def reverse_pixel_cnn_28_binary(x,
masks,
context=None,
nr_logistic_mix=10,
nr_resnet=1,
nr_filters=100,
dropout_p=0.0,
nonlinearity=None,
bn=True,
kernel_initializer=None,
kernel_regularizer=None,
is_training=False,
counters={}):
name = get_name("reverse_pixel_cnn_28_binary", counters)
x = x * broadcast_masks_tf(masks, num_channels=3)
x = tf.concat([x, broadcast_masks_tf(masks, num_channels=1)], axis=-1)
print("construct", name, "...")
print(" * nr_resnet: ", nr_resnet)
print(" * nr_filters: ", nr_filters)
print(" * nr_logistic_mix: ", nr_logistic_mix)
assert not bn, "auto-reggressive model should not use batch normalization"
with tf.variable_scope(name):
with arg_scope([gated_resnet],
gh=None,
sh=context,
nonlinearity=nonlinearity,
dropout_p=dropout_p):
with arg_scope([
gated_resnet, up_shifted_conv2d, up_left_shifted_conv2d,
up_shifted_deconv2d, up_left_shifted_deconv2d
],
bn=bn,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
is_training=is_training,
counters=counters):
xs = int_shape(x)
x_pad = tf.concat(
[x, tf.ones(xs[:-1] + [1])], 3
) # add channel of ones to distinguish image from padding later on
u_list = [
up_shift(
up_shifted_conv2d(x_pad,
num_filters=nr_filters,
filter_size=[2, 3]))
] # stream for pixels above
ul_list = [up_shift(up_shifted_conv2d(x_pad, num_filters=nr_filters, filter_size=[1,3])) + \
left_shift(up_left_shifted_conv2d(x_pad, num_filters=nr_filters, filter_size=[2,1]))] # stream for up and to the left
for rep in range(nr_resnet):
u_list.append(
gated_resnet(u_list[-1], conv=up_shifted_conv2d))
ul_list.append(
gated_resnet(ul_list[-1],
u_list[-1],
conv=up_left_shifted_conv2d))
x_out = nin(tf.nn.elu(ul_list[-1]), nr_filters)
return x_out
def context_encoder(contexts,
masks,
is_training,
nr_resnet=5,
nr_filters=100,
nonlinearity=None,
bn=False,
kernel_initializer=None,
kernel_regularizer=None,
counters={}):
name = get_name("context_encoder", counters)
print("construct", name, "...")
x = contexts * broadcast_masks_tf(masks, num_channels=3)
x = tf.concat([x, broadcast_masks_tf(masks, num_channels=1)], axis=-1)
if bn:
print("*** Attention *** using bn in the context encoder\n")
with tf.variable_scope(name):
with arg_scope([gated_resnet],
nonlinearity=nonlinearity,
counters=counters):
with arg_scope(
[gated_resnet, up_shifted_conv2d, up_left_shifted_conv2d],
bn=bn,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer,
is_training=is_training):
xs = int_shape(x)
x_pad = tf.concat(
[x, tf.ones(xs[:-1] + [1])], 3
) # add channel of ones to distinguish image from padding later on
u_list = [
up_shift(
up_shifted_conv2d(x_pad,
num_filters=nr_filters,
filter_size=[2, 3]))
] # stream for pixels above
ul_list = [up_shift(up_shifted_conv2d(x_pad, num_filters=nr_filters, filter_size=[1,3])) + \
left_shift(up_left_shifted_conv2d(x_pad, num_filters=nr_filters, filter_size=[2,1]))] # stream for up and to the left
receptive_field = (2, 3)
for rep in range(nr_resnet):
u_list.append(
gated_resnet(u_list[-1], conv=up_shifted_conv2d))
ul_list.append(
gated_resnet(ul_list[-1],
u_list[-1],
conv=up_left_shifted_conv2d))
receptive_field = (receptive_field[0] + 1,
receptive_field[1] + 2)
x_out = nin(tf.nn.elu(ul_list[-1]), nr_filters)
print(" * receptive_field", receptive_field)
return x_out