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encoders_decoders.py
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import tensorflow as tf
import numpy as np
import warnings
from tflearn.layers.core import fully_connected, dropout
from tflearn.layers.conv import conv_1d, avg_pool_1d
from tflearn.layers.normalization import batch_normalization
from tflearn.layers.core import fully_connected, dropout
from latent_3d_points.tf_utils import expand_scope_by_name, replicate_parameter_for_all_layers
import os
import sys
import collections
BASE_DIR = os.path.dirname(__file__)
sys.path.append(BASE_DIR)
sys.path.append(BASE_DIR + "/pointnet_plusplus/utils")
sys.path.append(BASE_DIR + "/pointnet_plusplus/tf_ops")
sys.path.append(BASE_DIR + "/pointnet_plusplus/tf_ops/3d_interpolation")
sys.path.append(BASE_DIR + "/pointnet_plusplus/tf_ops/grouping")
sys.path.append(BASE_DIR + "/pointnet_plusplus/tf_ops/sampling")
from pointnet_util import pointnet_sa_module, pointnet_fp_module
def ocEncoder_PointNET2_multilevel256_3mlp(input_points, verbose=True, is_training=None, bn_decay=None):
l0_xyz = input_points
l0_points = None
# Set Abstraction layers
l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=512, radius=0.1, nsample=64,
mlp=[64, 64, 128], mlp2=None, group_all=False,
is_training=is_training, bn_decay=bn_decay, scope='layer1', bn=False)
l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=256, radius=0.2, nsample=64,
mlp=[128, 128, 256], mlp2=None, group_all=False,
is_training=is_training, bn_decay=bn_decay, scope='layer2', bn=False)
l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=128, radius=0.3, nsample=64,
mlp=[256, 256, 256], mlp2=None, group_all=False,
is_training=is_training, bn_decay=bn_decay, scope='layer3', bn=False)
l4_xyz, l4_points, l4_indices = pointnet_sa_module(l3_xyz, l3_points, npoint=32, radius=0.4, nsample=64,
mlp=[256, 256, 256], mlp2=None, group_all=False,
is_training=is_training, bn_decay=bn_decay, scope='layer4', bn=False)
output_1 = encoder_with_convs_and_symmetry(l1_points, n_filters=[128, 128, 64])
output_2 = encoder_with_convs_and_symmetry(l2_points, n_filters=[256, 256, 64])
output_3 = encoder_with_convs_and_symmetry(l3_points, n_filters=[256, 256, 64])
output_4 = encoder_with_convs_and_symmetry(l4_points, n_filters=[256, 256, 64])
output_1234 = tf.concat( [output_1, output_2, output_3, output_4] , axis=1 )
print('output_1.shape = %s', output_1.shape)
print('output_2.shape = %s', output_2.shape)
print('output_3.shape = %s', output_3.shape)
print('output_4.shape = %s', output_4.shape)
return output_1234
def encoder_with_convs_and_symmetry(in_signal, n_filters=[64, 128, 256, 1024], filter_sizes=[1], strides=[1],
b_norm=True, non_linearity=tf.nn.relu, regularizer=None, weight_decay=0.001,
symmetry=tf.reduce_max, dropout_prob=None, pool=avg_pool_1d, pool_sizes=None,
scope=None,
reuse=False, padding='same', verbose=False, closing=None, conv_op=conv_1d):
if verbose:
print('encoder_with_convs_and_symmetry')
n_layers = len(n_filters)
filter_sizes = replicate_parameter_for_all_layers(filter_sizes, n_layers)
strides = replicate_parameter_for_all_layers(strides, n_layers)
dropout_prob = replicate_parameter_for_all_layers(dropout_prob, n_layers)
if n_layers < 1:
raise ValueError('More than 0 layers are expected.')
for i in range(n_layers):
if i == 0:
layer = in_signal
name = 'encoder_conv_layer_' + str(i)
scope_i = expand_scope_by_name(scope, name)
layer = conv_op(layer, nb_filter=n_filters[i], filter_size=filter_sizes[i], strides=strides[i],
regularizer=regularizer,
weight_decay=weight_decay, name=name, reuse=reuse, scope=scope_i, padding=padding)
if verbose:
print( name, 'conv params = ', np.prod(layer.W.get_shape().as_list()) + np.prod(layer.b.get_shape().as_list()) )
if b_norm:
name += '_bnorm'
scope_i = expand_scope_by_name(scope, name)
layer = batch_normalization(layer, name=name, reuse=reuse, scope=scope_i)
if verbose:
print( 'bnorm params = ', np.prod(layer.beta.get_shape().as_list()) + np.prod( layer.gamma.get_shape().as_list()) )
if non_linearity is not None:
layer = non_linearity(layer)
if pool is not None and pool_sizes is not None:
if pool_sizes[i] is not None:
layer = pool(layer, kernel_size=pool_sizes[i])
if dropout_prob is not None and dropout_prob[i] > 0:
layer = dropout(layer, 1.0 - dropout_prob[i])
if verbose:
print( layer )
print( 'output size:', np.prod(layer.get_shape().as_list()[1:]), '\n' )
if symmetry is not None:
layer = symmetry(layer, axis=1)
if verbose:
print
layer
if closing is not None:
layer = closing(layer)
print( layer )
return layer
def decoder_with_fc_only(latent_signal, layer_sizes=[], b_norm=True, non_linearity=tf.nn.relu,
regularizer=None, weight_decay=0.001, reuse=False, scope=None, dropout_prob=None,
b_norm_finish=False, verbose=False, nameprefix='decoder_fc_'):
'''A decoding network which maps points from the latent space back onto the data space.
'''
if verbose:
print( 'Building Decoder' )
n_layers = len(layer_sizes)
dropout_prob = replicate_parameter_for_all_layers(dropout_prob, n_layers)
if n_layers < 1:
raise ValueError('For an FC decoder with single a layer use simpler code.')
layer = latent_signal
for i in range(0, n_layers - 1):
name = nameprefix + str(i)
scope_i = expand_scope_by_name(scope, name)
print('***************')
print(scope)
print(name)
print(scope_i)
print('***************')
layer = fully_connected(layer, layer_sizes[i], activation='linear', weights_init='xavier', name=name,
regularizer=regularizer, weight_decay=weight_decay, reuse=reuse, scope=scope_i)
if verbose:
print( name, 'FC params = ', np.prod(layer.W.get_shape().as_list()) + np.prod(layer.b.get_shape().as_list()) )
if b_norm:
name += '_bnorm'
scope_i = expand_scope_by_name(scope, name)
layer = batch_normalization(layer, name=name, reuse=reuse, scope=scope_i)
if verbose:
print('bnorm params = ', np.prod(layer.beta.get_shape().as_list()) + np.prod( layer.gamma.get_shape().as_list()) )
if non_linearity is not None:
layer = non_linearity(layer)
if dropout_prob is not None and dropout_prob[i] > 0:
layer = dropout(layer, 1.0 - dropout_prob[i])
if verbose:
print( layer )
print( 'output size:', np.prod(layer.get_shape().as_list()[1:]), '\n' )
# Last decoding layer never has a non-linearity.
name = nameprefix + str(n_layers - 1)
scope_i = expand_scope_by_name(scope, name)
print('***************')
print(scope)
print(name)
print(scope_i)
print('***************')
layer = fully_connected(layer, layer_sizes[n_layers - 1], activation='linear', weights_init='xavier', name=name,
regularizer=regularizer, weight_decay=weight_decay, reuse=reuse, scope=scope_i)
if verbose:
print( name, 'FC params = ', np.prod(layer.W.get_shape().as_list()) + np.prod(layer.b.get_shape().as_list()) )
if b_norm_finish:
name += '_bnorm'
scope_i = expand_scope_by_name(scope, name)
layer = batch_normalization(layer, name=name, reuse=reuse, scope=scope_i)
if verbose:
print('bnorm params = ', np.prod(layer.beta.get_shape().as_list()) + np.prod(layer.gamma.get_shape().as_list()) )
if verbose:
print( layer )
print( 'output size:', np.prod(layer.get_shape().as_list()[1:]), '\n' )
return layer