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super_resolution.py
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super_resolution.py
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import os
import argparse
import torch
import cv2
from utils.file_utils import image_files, load_as_tensor, Tensor2PIL, split_to_batches
from utils.image_precossing import _sigmoid_to_tanh, _tanh_to_sigmoid, _add_batch_one
from derivable_models.derivable_generator import get_derivable_generator
from utils.manipulate import SR_loss, downsample_images
from inversion.inversion_methods import get_inversion
from inversion.losses import get_loss
from models.model_settings import MODEL_POOL
from utils.manipulate import convert_array_to_images
import warnings
warnings.filterwarnings("ignore")
def main(args):
os.makedirs(args.outputs, exist_ok=True)
generator = get_derivable_generator(args.gan_model, args.inversion_type, args)
loss = get_loss(args.loss_type, args)
sr_loss = SR_loss(loss, args.down, args.factor)
# to cuda
generator.cuda()
loss.cuda()
inversion = get_inversion(args.optimization, args)
image_list = image_files(args.target_images)
frameSize = MODEL_POOL[args.gan_model]['resolution']
for i, images in enumerate(split_to_batches(image_list, 1)):
print('%d: Super-resolving %d images ' % (i + 1, 1), end='')
pt_image_str = '%s\n'
print(pt_image_str % tuple(images))
image_name_list = []
image_tensor_list = []
for image in images:
image_name_list.append(os.path.split(image)[1])
image_tensor_list.append(_add_batch_one(load_as_tensor(image)))
y_gt = _sigmoid_to_tanh(torch.cat(image_tensor_list, dim=0)).cuda()
# Invert
latent_estimates, history = inversion.invert(generator, y_gt, sr_loss, batch_size=1, video=args.video)
# Get Images
y_estimate_list = torch.split(torch.clamp(_tanh_to_sigmoid(generator(latent_estimates)), min=0., max=1.).cpu(), 1, dim=0)
# Save
for img_id, image in enumerate(images):
up_nn, up_bic = downsample_images(image_tensor_list[img_id], factor=args.factor, mode=args.down)
y_nn_pil = Tensor2PIL(up_nn)
y_estimate_pil = Tensor2PIL(y_estimate_list[img_id])
y_estimate_pil.save(os.path.join(os.path.join(args.outputs, '%s.png' % image_name_list[img_id][:-4])))
y_nn_pil.save(os.path.join(os.path.join(args.outputs, '%s-nn.png' % image_name_list[img_id][:-4])))
# Create video
if args.video:
print('Create GAN-Inversion video.')
video = cv2.VideoWriter(
filename=os.path.join(args.outputs, '%s_sr.avi' % image_name_list[img_id][:-4]),
fourcc=cv2.VideoWriter_fourcc(*'MJPG'),
fps=args.fps,
frameSize=(frameSize, frameSize))
print('Save frames.')
for i, sample in enumerate(history):
image = generator(sample)
image_cv2 = convert_array_to_images(image.detach().cpu().numpy())[0][:, :, ::-1]
video.write(image_cv2)
video.release()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='SR using multi-code GAN prior')
# Image Path and Saving Path
parser.add_argument('-i', '--target_images',
default='./examples/superresolution',
help='Directory with images for SR')
parser.add_argument('-o', '--outputs',
default='sr_test',
help='Directory for storing generated images')
# Parameters for Multi-Code GAN Inversion
parser.add_argument('--inversion_type', default='PGGAN-Multi-Z',
help='Inversion type, PGGAN-Multi-Z for Multi-Code-GAN prior.')
parser.add_argument('--composing_layer', default=6,
help='Composing layer in multi-code gan inversion methods.', type=int)
parser.add_argument('--z_number', default=30,
help='Number of the latent codes.', type=int)
# Experiment Settings
# Super-resolution
parser.add_argument('--down', type=str, default='bilinear',
help='Downsampling method.')
parser.add_argument('--factor', type=int, default=16,
help='SR factor.')
# Loss Parameters
parser.add_argument('--image_size', default=256,
help='Size of images for perceptual model', type=int)
parser.add_argument('--loss_type', default='Combine',
help="['VGG', 'L1', 'L2', 'Combine']. 'Combine' means using L2 and Perceptual Loss.")
parser.add_argument('--vgg_loss_type', default='L1',
help="['L1', 'L2']. The loss used in perceptual loss.")
parser.add_argument('--vgg_layer', default=16,
help='The layer used in perceptual loss.', type=int)
parser.add_argument('--l1_lambda', default=0.,
help="Used when 'loss_type' is 'Combine'. Trade-off parameter for L1 loss.", type=float)
parser.add_argument('--l2_lambda', default=1.,
help="Used when 'loss_type' is 'Combine'. Trade-off parameter for L2 loss.", type=float)
parser.add_argument('--vgg_lambda', default=1.0,
help="Used when 'loss_type' is 'Combine'. Trade-off parameter for Perceptual loss.", type=float)
# Optimization Parameters
parser.add_argument('--optimization', default='GD',
help="['GD', 'Adam']. Optimization method used.") # inversion_type
parser.add_argument('--init_type', default='Zero',
help="['Zero', 'Normal']. Initialization method. Using zero init or Gaussian random vector.")
parser.add_argument('--lr', default=1.0,
help='Learning rate.', type=float)
parser.add_argument('--iterations', default=2000,
help='Number of optimization steps.', type=int)
# Generator Setting
parser.add_argument('--gan_model', default='pggan_celebahq',
help='The name of model used.', type=str)
# Video Settings
parser.add_argument('--video', type=bool, default=True,
help='Save video. False for no video.')
parser.add_argument('--fps', type=int, default=24,
help='Frame rate of the created video.')
args, other_args = parser.parse_known_args()
### RUN
main(args)