def point_cloud_generator(z,
pc_dims,
layer_sizes=[64, 128, 512, 1024],
non_linearity=tf.nn.relu,
b_norm=False,
b_norm_last=False,
dropout_prob=None):
''' used in nips submission.
'''
n_points, dummy = pc_dims
if (dummy != 3):
raise ValueError()
out_signal = decoder_with_fc_only(z,
layer_sizes=layer_sizes,
non_linearity=non_linearity,
b_norm=b_norm)
out_signal = non_linearity(out_signal)
if dropout_prob is not None:
out_signal = dropout(out_signal, dropout_prob)
if b_norm_last:
out_signal = batch_normalization(out_signal)
out_signal = fully_connected(out_signal,
np.prod([n_points, 3]),
activation='linear',
weights_init='xavier')
out_signal = tf.reshape(out_signal, [-1, n_points, 3])
return out_signal
def mlp_discriminator(in_signal,
non_linearity=tf.nn.relu,
reuse=False,
scope=None,
b_norm=True,
dropout_prob=None):
''' used in nips submission.
'''
encoder_args = {
'n_filters': [64, 128, 256, 256, 512],
'filter_sizes': [1, 1, 1, 1, 1],
'strides': [1, 1, 1, 1, 1]
}
encoder_args['reuse'] = reuse
encoder_args['scope'] = scope
encoder_args['non_linearity'] = non_linearity
encoder_args['dropout_prob'] = dropout_prob
encoder_args['b_norm'] = b_norm
layer = encoder_with_convs_and_symmetry(in_signal, **encoder_args)
name = 'decoding_logits'
scope_e = expand_scope_by_name(scope, name)
d_logit = decoder_with_fc_only(layer,
layer_sizes=[128, 64, 1],
b_norm=b_norm,
reuse=reuse,
scope=scope_e)
d_prob = tf.nn.sigmoid(d_logit)
return d_prob, d_logit
def scale_points_generator_fc(scale_feature,
scale_dim,
layer_sizes=[128, 128],
non_linearity=tf.nn.relu,
b_norm=False,
b_norm_last=False,
dropout_prob=None):
''' used in nips submission.
'''
n_points, dummy = scale_dim
# if (dummy != 3):
# raise ValueError()
out_signal = decoder_with_fc_only(scale_feature,
layer_sizes=layer_sizes,
non_linearity=non_linearity,
b_norm=b_norm)
out_signal = non_linearity(out_signal)
if dropout_prob is not None:
out_signal = dropout(out_signal, dropout_prob)
if b_norm_last:
out_signal = batch_normalization(out_signal)
out_signal = fully_connected(out_signal,
np.prod([n_points, dummy]),
activation='linear',
weights_init='xavier')
out_signal = tf.reshape(out_signal, [-1, n_points, dummy])
return out_signal
def latent_code_generator_2222(z,
out_dim,
layer_sizes=[256, 256, 256, 256],
b_norm=False,
non_linearity=tf.nn.relu,
reuse=False,
scope=None):
layer_sizes = layer_sizes + out_dim
out_signal = decoder_with_fc_only(z,
layer_sizes=layer_sizes,
b_norm=bnorm_default,
dropout_prob=dropout_default,
reuse=reuse,
scope=scope)
return out_signal
def latent_code_discriminator_222(in_signal,
layer_sizes=[256, 256, 256],
b_norm=False,
non_linearity=tf.nn.relu,
reuse=False,
scope=None):
layer_sizes = layer_sizes + [1]
d_logit = decoder_with_fc_only(in_signal,
layer_sizes=layer_sizes,
non_linearity=non_linearity,
b_norm=bnorm_default,
dropout_prob=dropout_default,
reuse=reuse,
scope=scope)
d_prob = tf.nn.sigmoid(d_logit)
return d_prob, d_logit
def latent_code_discriminator_two_layers(in_signal,
layer_sizes=[512, 512],
b_norm=False,
non_linearity=tf.nn.relu,
reuse=False,
scope=None):
''' Used in ICML submission.
'''
layer_sizes = layer_sizes + [1]
d_logit = decoder_with_fc_only(in_signal,
layer_sizes=layer_sizes,
non_linearity=non_linearity,
b_norm=b_norm,
reuse=reuse,
scope=scope)
d_prob = tf.nn.sigmoid(d_logit)
return d_prob, d_logit