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# Copyright 2017 Google Inc.
#
# 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.
"""Utilities for PixelDA model."""
import math
# Dependency imports
import tensorflow as tf
slim = tf.contrib.slim
flags = tf.app.flags
FLAGS = flags.FLAGS
def remove_depth(images):
"""Takes a batch of images and remove depth channel if present."""
if images.shape.as_list()[-1] == 4:
return images[:, :, :, 0:3]
return images
def image_grid(images, max_grid_size=4):
"""Given images and N, return first N^2 images as an NxN image grid.
Args:
images: a `Tensor` of size [batch_size, height, width, channels]
max_grid_size: Maximum image grid height/width
Returns:
Single image batch, of dim [1, h*n, w*n, c]
"""
images = remove_depth(images)
batch_size = images.shape.as_list()[0]
grid_size = min(int(math.sqrt(batch_size)), max_grid_size)
assert images.shape.as_list()[0] >= grid_size * grid_size
# If we have a depth channel
if images.shape.as_list()[-1] == 4:
images = images[:grid_size * grid_size, :, :, 0:3]
depth = tf.image.grayscale_to_rgb(images[:grid_size * grid_size, :, :, 3:4])
images = tf.reshape(images, [-1, images.shape.as_list()[2], 3])
split = tf.split(0, grid_size, images)
depth = tf.reshape(depth, [-1, images.shape.as_list()[2], 3])
depth_split = tf.split(0, grid_size, depth)
grid = tf.concat(split + depth_split, 1)
return tf.expand_dims(grid, 0)
else:
images = images[:grid_size * grid_size, :, :, :]
images = tf.reshape(
images, [-1, images.shape.as_list()[2],
images.shape.as_list()[3]])
split = tf.split(images, grid_size, 0)
grid = tf.concat(split, 1)
return tf.expand_dims(grid, 0)
def source_and_output_image_grid(output_images,
source_images=None,
max_grid_size=4):
"""Create NxN image grid for output, concatenate source grid if given.
Makes grid out of output_images and, if provided, source_images, and
concatenates them.
Args:
output_images: [batch_size, h, w, c] tensor of images
source_images: optional[batch_size, h, w, c] tensor of images
max_grid_size: Image grid height/width
Returns:
Single image batch, of dim [1, h*n, w*n, c]
"""
output_grid = image_grid(output_images, max_grid_size=max_grid_size)
if source_images is not None:
source_grid = image_grid(source_images, max_grid_size=max_grid_size)
# Make sure they have the same # of channels before concat
# Assumes either 1 or 3 channels
if output_grid.shape.as_list()[-1] != source_grid.shape.as_list()[-1]:
if output_grid.shape.as_list()[-1] == 1:
output_grid = tf.tile(output_grid, [1, 1, 1, 3])
if source_grid.shape.as_list()[-1] == 1:
source_grid = tf.tile(source_grid, [1, 1, 1, 3])
output_grid = tf.concat([output_grid, source_grid], 1)
return output_grid
def summarize_model(end_points):
"""Summarizes the given model via its end_points.
Args:
end_points: A dictionary of end_point names to `Tensor`.
"""
tf.summary.histogram('domain_logits_transferred',
tf.sigmoid(end_points['transferred_domain_logits']))
tf.summary.histogram('domain_logits_target',
tf.sigmoid(end_points['target_domain_logits']))
def summarize_transferred_grid(transferred_images,
source_images=None,
name='Transferred'):
"""Produces a visual grid summarization of the image transferrence.
Args:
transferred_images: A `Tensor` of size [batch_size, height, width, c].
source_images: A `Tensor` of size [batch_size, height, width, c].
name: Name to use in summary name
"""
if source_images is not None:
grid = source_and_output_image_grid(transferred_images, source_images)
else:
grid = image_grid(transferred_images)
tf.summary.image('%s_Images_Grid' % name, grid, max_outputs=1)
def summarize_transferred(source_images,
transferred_images,
max_images=20,
name='Transferred'):
"""Produces a visual summary of the image transferrence.
This summary displays the source image, transferred image, and a grayscale
difference image which highlights the differences between input and output.
Args:
source_images: A `Tensor` of size [batch_size, height, width, channels].
transferred_images: A `Tensor` of size [batch_size, height, width, channels]
max_images: The number of images to show.
name: Name to use in summary name
Raises:
ValueError: If number of channels in source and target are incompatible
"""
source_channels = source_images.shape.as_list()[-1]
transferred_channels = transferred_images.shape.as_list()[-1]
if source_channels < transferred_channels:
if source_channels != 1:
raise ValueError(
'Source must be 1 channel or same # of channels as target')
source_images = tf.tile(source_images, [1, 1, 1, transferred_channels])
if transferred_channels < source_channels:
if transferred_channels != 1:
raise ValueError(
'Target must be 1 channel or same # of channels as source')
transferred_images = tf.tile(transferred_images, [1, 1, 1, source_channels])
diffs = tf.abs(source_images - transferred_images)
diffs = tf.reduce_max(diffs, reduction_indices=[3], keep_dims=True)
diffs = tf.tile(diffs, [1, 1, 1, max(source_channels, transferred_channels)])
transition_images = tf.concat([
source_images,
transferred_images,
diffs,
], 2)
tf.summary.image(
'%s_difference' % name, transition_images, max_outputs=max_images)
def summaries_color_distributions(images, name):
"""Produces a histogram of the color distributions of the images.
Args:
images: A `Tensor` of size [batch_size, height, width, 3].
name: The name of the images being summarized.
"""
tf.summary.histogram('color_values/%s' % name, images)
def summarize_images(images, name):
"""Produces a visual summary of the given images.
Args:
images: A `Tensor` of size [batch_size, height, width, 3].
name: The name of the images being summarized.
"""
grid = image_grid(images)
tf.summary.image('%s_Images' % name, grid, max_outputs=1)
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