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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.
# ==============================================================================
"""Creates rotator network model.
This model performs the out-of-plane rotations given input image and action.
The action is either no-op, rotate clockwise or rotate counter-clockwise.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
def bilinear(input_x, input_y, output_size):
"""Define the bilinear transformation layer."""
shape_x = input_x.get_shape().as_list()
shape_y = input_y.get_shape().as_list()
weights_initializer = tf.truncated_normal_initializer(stddev=0.02,
seed=1)
biases_initializer = tf.constant_initializer(0.0)
matrix = tf.get_variable("Matrix", [shape_x[1], shape_y[1], output_size],
tf.float32, initializer=weights_initializer)
bias = tf.get_variable("Bias", [output_size],
initializer=biases_initializer)
# Add to GraphKeys.MODEL_VARIABLES
tf.contrib.framework.add_model_variable(matrix)
tf.contrib.framework.add_model_variable(bias)
# Define the transformation
h0 = tf.matmul(input_x, tf.reshape(matrix,
[shape_x[1], shape_y[1]*output_size]))
h0 = tf.reshape(h0, [-1, shape_y[1], output_size])
h1 = tf.tile(tf.reshape(input_y, [-1, shape_y[1], 1]),
[1, 1, output_size])
h1 = tf.multiply(h0, h1)
return tf.reduce_sum(h1, 1) + bias
def model(poses, actions, params, is_training):
"""Model for performing rotation."""
del is_training # Unused
return bilinear(poses, actions, params.z_dim)
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