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# Copyright 2017 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Training decoder as used in PTN (NIPS16)."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
slim = tf.contrib.slim
@tf.contrib.framework.add_arg_scope
def conv3d_transpose(inputs,
num_outputs,
kernel_size,
stride=1,
padding='SAME',
activation_fn=tf.nn.relu,
weights_initializer=tf.contrib.layers.xavier_initializer(),
biases_initializer=tf.zeros_initializer(),
reuse=None,
trainable=True,
scope=None):
"""Wrapper for conv3d_transpose layer.
This function wraps the tf.conv3d_transpose with basic non-linearity.
Tt creates a variable called `weights`, representing the kernel,
that is convoled with the input. A second varibale called `biases'
is added to the result of operation.
"""
with tf.variable_scope(
scope, 'Conv3d_transpose', [inputs], reuse=reuse):
dtype = inputs.dtype.base_dtype
kernel_d, kernel_h, kernel_w = kernel_size[0:3]
num_filters_in = inputs.get_shape()[4]
weights_shape = [kernel_d, kernel_h, kernel_w, num_outputs, num_filters_in]
weights = tf.get_variable('weights',
shape=weights_shape,
dtype=dtype,
initializer=weights_initializer,
trainable=trainable)
tf.contrib.framework.add_model_variable(weights)
input_shape = inputs.get_shape().as_list()
batch_size = input_shape[0]
depth = input_shape[1]
height = input_shape[2]
width = input_shape[3]
def get_deconv_dim(dim_size, stride_size):
# Only support padding='SAME'.
if isinstance(dim_size, tf.Tensor):
dim_size = tf.multiply(dim_size, stride_size)
elif dim_size is not None:
dim_size *= stride_size
return dim_size
out_depth = get_deconv_dim(depth, stride)
out_height = get_deconv_dim(height, stride)
out_width = get_deconv_dim(width, stride)
out_shape = [batch_size, out_depth, out_height, out_width, num_outputs]
outputs = tf.nn.conv3d_transpose(inputs, weights, out_shape,
[1, stride, stride, stride, 1],
padding=padding)
outputs.set_shape(out_shape)
if biases_initializer is not None:
biases = tf.get_variable('biases',
shape=[num_outputs,],
dtype=dtype,
initializer=biases_initializer,
trainable=trainable)
tf.contrib.framework.add_model_variable(biases)
outputs = tf.nn.bias_add(outputs, biases)
if activation_fn:
outputs = activation_fn(outputs)
return outputs
def model(identities, params, is_training):
"""Model transforming embedding to voxels."""
del is_training # Unused
f_dim = params.f_dim
# Please refer to the original implementation: github.com/xcyan/nips16_PTN
# In TF replication, we use a slightly different architecture.
with slim.arg_scope(
[slim.fully_connected, conv3d_transpose],
weights_initializer=tf.truncated_normal_initializer(stddev=0.02, seed=1)):
h0 = slim.fully_connected(
identities, 4 * 4 * 4 * f_dim * 8, activation_fn=tf.nn.relu)
h1 = tf.reshape(h0, [-1, 4, 4, 4, f_dim * 8])
h1 = conv3d_transpose(
h1, f_dim * 4, [4, 4, 4], stride=2, activation_fn=tf.nn.relu)
h2 = conv3d_transpose(
h1, int(f_dim * 3 / 2), [5, 5, 5], stride=2, activation_fn=tf.nn.relu)
h3 = conv3d_transpose(
h2, 1, [6, 6, 6], stride=2, activation_fn=tf.nn.sigmoid)
return h3
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