if (ff_model is None):
if os.path.exists(args.load_resnet):
print("Loading ResNet Model:")
ff_model = load_model(args.load_resnet)
from keras.applications.resnet50 import preprocess_input
if (ff_model is None):
if os.path.exists(args.load_effnet):
import efficientnet
print("Loading EfficientNet Model:")
ff_model = load_model(args.load_effnet)
from efficientnet import preprocess_input
if (ff_model
is not None): # predict initial dlatents with ResNet model
dlatents = ff_model.predict(
preprocess_input(
load_images(images_batch,
image_size=args.resnet_image_size)))
dlatents = np.mean(dlatents, axis=1)
# dlatents = misc.random_latents(1, Gs, random_state=np.random.RandomState(800))
if dlatents is not None:
generator.set_dlatents(dlatents)
dlabels = np.random.rand(args.batch_size, args.labels_size)
if dlabels is not None:
generator.set_dlabels(dlabels)
op = perceptual_model.optimize(
[generator.dlatent_variable, generator.dlabel_variable],
def main():
parser = argparse.ArgumentParser(description='Find latent representation of reference images using perceptual losses', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# Output directories setting
parser.add_argument('src_dir', help='Directory with images for encoding')
parser.add_argument('generated_images_dir', help='Directory for storing generated images')
parser.add_argument('guessed_images_dir', help='Directory for storing initially guessed images')
parser.add_argument('dlatent_dir', help='Directory for storing dlatent representations')
# General params
parser.add_argument('--model_res', default=1024, help='The dimension of images in the StyleGAN model', type=int)
parser.add_argument('--batch_size', default=1, help='Batch size for generator and perceptual model', type=int)
parser.add_argument('--use_resnet', default=True, help='Use pretrained ResNet for approximating dlatents', type=lambda x: (str(x).lower() == 'true'))
# Perceptual model params
parser.add_argument('--iterations', default=100, help='Number of optimization steps for each batch', type=int)
parser.add_argument('--lr', default=0.02, help='Learning rate for perceptual model', type=float)
parser.add_argument('--decay_rate', default=0.9, help='Decay rate for learning rate', type=float)
parser.add_argument('--decay_steps', default=10, help='Decay steps for learning rate decay (as a percent of iterations)', type=float)
parser.add_argument('--image_size', default=256, help='Size of images for perceptual model', type=int)
parser.add_argument('--resnet_image_size', default=256, help='Size of images for the Resnet model', type=int)
# Loss function options
parser.add_argument('--use_vgg_loss', default=0.4, help='Use VGG perceptual loss; 0 to disable, > 0 to scale.', type=float)
parser.add_argument('--use_vgg_layer', default=9, help='Pick which VGG layer to use.', type=int)
parser.add_argument('--use_pixel_loss', default=1.5, help='Use logcosh image pixel loss; 0 to disable, > 0 to scale.', type=float)
parser.add_argument('--use_mssim_loss', default=100, help='Use MS-SIM perceptual loss; 0 to disable, > 0 to scale.', type=float)
parser.add_argument('--use_lpips_loss', default=100, help='Use LPIPS perceptual loss; 0 to disable, > 0 to scale.', type=float)
parser.add_argument('--use_l1_penalty', default=1, help='Use L1 penalty on latents; 0 to disable, > 0 to scale.', type=float)
# Generator params
parser.add_argument('--randomize_noise', default=False, help='Add noise to dlatents during optimization', type=lambda x: (str(x).lower() == 'true'))
parser.add_argument('--tile_dlatents', default=False, help='Tile dlatents to use a single vector at each scale', type=lambda x: (str(x).lower() == 'true'))
parser.add_argument('--clipping_threshold', default=2.0, help='Stochastic clipping of gradient values outside of this threshold', type=float)
# Masking params
parser.add_argument('--mask_dir', default='masks/latent_interpolation', help='Directory for storing optional masks')
parser.add_argument('--face_mask', default=False, help='Generate a mask for predicting only the face area', type=lambda x: (str(x).lower() == 'true'))
parser.add_argument('--use_grabcut', default=True, help='Use grabcut algorithm on the face mask to better segment the foreground', type=lambda x: (str(x).lower() == 'true'))
parser.add_argument('--scale_mask', default=1.5, help='Look over a wider section of foreground for grabcut', type=float)
args, other_args = parser.parse_known_args()
args.decay_steps *= 0.01 * args.iterations # Calculate steps as a percent of total iterations
ref_images = [os.path.join(args.src_dir, x) for x in os.listdir(args.src_dir)]
ref_images = sorted(list(filter(os.path.isfile, ref_images)))
if len(ref_images) == 0:
raise Exception('%s is empty' % args.src_dir)
# Create output directories
os.makedirs('data', exist_ok=True)
os.makedirs(args.generated_images_dir, exist_ok=True)
os.makedirs(args.guessed_images_dir, exist_ok=True)
os.makedirs(args.dlatent_dir, exist_ok=True)
if args.face_mask:
os.makedirs(args.mask_dir, exist_ok=True)
# Initialize generator
tflib.init_tf()
with open_url(url_styleGAN, cache_dir='cache') as f:
generator_network, discriminator_network, Gs_network = pickle.load(f)
generator = Generator(model=Gs_network,
batch_size=args.batch_size,
clipping_threshold=args.clipping_threshold,
tiled_dlatent=args.tile_dlatents,
model_res=args.model_res,
randomize_noise=args.randomize_noise)
# Initialize perceptual model
perc_model = None
if args.use_lpips_loss > 1e-7:
with open_url(url_VGG_perceptual, cache_dir='cache') as f:
perc_model = pickle.load(f)
perceptual_model = PerceptualModel(args, perc_model=perc_model, batch_size=args.batch_size)
perceptual_model.build_perceptual_model(generator)
# Initialize ResNet model
resnet_model = None
if args.use_resnet:
print("\nLoading ResNet Model:")
resnet_model_fn = 'data/finetuned_resnet.h5'
gdown.download(url_resnet, resnet_model_fn, quiet=True)
resnet_model = load_model(resnet_model_fn)
# Optimize (only) dlatents by minimizing perceptual loss between reference and generated images in feature space
for images_batch in tqdm(split_to_batches(ref_images, args.batch_size), total=len(ref_images) // args.batch_size):
names = [os.path.splitext(os.path.basename(x))[0] for x in images_batch]
perceptual_model.set_reference_images(images_batch)
# predict initial dlatents with ResNet model
if resnet_model is not None:
dlatents = resnet_model.predict(preprocess_input(load_images(images_batch, image_size=args.resnet_image_size)))
generator.set_dlatents(dlatents)
# Generate and save initially guessed images
initial_dlatents = generator.get_dlatents()
initial_images = generator.generate_images()
for img_array, dlatent, img_name in zip(initial_images, initial_dlatents, names):
img = PIL.Image.fromarray(img_array, 'RGB')
img.save(os.path.join(args.guessed_images_dir, f'{img_name}.png'), 'PNG')
# Optimization process to find best latent vectors
op = perceptual_model.optimize(generator.dlatent_variable, iterations=args.iterations)
progress_bar = tqdm(op, leave=False, total=args.iterations)
best_loss = None
best_dlatent = None
for loss_dict in progress_bar:
progress_bar.set_description(" ".join(names) + ": " + "; ".join(["{} {:.4f}".format(k, v) for k, v in loss_dict.items()]))
if best_loss is None or loss_dict["loss"] < best_loss:
best_loss = loss_dict["loss"]
best_dlatent = generator.get_dlatents()
generator.stochastic_clip_dlatents()
print(" ".join(names), " Loss {:.4f}".format(best_loss))
# Save found dlatents
generator.set_dlatents(best_dlatent)
generated_dlatents = generator.get_dlatents()
for dlatent, img_name in zip(generated_dlatents, names):
np.save(os.path.join(args.dlatent_dir, f'{img_name}.npy'), dlatent)
generator.reset_dlatents()
# Concatenate and save dlalents vectors
list_dlatents = sorted(os.listdir(args.dlatent_dir))
final_w_vectors = np.array([np.load(args.dlatent_dir + dlatent) for dlatent in list_dlatents])
np.save(os.path.join(args.dlatent_dir, 'output_vectors.npy'), final_w_vectors)
# Perform face morphing by interpolating the latent space
w1, w2 = create_morphing_lists(final_w_vectors)
ref_images_1, ref_images_2 = create_morphing_lists(ref_images)
for i in range(len(ref_images_1)):
avg_w_vector = (0.5 * (w1[i] + w2[i])).reshape((-1, 18, 512))
generator.set_dlatents(avg_w_vector)
img_array = generator.generate_images()[0]
img = PIL.Image.fromarray(img_array, 'RGB')
img_name = os.path.splitext(os.path.basename(ref_images_1[i]))[0] + '_vs_' + os.path.splitext(os.path.basename(ref_images_2[i]))[0]
img.save(os.path.join(args.generated_images_dir, f'{img_name}.png'), 'PNG')
generator.reset_dlatents()
def main():
parser = argparse.ArgumentParser(description='Find latent representation of reference images using perceptual losses', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('src_dir', help='Directory with images for encoding')
parser.add_argument('generated_images_dir', help='Directory for storing generated images')
parser.add_argument('dlatent_dir', help='Directory for storing dlatent representations')
parser.add_argument('--data_dir', default='data', help='Directory for storing optional models')
parser.add_argument('--mask_dir', default='masks', help='Directory for storing optional masks')
parser.add_argument('--load_last', default='', help='Start with embeddings from directory')
parser.add_argument('--dlatent_avg', default='', help='Use dlatent from file specified here for truncation instead of dlatent_avg from Gs')
parser.add_argument('--model_url', default='https://drive.google.com/uc?id=1aPjeguDIRE0hs4_PHiRghK1Y2Qh3zOi1', help='Fetch a StyleGAN model to train on from this URL') # karras2019stylegan-ffhq-1024x1024.pkl
parser.add_argument('--model_res', default=1024, help='The dimension of images in the StyleGAN model', type=int)
parser.add_argument('--batch_size', default=1, help='Batch size for generator and perceptual model', type=int)
parser.add_argument('--optimizer', default='ggt', help='Optimization algorithm used for optimizing dlatents')
# Perceptual model params
parser.add_argument('--image_size', default=256, help='Size of images for perceptual model', type=int)
parser.add_argument('--resnet_image_size', default=256, help='Size of images for the Resnet model', type=int)
parser.add_argument('--lr', default=0.25, help='Learning rate for perceptual model', type=float)
parser.add_argument('--decay_rate', default=0.9, help='Decay rate for learning rate', type=float)
parser.add_argument('--iterations', default=100, help='Number of optimization steps for each batch', type=int)
parser.add_argument('--decay_steps', default=4, help='Decay steps for learning rate decay (as a percent of iterations)', type=float)
parser.add_argument('--early_stopping', default=True, help='Stop early once training stabilizes', type=str2bool, nargs='?', const=True)
parser.add_argument('--early_stopping_threshold', default=0.5, help='Stop after this threshold has been reached', type=float)
parser.add_argument('--early_stopping_patience', default=10, help='Number of iterations to wait below threshold', type=int)
parser.add_argument('--load_effnet', default='data/finetuned_effnet.h5', help='Model to load for EfficientNet approximation of dlatents')
parser.add_argument('--load_resnet', default='data/finetuned_resnet.h5', help='Model to load for ResNet approximation of dlatents')
parser.add_argument('--use_preprocess_input', default=True, help='Call process_input() first before using feed forward net', type=str2bool, nargs='?', const=True)
parser.add_argument('--use_best_loss', default=True, help='Output the lowest loss value found as the solution', type=str2bool, nargs='?', const=True)
parser.add_argument('--average_best_loss', default=0.25, help='Do a running weighted average with the previous best dlatents found', type=float)
parser.add_argument('--sharpen_input', default=True, help='Sharpen the input images', type=str2bool, nargs='?', const=True)
# Loss function options
parser.add_argument('--use_vgg_loss', default=0.4, help='Use VGG perceptual loss; 0 to disable, > 0 to scale.', type=float)
parser.add_argument('--use_vgg_layer', default=9, help='Pick which VGG layer to use.', type=int)
parser.add_argument('--use_pixel_loss', default=1.5, help='Use logcosh image pixel loss; 0 to disable, > 0 to scale.', type=float)
parser.add_argument('--use_mssim_loss', default=200, help='Use MS-SIM perceptual loss; 0 to disable, > 0 to scale.', type=float)
parser.add_argument('--use_lpips_loss', default=100, help='Use LPIPS perceptual loss; 0 to disable, > 0 to scale.', type=float)
parser.add_argument('--use_l1_penalty', default=0.5, help='Use L1 penalty on latents; 0 to disable, > 0 to scale.', type=float)
parser.add_argument('--use_discriminator_loss', default=0.5, help='Use trained discriminator to evaluate realism.', type=float)
parser.add_argument('--use_adaptive_loss', default=False, help='Use the adaptive robust loss function from Google Research for pixel and VGG feature loss.', type=str2bool, nargs='?', const=True)
# Generator params
parser.add_argument('--randomize_noise', default=False, help='Add noise to dlatents during optimization', type=str2bool, nargs='?', const=True)
parser.add_argument('--tile_dlatents', default=False, help='Tile dlatents to use a single vector at each scale', type=str2bool, nargs='?', const=True)
parser.add_argument('--clipping_threshold', default=2.0, help='Stochastic clipping of gradient values outside of this threshold', type=float)
# Masking params
parser.add_argument('--load_mask', default=False, help='Load segmentation masks', type=str2bool, nargs='?', const=True)
parser.add_argument('--face_mask', default=True, help='Generate a mask for predicting only the face area', type=str2bool, nargs='?', const=True)
parser.add_argument('--use_grabcut', default=True, help='Use grabcut algorithm on the face mask to better segment the foreground', type=str2bool, nargs='?', const=True)
parser.add_argument('--scale_mask', default=1.4, help='Look over a wider section of foreground for grabcut', type=float)
parser.add_argument('--composite_mask', default=True, help='Merge the unmasked area back into the generated image', type=str2bool, nargs='?', const=True)
parser.add_argument('--composite_blur', default=8, help='Size of blur filter to smoothly composite the images', type=int)
# Video params
parser.add_argument('--video_dir', default='videos', help='Directory for storing training videos')
parser.add_argument('--output_video', default=False, help='Generate videos of the optimization process', type=bool)
parser.add_argument('--video_codec', default='MJPG', help='FOURCC-supported video codec name')
parser.add_argument('--video_frame_rate', default=24, help='Video frames per second', type=int)
parser.add_argument('--video_size', default=512, help='Video size in pixels', type=int)
parser.add_argument('--video_skip', default=1, help='Only write every n frames (1 = write every frame)', type=int)
args, other_args = parser.parse_known_args()
args.decay_steps *= 0.01 * args.iterations # Calculate steps as a percent of total iterations
if args.output_video:
import cv2
synthesis_kwargs = dict(output_transform=dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=False), minibatch_size=args.batch_size)
ref_images = [os.path.join(args.src_dir, x) for x in os.listdir(args.src_dir)]
ref_images = list(filter(os.path.isfile, ref_images))
if len(ref_images) == 0:
raise Exception('%s is empty' % args.src_dir)
os.makedirs(args.data_dir, exist_ok=True)
os.makedirs(args.mask_dir, exist_ok=True)
os.makedirs(args.generated_images_dir, exist_ok=True)
os.makedirs(args.dlatent_dir, exist_ok=True)
os.makedirs(args.video_dir, exist_ok=True)
# Initialize generator and perceptual model
tflib.init_tf()
with dnnlib.util.open_url(args.model_url, cache_dir=config.cache_dir) as f:
generator_network, discriminator_network, Gs_network = pickle.load(f)
generator = Generator(Gs_network, args.batch_size, clipping_threshold=args.clipping_threshold, tiled_dlatent=args.tile_dlatents, model_res=args.model_res, randomize_noise=args.randomize_noise)
if (args.dlatent_avg != ''):
generator.set_dlatent_avg(np.load(args.dlatent_avg))
perc_model = None
if (args.use_lpips_loss > 0.00000001):
with dnnlib.util.open_url('https://drive.google.com/uc?id=1N2-m9qszOeVC9Tq77WxsLnuWwOedQiD2', cache_dir=config.cache_dir) as f:
perc_model = pickle.load(f)
perceptual_model = PerceptualModel(args, perc_model=perc_model, batch_size=args.batch_size)
perceptual_model.build_perceptual_model(generator, discriminator_network)
ff_model = None
# Optimize (only) dlatents by minimizing perceptual loss between reference and generated images in feature space
for images_batch in tqdm(split_to_batches(ref_images, args.batch_size), total=len(ref_images)//args.batch_size):
names = [os.path.splitext(os.path.basename(x))[0] for x in images_batch]
if args.output_video:
video_out = {}
for name in names:
video_out[name] = cv2.VideoWriter(os.path.join(args.video_dir, f'{name}.avi'),cv2.VideoWriter_fourcc(*args.video_codec), args.video_frame_rate, (args.video_size,args.video_size))
perceptual_model.set_reference_images(images_batch)
dlatents = None
if (args.load_last != ''): # load previous dlatents for initialization
for name in names:
dl = np.expand_dims(np.load(os.path.join(args.load_last, f'{name}.npy')),axis=0)
if (dlatents is None):
dlatents = dl
else:
dlatents = np.vstack((dlatents,dl))
else:
if (ff_model is None):
if os.path.exists(args.load_resnet):
from keras.applications.resnet50 import preprocess_input
print("Loading ResNet Model:")
ff_model = load_model(args.load_resnet)
if (ff_model is None):
if os.path.exists(args.load_effnet):
import efficientnet
from efficientnet import preprocess_input
print("Loading EfficientNet Model:")
ff_model = load_model(args.load_effnet)
if (ff_model is not None): # predict initial dlatents with ResNet model
if (args.use_preprocess_input):
dlatents = ff_model.predict(preprocess_input(load_images(images_batch,image_size=args.resnet_image_size)))
else:
dlatents = ff_model.predict(load_images(images_batch,image_size=args.resnet_image_size))
if dlatents is not None:
generator.set_dlatents(dlatents)
op = perceptual_model.optimize(generator.dlatent_variable, iterations=args.iterations, use_optimizer=args.optimizer)
pbar = tqdm(op, leave=False, total=args.iterations)
vid_count = 0
best_loss = None
best_dlatent = None
avg_loss_count = 0
if args.early_stopping:
avg_loss = prev_loss = None
for loss_dict in pbar:
if args.early_stopping: # early stopping feature
if prev_loss is not None:
if avg_loss is not None:
avg_loss = 0.5 * avg_loss + (prev_loss - loss_dict["loss"])
if avg_loss < args.early_stopping_threshold: # count while under threshold; else reset
avg_loss_count += 1
else:
avg_loss_count = 0
if avg_loss_count > args.early_stopping_patience: # stop once threshold is reached
print("")
break
else:
avg_loss = prev_loss - loss_dict["loss"]
pbar.set_description(" ".join(names) + ": " + "; ".join(["{} {:.4f}".format(k, v) for k, v in loss_dict.items()]))
if best_loss is None or loss_dict["loss"] < best_loss:
if best_dlatent is None or args.average_best_loss <= 0.00000001:
best_dlatent = generator.get_dlatents()
else:
best_dlatent = 0.25 * best_dlatent + 0.75 * generator.get_dlatents()
if args.use_best_loss:
generator.set_dlatents(best_dlatent)
best_loss = loss_dict["loss"]
if args.output_video and (vid_count % args.video_skip == 0):
batch_frames = generator.generate_images()
for i, name in enumerate(names):
video_frame = PIL.Image.fromarray(batch_frames[i], 'RGB').resize((args.video_size,args.video_size),PIL.Image.LANCZOS)
video_out[name].write(cv2.cvtColor(np.array(video_frame).astype('uint8'), cv2.COLOR_RGB2BGR))
generator.stochastic_clip_dlatents()
prev_loss = loss_dict["loss"]
if not args.use_best_loss:
best_loss = prev_loss
print(" ".join(names), " Loss {:.4f}".format(best_loss))
if args.output_video:
for name in names:
video_out[name].release()
# Generate images from found dlatents and save them
if args.use_best_loss:
generator.set_dlatents(best_dlatent)
generated_images = generator.generate_images()
generated_dlatents = generator.get_dlatents()
for img_array, dlatent, img_path, img_name in zip(generated_images, generated_dlatents, images_batch, names):
mask_img = None
if args.composite_mask and (args.load_mask or args.face_mask):
_, im_name = os.path.split(img_path)
mask_img = os.path.join(args.mask_dir, f'{im_name}')
if args.composite_mask and mask_img is not None and os.path.isfile(mask_img):
orig_img = PIL.Image.open(img_path).convert('RGB')
width, height = orig_img.size
imask = PIL.Image.open(mask_img).convert('L').resize((width, height))
imask = imask.filter(ImageFilter.GaussianBlur(args.composite_blur))
mask = np.array(imask)/255
mask = np.expand_dims(mask,axis=-1)
img_array = mask*np.array(img_array) + (1.0-mask)*np.array(orig_img)
img_array = img_array.astype(np.uint8)
#img_array = np.where(mask, np.array(img_array), orig_img)
img = PIL.Image.fromarray(img_array, 'RGB')
img.save(os.path.join(args.generated_images_dir, f'{img_name}.png'), 'PNG')
np.save(os.path.join(args.dlatent_dir, f'{img_name}.npy'), dlatent)
generator.reset_dlatents()
def optimize_latents(images_batch, latent_paths, ff_model, generator,
perceptual_model, s3):
if args['output_video']:
pass
# TODO add video
# video_out = cv2.VideoWriter(os.path.join(args['video_dir'], f'{name}.avi'),cv2.VideoWriter_fourcc(*args['video_codec']), args['video_frame_rate'], (args['video_size'],args['video_size']))
names = [os.path.splitext(os.path.basename(x))[0] for x in images_batch]
print(images_batch)
perceptual_model.set_reference_images(images_batch)
dlatents = None
if (ff_model is not None): # predict initial dlatents with ResNet model
if (args['use_preprocess_input']):
dlatents = ff_model.predict(
preprocess_input(
load_images(images_batch,
image_size=args['resnet_image_size'])))
else:
dlatents = ff_model.predict(
load_images(images_batch,
image_size=args['resnet_image_size']))
if dlatents is not None:
generator.set_dlatents(dlatents)
op = perceptual_model.optimize(generator.dlatent_variable,
iterations=args['iterations'],
use_optimizer=args['optimizer'])
pbar = tqdm(op, leave=False, total=args['iterations'])
vid_count = 0
best_loss = None
best_dlatent = None
avg_loss_count = 0
if args['early_stopping']:
avg_loss = prev_loss = None
for loss_dict in pbar:
if args['early_stopping']: # early stopping feature
if prev_loss is not None:
if avg_loss is not None:
avg_loss = 0.5 * avg_loss + (prev_loss - loss_dict["loss"])
if avg_loss < args[
'early_stopping_threshold']: # count while under threshold; else reset
avg_loss_count += 1
else:
avg_loss_count = 0
if avg_loss_count > args[
'early_stopping_patience']: # stop once threshold is reached
print("")
break
else:
avg_loss = prev_loss - loss_dict["loss"]
pbar.set_description(
" ".join(images_batch) + ": " +
"; ".join(["{} {:.4f}".format(k, v)
for k, v in loss_dict.items()]))
if best_loss is None or loss_dict["loss"] < best_loss:
if best_dlatent is None or args['average_best_loss'] <= 0.00000001:
best_dlatent = generator.get_dlatents()
else:
best_dlatent = 0.25 * best_dlatent + 0.75 * generator.get_dlatents(
)
if args['use_best_loss']:
generator.set_dlatents(best_dlatent)
best_loss = loss_dict["loss"]
generator.stochastic_clip_dlatents()
prev_loss = loss_dict["loss"]
if not args['use_best_loss']:
best_loss = prev_loss
print(" ".join(names), " Loss {:.4f}".format(best_loss))
# Generate images from found dlatents and save them
if args['use_best_loss']:
generator.set_dlatents(best_dlatent)
generated_images = generator.generate_images()
generated_dlatents = generator.get_dlatents()
for img_array, dlatent, img_path, img_name, latent_path in zip(
generated_images, generated_dlatents, images_batch, names,
latent_paths):
mask_img = None
if args['composite_mask'] and (args['load_mask'] or args['face_mask']):
_, im_name = os.path.split(img_path)
mask_img = os.path.join(args['mask_dir'], f'{im_name}')
if args['composite_mask'] and mask_img is not None and os.path.isfile(
mask_img):
orig_img = Image.open(img_path).convert('RGB')
width, height = orig_img.size
imask = Image.open(mask_img).convert('L').resize((width, height))
imask = imask.filter(
ImageFilter.GaussianBlur(args['composite_blur']))
mask = np.array(imask) / 255
mask = np.expand_dims(mask, axis=-1)
print(mask)
print(img_array)
print(orig_img)
img_array = mask * np.array(img_array) + (
1.0 - mask) * np.array(orig_img)
img_array = img_array.astype(np.uint8)
img_array = np.where(mask, np.array(img_array), orig_img)
img = Image.fromarray(img_array, 'RGB')
img_fname = f'{img_name[:-3]}.png'
imname = os.path.join(args['generated_images_dir'], img_fname)
img.save(imname, 'PNG')
s3.upload_file(imname, GENERATED_BUCKET, img_fname)
np.save(latent_path, dlatent)
generator.reset_dlatents()
def main():
parser = argparse.ArgumentParser(
description=
'Find latent representation of reference images using perceptual losses',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('src_dir', help='Directory with images for encoding')
parser.add_argument('generated_images_dir',
help='Directory for storing generated images')
parser.add_argument('dlatent_dir',
help='Directory for storing dlatent representations')
parser.add_argument('--data_dir',
default='data',
help='Directory for storing optional models')
parser.add_argument(
'--model_url',
default=
'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ',
help='Fetch a StyleGAN model to train on from this URL'
) # karras2019stylegan-ffhq-1024x1024.pkl
parser.add_argument('--model_res',
default=1024,
help='The dimension of images in the StyleGAN model',
type=int)
parser.add_argument('--batch_size',
default=1,
help='Batch size for generator and perceptual model',
type=int)
# Perceptual model params
parser.add_argument('--image_size',
default=256,
help='Size of images for perceptual model',
type=int)
parser.add_argument('--resnet_image_size',
default=256,
help='Size of images for the Resnet model',
type=int)
parser.add_argument('--lr',
default=0.02,
help='Learning rate for perceptual model',
type=float)
parser.add_argument('--decay_rate',
default=0.9,
help='Decay rate for learning rate',
type=float)
parser.add_argument('--iterations',
default=100,
help='Number of optimization steps for each batch',
type=int)
parser.add_argument(
'--decay_steps',
default=10,
help='Decay steps for learning rate decay (as a percent of iterations)',
type=float)
parser.add_argument(
'--load_resnet',
default='data/finetuned_resnet.h5',
help='Model to load for Resnet approximation of dlatents')
# Loss function options
parser.add_argument(
'--use_vgg_loss',
default=0.4,
help='Use VGG perceptual loss; 0 to disable, > 0 to scale.',
type=float)
parser.add_argument('--use_vgg_layer',
default=9,
help='Pick which VGG layer to use.',
type=int)
parser.add_argument(
'--use_pixel_loss',
default=1.5,
help='Use logcosh image pixel loss; 0 to disable, > 0 to scale.',
type=float)
parser.add_argument(
'--use_mssim_loss',
default=100,
help='Use MS-SIM perceptual loss; 0 to disable, > 0 to scale.',
type=float)
parser.add_argument(
'--use_lpips_loss',
default=100,
help='Use LPIPS perceptual loss; 0 to disable, > 0 to scale.',
type=float)
parser.add_argument(
'--use_l1_penalty',
default=1,
help='Use L1 penalty on latents; 0 to disable, > 0 to scale.',
type=float)
# Generator params
parser.add_argument('--randomize_noise',
default=False,
help='Add noise to dlatents during optimization',
type=bool)
parser.add_argument(
'--tile_dlatents',
default=False,
help='Tile dlatents to use a single vector at each scale',
type=bool)
parser.add_argument(
'--clipping_threshold',
default=2.0,
help='Stochastic clipping of gradient values outside of this threshold',
type=float)
# Video params
parser.add_argument('--video_dir',
default='videos',
help='Directory for storing training videos')
parser.add_argument('--output_video',
default=False,
help='Generate videos of the optimization process',
type=bool)
parser.add_argument('--video_codec',
default='MJPG',
help='FOURCC-supported video codec name')
parser.add_argument('--video_frame_rate',
default=24,
help='Video frames per second',
type=int)
parser.add_argument('--video_size',
default=512,
help='Video size in pixels',
type=int)
parser.add_argument(
'--video_skip',
default=1,
help='Only write every n frames (1 = write every frame)',
type=int)
args, other_args = parser.parse_known_args()
args.decay_steps *= 0.01 * args.iterations # Calculate steps as a percent of total iterations
if args.output_video:
import cv2
synthesis_kwargs = dict(output_transform=dict(
func=tflib.convert_images_to_uint8, nchw_to_nhwc=False),
minibatch_size=args.batch_size)
ref_images = [
os.path.join(args.src_dir, x) for x in os.listdir(args.src_dir)
]
ref_images = list(filter(os.path.isfile, ref_images))
if len(ref_images) == 0:
raise Exception('%s is empty' % args.src_dir)
os.makedirs(args.data_dir, exist_ok=True)
os.makedirs(args.generated_images_dir, exist_ok=True)
os.makedirs(args.dlatent_dir, exist_ok=True)
os.makedirs(args.video_dir, exist_ok=True)
# Initialize generator and perceptual model
tflib.init_tf()
with dnnlib.util.open_url(args.model_url, cache_dir=config.cache_dir) as f:
generator_network, discriminator_network, Gs_network = pickle.load(f)
generator = Generator(Gs_network,
args.batch_size,
clipping_threshold=args.clipping_threshold,
tiled_dlatent=args.tile_dlatents,
model_res=args.model_res,
randomize_noise=args.randomize_noise)
perc_model = None
if (args.use_lpips_loss > 0.00000001):
with dnnlib.util.open_url(
'https://drive.google.com/uc?id=1N2-m9qszOeVC9Tq77WxsLnuWwOedQiD2',
cache_dir=config.cache_dir) as f:
perc_model = pickle.load(f)
perceptual_model = PerceptualModel(args,
perc_model=perc_model,
batch_size=args.batch_size)
perceptual_model.build_perceptual_model(generator)
resnet_model = None
if os.path.exists(args.load_resnet):
print("Loading ResNet Model:")
resnet_model = load_model(args.load_resnet)
# Optimize (only) dlatents by minimizing perceptual loss between reference and generated images in feature space
for images_batch in tqdm(split_to_batches(ref_images, args.batch_size),
total=len(ref_images) // args.batch_size):
names = [
os.path.splitext(os.path.basename(x))[0] for x in images_batch
]
if args.output_video:
video_out = {}
for name in names:
video_out[name] = cv2.VideoWriter(
os.path.join(args.video_dir, f'{name}.avi'),
cv2.VideoWriter_fourcc(*args.video_codec),
args.video_frame_rate, (args.video_size, args.video_size))
perceptual_model.set_reference_images(images_batch)
dlatents = None
if (resnet_model is not None):
dlatents = resnet_model.predict(
preprocess_resnet_input(
load_images(images_batch,
image_size=args.resnet_image_size)))
if dlatents is not None:
generator.set_dlatents(dlatents)
op = perceptual_model.optimize(generator.dlatent_variable,
iterations=args.iterations)
pbar = tqdm(op, leave=False, total=args.iterations)
vid_count = 0
best_loss = None
best_dlatent = None
for loss_dict in pbar:
pbar.set_description(" ".join(names) + ": " + "; ".join(
["{} {:.4f}".format(k, v) for k, v in loss_dict.items()]))
if best_loss is None or loss_dict["loss"] < best_loss:
best_loss = loss_dict["loss"]
best_dlatent = generator.get_dlatents()
if args.output_video and (vid_count % args.video_skip == 0):
batch_frames = generator.generate_images()
for i, name in enumerate(names):
video_frame = PIL.Image.fromarray(
batch_frames[i], 'RGB').resize(
(args.video_size, args.video_size),
PIL.Image.LANCZOS)
video_out[name].write(
cv2.cvtColor(
np.array(video_frame).astype('uint8'),
cv2.COLOR_RGB2BGR))
generator.stochastic_clip_dlatents()
print(" ".join(names), " Loss {:.4f}".format(best_loss))
if args.output_video:
for name in names:
video_out[name].release()
# Generate images from found dlatents and save them
generator.set_dlatents(best_dlatent)
generated_images = generator.generate_images()
generated_dlatents = generator.get_dlatents()
for img_array, dlatent, img_name in zip(generated_images,
generated_dlatents, names):
img = PIL.Image.fromarray(img_array, 'RGB')
img.save(
os.path.join(args.generated_images_dir, f'{img_name}.png'),
'PNG')
np.save(os.path.join(args.dlatent_dir, f'{img_name}.npy'), dlatent)
generator.reset_dlatents()
def encode(resnet, learning_rate=0.02, iterations=200):
args = eden.utils.DictMap()
args_other = eden.utils.DictMap()
args.src_dir = os.path.join(stylegan, 'aligned_images')
args.generated_images_dir = os.path.join(stylegan, 'generated_images')
args.dlatent_dir = os.path.join(stylegan, 'latent_representations')
args.load_last = None
args.dlatent_avg = None
args.model_res = 1024
args.batch_size = 1
# Perceptual model params
args.image_size = 256
args.resnet_image_size = 256
args.lr = learning_rate
args.decay_rate = 0.9
args.iterations = iterations
args.decay_steps = 10
args.load_effnet = None
args.load_resnet = os.path.join(stylegan, resnet)
# Loss function options
args.use_vgg_loss = 0.4
args.use_vgg_layer = 9
args.use_pixel_loss = 1.5
args.use_mssim_loss = 100
args.use_lpips_loss = 100
args.use_l1_penalty = 1
# Generator params
args.randomize_noise = False
args.tile_dlatents = False
args.clipping_threshold = 2.0
# Masking params
args.load_mask = False
args.face_mask = False
args.use_grabcut = True
args.scale_mask = 1.5
# Video params
args.video_dir = os.path.join(stylegan, 'videos')
args.output_video = True
args.video_codec = 'MJPG'
args.video_frame_rate = 30
args.video_size = 1024
args.video_skip = 1
args.decay_steps *= 0.01 * args.iterations # Calculate steps as a percent of total iterations
if args.output_video:
synthesis_kwargs = dict(output_transform=dict(
func=tflib.convert_images_to_uint8, nchw_to_nhwc=False),
minibatch_size=args.batch_size)
ref_images = [
os.path.join(args.src_dir, x) for x in os.listdir(args.src_dir)
]
ref_images = list(filter(os.path.isfile, ref_images))
if len(ref_images) == 0:
raise Exception('%s is empty' % args.src_dir)
eden.utils.try_make_folder(args.generated_images_dir)
eden.utils.try_make_folder(args.dlatent_dir)
eden.utils.try_make_folder(args.video_dir)
# Initialize generator and perceptual model
generator = Generator(Gs,
args.batch_size,
clipping_threshold=args.clipping_threshold,
tiled_dlatent=args.tile_dlatents,
model_res=args.model_res,
randomize_noise=args.randomize_noise)
if (args.dlatent_avg is not None):
generator.set_dlatent_avg(np.load(args.dlatent_avg))
perc_model = None
if (args.use_lpips_loss > 0.00000001):
cache_dir = os.path.join(stylegan, config.cache_dir)
with dnnlib.util.open_url(
'https://drive.google.com/uc?id=1N2-m9qszOeVC9Tq77WxsLnuWwOedQiD2',
cache_dir=cache_dir) as f:
perc_model = pickle.load(f)
perceptual_model = PerceptualModel(args,
perc_model=perc_model,
batch_size=args.batch_size)
perceptual_model.build_perceptual_model(generator)
ff_model = None
# Optimize (only) dlatents by minimizing perceptual loss between reference and generated images in feature space
for images_batch in tqdm(split_to_batches(ref_images, args.batch_size),
total=len(ref_images) // args.batch_size):
names = [
os.path.splitext(os.path.basename(x))[0] for x in images_batch
]
if args.output_video:
video_out = {}
for name in names:
video_out[name] = cv2.VideoWriter(
os.path.join(args.video_dir, f'{name}.avi'),
cv2.VideoWriter_fourcc(*args.video_codec),
args.video_frame_rate, (args.video_size, args.video_size))
perceptual_model.set_reference_images(images_batch)
dlatents = None
if (args.load_last
is not None): # load previous dlatents for initialization
for name in names:
dl = np.expand_dims(np.load(
os.path.join(args.load_last, f'{name}.npy')),
axis=0)
if (dlatents is None):
dlatents = dl
else:
dlatents = np.vstack((dlatents, dl))
else:
if (ff_model is None):
if os.path.exists(args.load_resnet):
print("Loading ResNet Model:")
ff_model = load_model(args.load_resnet)
from keras.applications.resnet50 import preprocess_input
if (ff_model is None):
if os.path.exists(args.load_effnet):
import efficientnet
print("Loading EfficientNet Model:")
ff_model = load_model(args.load_effnet)
from efficientnet import preprocess_input
if (ff_model
is not None): # predict initial dlatents with ResNet model
dlatents = ff_model.predict(
preprocess_input(
load_images(images_batch,
image_size=args.resnet_image_size)))
if dlatents is not None:
generator.set_dlatents(dlatents)
op = perceptual_model.optimize(generator.dlatent_variable,
iterations=args.iterations)
pbar = tqdm(op, leave=False, total=args.iterations)
vid_count = 0
best_loss = None
best_dlatent = None
for loss_dict in pbar:
pbar.set_description(" ".join(names) + ": " + "; ".join(
["{} {:.4f}".format(k, v) for k, v in loss_dict.items()]))
if best_loss is None or loss_dict["loss"] < best_loss:
best_loss = loss_dict["loss"]
best_dlatent = generator.get_dlatents()
if args.output_video and (vid_count % args.video_skip == 0):
batch_frames = generator.generate_images()
for i, name in enumerate(names):
video_frame = Image.fromarray(
batch_frames[i], 'RGB').resize(
(args.video_size, args.video_size), Image.LANCZOS)
video_out[name].write(
cv2.cvtColor(
np.array(video_frame).astype('uint8'),
cv2.COLOR_RGB2BGR))
generator.stochastic_clip_dlatents()
print(" ".join(names), " Loss {:.4f}".format(best_loss))
if args.output_video:
for name in names:
video_out[name].release()
# Generate images from found dlatents and save them
generator.set_dlatents(best_dlatent)
generated_images = generator.generate_images()
generated_dlatents = generator.get_dlatents()
for img_array, dlatent, img_name in zip(generated_images,
generated_dlatents, names):
img = Image.fromarray(img_array, 'RGB')
img.save(
os.path.join(args.generated_images_dir, f'{img_name}.png'),
'PNG')
np.save(os.path.join(args.dlatent_dir, f'{img_name}.npy'), dlatent)
generator.reset_dlatents()
def styleGAN_encoder(path_A, path_B):
start_ = time.time()
decay_steps =10
decay_steps *= 0.01 * 100 # Calculate steps as a percent of total iterations
src_dir = 'aligned_images'
name_A = src_dir+'/%s.png' %os.path.basename(os.path.splitext(path_A)[0])
name_B = src_dir+'/%s.png' %os.path.basename(os.path.splitext(path_B)[0])
ref_images = [name_A,name_B]
ref_images = list(filter(os.path.isfile, ref_images))
os.makedirs('data', exist_ok=True)
os.makedirs('masks', exist_ok=True)
# Initialize generator and perceptual model
tflib.init_tf()
with dnnlib.util.open_url('https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ', cache_dir='cache') as f:
generator_network, discriminator_network, Gs_network = pickle.load(f)
generator = Generator(Gs_network, 1, clipping_threshold=2.0, tiled_dlatent=False, model_res=1024, randomize_noise=False)
print(generator.model_scale)
perc_model = None
if (100 > 0.00000001):
with dnnlib.util.open_url('https://drive.google.com/uc?id=1N2-m9qszOeVC9Tq77WxsLnuWwOedQiD2', cache_dir='cache') as f:
perc_model = pickle.load(f)
perceptual_model = PerceptualModel(perc_model=perc_model, batch_size=1)
perceptual_model.build_perceptual_model(generator)
ff_model = None
# Optimize (only) dlatents by minimizing perceptual loss between reference and generated images in feature space
for images_batch in tqdm(split_to_batches(ref_images, 1), total=len(ref_images)//1):
names = [os.path.splitext(os.path.basename(x))[0] for x in images_batch]
perceptual_model.set_reference_images(images_batch)
dlatents = None
if (ff_model is None):
if os.path.exists('data/finetuned_resnet.h5'):
print("Loading ResNet Model:")
ff_model = load_model('data/finetuned_resnet.h5')
from keras.applications.resnet50 import preprocess_input
if (ff_model is not None): # predict initial dlatents with ResNet model
dlatents = ff_model.predict(preprocess_input(load_images(images_batch,image_size=256)))
if dlatents is not None:
generator.set_dlatents(dlatents)
op = perceptual_model.optimize(generator.dlatent_variable, iterations=100)
pbar = tqdm(op, leave=False, total=100)
best_loss = None
best_dlatent = None
for loss_dict in pbar:
pbar.set_description(" ".join(names) + ": " + "; ".join(["{} {:.4f}".format(k, v)
for k, v in loss_dict.items()]))
if best_loss is None or loss_dict["loss"] < best_loss:
best_loss = loss_dict["loss"]
best_dlatent = generator.get_dlatents()
generator.stochastic_clip_dlatents()
print(" ".join(names), " Loss {:.4f}".format(best_loss))
print(best_dlatent)
# Generate images from found dlatents and save them
generator.set_dlatents(best_dlatent)
generated_images = generator.generate_images()
generated_dlatents = generator.get_dlatents()
print(generator.initial_dlatents)
for img_array, dlatent, img_name in zip(generated_images, generated_dlatents, names):
np.save(os.path.join('latent_representations', f'{img_name}.npy'), dlatent)
generator.reset_dlatents()
end_ = time.time()
logging.info('The time it takes for the StyleGAN Encoder: %.2fs' % (end_ - start_))
def styleGAN_encoder(args,path_A, path_B):
start_ = time.time()
args.decay_steps *= 0.01 * args.iterations # Calculate steps as a percent of total iterations
src_dir = args.src_dir
name_A = src_dir+'/%s.png' %os.path.basename(os.path.splitext(path_A)[0])
name_B = src_dir+'/%s.png' %os.path.basename(os.path.splitext(path_B)[0])
ref_images = [name_A,name_B]
ref_images = list(filter(os.path.isfile, ref_images))
os.makedirs(args.data_dir, exist_ok=True)
os.makedirs(args.dlatent_dir, exist_ok=True)
# Initialize generator and perceptual model
tflib.init_tf()
with dnnlib.util.open_url(args.model_url, cache_dir=config.cache_dir) as f:
generator_network, discriminator_network, Gs_network = pickle.load(f)
generator = Generator(Gs_network, args.batch_size, clipping_threshold=args.clipping_threshold, tiled_dlatent=args.tile_dlatents, model_res=args.model_res, randomize_noise=args.randomize_noise)
perc_model = None
if (args.use_lpips_loss > 0.00000001):
with dnnlib.util.open_url('https://drive.google.com/uc?id=1N2-m9qszOeVC9Tq77WxsLnuWwOedQiD2', cache_dir=config.cache_dir) as f:
perc_model = pickle.load(f)
perceptual_model = PerceptualModel(args, perc_model=perc_model, batch_size=args.batch_size)
perceptual_model.build_perceptual_model(generator)
ff_model = None
# Optimize (only) dlatents by minimizing perceptual loss between reference and generated images in feature space
for images_batch in tqdm(split_to_batches(ref_images, args.batch_size), total=len(ref_images)//args.batch_size):
names = [os.path.splitext(os.path.basename(x))[0] for x in images_batch]
perceptual_model.set_reference_images(images_batch)
dlatents = None
if (args.load_last != ''): # load previous dlatents for initialization
for name in names:
dl = np.expand_dims(np.load(os.path.join(args.load_last, f'{name}.npy')),axis=0)
if (dlatents is None):
dlatents = dl
else:
dlatents = np.vstack((dlatents,dl))
else:
if (ff_model is None):
if os.path.exists(args.load_resnet):
print("Loading ResNet Model:")
ff_model = load_model(args.load_resnet)
from keras.applications.resnet50 import preprocess_input
if (ff_model is None):
if os.path.exists(args.load_effnet):
import efficientnet
print("Loading EfficientNet Model:")
ff_model = load_model(args.load_effnet)
from efficientnet import preprocess_input
if (ff_model is not None): # predict initial dlatents with ResNet model
dlatents = ff_model.predict(preprocess_input(load_images(images_batch,image_size=args.resnet_image_size)))
if dlatents is not None:
generator.set_dlatents(dlatents)
op = perceptual_model.optimize(generator.dlatent_variable, iterations=args.iterations)
pbar = tqdm(op, leave=False, total=args.iterations)
best_loss = None
best_dlatent = None
for loss_dict in pbar:
pbar.set_description(" ".join(names) + ": " + "; ".join(["{} {:.4f}".format(k, v)
for k, v in loss_dict.items()]))
if best_loss is None or loss_dict["loss"] < best_loss:
best_loss = loss_dict["loss"]
best_dlatent = generator.get_dlatents()
generator.stochastic_clip_dlatents()
print(" ".join(names), " Loss {:.4f}".format(best_loss))
# Generate images from found dlatents and save them
generator.set_dlatents(best_dlatent)
generated_images = generator.generate_images()
generated_dlatents = generator.get_dlatents()
for img_array, dlatent, img_name in zip(generated_images, generated_dlatents, names):
np.save(os.path.join(args.dlatent_dir, f'{img_name}.npy'), dlatent)
generator.reset_dlatents()
end_ = time.time()
logging.info('The time it takes for the StyleGAN Encoder: %.2fs' % (end_ - start_))
def styleGAN_encoder(args):
start_ = time.time()
args.decay_steps *= 0.01 * args.iterations # Calculate steps as a percent of total iterations
if args.output_video:
import cv2
synthesis_kwargs = dict(output_transform=dict(
func=tflib.convert_images_to_uint8, nchw_to_nhwc=False),
minibatch_size=args.batch_size)
ref_images = [
os.path.join(args.src_dir, x) for x in os.listdir(args.src_dir)
]
ref_images = list(filter(os.path.isfile, ref_images))
if len(ref_images) == 0:
raise Exception('%s is empty' % args.src_dir)
os.makedirs(args.data_dir, exist_ok=True)
os.makedirs(args.mask_dir, exist_ok=True)
os.makedirs(args.generated_images_dir, exist_ok=True)
os.makedirs(args.dlatent_dir, exist_ok=True)
os.makedirs(args.video_dir, exist_ok=True)
# Initialize generator and perceptual model
tflib.init_tf()
with dnnlib.util.open_url(args.model_url, cache_dir=config.cache_dir) as f:
generator_network, discriminator_network, Gs_network = pickle.load(f)
generator = Generator(Gs_network,
args.batch_size,
clipping_threshold=args.clipping_threshold,
tiled_dlatent=args.tile_dlatents,
model_res=args.model_res,
randomize_noise=args.randomize_noise)
if (args.dlatent_avg != ''):
generator.set_dlatent_avg(np.load(args.dlatent_avg))
perc_model = None
if (args.use_lpips_loss > 0.00000001):
with dnnlib.util.open_url(
'https://drive.google.com/uc?id=1N2-m9qszOeVC9Tq77WxsLnuWwOedQiD2',
cache_dir=config.cache_dir) as f:
perc_model = pickle.load(f)
perceptual_model = PerceptualModel(args,
perc_model=perc_model,
batch_size=args.batch_size)
perceptual_model.build_perceptual_model(generator)
ff_model = None
# Optimize (only) dlatents by minimizing perceptual loss between reference and generated images in feature space
for images_batch in tqdm(split_to_batches(ref_images, args.batch_size),
total=len(ref_images) // args.batch_size):
names = [
os.path.splitext(os.path.basename(x))[0] for x in images_batch
]
if args.output_video:
video_out = {}
for name in names:
video_out[name] = cv2.VideoWriter(
os.path.join(args.video_dir, f'{name}.avi'),
cv2.VideoWriter_fourcc(*args.video_codec),
args.video_frame_rate, (args.video_size, args.video_size))
perceptual_model.set_reference_images(images_batch)
dlatents = None
if (args.load_last != ''): # load previous dlatents for initialization
for name in names:
dl = np.expand_dims(np.load(
os.path.join(args.load_last, f'{name}.npy')),
axis=0)
if (dlatents is None):
dlatents = dl
else:
dlatents = np.vstack((dlatents, dl))
else:
if (ff_model is None):
if os.path.exists(args.load_resnet):
print("Loading ResNet Model:")
ff_model = load_model(args.load_resnet)
from keras.applications.resnet50 import preprocess_input
if (ff_model is None):
if os.path.exists(args.load_effnet):
import efficientnet
print("Loading EfficientNet Model:")
ff_model = load_model(args.load_effnet)
from efficientnet import preprocess_input
if (ff_model
is not None): # predict initial dlatents with ResNet model
dlatents = ff_model.predict(
preprocess_input(
load_images(images_batch,
image_size=args.resnet_image_size)))
if dlatents is not None:
generator.set_dlatents(dlatents)
op = perceptual_model.optimize(generator.dlatent_variable,
iterations=args.iterations)
pbar = tqdm(op, leave=False, total=args.iterations)
vid_count = 0
best_loss = None
best_dlatent = None
for loss_dict in pbar:
pbar.set_description(" ".join(names) + ": " + "; ".join(
["{} {:.4f}".format(k, v) for k, v in loss_dict.items()]))
if best_loss is None or loss_dict["loss"] < best_loss:
best_loss = loss_dict["loss"]
best_dlatent = generator.get_dlatents()
if args.output_video and (vid_count % args.video_skip == 0):
batch_frames = generator.generate_images()
for i, name in enumerate(names):
video_frame = PIL.Image.fromarray(
batch_frames[i], 'RGB').resize(
(args.video_size, args.video_size),
PIL.Image.LANCZOS)
video_out[name].write(
cv2.cvtColor(
np.array(video_frame).astype('uint8'),
cv2.COLOR_RGB2BGR))
generator.stochastic_clip_dlatents()
print(" ".join(names), " Loss {:.4f}".format(best_loss))
if args.output_video:
for name in names:
video_out[name].release()
# Generate images from found dlatents and save them
generator.set_dlatents(best_dlatent)
generated_images = generator.generate_images()
generated_dlatents = generator.get_dlatents()
for img_array, dlatent, img_name in zip(generated_images,
generated_dlatents, names):
img = PIL.Image.fromarray(img_array, 'RGB')
img.save(
os.path.join(args.generated_images_dir, f'{img_name}.png'),
'PNG')
np.save(os.path.join(args.dlatent_dir, f'{img_name}.npy'), dlatent)
generator.reset_dlatents()
end_ = time.time()
logging.info('图像的StyleEncoder编码耗费时间: %.2fs' % (end_ - start_))
def main():
parser = argparse.ArgumentParser(
description=
'Find latent representation of reference images using perceptual loss')
parser.add_argument('name', help='Name of a combined image')
parser.add_argument('raw_dir',
help='Directory with a raw image for encoding')
parser.add_argument('aligned_dir', help='Directory with a aligned image')
parser.add_argument('generated_images_dir',
help='Directory for storing generated images')
parser.add_argument('dlatent_dir',
help='Directory for storing dlatent representations')
parser.add_argument('--data_dir',
default='data',
help='Directory for storing optional models')
parser.add_argument('--mask_dir',
default='masks',
help='Directory for storing optional masks')
parser.add_argument('--load_last',
default='',
help='Start with embeddings from directory')
parser.add_argument(
'--dlatent_avg',
default='',
help=
'Use dlatent from file specified here for truncation instead of dlatent_avg from Gs'
)
parser.add_argument(
'--model_url',
default=
'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ',
help='Fetch a StyleGAN model to train on from this URL'
) # karras2019stylegan-ffhq-1024x1024.pkl
parser.add_argument('--model_res',
default=1024,
help='The dimension of images in the StyleGAN model',
type=int)
parser.add_argument('--batch_size',
default=1,
help='Batch size for generator and perceptual model',
type=int)
#Perceptual model params
parser.add_argument('--image_size',
default=256,
help='Size of images for perceptual model',
type=int)
parser.add_argument('--resnet_image_size',
default=256,
help='Size of images for the Resnet model',
type=int)
parser.add_argument('--lr',
default=0.03,
help='Learning rate for perceptual model',
type=float)
parser.add_argument('--decay_rate',
default=0.9,
help='Decay rate for learning rate',
type=float)
parser.add_argument('--iterations',
default=1000,
help='Number of optimization steps for each batch',
type=int)
parser.add_argument(
'--decay_steps',
default=10,
help='Decay steps for learning rate decay (as a percent of iterations)',
type=float)
parser.add_argument(
'--load_effnet',
default='data/finetuned_effnet.h5',
help='Model to load for EfficientNet approximation of dlatents')
parser.add_argument(
'--load_resnet',
default='data/finetuned_resnet.h5',
help='Model to load for ResNet approximation of dlatents')
#Loss function options
parser.add_argument(
'--use_vgg_loss',
default=0.4,
help='Use VGG perceptual loss; 0 to disable, > 0 to scale.',
type=float)
parser.add_argument('--use_vgg_layer',
default=9,
help='Pick which VGG layer to use.',
type=int)
parser.add_argument(
'--use_pixel_loss',
default=1.5,
help='Use logcosh image pixel loss; 0 to disable, > 0 to scale.',
type=float)
parser.add_argument(
'--use_mssim_loss',
default=100,
help='Use MS-SIM perceptual loss; 0 to disable, > 0 to scale.',
type=float)
parser.add_argument(
'--use_lpips_loss',
default=100,
help='Use LPIPS perceptual loss; 0 to disable, > 0 to scale.',
type=float)
parser.add_argument(
'--use_l1_penalty',
default=1,
help='Use L1 penalty on latents; 0 to disable, > 0 to scale.',
type=float)
#Generator params
parser.add_argument('--randomize_noise',
default=False,
help='Add noise to dlatents during optimization',
type=bool)
parser.add_argument(
'--tile_dlatents',
default=False,
help='Tile dlatents to use a single vector at each scale',
type=bool)
parser.add_argument(
'--clipping_threshold',
default=2.0,
help='Stochastic clipping of gradient values outside of this threshold',
type=float)
# Masking params
parser.add_argument('--load_mask',
default=False,
help='Load segmentation masks',
type=bool)
parser.add_argument(
'--face_mask',
default=False,
help='Generate a mask for predicting only the face area',
type=bool)
parser.add_argument(
'--use_grabcut',
default=True,
help=
'Use grabcut algorithm on the face mask to better segment the foreground',
type=bool)
parser.add_argument(
'--scale_mask',
default=1.5,
help='Look over a wider section of foreground for grabcut',
type=float)
# Video params
parser.add_argument('--video_dir',
default='videos',
help='Directory for storing training videos')
parser.add_argument('--output_video',
default=False,
help='Generate videos of the optimization process',
type=bool)
parser.add_argument('--video_codec',
default='MJPG',
help='FOURCC-supported video codec name')
parser.add_argument('--video_frame_rate',
default=24,
help='Video frames per second',
type=int)
parser.add_argument('--video_size',
default=512,
help='Video size in pixels',
type=int)
parser.add_argument(
'--video_skip',
default=1,
help='Only write every n frames (1 = write every frame)',
type=int)
args, other_args = parser.parse_known_args()
args.decay_steps *= 0.01 * args.iterations
if args.output_video:
import cv2
synthesis_kwargs = dict(output_transform=dict(
func=tflib.convert_images_to_uint8, nchw_to_nhwc=False),
minibatch_size=args.batch_size)
#encoder_main
os.makedirs(args.raw_dir, exist_ok=True)
src_dir = args.raw_dir + args.name
img = PIL.Image.open(src_dir)
wpercent = (256 / float(img.size[0]))
hsize = int((float(img.size[1]) * float(wpercent)))
img = img.resize((256, hsize), PIL.Image.LANCZOS)
#align_images
os.makedirs(args.aligned_dir, exist_ok=True)
align_images(args.raw_dir, args.aligned_dir)
#encode_images
ref_images = [
os.path.join(args.aligned_dir, x) for x in os.listdir(args.aligned_dir)
]
ref_images = list(filter(os.path.isfile, ref_images))
if len(ref_images) == 0:
raise Exception('%s is empty' % args.aligned_dir)
os.makedirs(args.data_dir, exist_ok=True)
os.makedirs(args.mask_dir, exist_ok=True)
os.makedirs(args.generated_images_dir, exist_ok=True)
os.makedirs(args.dlatent_dir, exist_ok=True)
os.makedirs(args.video_dir, exist_ok=True)
tflib.init_tf()
#with dnnlib.util.open_url(URL_FFHQ, cache_dir=config.cache_dir) as f:
#generator_network, discriminator_network, Gs_network = pickle.load(f)
ffhq = '/content/gdrive/My Drive/data/karras2019stylegan-ffhq-1024x1024.pkl'
with open(ffhq, 'rb') as f:
_generator_network, discriminator_network, Gs_network = pickle.load(f)
generator = Generator(Gs_network,
args.batch_size,
clipping_threshold=args.clipping_threshold,
tiled_dlatent=args.tile_dlatents,
model_res=args.model_res,
randomize_noise=args.randomize_noise)
if (args.dlatent_avg != ''):
generator.set_dlatent_avg(np.load(args.dlatent_avg))
perc_model = None
if (args.use_lpips_loss > 0.00000001):
with dnnlib.util.open_url(
'https://drive.google.com/uc?id=1N2-m9qszOeVC9Tq77WxsLnuWwOedQiD2',
cache_dir=config.cache_dir) as f:
perc_model = pickle.load(f)
perceptual_model = PerceptualModel(args,
perc_model=perc_model,
batch_size=args.batch_size)
perceptual_model.build_perceptual_model(generator) #.generated_image
ff_model = None
for images_batch in tqdm(split_to_batches(ref_images, args.batch_size),
total=len(ref_images) // args.batch_size):
names = [
os.path.splitext(os.path.basename(x))[0] for x in images_batch
]
if args.output_video:
video_out = {}
for name in names:
video_out[name] = cv2.VideoWriter(
os.path.join(args.video_dir, f'{name}.avi'),
cv2.VideoWriter_fourcc(*args.video_codec),
args.video_frame_rate, (args.video_size, args.video_size))
perceptual_model.set_reference_images(images_batch)
dlatents = None
if (args.load_last != ''): # load previous dlatents for initialization
for name in names:
dl = np.expand_dims(np.load(
os.path.join(args.load_last, f'{name}.npy')),
axis=0)
if (dlatents is None):
dlatents = dl
else:
dlatents = np.vstack((dlatents, dl))
else:
if (ff_model is None):
if os.path.exists(args.load_resnet):
print("Loading ResNet Model:")
ff_model = load_model(args.load_resnet)
from keras.applications.resnet50 import preprocess_input
if (ff_model is None):
if os.path.exists(args.load_effnet):
import efficientnet
print("Loading EfficientNet Model:")
ff_model = load_model(args.load_effnet)
from efficientnet import preprocess_input
if (ff_model
is not None): # predict initial dlatents with ResNet model
dlatents = ff_model.predict(
preprocess_input(
load_images(images_batch,
image_size=args.resnet_image_size)))
if dlatents is not None:
generator.set_dlatents(dlatents)
op = perceptual_model.optimize(generator.dlatent_variable,
iterations=args.iterations)
pbar = tqdm(op, leave=False, total=args.iterations)
vid_count = 0
best_loss = None
best_dlatent = None
for loss_dict in pbar:
pbar.set_description(" ".join(names) + ": " + "; ".join(
["{} {:.4f}".format(k, v) for k, v in loss_dict.items()]))
if best_loss is None or loss_dict["loss"] < best_loss:
best_loss = loss_dict["loss"]
best_dlatent = generator.get_dlatents()
if args.output_video and (vid_count % args.video_skip == 0):
batch_frames = generator.generate_images()
for i, name in enumerate(names):
video_frame = PIL.Image.fromarray(
batch_frames[i], 'RGB').resize(
(args.video_size, args.video_size),
PIL.Image.LANCZOS)
video_out[name].write(
cv2.cvtColor(
np.array(video_frame).astype('uint8'),
cv2.COLOR_RGB2BGR))
generator.stochastic_clip_dlatents()
print(" ".join(names), " Loss {:.4f}".format(best_loss))
if args.output_video:
for name in names:
video_out[name].release()
# Generate images from found dlatents and save them
generator.set_dlatents(best_dlatent)
generated_images = generator.generate_images()
generated_dlatents = generator.get_dlatents()
for img_array, dlatent, img_name in zip(generated_images,
generated_dlatents, names):
img = PIL.Image.fromarray(img_array, 'RGB')
img.save(
os.path.join(args.generated_images_dir, f'{img_name}.png'),
'PNG')
np.save(os.path.join(args.dlatent_dir, f'{img_name}.npy'), dlatent)
generator.reset_dlatents()
