import os import argparse import pickle from tqdm import tqdm import PIL.Image from PIL import ImageFilter import numpy as np import dnnlib import dnnlib.tflib as tflib import config from encoder.generator_model import Generator from encoder.perceptual_model import PerceptualModel, load_images #from tensorflow.keras.models import load_model from keras.models import load_model from keras.applications.resnet50 import preprocess_input def split_to_batches(l, n): for i in range(0, len(l), n): yield l[i:i + n] def str2bool(v): if isinstance(v, bool): return v if v.lower() in ('yes', 'true', 't', 'y', '1'): return True elif v.lower() in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected.') 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='./data/karras2019stylegan-ffhq-1024x1024.pkl', help='Fetch a StyleGAN model to train on from this URL') parser.add_argument('--architecture', default='./data/vgg16_zhang_perceptual.pkl', help='Сonvolutional neural network model from this URL') 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(args.architecture, 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() if __name__ == "__main__": main()