def build_model(data_tensor, reuse, training, output_channels):
"""Create the hgru from Learning long-range..."""
conv_kernel = [
[3, 3, 1],
[3, 3, 3],
[3, 3, 3],
]
up_kernel = [2, 2, 1]
# filters = [28, 36, 48, 64, 80]
filters = [24, 32, 48, 64, 80]
print(data_tensor.dtype)
with tf.variable_scope('cnn', reuse=reuse):
# Unclear if we should include l0 in the down/upsample cascade
with tf.variable_scope('in_embedding', reuse=reuse):
in_emb = conv.conv3d_layer(bottom=data_tensor,
name='l0',
stride=[1, 1, 1],
padding='SAME',
num_filters=filters[0],
kernel_size=[5, 5, 1],
trainable=training,
use_bias=True)
in_emb = tf.nn.elu(in_emb)
print(in_emb.dtype)
# Downsample
l1 = conv.down_block(layer_name='l1',
bottom=in_emb,
kernel_size=conv_kernel,
num_filters=filters[1],
training=training,
reuse=reuse)
print(l1.dtype)
l2 = conv.down_block(layer_name='l2',
bottom=l1,
kernel_size=conv_kernel,
num_filters=filters[2],
training=training,
reuse=reuse)
print(l2.dtype)
l3 = conv.down_block(layer_name='l3',
bottom=l2,
kernel_size=conv_kernel,
num_filters=filters[3],
training=training,
reuse=reuse)
print(l3.dtype)
l4 = conv.down_block(layer_name='l4',
bottom=l3,
kernel_size=conv_kernel,
num_filters=filters[4],
training=training,
reuse=reuse)
print(l4.dtype)
# Upsample
ul3 = conv.up_block(layer_name='ul3',
bottom=l4,
skip_activity=l3,
kernel_size=up_kernel,
num_filters=filters[3],
training=training,
reuse=reuse)
ul3 = conv.down_block(layer_name='ul3_d',
bottom=ul3,
kernel_size=conv_kernel,
num_filters=filters[3],
training=training,
reuse=reuse,
include_pool=False)
ul2 = conv.up_block(layer_name='ul2',
bottom=ul3,
skip_activity=l2,
kernel_size=up_kernel,
num_filters=filters[2],
training=training,
reuse=reuse)
ul2 = conv.down_block(layer_name='ul2_d',
bottom=ul2,
kernel_size=conv_kernel,
num_filters=filters[2],
training=training,
reuse=reuse,
include_pool=False)
ul1 = conv.up_block(layer_name='ul1',
bottom=ul2,
skip_activity=l1,
kernel_size=up_kernel,
num_filters=filters[1],
training=training,
reuse=reuse)
ul1 = conv.down_block(layer_name='ul1_d',
bottom=ul1,
kernel_size=conv_kernel,
num_filters=filters[1],
training=training,
reuse=reuse,
include_pool=False)
ul0 = conv.up_block(layer_name='ul0',
bottom=ul1,
skip_activity=in_emb,
kernel_size=up_kernel,
num_filters=filters[0],
training=training,
reuse=reuse)
with tf.variable_scope('out_embedding', reuse=reuse):
out_emb = conv.conv3d_layer(bottom=ul0,
name='out_emb',
stride=[1, 1, 1],
padding='SAME',
num_filters=output_channels,
kernel_size=[5, 5, 1],
trainable=training,
use_bias=True)
return out_emb
def build_model(data_tensor, reuse, training, output_shape):
"""Create the hgru from Learning long-range..."""
if isinstance(output_shape, list):
output_shape = output_shape[0]
elif isinstance(output_shape, dict):
nhot_shape = output_shape['aux']
output_shape = output_shape['output']
use_aux = True
data_format = 'channels_last'
conv_kernel = [
[3, 3],
[3, 3],
[3, 3],
]
up_kernel = [2, 2]
filters = [28, 36, 48, 64, 80]
with tf.variable_scope('cnn', reuse=reuse):
# Unclear if we should include l0 in the down/upsample cascade
with tf.variable_scope('in_embedding', reuse=reuse):
in_emb = tf.layers.conv2d(inputs=data_tensor,
filters=filters[0],
kernel_size=5,
name='l0',
strides=(1, 1),
padding='same',
activation=tf.nn.elu,
data_format=data_format,
trainable=training,
use_bias=True)
# Downsample
l1 = conv.down_block(layer_name='l1',
bottom=in_emb,
kernel_size=conv_kernel,
num_filters=filters[1],
training=training,
reuse=reuse)
l2 = conv.down_block(layer_name='l2',
bottom=l1,
kernel_size=conv_kernel,
num_filters=filters[2],
training=training,
reuse=reuse)
l3 = conv.down_block(layer_name='l3',
bottom=l2,
kernel_size=conv_kernel,
num_filters=filters[3],
training=training,
reuse=reuse)
l4 = conv.down_block(layer_name='l4',
bottom=l3,
kernel_size=conv_kernel,
num_filters=filters[4],
training=training,
reuse=reuse)
# Upsample
ul3 = conv.up_block(layer_name='ul3',
bottom=l4,
skip_activity=l3,
kernel_size=up_kernel,
num_filters=filters[3],
training=training,
reuse=reuse)
ul3 = conv.down_block(layer_name='ul3_d',
bottom=ul3,
kernel_size=conv_kernel,
num_filters=filters[3],
training=training,
reuse=reuse,
include_pool=False)
ul2 = conv.up_block(layer_name='ul2',
bottom=ul3,
skip_activity=l2,
kernel_size=up_kernel,
num_filters=filters[2],
training=training,
reuse=reuse)
ul2 = conv.down_block(layer_name='ul2_d',
bottom=ul2,
kernel_size=conv_kernel,
num_filters=filters[2],
training=training,
reuse=reuse,
include_pool=False)
ul1 = conv.up_block(layer_name='ul1',
bottom=ul2,
skip_activity=l1,
kernel_size=up_kernel,
num_filters=filters[1],
training=training,
reuse=reuse)
ul1 = conv.down_block(layer_name='ul1_d',
bottom=ul1,
kernel_size=conv_kernel,
num_filters=filters[1],
training=training,
reuse=reuse,
include_pool=False)
ul0 = conv.up_block(layer_name='ul0',
bottom=ul1,
skip_activity=in_emb,
kernel_size=up_kernel,
num_filters=filters[0],
training=training,
reuse=reuse)
with tf.variable_scope('exclusion', reuse=reuse):
activity = conv.conv_layer(bottom=ul0,
name='pre_readout_conv',
num_filters=output_shape,
kernel_size=1,
trainable=training,
use_bias=True)
extra_activities = {}
return activity, extra_activities
def build_model(data_tensor, reuse, training, output_shape):
"""Create the hgru from Learning long-range..."""
if isinstance(output_shape, list):
output_shape = output_shape[0]
data_format = 'channels_last'
conv_kernel = [
[3, 3],
[3, 3],
[3, 3],
]
up_kernel = [2, 2]
filters = [28, 36, 48, 64, 80]
with tf.variable_scope('cnn', reuse=reuse):
# Unclear if we should include l0 in the down/upsample cascade
with tf.variable_scope('in_embedding', reuse=reuse):
in_emb = tf.layers.conv2d(inputs=data_tensor,
filters=filters[0],
kernel_size=5,
name='l0',
strides=(1, 1),
padding='same',
activation=tf.nn.elu,
data_format=data_format,
trainable=training,
use_bias=True)
# Downsample
l1 = conv.down_block(layer_name='l1',
bottom=in_emb,
kernel_size=conv_kernel,
num_filters=filters[1],
training=training,
reuse=reuse)
l2 = conv.down_block(layer_name='l2',
bottom=l1,
kernel_size=conv_kernel,
num_filters=filters[2],
training=training,
reuse=reuse)
l3 = conv.down_block(layer_name='l3',
bottom=l2,
kernel_size=conv_kernel,
num_filters=filters[3],
training=training,
reuse=reuse)
l4 = conv.down_block(layer_name='l4',
bottom=l3,
kernel_size=conv_kernel,
num_filters=filters[4],
training=training,
reuse=reuse)
# Upsample
ul3 = conv.up_block(layer_name='ul3',
bottom=l4,
skip_activity=l3,
kernel_size=up_kernel,
num_filters=filters[3],
training=training,
reuse=reuse)
ul3 = conv.down_block(layer_name='ul3_d',
bottom=ul3,
kernel_size=conv_kernel,
num_filters=filters[3],
training=training,
reuse=reuse,
include_pool=False)
ul2 = conv.up_block(layer_name='ul2',
bottom=ul3,
skip_activity=l2,
kernel_size=up_kernel,
num_filters=filters[2],
training=training,
reuse=reuse)
ul2 = conv.down_block(layer_name='ul2_d',
bottom=ul2,
kernel_size=conv_kernel,
num_filters=filters[2],
training=training,
reuse=reuse,
include_pool=False)
ul1 = conv.up_block(layer_name='ul1',
bottom=ul2,
skip_activity=l1,
kernel_size=up_kernel,
num_filters=filters[1],
training=training,
reuse=reuse)
ul1 = conv.down_block(layer_name='ul1_d',
bottom=ul1,
kernel_size=conv_kernel,
num_filters=filters[1],
training=training,
reuse=reuse,
include_pool=False)
ul0 = conv.up_block(layer_name='ul0',
bottom=ul1,
skip_activity=in_emb,
kernel_size=up_kernel,
num_filters=filters[0],
training=training,
reuse=reuse)
with tf.variable_scope('readout_1', reuse=reuse):
activity = conv.conv_layer(bottom=ul0,
name='pre_readout_conv',
num_filters=2,
kernel_size=1,
trainable=training,
use_bias=False)
pool_aux = {'pool_type': 'max'}
activity = pooling.global_pool(bottom=activity,
name='pre_readout_pool',
aux=pool_aux)
activity = normalization.batch(bottom=activity,
renorm=True,
name='readout_1_bn',
training=training)
with tf.variable_scope('readout_2', reuse=reuse):
activity = tf.layers.flatten(activity, name='flat_readout')
activity = tf.layers.dense(inputs=activity, units=output_shape)
extra_activities = {'l4': l4}
return activity, extra_activities
