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authorMatt Cooper <matthew_cooper@brown.edu>2016-08-12 16:48:46 -0400
committerMatt Cooper <matthew_cooper@brown.edu>2016-08-12 16:48:46 -0400
commit0a3fd5b62065333669c7b391c626cb2505217617 (patch)
tree04be2e559272d62e22c08258d0c72d759a00265d
First commit
-rw-r--r--.gitignore102
-rw-r--r--Code/avg_runner.py173
-rw-r--r--Code/constants.py198
-rw-r--r--Code/d_model.py187
-rw-r--r--Code/d_scale_model.py153
-rw-r--r--Code/g_model.py428
-rw-r--r--Code/loss_functions.py118
-rw-r--r--Code/loss_functions_test.py304
-rw-r--r--Code/process_data.py71
-rw-r--r--Code/tfutils.py133
-rw-r--r--Code/tfutils_test.py102
-rw-r--r--Code/utils.py212
-rw-r--r--New Figure 1/New Figure 1 – Deep Multiscale Video Prediction Beyond Mean Square Error-01.pngbin0 -> 58383 bytes
-rw-r--r--New Figure 1/New Figure 1 – Deep Multiscale Video Prediction Beyond Mean Square Error.ai2134
-rw-r--r--New Figure 1/New Figure 1 – Deep Multiscale Video Prediction Beyond Mean Square Error.pdf867
-rw-r--r--deep_multi-scale_video_prediction_beyond_mean_square_error.pdfbin0 -> 6462543 bytes
16 files changed, 5182 insertions, 0 deletions
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..377a3e3
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,102 @@
+*.idea
+*.iml
+*.pyc
+
+Data/
+Comparison/
+Save/
+
+##
+# GitHub Python gitignore
+##
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos
+into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# IPython Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# dotenv
+.env
+
+# virtualenv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+
+# Rope project settings
+.ropeproject
diff --git a/Code/avg_runner.py b/Code/avg_runner.py
new file mode 100644
index 0000000..5de994b
--- /dev/null
+++ b/Code/avg_runner.py
@@ -0,0 +1,173 @@
+import tensorflow as tf
+import getopt
+import sys
+
+from utils import get_train_batch, get_test_batch
+import constants as c
+from g_model import GeneratorModel
+from d_model import DiscriminatorModel
+
+
+class AVGRunner:
+ def __init__(self, model_load_path, num_test_rec):
+ """
+ Initializes the Adversarial Video Generation Runner.
+
+ @param model_load_path: The path from which to load a previously-saved model.
+ Default = None.
+ @param num_test_rec: The number of recursive generations to produce when testing. Recursive
+ generations use previous generations as input to predict further into
+ the future.
+ """
+
+ self.global_step = 0
+ self.num_test_rec = num_test_rec
+
+ self.sess = tf.Session()
+ self.summary_writer = tf.train.SummaryWriter(c.SUMMARY_SAVE_DIR, graph=self.sess.graph)
+
+ if c.ADVERSARIAL:
+ print 'Init discriminator...'
+ self.d_model = DiscriminatorModel(self.sess,
+ self.summary_writer,
+ c.TRAIN_HEIGHT,
+ c.TRAIN_WIDTH,
+ c.SCALE_CONV_FMS_D,
+ c.SCALE_KERNEL_SIZES_D,
+ c.SCALE_FC_LAYER_SIZES_D)
+
+ print 'Init generator...'
+ self.g_model = GeneratorModel(self.sess,
+ self.summary_writer,
+ c.TRAIN_HEIGHT,
+ c.TRAIN_WIDTH,
+ c.TEST_HEIGHT,
+ c.TEST_WIDTH,
+ c.SCALE_FMS_G,
+ c.SCALE_KERNEL_SIZES_G)
+
+ print 'Init variables...'
+ self.saver = tf.train.Saver(keep_checkpoint_every_n_hours=2)
+ self.sess.run(tf.initialize_all_variables())
+
+ # if load path specified, load a saved model
+ if model_load_path is not None:
+ self.saver.restore(self.sess, model_load_path)
+ print 'Model restored from ' + model_load_path
+
+ def train(self):
+ """
+ Runs a training loop on the model networks.
+ """
+ while True:
+ if c.ADVERSARIAL:
+ # update discriminator
+ batch = get_train_batch()
+ print 'Training discriminator...'
+ self.d_model.train_step(batch, self.g_model)
+
+ # update generator
+ batch = get_train_batch()
+ print 'Training generator...'
+ self.global_step = self.g_model.train_step(
+ batch, discriminator=(self.d_model if c.ADVERSARIAL else None))
+
+ # save the models
+ if self.global_step % c.MODEL_SAVE_FREQ == 0:
+ print '-' * 30
+ print 'Saving models...'
+ self.saver.save(self.sess,
+ c.MODEL_SAVE_DIR + 'model.ckpt',
+ global_step=self.global_step)
+ print 'Saved models!'
+ print '-' * 30
+
+ # test generator model
+ if self.global_step % c.TEST_FREQ == 0:
+ self.test()
+
+ def test(self):
+ """
+ Runs one test step on the generator network.
+ """
+ batch = get_test_batch(c.BATCH_SIZE, num_rec_out=self.num_test_rec)
+ self.g_model.test_batch(
+ batch, self.global_step, num_rec_out=self.num_test_rec)
+
+
+def usage():
+ print 'Options:'
+ print '-l/--load_path= <Relative/path/to/saved/model>'
+ print '-t/--test_dir= <Directory of test images>'
+ print '-r/--recursions= <# recursive predictions to make on test>'
+ print '-a/--adversarial= <{t/f}> (Whether to use adversarial training. Default=True)'
+ print '-n/--name= <Subdirectory of ../Data/Save/*/ in which to save output of this run>'
+ print '-O/--overwrite (Overwrites all previous data for the model with this save name)'
+ print '-T/--test_only (Only runs a test step -- no training)'
+ print '-H/--help (prints usage)'
+ print '--stats_freq= <how often to print loss/train error stats, in # steps>'
+ print '--summary_freq= <how often to save loss/error summaries, in # steps>'
+ print '--img_save_freq= <how often to save generated images, in # steps>'
+ print '--test_freq= <how often to test the model on test data, in # steps>'
+ print '--model_save_freq= <how often to save the model, in # steps>'
+
+
+def main():
+ ##
+ # Handle command line input.
+ ##
+
+ load_path = None
+ test_only = False
+ num_test_rec = 1 # number of recursive predictions to make on test
+ try:
+ opts, _ = getopt.getopt(sys.argv[1:], 'l:t:r:a:n:OTH',
+ ['load_path=', 'test_dir=', 'recursions=', 'adversarial=', 'name=',
+ 'overwrite', 'test_only', 'help', 'stats_freq=', 'summary_freq=',
+ 'img_save_freq=', 'test_freq=', 'model_save_freq='])
+ except getopt.GetoptError:
+ usage()
+ sys.exit(2)
+
+ for opt, arg in opts:
+ if opt in ('-l', '--load_path'):
+ load_path = arg
+ if opt in ('-t', '--test_dir'):
+ c.set_test_dir(arg)
+ if opt in ('-r', '--recursions'):
+ num_test_rec = int(arg)
+ if opt in ('-a', '--adversarial'):
+ c.ADVERSARIAL = (arg.lower() == 'true' or arg.lower() == 't')
+ if opt in ('-n', '--name'):
+ c.set_save_name(arg)
+ if opt in ('-O', '--overwrite'):
+ c.clear_save_name()
+ if opt in ('-H', '--help'):
+ usage()
+ sys.exit(2)
+ if opt in ('-T', '--test_only'):
+ test_only = True
+ if opt == '--stats_freq':
+ c.STATS_FREQ = int(arg)
+ if opt == '--summary_freq':
+ c.SUMMARY_FREQ = int(arg)
+ if opt == '--img_save_freq':
+ c.IMG_SAVE_FREQ = int(arg)
+ if opt == '--test_freq':
+ c.TEST_FREQ = int(arg)
+ if opt == '--model_save_freq':
+ c.MODEL_SAVE_FREQ = int(arg)
+
+ ##
+ # Init and run the predictor
+ ##
+
+ runner = AVGRunner(load_path, num_test_rec)
+ if test_only:
+ runner.test()
+ else:
+ runner.train()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/Code/constants.py b/Code/constants.py
new file mode 100644
index 0000000..afe8f9d
--- /dev/null
+++ b/Code/constants.py
@@ -0,0 +1,198 @@
+import numpy as np
+import os
+from glob import glob
+import shutil
+from datetime import datetime
+from scipy.ndimage import imread
+
+##
+# Data
+##
+
+def get_date_str():
+ """
+ @return: A string representing the current date/time that can be used as a directory name.
+ """
+ return str(datetime.now()).replace(' ', '_').replace(':', '.')[:-10]
+
+def get_dir(directory):
+ """
+ Creates the given directory if it does not exist.
+
+ @param directory: The path to the directory.
+ @return: The path to the directory.
+ """
+ if not os.path.exists(directory):
+ os.makedirs(directory)
+ return directory
+
+def clear_dir(directory):
+ """
+ Removes all files in the given directory.
+
+ @param directory: The path to the directory.
+ """
+ for f in os.listdir(directory):
+ path = os.path.join(directory, f)
+ try:
+ if os.path.isfile(path):
+ os.unlink(path)
+ elif os.path.isdir(path):
+ shutil.rmtree(path)
+ except Exception as e:
+ print(e)
+
+def get_test_frame_dims():
+ img_path = glob(TEST_DIR + '*/*')[0]
+ img = imread(img_path, mode='RGB')
+ shape = np.shape(img)
+
+ return shape[0], shape[1]
+
+def set_test_dir(directory):
+ """
+ Edits all constants dependent on TEST_DIR.
+
+ @param directory: The new test directory.
+ """
+ global TEST_DIR, TEST_HEIGHT, TEST_WIDTH
+
+ TEST_DIR = directory
+ TEST_HEIGHT, TEST_WIDTH = get_test_frame_dims()
+
+# root directory for all data
+DATA_DIR = get_dir('../Data/')
+# directory of unprocessed training frames
+TRAIN_DIR = DATA_DIR + 'Ms_Pacman/Train/'
+# directory of unprocessed test frames
+TEST_DIR = DATA_DIR + 'Ms_Pacman/Test/'
+# Directory of processed training clips.
+# hidden so finder doesn't freeze w/ so many files. DON'T USE `ls` COMMAND ON THIS DIR!
+TRAIN_DIR_CLIPS = get_dir(DATA_DIR + '.Clips/')
+
+# For processing clips. l2 diff between frames must be greater than this
+MOVEMENT_THRESHOLD = 100
+# total number of processed clips in TRAIN_DIR_CLIPS
+NUM_CLIPS = len(glob(TRAIN_DIR_CLIPS + '*'))
+
+# the height and width of the full frames to test on
+TEST_HEIGHT, TEST_WIDTH = get_test_frame_dims()
+# the height and width of the patches to train on
+TRAIN_HEIGHT = TRAIN_WIDTH = 32
+
+##
+# Output
+##
+
+def set_save_name(name):
+ """
+ Edits all constants dependent on SAVE_NAME.
+
+ @param name: The new save name.
+ """
+ global SAVE_NAME, MODEL_SAVE_DIR, SUMMARY_SAVE_DIR, IMG_SAVE_DIR
+
+ SAVE_NAME = name
+ MODEL_SAVE_DIR = get_dir(SAVE_DIR + 'Models/' + SAVE_NAME)
+ SUMMARY_SAVE_DIR = get_dir(SAVE_DIR + 'Summaries/' + SAVE_NAME)
+ IMG_SAVE_DIR = get_dir(SAVE_DIR + 'Images/' + SAVE_NAME)
+
+def clear_save_name():
+ """
+ Clears all saved content for SAVE_NAME.
+ """
+ clear_dir(MODEL_SAVE_DIR)
+ clear_dir(SUMMARY_SAVE_DIR)
+ clear_dir(IMG_SAVE_DIR)
+
+
+# root directory for all saved content
+SAVE_DIR = get_dir('../Save/')
+
+# inner directory to differentiate between runs
+SAVE_NAME = 'Default/'
+# directory for saved models
+MODEL_SAVE_DIR = get_dir(SAVE_DIR + 'Models/' + SAVE_NAME)
+# directory for saved TensorBoard summaries
+SUMMARY_SAVE_DIR = get_dir(SAVE_DIR + 'Summaries/' + SAVE_NAME)
+# directory for saved images
+IMG_SAVE_DIR = get_dir(SAVE_DIR + 'Images/' + SAVE_NAME)
+
+
+STATS_FREQ = 10 # how often to print loss/train error stats, in # steps
+SUMMARY_FREQ = 100 # how often to save the summaries, in # steps
+IMG_SAVE_FREQ = 1000 # how often to save generated images, in # steps
+TEST_FREQ = 5000 # how often to test the model on test data, in # steps
+MODEL_SAVE_FREQ = 10000 # how often to save the model, in # steps
+
+##
+# General training
+##
+
+# whether to use adversarial training vs. basic training of the generator
+ADVERSARIAL = True
+# the training minibatch size
+BATCH_SIZE = 8
+# the number of history frames to give as input to the network
+HIST_LEN = 4
+
+##
+# Loss parameters
+##
+
+# for lp loss. e.g, 1 or 2 for l1 and l2 loss, respectively)
+L_NUM = 2
+# the power to which each gradient term is raised in GDL loss
+ALPHA_NUM = 1
+# the percentage of the adversarial loss to use in the combined loss
+LAM_ADV = 0.05
+# the percentage of the lp loss to use in the combined loss
+LAM_LP = 1
+# the percentage of the GDL loss to use in the combined loss
+LAM_GDL = 1
+
+##
+# Generator model
+##
+
+# learning rate for the generator model
+LRATE_G = 0.00004 # Value in paper is 0.04
+# padding for convolutions in the generator model
+PADDING_G = 'SAME'
+# feature maps for each convolution of each scale network in the generator model
+# e.g SCALE_FMS_G[1][2] is the input of the 3rd convolution in the 2nd scale network.
+SCALE_FMS_G = [[3 * HIST_LEN, 128, 256, 128, 3],
+ [3 * (HIST_LEN + 1), 128, 256, 128, 3],
+ [3 * (HIST_LEN + 1), 128, 256, 512, 256, 128, 3],
+ [3 * (HIST_LEN + 1), 128, 256, 512, 256, 128, 3]]
+# kernel sizes for each convolution of each scale network in the generator model
+SCALE_KERNEL_SIZES_G = [[3, 3, 3, 3],
+ [5, 3, 3, 5],
+ [5, 3, 3, 3, 3, 5],
+ [7, 5, 5, 5, 5, 7]]
+
+
+##
+# Discriminator model
+##
+
+# learning rate for the discriminator model
+LRATE_D = 0.02
+# padding for convolutions in the discriminator model
+PADDING_D = 'VALID'
+# feature maps for each convolution of each scale network in the discriminator model
+SCALE_CONV_FMS_D = [[3, 64],
+ [3, 64, 128, 128],
+ [3, 128, 256, 256],
+ [3, 128, 256, 512, 128]]
+# kernel sizes for each convolution of each scale network in the discriminator model
+SCALE_KERNEL_SIZES_D = [[3],
+ [3, 3, 3],
+ [5, 5, 5],
+ [7, 7, 5, 5]]
+# layer sizes for each fully-connected layer of each scale network in the discriminator model
+# layer connecting conv to fully-connected is dynamically generated when creating the model
+SCALE_FC_LAYER_SIZES_D = [[512, 256, 1],
+ [1024, 512, 1],
+ [1024, 512, 1],
+ [1024, 512, 1]]
diff --git a/Code/d_model.py b/Code/d_model.py
new file mode 100644
index 0000000..7b1cb12
--- /dev/null
+++ b/Code/d_model.py
@@ -0,0 +1,187 @@
+import tensorflow as tf
+import numpy as np
+from skimage.transform import resize
+
+from d_scale_model import DScaleModel
+from loss_functions import adv_loss
+import constants as c
+
+
+# noinspection PyShadowingNames
+class DiscriminatorModel:
+ def __init__(self, session, summary_writer, height, width, scale_conv_layer_fms,
+ scale_kernel_sizes, scale_fc_layer_sizes):
+ """
+ Initializes a GeneratorModel.
+
+ @param session: The TensorFlow session.
+ @param summary_writer: The writer object to record TensorBoard summaries
+ @param height: The height of the input images.
+ @param width: The width of the input images.
+ @param scale_conv_layer_fms: The number of feature maps in each convolutional layer of each
+ scale network.
+ @param scale_kernel_sizes: The size of the kernel for each layer of each scale network.
+ @param scale_fc_layer_sizes: The number of nodes in each fully-connected layer of each scale
+ network.
+
+ @type session: tf.Session
+ @type summary_writer: tf.train.SummaryWriter
+ @type height: int
+ @type width: int
+ @type scale_conv_layer_fms: list<list<int>>
+ @type scale_kernel_sizes: list<list<int>>
+ @type scale_fc_layer_sizes: list<list<int>>
+ """
+ self.sess = session
+ self.summary_writer = summary_writer
+ self.height = height
+ self.width = width
+ self.scale_conv_layer_fms = scale_conv_layer_fms
+ self.scale_kernel_sizes = scale_kernel_sizes
+ self.scale_fc_layer_sizes = scale_fc_layer_sizes
+ self.num_scale_nets = len(scale_conv_layer_fms)
+
+ self.define_graph()
+
+ # noinspection PyAttributeOutsideInit
+ def define_graph(self):
+ """
+ Sets up the model graph in TensorFlow.
+ """
+ with tf.name_scope('discriminator'):
+ ##
+ # Setup scale networks. Each will make the predictions for images at a given scale.
+ ##
+
+ self.scale_nets = []
+ for scale_num in xrange(self.num_scale_nets):
+ with tf.name_scope('scale_net_' + str(scale_num)):
+ scale_factor = 1. / 2 ** ((self.num_scale_nets - 1) - scale_num)
+ self.scale_nets.append(DScaleModel(scale_num,
+ int(self.height * scale_factor),
+ int(self.width * scale_factor),
+ self.scale_conv_layer_fms[scale_num],
+ self.scale_kernel_sizes[scale_num],
+ self.scale_fc_layer_sizes[scale_num]))
+
+ # A list of the prediction tensors for each scale network
+ self.scale_preds = []
+ for scale_num in xrange(self.num_scale_nets):
+ self.scale_preds.append(self.scale_nets[scale_num].preds)
+
+ ##
+ # Data
+ ##
+
+ self.labels = tf.placeholder(tf.float32, shape=[None, 1], name='labels')
+
+ ##
+ # Training
+ ##
+
+ with tf.name_scope('training'):
+ # global loss is the combined loss from every scale network
+ self.global_loss = adv_loss(self.scale_preds, self.labels)
+ self.global_step = tf.Variable(0, trainable=False, name='global_step')
+ self.optimizer = tf.train.GradientDescentOptimizer(c.LRATE_D, name='optimizer')
+ self.train_op = self.optimizer.minimize(self.global_loss,
+ global_step=self.global_step,
+ name='train_op')
+
+ # add summaries to visualize in TensorBoard
+ loss_summary = tf.scalar_summary('loss_D', self.global_loss)
+ self.summaries = tf.merge_summary([loss_summary])
+
+ def build_feed_dict(self, input_frames, gt_output_frames, generator):
+ """
+ Builds a feed_dict with resized inputs and outputs for each scale network.
+
+ @param input_frames: An array of shape
+ [batch_size x self.height x self.width x (3 * HIST_LEN)], The frames to
+ use for generation.
+ @param gt_output_frames: An array of shape [batch_size x self.height x self.width x 3], The
+ ground truth outputs for each sequence in input_frames.
+ @param generator: The generator model.
+
+ @return: The feed_dict needed to run this network, all scale_nets, and the generator
+ predictions.
+ """
+ feed_dict = {}
+ batch_size = np.shape(gt_output_frames)[0]
+
+ ##
+ # Get generated frames from GeneratorModel
+ ##
+
+ g_feed_dict = {generator.input_frames_train: input_frames,
+ generator.gt_frames_train: gt_output_frames}
+ g_scale_preds = self.sess.run(generator.scale_preds_train, feed_dict=g_feed_dict)
+
+ ##
+ # Create discriminator feed dict
+ ##
+ for scale_num in xrange(self.num_scale_nets):
+ scale_net = self.scale_nets[scale_num]
+
+ # resize gt_output_frames
+ scaled_gt_output_frames = np.empty([batch_size, scale_net.height, scale_net.width, 3])
+ for i, img in enumerate(gt_output_frames):
+ # for skimage.transform.resize, images need to be in range [0, 1], so normalize to
+ # [0, 1] before resize and back to [-1, 1] after
+ sknorm_img = (img / 2) + 0.5
+ resized_frame = resize(sknorm_img, [scale_net.height, scale_net.width, 3])
+ scaled_gt_output_frames[i] = (resized_frame - 0.5) * 2
+
+ # combine with resized gt_output_frames to get inputs for prediction
+ scaled_input_frames = np.concatenate([g_scale_preds[scale_num],
+ scaled_gt_output_frames])
+
+ # convert to np array and add to feed_dict
+ feed_dict[scale_net.input_frames] = scaled_input_frames
+
+ # add labels for each image to feed_dict
+ batch_size = np.shape(input_frames)[0]
+ feed_dict[self.labels] = np.concatenate([np.zeros([batch_size, 1]),
+ np.ones([batch_size, 1])])
+
+ return feed_dict
+
+ def train_step(self, batch, generator):
+ """
+ Runs a training step using the global loss on each of the scale networks.
+
+ @param batch: An array of shape
+ [BATCH_SIZE x self.height x self.width x (3 * (HIST_LEN + 1))]. The input
+ and output frames, concatenated along the channel axis (index 3).
+ @param generator: The generator model.
+
+ @return: The global step.
+ """
+ ##
+ # Split into inputs and outputs
+ ##
+
+ input_frames = batch[:, :, :, :-3]
+ gt_output_frames = batch[:, :, :, -3:]
+
+ ##
+ # Train
+ ##
+
+ feed_dict = self.build_feed_dict(input_frames, gt_output_frames, generator)
+
+ _, global_loss, global_step, summaries = self.sess.run(
+ [self.train_op, self.global_loss, self.global_step, self.summaries],
+ feed_dict=feed_dict)
+
+ ##
+ # User output
+ ##
+
+ if global_step % c.STATS_FREQ == 0:
+ print 'DiscriminatorModel: step %d | global loss: %f' % (global_step, global_loss)
+ if global_step % c.SUMMARY_FREQ == 0:
+ print 'DiscriminatorModel: saved summaries'
+ self.summary_writer.add_summary(summaries, global_step)
+
+ return global_step
diff --git a/Code/d_scale_model.py b/Code/d_scale_model.py
new file mode 100644
index 0000000..766e01a
--- /dev/null
+++ b/Code/d_scale_model.py
@@ -0,0 +1,153 @@
+import tensorflow as tf
+from tfutils import w, b, conv_out_size
+import constants as c
+
+
+# noinspection PyShadowingNames
+class DScaleModel:
+ """
+ A DScaleModel is a network that takes as input one video frame and attempts to discriminate
+ whether or not the output frame is a real-world image or one generated by a generator network.
+ Multiple of these are used together in a DiscriminatorModel to make predictions on frames at
+ increasing scales.
+ """
+
+ def __init__(self, scale_index, height, width, conv_layer_fms, kernel_sizes, fc_layer_sizes):
+ """
+ Initializes the DScaleModel.
+
+ @param scale_index: The index number of this height in the GeneratorModel.
+ @param height: The height of the input images.
+ @param width: The width of the input images.
+ @param conv_layer_fms: The number of output feature maps for each convolution.
+ @param kernel_sizes: The size of the kernel for each convolutional layer.
+ @param fc_layer_sizes: The number of nodes in each fully-connected layer.
+
+ @type scale_index: int
+ @type height: int
+ @type width: int
+ @type conv_layer_fms: list<int>
+ @type kernel_sizes: list<int> (len = len(scale_layer_fms) - 1)
+ @type fc_layer_sizes: list<int>
+ """
+ assert len(kernel_sizes) == len(conv_layer_fms) - 1, \
+ 'len(kernel_sizes) must = len(conv_layer_fms) - 1'
+
+ self.scale_index = scale_index
+ self.height = height
+ self.width = width
+ self.conv_layer_fms = conv_layer_fms
+ self.kernel_sizes = kernel_sizes
+ self.fc_layer_sizes = fc_layer_sizes
+
+ self.define_graph()
+
+ # noinspection PyAttributeOutsideInit
+ def define_graph(self):
+ """
+ Sets up the model graph in TensorFlow.
+ """
+
+ ##
+ # Input data
+ ##
+ with tf.name_scope('input'):
+ self.input_frames = tf.placeholder(
+ tf.float32, shape=[None, self.height, self.width, self.conv_layer_fms[0]])
+
+ # use variable batch_size for more flexibility
+ self.batch_size = tf.shape(self.input_frames)[0]
+
+ ##
+ # Layer setup
+ ##
+
+ with tf.name_scope('setup'):
+ # convolution
+ with tf.name_scope('convolutions'):
+ conv_ws = []
+ conv_bs = []
+ last_out_height = self.height
+ last_out_width = self.width
+ for i in xrange(len(self.kernel_sizes)):
+ conv_ws.append(w([self.kernel_sizes[i],
+ self.kernel_sizes[i],
+ self.conv_layer_fms[i],
+ self.conv_layer_fms[i + 1]]))
+ conv_bs.append(b([self.conv_layer_fms[i + 1]]))
+
+ last_out_height = conv_out_size(
+ last_out_height, c.PADDING_D, self.kernel_sizes[i], 1)
+ last_out_width = conv_out_size(
+ last_out_width, c.PADDING_D, self.kernel_sizes[i], 1)
+
+ # fully-connected
+ with tf.name_scope('full-connected'):
+ # Add in an initial layer to go from the last conv to the first fully-connected.
+ # Use /2 for the height and width because there is a 2x2 pooling layer
+ self.fc_layer_sizes.insert(
+ 0, (last_out_height / 2) * (last_out_width / 2) * self.conv_layer_fms[-1])
+
+ fc_ws = []
+ fc_bs = []
+ for i in xrange(len(self.fc_layer_sizes) - 1):
+ fc_ws.append(w([self.fc_layer_sizes[i],
+ self.fc_layer_sizes[i + 1]]))
+ fc_bs.append(b([self.fc_layer_sizes[i + 1]]))
+
+ ##
+ # Forward pass calculation
+ ##
+
+ def generate_predictions():
+ """
+ Runs self.input_frames through the network to generate a prediction from 0
+ (generated img) to 1 (real img).
+
+ @return: A tensor of predictions of shape [self.batch_size x 1].
+ """
+ with tf.name_scope('calculation'):
+ preds = tf.zeros([self.batch_size, 1])
+ last_input = self.input_frames
+
+ # convolutions
+ with tf.name_scope('convolutions'):
+ for i in xrange(len(conv_ws)):
+ # Convolve layer and activate with ReLU
+ preds = tf.nn.conv2d(
+ last_input, conv_ws[i], [1, 1, 1, 1], padding=c.PADDING_D)
+ preds = tf.nn.relu(preds + conv_bs[i])
+
+ last_input = preds
+
+ # pooling layer
+ with tf.name_scope('pooling'):
+ preds = tf.nn.max_pool(preds, [1, 2, 2, 1], [1, 2, 2, 1], padding=c.PADDING_D)
+
+ # flatten preds for dense layers
+ shape = preds.get_shape().as_list()
+ # -1 can be used as one dimension to size dynamically
+ preds = tf.reshape(preds, [-1, shape[1] * shape[2] * shape[3]])
+
+ # fully-connected layers
+ with tf.name_scope('fully-connected'):
+ for i in xrange(len(fc_ws)):
+ preds = tf.matmul(preds, fc_ws[i]) + fc_bs[i]
+
+ # Activate with ReLU (or Sigmoid for last layer)
+ if i == len(fc_ws) - 1:
+ preds = tf.sigmoid(preds)
+ else:
+ preds = tf.nn.relu(preds)
+
+ # clip preds between [.1, 0.9] for stability
+ with tf.name_scope('clip'):
+ preds = tf.clip_by_value(preds, 0.1, 0.9)
+
+ return preds
+
+ self.preds = generate_predictions()
+
+ ##
+ # Training handled by DiscriminatorModel
+ ##
diff --git a/Code/g_model.py b/Code/g_model.py
new file mode 100644
index 0000000..eef24ab
--- /dev/null
+++ b/Code/g_model.py
@@ -0,0 +1,428 @@
+import tensorflow as tf
+import numpy as np
+from scipy.misc import imsave
+from skimage.transform import resize
+from copy import deepcopy
+
+import constants as c
+from loss_functions import combined_loss
+from utils import psnr_error, sharp_diff_error
+from tfutils import w, b
+
+# noinspection PyShadowingNames
+class GeneratorModel:
+ def __init__(self, session, summary_writer, height_train, width_train, height_test,
+ width_test, scale_layer_fms, scale_kernel_sizes):
+ """
+ Initializes a GeneratorModel.
+
+ @param session: The TensorFlow Session.
+ @param summary_writer: The writer object to record TensorBoard summaries
+ @param height_train: The height of the input images for training.
+ @param width_train: The width of the input images for training.
+ @param height_train: The height of the input images for testing.
+ @param width_train: The width of the input images for testing.
+ @param scale_layer_fms: The number of feature maps in each layer of each scale network.
+ @param scale_kernel_sizes: The size of the kernel for each layer of each scale network.
+
+ @type session: tf.Session
+ @type summary_writer: tf.train.SummaryWriter
+ @type height_train: int
+ @type width_train: int
+ @type height_test: int
+ @type width_test: int
+ @type scale_layer_fms: list<list<int>>
+ @type scale_kernel_sizes: list<list<int>>
+ """
+ self.sess = session
+ self.summary_writer = summary_writer
+ self.height_train = height_train
+ self.width_train = width_train
+ self.height_test = height_test
+ self.width_test = width_test
+ self.scale_layer_fms = scale_layer_fms
+ self.scale_kernel_sizes = scale_kernel_sizes
+ self.num_scale_nets = len(scale_layer_fms)
+
+ self.define_graph()
+
+ # noinspection PyAttributeOutsideInit
+ def define_graph(self):
+ """
+ Sets up the model graph in TensorFlow.
+ """
+ with tf.name_scope('generator'):
+ ##
+ # Data
+ ##
+
+ with tf.name_scope('data'):
+ self.input_frames_train = tf.placeholder(
+ tf.float32, shape=[None, self.height_train, self.width_train, 3 * c.HIST_LEN])
+ self.gt_frames_train = tf.placeholder(
+ tf.float32, shape=[None, self.height_train, self.width_train, 3])
+
+ self.input_frames_test = tf.placeholder(
+ tf.float32, shape=[None, self.height_test, self.width_test, 3 * c.HIST_LEN])
+ self.gt_frames_test = tf.placeholder(
+ tf.float32, shape=[None, self.height_test, self.width_test, 3])
+
+ # use variable batch_size for more flexibility
+ self.batch_size_train = tf.shape(self.input_frames_train)[0]
+ self.batch_size_test = tf.shape(self.input_frames_test)[0]
+
+ ##
+ # Scale network setup and calculation
+ ##
+
+ self.summaries_train = []
+ self.scale_preds_train = [] # the generated images at each scale
+ self.scale_gts_train = [] # the ground truth images at each scale
+ self.d_scale_preds = [] # the predictions from the discriminator model
+
+ self.summaries_test = []
+ self.scale_preds_test = [] # the generated images at each scale
+ self.scale_gts_test = [] # the ground truth images at each scale
+
+ for scale_num in xrange(self.num_scale_nets):
+ with tf.name_scope('scale_' + str(scale_num)):
+ with tf.name_scope('setup'):
+ ws = []
+ bs = []
+
+ # create weights for kernels
+ for i in xrange(len(self.scale_kernel_sizes[scale_num])):
+ ws.append(w([self.scale_kernel_sizes[scale_num][i],
+ self.scale_kernel_sizes[scale_num][i],
+ self.scale_layer_fms[scale_num][i],
+ self.scale_layer_fms[scale_num][i + 1]]))
+ bs.append(b([self.scale_layer_fms[scale_num][i + 1]]))
+
+ with tf.name_scope('calculation'):
+ def calculate(height, width, inputs, gts, last_gen_frames):
+ # scale inputs and gts
+ scale_factor = 1. / 2 ** ((self.num_scale_nets - 1) - scale_num)
+ scale_height = int(height * scale_factor)
+ scale_width = int(width * scale_factor)
+
+ inputs = tf.image.resize_images(inputs, scale_height, scale_width)
+ scale_gts = tf.image.resize_images(gts, scale_height, scale_width)
+
+ # for all scales but the first, add the frame generated by the last
+ # scale to the input
+ if scale_num > 0:
+ last_gen_frames = tf.image.resize_images(last_gen_frames,
+ scale_height,
+ scale_width)
+ inputs = tf.concat(3, [inputs, last_gen_frames])
+
+ # generated frame predictions
+ preds = inputs
+
+ # perform convolutions
+ with tf.name_scope('convolutions'):
+ for i in xrange(len(self.scale_kernel_sizes[scale_num])):
+ # Convolve layer
+ preds = tf.nn.conv2d(
+ preds, ws[i], [1, 1, 1, 1], padding=c.PADDING_G)
+
+ # Activate with ReLU (or Tanh for last layer)
+ if i == len(self.scale_kernel_sizes[scale_num]) - 1:
+ preds = tf.nn.tanh(preds + bs[i])
+ else:
+ preds = tf.nn.relu(preds + bs[i])
+
+ return preds, scale_gts
+
+ ##
+ # Perform train calculation
+ ##
+
+ # for all scales but the first, add the frame generated by the last
+ # scale to the input
+ if scale_num > 0:
+ last_scale_pred_train = self.scale_preds_train[scale_num - 1]
+ else:
+ last_scale_pred_train = None
+
+ # calculate
+ train_preds, train_gts = calculate(self.height_train,
+ self.width_train,
+ self.input_frames_train,
+ self.gt_frames_train,
+ last_scale_pred_train)
+ self.scale_preds_train.append(train_preds)
+ self.scale_gts_train.append(train_gts)
+
+ # We need to run the network first to get generated frames, run the
+ # discriminator on those frames to get d_scale_preds, then run this
+ # again for the loss optimization.
+ if c.ADVERSARIAL:
+ self.d_scale_preds.append(tf.placeholder(tf.float32, [None, 1]))
+
+ ##
+ # Perform test calculation
+ ##
+
+ # for all scales but the first, add the frame generated by the last
+ # scale to the input
+ if scale_num > 0:
+ last_scale_pred_test = self.scale_preds_test[scale_num - 1]
+ else:
+ last_scale_pred_test = None
+
+ # calculate
+ test_preds, test_gts = calculate(self.height_test,
+ self.width_test,
+ self.input_frames_test,
+ self.gt_frames_test,
+ last_scale_pred_test)
+ self.scale_preds_test.append(test_preds)
+ self.scale_gts_test.append(test_gts)
+
+ ##
+ # Training
+ ##
+
+ with tf.name_scope('train'):
+ # global loss is the combined loss from every scale network
+ self.global_loss = combined_loss(self.scale_preds_train,
+ self.scale_gts_train,
+ self.d_scale_preds)
+ self.global_step = tf.Variable(0, trainable=False)
+ self.optimizer = tf.train.AdamOptimizer(learning_rate=c.LRATE_G, name='optimizer')
+ self.train_op = self.optimizer.minimize(self.global_loss,
+ global_step=self.global_step,
+ name='train_op')
+
+ # train loss summary
+ loss_summary = tf.scalar_summary('train_loss_G', self.global_loss)
+ self.summaries_train.append(loss_summary)
+
+ ##
+ # Error
+ ##
+
+ with tf.name_scope('error'):
+ # error computation
+ # get error at largest scale
+ self.psnr_error_train = psnr_error(self.scale_preds_train[-1],
+ self.gt_frames_train)
+ self.sharpdiff_error_train = sharp_diff_error(self.scale_preds_train[-1],
+ self.gt_frames_train)
+ self.psnr_error_test = psnr_error(self.scale_preds_test[-1],
+ self.gt_frames_test)
+ self.sharpdiff_error_test = sharp_diff_error(self.scale_preds_test[-1],
+ self.gt_frames_test)
+ # train error summaries
+ summary_psnr_train = tf.scalar_summary('train_PSNR',
+ self.psnr_error_train)
+ summary_sharpdiff_train = tf.scalar_summary('train_SharpDiff',
+ self.sharpdiff_error_train)
+ self.summaries_train += [summary_psnr_train, summary_sharpdiff_train]
+
+ # test error
+ summary_psnr_test = tf.scalar_summary('test_PSNR',
+ self.psnr_error_test)
+ summary_sharpdiff_test = tf.scalar_summary('test_SharpDiff',
+ self.sharpdiff_error_test)
+ self.summaries_test += [summary_psnr_test, summary_sharpdiff_test]
+
+ # add summaries to visualize in TensorBoard
+ self.summaries_train = tf.merge_summary(self.summaries_train)
+ self.summaries_test = tf.merge_summary(self.summaries_test)
+
+ def train_step(self, batch, discriminator=None):
+ """
+ Runs a training step using the global loss on each of the scale networks.
+
+ @param batch: An array of shape
+ [c.BATCH_SIZE x self.height x self.width x (3 * (c.HIST_LEN + 1))].
+ The input and output frames, concatenated along the channel axis (index 3).
+ @param discriminator: The discriminator model. Default = None, if not adversarial.
+
+ @return: The global step.
+ """
+ ##
+ # Split into inputs and outputs
+ ##
+
+ input_frames = batch[:, :, :, :-3]
+ gt_frames = batch[:, :, :, -3:]
+
+ ##
+ # Train
+ ##
+
+ feed_dict = {self.input_frames_train: input_frames, self.gt_frames_train: gt_frames}
+
+ if c.ADVERSARIAL:
+ # Run the generator first to get generated frames
+ scale_preds = self.sess.run(self.scale_preds_train, feed_dict=feed_dict)
+
+ # Run the discriminator nets on those frames to get predictions
+ d_feed_dict = {}
+ for scale_num, gen_frames in enumerate(scale_preds):
+ d_feed_dict[discriminator.scale_nets[scale_num].input_frames] = gen_frames
+ d_scale_preds = self.sess.run(discriminator.scale_preds, feed_dict=d_feed_dict)
+
+ # Add discriminator predictions to the
+ for i, preds in enumerate(d_scale_preds):
+ feed_dict[self.d_scale_preds[i]] = preds
+
+ _, global_loss, global_psnr_error, global_sharpdiff_error, global_step, summaries = \
+ self.sess.run([self.train_op,
+ self.global_loss,
+ self.psnr_error_train,
+ self.sharpdiff_error_train,
+ self.global_step,
+ self.summaries_train],
+ feed_dict=feed_dict)
+
+ ##
+ # User output
+ ##
+ if global_step % c.STATS_FREQ == 0:
+ print 'GeneratorModel : Step ', global_step
+ print ' Global Loss : ', global_loss
+ print ' PSNR Error : ', global_psnr_error
+ print ' Sharpdiff Error: ', global_sharpdiff_error
+ if global_step % c.SUMMARY_FREQ == 0:
+ self.summary_writer.add_summary(summaries, global_step)
+ print 'GeneratorModel: saved summaries'
+ if global_step % c.IMG_SAVE_FREQ == 0:
+ print '-' * 30
+ print 'Saving images...'
+
+ # if not adversarial, we didn't get the preds for each scale net before for the
+ # discriminator prediction, so do it now
+ if not c.ADVERSARIAL:
+ scale_preds = self.sess.run(self.scale_preds_train, feed_dict=feed_dict)
+
+ # re-generate scale gt_frames to avoid having to run through TensorFlow.
+ scale_gts = []
+ for scale_num in xrange(self.num_scale_nets):
+ scale_factor = 1. / 2 ** ((self.num_scale_nets - 1) - scale_num)
+ scale_height = int(self.height_train * scale_factor)
+ scale_width = int(self.width_train * scale_factor)
+
+ # resize gt_output_frames for scale and append to scale_gts_train
+ scaled_gt_frames = np.empty([c.BATCH_SIZE, scale_height, scale_width, 3])
+ for i, img in enumerate(gt_frames):
+ # for skimage.transform.resize, images need to be in range [0, 1], so normalize
+ # to [0, 1] before resize and back to [-1, 1] after
+ sknorm_img = (img / 2) + 0.5
+ resized_frame = resize(sknorm_img, [scale_height, scale_width, 3])
+ scaled_gt_frames[i] = (resized_frame - 0.5) * 2
+ scale_gts.append(scaled_gt_frames)
+
+ # for every clip in the batch, save the inputs, scale preds and scale gts
+ for pred_num in xrange(len(input_frames)):
+ pred_dir = c.get_dir(c.IMG_SAVE_DIR + 'Step_' + str(global_step) + '/' + str(
+ pred_num) + '/')
+
+ # save input images
+ for frame_num in xrange(c.HIST_LEN):
+ img = input_frames[pred_num, :, :, (frame_num * 3):((frame_num + 1) * 3)]
+ imsave(pred_dir + 'input_' + str(frame_num) + '.png', img)
+
+ # save preds and gts at each scale
+ # noinspection PyUnboundLocalVariable
+ for scale_num, scale_pred in enumerate(scale_preds):
+ gen_img = scale_pred[pred_num]
+
+ path = pred_dir + 'scale' + str(scale_num)
+ gt_img = scale_gts[scale_num][pred_num]
+
+ imsave(path + '_gen.png', gen_img)
+ imsave(path + '_gt.png', gt_img)
+
+ print 'Saved images!'
+ print '-' * 30
+
+ return global_step
+
+ def test_batch(self, batch, global_step, num_rec_out=1, save_imgs=True):
+ """
+ Runs a training step using the global loss on each of the scale networks.
+
+ @param batch: An array of shape
+ [batch_size x self.height x self.width x (3 * (c.HIST_LEN+ num_rec_out))].
+ A batch of the input and output frames, concatenated along the channel axis
+ (index 3).
+ @param global_step: The global step.
+ @param num_rec_out: The number of outputs to predict. Outputs > 1 are computed recursively,
+ using previously-generated frames as input. Default = 1.
+ @param save_imgs: Whether or not to save the input/output images to file. Default = True.
+
+ @return: A tuple of (psnr error, sharpdiff error) for the batch.
+ """
+ if num_rec_out < 1:
+ raise ValueError('num_rec_out must be >= 1')
+
+ print '-' * 30
+ print 'Testing:'
+
+ ##
+ # Split into inputs and outputs
+ ##
+
+ input_frames = batch[:, :, :, :3 * c.HIST_LEN]
+ gt_frames = batch[:, :, :, 3 * c.HIST_LEN:]
+
+ ##
+ # Generate num_rec_out recursive predictions
+ ##
+
+ working_input_frames = deepcopy(input_frames) # input frames that will shift w/ recursion
+ rec_preds = []
+ rec_summaries = []
+ for rec_num in xrange(num_rec_out):
+ working_gt_frames = gt_frames[:, :, :, 3 * rec_num:3 * (rec_num + 1)]
+
+ feed_dict = {self.input_frames_test: working_input_frames,
+ self.gt_frames_test: working_gt_frames}
+ preds, psnr, sharpdiff, summaries = self.sess.run([self.scale_preds_test[-1],
+ self.psnr_error_test,
+ self.sharpdiff_error_test,
+ self.summaries_test],
+ feed_dict=feed_dict)
+
+ # remove first input and add new pred as last input
+ working_input_frames = np.concatenate(
+ [working_input_frames[:, :, :, 3:], preds], axis=3)
+
+ # add predictions and summaries
+ rec_preds.append(preds)
+ rec_summaries.append(summaries)
+
+ print 'Recursion ', rec_num
+ print 'PSNR Error : ', psnr
+ print 'Sharpdiff Error: ', sharpdiff
+
+ # write summaries
+ # TODO: Think of a good way to write rec output summaries - rn, just using first output.
+ self.summary_writer.add_summary(rec_summaries[0], global_step)
+
+ ##
+ # Save images
+ ##
+
+ if save_imgs:
+ for pred_num in xrange(len(input_frames)):
+ pred_dir = c.get_dir(
+ c.IMG_SAVE_DIR + 'Tests/Step_' + str(global_step) + '/' + str(pred_num) + '/')
+
+ # save input images
+ for frame_num in xrange(c.HIST_LEN):
+ img = input_frames[pred_num, :, :, (frame_num * 3):((frame_num + 1) * 3)]
+ imsave(pred_dir + 'input_' + str(frame_num) + '.png', img)
+
+ # save recursive outputs
+ for rec_num in xrange(num_rec_out):
+ gen_img = rec_preds[rec_num][pred_num]
+ gt_img = gt_frames[pred_num, :, :, 3 * rec_num:3 * (rec_num + 1)]
+ imsave(pred_dir + 'gen_' + str(rec_num) + '.png', gen_img)
+ imsave(pred_dir + 'gt_' + str(rec_num) + '.png', gt_img)
+
+ print '-' * 30
diff --git a/Code/loss_functions.py b/Code/loss_functions.py
new file mode 100644
index 0000000..994d226
--- /dev/null
+++ b/Code/loss_functions.py
@@ -0,0 +1,118 @@
+import tensorflow as tf
+import numpy as np
+
+from tfutils import log10
+import constants as c
+
+def combined_loss(gen_frames, gt_frames, d_preds, lam_adv=1, lam_lp=1, lam_gdl=1, l_num=2, alpha=2):
+ """
+ Calculates the sum of the combined adversarial, lp and GDL losses in the given proportion. Used
+ for training the generative model.
+
+ @param gen_frames: A list of tensors of the generated frames at each scale.
+ @param gt_frames: A list of tensors of the ground truth frames at each scale.
+ @param d_preds: A list of tensors of the classifications made by the discriminator model at each
+ scale.
+ @param lam_adv: The percentage of the adversarial loss to use in the combined loss.
+ @param lam_lp: The percentage of the lp loss to use in the combined loss.
+ @param lam_gdl: The percentage of the GDL loss to use in the combined loss.
+ @param l_num: 1 or 2 for l1 and l2 loss, respectively).
+ @param alpha: The power to which each gradient term is raised in GDL loss.
+
+ @return: The combined adversarial, lp and GDL losses.
+ """
+ batch_size = tf.shape(gen_frames[0])[0] # variable batch size as a tensor
+
+ loss = lam_lp * lp_loss(gen_frames, gt_frames, l_num)
+ loss += lam_gdl * gdl_loss(gen_frames, gt_frames, alpha)
+ if c.ADVERSARIAL: loss += lam_adv * adv_loss(d_preds, tf.ones([batch_size, 1]))
+
+ return loss
+
+
+def bce_loss(preds, targets):
+ """
+ Calculates the sum of binary cross-entropy losses between predictions and ground truths.
+
+ @param preds: A 1xN tensor. The predicted classifications of each frame.
+ @param targets: A 1xN tensor The target labels for each frame. (Either 1 or -1). Not "truths"
+ because the generator passes in lies to determine how well it confuses the
+ discriminator.
+
+ @return: The sum of binary cross-entropy losses.
+ """
+ return tf.squeeze(-1 * (tf.matmul(targets, log10(preds), transpose_a=True) +
+ tf.matmul(1 - targets, log10(1 - preds), transpose_a=True)))
+
+
+def lp_loss(gen_frames, gt_frames, l_num):
+ """
+ Calculates the sum of lp losses between the predicted and ground truth frames.
+
+ @param gen_frames: The predicted frames at each scale.
+ @param gt_frames: The ground truth frames at each scale
+ @param l_num: 1 or 2 for l1 and l2 loss, respectively).
+
+ @return: The lp loss.
+ """
+ # calculate the loss for each scale
+ scale_losses = []
+ for i in xrange(len(gen_frames)):
+ scale_losses.append(tf.reduce_sum(tf.abs(gen_frames[i] - gt_frames[i])**l_num))
+
+ # condense into one tensor and avg
+ return tf.reduce_mean(tf.pack(scale_losses))
+
+
+def gdl_loss(gen_frames, gt_frames, alpha):
+ """
+ Calculates the sum of GDL losses between the predicted and ground truth frames.
+
+ @param gen_frames: The predicted frames at each scale.
+ @param gt_frames: The ground truth frames at each scale
+ @param alpha: The power to which each gradient term is raised.
+
+ @return: The GDL loss.
+ """
+ # calculate the loss for each scale
+ scale_losses = []
+ for i in xrange(len(gen_frames)):
+ # create filters [-1, 1] and [[1],[-1]] for diffing to the left and down respectively.
+ pos = tf.constant(np.identity(3), dtype=tf.float32)
+ neg = -1 * pos
+ filter_x = tf.expand_dims(tf.pack([neg, pos]), 0) # [-1, 1]
+ filter_y = tf.pack([tf.expand_dims(pos, 0), tf.expand_dims(neg, 0)]) # [[1],[-1]]
+ strides = [1, 1, 1, 1] # stride of (1, 1)
+ padding = 'SAME'
+
+ gen_dx = tf.abs(tf.nn.conv2d(gen_frames[i], filter_x, strides, padding=padding))
+ gen_dy = tf.abs(tf.nn.conv2d(gen_frames[i], filter_y, strides, padding=padding))
+ gt_dx = tf.abs(tf.nn.conv2d(gt_frames[i], filter_x, strides, padding=padding))
+ gt_dy = tf.abs(tf.nn.conv2d(gt_frames[i], filter_y, strides, padding=padding))
+
+ grad_diff_x = tf.abs(gt_dx - gen_dx)
+ grad_diff_y = tf.abs(gt_dy - gen_dy)
+
+ scale_losses.append(tf.reduce_sum((grad_diff_x ** alpha + grad_diff_y ** alpha)))
+
+ # condense into one tensor and avg
+ return tf.reduce_mean(tf.pack(scale_losses))
+
+
+def adv_loss(preds, labels):
+ """
+ Calculates the sum of BCE losses between the predicted classifications and true labels.
+
+ @param preds: The predicted classifications at each scale.
+ @param labels: The true labels. (Same for every scale).
+
+ @return: The adversarial loss.
+ """
+ # calculate the loss for each scale
+ scale_losses = []
+ for i in xrange(len(preds)):
+ loss = bce_loss(preds[i], labels)
+ scale_losses.append(loss)
+
+ # condense into one tensor and avg
+ return tf.reduce_mean(tf.pack(scale_losses))
diff --git a/Code/loss_functions_test.py b/Code/loss_functions_test.py
new file mode 100644
index 0000000..6b015f2
--- /dev/null
+++ b/Code/loss_functions_test.py
@@ -0,0 +1,304 @@
+from loss_functions import *
+
+sess = tf.Session()
+BATCH_SIZE = 2
+NUM_SCALES = 5
+MAX_P = 5
+MAX_ALPHA = 1
+
+
+# noinspection PyClassHasNoInit
+class TestBCELoss:
+ def test_false_correct(self):
+ targets = tf.constant(np.zeros([5, 1]))
+ preds = 1e-7 * tf.constant(np.ones([5, 1]))
+ res = sess.run(bce_loss(preds, targets))
+
+ log_con = np.log10(1 - 1e-7)
+ res_tru = -1 * np.sum(np.array([log_con] * 5))
+ assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
+
+ def test_false_incorrect(self):
+ targets = tf.constant(np.zeros([5, 1]))
+ preds = tf.constant(np.ones([5, 1])) - 1e-7
+ res = sess.run(bce_loss(preds, targets))
+
+ log_con = np.log10(1e-7)
+ res_tru = -1 * np.sum(np.array([log_con] * 5))
+ assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
+
+ def test_false_half(self):
+ targets = tf.constant(np.zeros([5, 1]))
+ preds = 0.5 * tf.constant(np.ones([5, 1]))
+ res = sess.run(bce_loss(preds, targets))
+
+ log_con = np.log10(0.5)
+ res_tru = -1 * np.sum(np.array([log_con] * 5))
+ assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
+
+ def test_true_correct(self):
+ targets = tf.constant(np.ones([5, 1]))
+ preds = tf.constant(np.ones([5, 1])) - 1e-7
+ res = sess.run(bce_loss(preds, targets))
+
+ log = np.log10(1 - 1e-7)
+ res_tru = -1 * np.sum(np.array([log] * 5))
+ assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
+
+ def test_true_incorrect(self):
+ targets = tf.constant(np.ones([5, 1]))
+ preds = 1e-7 * tf.constant(np.ones([5, 1]))
+ res = sess.run(bce_loss(preds, targets))
+
+ log = np.log10(1e-7)
+ res_tru = -1 * np.sum(np.array([log] * 5))
+ assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
+
+ def test_true_half(self):
+ targets = tf.constant(np.ones([5, 1]))
+ preds = 0.5 * tf.constant(np.ones([5, 1]))
+ res = sess.run(bce_loss(preds, targets))
+
+ log = np.log10(0.5)
+ res_tru = -1 * np.sum(np.array([log] * 5))
+ assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
+
+
+# noinspection PyClassHasNoInit
+class TestLPLoss:
+ def test_same_images(self):
+ # generate scales
+ scale_preds = []
+ scale_truths = []
+
+ res_tru = 0
+ for i in xrange(1, NUM_SCALES + 1):
+ scale_preds.append(tf.constant(np.ones([BATCH_SIZE, 2**i, 2**i, 3])))
+ scale_truths.append(tf.constant(np.ones([BATCH_SIZE, 2**i, 2**i, 3])))
+
+ for p in xrange(1, MAX_P + 1):
+ res = sess.run(lp_loss(scale_preds, scale_truths, p))
+ assert res == res_tru, 'failed on p = %d' % p
+
+ def test_opposite_images(self):
+ # generate scales
+ scale_preds = []
+ scale_truths = []
+
+ res_tru = 0
+ for i in xrange(1, NUM_SCALES + 1):
+ scale_preds.append(tf.constant(np.zeros([BATCH_SIZE, 2**i, 2 ** i, 3])))
+ scale_truths.append(tf.constant(np.ones([BATCH_SIZE, 2**i, 2 ** i, 3])))
+
+ res_tru += BATCH_SIZE * 2**i * 2**i * 3
+
+ for p in xrange(1, MAX_P + 1):
+ res = sess.run(lp_loss(scale_preds, scale_truths, p))
+ assert res == res_tru, 'failed on p = %d' % p
+
+ def test_some_correct(self):
+ # generate scales
+ scale_preds = []
+ scale_truths = []
+
+ res_tru = 0
+ for i in xrange(1, NUM_SCALES + 1):
+ # generate batch of 3-deep identity matrices
+ preds = np.empty([BATCH_SIZE, 2**i, 2**i, 3])
+ imat = np.identity(2**i)
+ for elt in xrange(BATCH_SIZE):
+ preds[elt] = np.dstack([imat, imat, imat])
+
+ scale_preds.append(tf.constant(preds))
+ scale_truths.append(tf.constant(np.zeros([BATCH_SIZE, 2**i, 2**i, 3])))
+
+ res_tru += BATCH_SIZE * 2**i * 3
+
+ for p in xrange(1, MAX_P + 1):
+ res = sess.run(lp_loss(scale_preds, scale_truths, p))
+ assert res == res_tru, 'failed on p = %d' % p
+
+ def test_l_high(self):
+ # generate scales
+ scale_preds = []
+ scale_truths = []
+
+ res_tru = 0
+ for i in xrange(1, NUM_SCALES + 1):
+ # opposite images
+ preds = np.empty([BATCH_SIZE, 2**i, 2**i, 3])
+ preds.fill(3)
+ scale_preds.append(tf.constant(preds))
+ scale_truths.append(tf.constant(np.zeros([BATCH_SIZE, 2**i, 2**i, 3])))
+
+ res_tru += BATCH_SIZE * 2**i * 2**i * 3
+
+ for p in xrange(1, MAX_P + 1):
+ res = sess.run(lp_loss(scale_preds, scale_truths, p))
+ assert res == res_tru * (3**p), 'failed on p = %d' % p
+
+
+# noinspection PyClassHasNoInit
+class TestGDLLoss:
+ def test_same_uniform(self):
+ # generate scales
+ scale_preds = []
+ scale_truths = []
+
+ res_tru = 0
+ for i in xrange(1, NUM_SCALES + 1):
+ scale_preds.append(tf.ones([BATCH_SIZE, 2 ** i, 2 ** i, 3]))
+ scale_truths.append(tf.ones([BATCH_SIZE, 2 ** i, 2 ** i, 3]))
+
+ for a in xrange(1, MAX_ALPHA + 1):
+ res = sess.run(gdl_loss(scale_preds, scale_truths, a))
+ assert res == res_tru, 'failed on alpha = %d' % a
+
+ def test_same_nonuniform(self):
+ # generate scales
+ scale_preds = []
+ scale_truths = []
+
+ res_tru = 0
+ for i in xrange(1, NUM_SCALES + 1):
+ # generate batch of 3-deep identity matrices
+ arr = np.empty([BATCH_SIZE, 2 ** i, 2 ** i, 3])
+ imat = np.identity(2 ** i)
+ for elt in xrange(BATCH_SIZE):
+ arr[elt] = np.dstack([imat, imat, imat])
+
+ scale_preds.append(tf.constant(arr, dtype=tf.float32))
+ scale_truths.append(tf.constant(arr, dtype=tf.float32))
+
+ for a in xrange(1, MAX_ALPHA + 1):
+ res = sess.run(gdl_loss(scale_preds, scale_truths, a))
+ assert res == res_tru, 'failed on alpha = %d' % a
+
+ # TODO: Not 0 loss as expected because the 1s array is padded by 0s, so there is some gradient.
+ def test_diff_uniform(self):
+ # generate scales
+ scale_preds = []
+ scale_truths = []
+
+ res_tru = 0
+ for i in xrange(1, NUM_SCALES + 1):
+ scale_preds.append(tf.zeros([BATCH_SIZE, 2 ** i, 2 ** i, 3]))
+ scale_truths.append(tf.ones([BATCH_SIZE, 2 ** i, 2 ** i, 3]))
+
+ # every diff should have an abs value of 1, so no need for alpha handling
+ res_tru += BATCH_SIZE * 2 ** i * 2 * 3
+
+ for a in xrange(1, MAX_ALPHA + 1):
+ res = sess.run(gdl_loss(scale_preds, scale_truths, a))
+ assert res == res_tru, 'failed on alpha = %d' % a
+
+ def test_diff_one_uniform_one_not(self):
+ # generate scales
+ scale_preds = []
+ scale_truths = []
+
+ res_trus = np.zeros(MAX_ALPHA - 1)
+ for i in xrange(1, NUM_SCALES + 1):
+ # generate batch of 3-deep matrices with 3s on the diagonals
+ preds = np.empty([BATCH_SIZE, 2 ** i, 2 ** i, 3])
+ imat = np.identity(2 ** i) * 3
+ for elt in xrange(BATCH_SIZE):
+ preds[elt] = np.dstack([imat, imat, imat])
+
+ scale_preds.append(tf.constant(preds, dtype=tf.float32))
+ scale_truths.append(tf.zeros([BATCH_SIZE, 2 ** i, 2 ** i, 3]))
+
+ # every diff has an abs value of 3, so we can multiply that, raised to alpha
+ # for each alpha check, times the number of diffs in a batch:
+ # BATCH_SIZE * (diffs to left + down) * (diffs from up and right) * (# 3s in height) *
+ # (# channels)
+ num_diffs = BATCH_SIZE * 2 * 2 * 2**i * 3
+
+ for a in xrange(1, MAX_ALPHA):
+ res_trus[a] += num_diffs * 3**a
+
+ for a, res_tru in enumerate(res_trus):
+ res = sess.run(gdl_loss(scale_preds, scale_truths, a + 1))
+ assert res == res_tru, 'failed on alpha = %d' % (a + 1)
+
+
+# noinspection PyClassHasNoInit
+class TestAdvLoss:
+ def test_false_correct(self):
+ # generate scales
+ scale_preds = []
+ targets = tf.constant(np.zeros([5, 1]))
+
+ res_tru = 0
+ log_con = np.log10(1 - 1e-7)
+ for i in xrange(NUM_SCALES):
+ scale_preds.append(1e-7 * tf.constant(np.ones([5, 1])))
+ res_tru += -1 * np.sum(np.array([log_con] * 5))
+
+ res = sess.run(adv_loss(scale_preds, targets))
+ assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
+
+ def test_false_incorrect(self):
+ scale_preds = []
+ targets = tf.constant(np.zeros([5, 1]))
+
+ res_tru = 0
+ log_con = np.log10(1e-7)
+ for i in xrange(NUM_SCALES):
+ scale_preds.append(tf.constant(np.ones([5, 1])) - 1e-7)
+ res_tru += -1 * np.sum(np.array([log_con] * 5))
+
+ res = sess.run(adv_loss(scale_preds, targets))
+ assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
+
+ def test_false_half(self):
+ scale_preds = []
+ targets = tf.constant(np.zeros([5, 1]))
+
+ res_tru = 0
+ log_con = np.log10(0.5)
+ for i in xrange(NUM_SCALES):
+ scale_preds.append(0.5 * tf.constant(np.ones([5, 1])))
+ res_tru += -1 * np.sum(np.array([log_con] * 5))
+
+ res = sess.run(adv_loss(scale_preds, targets))
+ assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
+
+ def test_true_correct(self):
+ scale_preds = []
+ targets = tf.constant(np.ones([5, 1]))
+
+ res_tru = 0
+ log = np.log10(1 - 1e-7)
+ for i in xrange(NUM_SCALES):
+ scale_preds.append(tf.constant(np.ones([5, 1])) - 1e-7)
+ res_tru += -1 * np.sum(np.array([log] * 5))
+
+ res = sess.run(adv_loss(scale_preds, targets))
+ assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
+
+ def test_true_incorrect(self):
+ scale_preds = []
+ targets = tf.constant(np.ones([5, 1]))
+
+ res_tru = 0
+ log = np.log10(1e-7)
+ for i in xrange(NUM_SCALES):
+ scale_preds.append(1e-7 * tf.constant(np.ones([5, 1])))
+ res_tru += -1 * np.sum(np.array([log] * 5))
+
+ res = sess.run(adv_loss(scale_preds, targets))
+ assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
+
+ def test_true_half(self):
+ scale_preds = []
+ targets = tf.constant(np.ones([5, 1]))
+
+ res_tru = 0
+ log = np.log10(0.5)
+ for i in xrange(NUM_SCALES):
+ scale_preds.append(0.5 * tf.constant(np.ones([5, 1])))
+ res_tru += -1 * np.sum(np.array([log] * 5))
+
+ res = sess.run(adv_loss(scale_preds, targets))
+ assert np.array_equal(np.around(res, 7), np.around(res_tru, 7))
diff --git a/Code/process_data.py b/Code/process_data.py
new file mode 100644
index 0000000..170959a
--- /dev/null
+++ b/Code/process_data.py
@@ -0,0 +1,71 @@
+import numpy as np
+import getopt
+import sys
+from glob import glob
+
+import constants as c
+from utils import process_clip
+
+
+def process_training_data(num_clips):
+ """
+ Processes random training clips from the full training data. Saves to TRAIN_DIR_CLIPS by
+ default.
+
+ @param num_clips: The number of clips to process. Default = 5000000 (set in __main__).
+
+ @warning: This can take a couple of hours to complete with large numbers of clips.
+ """
+ num_prev_clips = len(glob(c.TRAIN_DIR_CLIPS + '*'))
+
+ for clip_num in xrange(num_prev_clips, num_clips + num_prev_clips):
+ clip = process_clip()
+
+ np.savez_compressed(c.TRAIN_DIR_CLIPS + str(clip_num), clip)
+
+ if (clip_num + 1) % 100 == 0: print 'Processed %d clips' % (clip_num + 1)
+
+
+def usage():
+ print 'Options:'
+ print '-n/--num_clips= <# clips to process for training>'
+ print '-t/--train_dir= <Directory of full training frames>'
+ print '-c/--clips_dir= <Save directory for processed clips>'
+ print " (I suggest making this a hidden dir so the filesystem doesn't freeze"
+ print " with so many files. DON'T `ls` THIS DIRECTORY!)"
+ print '-o/--overwrite (Overwrites the previous data in the training dir)'
+
+
+def main():
+ ##
+ # Handle command line input
+ ##
+
+ num_clips = 5000000
+
+ try:
+ opts, _ = getopt.getopt(sys.argv[1:], 'n:t:c:o',
+ ['num_clips=', 'train_dir=', 'clips_dir=', 'overwrite'])
+ except getopt.GetoptError:
+ usage()
+ sys.exit(2)
+
+ for opt, arg in opts:
+ if opt in ('-n', '--num_clips'):
+ num_clips = int(arg)
+ if opt in ('-t', '--train_dir'):
+ c.TRAIN_DIR = c.get_dir(arg)
+ if opt in ('-c', '--clips_dir'):
+ c.TRAIN_DIR_CLIPS = c.get_dir(arg)
+ if opt in ('-o', '--overwrite'):
+ c.clear_dir(c.TRAIN_DIR_CLIPS)
+
+ ##
+ # Process data for training
+ ##
+
+ process_training_data(num_clips)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/Code/tfutils.py b/Code/tfutils.py
new file mode 100644
index 0000000..22baf95
--- /dev/null
+++ b/Code/tfutils.py
@@ -0,0 +1,133 @@
+import tensorflow as tf
+import numpy as np
+
+
+def w(shape, stddev=0.01):
+ """
+ @return A weight layer with the given shape and standard deviation. Initialized with a
+ truncated normal distribution.
+ """
+ return tf.Variable(tf.truncated_normal(shape, stddev=stddev))
+
+
+def b(shape, const=0.1):
+ """
+ @return A bias layer with the given shape.
+ """
+ return tf.Variable(tf.constant(const, shape=shape))
+
+
+def conv_out_size(i, p, k, s):
+ """
+ Gets the output size for a 2D convolution. (Assumes square input and kernel).
+
+ @param i: The side length of the input.
+ @param p: The padding type (either 'SAME' or 'VALID').
+ @param k: The side length of the kernel.
+ @param s: The stride.
+
+ @type i: int
+ @type p: string
+ @type k: int
+ @type s: int
+
+ @return The side length of the output.
+ """
+ # convert p to a number
+ if p == 'SAME':
+ p = k // 2
+ elif p == 'VALID':
+ p = 0
+ else:
+ raise ValueError('p must be "SAME" or "VALID".')
+
+ return int(((i + (2 * p) - k) / s) + 1)
+
+
+def log10(t):
+ """
+ Calculates the base-10 log of each element in t.
+
+ @param t: The tensor from which to calculate the base-10 log.
+
+ @return: A tensor with the base-10 log of each element in t.
+ """
+
+ numerator = tf.log(t)
+ denominator = tf.log(tf.constant(10, dtype=numerator.dtype))
+ return numerator / denominator
+
+
+def batch_pad_to_bounding_box(images, offset_height, offset_width, target_height, target_width):
+ """
+ Zero-pads a batch of images with the given dimensions.
+
+ @param images: 4-D tensor with shape [batch_size, height, width, channels]
+ @param offset_height: Number of rows of zeros to add on top.
+ @param offset_width: Number of columns of zeros to add on the left.
+ @param target_height: Height of output images.
+ @param target_width: Width of output images.
+
+ @return: The batch of images, all zero-padded with the specified dimensions.
+ """
+ batch_size, height, width, channels = tf.Session().run(tf.shape(images))
+
+ if not offset_height >= 0:
+ raise ValueError('offset_height must be >= 0')
+ if not offset_width >= 0:
+ raise ValueError('offset_width must be >= 0')
+ if not target_height >= height + offset_height:
+ raise ValueError('target_height must be >= height + offset_height')
+ if not target_width >= width + offset_width:
+ raise ValueError('target_width must be >= width + offset_width')
+
+ num_tpad = offset_height
+ num_lpad = offset_width
+ num_bpad = target_height - (height + offset_height)
+ num_rpad = target_width - (width + offset_width)
+
+ tpad = np.zeros([batch_size, num_tpad, width, channels])
+ bpad = np.zeros([batch_size, num_bpad, width, channels])
+ lpad = np.zeros([batch_size, target_height, num_lpad, channels])
+ rpad = np.zeros([batch_size, target_height, num_rpad, channels])
+
+ padded = images
+ if num_tpad > 0 and num_bpad > 0: padded = tf.concat(1, [tpad, padded, bpad])
+ elif num_tpad > 0: padded = tf.concat(1, [tpad, padded])
+ elif num_bpad > 0: padded = tf.concat(1, [padded, bpad])
+ if num_lpad > 0 and num_rpad > 0: padded = tf.concat(2, [lpad, padded, rpad])
+ elif num_lpad > 0: padded = tf.concat(2, [lpad, padded])
+ elif num_rpad > 0: padded = tf.concat(2, [padded, rpad])
+
+ return padded
+
+
+def batch_crop_to_bounding_box(images, offset_height, offset_width, target_height, target_width):
+ """
+ Crops a batch of images to the given dimensions.
+
+ @param images: 4-D tensor with shape [batch, height, width, channels]
+ @param offset_height: Vertical coordinate of the top-left corner of the result in the input.
+ @param offset_width: Horizontal coordinate of the top-left corner of the result in the input.
+ @param target_height: Height of output images.
+ @param target_width: Width of output images.
+
+ @return: The batch of images, all cropped the specified dimensions.
+ """
+ batch_size, height, width, channels = tf.Session().run(tf.shape(images))
+
+ if not offset_height >= 0:
+ raise ValueError('offset_height must be >= 0')
+ if not offset_width >= 0:
+ raise ValueError('offset_width must be >= 0')
+ if not target_height + offset_height <= height:
+ raise ValueError('target_height + offset_height must be <= height')
+ if not target_width <= width - offset_width:
+ raise ValueError('target_width + offset_width must be <= width')
+
+ top = offset_height
+ bottom = target_height + offset_height
+ left = offset_width
+ right = target_width + offset_width
+
+ return images[:, top:bottom, left:right, :]
diff --git a/Code/tfutils_test.py b/Code/tfutils_test.py
new file mode 100644
index 0000000..4e2b490
--- /dev/null
+++ b/Code/tfutils_test.py
@@ -0,0 +1,102 @@
+from tfutils import *
+
+imgs = tf.constant(np.ones([2, 2, 2, 3]))
+sess = tf.Session()
+
+
+# noinspection PyClassHasNoInit,PyMethodMayBeStatic
+class TestPad:
+ def test_rb(self):
+ res = sess.run(batch_pad_to_bounding_box(imgs, 0, 0, 4, 4))
+ assert np.array_equal(res, np.array([[[[1, 1, 1],
+ [1, 1, 1],
+ [0, 0, 0],
+ [0, 0, 0]],
+ [[1, 1, 1],
+ [1, 1, 1],
+ [0, 0, 0],
+ [0, 0, 0]],
+ [[0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0]],
+ [[0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0]]
+ ],
+ [[[1, 1, 1],
+ [1, 1, 1],
+ [0, 0, 0],
+ [0, 0, 0]],
+ [[1, 1, 1],
+ [1, 1, 1],
+ [0, 0, 0],
+ [0, 0, 0]],
+ [[0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0]],
+ [[0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0]]
+ ]], dtype=float))
+
+ def test_center(self):
+ res = sess.run(batch_pad_to_bounding_box(imgs, 1, 1, 4, 4))
+ assert np.array_equal(res, np.array([[[[0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0]],
+ [[0, 0, 0],
+ [1, 1, 1],
+ [1, 1, 1],
+ [0, 0, 0]],
+ [[0, 0, 0],
+ [1, 1, 1],
+ [1, 1, 1],
+ [0, 0, 0]],
+ [[0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0]]
+ ],
+ [[[0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0]],
+ [[0, 0, 0],
+ [1, 1, 1],
+ [1, 1, 1],
+ [0, 0, 0]],
+ [[0, 0, 0],
+ [1, 1, 1],
+ [1, 1, 1],
+ [0, 0, 0]],
+ [[0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0],
+ [0, 0, 0]]
+ ]], dtype=float))
+
+
+padded = batch_pad_to_bounding_box(imgs, 1, 1, 4, 4)
+
+
+# noinspection PyClassHasNoInit
+class TestCrop:
+ def test_rb(self):
+ res = sess.run(batch_crop_to_bounding_box(padded, 0, 0, 2, 2))
+ assert np.array_equal(res, np.array([[[[0, 0, 0],
+ [0, 0, 0]],
+ [[0, 0, 0],
+ [1, 1, 1]]],
+ [[[0, 0, 0],
+ [0, 0, 0]],
+ [[0, 0, 0],
+ [1, 1, 1]]]]))
+
+ def test_center(self):
+ res = sess.run(batch_crop_to_bounding_box(padded, 1, 1, 2, 2))
+ assert np.array_equal(res, np.ones([2, 2, 2, 3]))
diff --git a/Code/utils.py b/Code/utils.py
new file mode 100644
index 0000000..2b97bdb
--- /dev/null
+++ b/Code/utils.py
@@ -0,0 +1,212 @@
+import tensorflow as tf
+import numpy as np
+from scipy.ndimage import imread
+from glob import glob
+
+import constants as c
+from tfutils import log10
+
+##
+# Data
+##
+
+def normalize_frames(frames):
+ """
+ Convert frames from int8 [0, 255] to float32 [-1, 1].
+
+ @param frames: A numpy array. The frames to be converted.
+
+ @return: The normalized frames.
+ """
+ new_frames = frames.astype(np.float32)
+ new_frames /= (255 / 2)
+ new_frames -= 1
+
+ return new_frames
+
+
+def denormalize_frames(frames):
+ """
+ Performs the inverse operation of normalize_frames.
+
+ @param frames: A numpy array. The frames to be converted.
+
+ @return: The denormalized frames.
+ """
+ new_frames = frames + 1
+ new_frames *= (255 / 2)
+ # noinspection PyUnresolvedReferences
+ new_frames = new_frames.astype(np.uint8)
+
+ return new_frames
+
+def clip_l2_diff(clip):
+ """
+ @param clip: A numpy array of shape [c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + 1))].
+ @return: The sum of l2 differences between the frame pixels of each sequential pair of frames.
+ """
+ diff = 0
+ for i in xrange(c.HIST_LEN):
+ frame = clip[:, :, 3 * i:3 * (i + 1)]
+ next_frame = clip[:, :, 3 * (i + 1):3 * (i + 2)]
+ # noinspection PyTypeChecker
+ diff += np.sum(np.square(next_frame - frame))
+
+ return diff
+
+def get_full_clips(data_dir, num_clips, num_rec_out=1):
+ """
+ Loads a batch of random clips from the unprocessed train or test data.
+
+ @param data_dir: The directory of the data to read. Should be either c.TRAIN_DIR or c.TEST_DIR.
+ @param num_clips: The number of clips to read.
+ @param num_rec_out: The number of outputs to predict. Outputs > 1 are computed recursively,
+ using the previously-generated frames as input. Default = 1.
+
+ @return: An array of shape
+ [num_clips, c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + num_rec_out))].
+ A batch of frame sequences with values normalized in range [-1, 1].
+ """
+ clips = np.empty([num_clips,
+ c.TEST_HEIGHT,
+ c.TEST_WIDTH,
+ (3 * (c.HIST_LEN + num_rec_out))])
+
+ # get num_clips random episodes
+ ep_dirs = np.random.choice(glob(data_dir + '*'), num_clips)
+
+ # get a random clip of length HIST_LEN + 1 from each episode
+ for clip_num, ep_dir in enumerate(ep_dirs):
+ ep_frame_paths = glob(ep_dir + '/*')
+ start_index = np.random.choice(len(ep_frame_paths) - (c.HIST_LEN + num_rec_out - 1))
+ clip_frame_paths = ep_frame_paths[start_index:start_index + (c.HIST_LEN + num_rec_out)]
+
+ # read in frames
+ for frame_num, frame_path in enumerate(clip_frame_paths):
+ frame = imread(frame_path, mode='RGB')
+ norm_frame = normalize_frames(frame)
+
+ clips[clip_num, :, :, frame_num * 3:(frame_num + 1) * 3] = norm_frame
+
+ return clips
+
+def process_clip():
+ """
+ Gets a clip from the train dataset, cropped randomly to c.TRAIN_HEIGHT x c.TRAIN_WIDTH.
+
+ @return: An array of shape [c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + 1))].
+ A frame sequence with values normalized in range [-1, 1].
+ """
+ clip = get_full_clips(c.TRAIN_DIR, 1)[0]
+
+ # Randomly crop the clip. With 0.05 probability, take the first crop offered, otherwise,
+ # repeat until we have a clip with movement in it.
+ take_first = np.random.choice(2, p=[0.95, 0.05])
+ cropped_clip = np.empty([c.TRAIN_HEIGHT, c.TRAIN_WIDTH, 3 * (c.HIST_LEN + 1)])
+ for i in xrange(100): # cap at 100 trials in case the clip has no movement anywhere
+ crop_x = np.random.choice(c.TEST_WIDTH - c.TRAIN_WIDTH + 1)
+ crop_y = np.random.choice(c.TEST_HEIGHT - c.TRAIN_HEIGHT + 1)
+ cropped_clip = clip[crop_y:crop_y + c.TRAIN_HEIGHT, crop_x:crop_x + c.TRAIN_WIDTH, :]
+
+ if take_first or clip_l2_diff(cropped_clip) > c.MOVEMENT_THRESHOLD:
+ break
+
+ return cropped_clip
+
+def get_train_batch():
+ """
+ Loads c.BATCH_SIZE clips from the database of preprocessed training clips.
+
+ @return: An array of shape
+ [c.BATCH_SIZE, c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + 1))].
+ """
+ clips = np.empty([c.BATCH_SIZE, c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + 1))],
+ dtype=np.float32)
+ for i in xrange(c.BATCH_SIZE):
+ path = c.TRAIN_DIR_CLIPS + str(np.random.choice(c.NUM_CLIPS)) + '.npz'
+ clip = np.load(path)['arr_0']
+
+ clips[i] = clip
+
+ return clips
+
+
+def get_test_batch(test_batch_size, num_rec_out=1):
+ """
+ Gets a clip from the test dataset.
+
+ @param test_batch_size: The number of clips.
+ @param num_rec_out: The number of outputs to predict. Outputs > 1 are computed recursively,
+ using the previously-generated frames as input. Default = 1.
+
+ @return: An array of shape:
+ [test_batch_size, c.TEST_HEIGHT, c.TEST_WIDTH, (3 * (c.HIST_LEN + num_rec_out))].
+ A batch of frame sequences with values normalized in range [-1, 1].
+ """
+ return get_full_clips(c.TEST_DIR, test_batch_size, num_rec_out=num_rec_out)
+
+
+##
+# Error calculation
+##
+
+# TODO: Add SSIM error http://www.cns.nyu.edu/pub/eero/wang03-reprint.pdf
+# TODO: Unit test error functions.
+
+def psnr_error(gen_frames, gt_frames):
+ """
+ Computes the Peak Signal to Noise Ratio error between the generated images and the ground
+ truth images.
+
+ @param gen_frames: A tensor of shape [batch_size, height, width, 3]. The frames generated by the
+ generator model.
+ @param gt_frames: A tensor of shape [batch_size, height, width, 3]. The ground-truth frames for
+ each frame in gen_frames.
+
+ @return: A scalar tensor. The mean Peak Signal to Noise Ratio error over each frame in the
+ batch.
+ """
+ shape = tf.shape(gen_frames)
+ num_pixels = tf.to_float(shape[1] * shape[2])
+ square_diff = tf.square(gt_frames - gen_frames)
+
+ batch_errors = 10 * log10(1 / ((1 / num_pixels) * tf.reduce_sum(square_diff, [1, 2, 3])))
+ return tf.reduce_mean(batch_errors)
+
+def sharp_diff_error(gen_frames, gt_frames):
+ """
+ Computes the Sharpness Difference error between the generated images and the ground truth
+ images.
+
+ @param gen_frames: A tensor of shape [batch_size, height, width, 3]. The frames generated by the
+ generator model.
+ @param gt_frames: A tensor of shape [batch_size, height, width, 3]. The ground-truth frames for
+ each frame in gen_frames.
+
+ @return: A scalar tensor. The Sharpness Difference error over each frame in the batch.
+ """
+ shape = tf.shape(gen_frames)
+ num_pixels = tf.to_float(shape[1] * shape[2])
+
+ # gradient difference
+ # create filters [-1, 1] and [[1],[-1]] for diffing to the left and down respectively.
+ # TODO: Could this be simplified with one filter [[-1, 2], [0, -1]]?
+ pos = tf.constant(np.identity(3), dtype=tf.float32)
+ neg = -1 * pos
+ filter_x = tf.expand_dims(tf.pack([neg, pos]), 0) # [-1, 1]
+ filter_y = tf.pack([tf.expand_dims(pos, 0), tf.expand_dims(neg, 0)]) # [[1],[-1]]
+ strides = [1, 1, 1, 1] # stride of (1, 1)
+ padding = 'SAME'
+
+ gen_dx = tf.abs(tf.nn.conv2d(gen_frames, filter_x, strides, padding=padding))
+ gen_dy = tf.abs(tf.nn.conv2d(gen_frames, filter_y, strides, padding=padding))
+ gt_dx = tf.abs(tf.nn.conv2d(gt_frames, filter_x, strides, padding=padding))
+ gt_dy = tf.abs(tf.nn.conv2d(gt_frames, filter_y, strides, padding=padding))
+
+ gen_grad_sum = gen_dx + gen_dy
+ gt_grad_sum = gt_dx + gt_dy
+
+ grad_diff = tf.abs(gt_grad_sum - gen_grad_sum)
+
+ batch_errors = 10 * log10(1 / ((1 / num_pixels) * tf.reduce_sum(grad_diff, [1, 2, 3])))
+ return tf.reduce_mean(batch_errors)
diff --git a/New Figure 1/New Figure 1 – Deep Multiscale Video Prediction Beyond Mean Square Error-01.png b/New Figure 1/New Figure 1 – Deep Multiscale Video Prediction Beyond Mean Square Error-01.png
new file mode 100644
index 0000000..f272f17
--- /dev/null
+++ b/New Figure 1/New Figure 1 – Deep Multiscale Video Prediction Beyond Mean Square Error-01.png
Binary files differ
diff --git a/New Figure 1/New Figure 1 – Deep Multiscale Video Prediction Beyond Mean Square Error.ai b/New Figure 1/New Figure 1 – Deep Multiscale Video Prediction Beyond Mean Square Error.ai
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+ <xmpG:Colorants>
+ <rdf:Seq>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>White</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>255</xmpG:red>
+ <xmpG:green>255</xmpG:green>
+ <xmpG:blue>255</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>Black</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>0</xmpG:green>
+ <xmpG:blue>0</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>RGB Red</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>255</xmpG:red>
+ <xmpG:green>0</xmpG:green>
+ <xmpG:blue>0</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>RGB Yellow</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>255</xmpG:red>
+ <xmpG:green>255</xmpG:green>
+ <xmpG:blue>0</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>RGB Green</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>255</xmpG:green>
+ <xmpG:blue>0</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>RGB Cyan</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>255</xmpG:green>
+ <xmpG:blue>255</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>RGB Blue</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>0</xmpG:green>
+ <xmpG:blue>255</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>RGB Magenta</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>255</xmpG:red>
+ <xmpG:green>0</xmpG:green>
+ <xmpG:blue>255</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=193 G=39 B=45</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>193</xmpG:red>
+ <xmpG:green>39</xmpG:green>
+ <xmpG:blue>45</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=237 G=28 B=36</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>237</xmpG:red>
+ <xmpG:green>28</xmpG:green>
+ <xmpG:blue>36</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=241 G=90 B=36</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>241</xmpG:red>
+ <xmpG:green>90</xmpG:green>
+ <xmpG:blue>36</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=247 G=147 B=30</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>247</xmpG:red>
+ <xmpG:green>147</xmpG:green>
+ <xmpG:blue>30</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=251 G=176 B=59</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>251</xmpG:red>
+ <xmpG:green>176</xmpG:green>
+ <xmpG:blue>59</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=252 G=238 B=33</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>252</xmpG:red>
+ <xmpG:green>238</xmpG:green>
+ <xmpG:blue>33</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=217 G=224 B=33</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>217</xmpG:red>
+ <xmpG:green>224</xmpG:green>
+ <xmpG:blue>33</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=140 G=198 B=63</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>140</xmpG:red>
+ <xmpG:green>198</xmpG:green>
+ <xmpG:blue>63</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=57 G=181 B=74</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>57</xmpG:red>
+ <xmpG:green>181</xmpG:green>
+ <xmpG:blue>74</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=0 G=146 B=69</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>146</xmpG:green>
+ <xmpG:blue>69</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=0 G=104 B=55</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>104</xmpG:green>
+ <xmpG:blue>55</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=34 G=181 B=115</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>34</xmpG:red>
+ <xmpG:green>181</xmpG:green>
+ <xmpG:blue>115</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=0 G=169 B=157</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>169</xmpG:green>
+ <xmpG:blue>157</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=41 G=171 B=226</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>41</xmpG:red>
+ <xmpG:green>171</xmpG:green>
+ <xmpG:blue>226</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=0 G=113 B=188</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>113</xmpG:green>
+ <xmpG:blue>188</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=46 G=49 B=146</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>46</xmpG:red>
+ <xmpG:green>49</xmpG:green>
+ <xmpG:blue>146</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=27 G=20 B=100</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>27</xmpG:red>
+ <xmpG:green>20</xmpG:green>
+ <xmpG:blue>100</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=102 G=45 B=145</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>102</xmpG:red>
+ <xmpG:green>45</xmpG:green>
+ <xmpG:blue>145</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=147 G=39 B=143</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>147</xmpG:red>
+ <xmpG:green>39</xmpG:green>
+ <xmpG:blue>143</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=158 G=0 B=93</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>158</xmpG:red>
+ <xmpG:green>0</xmpG:green>
+ <xmpG:blue>93</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=212 G=20 B=90</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>212</xmpG:red>
+ <xmpG:green>20</xmpG:green>
+ <xmpG:blue>90</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=237 G=30 B=121</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>237</xmpG:red>
+ <xmpG:green>30</xmpG:green>
+ <xmpG:blue>121</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=199 G=178 B=153</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>199</xmpG:red>
+ <xmpG:green>178</xmpG:green>
+ <xmpG:blue>153</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=153 G=134 B=117</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>153</xmpG:red>
+ <xmpG:green>134</xmpG:green>
+ <xmpG:blue>117</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=115 G=99 B=87</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>115</xmpG:red>
+ <xmpG:green>99</xmpG:green>
+ <xmpG:blue>87</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=83 G=71 B=65</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>83</xmpG:red>
+ <xmpG:green>71</xmpG:green>
+ <xmpG:blue>65</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=198 G=156 B=109</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>198</xmpG:red>
+ <xmpG:green>156</xmpG:green>
+ <xmpG:blue>109</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=166 G=124 B=82</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>166</xmpG:red>
+ <xmpG:green>124</xmpG:green>
+ <xmpG:blue>82</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=140 G=98 B=57</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>140</xmpG:red>
+ <xmpG:green>98</xmpG:green>
+ <xmpG:blue>57</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=117 G=76 B=36</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>117</xmpG:red>
+ <xmpG:green>76</xmpG:green>
+ <xmpG:blue>36</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=96 G=56 B=19</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>96</xmpG:red>
+ <xmpG:green>56</xmpG:green>
+ <xmpG:blue>19</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=66 G=33 B=11</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>66</xmpG:red>
+ <xmpG:green>33</xmpG:green>
+ <xmpG:blue>11</xmpG:blue>
+ </rdf:li>
+ </rdf:Seq>
+ </xmpG:Colorants>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:groupName>Grays</xmpG:groupName>
+ <xmpG:groupType>1</xmpG:groupType>
+ <xmpG:Colorants>
+ <rdf:Seq>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=0 G=0 B=0</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>0</xmpG:green>
+ <xmpG:blue>0</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=26 G=26 B=26</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>26</xmpG:red>
+ <xmpG:green>26</xmpG:green>
+ <xmpG:blue>26</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=51 G=51 B=51</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>51</xmpG:red>
+ <xmpG:green>51</xmpG:green>
+ <xmpG:blue>51</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=77 G=77 B=77</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>77</xmpG:red>
+ <xmpG:green>77</xmpG:green>
+ <xmpG:blue>77</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=102 G=102 B=102</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>102</xmpG:red>
+ <xmpG:green>102</xmpG:green>
+ <xmpG:blue>102</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=128 G=128 B=128</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>128</xmpG:red>
+ <xmpG:green>128</xmpG:green>
+ <xmpG:blue>128</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=153 G=153 B=153</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>153</xmpG:red>
+ <xmpG:green>153</xmpG:green>
+ <xmpG:blue>153</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=179 G=179 B=179</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>179</xmpG:red>
+ <xmpG:green>179</xmpG:green>
+ <xmpG:blue>179</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=204 G=204 B=204</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>204</xmpG:red>
+ <xmpG:green>204</xmpG:green>
+ <xmpG:blue>204</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=230 G=230 B=230</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>230</xmpG:red>
+ <xmpG:green>230</xmpG:green>
+ <xmpG:blue>230</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=242 G=242 B=242</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>242</xmpG:red>
+ <xmpG:green>242</xmpG:green>
+ <xmpG:blue>242</xmpG:blue>
+ </rdf:li>
+ </rdf:Seq>
+ </xmpG:Colorants>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:groupName>Web Color Group</xmpG:groupName>
+ <xmpG:groupType>1</xmpG:groupType>
+ <xmpG:Colorants>
+ <rdf:Seq>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=63 G=169 B=245</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>63</xmpG:red>
+ <xmpG:green>169</xmpG:green>
+ <xmpG:blue>245</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=122 G=201 B=67</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>122</xmpG:red>
+ <xmpG:green>201</xmpG:green>
+ <xmpG:blue>67</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=255 G=147 B=30</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>255</xmpG:red>
+ <xmpG:green>147</xmpG:green>
+ <xmpG:blue>30</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=255 G=29 B=37</xmpG:swatchName>
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+ <rdf:Seq>
+ <rdf:li>Cyan</rdf:li>
+ <rdf:li>Magenta</rdf:li>
+ <rdf:li>Yellow</rdf:li>
+ <rdf:li>Black</rdf:li>
+ </rdf:Seq>
+ </xmpTPg:PlateNames>
+ <xmpTPg:SwatchGroups>
+ <rdf:Seq>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:groupName>Default Swatch Group</xmpG:groupName>
+ <xmpG:groupType>0</xmpG:groupType>
+ <xmpG:Colorants>
+ <rdf:Seq>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>White</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>255</xmpG:red>
+ <xmpG:green>255</xmpG:green>
+ <xmpG:blue>255</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>Black</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>0</xmpG:green>
+ <xmpG:blue>0</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>RGB Red</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>255</xmpG:red>
+ <xmpG:green>0</xmpG:green>
+ <xmpG:blue>0</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>RGB Yellow</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>255</xmpG:red>
+ <xmpG:green>255</xmpG:green>
+ <xmpG:blue>0</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>RGB Green</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>255</xmpG:green>
+ <xmpG:blue>0</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>RGB Cyan</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>255</xmpG:green>
+ <xmpG:blue>255</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>RGB Blue</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>0</xmpG:green>
+ <xmpG:blue>255</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>RGB Magenta</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>255</xmpG:red>
+ <xmpG:green>0</xmpG:green>
+ <xmpG:blue>255</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=193 G=39 B=45</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>193</xmpG:red>
+ <xmpG:green>39</xmpG:green>
+ <xmpG:blue>45</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=237 G=28 B=36</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>237</xmpG:red>
+ <xmpG:green>28</xmpG:green>
+ <xmpG:blue>36</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=241 G=90 B=36</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>241</xmpG:red>
+ <xmpG:green>90</xmpG:green>
+ <xmpG:blue>36</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=247 G=147 B=30</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>247</xmpG:red>
+ <xmpG:green>147</xmpG:green>
+ <xmpG:blue>30</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=251 G=176 B=59</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>251</xmpG:red>
+ <xmpG:green>176</xmpG:green>
+ <xmpG:blue>59</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=252 G=238 B=33</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>252</xmpG:red>
+ <xmpG:green>238</xmpG:green>
+ <xmpG:blue>33</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=217 G=224 B=33</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>217</xmpG:red>
+ <xmpG:green>224</xmpG:green>
+ <xmpG:blue>33</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=140 G=198 B=63</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>140</xmpG:red>
+ <xmpG:green>198</xmpG:green>
+ <xmpG:blue>63</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=57 G=181 B=74</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>57</xmpG:red>
+ <xmpG:green>181</xmpG:green>
+ <xmpG:blue>74</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=0 G=146 B=69</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>146</xmpG:green>
+ <xmpG:blue>69</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=0 G=104 B=55</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>104</xmpG:green>
+ <xmpG:blue>55</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=34 G=181 B=115</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>34</xmpG:red>
+ <xmpG:green>181</xmpG:green>
+ <xmpG:blue>115</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=0 G=169 B=157</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>169</xmpG:green>
+ <xmpG:blue>157</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=41 G=171 B=226</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>41</xmpG:red>
+ <xmpG:green>171</xmpG:green>
+ <xmpG:blue>226</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=0 G=113 B=188</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>113</xmpG:green>
+ <xmpG:blue>188</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=46 G=49 B=146</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>46</xmpG:red>
+ <xmpG:green>49</xmpG:green>
+ <xmpG:blue>146</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=27 G=20 B=100</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>27</xmpG:red>
+ <xmpG:green>20</xmpG:green>
+ <xmpG:blue>100</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=102 G=45 B=145</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>102</xmpG:red>
+ <xmpG:green>45</xmpG:green>
+ <xmpG:blue>145</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=147 G=39 B=143</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>147</xmpG:red>
+ <xmpG:green>39</xmpG:green>
+ <xmpG:blue>143</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=158 G=0 B=93</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>158</xmpG:red>
+ <xmpG:green>0</xmpG:green>
+ <xmpG:blue>93</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=212 G=20 B=90</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>212</xmpG:red>
+ <xmpG:green>20</xmpG:green>
+ <xmpG:blue>90</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=237 G=30 B=121</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>237</xmpG:red>
+ <xmpG:green>30</xmpG:green>
+ <xmpG:blue>121</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=199 G=178 B=153</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>199</xmpG:red>
+ <xmpG:green>178</xmpG:green>
+ <xmpG:blue>153</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=153 G=134 B=117</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>153</xmpG:red>
+ <xmpG:green>134</xmpG:green>
+ <xmpG:blue>117</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=115 G=99 B=87</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>115</xmpG:red>
+ <xmpG:green>99</xmpG:green>
+ <xmpG:blue>87</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=83 G=71 B=65</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>83</xmpG:red>
+ <xmpG:green>71</xmpG:green>
+ <xmpG:blue>65</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=198 G=156 B=109</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>198</xmpG:red>
+ <xmpG:green>156</xmpG:green>
+ <xmpG:blue>109</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=166 G=124 B=82</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>166</xmpG:red>
+ <xmpG:green>124</xmpG:green>
+ <xmpG:blue>82</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=140 G=98 B=57</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>140</xmpG:red>
+ <xmpG:green>98</xmpG:green>
+ <xmpG:blue>57</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=117 G=76 B=36</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>117</xmpG:red>
+ <xmpG:green>76</xmpG:green>
+ <xmpG:blue>36</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=96 G=56 B=19</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>96</xmpG:red>
+ <xmpG:green>56</xmpG:green>
+ <xmpG:blue>19</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=66 G=33 B=11</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>66</xmpG:red>
+ <xmpG:green>33</xmpG:green>
+ <xmpG:blue>11</xmpG:blue>
+ </rdf:li>
+ </rdf:Seq>
+ </xmpG:Colorants>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:groupName>Grays</xmpG:groupName>
+ <xmpG:groupType>1</xmpG:groupType>
+ <xmpG:Colorants>
+ <rdf:Seq>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=0 G=0 B=0</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>0</xmpG:red>
+ <xmpG:green>0</xmpG:green>
+ <xmpG:blue>0</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=26 G=26 B=26</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>26</xmpG:red>
+ <xmpG:green>26</xmpG:green>
+ <xmpG:blue>26</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=51 G=51 B=51</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>51</xmpG:red>
+ <xmpG:green>51</xmpG:green>
+ <xmpG:blue>51</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=77 G=77 B=77</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>77</xmpG:red>
+ <xmpG:green>77</xmpG:green>
+ <xmpG:blue>77</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=102 G=102 B=102</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>102</xmpG:red>
+ <xmpG:green>102</xmpG:green>
+ <xmpG:blue>102</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=128 G=128 B=128</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>128</xmpG:red>
+ <xmpG:green>128</xmpG:green>
+ <xmpG:blue>128</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=153 G=153 B=153</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>153</xmpG:red>
+ <xmpG:green>153</xmpG:green>
+ <xmpG:blue>153</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=179 G=179 B=179</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>179</xmpG:red>
+ <xmpG:green>179</xmpG:green>
+ <xmpG:blue>179</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=204 G=204 B=204</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>204</xmpG:red>
+ <xmpG:green>204</xmpG:green>
+ <xmpG:blue>204</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=230 G=230 B=230</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>230</xmpG:red>
+ <xmpG:green>230</xmpG:green>
+ <xmpG:blue>230</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=242 G=242 B=242</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>242</xmpG:red>
+ <xmpG:green>242</xmpG:green>
+ <xmpG:blue>242</xmpG:blue>
+ </rdf:li>
+ </rdf:Seq>
+ </xmpG:Colorants>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:groupName>Web Color Group</xmpG:groupName>
+ <xmpG:groupType>1</xmpG:groupType>
+ <xmpG:Colorants>
+ <rdf:Seq>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=63 G=169 B=245</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>63</xmpG:red>
+ <xmpG:green>169</xmpG:green>
+ <xmpG:blue>245</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=122 G=201 B=67</xmpG:swatchName>
+ <xmpG:mode>RGB</xmpG:mode>
+ <xmpG:type>PROCESS</xmpG:type>
+ <xmpG:red>122</xmpG:red>
+ <xmpG:green>201</xmpG:green>
+ <xmpG:blue>67</xmpG:blue>
+ </rdf:li>
+ <rdf:li rdf:parseType="Resource">
+ <xmpG:swatchName>R=255 G=147 B=30</xmpG:swatchName>
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