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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 | |
| 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 | |