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195 | """ Wrapper functions for TensorFlow layers.
Author: Charles R. Qi
Date: November 2016
downladed from: https://github.com/charlesq34/pointnet
"""
import numpy as np
import tensorflow as tf
def _variable_on_cpu(name, shape, initializer, use_fp16=False):
"""Helper to create a Variable stored on CPU memory.
Args:
name: name of the variable
shape: list of ints
initializer: initializer for Variable
Returns:
Variable Tensor
"""
with tf.device('/cpu:0'):
dtype = tf.float16 if use_fp16 else tf.float32
var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype)
return var
def _variable_with_weight_decay(name, shape, stddev, wd, use_xavier=True):
"""Helper to create an initialized Variable with weight decay.
Note that the Variable is initialized with a truncated normal distribution.
A weight decay is added only if one is specified.
Args:
name: name of the variable
shape: list of ints
stddev: standard deviation of a truncated Gaussian
wd: add L2Loss weight decay multiplied by this float. If None, weight
decay is not added for this Variable.
use_xavier: bool, whether to use xavier initializer
Returns:
Variable Tensor
"""
if use_xavier:
initializer = tf.contrib.layers.xavier_initializer()
else:
initializer = tf.truncated_normal_initializer(stddev=stddev)
var = _variable_on_cpu(name, shape, initializer)
if wd is not None:
weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
return var
def fully_connected(inputs,
num_outputs,
scope,
use_xavier=True,
stddev=1e-3,
weight_decay=0.0,
activation_fn=tf.nn.relu,
bn=False,
bn_decay=None,
is_training=None):
""" Fully connected layer with non-linear operation.
Args:
inputs: 2-D tensor BxN
num_outputs: int
Returns:
Variable tensor of size B x num_outputs.
"""
with tf.variable_scope(scope) as sc:
num_input_units = inputs.get_shape()[-1].value
weights = _variable_with_weight_decay('weights',
shape=[num_input_units, num_outputs],
use_xavier=use_xavier,
stddev=stddev,
wd=weight_decay)
outputs = tf.matmul(inputs, weights)
biases = _variable_on_cpu('biases', [num_outputs],
tf.constant_initializer(0.0))
outputs = tf.nn.bias_add(outputs, biases)
if bn:
outputs = batch_norm_for_fc(outputs, is_training, bn_decay, 'bn')
if activation_fn is not None:
outputs = activation_fn(outputs)
return outputs
def batch_norm_template(inputs, is_training, scope, moments_dims, bn_decay):
""" Batch normalization on convolutional maps and beyond...
Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
Args:
inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC
is_training: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
moments_dims: a list of ints, indicating dimensions for moments calculation
bn_decay: float or float tensor variable, controling moving average weight
Return:
normed: batch-normalized maps
"""
with tf.variable_scope(scope) as sc:
num_channels = inputs.get_shape()[-1].value
beta = tf.Variable(tf.constant(0.0, shape=[num_channels]),
name='beta', trainable=True)
gamma = tf.Variable(tf.constant(1.0, shape=[num_channels]),
name='gamma', trainable=True)
batch_mean, batch_var = tf.nn.moments(inputs, moments_dims, name='moments')
decay = bn_decay if bn_decay is not None else 0.9
ema = tf.train.ExponentialMovingAverage(decay=decay)
# Operator that maintains moving averages of variables.
ema_apply_op = tf.cond(is_training,
lambda: ema.apply([batch_mean, batch_var]),
lambda: tf.no_op())
# Update moving average and return current batch's avg and var.
def mean_var_with_update():
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
# ema.average returns the Variable holding the average of var.
mean, var = tf.cond(is_training,
mean_var_with_update,
lambda: (ema.average(batch_mean), ema.average(batch_var)))
normed = tf.nn.batch_normalization(inputs, mean, var, beta, gamma, 1e-3)
return normed
def batch_norm_for_fc(inputs, is_training, bn_decay, scope):
""" Batch normalization on FC data.
Args:
inputs: Tensor, 2D BxC input
is_training: boolean tf.Varialbe, true indicates training phase
bn_decay: float or float tensor variable, controling moving average weight
scope: string, variable scope
Return:
normed: batch-normalized maps
"""
return batch_norm_template(inputs, is_training, scope, [0,], bn_decay)
def dropout(inputs,
is_training,
scope,
keep_prob=0.5,
noise_shape=None):
""" Dropout layer.
Args:
inputs: tensor
is_training: boolean tf.Variable
scope: string
keep_prob: float in [0,1]
noise_shape: list of ints
Returns:
tensor variable
"""
with tf.variable_scope(scope) as sc:
outputs = tf.cond(is_training,
lambda: tf.nn.dropout(inputs, keep_prob, noise_shape),
lambda: inputs)
return outputs
def max_pool2d(inputs,
kernel_size,
scope,
stride=[2, 2],
padding='VALID'):
""" 2D max pooling.
Args:
inputs: 4-D tensor BxHxWxC
kernel_size: a list of 2 ints
stride: a list of 2 ints
Returns:
Variable tensor
"""
with tf.variable_scope(scope) as sc:
kernel_h, kernel_w = kernel_size
stride_h, stride_w = stride
outputs = tf.nn.max_pool(inputs,
ksize=[1, kernel_h, kernel_w, 1],
strides=[1, stride_h, stride_w, 1],
padding=padding,
name=sc.name)
return outputs
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