Spaces:
Running
Running
File size: 6,237 Bytes
0b8359d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 |
# 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.
# ==============================================================================
"""Defines the various loss functions in use by the PTN model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
slim = tf.contrib.slim
def add_rotator_image_loss(inputs, outputs, step_size, weight_scale):
"""Computes the image loss of deep rotator model.
Args:
inputs: Input dictionary to the model containing keys
such as `images_k'.
outputs: Output dictionary returned by the model containing keys
such as `images_k'.
step_size: A scalar representing the number of recurrent
steps (number of repeated out-of-plane rotations)
in the deep rotator network (int).
weight_scale: A reweighting factor applied over the image loss (float).
Returns:
A `Tensor' scalar that returns averaged L2 loss
(divided by batch_size and step_size) between the
ground-truth images (RGB) and predicted images (tf.float32).
"""
batch_size = tf.shape(inputs['images_0'])[0]
image_loss = 0
for k in range(1, step_size + 1):
image_loss += tf.nn.l2_loss(
inputs['images_%d' % k] - outputs['images_%d' % k])
image_loss /= tf.to_float(step_size * batch_size)
slim.summaries.add_scalar_summary(
image_loss, 'image_loss', prefix='losses')
image_loss *= weight_scale
return image_loss
def add_rotator_mask_loss(inputs, outputs, step_size, weight_scale):
"""Computes the mask loss of deep rotator model.
Args:
inputs: Input dictionary to the model containing keys
such as `masks_k'.
outputs: Output dictionary returned by the model containing
keys such as `masks_k'.
step_size: A scalar representing the number of recurrent
steps (number of repeated out-of-plane rotations)
in the deep rotator network (int).
weight_scale: A reweighting factor applied over the mask loss (float).
Returns:
A `Tensor' that returns averaged L2 loss
(divided by batch_size and step_size) between the ground-truth masks
(object silhouettes) and predicted masks (tf.float32).
"""
batch_size = tf.shape(inputs['images_0'])[0]
mask_loss = 0
for k in range(1, step_size + 1):
mask_loss += tf.nn.l2_loss(
inputs['masks_%d' % k] - outputs['masks_%d' % k])
mask_loss /= tf.to_float(step_size * batch_size)
slim.summaries.add_scalar_summary(
mask_loss, 'mask_loss', prefix='losses')
mask_loss *= weight_scale
return mask_loss
def add_volume_proj_loss(inputs, outputs, num_views, weight_scale):
"""Computes the projection loss of voxel generation model.
Args:
inputs: Input dictionary to the model containing keys such as
`images_1'.
outputs: Output dictionary returned by the model containing keys
such as `masks_k' and ``projs_k'.
num_views: A integer scalar represents the total number of
viewpoints for each of the object (int).
weight_scale: A reweighting factor applied over the projection loss (float).
Returns:
A `Tensor' that returns the averaged L2 loss
(divided by batch_size and num_views) between the ground-truth
masks (object silhouettes) and predicted masks (tf.float32).
"""
batch_size = tf.shape(inputs['images_1'])[0]
proj_loss = 0
for k in range(num_views):
proj_loss += tf.nn.l2_loss(
outputs['masks_%d' % (k + 1)] - outputs['projs_%d' % (k + 1)])
proj_loss /= tf.to_float(num_views * batch_size)
slim.summaries.add_scalar_summary(
proj_loss, 'proj_loss', prefix='losses')
proj_loss *= weight_scale
return proj_loss
def add_volume_loss(inputs, outputs, num_views, weight_scale):
"""Computes the volume loss of voxel generation model.
Args:
inputs: Input dictionary to the model containing keys such as
`images_1' and `voxels'.
outputs: Output dictionary returned by the model containing keys
such as `voxels_k'.
num_views: A scalar representing the total number of
viewpoints for each object (int).
weight_scale: A reweighting factor applied over the volume
loss (tf.float32).
Returns:
A `Tensor' that returns the averaged L2 loss
(divided by batch_size and num_views) between the ground-truth
volumes and predicted volumes (tf.float32).
"""
batch_size = tf.shape(inputs['images_1'])[0]
vol_loss = 0
for k in range(num_views):
vol_loss += tf.nn.l2_loss(
inputs['voxels'] - outputs['voxels_%d' % (k + 1)])
vol_loss /= tf.to_float(num_views * batch_size)
slim.summaries.add_scalar_summary(
vol_loss, 'vol_loss', prefix='losses')
vol_loss *= weight_scale
return vol_loss
def regularization_loss(scopes, params):
"""Computes the weight decay as regularization during training.
Args:
scopes: A list of different components of the model such as
``encoder'', ``decoder'' and ``projector''.
params: Parameters of the model.
Returns:
Regularization loss (tf.float32).
"""
reg_loss = tf.zeros(dtype=tf.float32, shape=[])
if params.weight_decay > 0:
is_trainable = lambda x: x in tf.trainable_variables()
is_weights = lambda x: 'weights' in x.name
for scope in scopes:
scope_vars = filter(is_trainable,
tf.contrib.framework.get_model_variables(scope))
scope_vars = filter(is_weights, scope_vars)
if scope_vars:
reg_loss += tf.add_n([tf.nn.l2_loss(var) for var in scope_vars])
slim.summaries.add_scalar_summary(
reg_loss, 'reg_loss', prefix='losses')
reg_loss *= params.weight_decay
return reg_loss
|