def input_block(bmode, pd, num_filters, reuse, scope, init):
x_bmode = conv_3d(bmode,
num_filters // 2,
5,
activation='linear',
padding='same',
reuse=reuse,
scope=scope + "_1_bmode",
weights_init=init)
x_pd = conv_3d(pd,
num_filters // 2,
5,
activation='linear',
padding='same',
reuse=reuse,
scope=scope + "_1_pd",
weights_init=init)
x = tflearn.layers.merge_ops.merge([x_bmode, x_pd], axis=4, mode='concat')
x = conv_3d(x,
num_filters,
5,
activation='linear',
padding='same',
reuse=reuse,
scope=scope + "_1",
weights_init=init)
x = tflearn.activation(x, "prelu")
return x
def sentnet_v0(width, height, frame_count, lr, output=9):
network = input_data(shape=[None, width, height, frame_count, 1], name='input')
network = conv_3d(network, 96, 11, strides=4, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 256, 5, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 384, 3, 3, activation='relu')
network = conv_3d(network, 384, 3, 3, activation='relu')
network = conv_3d(network, 256, 3, 3, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, output, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log')
return model
def sentnet_v0(width, height, frame_count, lr, output=9):
network = input_data(shape=[None, width, height, frame_count, 1], name='input')
network = conv_3d(network, 96, 11, strides=4, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 256, 5, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = conv_3d(network, 384, 3, 3, activation='relu')
network = conv_3d(network, 384, 3, 3, activation='relu')
network = conv_3d(network, 256, 3, 3, activation='relu')
network = max_pool_3d(network, 3, strides=2)
#network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, output, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir='log')
return model
def get_network(do_dropout=True):
# input images
p_sz = config.patch_size
network = input_data(shape=[None, p_sz, p_sz, p_sz, 2], name='input')
network = conv_3d(network, 100, 3, activation='relu', regularizer="L2")
#network = max_pool_3d(network, 2)
network = batch_normalization(network) # local_response_normalization
network = conv_3d(network, 100, 3, activation='relu', regularizer="L2")
#network = max_pool_3d(network, 2)
network = batch_normalization(network)
if do_dropout:
network = dropout(network, 0.5)
network = conv_3d(network, 100, 3, activation='relu', regularizer="L2")
#network = max_pool_3d(network, 2)
network = batch_normalization(network)
network = conv_3d(network, 100, 3, activation='relu', regularizer="L2")
#network = max_pool_3d(network, 2)
network = batch_normalization(network)
if do_dropout:
network = dropout(network, 0.5)
'''network = conv_3d(network, 100, 3, activation='relu', regularizer="L2")
#network = max_pool_3d(network, 2)
network = batch_normalization(network)
network = conv_3d(network, 100, 3, activation='relu', regularizer="L1")
#network = max_pool_3d(network, 2)
network = batch_normalization(network)'''
# fully connected layers
network = fully_connected(network, 100, activation='relu', regularizer="L2") # 50
if do_dropout:
network = dropout(network, 0.5)
#network = local_response_normalization(network)
network = batch_normalization(network)
network = fully_connected(network, 50, activation='relu', regularizer="L2") # 30
if do_dropout:
network = dropout(network, 0.5)
network = batch_normalization(network)
# softmax + output layers
network = fully_connected(network, 3, activation='softmax', name='soft')
network = regression(
network,
optimizer='adam',
learning_rate=0.0001,
loss='categorical_crossentropy',
name='target',
batch_size=75) # 0.000005
return network
def conv_block2(input, feature, num_convs, reuse, init):
x = tflearn.layers.merge_ops.merge([input, feature], axis=4, mode='concat')
n_channels = int(input.get_shape()[-1])
if num_convs == 1:
with tf.variable_scope("conv_" + str(1)):
x = conv_3d(x,
n_channels * 2,
filter_size=5,
strides=1,
activation='linear',
padding='same',
weights_init=init,
scope="conv_" + str(1),
reuse=reuse)
# x = tflearn.layers.normalization.batch_normalization(x, reuse=reuse, scope="bn_" + str(1))
# input = tflearn.layers.normalization.batch_normalization(x, reuse=reuse, scope="bn_inp_" + str(1))
input = x
x = x + input
# x = tflearn.layers.normalization.batch_normalization(x, reuse=reuse, scope="bn_merged_" + str(1))
x = tflearn.activation(x, "prelu")
return x
with tf.variable_scope("conv_" + str(1)):
x = conv_3d(x,
n_channels * 2,
filter_size=5,
strides=1,
activation='linear',
padding='same',
weights_init=init,
scope="conv_" + str(1),
reuse=reuse)
# x = tflearn.layers.normalization.batch_normalization(x, reuse=reuse, scope="bn_" + str(1))
x = tflearn.activation(x, "prelu")
for i in range(1, num_convs):
with tf.variable_scope("conv_" + str(i + 1)):
x = conv_3d(x,
n_channels,
filter_size=5,
strides=1,
activation='linear',
padding='same',
weights_init=init,
scope="conv2_" + str(i + 1),
reuse=reuse)
# x = tflearn.layers.normalization.batch_normalization(x, reuse=reuse, scope="bn_" + str(i+1))
# input = tflearn.layers.normalization.batch_normalization(x, reuse=reuse, scope="bn_inp_" + str(i+1))
input = x
if i == num_convs - 1:
x = x + input
# x = tflearn.layers.normalization.batch_normalization(x, reuse=reuse, scope="bn_merged_" + str(i + 1))
x = tflearn.activation(x, "prelu")
return x
def tf_bottleneck(inputs, nb_filter, name="bottleneck"): with tf.variable_scope(name): original = tf.identity(inputs, name="identity") with tf.contrib.framework.arg_scope([conv_3d, conv_3d_transpose], strides=[1, 1, 1, 1, 1], activation='relu', reuse=False): shape = original.get_shape().as_list() conv_4x4i = original conv_4x4i = conv_3d(incoming=conv_4x4i, name="conv_4x4i", filter_size=4, nb_filter=nb_filter, bias=False) conv_4x4o = conv_3d(incoming=conv_4x4i, name="conv_4x4o", filter_size=4, nb_filter=nb_filter, bias=False, activation=tf.identity, ) summation = tf.add(original, conv_4x4o, name="summation") return summation
def create_cnn_3d_alex():
#img_prep = ImagePreprocessing()
#img_prep.add_featurewise_zero_center(mean=0.25)
network = input_data(
shape=[None, IMG_SIZE_PX, IMG_SIZE_PX, IMG_SIZE_PX, 1])
network = conv_3d(network, 96, 11, strides=4, regularizer='L2')
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 3, strides=2)
network = conv_3d(network, 256, 5, regularizer='L2')
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 3, strides=2)
network = conv_3d(network, 384, 3, regularizer='L2')
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = conv_3d(network, 384, 3, regularizer='L2')
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = conv_3d(network, 256, 3, regularizer='L2')
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 3, strides=2)
network = fully_connected(network, 4096, regularizer='L2')
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = dropout(network, keep_rate)
network = fully_connected(network, 4096, regularizer='L2')
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = dropout(network, keep_rate)
output = fully_connected(network, num_class, activation='softmax')
network = regression(output,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.0001)
return network
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 create_cnn_3d_network():
# Building 'AlexNet'
network = input_data(
shape=[None, IMG_SIZE_PX, IMG_SIZE_PX, IMG_SIZE_PX, 1])
network = conv_3d(network, 32, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 3, strides=2)
network = conv_3d(network, 64, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 3, strides=2)
network = conv_3d(network, 128, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = conv_3d(network, 256, 3)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = max_pool_3d(network, 3, strides=2)
network = fully_connected(network, 2048)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = dropout(network, keep_rate)
network = fully_connected(network, 2048)
network = tflearn.activation(tflearn.batch_normalization(network),
activation='relu')
network = dropout(network, keep_rate)
network = fully_connected(network, num_class)
network = regression(network,
optimizer='adam',
loss='softmax_categorical_crossentropy',
learning_rate=0.0001)
return network
def tf_bottleneck(inputs, nb_filter, name="bottleneck"):
with tf.variable_scope(name):
original = tf.identity(inputs, name="identity")
with tf.contrib.framework.arg_scope([conv_3d, conv_3d_transpose], strides=[1, 1, 1, 1, 1], activation='leaky_relu'):
shape = original.get_shape().as_list()
conv_4x4i = original #conv_3d(incoming=original, name="conv_4x4i", filter_size=4, nb_filter=nb_filter) # From 256 to 64 in Residual pape, bias=Falser
# original = tf.nn.dropout(original, keep_prob=0.5)
conv_4x4i = conv_3d(incoming=conv_4x4i, name="conv_4x4i", filter_size=4, nb_filter=nb_filter, bias=False) # From 256 to 64 in Residual paper
# conv_4x4i = tf.nn.dropout(conv_4x4i, keep_prob=0.5)
conv_4x4m = conv_3d(incoming=conv_4x4i, name="conv_4x4m", filter_size=4, nb_filter=nb_filter, bias=False)
# conv_4x4o = tf.nn.dropout(conv_4x4o, keep_prob=0.5)
conv_4x4o = conv_3d(incoming=conv_4x4m, name="conv_4x4o", filter_size=4, nb_filter=nb_filter, bias=False, activation=tf.identity,
# output_shape=[shape[1], shape[2], shape[3]]
)
summation = tf.add(original, conv_4x4o, name="summation")
# summation = elu(summation)
# return batch_normalization(summation)
ret = InstanceNorm('bn', tf.squeeze(summation, axis=0))
ret = tf.expand_dims(ret, axis=0)
return ret
def add_conv_3d_layers(self, num_filters, filter_size, activation, num_layers=1):
"""
Args:
num_filters (int): Number of convolutional filters to use.
filter_size (int): Size of each filter.
activation (str): Activation function to use.
num_layers (int): The number of 3d convolutional layers to add.
"""
for i in range(num_layers):
self.network = conv_3d(self.network, num_filters, filter_size,
activation=activation)
def down_block(input_tensor, reuse, scope, init):
n_channels = int(input_tensor.get_shape()[-1])
x = conv_3d(input_tensor,
n_channels * 2,
filter_size=2,
strides=2,
activation='linear',
padding='same',
weights_init=init,
scope=scope + "_1_ds",
reuse=reuse)
# x = tflearn.layers.normalization.batch_normalization(x, reuse=reuse, scope=scope + "bn")
x = tflearn.activation(x, "prelu")
return x
def conv_block(input, num_convs, reuse, scope, init):
x = input
n_channels = int(x.get_shape()[-1])
for i in range(num_convs):
with tf.variable_scope("conv_" + str(i + 1)):
x = conv_3d(x,
n_channels,
filter_size=5,
strides=1,
activation='linear',
padding='same',
weights_init=init,
scope=scope + "_2",
reuse=reuse)
if i == num_convs - 1:
x = x + input
# x = tflearn.layers.normalization.batch_normalization(x, reuse=reuse, scope=scope+"bn"+str(i))
x = tflearn.activation(x, "prelu")
return x
from tflearn.data_utils import shuffle, to_categorical from tflearn.layers.core import input_data, dropout, fully_connected from tflearn.layers.conv import conv_3d, max_pool_3d, avg_pool_3d from tflearn.layers.estimator import regression num_of_categories = int(max(Y)+1) Y = to_categorical(Y, num_of_categories) img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() # Building 'VGG Network' network = input_data(shape=[None, 197, 233, 189, 1]) network = conv_3d(network, 4, 3, activation='relu') network = conv_3d(network, 4, 3, activation='relu') network = max_pool_3d(network, [1, 2, 2, 2, 1], strides=2) network = conv_3d(network, 8, 3, activation='relu') network = conv_3d(network, 8, 3, activation='relu') network = max_pool_3d(network, [1, 2, 2, 2, 1], strides=2) network = conv_3d(network, 64, 3, activation='relu') network = conv_3d(network, 64, 3, activation='relu') network = max_pool_3d(network, [1, 2, 2, 2, 1], strides=2) network = conv_3d(network, 128, 3, activation='relu') network = conv_3d(network, 128, 3, activation='relu') network = max_pool_3d(network, [1, 2, 2, 2, 1], strides=2)
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 inception_v3_3d(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
network = input_data(shape=[None, width, height,3, 1], name='input')
conv1_7_7 = conv_3d(network, 64, 7, strides=2, activation='relu', name = 'conv1_7_7_s2')
pool1_3_3 = max_pool_3d(conv1_7_7, 3,strides=2)
#pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_3d(pool1_3_3, 64,1, activation='relu',name = 'conv2_3_3_reduce')
conv2_3_3 = conv_3d(conv2_3_3_reduce, 192,3, activation='relu', name='conv2_3_3')
#conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_3d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
inception_3a_1_1 = conv_3d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_3d(pool2_3_3, 96,1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_3d(inception_3a_3_3_reduce, 128,filter_size=3, activation='relu', name = 'inception_3a_3_3')
inception_3a_5_5_reduce = conv_3d(pool2_3_3,16, filter_size=1,activation='relu', name ='inception_3a_5_5_reduce' )
inception_3a_5_5 = conv_3d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name= 'inception_3a_5_5')
inception_3a_pool = max_pool_3d(pool2_3_3, kernel_size=3, strides=1, )
inception_3a_pool_1_1 = conv_3d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=4)
inception_3b_1_1 = conv_3d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' )
inception_3b_3_3_reduce = conv_3d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_3d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu',name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_3d(inception_3a_output, 32, filter_size=1, activation='relu', name = 'inception_3b_5_5_reduce')
inception_3b_5_5 = conv_3d(inception_3b_5_5_reduce, 96, filter_size=5, name = 'inception_3b_5_5')
inception_3b_pool = max_pool_3d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_3d(inception_3b_pool, 64, filter_size=1,activation='relu', name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat',axis=4,name='inception_3b_output')
pool3_3_3 = max_pool_3d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
inception_4a_1_1 = conv_3d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_3d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_3d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_3d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_3d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_3d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_3d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=4, name='inception_4a_output')
inception_4b_1_1 = conv_3d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_3d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_3d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_3d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_3d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_3d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_3d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=4, name='inception_4b_output')
inception_4c_1_1 = conv_3d(inception_4b_output, 128, filter_size=1, activation='relu',name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_3d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_3d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_3d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_3d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_3d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_3d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=4,name='inception_4c_output')
inception_4d_1_1 = conv_3d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_3d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_3d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_3d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_3d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_3d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_3d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=4, name='inception_4d_output')
inception_4e_1_1 = conv_3d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_3d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_3d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_3d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_3d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_3d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_3d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,inception_4e_pool_1_1],axis=4, mode='concat')
pool4_3_3 = max_pool_3d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
inception_5a_1_1 = conv_3d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_3d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_3d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_3d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_3d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_3d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_3d(inception_5a_pool, 128, filter_size=1,activation='relu', name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=4,mode='concat')
inception_5b_1_1 = conv_3d(inception_5a_output, 384, filter_size=1,activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_3d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_3d(inception_5b_3_3_reduce, 384, filter_size=3,activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_3d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_3d(inception_5b_5_5_reduce,128, filter_size=5, activation='relu', name='inception_5b_5_5' )
inception_5b_pool = max_pool_3d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_3d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=4, mode='concat')
pool5_7_7 = avg_pool_3d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
loss = fully_connected(pool5_7_7, output,activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network, checkpoint_path=model_name,
max_checkpoints=1, tensorboard_verbose=0,tensorboard_dir='log')
return model
Y = [i[1] for i in train] y1=tflearn.data_utils.to_categorical(Y, nb_classes=2) v1 = np.array([i[0] for i in vald]).reshape(-1,20,50,50,1) V = [i[1] for i in vald] v2=tflearn.data_utils.to_categorical(V, nb_classes=2) num_classes = 2 img_prep = tflearn.ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() img_aug = tflearn.ImageAugmentation() img_aug.add_random_blur(sigma_max=3.0) img_aug.add_random_flip_leftright() img_aug.add_random_flip_updown() img_aug.add_random_rotation(max_angle=25.) network = input_data(shape=[None, 20,50,50,1],data_preprocessing=img_prep,data_augmentation=img_aug,name='input') network = conv_3d(network, 32, 3,3, activation='relu') network = max_pool_3d(network, 2,2) #network = dropout(network, 0.25) #network = conv_3d(network, 64, 2,2, activation='relu') network = conv_3d(network, 64, 3,3, activation='relu') network = max_pool_3d(network, 2,2) #network = dropout(network, 0.25) #network = conv_3d(network, 128, 3,3, activation='relu') #network = conv_3d(network, 128, 2,2, activation='relu') #network = max_pool_3d(network, 2,2) #network = dropout(network, 0.25) #network = conv_3d(network, 256, 3,3, activation='relu') #network = conv_3d(network, 256, 1,1, activation='relu') #network = max_pool_3d(network, 2,2) #network = dropout(network, 0.25) #network = conv_3d(network, 512, 1,1, activation='relu')
def one_hot(v):
return np.eye(2)[v.astype(int)].reshape((-1, 2))
Y = np.ravel(Y.T)
Y_test = np.ravel(Y_test.T)
Y_oh = one_hot(Y)
Y_test_oh = one_hot(Y_test)
# Convolutional network building
with tf.device('/gpu:%s' % arguments['--gpu-id']):
network = input_data(shape=[None, 30, 30, 30, 1])
network = conv_3d(network, 16, 5, activation=tf.nn.relu)
network = max_pool_3d(network, 2)
network = conv_3d(network, 32, 5, activation=tf.nn.relu)
network = max_pool_3d(network, 2)
network = fully_connected(network, 128, activation=tf.nn.relu)
network = fully_connected(network, 2, activation=tf.nn.relu)
network = regression(network,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=1e-7)
# Train using classifier
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit(X,
Y_oh,
n_epoch=epochs,
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 residual_block_concat(incoming,
nb_blocks,
out_channels,
downsample=False,
downsample_strides=2,
activation='relu',
batch_norm=True,
bias=True,
weights_init='variance_scaling',
bias_init='zeros',
regularizer='L2',
weight_decay=0.0001,
trainable=True,
restore=True,
reuse=False,
scope=None,
name="ResidualBlock"):
""" Residual Block.
A residual block as described in MSRA's Deep Residual Network paper.
Full pre-activation architecture is used here.
Input:
4-D Tensor [batch, height, width, in_channels].
Output:
4-D Tensor [batch, new height, new width, nb_filter].
Arguments:
incoming: `Tensor`. Incoming 4-D Layer.
nb_blocks: `int`. Number of layer blocks.
out_channels: `int`. The number of convolutional filters of the
convolution layers.
downsample: `bool`. If True, apply downsampling using
'downsample_strides' for strides.
downsample_strides: `int`. The strides to use when downsampling.
activation: `str` (name) or `function` (returning a `Tensor`).
Activation applied to this layer (see tflearn.activations).
Default: 'linear'.
batch_norm: `bool`. If True, apply batch normalization.
bias: `bool`. If True, a bias is used.
weights_init: `str` (name) or `Tensor`. Weights initialization.
(see tflearn.initializations) Default: 'uniform_scaling'.
bias_init: `str` (name) or `tf.Tensor`. Bias initialization.
(see tflearn.initializations) Default: 'zeros'.
regularizer: `str` (name) or `Tensor`. Add a regularizer to this
layer weights (see tflearn.regularizers). Default: None.
weight_decay: `float`. Regularizer decay parameter. Default: 0.001.
trainable: `bool`. If True, weights will be trainable.
restore: `bool`. If True, this layer weights will be restored when
loading a model.
reuse: `bool`. If True and 'scope' is provided, this layer variables
will be reused (shared).
scope: `str`. Define this layer scope (optional). A scope can be
used to share variables between layers. Note that scope will
override name.
name: A name for this layer (optional). Default: 'ShallowBottleneck'.
"""
resnet = incoming
in_channels = incoming.get_shape().as_list()[-1]
# Variable Scope fix for older TF
try:
vscope = tf.variable_scope(scope,
default_name=name,
values=[incoming],
reuse=reuse)
except Exception:
vscope = tf.variable_op_scope([incoming], scope, name, reuse=reuse)
with vscope as scope:
name = scope.name #TODO
for i in range(nb_blocks):
identity = resnet
if not downsample:
downsample_strides = 1
if batch_norm:
resnet = tflearn.batch_normalization(resnet)
resnet = tflearn.activation(resnet, activation)
resnet = conv_3d(resnet, out_channels, 3, downsample_strides,
'same', 'linear', bias, weights_init, bias_init,
regularizer, weight_decay, trainable, restore)
if batch_norm:
resnet = tflearn.batch_normalization(resnet)
resnet = tflearn.activation(resnet, activation)
resnet = conv_3d(resnet, out_channels, 3, 1, 'same', 'linear',
bias, weights_init, bias_init, regularizer,
weight_decay, trainable, restore)
# Downsampling
if downsample_strides > 1:
identity = tflearn.avg_pool_3d(identity, 1, downsample_strides)
# Projection to new dimension
if in_channels != out_channels:
ch = (out_channels - in_channels) // 2
identity = tf.pad(identity,
[[0, 0], [0, 0], [0, 0], [0, 0], [ch, ch]])
in_channels = out_channels
resnet = tf.concat(1, [resnet, identity])
return resnet
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')
'''
def inception_v3_3d(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'):
network = input_data(shape=[None, width, height,3, 1], name='input')
conv1_7_7 = conv_3d(network, 64, 7, strides=2, activation='relu', name = 'conv1_7_7_s2')
pool1_3_3 = max_pool_3d(conv1_7_7, 3,strides=2)
#pool1_3_3 = local_response_normalization(pool1_3_3)
conv2_3_3_reduce = conv_3d(pool1_3_3, 64,1, activation='relu',name = 'conv2_3_3_reduce')
conv2_3_3 = conv_3d(conv2_3_3_reduce, 192,3, activation='relu', name='conv2_3_3')
#conv2_3_3 = local_response_normalization(conv2_3_3)
pool2_3_3 = max_pool_3d(conv2_3_3, kernel_size=3, strides=2, name='pool2_3_3_s2')
inception_3a_1_1 = conv_3d(pool2_3_3, 64, 1, activation='relu', name='inception_3a_1_1')
inception_3a_3_3_reduce = conv_3d(pool2_3_3, 96,1, activation='relu', name='inception_3a_3_3_reduce')
inception_3a_3_3 = conv_3d(inception_3a_3_3_reduce, 128,filter_size=3, activation='relu', name = 'inception_3a_3_3')
inception_3a_5_5_reduce = conv_3d(pool2_3_3,16, filter_size=1,activation='relu', name ='inception_3a_5_5_reduce' )
inception_3a_5_5 = conv_3d(inception_3a_5_5_reduce, 32, filter_size=5, activation='relu', name= 'inception_3a_5_5')
inception_3a_pool = max_pool_3d(pool2_3_3, kernel_size=3, strides=1, )
inception_3a_pool_1_1 = conv_3d(inception_3a_pool, 32, filter_size=1, activation='relu', name='inception_3a_pool_1_1')
# merge the inception_3a__
inception_3a_output = merge([inception_3a_1_1, inception_3a_3_3, inception_3a_5_5, inception_3a_pool_1_1], mode='concat', axis=4)
inception_3b_1_1 = conv_3d(inception_3a_output, 128,filter_size=1,activation='relu', name= 'inception_3b_1_1' )
inception_3b_3_3_reduce = conv_3d(inception_3a_output, 128, filter_size=1, activation='relu', name='inception_3b_3_3_reduce')
inception_3b_3_3 = conv_3d(inception_3b_3_3_reduce, 192, filter_size=3, activation='relu',name='inception_3b_3_3')
inception_3b_5_5_reduce = conv_3d(inception_3a_output, 32, filter_size=1, activation='relu', name = 'inception_3b_5_5_reduce')
inception_3b_5_5 = conv_3d(inception_3b_5_5_reduce, 96, filter_size=5, name = 'inception_3b_5_5')
inception_3b_pool = max_pool_3d(inception_3a_output, kernel_size=3, strides=1, name='inception_3b_pool')
inception_3b_pool_1_1 = conv_3d(inception_3b_pool, 64, filter_size=1,activation='relu', name='inception_3b_pool_1_1')
#merge the inception_3b_*
inception_3b_output = merge([inception_3b_1_1, inception_3b_3_3, inception_3b_5_5, inception_3b_pool_1_1], mode='concat',axis=4,name='inception_3b_output')
pool3_3_3 = max_pool_3d(inception_3b_output, kernel_size=3, strides=2, name='pool3_3_3')
inception_4a_1_1 = conv_3d(pool3_3_3, 192, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4a_3_3_reduce = conv_3d(pool3_3_3, 96, filter_size=1, activation='relu', name='inception_4a_3_3_reduce')
inception_4a_3_3 = conv_3d(inception_4a_3_3_reduce, 208, filter_size=3, activation='relu', name='inception_4a_3_3')
inception_4a_5_5_reduce = conv_3d(pool3_3_3, 16, filter_size=1, activation='relu', name='inception_4a_5_5_reduce')
inception_4a_5_5 = conv_3d(inception_4a_5_5_reduce, 48, filter_size=5, activation='relu', name='inception_4a_5_5')
inception_4a_pool = max_pool_3d(pool3_3_3, kernel_size=3, strides=1, name='inception_4a_pool')
inception_4a_pool_1_1 = conv_3d(inception_4a_pool, 64, filter_size=1, activation='relu', name='inception_4a_pool_1_1')
inception_4a_output = merge([inception_4a_1_1, inception_4a_3_3, inception_4a_5_5, inception_4a_pool_1_1], mode='concat', axis=4, name='inception_4a_output')
inception_4b_1_1 = conv_3d(inception_4a_output, 160, filter_size=1, activation='relu', name='inception_4a_1_1')
inception_4b_3_3_reduce = conv_3d(inception_4a_output, 112, filter_size=1, activation='relu', name='inception_4b_3_3_reduce')
inception_4b_3_3 = conv_3d(inception_4b_3_3_reduce, 224, filter_size=3, activation='relu', name='inception_4b_3_3')
inception_4b_5_5_reduce = conv_3d(inception_4a_output, 24, filter_size=1, activation='relu', name='inception_4b_5_5_reduce')
inception_4b_5_5 = conv_3d(inception_4b_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4b_5_5')
inception_4b_pool = max_pool_3d(inception_4a_output, kernel_size=3, strides=1, name='inception_4b_pool')
inception_4b_pool_1_1 = conv_3d(inception_4b_pool, 64, filter_size=1, activation='relu', name='inception_4b_pool_1_1')
inception_4b_output = merge([inception_4b_1_1, inception_4b_3_3, inception_4b_5_5, inception_4b_pool_1_1], mode='concat', axis=4, name='inception_4b_output')
inception_4c_1_1 = conv_3d(inception_4b_output, 128, filter_size=1, activation='relu',name='inception_4c_1_1')
inception_4c_3_3_reduce = conv_3d(inception_4b_output, 128, filter_size=1, activation='relu', name='inception_4c_3_3_reduce')
inception_4c_3_3 = conv_3d(inception_4c_3_3_reduce, 256, filter_size=3, activation='relu', name='inception_4c_3_3')
inception_4c_5_5_reduce = conv_3d(inception_4b_output, 24, filter_size=1, activation='relu', name='inception_4c_5_5_reduce')
inception_4c_5_5 = conv_3d(inception_4c_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4c_5_5')
inception_4c_pool = max_pool_3d(inception_4b_output, kernel_size=3, strides=1)
inception_4c_pool_1_1 = conv_3d(inception_4c_pool, 64, filter_size=1, activation='relu', name='inception_4c_pool_1_1')
inception_4c_output = merge([inception_4c_1_1, inception_4c_3_3, inception_4c_5_5, inception_4c_pool_1_1], mode='concat', axis=4,name='inception_4c_output')
inception_4d_1_1 = conv_3d(inception_4c_output, 112, filter_size=1, activation='relu', name='inception_4d_1_1')
inception_4d_3_3_reduce = conv_3d(inception_4c_output, 144, filter_size=1, activation='relu', name='inception_4d_3_3_reduce')
inception_4d_3_3 = conv_3d(inception_4d_3_3_reduce, 288, filter_size=3, activation='relu', name='inception_4d_3_3')
inception_4d_5_5_reduce = conv_3d(inception_4c_output, 32, filter_size=1, activation='relu', name='inception_4d_5_5_reduce')
inception_4d_5_5 = conv_3d(inception_4d_5_5_reduce, 64, filter_size=5, activation='relu', name='inception_4d_5_5')
inception_4d_pool = max_pool_3d(inception_4c_output, kernel_size=3, strides=1, name='inception_4d_pool')
inception_4d_pool_1_1 = conv_3d(inception_4d_pool, 64, filter_size=1, activation='relu', name='inception_4d_pool_1_1')
inception_4d_output = merge([inception_4d_1_1, inception_4d_3_3, inception_4d_5_5, inception_4d_pool_1_1], mode='concat', axis=4, name='inception_4d_output')
inception_4e_1_1 = conv_3d(inception_4d_output, 256, filter_size=1, activation='relu', name='inception_4e_1_1')
inception_4e_3_3_reduce = conv_3d(inception_4d_output, 160, filter_size=1, activation='relu', name='inception_4e_3_3_reduce')
inception_4e_3_3 = conv_3d(inception_4e_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_4e_3_3')
inception_4e_5_5_reduce = conv_3d(inception_4d_output, 32, filter_size=1, activation='relu', name='inception_4e_5_5_reduce')
inception_4e_5_5 = conv_3d(inception_4e_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_4e_5_5')
inception_4e_pool = max_pool_3d(inception_4d_output, kernel_size=3, strides=1, name='inception_4e_pool')
inception_4e_pool_1_1 = conv_3d(inception_4e_pool, 128, filter_size=1, activation='relu', name='inception_4e_pool_1_1')
inception_4e_output = merge([inception_4e_1_1, inception_4e_3_3, inception_4e_5_5,inception_4e_pool_1_1],axis=4, mode='concat')
pool4_3_3 = max_pool_3d(inception_4e_output, kernel_size=3, strides=2, name='pool_3_3')
inception_5a_1_1 = conv_3d(pool4_3_3, 256, filter_size=1, activation='relu', name='inception_5a_1_1')
inception_5a_3_3_reduce = conv_3d(pool4_3_3, 160, filter_size=1, activation='relu', name='inception_5a_3_3_reduce')
inception_5a_3_3 = conv_3d(inception_5a_3_3_reduce, 320, filter_size=3, activation='relu', name='inception_5a_3_3')
inception_5a_5_5_reduce = conv_3d(pool4_3_3, 32, filter_size=1, activation='relu', name='inception_5a_5_5_reduce')
inception_5a_5_5 = conv_3d(inception_5a_5_5_reduce, 128, filter_size=5, activation='relu', name='inception_5a_5_5')
inception_5a_pool = max_pool_3d(pool4_3_3, kernel_size=3, strides=1, name='inception_5a_pool')
inception_5a_pool_1_1 = conv_3d(inception_5a_pool, 128, filter_size=1,activation='relu', name='inception_5a_pool_1_1')
inception_5a_output = merge([inception_5a_1_1, inception_5a_3_3, inception_5a_5_5, inception_5a_pool_1_1], axis=4,mode='concat')
inception_5b_1_1 = conv_3d(inception_5a_output, 384, filter_size=1,activation='relu', name='inception_5b_1_1')
inception_5b_3_3_reduce = conv_3d(inception_5a_output, 192, filter_size=1, activation='relu', name='inception_5b_3_3_reduce')
inception_5b_3_3 = conv_3d(inception_5b_3_3_reduce, 384, filter_size=3,activation='relu', name='inception_5b_3_3')
inception_5b_5_5_reduce = conv_3d(inception_5a_output, 48, filter_size=1, activation='relu', name='inception_5b_5_5_reduce')
inception_5b_5_5 = conv_3d(inception_5b_5_5_reduce,128, filter_size=5, activation='relu', name='inception_5b_5_5' )
inception_5b_pool = max_pool_3d(inception_5a_output, kernel_size=3, strides=1, name='inception_5b_pool')
inception_5b_pool_1_1 = conv_3d(inception_5b_pool, 128, filter_size=1, activation='relu', name='inception_5b_pool_1_1')
inception_5b_output = merge([inception_5b_1_1, inception_5b_3_3, inception_5b_5_5, inception_5b_pool_1_1], axis=4, mode='concat')
pool5_7_7 = avg_pool_3d(inception_5b_output, kernel_size=7, strides=1)
pool5_7_7 = dropout(pool5_7_7, 0.4)
loss = fully_connected(pool5_7_7, output,activation='softmax')
network = regression(loss, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=lr, name='targets')
model = tflearn.DNN(network, checkpoint_path=model_name,
max_checkpoints=1, tensorboard_verbose=0,tensorboard_dir='log')
return model
def vol3d_encoder(self, x, name='Vol3D_Encoder'):
with argscope([Conv3D], kernel_shape=4, padding='SAME', nl=tf.nn.elu):
# x = x - VGG19_MEAN_TENSOR
x = tf_2tanh(x)
# x = x/255.0
x = tf.expand_dims(x, axis=0) # to 1 256 256 256 3
x = tf.transpose(x, [4, 1, 2, 3, 0]) #
"""
# x = (LinearWrap(x)
# .Conv3D('conv1a', 16, strides = 2, padding='SAME') #
# .Conv3D('conv2a', 32, strides = 2, padding='SAME') #
# .Conv3D('conv3a', 64, strides = 2, padding='SAME') #
# .Conv3D('conv4a', 128, strides = 2, padding='SAME') #
# .Conv3D('conv5a', 256, strides = 2, padding='SAME') #
# .Conv3D('conv6a', 1024, strides = 2, padding='SAME', use_bias=True, nl=tf.tanh) # 4x4x4x1024
# ())
"""
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):
# Encoder
e1a = conv_3d(incoming=x, name="e1a", nb_filter=16, bias=False)
r1a = tf_bottleneck(e1a, name="r1a", nb_filter=16)
# r1a = tf.nn.dropout(r1a, keep_prob=0.5)
e2a = conv_3d(incoming=r1a, name="e2a", nb_filter=32, bias=False)
r2a = tf_bottleneck(e2a, name="r2a", nb_filter=32)
# r2a = tf.nn.dropout(r2a, keep_prob=0.5)
e3a = conv_3d(incoming=r2a, name="e3a", nb_filter=64, bias=False)
r3a = tf_bottleneck(e3a, name="r3a", nb_filter=64)
# r3a = tf.nn.dropout(r3a, keep_prob=0.5)
e4a = conv_3d(incoming=r3a, name="e4a", nb_filter=128, bias=False)
r4a = tf_bottleneck(e4a, name="r4a", nb_filter=128)
# r4a = tf.nn.dropout(r4a, keep_prob=0.5)
e5a = conv_3d(incoming=r4a, name="e5a", nb_filter=256, bias=False)
r5a = tf_bottleneck(e5a, name="r5a", nb_filter=256)
#r5a = tf.nn.dropout(r5a, keep_prob=0.5)
e6a = conv_3d(incoming=r5a, name="e6a", nb_filter=1024, bias=False)
r6a = tf_bottleneck(e6a, name="r6a", nb_filter=1024)
# 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 :", x.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())
print("E6a :", e6a.get_shape().as_list())
print("R6a :", r6a.get_shape().as_list())
x = r6a
x = tf.transpose(x, [4, 1, 2, 3, 0]) ##
x = tf.reshape(x, [-1, 4, 4, 3]) #
x = tf.batch_to_space(x, crops=[[0,0],[0,0]], block_size=64,name='b2s')
# x = x*255.0
x = tf_2imag(x)
x = INLReLU(x)
# x = x + VGG19_MEAN_TENSOR
return x
def test1(x_train, y_train, x_test, y_test):
# Train using classifier
#define network
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',
weight_decay=0.05)
print('conv1', conv1.get_shape().as_list())
batch_norm = batch_normalization(conv1)
#act1=tflearn.activations.relu(batch_norm)
#pool1=max_pool_3d(act1,[1,1,2,2,1],strides=[1,1,1,1,1])
conv2 = conv_3d(batch_norm,
12, [24, 3, 3],
padding='VALID',
strides=[1, 1, 1, 1, 1],
activation='prelu',
weight_decay=0.05)
print('conv2', conv2.get_shape().as_list())
batch_norm = batch_normalization(conv2)
#act = tflearn.activations.relu(batch_norm)
#pool2=max_pool_3d(act,[1,1,2,2,1],strides=[1,1,1,1,1])
net = residual_block_concat(batch_norm,
2,
16,
batch_norm=None,
downsample_strides=1,
weight_decay=0.05)
#net = residual_block(net, 5, 16)
#net = residual_block(net, 1, 32, )
#net = residual_block(net, 4, 32)
#net = residual_block(net, 1, 64, downsample=True)
#net = residual_block(net, 2, 64)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
'''
conv3=conv_3d(batch_norm,24,[24,1,1],padding='VALID',strides=[1,5,1,1,1],activation='prelu')
print('conv3', conv3.get_shape().as_list())
batch_norm = batch_normalization(conv3)
#act=tflearn.activations.relu(batch_norm)
#pool3=max_pool_3d(act,[1,1,2,2,1],strides=[1,1,1,1,1])
'''
flat = flatten(net)
print('flat', flat.get_shape().as_list())
ip1 = fully_connected(
flat,
100,
activation='prelu',
)
dro = dropout(ip1, 0.9)
ip2 = fully_connected(
dro,
20,
activation='softmax',
)
network = regression(ip2,
optimizer='Adagrad',
loss='categorical_crossentropy',
learning_rate=0.01)
model = tflearn.DNN(network,
tensorboard_verbose=0,
tensorboard_dir="./tflearn_logs/")
model.fit(x_train,
y_train,
n_epoch=200,
shuffle=True,
validation_set=(x_test, y_test),
show_metric=True,
batch_size=32,
run_id='3d_net')
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
