def print_status(self, mse, psnr, log=False):
if self.step == 0:
logging.info("Initial MSE:%f PSNR:%f" % (mse, psnr))
else:
processing_time = (time.time() - self.start_time) / self.step
if self.use_l1_loss:
line_a = "%s Step:%s MSE:%f PSNR:%f (Training Loss:%0.3f)" % (
util.get_now_date(), "{:,}".format(self.step), mse, psnr,
self.training_loss_sum / self.training_step)
else:
line_a = "%s Step:%s MSE:%f PSNR:%f (Training PSNR:%0.3f)" % (
util.get_now_date(), "{:,}".format(self.step), mse, psnr,
self.training_psnr_sum / self.training_step)
estimated = processing_time * (
self.total_epochs - self.epochs_completed) * (
self.training_images // self.batch_num)
h = estimated // (60 * 60)
estimated -= h * 60 * 60
m = estimated // 60
s = estimated - m * 60
line_b = "Epoch:%d LR:%f (%2.3fsec/step) Estimated:%d:%d:%d" % (
self.epochs_completed, self.lr, processing_time, h, m, s)
if log:
logging.info(line_a)
logging.info(line_b)
else:
print(line_a)
print(line_b)
def main(not_parsed_args):
if len(not_parsed_args) > 1:
print("Unknown args:%s" % not_parsed_args)
exit()
model = DCSCN.SuperResolution(FLAGS, model_name=FLAGS.model_name)
model.train = model.load_dynamic_datasets(
FLAGS.data_dir + "/" + FLAGS.dataset, FLAGS.batch_image_size,
FLAGS.stride_size)
model.test = model.load_datasets(
FLAGS.data_dir + "/" + FLAGS.test_dataset,
FLAGS.batch_dir + "/" + FLAGS.test_dataset, FLAGS.batch_image_size,
FLAGS.stride_size)
model.build_graph()
model.build_optimizer()
model.build_summary_saver()
logging.info("\n" + str(sys.argv))
logging.info("Test Data:" + FLAGS.test_dataset + " Training Data:" +
FLAGS.dataset)
final_mse = final_psnr = 0
test_filenames = util.get_files_in_directory(FLAGS.data_dir + "/" +
FLAGS.test_dataset)
for i in range(FLAGS.tests):
train(model, FLAGS, i)
total_psnr = total_mse = 0
for filename in test_filenames:
mse = model.do_for_evaluate(filename,
FLAGS.output_dir,
output=i is (FLAGS.tests - 1),
print_console=False)
total_mse += mse
total_psnr += util.get_psnr(mse, max_value=FLAGS.max_value)
logging.info("\nTrial(%d) %s" % (i, util.get_now_date()))
model.print_steps_completed(output_to_logging=True)
logging.info("MSE:%f, PSNR:%f\n" % (total_mse / len(test_filenames),
total_psnr / len(test_filenames)))
final_mse += total_mse
final_psnr += total_psnr
logging.info("=== summary [%d] %s [%s] ===" %
(FLAGS.tests, model.name, util.get_now_date()))
util.print_num_of_total_parameters(output_to_logging=True)
n = len(test_filenames) * FLAGS.tests
logging.info("\n=== Final Average [%s] MSE:%f, PSNR:%f ===" %
(FLAGS.test_dataset, final_mse / n, final_psnr / n))
model.copy_log_to_archive("archive")
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 = BICUBIC_METHOD_STRING
self.pixel_shuffler = flags.pixel_shuffler
self.pixel_shuffler_filters = flags.pixel_shuffler_filters
self.self_ensemble = flags.self_ensemble
# 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 = 2 + self.scale
# initialize variables
self.name = self.get_model_name(model_name)
self.total_epochs = 0
util.make_dir(self.checkpoint_dir)
logging.info("\nDCSCN v2-------------------------------------")
logging.info("%s [%s]" % (util.get_now_date(), self.name))
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 main(not_parsed_args):
if len(not_parsed_args) > 1:
print("Unknown args:%s" % not_parsed_args)
exit()
model = LFFN.SuperResolution(FLAGS, model_name=FLAGS.model_name)
model.build_graph()
model.build_optimizer()
model.build_summary_saver()
logging.info("\n" + str(sys.argv))
logging.info("Test Data:" + FLAGS.test_dataset + " Training Data:" + FLAGS.train_dir+FLAGS.data_train)
util.print_num_of_total_parameters(output_to_logging=True)
total_psnr = total_mse = 0
for i in range(FLAGS.tests):
mse = train(model, FLAGS, i) # begin train
psnr = util.get_psnr(mse, max_value=FLAGS.max_value)
total_mse += mse
total_psnr += psnr
logging.info("\nTrial(%d) %s" % (i, util.get_now_date()))
model.print_steps_completed(output_to_logging=True)
logging.info("MSE:%f, PSNR:%f\n" % (mse, psnr))
if FLAGS.tests > 1:
logging.info("\n=== Final Average [%s] MSE:%f, PSNR:%f ===" % (
FLAGS.test_dataset, total_mse / FLAGS.tests, total_psnr / FLAGS.tests))
model.copy_log_to_archive("archive")
def print_status(self, mse):
psnr = util.get_psnr(mse, max_value=self.max_value)
if self.step == 0:
print("Initial MSE:%f PSNR:%f" % (mse, psnr))
else:
processing_time = (time.time() - self.start_time) / self.step
print("%s Step:%d MSE:%f PSNR:%f (Training PSNR:%0.3f)" % (
util.get_now_date(), self.step, mse, psnr, self.training_psnr_sum / self.training_step))
print("Epoch:%d (Step:%s) LR:%f (%2.3fsec/step) MinPSNR:%0.3f" % (
self.epochs_completed, "{:,}".format(self.step), self.lr, processing_time,
util.get_psnr(self.min_validation_mse)))
def main(not_parsed_args):
if len(not_parsed_args) > 1:
print("Unknown args:%s" % not_parsed_args)
exit()
model = DCSCN.SuperResolution(FLAGS,
model_name=FLAGS.model_name,
is_module_training=True)
# if FLAGS.build_batch:
# model.load_datasets(FLAGS.data_dir + "/" + FLAGS.dataset, FLAGS.batch_dir + "/" + FLAGS.dataset,
# FLAGS.batch_image_size, FLAGS.stride_size)
# else:
# model.load_dynamic_datasets(FLAGS.data_dir + "/" + FLAGS.dataset, FLAGS.batch_image_size)
if FLAGS.build_batch:
# Not implemented for MISR
logging.error("'build_batch' not implemented for MISR")
raise NotImplementedError
else:
model.load_dynamic_datasets_misr(
data_dir=FLAGS.data_dir,
batch_image_size=FLAGS.batch_image_size,
dataset_name=FLAGS.dataset)
model.build_graph()
model.build_optimizer()
model.build_summary_saver()
logging.info("\n" + str(sys.argv))
logging.info("Test Data:" + FLAGS.test_dataset + " Training Data:" +
FLAGS.dataset)
util.print_num_of_total_parameters(output_to_logging=True)
total_psnr = total_ssim = 0
for i in range(FLAGS.tests):
psnr, ssim, cpsnr = train_misr(model, FLAGS, i)
total_psnr += psnr
total_ssim += ssim
logging.info("\nTrial(%d) %s" % (i, util.get_now_date()))
model.print_steps_completed(output_to_logging=True)
logging.info("PSNR:%f, SSIM:%f\n" % (psnr, ssim))
if FLAGS.tests > 1:
logging.info("\n=== Final Average [%s] PSNR:%f, SSIM:%f ===" %
(FLAGS.test_dataset, total_psnr / FLAGS.tests,
total_ssim / FLAGS.tests))
model.copy_log_to_archive("archive")
def main(not_parsed_args):
if len(not_parsed_args) > 1:
print("Unknown args:%s" % not_parsed_args)
exit()
model = DCSCN.SuperResolution(FLAGS, model_name=FLAGS.model_name)
# script allows you to split training images into batches in advance.
if FLAGS.build_batch:
model.load_datasets(FLAGS.data_dir + "/" + FLAGS.dataset,
FLAGS.batch_dir + "/" + FLAGS.dataset,
FLAGS.batch_image_size, FLAGS.stride_size)
else:
model.load_dynamic_datasets(FLAGS.data_dir + "/" + FLAGS.dataset,
FLAGS.batch_image_size)
model.build_graph()
model.build_optimizer()
model.build_summary_saver()
logging.info("\n" + str(sys.argv))
logging.info("Test Data:" + FLAGS.test_dataset + " Training Data:" +
FLAGS.dataset)
util.print_num_of_total_parameters(output_to_logging=True)
total_psnr = total_ssim = 0
for i in range(FLAGS.tests):
psnr, ssim = train(model, FLAGS, i)
total_psnr += psnr
total_ssim += ssim
logging.info("\nTrial(%d) %s" % (i, util.get_now_date()))
model.print_steps_completed(output_to_logging=True)
logging.info("PSNR:%f, SSIM:%f\n" % (psnr, ssim))
if FLAGS.tests > 1:
logging.info("\n=== Final Average [%s] PSNR:%f, SSIM:%f ===" %
(FLAGS.test_dataset, total_psnr / FLAGS.tests,
total_ssim / FLAGS.tests))
model.copy_log_to_archive("archive")
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))
