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.build_graph()
model.build_summary_saver()
model.init_all_variables()
if FLAGS.test_dataset == "all":
test_list = ['set5', 'set14', 'bsd100']
else:
test_list = [FLAGS.test_dataset]
# FLAGS.tests refer to the number of training sets to be used
for i in range(FLAGS.tests):
model.load_model(FLAGS.load_model_name,
trial=i,
output_log=True if FLAGS.tests > 1 else False)
if FLAGS.compute_bicubic:
for test_data in test_list:
evaluate_bicubic(model, test_data)
for test_data in test_list:
evaluate_model(model, test_data)
model.save_model(name="inference_model", output_log=True)
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)
if (FLAGS.frozenInference):
model.load_graph(FLAGS.frozen_graph_path)
model.build_summary_saver(
with_saver=False
) # no need because we are not saving any variables
else:
model.build_graph()
model.build_summary_saver()
model.init_all_variables()
if FLAGS.test_dataset == "all":
test_list = ['set5', 'set14', 'bsd100']
else:
test_list = [FLAGS.test_dataset]
for i in range(FLAGS.tests):
if (not FLAGS.frozenInference):
model.load_model(FLAGS.load_model_name,
trial=i,
output_log=True if FLAGS.tests > 1 else False)
if FLAGS.compute_bicubic:
for test_data in test_list:
print(test_data)
evaluate_bicubic(model, test_data)
for test_data in test_list:
evaluate_model(model, test_data)
def main(not_parsed_args):
if len(not_parsed_args) > 1:
print("Unknown args:%s" % not_parsed_args)
exit()
# modifying process/build options for faster processing
if FLAGS.load_model_name == "":
FLAGS.load_model_name = "default"
FLAGS.save_loss = False
FLAGS.save_weights = False
FLAGS.save_images = False
model = DCSCN.SuperResolution(FLAGS, model_name=FLAGS.model_name)
model.build_graph()
model.build_summary_saver()
model.init_all_variables()
logging.info("evaluate model performance")
if FLAGS.test_dataset == "all":
test_list = ['set5', 'set14', 'bsd100']
else:
test_list = [FLAGS.test_dataset]
for i in range(FLAGS.tests):
model.load_model(FLAGS.load_model_name, i,
True if FLAGS.tests > 1 else False)
for test_data in test_list:
test(model, test_data)
def main(_):
model = DCSCN.SuperResolution(FLAGS, model_name=FLAGS.model_name)
model.build_graph()
model.init_all_variables()
model.load_model()
model.do_for_file(FLAGS.file, FLAGS.output_dir)
def load_model(flags, model_path):
model = DCSCN.SuperResolution(FLAGS, model_name=model_path)
model.build_graph()
model.build_optimizer()
model.build_summary_saver()
model.init_all_variables()
model.load_model(name=model_path)
return model
def main(_):
model = DCSCN.SuperResolution(FLAGS, model_name=FLAGS.model_name)
model.build_graph()
model.build_optimizer()
model.build_summary_saver()
model.init_all_variables()
model.load_model()
model.export_model()
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 main(_):
model = DCSCN.SuperResolution(FLAGS, model_name=FLAGS.model_name)
model.build_graph()
model.build_optimizer()
model.build_summary_saver()
model.init_all_variables()
model.load_model()
assert (os.path.isdir(FLAGS.dir))
filelist = os.listdir(FLAGS.dir)
for file_name in filelist[:]:
if file_name.endswith(".png"):
model.do_for_file(FLAGS.dir + '/' + file_name, FLAGS.output_dir)
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)
model.build_graph()
model.build_summary_saver()
model.init_all_variables()
if FLAGS.test_dataset == "all":
test_list = ['set5', 'set14', 'bsd100']
else:
test_list = [FLAGS.test_dataset]
for i in range(FLAGS.tests):
model.load_model(FLAGS.load_model_name, trial=i, output_log=True if FLAGS.tests > 1 else False)
for test_data in test_list:
test(model, test_data)
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)
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_mse = 0
for i in range(FLAGS.tests):
mse = train(model, FLAGS, i)
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 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.load_datasets("training", FLAGS.data_dir + "/" + FLAGS.dataset, FLAGS.batch_dir + "/" + FLAGS.dataset,
FLAGS.batch_image_size, FLAGS.stride_size)
model.load_datasets("test", 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_psnr = final_ssim = 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_ssim = 0
for filename in test_filenames:
psnr, ssim = model.do_for_evaluate(filename, FLAGS.output_dir, output=i is (FLAGS.tests - 1))
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" % (total_psnr / len(test_filenames), total_ssim / len(test_filenames)))
final_psnr += total_psnr
final_ssim += total_ssim
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=== Average [%s] PSNR:%f, SSIM:%f ===" % (FLAGS.test_dataset, final_psnr / n, final_ssim / n))
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 main(_):
model = DCSCN.SuperResolution(FLAGS, model_name=FLAGS.model_name)
model.build_graph()
model.build_optimizer()
model.build_summary_saver()
model.init_all_variables()
model.load_model()
video=FLAGS.file
lrclip=VideoFileClip(video).subclip(0,20)
audioclip=lrclip.audio
audioclip.write_audiofile('audio'+video[:-4]+'.mp3')
hrclip=lrclip.fl_image(model.doframe)
# hrclip.ipython_display()
# lrclip.ipython_display()
'''
Uncomment the line if you run the code in jupyter notebook
'''
hroutput='hr'+video
hrclip.write_videofile(hroutput, audio='audio'+video[:-4]+'.mp3', threads=8, progress_bar=False)
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.load_graph()
model.build_summary_saver(with_saver=False) # no need because we are not saving any variables
model.init_all_variables()
if FLAGS.test_dataset == "all":
test_list = ['set5', 'set14', 'bsd100']
else:
test_list = [FLAGS.test_dataset]
# FLAGS.tests refer to the number of training sets to be used
for i in range(FLAGS.tests):
if FLAGS.compute_bicubic:
for test_data in test_list:
evaluate_bicubic(model, test_data)
for test_data in test_list:
evaluate_model(model, test_data)