def super_resolution(model_args, filename, color, resize, denoise):
model = ESPCN(model_args)
with tf.Session() as sess:
data_batch, data_iterator = get_data(filename, color, resize, denoise)
print(data_batch.get_shape())
predicted_batch = model.load_model(data_batch)
if args.ckpt_path is None:
print("Path to the checkpoint file was not provided")
exit(1)
if os.path.isdir(args.ckpt_path):
args.ckpt_path = tf.train.latest_checkpoint(args.ckpt_path)
print(args.ckpt_path)
saver = tf.train.Saver()
saver.restore(sess, args.ckpt_path)
sess.run(data_iterator.initializer)
# temp = sess.run(data_batch)
# print(temp.shape)
img = sess.run(predicted_batch)
if color:
img = np.dstack(
(img[0, :, :, :], img[1, :, :, :], img[2, :, :, :]))
print(img.shape)
rgb = cv2.cvtColor(img, cv2.COLOR_YCrCb2RGB)
print(rgb.dtype)
# print(rgb[0,::])
cv2.imshow('image', rgb)
cv2.waitKey(0)
cv2.destroyAllWindows()
rgb = rgb * 255.0 # back to uint8
print(rgb.dtype)
# print(rgb[0, ::])
cv2.imwrite("sdog.png", rgb)
else:
print(type(img))
print(img.shape)
print(img.dtype)
# img = img.astype(np.uint8)
cv2.imshow('image', img[0, :, :, 0])
cv2.waitKey(0)
cv2.destroyAllWindows()
def main():
args = get_arguments()
if not os.path.exists(args.output_folder):
os.mkdir(args.output_folder)
if (not args.random_weights) and (args.ckpt_path is None):
print("Path to the checkpoint file was not provided")
exit(1)
if args.model == 'srcnn':
model = SRCNN(args)
elif args.model == 'espcn':
model = ESPCN(args)
elif args.model == 'ldsp':
model = LDSP(args)
elif args.model == 'vespcn':
model = VESPCN(args)
elif args.model == 'vsrnet':
model = VSRnet(args)
else:
exit(1)
with tf.Session() as sess:
input_ph = model.get_placeholder()
predicted = model.load_model(input_ph)
if args.model == 'vespcn':
predicted = predicted[2]
predicted = tf.identity(predicted, name='y')
if args.random_weights:
print("Random Weights Loaded.")
init = tf.global_variables_initializer()
sess.run(init)
else:
print("Checkpoint Weights Loaded.")
if os.path.isdir(args.ckpt_path):
args.ckpt_path = tf.train.latest_checkpoint(args.ckpt_path)
saver = tf.train.Saver()
saver.restore(sess, args.ckpt_path)
output_graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['y'])
tf.train.write_graph(output_graph_def, args.output_folder, args.model + '.pb', as_text=False)
def main():
args = get_arguments()
if args.model == 'srcnn':
model = SRCNN(args)
elif args.model == 'espcn':
model = ESPCN(args)
elif args.model == 'ldsp':
model = LDSP(args)
elif args.model == 'vespcn':
model = VESPCN(args)
elif args.model == 'vsrnet':
model = VSRnet(args)
else:
exit(1)
with tf.Session() as sess:
data_batch, data_initializer = model.get_data()
predicted_batch = model.load_model(data_batch)
loss = model.get_loss(data_batch, predicted_batch)
global_step = tf.Variable(0, trainable=False)
if args.use_lr_decay:
lr = tf.train.exponential_decay(args.learning_rate,
global_step,
args.lr_decay_epochs *
model.dataset.examples_num,
args.lr_decay_rate,
staircase=args.staircase_lr_decay)
else:
lr = args.learning_rate
if args.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(lr)
elif args.optimizer == 'momentum':
optimizer = tf.train.MomentumOptimizer(lr, args.momentum)
elif args.optimizer == 'sgd':
optimizer = tf.train.GradientDescentOptimizer(lr)
grads_vars = optimizer.compute_gradients(loss)
grads_vars_final = []
for gradient, variable in grads_vars:
assert gradient is not None, variable.name
if variable.name in model.lr_multipliers.keys():
gradient *= model.lr_multipliers[variable.name]
grads_vars_final.append((gradient, variable))
variable_name = variable.name.replace(':', '_')
scope = 'TrainLogs/' + variable_name + '/Values/'
tf.summary.scalar(scope + 'MIN', tf.reduce_min(variable))
tf.summary.scalar(scope + 'MAX', tf.reduce_max(variable))
tf.summary.scalar(scope + 'L2', tf.norm(variable))
tf.summary.scalar(scope + 'AVG', tf.reduce_mean(variable))
scope = 'TrainLogs/' + variable_name + '/Gradients/'
tf.summary.scalar(scope + 'MIN', tf.reduce_min(gradient))
tf.summary.scalar(scope + 'MAX', tf.reduce_max(gradient))
tf.summary.scalar(scope + 'L2', tf.norm(gradient))
tf.summary.scalar(scope + 'AVG', tf.reduce_mean(gradient))
train_op = optimizer.apply_gradients(grads_vars_final,
global_step=global_step)
tf.summary.scalar('Learning_rate', lr)
summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(args.logdir, sess.graph)
saver = tf.train.Saver()
last_epoch = 0
if args.ckpt_path is None:
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess.run(init_op)
else:
if os.path.isdir(args.ckpt_path):
args.ckpt_path = tf.train.latest_checkpoint(args.ckpt_path)
last_epoch = int(args.ckpt_path.split('.')[0].split('_')[-1])
saver.restore(sess, args.ckpt_path)
sess.run(data_initializer)
num_steps_in_epoch = model.dataset.examples_num // args.batch_size + \
1 if model.dataset.examples_num % args.batch_size != 0 else 0
for epoch in range(args.num_epochs):
print("Epoch: ", epoch + last_epoch)
bar = tqdm(range(num_steps_in_epoch),
total=num_steps_in_epoch,
unit='step',
smoothing=1.0)
for i in bar:
_, cur_loss, cur_summary, = sess.run([train_op, loss, summary])
bar.set_description('Loss: ' + str(cur_loss))
bar.refresh()
if (i + 1) % args.steps_per_log == 0:
summary_writer.add_summary(
cur_summary,
(last_epoch + epoch) * num_steps_in_epoch + i)
if epoch % args.epochs_per_save == 0:
saver.save(
sess,
os.path.join(
args.logdir,
'model_' + str(last_epoch + epoch + 1) + '.ckpt'))
def main():
args = get_arguments()
if args.model == 'srcnn':
model = SRCNN(args)
elif args.model == 'espcn':
model = ESPCN(args)
elif args.model == 'ldsp':
model = LDSP(args)
elif args.model == 'vespcn':
model = VESPCN(args)
elif args.model == 'vsrnet':
model = VSRnet(args)
else:
exit(1)
with tf.Session() as sess:
data_batch, data_initializer = model.get_data()
predicted_batch = model.load_model(data_batch)
metrics = model.calculate_metrics(data_batch, predicted_batch)
if args.ckpt_path is None:
print("Path to the checkpoint file was not provided")
exit(1)
if os.path.isdir(args.ckpt_path):
args.ckpt_path = tf.train.latest_checkpoint(args.ckpt_path)
saver = tf.train.Saver()
saver.restore(sess, args.ckpt_path)
summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(args.logdir, sess.graph)
sess.run(data_initializer)
steps = model.dataset.examples_num // args.batch_size + (
1 if model.dataset.examples_num % args.batch_size > 0 else 0)
epoch = int(args.ckpt_path.split('.')[0].split('_')[-1])
logged_iterations = 0
metrics_results = [[metric[0], np.array([])] for metric in metrics]
time_res = 0.0
for i in tqdm(range(steps), total=steps, unit='step'):
start = time()
results = sess.run([metric[1] for metric in metrics] + [summary])
time_res += time() - start
cur_metrics_results = results[:-1]
for j in range(len(cur_metrics_results)):
if len(cur_metrics_results[j].shape) == len(
metrics_results[j][1].shape):
metrics_results[j][1] = np.concatenate(
(metrics_results[j][1], cur_metrics_results[j]))
else:
metrics_results[j][1] = np.concatenate(
(metrics_results[j][1], [cur_metrics_results[j]]))
cur_summary_results = results[-1]
if (i + 1) % args.steps_per_log == 0:
summary_writer.add_summary(cur_summary_results,
epoch * steps + logged_iterations)
logged_iterations += 1
mean_metrics = [(metric[0], np.mean(metric[1]))
for metric in metrics_results]
mean_metrics.append(("Time", time_res / model.dataset.examples_num))
metric_summaries = []
for metric in mean_metrics:
print("Mean " + metric[0] + ': ', metric[1])
metric_summaries.append(tf.summary.scalar(metric[0], metric[1]))
metric_summary = tf.summary.merge(metric_summaries)
metric_summary_res = sess.run(metric_summary)
summary_writer.add_summary(metric_summary_res, epoch)
def main():
args = get_arguments()
if not os.path.exists(args.output_folder):
os.mkdir(args.output_folder)
if args.ckpt_path is None:
print("Path to the checkpoint file was not provided")
exit(1)
if args.model == 'srcnn':
model = SRCNN(args)
elif args.model == 'espcn':
model = ESPCN(args)
elif args.model == 'vespcn':
model = VESPCN(args)
elif args.model == 'vsrnet':
model = VSRnet(args)
else:
exit(1)
with tf.Session() as sess:
input_ph = model.get_placeholder()
predicted = model.load_model(input_ph)
if args.model == 'vespcn':
predicted = predicted[2]
predicted = tf.identity(predicted, name='y')
if os.path.isdir(args.ckpt_path):
args.ckpt_path = tf.train.latest_checkpoint(args.ckpt_path)
saver = tf.train.Saver()
saver.restore(sess, args.ckpt_path)
with open(os.path.join(args.output_folder, args.model + '.model'),
'wb') as native_mf:
weights = model.get_model_weights(sess)
if args.model == 'srcnn':
prepare_native_mf_srcnn(weights, native_mf)
elif args.model == 'espcn':
prepare_native_mf_espcn(weights, native_mf, args.scale_factor)
elif args.model == 'vespcn':
prepare_native_mf_vespcn(weights, native_mf, args.scale_factor)
elif args.model == 'vsrnet':
prepare_native_mf_vsrnet(weights, native_mf)
with open(os.path.join(args.output_folder, 'dnn_' + args.model + '.h'),
'w') as header:
header.write('/**\n')
header.write(' * @file\n')
header.write(' * Default cnn weights for x' +
str(args.scale_factor) + ' upscaling with ' +
args.model + ' model.\n')
header.write(' */\n\n')
header.write('#ifndef AVFILTER_DNN_' + args.model.upper() + '_H\n')
header.write('#define AVFILTER_DNN_' + args.model.upper() + '_H\n')
variables = tf.trainable_variables()
var_dict = OrderedDict()
for variable in variables:
var_name = variable.name.split(':')[0].replace('/', '_')
value = variable.eval()
if 'kernel' in var_name:
value = np.transpose(value, axes=(3, 0, 1, 2))
var_dict[var_name] = value
for name, value in var_dict.items():
dump_to_file(header, value, name)
header.write('#endif\n')
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess, sess.graph_def, ['y'])
tf.train.write_graph(output_graph_def,
args.output_folder,
args.model + '.pb',
as_text=False)