def make_stack_net_v1(inp_shape, batch_size, params):
"""Basic Stacked VCNN. Like maskedCNN, but moving layers around to remove blind spots"""
n_filters = 16
n_per_block = 3
inputs = {
'conditioned_occ': tf.keras.Input(batch_size=batch_size,
shape=inp_shape)
}
x = inputs['conditioned_occ']
def bs(x):
return nn.BackShiftConv3D(n_filters, use_bias=False, nln=tf.nn.elu)(x)
def bds(x):
return nn.BackDownShiftConv3D(n_filters, use_bias=False,
nln=tf.nn.elu)(x)
def bdrs(x):
return nn.BackDownRightShiftConv3D(n_filters,
use_bias=False,
nln=tf.nn.elu)(x)
#Front
f_list = [nn.BackShift()(bs(x))]
#Upper Front
uf_list = [nn.BackShift()(bs(x)) + \
nn.DownShift()(bds(x))]
#Left Upper Front
luf_list = [nn.BackShift()(bs(x)) + \
nn.DownShift()(bds(x)) + \
nn.RightShift()(bdrs(x))]
for _ in range(n_per_block):
f_list.append(bs(f_list[-1]))
uf_list.append(bds(uf_list[-1]) + f_list[-1])
luf_list.append(bdrs(luf_list[-1]) + uf_list[-1])
x = nn.Conv3D(n_filters=1, filter_size=[1, 1, 1],
use_bias=True)(luf_list[-1])
if params['final_activation'] == 'sigmoid':
x = tf.nn.sigmoid(x)
elif params['final_activation'] == 'elu':
x = tf.nn.elu(x)
elif params['final_activation'] == None:
pass
else:
raise ("Unknown param valies for [final activation]: {}".format(
params['final_activation']))
output = {"predicted_occ": x, "predicted_free": 1 - x}
return tf.keras.Model(inputs=inputs, output=x)
def make_stack_net_v4(inp_shape, batch_size, params):
"""
Autoencoder combined with VCNN
"""
inputs = {
'conditioned_occ': tf.keras.Input(batch_size=batch_size,
shape=inp_shape),
'known_occ': tf.keras.Input(batch_size=batch_size, shape=inp_shape),
'known_free': tf.keras.Input(batch_size=batch_size, shape=inp_shape),
}
# Autoencoder
x = tfl.concatenate([inputs['known_occ'], inputs['known_free']], axis=4)
for n_filter in [64, 128, 256, 512]:
x = tfl.Conv3D(n_filter, (
2,
2,
2,
), use_bias=True, padding="same")(x)
x = tfl.Activation(tf.nn.relu)(x)
x = tfl.MaxPool3D((2, 2, 2))(x)
x = tfl.Flatten()(x)
x = tfl.Dense(params['num_latent_layers'], activation='relu')(x)
x = tfl.Dense(32768, activation='relu')(x)
x = tfl.Reshape((4, 4, 4, 512))(x)
auto_encoder_features = x
for n_filter in [256, 128, 64, 12]:
x = tfl.Conv3DTranspose(n_filter, (
2,
2,
2,
),
use_bias=True,
strides=2)(x)
x = tfl.Activation(tf.nn.relu)(x)
x = tfl.Conv3D(1, (1, 1, 1), use_bias=True)(x)
ae_output_before_activation = x
# autoencoder_output = tfl.Activation(tf.nn.sigmoid)(x)
# VCNN
filter_size = [2, 2, 2]
# n_filters = [64, 128, 256, 512]
x = inputs['conditioned_occ']
conv_args_strided = {
'use_bias': True,
'nln': tf.nn.elu,
'strides': [1, 2, 2, 2, 1]
}
def bs_strided(x, n_filters):
return nn.BackShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args_strided)(x)
def bds_strided(x, n_filters):
return nn.BackDownShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args_strided)(x)
def bdrs_strided(x, n_filters):
return nn.BackDownRightShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args_strided)(x)
conv_args = {
'use_bias': True,
'nln': tf.nn.elu,
'strides': [1, 1, 1, 1, 1]
}
def bs(x, n_filters):
return nn.BackShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args)(x)
def bds(x, n_filters):
return nn.BackDownShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args)(x)
def bdrs(x, n_filters):
return nn.BackDownRightShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args)(x)
flf = 4 # num_first_layer_filters
# Front, #Upper Front, and #Left Upper Front
f_1 = nn.BackShift()(bs(x, flf))
uf_1 = nn.BackShift()(bs(x, flf)) + \
nn.DownShift()(bds(x, flf))
luf_1 = nn.BackShift()(bs(x, flf)) + \
nn.DownShift()(bds(x, flf)) + \
nn.RightShift()(bdrs(x, flf))
for i in range(2):
f_1 = bs(f_1, flf)
uf_1 = bds(uf_1, flf) + f_1
luf_1 = bdrs(luf_1, flf) + uf_1
f_list = [f_1]
uf_list = [uf_1]
luf_list = [luf_1]
for fs in [64, 128, 256, 512]:
f_list.append(bs_strided(f_list[-1], fs))
uf_list.append(bds_strided(uf_list[-1], fs) + f_list[-1])
luf_list.append(bdrs_strided(luf_list[-1], fs) + uf_list[-1])
f = f_list.pop()
uf = uf_list.pop()
luf = tf.concat([luf_list.pop(), auto_encoder_features], axis=4)
for fs in [256, 128, 64, 4]:
f = tf.concat([
tfl.Conv3DTranspose(fs, [2, 2, 2], strides=[2, 2, 2])(f),
f_list.pop()
],
axis=4)
f = tfl.Activation(tf.nn.elu)(f)
uf = tf.concat([
tfl.Conv3DTranspose(fs, [2, 2, 2], strides=[2, 2, 2])(uf),
uf_list.pop(), f
],
axis=4)
uf = tfl.Activation(tf.nn.elu)(uf)
luf = tf.concat([
tfl.Conv3DTranspose(fs, [2, 2, 2], strides=[2, 2, 2])(luf),
luf_list.pop(), uf
],
axis=4)
luf = tfl.Activation(tf.nn.elu)(luf)
x = luf
x = nn.Conv3D(n_filters=1, filter_size=[1, 1, 1], use_bias=True)(x)
output = {"p_occ_logits": x, "aux_logits": ae_output_before_activation}
return tf.keras.Model(inputs=inputs, outputs=output)
def make_cvcnn(inp_shape, batch_size, params):
inputs = {
'conditioned_occ': tf.keras.Input(batch_size=batch_size,
shape=inp_shape),
'ae_features': tf.keras.Input(batch_size=batch_size,
shape=(4, 4, 4, 512))
}
auto_encoder_features = inputs['ae_features']
# VCNN
filter_size = [2, 2, 2]
# n_filters = [64, 128, 256, 512]
x = inputs['conditioned_occ']
conv_args_strided = {
'use_bias': True,
'nln': tf.nn.elu,
'strides': [1, 2, 2, 2, 1]
}
def bs_strided(x, n_filters):
return nn.BackShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args_strided)(x)
def bds_strided(x, n_filters):
return nn.BackDownShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args_strided)(x)
def bdrs_strided(x, n_filters):
return nn.BackDownRightShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args_strided)(x)
conv_args = {
'use_bias': True,
'nln': tf.nn.elu,
'strides': [1, 1, 1, 1, 1]
}
def bs(x, n_filters):
return nn.BackShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args)(x)
def bds(x, n_filters):
return nn.BackDownShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args)(x)
def bdrs(x, n_filters):
return nn.BackDownRightShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args)(x)
flf = 4 # num_first_layer_filters
# Front, #Upper Front, and #Left Upper Front
f_1 = nn.BackShift()(bs(x, flf))
uf_1 = nn.BackShift()(bs(x, flf)) + \
nn.DownShift()(bds(x, flf))
luf_1 = nn.BackShift()(bs(x, flf)) + \
nn.DownShift()(bds(x, flf)) + \
nn.RightShift()(bdrs(x, flf))
for i in range(2):
f_1 = bs(f_1, flf)
uf_1 = bds(uf_1, flf) + f_1
luf_1 = bdrs(luf_1, flf) + uf_1
f_list = [f_1]
uf_list = [uf_1]
luf_list = [luf_1]
for fs in [64, 128, 256, 512]:
f_list.append(bs_strided(f_list[-1], fs))
uf_list.append(bds_strided(uf_list[-1], fs) + f_list[-1])
luf_list.append(bdrs_strided(luf_list[-1], fs) + uf_list[-1])
f = f_list.pop()
uf = uf_list.pop()
luf = tf.concat([luf_list.pop(), auto_encoder_features], axis=4)
for fs in [256, 128, 64, 4]:
f = tf.concat([
tfl.Conv3DTranspose(fs, [2, 2, 2], strides=[2, 2, 2])(f),
f_list.pop()
],
axis=4)
f = tfl.Activation(tf.nn.elu)(f)
uf = tf.concat([
tfl.Conv3DTranspose(fs, [2, 2, 2], strides=[2, 2, 2])(uf),
uf_list.pop(), f
],
axis=4)
uf = tfl.Activation(tf.nn.elu)(uf)
luf = tf.concat([
tfl.Conv3DTranspose(fs, [2, 2, 2], strides=[2, 2, 2])(luf),
luf_list.pop(), uf
],
axis=4)
luf = tfl.Activation(tf.nn.elu)(luf)
x = luf
x = nn.Conv3D(n_filters=1, filter_size=[1, 1, 1], use_bias=True)(x)
output = {"p_occ_logits": x}
return tf.keras.Model(inputs=inputs, outputs=output)
def make_stack_net_v2(inp_shape, batch_size, params):
"""Stacked VCNN with hourglass shape and unet connections"""
filter_size = [2, 2, 2]
n_filters = [64, 128, 256, 512]
inputs = {
'conditioned_occ': tf.keras.Input(batch_size=batch_size,
shape=inp_shape)
}
x = inputs['conditioned_occ']
# inputs = tf.keras.Input(batch_size=batch_size, shape=inp_shape)
# x = inputs
conv_args_strided = {
'use_bias': True,
# 'filter_size': filter_size,
'nln': tf.nn.elu,
'strides': [1, 2, 2, 2, 1]
}
def bs_strided(x, n_filters):
return nn.BackShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args_strided)(x)
def bds_strided(x, n_filters):
return nn.BackDownShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args_strided)(x)
def bdrs_strided(x, n_filters):
return nn.BackDownRightShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args_strided)(x)
conv_args = {
'use_bias': True,
# 'filter_size': filter_size,
'nln': tf.nn.elu,
'strides': [1, 1, 1, 1, 1]
}
def bs(x, n_filters):
return nn.BackShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args)(x)
def bds(x, n_filters):
return nn.BackDownShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args)(x)
def bdrs(x, n_filters):
return nn.BackDownRightShiftConv3D(n_filters,
filter_size=filter_size,
**conv_args)(x)
flf = 4 # first_layer_filters
# Front, #Upper Front, and #Left Upper Front
f_1 = nn.BackShift()(bs(x, flf))
uf_1 = nn.BackShift()(bs(x, flf)) + \
nn.DownShift()(bds(x, flf))
luf_1 = nn.BackShift()(bs(x, flf)) + \
nn.DownShift()(bds(x, flf)) + \
nn.RightShift()(bdrs(x, flf))
for i in range(2):
f_1 = bs(f_1, flf)
uf_1 = bds(uf_1, flf) + f_1
luf_1 = bdrs(luf_1, flf) + uf_1
f_list = [f_1]
uf_list = [uf_1]
luf_list = [luf_1]
for fs in n_filters:
f_list.append(bs_strided(f_list[-1], fs))
uf_list.append(bds_strided(uf_list[-1], fs) + f_list[-1])
luf_list.append(bdrs_strided(luf_list[-1], fs) + uf_list[-1])
f = f_list.pop()
uf = uf_list.pop()
luf = luf_list.pop()
for fs in reversed(n_filters):
f = tf.concat([
tfl.Conv3DTranspose(fs, [2, 2, 2], strides=[2, 2, 2])(f),
f_list.pop()
],
axis=4)
uf = tf.concat([
tfl.Conv3DTranspose(fs, [2, 2, 2], strides=[2, 2, 2])(uf),
uf_list.pop()
],
axis=4) + f
luf = tf.concat([
tfl.Conv3DTranspose(fs, [2, 2, 2], strides=[2, 2, 2])(luf),
luf_list.pop()
],
axis=4) + uf
x = nn.Conv3D(n_filters=1, filter_size=[1, 1, 1], use_bias=True)(luf)
if params['final_activation'] == 'sigmoid':
x = tf.nn.sigmoid(x)
elif params['final_activation'] == 'elu':
x = tf.nn.elu(x)
elif params['final_activation'] == None or params[
'final_activation'] == 'None':
pass
else:
raise ("Unknown param valies for [final activation]: {}".format(
params['final_activation']))
output = x
return tf.keras.Model(inputs=inputs, outputs=output)