def main(_):
#print("Super Resolution (tensorflow version:%s)" % tf.__version__)
print("%s\n" % util.get_now_date()) #开始时间
if FLAGS.model_name is "": #保存模型名字
model_name = "model_F%d_D%d_LR%f" % (
FLAGS.feature_num, FLAGS.inference_depth, FLAGS.initial_lr)
else:
model_name = "model_%s" % FLAGS.model_name
model = sr.SuperResolution(FLAGS, model_name=model_name) #建立DRCN模型
test_filenames = util.build_test_filenames(FLAGS.data_dir, FLAGS.dataset,
FLAGS.scale) #获取测试文件
if FLAGS.is_training:
if FLAGS.dataset == "test":
training_filenames = util.build_test_filenames(
FLAGS.data_dir, FLAGS.dataset, FLAGS.scale)
else:
training_filenames = util.get_files_in_directory(
FLAGS.data_dir + "/" + FLAGS.training_set + "/") #获得训练文件
print("Loading and building cache images...")
model.load_datasets(
FLAGS.cache_dir, training_filenames, test_filenames,
FLAGS.batch_size,
FLAGS.stride_size) #模型用的训练数据、测试数据、缓存图像数据目录、隔多少个数据取一次mini-batch
else:
FLAGS.load_model = True
model.build_embedding_graph() # embedding层
model.build_inference_graph() # inference层
model.build_reconstruction_graph() # reconstruction层
model.build_optimizer() #创建损失函数及优化器
model.init_all_variables(load_initial_data=FLAGS.load_model) #初始化变量
if FLAGS.is_training:
train(training_filenames, test_filenames, model)
psnr = 0
total_mse = 0
for filename in test_filenames:
mse = model.do_super_resolution_for_test(
filename, FLAGS.output_dir) # 对测试图片进行超分辨
total_mse += mse
psnr += util.get_psnr(mse) #
print("\n--- summary --- %s" % util.get_now_date()) #结束时间
model.print_steps_completed() #输出完成训练所需的总epoch，steps，time等
util.print_num_of_total_parameters() #输出一共有多少参数
print("Final MSE:%f, PSNR:%f" %
(total_mse / len(test_filenames), psnr / len(test_filenames)))
def main(_):
print("Super Resolution (tensorflow version:%s)" % tf.__version__)
print("%s\n" % util.get_now_date())
if FLAGS.model_name is "":
model_name = "model_F%d_D%d_LR%f" % (
FLAGS.feature_num, FLAGS.inference_depth, FLAGS.initial_lr)
else:
model_name = "model_%s" % FLAGS.model_name
model = sr.SuperResolution(FLAGS, model_name=model_name)
test_filenames = util.build_test_filenames(FLAGS.data_dir, FLAGS.dataset,
FLAGS.scale)
if FLAGS.is_training:
if FLAGS.dataset == "test":
training_filenames = util.build_test_filenames(
FLAGS.data_dir, FLAGS.dataset, FLAGS.scale)
else:
training_filenames = util.get_files_in_directory(
FLAGS.data_dir + "/" + FLAGS.training_set + "/")
print("Loading and building cache images...")
model.load_datasets(FLAGS.cache_dir, training_filenames,
test_filenames, FLAGS.batch_size,
FLAGS.stride_size)
else:
FLAGS.load_model = True
model.build_embedding_graph()
model.build_inference_graph()
model.build_reconstruction_graph()
model.build_optimizer()
model.init_all_variables(load_initial_data=FLAGS.load_model)
if FLAGS.is_training:
train(training_filenames, test_filenames, model)
psnr = 0
total_mse = 0
for filename in test_filenames:
mse = model.do_super_resolution_for_test(filename, FLAGS.output_dir)
total_mse += mse
psnr += util.get_psnr(mse)
print("\n--- summary --- %s" % util.get_now_date())
model.print_steps_completed()
util.print_num_of_total_parameters()
print("Final MSE:%f, PSNR:%f" %
(total_mse / len(test_filenames), psnr / len(test_filenames)))
def main(_):
print("Super Resolution (tensorflow version:%s)" % tf.__version__)
print("%s\n" % util.get_now_date())
if FLAGS.model_name is "":
model_name = "model_F%d_D%d_LR%f" % (FLAGS.feature_num, FLAGS.inference_depth, FLAGS.initial_lr)
else:
model_name = "model_%s" % FLAGS.model_name
model = sr.SuperResolution(FLAGS, model_name=model_name)
test_filename = util.get_files_in_directory(FLAGS.data_dir + "/" + FLAGS.file + "/")
test_filenames = [test_filename[i:i+5] for i in range(0, len(test_filename), 5)]
label_filenames = util.get_files_in_directory(FLAGS.data_dir + "/" + FLAGS.label_file + "/")
FLAGS.load_model = True
model.build_lap_graph()
model.build_merge_graph()
model.build_inference_graph()
model.build_optimizer()
model.init_all_variables(load_initial_data=FLAGS.load_model)
for i in range(len(label_filenames)):
mse_bic, mse = model.do_super_resolution(label_filenames[i], test_filenames[i], FLAGS.output_dir)
psnr_bic = util.get_psnr(mse_bic)
psnr = util.get_psnr(mse)
path, name = os.path.split(label_filenames[i])
print("%s MSE:%f, PSNR_bil:%f, PSNR:%f\n" % (name, mse, psnr_bic, psnr))
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 (%f)" % (util.get_now_date(), self.step, mse, psnr, self.training_psnr))
print("Epoch:%d LR:%f α:%f (%2.2fsec/step)" % (self.epochs_completed, self.lr, self.loss_alpha, processing_time))
def print_status(self, step, mse):
processing_time = (time.time() - self.start_time) / step
if self.trained > 0:
training_psnr = self.training_psnr / self.trained
else:
training_psnr = 0
print("%s Step:%d MSE:%f PSNR:%f (%f)" %
(util.get_now_date(), step, mse,
util.get_psnr(mse, max_value=self.max_value), training_psnr))
print(
"Epoch:%d LR:%f α:%f (%2.2fsec/step)" %
(self.epochs_completed, self.lr, self.loss_alpha, processing_time))
def main(_):
print("Super Resolution (tensorflow version:%s)" % tf.__version__)
print("%s\n" % util.get_now_date())
if FLAGS.model_name is "":
model_name = "model_F%d_D%d_LR%f" % (FLAGS.feature_num, FLAGS.inference_depth, FLAGS.initial_lr)
else:
model_name = "model_%s" % FLAGS.model_name
model = sr.SuperResolution(FLAGS, model_name=model_name)
test_filenames = [FLAGS.file]
FLAGS.load_model = True
model.build_embedding_graph()
model.build_inference_graph()
model.build_reconstruction_graph()
model.build_optimizer()
model.init_all_variables(load_initial_data=FLAGS.load_model)
model.do_super_resolution(FLAGS.file, FLAGS.output_dir)