def log_to_tensorboard(self, test_filename, psnr, save_meta_data=True):
if self.enable_log is False:
return
# todo
save_meta_data = False
org_image = util.set_image_alignment(
util.load_image(test_filename, print_console=False), self.scale)
if len(org_image.shape
) >= 3 and org_image.shape[2] == 3 and self.channels == 1:
org_image = util.convert_rgb_to_y(org_image)
input_image = util.resize_image_by_pil(
org_image,
1.0 / self.scale,
resampling_method=self.resampling_method)
bicubic_image = util.resize_image_by_pil(
input_image, self.scale, resampling_method=self.resampling_method)
if self.max_value != 255.0:
input_image = np.multiply(input_image, self.max_value /
255.0) # type: np.ndarray
bicubic_image = np.multiply(bicubic_image, self.max_value /
255.0) # type: np.ndarray
org_image = np.multiply(org_image,
self.max_value / 255.0) # type: np.ndarray
feed_dict = {
self.x:
input_image.reshape([
1, input_image.shape[0], input_image.shape[1],
input_image.shape[2]
]),
self.x2:
bicubic_image.reshape([
1, bicubic_image.shape[0], bicubic_image.shape[1],
bicubic_image.shape[2]
]),
self.y:
org_image.reshape([
1, org_image.shape[0], org_image.shape[1], org_image.shape[2]
]),
self.dropout:
1.0,
self.is_training:
0
}
if save_meta_data:
# profiler = tf.profiler.Profile(self.sess.graph)
run_metadata = tf.RunMetadata()
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
summary_str, _ = self.sess.run([self.summary_op, self.loss],
feed_dict=feed_dict,
options=run_options,
run_metadata=run_metadata)
self.test_writer.add_run_metadata(run_metadata,
"step%d" % self.epochs_completed)
filename = self.checkpoint_dir + "/" + self.name + "_metadata.txt"
with open(filename, "w") as out:
out.write(str(run_metadata))
# filename = self.checkpoint_dir + "/" + self.name + "_memory.txt"
# tf.profiler.write_op_log(
# tf.get_default_graph(),
# log_dir=self.checkpoint_dir,
# #op_log=op_log,
# run_meta=run_metadata)
tf.contrib.tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(),
run_meta=run_metadata,
tfprof_options=tf.contrib.tfprof.model_analyzer.
PRINT_ALL_TIMING_MEMORY)
else:
summary_str, _ = self.sess.run([self.summary_op, self.loss],
feed_dict=feed_dict)
self.train_writer.add_summary(summary_str, self.epochs_completed)
if not self.use_l1_loss:
if self.training_step != 0:
util.log_scalar_value(
self.train_writer, 'PSNR',
self.training_psnr_sum / self.training_step,
self.epochs_completed)
util.log_scalar_value(self.train_writer, 'LR', self.lr,
self.epochs_completed)
self.train_writer.flush()
util.log_scalar_value(self.test_writer, 'PSNR', psnr,
self.epochs_completed)
self.test_writer.flush()
def do_for_evaluate_with_output(self,
file_path,
output_directory,
print_console=False):
filename, extension = os.path.splitext(file_path)
output_directory += "/" + self.name + "/"
util.make_dir(output_directory)
true_image = util.set_image_alignment(
util.load_image(file_path, print_console=False), self.scale)
input_image = util.resize_image_by_pil(
true_image,
1.0 / self.scale,
resampling_method=self.resampling_method)
input_bicubic_image = util.resize_image_by_pil(
input_image, self.scale, resampling_method=self.resampling_method)
util.save_image(
output_directory + filename + "_input_bicubic" + extension,
input_bicubic_image)
if true_image.shape[2] == 3 and self.channels == 1:
# for color images
input_y_image = loader.build_input_image(true_image,
channels=self.channels,
scale=self.scale,
alignment=self.scale,
convert_ycbcr=True)
input_bicubic_y_image = util.resize_image_by_pil(
input_y_image,
self.scale,
resampling_method=self.resampling_method)
true_ycbcr_image = util.convert_rgb_to_ycbcr(true_image)
output_y_image = self.do(input_y_image, input_bicubic_y_image)
psnr, ssim = util.compute_psnr_and_ssim(
true_ycbcr_image[:, :, 0:1],
output_y_image,
border_size=self.psnr_calc_border_size)
loss_image = util.get_loss_image(
true_ycbcr_image[:, :, 0:1],
output_y_image,
border_size=self.psnr_calc_border_size)
output_color_image = util.convert_y_and_cbcr_to_rgb(
output_y_image, true_ycbcr_image[:, :, 1:3])
util.save_image(output_directory + file_path, true_image)
util.save_image(output_directory + filename + "_input" + extension,
input_y_image)
util.save_image(
output_directory + filename + "_input_bicubic_y" + extension,
input_bicubic_y_image)
util.save_image(
output_directory + filename + "_true_y" + extension,
true_ycbcr_image[:, :, 0:1])
util.save_image(
output_directory + filename + "_result" + extension,
output_y_image)
util.save_image(
output_directory + filename + "_result_c" + extension,
output_color_image)
util.save_image(output_directory + filename + "_loss" + extension,
loss_image)
elif true_image.shape[2] == 1 and self.channels == 1:
# for monochrome images
input_image = loader.build_input_image(true_image,
channels=self.channels,
scale=self.scale,
alignment=self.scale)
input_bicubic_y_image = util.resize_image_by_pil(
input_image,
self.scale,
resampling_method=self.resampling_method)
output_image = self.do(input_image, input_bicubic_y_image)
psnr, ssim = util.compute_psnr_and_ssim(
true_image,
output_image,
border_size=self.psnr_calc_border_size)
util.save_image(output_directory + file_path, true_image)
util.save_image(
output_directory + filename + "_result" + extension,
output_image)
else:
return None, None
if print_console:
print("[%s] PSNR:%f, SSIM:%f" % (filename, psnr, ssim))
return psnr, ssim
def load_and_evaluate_tflite_graph(
output_dir,
data_dir,
test_data,
model_path=os.path.join(os.getcwd(),
'model_to_freeze/converted_model.tflite')):
# https://stackoverflow.com/questions/50443411/how-to-load-a-tflite-model-in-script
# https://www.tensorflow.org/lite/convert/python_api#tensorflow_lite_python_interpreter_
output_directory = output_dir
output_directory += "/" + "tflite" + "/"
util.make_dir(output_directory)
test_filepaths = util.get_files_in_directory(data_dir + "/" + test_data)
total_psnr = total_ssim = total_time = 0
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path=model_path)
# interpreter = tf.contrib.lite.Interpreter(model_path=model_path)
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
for file_path in test_filepaths:
# split filename from extension
filename, extension = os.path.splitext(file_path)
# prepare true image
true_image = util.set_image_alignment(
util.load_image(file_path, print_console=False), FLAGS.scale)
# start the timer
if true_image.shape[2] == 3 and FLAGS.channels == 1:
# prepare input and ground truth images
input_y_image = loader.build_input_image(true_image,
channels=FLAGS.channels,
scale=FLAGS.scale,
alignment=FLAGS.scale,
convert_ycbcr=True)
input_bicubic_y_image = util.resize_image_by_pil(
input_y_image,
FLAGS.scale,
resampling_method=FLAGS.resampling_method)
true_ycbcr_image = util.convert_rgb_to_ycbcr(true_image)
# pass inputs through the model (need to recast and reshape inputs)
input_y_image_reshaped = input_y_image.astype('float32')
input_y_image_reshaped = input_y_image_reshaped.reshape(
1, input_y_image.shape[0], input_y_image.shape[1],
FLAGS.channels)
input_bicubic_y_image_reshaped = input_bicubic_y_image.astype(
'float32')
input_bicubic_y_image_reshaped = input_bicubic_y_image_reshaped.reshape(
1, input_bicubic_y_image.shape[0],
input_bicubic_y_image.shape[1], FLAGS.channels)
interpreter.set_tensor(input_details[0]['index'],
input_y_image_reshaped) # pass x
interpreter.set_tensor(input_details[1]['index'],
input_bicubic_y_image_reshaped) # pass x2
start = time.time()
interpreter.invoke()
end = time.time()
output_y_image = interpreter.get_tensor(
output_details[0]['index']) # get y
# resize the output into an image
output_y_image = output_y_image.reshape(output_y_image.shape[1],
output_y_image.shape[2],
FLAGS.channels)
# calculate psnr and ssim for the output
psnr, ssim = util.compute_psnr_and_ssim(
true_ycbcr_image[:, :, 0:1],
output_y_image,
border_size=FLAGS.psnr_calc_border_size)
# get the loss image
loss_image = util.get_loss_image(
true_ycbcr_image[:, :, 0:1],
output_y_image,
border_size=FLAGS.psnr_calc_border_size)
# get output color image
output_color_image = util.convert_y_and_cbcr_to_rgb(
output_y_image, true_ycbcr_image[:, :, 1:3])
# save all images
util.save_image(output_directory + file_path, true_image)
util.save_image(output_directory + filename + "_input" + extension,
input_y_image)
util.save_image(
output_directory + filename + "_input_bicubic" + extension,
input_bicubic_y_image)
util.save_image(
output_directory + filename + "_true_y" + extension,
true_ycbcr_image[:, :, 0:1])
util.save_image(
output_directory + filename + "_result" + extension,
output_y_image)
util.save_image(
output_directory + filename + "_result_c" + extension,
output_color_image)
util.save_image(output_directory + filename + "_loss" + extension,
loss_image)
elapsed_time = end - start
total_psnr += psnr
total_ssim += ssim
total_time += elapsed_time
testSize = len(test_filepaths)
print("Model Average [%s] PSNR:%f, SSIM:%f, Elapsed Time:%f" %
(test_data, total_psnr / testSize, total_ssim / testSize,
total_time / testSize))
def do_for_evaluate(self,
file_path,
output_directory="output",
output=True,
print_console=True):
filename, extension = os.path.splitext(file_path)
output_directory += "/"
true_image = util.set_image_alignment(util.load_image(file_path),
self.scale)
if true_image.shape[2] == 3 and self.channels == 1:
input_y_image = loader.build_input_image(true_image,
channels=self.channels,
scale=self.scale,
alignment=self.scale,
convert_ycbcr=True,
jpeg_mode=self.jpeg_mode)
# for color images
if output:
input_bicubic_y_image = util.resize_image_by_pil(
input_y_image, self.scale)
true_ycbcr_image = util.convert_rgb_to_ycbcr(
true_image, jpeg_mode=self.jpeg_mode)
output_y_image = self.do(input_y_image, input_bicubic_y_image)
mse = util.compute_mse(true_ycbcr_image[:, :, 0:1],
output_y_image,
border_size=self.scale)
loss_image = util.get_loss_image(true_ycbcr_image[:, :, 0:1],
output_y_image,
border_size=self.scale)
output_color_image = util.convert_y_and_cbcr_to_rgb(
output_y_image,
true_ycbcr_image[:, :, 1:3],
jpeg_mode=self.jpeg_mode)
util.save_image(output_directory + file_path, true_image)
util.save_image(
output_directory + filename + "_input" + extension,
input_y_image)
util.save_image(
output_directory + filename + "_input_bicubic" + extension,
input_bicubic_y_image)
util.save_image(
output_directory + filename + "_true_y" + extension,
true_ycbcr_image[:, :, 0:1])
util.save_image(
output_directory + filename + "_result" + extension,
output_y_image)
util.save_image(
output_directory + filename + "_result_c" + extension,
output_color_image)
util.save_image(
output_directory + filename + "_loss" + extension,
loss_image)
else:
true_y_image = util.convert_rgb_to_y(true_image,
jpeg_mode=self.jpeg_mode)
output_y_image = self.do(input_y_image)
mse = util.compute_mse(true_y_image,
output_y_image,
border_size=self.scale)
elif true_image.shape[2] == 1 and self.channels == 1:
# for monochrome images
input_image = loader.build_input_image(true_image,
channels=self.channels,
scale=self.scale,
alignment=self.scale)
output_image = self.do(input_image)
mse = util.compute_mse(true_image,
output_image,
border_size=self.scale)
if output:
util.save_image(output_directory + file_path, true_image)
util.save_image(
output_directory + filename + "_result" + extension,
output_image)
if print_console:
print("MSE:%f PSNR:%f" % (mse, util.get_psnr(mse)))
return mse
def do_for_evaluate_with_output(self,
file_path,
output_directory,
print_console=False):
filename, extension = os.path.splitext(file_path)
output_directory += "/" + self.name + "/"
util.make_dir(output_directory)
true_image = util.set_image_alignment(
util.load_image(file_path, print_console=False), self.scale)
if true_image.shape[2] == 3 and self.channels == 3:
# for color images
input_image = util.build_input_image(true_image,
scale=self.scale,
alignment=self.scale)
input_bicubic_image = util.resize_image_by_pil(
input_image,
self.scale,
resampling_method=self.resampling_method)
output_image, spend_time = self.do(input_image) # SR
SR_y = eva.convert_rgb_to_y(output_image)
HR_y = eva.convert_rgb_to_y(true_image)
psnr_predicted = eva.PSNR(np.uint8(HR_y),
np.uint8(SR_y),
shave_border=self.psnr_calc_border_size)
ssim_predicted = eva.compute_ssim(np.squeeze(HR_y),
np.squeeze(SR_y))
mse = util.compute_mse(HR_y,
SR_y,
border_size=self.psnr_calc_border_size)
loss_image = util.get_loss_image(
HR_y, SR_y, border_size=self.psnr_calc_border_size)
util.save_image(output_directory + file_path[29:], true_image)
util.save_image(
output_directory + filename[28:] + "_input" + extension,
input_image)
util.save_image(
output_directory + filename[28:] + "_input_bicubic" +
extension, input_bicubic_image)
util.save_image(
output_directory + filename[28:] + "_sr" + extension,
output_image)
util.save_image(
output_directory + filename[28:] + "_loss" + extension,
loss_image)
elif true_image.shape[2] == 1 and self.channels == 1:
# for monochrome images
input_image = util.build_input_image(true_image,
scale=self.scale,
alignment=self.scale)
output_image, spend_time = self.do(input_image)
psnr_predicted = eva.PSNR(np.uint8(true_image),
np.uint8(output_image),
shave_border=self.psnr_calc_border_size)
ssim_predicted = eva.compute_ssim(np.squeeze(true_image),
np.squeeze(output_image))
mse = util.compute_mse(true_image,
output_image,
border_size=self.psnr_calc_border_size)
util.save_image(output_directory + file_path, true_image)
util.save_image(output_directory + filename + "_sr" + extension,
output_image)
else:
psnr_predicted = 0.0
ssim_predicted = 0.0
mse = 0.0
spend_time = 0.0
if print_console:
print("[%s] psnr:%f, ssim:%f, time:%f" %
(filename, psnr_predicted, ssim_predicted, spend_time))
return mse, psnr_predicted, ssim_predicted, spend_time
def log_to_tensorboard(self, test_filename, psnr, save_meta_data=True):
# todo
save_meta_data = False
org_image = util.set_image_alignment(
util.load_image(test_filename, print_console=False), self.scale)
if len(org_image.shape
) >= 3 and org_image.shape[2] == 3 and self.channels == 1:
org_image = util.convert_rgb_to_y(org_image)
input_image = util.resize_image_by_pil(
org_image,
1.0 / self.scale,
resampling_method=self.resampling_method)
feed_dict = {
self.x:
input_image.reshape([
1, input_image.shape[0], input_image.shape[1],
input_image.shape[2]
]),
self.y:
org_image.reshape([
1, org_image.shape[0], org_image.shape[1], org_image.shape[2]
]),
self.is_training:
0
}
if save_meta_data:
run_metadata = tf.RunMetadata()
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
summary_str, _ = self.sess.run([self.summary_op, self.mae],
feed_dict=feed_dict,
options=run_options,
run_metadata=run_metadata)
self.test_writer.add_run_metadata(run_metadata,
"step%d" % self.epochs_completed)
filename = self.checkpoint_dir + "/" + self.name + "_metadata.txt"
with open(filename, "w") as out:
out.write(str(run_metadata))
tf.contrib.tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(),
run_meta=run_metadata,
tfprof_options=tf.contrib.tfprof.model_analyzer.
PRINT_ALL_TIMING_MEMORY)
else:
summary_str, _ = self.sess.run([self.summary_op, self.mae],
feed_dict=feed_dict)
self.train_writer.add_summary(summary_str, self.epochs_completed)
util.log_scalar_value(self.train_writer, 'training_PSNR',
self.training_psnr_sum / self.training_step,
self.epochs_completed)
util.log_scalar_value(self.train_writer, 'LR', self.lr,
self.epochs_completed)
self.train_writer.flush()
util.log_scalar_value(self.test_writer, 'PSNR', psnr,
self.epochs_completed)
self.test_writer.flush()
def do_for_evaluate_with_output(self,
file_path,
output_directory=None,
print_console=False):
true_image = util.set_image_alignment(
util.load_image(file_path, print_console=False), self.scale)
# Assuming the image is color
assert true_image.shape[
2] == 3 and self.channels == 1, "Only 3-channel images are supported"
input_image = loader.build_input_image(true_image,
scale=self.scale,
alignment=self.scale)
input_y_image = util.convert_rgb_to_y(input_image)
true_y_image = util.convert_rgb_to_y(true_image)
input_bicubic_y_image = util.resize_image_by_pil(
input_y_image,
self.scale,
resampling_method=self.resampling_method)
output_y_image = self.do(input_y_image, input_bicubic_y_image)
psnr, ssim = util.compute_psnr_and_ssim(
true_y_image,
output_y_image,
border_size=self.psnr_calc_border_size)
if output_directory:
true_ycbcr_image = util.convert_rgb_to_ycbcr(true_image)
_, true_cbcr = util.convert_ycbcr_to_y_cbcr(true_ycbcr_image)
output_color_image = util.convert_y_and_cbcr_to_rgb(
output_y_image, true_cbcr)
loss_image = util.get_loss_image(
true_y_image,
output_y_image,
border_size=self.psnr_calc_border_size)
filename, extension = os.path.splitext(file_path)
output_directory += "/" + self.name + "/"
util.make_dir(output_directory)
util.save_image(output_directory + file_path, true_image)
util.save_image(output_directory + filename + "_input" + extension,
input_y_image)
util.save_image(
output_directory + filename + "_input_bicubic" + extension,
input_bicubic_y_image)
util.save_image(
output_directory + filename + "_true_y" + extension,
true_ycbcr_image[:, :, 0:1])
util.save_image(
output_directory + filename + "_result" + extension,
output_y_image)
util.save_image(
output_directory + filename + "_result_c" + extension,
output_color_image)
util.save_image(output_directory + filename + "_loss" + extension,
loss_image)
if print_console:
print("[%s] PSNR:%f, SSIM:%f" % (filename, psnr, ssim))
return psnr, ssim
