf.nn.sigmoid)
decoder = tflearn.reshape(decoder, [-1, 30, 30, 30, num_filters])
decoder = conv_3d_transpose(decoder,
1,
filter_size, [30, 30, 30],
activation=tf.nn.sigmoid)
else:
decoder = tflearn.fully_connected(encoder,
64,
activation=tf.nn.relu)
decoder = tflearn.fully_connected(decoder,
256,
activation=tf.nn.relu)
decoder = tflearn.fully_connected(decoder,
27000,
activation=tf.nn.relu)
def sparsity_loss(y_pred, y_true):
return tf.reduce_mean(tf.square(y_pred - y_true)) + \
sparsity_weight * div
# Regression, with mean square error
net = tflearn.regression(decoder,
optimizer='adam',
learning_rate=1e-4,
loss=sparsity_loss if sparsity else 'mean_square',
metric=None)
# Training the auto encoder
model = tflearn.DNN(net, tensorboard_verbose=0)
encoding_model = tflearn.DNN(encoder, session=model.session)
saver = tf.train.Saver()
checkpoint_path = CKPT_FORMAT.format(id=checkpoint or ID_)
if is_training:
model.fit(X, X, n_epoch=epochs, run_id="auto_encoder", batch_size=256)
saver.save(encoding_model.session, checkpoint_path)
else:
saver.restore(encoding_model.models, checkpoint_path)
return {'model': model, 'encoding_model': encoding_model}
def generator_fusionnet(images, name='generator'): dimx = DIMX dimy = DIMY dimz = DIMZ with tf.variable_scope(name): # return images e1 = conv_3d(incoming=images, nb_filter=NB_FILTERS*1, filter_size=4, strides=[1, 1, 1, 1, 1], # DIMZ/1, DIMY/2, DIMX/2, regularizer='L1', activation='elu') e1 = batch_normalization(incoming=e1) ### e2 = conv_3d(incoming=e1, nb_filter=NB_FILTERS*1, filter_size=4, strides=[1, 2, 2, 2, 1], # DIMZ/2, DIMY/4, DIMX/4, regularizer='L1', activation='elu') e2 = batch_normalization(incoming=e2) ### e3 = conv_3d(incoming=e2, nb_filter=NB_FILTERS*2, filter_size=4, strides=[1, 2, 2, 2, 1], # DIMZ/4, DIMY/8, DIMX/8, regularizer='L1', activation='elu') e3 = batch_normalization(incoming=e3) ### e4 = conv_3d(incoming=e3, nb_filter=NB_FILTERS*2, filter_size=4, strides=[1, 2, 2, 2, 1], # DIMZ/8, DIMY/16, DIMX/16, regularizer='L1', activation='elu') e4 = batch_normalization(incoming=e4) ### e5 = conv_3d(incoming=e4, nb_filter=NB_FILTERS*4, filter_size=4, strides=[1, 2, 2, 2, 1], # DIMZ/16, DIMY/32, DIMX/32, regularizer='L1', activation='elu') e5 = batch_normalization(incoming=e5) ### e6 = conv_3d(incoming=e5, nb_filter=NB_FILTERS*4, filter_size=4, strides=[1, 2, 2, 2, 1], # DIMZ/32, DIMY/64, DIMX/64, regularizer='L1', activation='elu') e6 = batch_normalization(incoming=e6) ### e7 = conv_3d(incoming=e6, nb_filter=NB_FILTERS*8, filter_size=4, strides=[1, 2, 2, 2, 1], # DIMZ/64, DIMY/128, DIMX/128, regularizer='L1', activation='elu') e7 = batch_normalization(incoming=e7) ### Middle e8 = conv_3d(incoming=e7, nb_filter=NB_FILTERS*8, filter_size=4, strides=[1, 2, 2, 2, 1], # DIMZ/128, DIMY/256, DIMX/256, regularizer='L1', activation='elu') # print "Dim8: ", dimz, dimy, dimx dimz, dimy, dimx = dimz/2, dimy/2, dimx/2 e8 = batch_normalization(incoming=e8) ################### Decoder # print "Dim D7a: ", dimz, dimy, dimx d7 = conv_3d_transpose(incoming=e8, nb_filter=NB_FILTERS*8, filter_size=4, strides=[1, 2, 2, 2, 1], # DIMZ/64, DIMY/128, DIMX/128, regularizer='L1', activation='elu', output_shape=[2, 4, 4]) d7 = batch_normalization(incoming=d7) d7 = dropout(incoming=d7, keep_prob=0.5) d7 = merge(tensors_list=[d7, e7], mode='elemwise_sum') # d7 = d7+e7 ### d6 = conv_3d_transpose(incoming=d7, nb_filter=NB_FILTERS*4, filter_size=4, strides=[1, 2, 2, 2, 1], # DIMZ/32, DIMY/64, DIMX/64, regularizer='L1', activation='elu', output_shape=[4, 8, 8]) d6 = batch_normalization(incoming=d6) d6 = dropout(incoming=d6, keep_prob=0.5) d6 = merge(tensors_list=[d6, e6], mode='elemwise_sum') # d6 = d6+e6 ### d5 = conv_3d_transpose(incoming=d6, nb_filter=NB_FILTERS*4, filter_size=4, strides=[1, 2, 2, 2, 1], # DIMZ/16, DIMY/32, DIMX/32, regularizer='L1', activation='elu', output_shape=[8, 16, 16]) d5 = batch_normalization(incoming=d5) d5 = dropout(incoming=d5, keep_prob=0.5) d5 = merge(tensors_list=[d5, e5], mode='elemwise_sum') # d5 = d5+e5 ### d4 = conv_3d_transpose(incoming=d5, nb_filter=NB_FILTERS*2, filter_size=4, strides=[1, 2, 2, 2, 1], # DIMZ/8, DIMY/16, DIMX/16, regularizer='L1', activation='elu', output_shape=[16, 32, 32]) d4 = batch_normalization(incoming=d4) d4 = merge(tensors_list=[d4, e4], mode='elemwise_sum') # d4 = d4+e4 ### d3 = conv_3d_transpose(incoming=d4, nb_filter=NB_FILTERS*2, filter_size=4, strides=[1, 2, 2, 2, 1], # DIMZ/4, DIMY/8, DIMX/8, regularizer='L1', activation='elu', output_shape=[32, 64, 64]) d3 = batch_normalization(incoming=d3) d3 = merge(tensors_list=[d3, e3], mode='elemwise_sum') # d3 = d3+e3 ### d2 = conv_3d_transpose(incoming=d3, nb_filter=NB_FILTERS*1, filter_size=4, strides=[1, 2, 2, 2, 1], # DIMZ/2, DIMY/4, DIMX/4, regularizer='L1', activation='elu', output_shape=[64, 128, 128]) d2 = batch_normalization(incoming=d2) d2 = merge(tensors_list=[d2, e2], mode='elemwise_sum') # d2 = d2+e2 ### d1 = conv_3d_transpose(incoming=d2, nb_filter=NB_FILTERS*1, filter_size=4, strides=[1, 2, 2, 2, 1], # DIMZ/1, DIMY/2, DIMX/2, regularizer='L1', activation='elu', output_shape=[128, 256, 256]) d1 = batch_normalization(incoming=d1) d1 = merge(tensors_list=[d1, e1], mode='elemwise_sum') # d1 = d1+e1 ### out = conv_3d_transpose(incoming=d1, nb_filter=1, filter_size=4, strides=[1, 1, 1, 1, 1], # DIMZ/1, DIMY/1, DIMX/1, regularizer='L1', activation='tanh', output_shape=[128, 256, 256]) return out, e8
def vol3d_decoder(self, x, name='Vol3D_Decoder'):
with argscope([Conv3DTranspose], kernel_shape=4, padding='SAME', nl=tf.nn.elu):
# x = x - VGG19_MEAN_TENSOR
# x = BNLReLU(x)
x = tf_2tanh(x)
# x = x/255.0
x = tf.space_to_batch(x, paddings=[[0,0],[0,0]], block_size=64 ,name='s2b')
x = tf.reshape(x, [-1, 4, 4, 4, 3])
x = tf.transpose(x, [4, 1, 2, 3, 0]) # #
"""
x = (LinearWrap(x)
.Conv3DTranspose('conv6a', 256, strides = 2, padding='SAME') #
.Conv3DTranspose('conv5a', 128, strides = 2, padding='SAME') #
.Conv3DTranspose('conv4a', 64, strides = 2, padding='SAME') #
.Conv3DTranspose('conv3a', 32, strides = 2, padding='SAME') #
.Conv3DTranspose('conv2a', 16, strides = 2, padding='SAME') #
.Conv3DTranspose('conv1a', 1, strides = 2, padding='SAME', use_bias=True, nl=tf.tanh) #
())
"""
with tf.contrib.framework.arg_scope([conv_3d], filter_size=4, strides=[1, 2, 2, 2, 1], activation='relu', reuse=False):
with tf.contrib.framework.arg_scope([conv_3d_transpose], filter_size=4, strides=[1, 2, 2, 2, 1], activation='relu', reuse=False):
d6b = conv_3d_transpose(incoming=x , name="d6b", nb_filter=256, output_shape=[int(s) for s in [-(-DIMZ//(2**5)), -(-DIMY//(2**5)), -(-DIMX/(2**5))]], bias=False)
r6b = tf_bottleneck(d6b, name="r6b", nb_filter=256)
# Decoder
d5b = conv_3d_transpose(incoming=r6b, name="d5b", nb_filter=128, output_shape=[int(s) for s in [-(-DIMZ//(2**4)), -(-DIMY//(2**4)), -(-DIMX//(2**4))]], bias=False)
r5b = tf_bottleneck(d5b, name="r5b", nb_filter=128)
d4b = conv_3d_transpose(incoming=r5b, name="d4b", nb_filter=64, output_shape=[int(s) for s in [-(-DIMZ//(2**3)), -(-DIMY//(2**3)), -(-DIMX//(2**3))]], bias=False)
r4b = tf_bottleneck(d4b, name="r4b", nb_filter=64)
d3b = conv_3d_transpose(incoming=r4b, name="d3b", nb_filter=32, output_shape=[int(s) for s in [-(-DIMZ//(2**2)), -(-DIMY//(2**2)), -(-DIMX//(2**2))]], bias=False)
r3b = tf_bottleneck(d3b, name="r3b", nb_filter=32)
d2b = conv_3d_transpose(incoming=r3b, name="d1b", nb_filter=16, output_shape=[int(s) for s in [-(-DIMZ//(2**1)), -(-DIMY//(2**1)), -(-DIMX//(2**1))]], bias=False)
r2b = tf_bottleneck(d2b, name="r2b", nb_filter=16)
out = conv_3d_transpose(incoming=r2b, name="out", nb_filter=1,
activation='tanh',
output_shape=[int(s) for s in [-(-DIMZ//(2**0)), -(-DIMY//(2**0)), -(-DIMX//(2**0))]])
print("D6b :", d6b.get_shape().as_list())
print("R6b :", r6b.get_shape().as_list())
print("D5b :", d5b.get_shape().as_list())
print("R5b :", r5b.get_shape().as_list())
print("D4b :", d4b.get_shape().as_list())
print("R4b :", r4b.get_shape().as_list())
print("D3b :", d3b.get_shape().as_list())
print("R3b :", r3b.get_shape().as_list())
print("D2b :", d2b.get_shape().as_list())
print("R2b :", r2b.get_shape().as_list())
print("Out :", out.get_shape().as_list())
x = out
x = tf.transpose(x, [4, 1, 2, 3, 0]) #
x = tf.squeeze(x)
# x = x*255.0
x = tf_2imag(x)
# x = x + VGG19_MEAN_TENSOR
return x
def self(x_train, y_train, x_test, y_test):
int_put = input_data(shape=[None, 224, 5, 5, 1], )
conv1 = conv_3d(
int_put,
24,
[24, 3, 3],
padding='VALID',
strides=[1, 1, 1, 1, 1],
activation='prelu',
)
print('conv1', conv1.get_shape().as_list())
batch_norm = batch_normalization(conv1)
conv2 = conv_3d(
batch_norm,
12,
[24, 3, 3],
padding='VALID',
strides=[1, 1, 1, 1, 1],
activation='prelu',
)
print('conv2', conv2.get_shape().as_list())
batch_norm_con = batch_normalization(conv2)
decon2 = conv_3d_transpose(batch_norm_con,
24, [24, 3, 3],
padding='VALID',
output_shape=[201, 3, 3, 24])
batch_norm = batch_normalization(decon2)
print('a')
decon2 = conv_3d_transpose(batch_norm,
1, [24, 3, 3],
padding='VALID',
output_shape=[224, 5, 5, 1])
batch_norm = batch_normalization(decon2)
network = regression(batch_norm,
optimizer='Adagrad',
loss='mean_square',
learning_rate=0.01,
metric='R2')
model = tflearn.DNN(network,
tensorboard_verbose=0,
tensorboard_dir="./tflearn_logs/")
for i in range(10):
model.fit(x_train,
x_train,
n_epoch=20,
shuffle=True,
show_metric=True,
validation_set=(x_test, x_test),
batch_size=32,
run_id='3d_net_self')
x_pre = model.predict(x_train)
x_pre = np.array(x_pre)
x_true = np.array(x_train)
psnr(x_true, x_pre)
model.save('my_model_self.tflearn')
'''
