-
Notifications
You must be signed in to change notification settings - Fork 12
/
gen_samples.py
230 lines (185 loc) · 9.93 KB
/
gen_samples.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
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
"""Generate images and shapes using pretrained network pickle."""
import os
import re
from typing import List, Optional, Tuple, Union
import click
import dnnlib
import numpy as np
import PIL.Image
import torch
from tqdm import tqdm
import mrcfile
import legacy
from camera_utils import LookAtPoseSampler, FOV_to_intrinsics
from torch_utils import misc
from training.triplane import TriPlaneGenerator
#----------------------------------------------------------------------------
def parse_range(s: Union[str, List]) -> List[int]:
'''Parse a comma separated list of numbers or ranges and return a list of ints.
Example: '1,2,5-10' returns [1, 2, 5, 6, 7]
'''
if isinstance(s, list): return s
ranges = []
range_re = re.compile(r'^(\d+)-(\d+)$')
for p in s.split(','):
if m := range_re.match(p):
ranges.extend(range(int(m.group(1)), int(m.group(2))+1))
else:
ranges.append(int(p))
return ranges
#----------------------------------------------------------------------------
def parse_vec2(s: Union[str, Tuple[float, float]]) -> Tuple[float, float]:
'''Parse a floating point 2-vector of syntax 'a,b'.
Example:
'0,1' returns (0,1)
'''
if isinstance(s, tuple): return s
parts = s.split(',')
if len(parts) == 2:
return (float(parts[0]), float(parts[1]))
raise ValueError(f'cannot parse 2-vector {s}')
#----------------------------------------------------------------------------
def make_transform(translate: Tuple[float,float], angle: float):
m = np.eye(3)
s = np.sin(angle/360.0*np.pi*2)
c = np.cos(angle/360.0*np.pi*2)
m[0][0] = c
m[0][1] = s
m[0][2] = translate[0]
m[1][0] = -s
m[1][1] = c
m[1][2] = translate[1]
return m
#----------------------------------------------------------------------------
def create_samples(N=256, voxel_origin=[0, 0, 0], cube_length=2.0):
# NOTE: the voxel_origin is actually the (bottom, left, down) corner, not the middle
voxel_origin = np.array(voxel_origin) - cube_length/2
voxel_size = cube_length / (N - 1)
overall_index = torch.arange(0, N ** 3, 1, out=torch.LongTensor())
samples = torch.zeros(N ** 3, 3)
# transform first 3 columns
# to be the x, y, z index
samples[:, 2] = overall_index % N
samples[:, 1] = (overall_index.float() / N) % N
samples[:, 0] = ((overall_index.float() / N) / N) % N
# transform first 3 columns
# to be the x, y, z coordinate
samples[:, 0] = (samples[:, 0] * voxel_size) + voxel_origin[2]
samples[:, 1] = (samples[:, 1] * voxel_size) + voxel_origin[1]
samples[:, 2] = (samples[:, 2] * voxel_size) + voxel_origin[0]
num_samples = N ** 3
return samples.unsqueeze(0), voxel_origin, voxel_size
#----------------------------------------------------------------------------
@click.command()
@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
@click.option('--seeds', type=parse_range, help='List of random seeds (e.g., \'0,1,4-6\')', required=True)
@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
@click.option('--trunc-cutoff', 'truncation_cutoff', type=int, help='Truncation cutoff', default=14, show_default=True)
@click.option('--class', 'class_idx', type=int, help='Class label (unconditional if not specified)')
@click.option('--outdir', help='Where to save the out images', type=str, required=True, metavar='DIR')
@click.option('--shapes', help='Export shapes as .mrc files viewable in ChimeraX', type=bool, required=False, metavar='BOOL', default=False, show_default=True)
@click.option('--shape-res', help='', type=int, required=False, metavar='int', default=512, show_default=True)
@click.option('--fov-deg', help='Field of View of camera in degrees', type=int, required=False, metavar='float', default=18.837, show_default=True)
@click.option('--shape-format', help='Shape Format', type=click.Choice(['.mrc', '.ply']), default='.mrc')
@click.option('--reload_modules', help='Overload persistent modules?', type=bool, required=False, metavar='BOOL', default=False, show_default=True)
def generate_images(
network_pkl: str,
seeds: List[int],
truncation_psi: float,
truncation_cutoff: int,
outdir: str,
shapes: bool,
shape_res: int,
fov_deg: float,
shape_format: str,
class_idx: Optional[int],
reload_modules: bool,
):
"""Generate images using pretrained network pickle.
Examples:
\b
# Generate an image using pre-trained FFHQ model.
python gen_samples.py --outdir=out --trunc=0.7 --seeds=0-5 --shapes=True\\
--network=ffhq-rebalanced-128.pkl
"""
print('Loading networks from "%s"...' % network_pkl)
device = torch.device('cuda')
with dnnlib.util.open_url(network_pkl) as f:
G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore
# Specify reload_modules=True if you want code modifications to take effect; otherwise uses pickled code
if reload_modules:
print("Reloading Modules!")
G_new = TriPlaneGenerator(*G.init_args, **G.init_kwargs).eval().requires_grad_(False).to(device)
misc.copy_params_and_buffers(G, G_new, require_all=True)
G_new.neural_rendering_resolution = G.neural_rendering_resolution
G_new.rendering_kwargs = G.rendering_kwargs
G = G_new
os.makedirs(outdir, exist_ok=True)
cam2world_pose = LookAtPoseSampler.sample(3.14/2, 3.14/2, torch.tensor([0, 0, 0.2], device=device), radius=2.7, device=device)
intrinsics = FOV_to_intrinsics(fov_deg, device=device)
# Generate images.
for seed_idx, seed in enumerate(seeds):
print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device)
imgs = []
angle_p = -0.2
for angle_y, angle_p in [(.4, angle_p), (0, angle_p), (-.4, angle_p)]:
cam_pivot = torch.tensor(G.rendering_kwargs.get('avg_camera_pivot', [0, 0, 0]), device=device)
cam_radius = G.rendering_kwargs.get('avg_camera_radius', 2.7)
cam2world_pose = LookAtPoseSampler.sample(np.pi/2 + angle_y, np.pi/2 + angle_p, cam_pivot, radius=cam_radius, device=device)
conditioning_cam2world_pose = LookAtPoseSampler.sample(np.pi/2, np.pi/2, cam_pivot, radius=cam_radius, device=device)
camera_params = torch.cat([cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9)], 1)
conditioning_params = torch.cat([conditioning_cam2world_pose.reshape(-1, 16), intrinsics.reshape(-1, 9)], 1)
ws = G.mapping(z, conditioning_params, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff)
img = G.synthesis(ws, camera_params)['image']
img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
imgs.append(img)
img = torch.cat(imgs, dim=2)
PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/seed{seed:04d}.png')
if shapes:
# extract a shape.mrc with marching cubes. You can view the .mrc file using ChimeraX from UCSF.
max_batch=1000000
samples, voxel_origin, voxel_size = create_samples(N=shape_res, voxel_origin=[0, 0, 0], cube_length=G.rendering_kwargs['box_warp'] * 1)#.reshape(1, -1, 3)
samples = samples.to(z.device)
sigmas = torch.zeros((samples.shape[0], samples.shape[1], 1), device=z.device)
transformed_ray_directions_expanded = torch.zeros((samples.shape[0], max_batch, 3), device=z.device)
transformed_ray_directions_expanded[..., -1] = -1
head = 0
with tqdm(total = samples.shape[1]) as pbar:
with torch.no_grad():
while head < samples.shape[1]:
torch.manual_seed(0)
sigma = G.sample(samples[:, head:head+max_batch], transformed_ray_directions_expanded[:, :samples.shape[1]-head], z, conditioning_params, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, noise_mode='const')['sigma']
sigmas[:, head:head+max_batch] = sigma
head += max_batch
pbar.update(max_batch)
sigmas = sigmas.reshape((shape_res, shape_res, shape_res)).cpu().numpy()
sigmas = np.flip(sigmas, 0)
# Trim the border of the extracted cube
pad = int(30 * shape_res / 256)
pad_value = -1000
sigmas[:pad] = pad_value
sigmas[-pad:] = pad_value
sigmas[:, :pad] = pad_value
sigmas[:, -pad:] = pad_value
sigmas[:, :, :pad] = pad_value
sigmas[:, :, -pad:] = pad_value
if shape_format == '.ply':
from shape_utils import convert_sdf_samples_to_ply
convert_sdf_samples_to_ply(np.transpose(sigmas, (2, 1, 0)), [0, 0, 0], 1, os.path.join(outdir, f'seed{seed:04d}.ply'), level=10)
elif shape_format == '.mrc': # out mrc
with mrcfile.new_mmap(os.path.join(outdir, f'seed{seed:04d}.mrc'), overwrite=True, shape=sigmas.shape, mrc_mode=2) as mrc:
mrc.data[:] = sigmas
#----------------------------------------------------------------------------
if __name__ == "__main__":
generate_images() # pylint: disable=no-value-for-parameter
#----------------------------------------------------------------------------