-
Notifications
You must be signed in to change notification settings - Fork 128
/
train.py
542 lines (479 loc) · 31.2 KB
/
train.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
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
# 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.
"""Train a GAN using the techniques described in the paper
"Efficient Geometry-aware 3D Generative Adversarial Networks."
Code adapted from
"Alias-Free Generative Adversarial Networks"."""
import os
import click
import re
import json
import tempfile
import torch
import dnnlib
from training import training_loop
from metrics import metric_main
from torch_utils import training_stats
from torch_utils import custom_ops
# ----------------------------------------------------------------------------
def subprocess_fn(rank, c, temp_dir):
dnnlib.util.Logger(file_name=os.path.join(
c.run_dir, 'log.txt'), file_mode='a', should_flush=True)
# Init torch.distributed.
if c.num_gpus > 1:
init_file = os.path.abspath(os.path.join(
temp_dir, '.torch_distributed_init'))
if os.name == 'nt':
init_method = 'file:///' + init_file.replace('\\', '/')
torch.distributed.init_process_group(
backend='gloo', init_method=init_method, rank=rank, world_size=c.num_gpus)
else:
init_method = f'file://{init_file}'
torch.distributed.init_process_group(
backend='nccl', init_method=init_method, rank=rank, world_size=c.num_gpus)
# Init torch_utils.
sync_device = torch.device('cuda', rank) if c.num_gpus > 1 else None
training_stats.init_multiprocessing(rank=rank, sync_device=sync_device)
if rank != 0:
custom_ops.verbosity = 'none'
# Execute training loop.
training_loop.training_loop(rank=rank, **c)
# ----------------------------------------------------------------------------
def launch_training(c, desc, outdir, dry_run):
dnnlib.util.Logger(should_flush=True)
# Pick output directory.
prev_run_dirs = []
if os.path.isdir(outdir):
prev_run_dirs = [x for x in os.listdir(
outdir) if os.path.isdir(os.path.join(outdir, x))]
prev_run_ids = [re.match(r'^\d+', x) for x in prev_run_dirs]
prev_run_ids = [int(x.group()) for x in prev_run_ids if x is not None]
cur_run_id = max(prev_run_ids, default=-1) + 1
c.run_dir = os.path.join(outdir, f'{cur_run_id:05d}-{desc}')
assert not os.path.exists(c.run_dir)
c.exp_name = f'{cur_run_id:05d}-{desc}'
# Print options.
print()
print('Training options:')
print(json.dumps(c, indent=2))
print()
print(f'Output directory: {c.run_dir}')
print(f'Number of GPUs: {c.num_gpus}')
print(f'Batch size: {c.batch_size} images')
print(f'Training duration: {c.total_kimg} kimg')
print(f'Dataset path: {c.training_set_kwargs.path}')
print(f'Dataset size: {c.training_set_kwargs.max_size} images')
print(f'Dataset resolution: {c.training_set_kwargs.resolution}')
print(f'Dataset labels: {c.training_set_kwargs.use_labels}')
print(f'Dataset x-flips: {c.training_set_kwargs.xflip}')
print()
# Dry run?
if dry_run:
print('Dry run; exiting.')
return
# Create output directory.
print('Creating output directory...')
os.makedirs(c.run_dir)
with open(os.path.join(c.run_dir, 'training_options.json'), 'wt') as f:
json.dump(c, f, indent=2)
# Launch processes.
print('Launching processes...')
torch.multiprocessing.set_start_method('spawn')
with tempfile.TemporaryDirectory() as temp_dir:
if c.num_gpus == 1:
subprocess_fn(rank=0, c=c, temp_dir=temp_dir)
else:
torch.multiprocessing.spawn(
fn=subprocess_fn, args=(c, temp_dir), nprocs=c.num_gpus)
# ----------------------------------------------------------------------------
def init_dataset_kwargs(data):
try:
dataset_kwargs = dnnlib.EasyDict(
class_name='training.dataset.ImageFolderDataset', path=data, use_labels=True, max_size=None, xflip=False)
# Subclass of training.dataset.Dataset.
dataset_obj = dnnlib.util.construct_class_by_name(**dataset_kwargs)
# Be explicit about resolution.
dataset_kwargs.resolution = dataset_obj.resolution
# Be explicit about labels.
dataset_kwargs.use_labels = dataset_obj.has_labels
# Be explicit about dataset size.
dataset_kwargs.max_size = len(dataset_obj)
return dataset_kwargs, dataset_obj.name
except IOError as err:
raise click.ClickException(f'--data: {err}')
# ----------------------------------------------------------------------------
def init_conditional_dataset_kwargs(data, mask_data, data_type, resolution=None):
try:
if data_type == 'seg':
dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageSegFolderDataset', path=data, mask_path=mask_data,
data_type=data_type, use_labels=True, max_size=None, xflip=False, resolution=resolution)
# Subclass of training.dataset.Dataset.
dataset_obj = dnnlib.util.construct_class_by_name(**dataset_kwargs)
# Be explicit about resolution.
dataset_kwargs.resolution = dataset_obj.resolution
# Be explicit about labels.
dataset_kwargs.use_labels = dataset_obj.has_labels
# Be explicit about dataset size.
dataset_kwargs.max_size = len(dataset_obj)
return dataset_kwargs, dataset_obj.name
elif data_type == 'edge':
dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageEdgeFolderDataset', path=data,
mask_path=mask_data, data_type=data_type, use_labels=True, max_size=None, xflip=False)
# Subclass of training.dataset.Dataset.
dataset_obj = dnnlib.util.construct_class_by_name(**dataset_kwargs)
# Be explicit about resolution.
dataset_kwargs.resolution = dataset_obj.resolution
# Be explicit about labels.
dataset_kwargs.use_labels = dataset_obj.has_labels
# Be explicit about dataset size.
dataset_kwargs.max_size = len(dataset_obj)
return dataset_kwargs, dataset_obj.name
else:
raise click.ClickException(f'Unknown data_type: {data_type}')
except IOError as err:
raise click.ClickException(f'--data: {err}')
# ----------------------------------------------------------------------------
def parse_comma_separated_list(s):
if isinstance(s, list):
return s
if s is None or s.lower() == 'none' or s == '':
return []
return s.split(',')
# ----------------------------------------------------------------------------
@click.command()
# Required.
@click.option('--outdir', help='Where to save the results', metavar='DIR', required=True)
@click.option('--cfg', help='Base configuration', type=str, required=True)
@click.option('--data', help='Training data', metavar='[ZIP|DIR]', type=str, required=True)
@click.option('--mask_data', help='Training data', metavar='[ZIP|DIR]', type=str, required=True)
@click.option('--data_type', help='Training data type', type=str, default='seg', show_default=True)
@click.option('--gpus', help='Number of GPUs to use', metavar='INT', type=click.IntRange(min=1), required=True)
@click.option('--batch', help='Total batch size', metavar='INT', type=click.IntRange(min=1), required=True)
@click.option('--gamma', help='R1 regularization weight', metavar='FLOAT', type=click.FloatRange(min=0), required=True)
# Optional features.
@click.option('--cond', help='Train conditional model', metavar='BOOL', type=bool, default=True, show_default=True)
@click.option('--mirror', help='Enable dataset x-flips', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--aug', help='Augmentation mode', type=click.Choice(['noaug', 'ada', 'fixed']), default='noaug', show_default=True)
@click.option('--resume', help='Resume from given network pickle', metavar='[PATH|URL]', type=str)
@click.option('--freezed', help='Freeze first layers of D', metavar='INT', type=click.IntRange(min=0), default=0, show_default=True)
# Misc hyperparameters.
@click.option('--p', help='Probability for --aug=fixed', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0.2, show_default=True)
@click.option('--target', help='Target value for --aug=ada', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0.6, show_default=True)
@click.option('--batch-gpu', help='Limit batch size per GPU', metavar='INT', type=click.IntRange(min=1))
@click.option('--cbase', help='Capacity multiplier', metavar='INT', type=click.IntRange(min=1), default=32768, show_default=True)
@click.option('--cmax', help='Max. feature maps', metavar='INT', type=click.IntRange(min=1), default=512, show_default=True)
@click.option('--glr', help='G learning rate [default: varies]', metavar='FLOAT', type=click.FloatRange(min=0))
@click.option('--dlr', help='D learning rate', metavar='FLOAT', type=click.FloatRange(min=0), default=0.002, show_default=True)
@click.option('--map-depth', help='Mapping network depth [default: varies]', metavar='INT', type=click.IntRange(min=1), default=2, show_default=True)
@click.option('--mbstd-group', help='Minibatch std group size', metavar='INT', type=click.IntRange(min=1), default=4, show_default=True)
# Misc settings.
@click.option('--desc', help='String to include in result dir name', metavar='STR', type=str)
@click.option('--metrics', help='Quality metrics', metavar='[NAME|A,B,C|none]', type=parse_comma_separated_list, default='fid50k_full', show_default=True)
@click.option('--kimg', help='Total training duration', metavar='KIMG', type=click.IntRange(min=1), default=25000, show_default=True)
@click.option('--tick', help='How often to print progress', metavar='KIMG', type=click.IntRange(min=1), default=4, show_default=True)
@click.option('--snap', help='How often to save snapshots', metavar='TICKS', type=click.IntRange(min=1), default=10, show_default=True)
@click.option('--seed', help='Random seed', metavar='INT', type=click.IntRange(min=0), default=0, show_default=True)
# @click.option('--fp32', help='Disable mixed-precision', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--nobench', help='Disable cuDNN benchmarking', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--workers', help='DataLoader worker processes', metavar='INT', type=click.IntRange(min=1), default=3, show_default=True)
@click.option('-n', '--dry-run', help='Print training options and exit', is_flag=True)
# @click.option('--sr_module', help='Superresolution module', metavar='STR', type=str, required=True)
@click.option('--neural_rendering_resolution_initial', help='Resolution to render at', metavar='INT', type=click.IntRange(min=1), default=64, required=False)
@click.option('--neural_rendering_resolution_final', help='Final resolution to render at, if blending', metavar='INT', type=click.IntRange(min=1), required=False, default=None)
@click.option('--neural_rendering_resolution_fade_kimg', help='Kimg to blend resolution over', metavar='INT', type=click.IntRange(min=0), required=False, default=1000, show_default=True)
@click.option('--blur_fade_kimg', help='Blur over how many', metavar='INT', type=click.IntRange(min=1), required=False, default=200)
@click.option('--gen_pose_cond', help='If true, enable generator pose conditioning.', metavar='BOOL', type=bool, required=False, default=False)
@click.option('--c-scale', help='Scale factor for generator pose conditioning.', metavar='FLOAT', type=click.FloatRange(min=0), required=False, default=1)
@click.option('--c-noise', help='Add noise for generator pose conditioning.', metavar='FLOAT', type=click.FloatRange(min=0), required=False, default=0)
@click.option('--gpc_reg_prob', help='Strength of swapping regularization. None means no generator pose conditioning, i.e. condition with zeros.', metavar='FLOAT', type=click.FloatRange(min=0), required=False, default=0.5)
@click.option('--gpc_reg_fade_kimg', help='Length of swapping prob fade', metavar='INT', type=click.IntRange(min=0), required=False, default=1000)
@click.option('--disc_c_noise', help='Strength of discriminator pose conditioning regularization, in standard deviations.', metavar='FLOAT', type=click.FloatRange(min=0), required=False, default=0)
@click.option('--sr_noise_mode', help='Type of noise for superresolution', metavar='STR', type=click.Choice(['random', 'none']), required=False, default='none')
@click.option('--resume_blur', help='Enable to blur even on resume', metavar='BOOL', type=bool, required=False, default=False)
@click.option('--sr_num_fp16_res', help='Number of fp16 layers in superresolution', metavar='INT', type=click.IntRange(min=0), default=4, required=False, show_default=True)
@click.option('--g_num_fp16_res', help='Number of fp16 layers in generator', metavar='INT', type=click.IntRange(min=0), default=0, required=False, show_default=True)
@click.option('--d_num_fp16_res', help='Number of fp16 layers in discriminator', metavar='INT', type=click.IntRange(min=0), default=4, required=False, show_default=True)
@click.option('--sr_first_cutoff', help='First cutoff for AF superresolution', metavar='INT', type=click.IntRange(min=2), default=2, required=False, show_default=True)
@click.option('--sr_first_stopband', help='First cutoff for AF superresolution', metavar='FLOAT', type=click.FloatRange(min=2), default=2**2.1, required=False, show_default=True)
@click.option('--style_mixing_prob', help='Style-mixing regularization probability for training.', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0, required=False, show_default=True)
@click.option('--sr-module', help='Superresolution module override', metavar='STR', type=str, required=False, default=None)
@click.option('--density_reg', help='Density regularization strength.', metavar='FLOAT', type=click.FloatRange(min=0), default=0.25, required=False, show_default=True)
@click.option('--density_reg_every', help='lazy density reg', metavar='int', type=click.FloatRange(min=1), default=4, required=False, show_default=True)
@click.option('--density_reg_p_dist', help='density regularization strength.', metavar='FLOAT', type=click.FloatRange(min=0), default=0.004, required=False, show_default=True)
@click.option('--reg_type', help='Type of regularization', metavar='STR', type=click.Choice(['l1', 'l1-alt', 'monotonic-detach', 'monotonic-fixed', 'total-variation']), required=False, default='l1')
@click.option('--decoder_lr_mul', help='decoder learning rate multiplier.', metavar='FLOAT', type=click.FloatRange(min=0), default=1, required=False, show_default=True)
@click.option('--wandb_log', help='whether to use wandb', metavar='BOOL', type=bool, default=True, show_default=True)
@click.option('--no_eval', help='whether to disable evaluation metrics', metavar='BOOL', type=bool, default=True, show_default=True)
@click.option('--random_c_prob', help='Probablity of random poses', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0, required=False, show_default=True)
@click.option('--debug', help='whether to use debug mode', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--render_mask', help='whether to render masks', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--dis_mask', help='whether to use Dis for masks', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--lambda_l1', help='L1 loss weight', metavar='FLOAT', type=click.FloatRange(min=0), default=0, required=False, show_default=True)
@click.option('--lambda_lpips', help='LPIPS loss weight', metavar='FLOAT', type=click.FloatRange(min=0), default=10, required=False, show_default=True)
@click.option('--lambda_d_semantic', help='D_semantic loss weight', metavar='FLOAT', type=click.FloatRange(min=0), default=1, required=False, show_default=True)
@click.option('--seg_weight', help='Cross Entropy Class Weight', metavar='int', type=click.FloatRange(min=0, max=2), default=0, required=False, show_default=True)
@click.option('--edge_weight', help='L1 loss weight for edges', metavar='FLOAT', type=click.FloatRange(min=0), default=2, required=False, show_default=True)
@click.option('--only_raw_recons', help='whether to only use reconstruction loss for nerf renderings', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--semantic_channels', help='number of semantic channels', metavar='INT', type=click.IntRange(min=1), default=19, show_default=True)
@click.option('--use_bg', help='whether to use bg model in nerf', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--silhouette_loss', help='whether to use silhouette loss in nerf', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--data_resolution', help='Data Resolution', metavar='INT', type=int, default=0, required=False)
@click.option('--geometry_layer', help='Geometry Layer', metavar='INT', type=int, default=7, required=False)
@click.option('--lambda_cross_view', help='Cross View loss weight', metavar='FLOAT', type=click.FloatRange(min=0), default=0, required=False, show_default=True)
def main(**kwargs):
"""Train a GAN using the techniques described in the paper
"Alias-Free Generative Adversarial Networks".
Examples:
\b
# Train StyleGAN3-T for AFHQv2 using 8 GPUs.
python train.py --outdir=~/training-runs --cfg=stylegan3-t --data=~/datasets/afhqv2-512x512.zip \\
--gpus=8 --batch=32 --gamma=8.2 --mirror=1
\b
# Fine-tune StyleGAN3-R for MetFaces-U using 1 GPU, starting from the pre-trained FFHQ-U pickle.
python train.py --outdir=~/training-runs --cfg=stylegan3-r --data=~/datasets/metfacesu-1024x1024.zip \\
--gpus=8 --batch=32 --gamma=6.6 --mirror=1 --kimg=5000 --snap=5 \\
--resume=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhqu-1024x1024.pkl
\b
# Train StyleGAN2 for FFHQ at 1024x1024 resolution using 8 GPUs.
python train.py --outdir=~/training-runs --cfg=stylegan2 --data=~/datasets/ffhq-1024x1024.zip \\
--gpus=8 --batch=32 --gamma=10 --mirror=1 --aug=noaug
"""
# Initialize config.
opts = dnnlib.EasyDict(kwargs) # Command line arguments.
c = dnnlib.EasyDict() # Main config dict.
c.G_kwargs = dnnlib.EasyDict(
class_name=None, z_dim=512, w_dim=512, mapping_kwargs=dnnlib.EasyDict())
c.D_kwargs = dnnlib.EasyDict(class_name='training.networks_stylegan2.Discriminator', block_kwargs=dnnlib.EasyDict(
), mapping_kwargs=dnnlib.EasyDict(), epilogue_kwargs=dnnlib.EasyDict())
c.G_opt_kwargs = dnnlib.EasyDict(
class_name='torch.optim.Adam', betas=[0, 0.99], eps=1e-8)
c.D_opt_kwargs = dnnlib.EasyDict(
class_name='torch.optim.Adam', betas=[0, 0.99], eps=1e-8)
c.loss_kwargs = dnnlib.EasyDict(class_name='training.loss.Pix2Pix3DLoss')
c.data_loader_kwargs = dnnlib.EasyDict(pin_memory=True, prefetch_factor=2)
# Training set.
# c.training_set_kwargs, dataset_name = init_dataset_kwargs(data=opts.data)
if opts.data_resolution == 0:
opts.data_resolution = None
c.training_set_kwargs, dataset_name = init_conditional_dataset_kwargs(
data=opts.data, mask_data=opts.mask_data, data_type=opts.data_type, resolution=opts.data_resolution)
if opts.cond and not c.training_set_kwargs.use_labels:
raise click.ClickException(
'--cond=True requires labels specified in dataset.json')
c.training_set_kwargs.use_labels = opts.cond
c.training_set_kwargs.xflip = opts.mirror
# Hyperparameters & settings.
c.num_gpus = opts.gpus
c.batch_size = opts.batch
c.batch_gpu = opts.batch_gpu or opts.batch // opts.gpus
c.G_kwargs.channel_base = c.D_kwargs.channel_base = opts.cbase
c.G_kwargs.channel_max = c.D_kwargs.channel_max = opts.cmax
c.G_kwargs.mapping_kwargs.num_layers = opts.map_depth
c.D_kwargs.block_kwargs.freeze_layers = opts.freezed
c.D_kwargs.epilogue_kwargs.mbstd_group_size = opts.mbstd_group
c.loss_kwargs.r1_gamma = opts.gamma
c.G_opt_kwargs.lr = (
0.002 if opts.cfg == 'stylegan2' else 0.0025) if opts.glr is None else opts.glr
c.D_opt_kwargs.lr = opts.dlr
c.metrics = opts.metrics
c.total_kimg = opts.kimg
c.kimg_per_tick = opts.tick
c.image_snapshot_ticks = c.network_snapshot_ticks = opts.snap
c.random_seed = c.training_set_kwargs.random_seed = opts.seed
c.data_loader_kwargs.num_workers = 0
# Loss parameters
c.loss_kwargs.random_c_prob = opts.random_c_prob
c.loss_kwargs.lambda_l1 = opts.lambda_l1
c.loss_kwargs.lambda_lpips = opts.lambda_lpips
c.loss_kwargs.lambda_D_semantic = opts.lambda_d_semantic
c.loss_kwargs.seg_weight = opts.seg_weight
c.loss_kwargs.edge_weight = opts.edge_weight
c.loss_kwargs.only_raw_recons = opts.only_raw_recons
c.loss_kwargs.silhouette_loss = opts.silhouette_loss
c.loss_kwargs.lambda_cross_view = opts.lambda_cross_view
# Mask parameters
if opts.data_type == 'seg':
c.G_kwargs.mapping_kwargs.in_resolution = c.training_set_kwargs.resolution
c.G_kwargs.mapping_kwargs.in_channels = opts.semantic_channels
# c.G_kwargs.mapping_kwargs.class_name = 'training.triplane_cond.MaskMappingNetwork'
c.G_kwargs.mapping_kwargs.class_name = 'training.triplane_cond.MaskMappingNetwork_disentangle'
elif opts.data_type == 'edge':
c.G_kwargs.mapping_kwargs.in_resolution = c.training_set_kwargs.resolution
c.G_kwargs.mapping_kwargs.in_channels = 1
c.G_kwargs.mapping_kwargs.geometry_layer = opts.geometry_layer
# c.G_kwargs.mapping_kwargs.class_name = 'training.triplane_cond.EdgeMappingNetwork'
c.G_kwargs.mapping_kwargs.class_name = 'training.triplane_cond.EdgeMappingNetwork_disentangle'
else:
raise click.ClickException(
f'--data_type={opts.data_type} not supported.')
# Sanity checks.
if c.batch_size % c.num_gpus != 0:
raise click.ClickException('--batch must be a multiple of --gpus')
if c.batch_size % (c.num_gpus * c.batch_gpu) != 0:
raise click.ClickException(
'--batch must be a multiple of --gpus times --batch-gpu')
if c.batch_gpu < c.D_kwargs.epilogue_kwargs.mbstd_group_size:
raise click.ClickException(
'--batch-gpu cannot be smaller than --mbstd')
if any(not metric_main.is_valid_metric(metric) for metric in c.metrics):
raise click.ClickException('\n'.join(
['--metrics can only contain the following values:'] + metric_main.list_valid_metrics()))
# Base configuration.
c.ema_kimg = c.batch_size * 10 / 32
# c.G_kwargs.class_name = 'training.triplane.TriPlaneGenerator'
if opts.render_mask:
# c.G_kwargs.class_name = 'training.triplane_cond.TriPlaneSemanticGenerator'
c.G_kwargs.class_name = 'training.triplane_cond.TriPlaneSemanticEntangleGenerator'
if opts.use_bg:
c.G_kwargs.class_name = 'training.triplane_cond.TriPlaneSemanticEntangleGenerator_withBG'
else:
c.G_kwargs.class_name = 'training.triplane_cond.TriPlaneGenerator'
c.D_kwargs.class_name = 'training.dual_discriminator.DualDiscriminator'
# Speed up training by using regular convolutions instead of grouped convolutions.
c.G_kwargs.fused_modconv_default = 'inference_only'
# Filter mode for raw images ['antialiased', 'none', float [0-1]]
c.loss_kwargs.filter_mode = 'antialiased'
# Regularization for discriminator pose conditioning
c.D_kwargs.disc_c_noise = opts.disc_c_noise
if c.training_set_kwargs.resolution == 512:
sr_module = 'training.superresolution.SuperresolutionHybrid8XDC'
sr_module_semantic = 'training.superresolution.SuperresolutionHybrid8XDC_semantic'
elif c.training_set_kwargs.resolution == 256:
sr_module = 'training.superresolution.SuperresolutionHybrid4X'
sr_module_semantic = 'training.superresolution.SuperresolutionHybrid4X_semantic'
elif c.training_set_kwargs.resolution == 128:
sr_module = 'training.superresolution.SuperresolutionHybrid2X'
sr_module_semantic = 'training.superresolution.SuperresolutionHybrid2X_semantic'
else:
assert False, f"Unsupported resolution {c.training_set_kwargs.resolution}; make a new superresolution module"
if opts.sr_module != None:
sr_module = opts.sr_module
rendering_options = {
'image_resolution': c.training_set_kwargs.resolution,
'disparity_space_sampling': False,
'clamp_mode': 'softplus',
'superresolution_module': sr_module,
'superresolution_module_semantic': sr_module_semantic,
# if true, fill generator pose conditioning label with dummy zero vector
'c_gen_conditioning_zero': not opts.gen_pose_cond,
'gpc_reg_prob': opts.gpc_reg_prob if opts.gen_pose_cond else None,
'c_scale': opts.c_scale, # mutliplier for generator pose conditioning label
# [random or none], whether to inject pixel noise into super-resolution layers
'superresolution_noise_mode': opts.sr_noise_mode,
'density_reg': opts.density_reg, # strength of density regularization
# distance at which to sample perturbed points for density regularization
'density_reg_p_dist': opts.density_reg_p_dist,
# for experimenting with variations on density regularization
'reg_type': opts.reg_type,
'decoder_lr_mul': opts.decoder_lr_mul, # learning rate multiplier for decoder
'sr_antialias': True,
}
if opts.cfg == 'ffhq' or opts.cfg == 'celeba':
rendering_options.update({
# number of uniform samples to take per ray.
'depth_resolution': 48,
# number of importance samples to take per ray.
'depth_resolution_importance': 48,
# near point along each ray to start taking samples.
'ray_start': 2.25,
'ray_end': 3.3, # far point along each ray to stop taking samples.
# the side-length of the bounding box spanned by the tri-planes; box_warp=1 means [-0.5, -0.5, -0.5] -> [0.5, 0.5, 0.5].
'box_warp': 1,
# used only in the visualizer to specify camera orbit radius.
'avg_camera_radius': 2.7,
# used only in the visualizer to control center of camera rotation.
'avg_camera_pivot': [0, 0, 0.2],
})
elif opts.cfg == 'afhq':
rendering_options.update({
'depth_resolution': 48,
'depth_resolution_importance': 48,
'ray_start': 2.25,
'ray_end': 3.3,
'box_warp': 1,
'avg_camera_radius': 2.7,
'avg_camera_pivot': [0, 0, -0.06],
})
elif opts.cfg == 'shapenet':
rendering_options.update({
'depth_resolution': 64,
'depth_resolution_importance': 64,
'ray_start': 0.1,
'ray_end': 2.6,
'box_warp': 1.6,
'white_back': True,
'avg_camera_radius': 1.7,
'avg_camera_pivot': [0, 0, 0],
})
else:
assert False, "Need to specify config"
if opts.density_reg > 0:
c.G_reg_interval = opts.density_reg_every
c.G_kwargs.rendering_kwargs = rendering_options
c.G_kwargs.num_fp16_res = 0
# Blur the images seen by the discriminator.
c.loss_kwargs.blur_init_sigma = 10
# Fade out the blur during the first N kimg.
c.loss_kwargs.blur_fade_kimg = c.batch_size * opts.blur_fade_kimg / 32
c.loss_kwargs.gpc_reg_prob = opts.gpc_reg_prob if opts.gen_pose_cond else None
c.loss_kwargs.gpc_reg_fade_kimg = opts.gpc_reg_fade_kimg
c.loss_kwargs.dual_discrimination = True
c.loss_kwargs.neural_rendering_resolution_initial = opts.neural_rendering_resolution_initial
c.loss_kwargs.neural_rendering_resolution_final = opts.neural_rendering_resolution_final
c.loss_kwargs.neural_rendering_resolution_fade_kimg = opts.neural_rendering_resolution_fade_kimg
c.G_kwargs.sr_num_fp16_res = opts.sr_num_fp16_res
c.G_kwargs.sr_kwargs = dnnlib.EasyDict(
channel_base=opts.cbase, channel_max=opts.cmax, fused_modconv_default='inference_only')
c.loss_kwargs.style_mixing_prob = opts.style_mixing_prob
# Augmentation.
if opts.aug != 'noaug':
c.augment_kwargs = dnnlib.EasyDict(class_name='training.augment.AugmentPipe', xflip=1, rotate90=1, xint=1,
scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1)
if opts.aug == 'ada':
c.ada_target = opts.target
if opts.aug == 'fixed':
c.augment_p = opts.p
# Resume.
if opts.resume is not None:
c.resume_pkl = opts.resume
c.ada_kimg = 100 # Make ADA react faster at the beginning.
c.ema_rampup = None # Disable EMA rampup.
if not opts.resume_blur:
c.loss_kwargs.blur_init_sigma = 0 # Disable blur rampup.
c.loss_kwargs.gpc_reg_fade_kimg = 0 # Disable swapping rampup
# Performance-related toggles.
# if opts.fp32:
# c.G_kwargs.num_fp16_res = c.D_kwargs.num_fp16_res = 0
# c.G_kwargs.conv_clamp = c.D_kwargs.conv_clamp = None
c.G_kwargs.num_fp16_res = opts.g_num_fp16_res
c.G_kwargs.conv_clamp = 256 if opts.g_num_fp16_res > 0 else None
c.D_kwargs.num_fp16_res = opts.d_num_fp16_res
c.D_kwargs.conv_clamp = 256 if opts.d_num_fp16_res > 0 else None
if opts.nobench:
c.cudnn_benchmark = False
# Description string.
desc = f'{opts.cfg:s}-{dataset_name:s}-gpus{c.num_gpus:d}-batch{c.batch_size:d}-gamma{c.loss_kwargs.r1_gamma:g}'
if opts.desc is not None:
desc += f'-{opts.desc}'
# Logging.
c.wandb_log = opts.wandb_log
c.no_eval = opts.no_eval
c.debug = opts.debug
# Dis for masks
if opts.dis_mask:
c.D_semantic_kwargs = c.D_kwargs.copy()
else:
c.D_semantic_kwargs = None
# Launch.
launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run)
# ----------------------------------------------------------------------------
if __name__ == "__main__":
main() # pylint: disable=no-value-for-parameter
# ----------------------------------------------------------------------------