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jblitzar
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github-python

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"""
import os
import sys
from argparse import ArgumentParser
from pathlib import Path
sys.path.append('third_party/stylegan2_ada_pytorch')
from reconstruct.alpha_projector import AlphaProjector
from utils import io_utils, image_ops
from utils.data_utils import PersonalizedDataset
from utils.io_utils import load_latents
from utils.image_ops import Degradation
import torch
import numpy as np
torch.manual_seed(2)
np.random.seed(2)
def project(args):
dataset = PersonalizedDataset(args.images_dir,
mask_dir=args.mask_dir)
anchors = load_latents(args.anchor_dir, to_w=True)
generator = io_utils.load_net(args.generator_path)
if args.mask_dir is not None:
deg_func = Degradation.hole
elif args.sr_factor is not None:
deg_func = lambda x, **kwargs: Degradation.downsample(x, args.sr_factor)
else:
deg_func = lambda x, **kwargs: x
alpha_projector = AlphaProjector(args.device, generator,
args.debug_output_dir, args.is_wplus,
anchors, deg_func,
beta=args.beta)
for sample in dataset:
sample.img = deg_func(sample.img, mask=sample.mask)
sample.save_input(args.output_dir)
sample = alpha_projector.reconstruct(sample)
if sample.mask is not None:
blended_recon = image_ops.blend(sample.img, sample.recon_img, sample.mask)
sample.set(recon_img=blended_recon)
sample.save_latent(args.output_dir)
sample.save_recon(args.output_dir)
# TODO(3): SR blending, face segmenetation model?
def parse_args(raw_args=None):
parser = ArgumentParser('Projection arguments')
parser.add_argument('--device', type=str, default="0")
parser.add_argument('--verbose', type=io_utils.str2bool, default="True")
parser.add_argument('--images_dir', type=io_utils.existing_path, required=True)
parser.add_argument('--output_dir', type=Path, required=True)
parser.add_argument('--generator_path', type=io_utils.existing_path, required=True)
parser.add_argument('--anchor_dir', type=io_utils.existing_path, required=True)
parser.add_argument('--beta', type=io_utils.float_or_none, default=0.03,
help='Controls the maximal allowed dilation of the personalized space.'
'Pass None to not restrict dilation.')
parser.add_argument('--is_wplus', type=io_utils.str2bool, default="True")
parser.add_argument('--mask_dir', type=io_utils.existing_path)
parser.add_argument('--sr_factor', type=float)
# TODO(2): support W/W+ projection
args = parser.parse_args(raw_args)
return args
def process_args(args):
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.device)
args.device = torch.device(f'cuda')
args.output_dir.mkdir(exist_ok=True, parents=True)
args.debug_output_dir = None
if args.verbose:
args.debug_output_dir = args.output_dir.joinpath('debug')
args.debug_output_dir.mkdir(exist_ok=True, parents=True)
if args.sr_factor is not None and args.sr_factor > 1:
args.sr_factor = 1 / args.sr_factor
return args