def __init__(self, flags, model_name=""):
super().__init__(flags)
# Model Parameters
self.scale = flags.scale
self.layers = flags.layers
self.depth_wise_convolution = flags.depth_wise_convolution
self.resampling_method = BICUBIC_METHOD_STRING
self.self_ensemble = flags.self_ensemble
# Training Parameters
self.optimizer = flags.optimizer
self.beta1 = flags.beta1
self.beta2 = flags.beta2
self.momentum = flags.momentum
self.batch_num = flags.batch_num
self.batch_image_size = flags.batch_image_size
self.clipping_norm = flags.clipping_norm
# Learning Rate Control for Training
self.initial_lr = flags.initial_lr
self.lr_decay = flags.lr_decay
self.lr_decay_epoch = flags.lr_decay_epoch
# Dataset or Others
self.training_images = int(
math.ceil(flags.training_images / flags.batch_num) *
flags.batch_num)
# Image Processing Parameters
self.max_value = flags.max_value
self.channels = flags.channels
self.output_channels = flags.channels
self.psnr_calc_border_size = flags.psnr_calc_border_size
if self.psnr_calc_border_size < 0:
self.psnr_calc_border_size = 2 + self.scale
# initialize variables
self.name = self.get_model_name(model_name)
self.total_epochs = 0
lr = self.initial_lr
while lr > flags.end_lr:
self.total_epochs += self.lr_decay_epoch
lr *= self.lr_decay
# initialize environment
util.make_dir(self.checkpoint_dir)
util.make_dir(flags.graph_dir)
util.make_dir(self.tf_log_dir)
if flags.initialize_tf_log:
util.clean_dir(self.tf_log_dir)
util.set_logging(flags.log_filename,
stream_log_level=logging.INFO,
file_log_level=logging.INFO,
tf_log_level=tf.logging.WARN)
logging.info("\nLFFN-------------------------------------")
logging.info("%s [%s]" % (util.get_now_date(), self.name))
self.init_train_step()
def build_batch(self, data_dir):
""" Build batch images and. """
print("Building batch images for %s..." % self.batch_dir)
filenames = util.get_files_in_directory(data_dir)
images_count = 0
util.make_dir(self.batch_dir)
util.clean_dir(self.batch_dir)
util.make_dir(self.batch_dir + "/" + INPUT_IMAGE_DIR)
util.make_dir(self.batch_dir + "/" + INTERPOLATED_IMAGE_DIR)
util.make_dir(self.batch_dir + "/" + TRUE_IMAGE_DIR)
processed_images = 0
for filename in filenames:
output_window_size = self.batch_image_size * self.scale
output_window_stride = self.stride * self.scale
input_image, input_interpolated_image, true_image = \
build_image_set(filename, channels=self.channels, resampling_method=self.resampling_method,
scale=self.scale, print_console=False)
# split into batch images
input_batch_images = util.get_split_images(input_image, self.batch_image_size, stride=self.stride)
input_interpolated_batch_images = util.get_split_images(input_interpolated_image, output_window_size,
stride=output_window_stride)
if input_batch_images is None or input_interpolated_batch_images is None:
# if the original image size * scale is less than batch image size
continue
input_count = input_batch_images.shape[0]
true_batch_images = util.get_split_images(true_image, output_window_size, stride=output_window_stride)
for i in range(input_count):
self.save_input_batch_image(images_count, input_batch_images[i])
self.save_interpolated_batch_image(images_count, input_interpolated_batch_images[i])
self.save_true_batch_image(images_count, true_batch_images[i])
images_count += 1
processed_images += 1
if processed_images % 10 == 0:
print('.', end='', flush=True)
print("Finished")
self.count = images_count
print("%d mini-batch images are built(saved)." % images_count)
config = configparser.ConfigParser()
config.add_section("batch")
config.set("batch", "count", str(images_count))
config.set("batch", "scale", str(self.scale))
config.set("batch", "batch_image_size", str(self.batch_image_size))
config.set("batch", "stride", str(self.stride))
config.set("batch", "channels", str(self.channels))
with open(self.batch_dir + "/batch_images.ini", "w") as configfile:
config.write(configfile)
def build_batch(self, data_dir, batch_dir):
""" load from input files. Then save batch images on file to reduce memory consumption. """
print("Building batch images for %s..." % batch_dir)
filenames = util.get_files_in_directory(data_dir)
images_count = 0
util.make_dir(batch_dir)
util.clean_dir(batch_dir)
util.make_dir(batch_dir + "/" + INPUT_IMAGE_DIR)
util.make_dir(batch_dir + "/" + INTERPOLATED_IMAGE_DIR)
util.make_dir(batch_dir + "/" + TRUE_IMAGE_DIR)
for filename in filenames:
output_window_size = self.batch_image_size * self.scale
output_window_stride = self.stride * self.scale
input_image, input_interpolated_image = self.input.load_input_image(filename, rescale=True,
resampling_method=self.resampling_method)
test_image = self.true.load_test_image(filename)
# split into batch images
input_batch_images = util.get_split_images(input_image, self.batch_image_size, stride=self.stride)
input_interpolated_batch_images = util.get_split_images(input_interpolated_image, output_window_size,
stride=output_window_stride)
if input_batch_images is None or input_interpolated_batch_images is None:
continue
input_count = input_batch_images.shape[0]
test_batch_images = util.get_split_images(test_image, output_window_size, stride=output_window_stride)
for i in range(input_count):
save_input_batch_image(batch_dir, images_count, input_batch_images[i])
save_interpolated_batch_image(batch_dir, images_count, input_interpolated_batch_images[i])
save_true_batch_image(batch_dir, images_count, test_batch_images[i])
images_count += 1
print("%d mini-batch images are built(saved)." % images_count)
config = configparser.ConfigParser()
config.add_section("batch")
config.set("batch", "count", str(images_count))
config.set("batch", "scale", str(self.scale))
config.set("batch", "batch_image_size", str(self.batch_image_size))
config.set("batch", "stride", str(self.stride))
config.set("batch", "channels", str(self.channels))
config.set("batch", "jpeg_mode", str(self.jpeg_mode))
config.set("batch", "max_value", str(self.max_value))
with open(batch_dir + "/batch_images.ini", "w") as configfile:
config.write(configfile)
def load_datasets(self, data_dir, batch_dir, batch_image_size, stride_size=0):
""" build input patch images and loads as a datasets
Opens image directory as a datasets.
Each images are splitted into patch images and converted to input image. Since loading
(especially from PNG/JPG) and building input-LR images needs much computation in the
training phase, building pre-processed images makes training much faster. However, images
are limited by divided grids.
"""
batch_dir += "/scale%d" % self.scale
self.train = loader.BatchDataSets(self.scale, batch_dir, batch_image_size, stride_size, channels=self.channels,
resampling_method=self.resampling_method)
if not self.train.is_batch_exist():
util.make_dir(batch_dir)
util.clean_dir(batch_dir)
util.make_dir(batch_dir + "/" + INPUT_IMAGE_DIR)
util.make_dir(batch_dir + "/" + INTERPOLATED_IMAGE_DIR)
util.make_dir(batch_dir + "/" + TRUE_IMAGE_DIR)
self.train.build_batch_threaded(data_dir, batch_dir, self.threads)
else:
self.train.load_batch_counts()
self.train.load_all_batch_images(self.threads)
def __init__(self, flags, model_name=""):
super().__init__(flags)
# Model Parameters
self.layers = flags.layers
self.filters = flags.filters
self.min_filters = min(flags.filters, flags.min_filters)
self.filters_decay_gamma = flags.filters_decay_gamma
self.use_nin = flags.use_nin
self.nin_filters = flags.nin_filters
self.nin_filters2 = flags.nin_filters2
self.reconstruct_layers = max(flags.reconstruct_layers, 1)
self.reconstruct_filters = flags.reconstruct_filters
self.resampling_method = BICUBIC_METHOD_STRING
self.pixel_shuffler = flags.pixel_shuffler
self.self_ensemble = flags.self_ensemble
# Training Parameters
self.l2_decay = flags.l2_decay
self.optimizer = flags.optimizer
self.beta1 = flags.beta1
self.beta2 = flags.beta2
self.momentum = flags.momentum
self.batch_num = flags.batch_num
self.batch_image_size = flags.batch_image_size
if flags.stride_size == 0:
self.stride_size = flags.batch_image_size // 2
else:
self.stride_size = flags.stride_size
self.clipping_norm = flags.clipping_norm
# Learning Rate Control for Training
self.initial_lr = flags.initial_lr
self.lr_decay = flags.lr_decay
self.lr_decay_epoch = flags.lr_decay_epoch
# Dataset or Others
self.dataset = flags.dataset
self.test_dataset = flags.test_dataset
self.training_image_count = max(
1, (flags.training_images // flags.batch_num)) * flags.batch_num
self.train = None
self.test = None
# Image Processing Parameters
self.scale = flags.scale
self.max_value = flags.max_value
self.channels = flags.channels
self.jpeg_mode = flags.jpeg_mode
self.output_channels = 1
# Environment (all directory name should not contain '/' after )
self.batch_dir = flags.batch_dir
# initialize variables
self.name = self.get_model_name(model_name)
self.total_epochs = 0
lr = self.initial_lr
while lr > flags.end_lr:
self.total_epochs += self.lr_decay_epoch
lr *= self.lr_decay
# initialize environment
util.make_dir(self.checkpoint_dir)
util.make_dir(flags.graph_dir)
util.make_dir(self.tf_log_dir)
if flags.initialise_tf_log:
util.clean_dir(self.tf_log_dir)
util.set_logging(flags.log_filename,
stream_log_level=logging.INFO,
file_log_level=logging.INFO,
tf_log_level=tf.logging.WARN)
logging.info("\nDCSCN v2-------------------------------------")
logging.info("%s [%s]" % (util.get_now_date(), self.name))
self.init_train_step()
def __init__(self, flags, model_name=""):
super().__init__(flags)
# Model Parameters
self.scale = flags.scale
self.layers = flags.layers
self.filters = flags.filters
self.min_filters = min(flags.filters, flags.min_filters)
self.filters_decay_gamma = flags.filters_decay_gamma
self.use_nin = flags.use_nin
self.nin_filters = flags.nin_filters
self.nin_filters2 = flags.nin_filters2
self.reconstruct_layers = max(flags.reconstruct_layers, 1)
self.reconstruct_filters = flags.reconstruct_filters
self.resampling_method = flags.resampling_method
self.pixel_shuffler = flags.pixel_shuffler
self.pixel_shuffler_filters = flags.pixel_shuffler_filters
self.self_ensemble = flags.self_ensemble
self.depthwise_seperable = flags.depthwise_seperable
self.bottleneck = flags.bottleneck
# Training Parameters
self.l2_decay = flags.l2_decay
self.optimizer = flags.optimizer
self.beta1 = flags.beta1
self.beta2 = flags.beta2
self.epsilon = flags.epsilon
self.momentum = flags.momentum
self.batch_num = flags.batch_num
self.batch_image_size = flags.batch_image_size
if flags.stride_size == 0:
self.stride_size = flags.batch_image_size // 2
else:
self.stride_size = flags.stride_size
self.clipping_norm = flags.clipping_norm
self.use_l1_loss = flags.use_l1_loss
# Learning Rate Control for Training
self.initial_lr = flags.initial_lr
self.lr_decay = flags.lr_decay
self.lr_decay_epoch = flags.lr_decay_epoch
# Dataset or Others
self.training_images = int(
math.ceil(flags.training_images / flags.batch_num) *
flags.batch_num)
self.train = None
self.test = None
self.gpu_device_id = flags.gpu_device_id
# Image Processing Parameters
self.max_value = flags.max_value
self.channels = flags.channels
self.output_channels = 1
self.psnr_calc_border_size = flags.psnr_calc_border_size
if self.psnr_calc_border_size < 0:
self.psnr_calc_border_size = self.scale
self.input_image_width = flags.input_image_width
self.input_image_height = flags.input_image_height
# Environment (all directory name should not contain tailing '/' )
self.batch_dir = flags.batch_dir
# initialize variables
self.name = self.get_model_name(model_name,
name_postfix=flags.name_postfix)
self.total_epochs = 0
lr = self.initial_lr
while lr > flags.end_lr:
self.total_epochs += self.lr_decay_epoch
lr *= self.lr_decay
# initialize environment
util.make_dir(self.checkpoint_dir)
util.make_dir(flags.graph_dir)
util.make_dir(self.tf_log_dir)
if flags.initialize_tf_log:
util.clean_dir(self.tf_log_dir)
util.set_logging(flags.log_filename,
stream_log_level=logging.INFO,
file_log_level=logging.INFO,
tf_log_level=tf.logging.WARN)
logging.info("\nDCSCN v2-------------------------------------")
logging.info("%s [%s]" % (util.get_now_date(), self.name))
self.init_train_step()
def __init__(self, flags, model_name=""):
# Model Parameters
self.filters = flags.filters
self.min_filters = flags.min_filters
self.nin_filters = flags.nin_filters
self.nin_filters2 = flags.nin_filters2 if flags.nin_filters2 != 0 else flags.nin_filters // 2
self.cnn_size = flags.cnn_size
self.last_cnn_size = flags.last_cnn_size
self.cnn_stride = 1
self.layers = flags.layers
self.nin = flags.nin
self.bicubic_init = flags.bicubic_init
self.dropout = flags.dropout
self.activator = flags.activator
self.filters_decay_gamma = flags.filters_decay_gamma
# Training Parameters
self.initializer = flags.initializer
self.weight_dev = flags.weight_dev
self.l2_decay = flags.l2_decay
self.optimizer = flags.optimizer
self.beta1 = flags.beta1
self.beta2 = flags.beta2
self.momentum = flags.momentum
self.batch_num = flags.batch_num
self.batch_image_size = flags.batch_image_size
if flags.stride_size == 0:
self.stride_size = flags.batch_image_size // 2
else:
self.stride_size = flags.stride_size
# Learning Rate Control for Training
self.initial_lr = flags.initial_lr
self.lr_decay = flags.lr_decay
self.lr_decay_epoch = flags.lr_decay_epoch
# Dataset or Others
self.dataset = flags.dataset
self.test_dataset = flags.test_dataset
# Image Processing Parameters
self.scale = flags.scale
self.max_value = flags.max_value
self.channels = flags.channels
self.jpeg_mode = flags.jpeg_mode
self.output_channels = self.scale * self.scale
# Environment (all directory name should not contain '/' after )
self.checkpoint_dir = flags.checkpoint_dir
self.tf_log_dir = flags.tf_log_dir
# Debugging or Logging
self.debug = flags.debug
self.save_loss = flags.save_loss
self.save_weights = flags.save_weights
self.save_images = flags.save_images
self.save_images_num = flags.save_images_num
self.log_weight_image_num = 32
# initialize variables
self.name = self.get_model_name(model_name)
self.batch_input = self.batch_num * [None]
self.batch_input_quad = self.batch_num * [None]
self.batch_true_quad = self.batch_num * [None]
self.receptive_fields = 2 * self.layers + self.cnn_size - 2
self.complexity = 0
# initialize environment
util.make_dir(self.checkpoint_dir)
util.make_dir(flags.graph_dir)
util.make_dir(self.tf_log_dir)
if flags.initialise_tf_log:
util.clean_dir(self.tf_log_dir)
util.set_logging(flags.log_filename,
stream_log_level=logging.INFO,
file_log_level=logging.INFO,
tf_log_level=tf.logging.WARN)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.InteractiveSession(config=config)
self.init_train_step()
logging.info("\nDCSCN -------------------------------------")
logging.info("%s [%s]" % (util.get_now_date(), self.name))
