forked from NVlabs/stylegan2
-
Notifications
You must be signed in to change notification settings - Fork 16
/
run_projector.py
executable file
·148 lines (116 loc) · 6.8 KB
/
run_projector.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
# Copyright (c) 2019, NVIDIA Corporation. All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, visit
# https://nvlabs.github.io/stylegan2/license.html
import argparse
import numpy as np
import dnnlib
import dnnlib.tflib as tflib
import re
import sys
import projector
import pretrained_networks
from training import dataset
from training import misc
#----------------------------------------------------------------------------
def project_image(proj, targets, png_prefix, num_snapshots):
snapshot_steps = set(proj.num_steps - np.linspace(0, proj.num_steps, num_snapshots, endpoint=False, dtype=int))
misc.save_image_grid(targets, png_prefix + 'target.png', drange=[-1,1])
proj.start(targets)
while proj.get_cur_step() < proj.num_steps:
print('\r%d / %d ... ' % (proj.get_cur_step(), proj.num_steps), end='', flush=True)
proj.step()
if proj.get_cur_step() in snapshot_steps:
misc.save_image_grid(proj.get_images(), png_prefix + 'step%04d.png' % proj.get_cur_step(), drange=[-1,1])
print('\r%-30s\r' % '', end='', flush=True)
#----------------------------------------------------------------------------
def project_generated_images(network_pkl, seeds, num_snapshots, truncation_psi):
print('Loading networks from "%s"...' % network_pkl)
_G, _D, Gs = pretrained_networks.load_networks(network_pkl)
proj = projector.Projector()
proj.set_network(Gs)
noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.randomize_noise = False
Gs_kwargs.truncation_psi = truncation_psi
for seed_idx, seed in enumerate(seeds):
print('Projecting seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
rnd = np.random.RandomState(seed)
z = rnd.randn(1, *Gs.input_shape[1:])
tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars})
images = Gs.run(z, None, **Gs_kwargs)
project_image(proj, targets=images, png_prefix=dnnlib.make_run_dir_path('seed%04d-' % seed), num_snapshots=num_snapshots)
#----------------------------------------------------------------------------
def project_real_images(network_pkl, dataset_name, data_dir, num_images, num_snapshots):
print('Loading networks from "%s"...' % network_pkl)
_G, _D, Gs = pretrained_networks.load_networks(network_pkl)
proj = projector.Projector()
proj.set_network(Gs)
print('Loading images from "%s"...' % dataset_name)
dataset_obj = dataset.load_dataset(data_dir=data_dir, tfrecord_dir=dataset_name, max_label_size=0, repeat=False, shuffle_mb=0)
assert dataset_obj.shape == Gs.output_shape[1:]
for image_idx in range(num_images):
print('Projecting image %d/%d ...' % (image_idx, num_images))
images, _labels = dataset_obj.get_minibatch_np(1)
images = misc.adjust_dynamic_range(images, [0, 255], [-1, 1])
project_image(proj, targets=images, png_prefix=dnnlib.make_run_dir_path('image%04d-' % image_idx), num_snapshots=num_snapshots)
#----------------------------------------------------------------------------
def _parse_num_range(s):
'''Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.'''
range_re = re.compile(r'^(\d+)-(\d+)$')
m = range_re.match(s)
if m:
return list(range(int(m.group(1)), int(m.group(2))+1))
vals = s.split(',')
return [int(x) for x in vals]
#----------------------------------------------------------------------------
_examples = '''examples:
# Project generated images
python %(prog)s project-generated-images --network=gdrive:networks/stylegan2-car-config-f.pkl --seeds=0,1,5
# Project real images
python %(prog)s project-real-images --network=gdrive:networks/stylegan2-car-config-f.pkl --dataset=car --data-dir=~/datasets
'''
#----------------------------------------------------------------------------
def main():
parser = argparse.ArgumentParser(
description='''StyleGAN2 projector.
Run 'python %(prog)s <subcommand> --help' for subcommand help.''',
epilog=_examples,
formatter_class=argparse.RawDescriptionHelpFormatter
)
subparsers = parser.add_subparsers(help='Sub-commands', dest='command')
project_generated_images_parser = subparsers.add_parser('project-generated-images', help='Project generated images')
project_generated_images_parser.add_argument('--network', help='Network pickle filename', dest='network_pkl', required=True)
project_generated_images_parser.add_argument('--seeds', type=_parse_num_range, help='List of random seeds', default=range(3))
project_generated_images_parser.add_argument('--num-snapshots', type=int, help='Number of snapshots (default: %(default)s)', default=5)
project_generated_images_parser.add_argument('--truncation-psi', type=float, help='Truncation psi (default: %(default)s)', default=1.0)
project_generated_images_parser.add_argument('--result-dir', help='Root directory for run results (default: %(default)s)', default='results', metavar='DIR')
project_real_images_parser = subparsers.add_parser('project-real-images', help='Project real images')
project_real_images_parser.add_argument('--network', help='Network pickle filename', dest='network_pkl', required=True)
project_real_images_parser.add_argument('--data-dir', help='Dataset root directory', required=True)
project_real_images_parser.add_argument('--dataset', help='Training dataset', dest='dataset_name', required=True)
project_real_images_parser.add_argument('--num-snapshots', type=int, help='Number of snapshots (default: %(default)s)', default=5)
project_real_images_parser.add_argument('--num-images', type=int, help='Number of images to project (default: %(default)s)', default=3)
project_real_images_parser.add_argument('--result-dir', help='Root directory for run results (default: %(default)s)', default='results', metavar='DIR')
args = parser.parse_args()
subcmd = args.command
if subcmd is None:
print ('Error: missing subcommand. Re-run with --help for usage.')
sys.exit(1)
kwargs = vars(args)
sc = dnnlib.SubmitConfig()
sc.num_gpus = 1
sc.submit_target = dnnlib.SubmitTarget.LOCAL
sc.local.do_not_copy_source_files = True
sc.run_dir_root = kwargs.pop('result_dir')
sc.run_desc = kwargs.pop('command')
func_name_map = {
'project-generated-images': 'run_projector.project_generated_images',
'project-real-images': 'run_projector.project_real_images'
}
dnnlib.submit_run(sc, func_name_map[subcmd], **kwargs)
#----------------------------------------------------------------------------
if __name__ == "__main__":
main()
#----------------------------------------------------------------------------