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3ce19a2 | 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 | # PyTorch StudioGAN: https://github.com/POSTECH-CVLab/PyTorch-StudioGAN
# The MIT License (MIT)
# See license file or visit https://github.com/POSTECH-CVLab/PyTorch-StudioGAN for details
# src/models/model.py
import copy
from torch.nn import DataParallel
from torch.nn.parallel import DistributedDataParallel as DDP
import torch
from sync_batchnorm.batchnorm import convert_model
from utils.ema import Ema
from utils.ema import EmaStylegan2
import utils.misc as misc
def load_generator_discriminator(DATA, OPTIMIZATION, MODEL, STYLEGAN, MODULES, RUN, device, logger):
if device == 0:
logger.info("Build a Generative Adversarial Network.")
module = __import__("models.{backbone}".format(backbone=MODEL.backbone), fromlist=["something"])
if device == 0:
logger.info("Modules are located on './src/models.{backbone}'.".format(backbone=MODEL.backbone))
if MODEL.backbone in ["stylegan2", "stylegan3"]:
channel_base, channel_max = 32768 if MODEL.backbone == "stylegan3" or DATA.img_size >= 512 or \
DATA.name in ["CIFAR10", "CIFAR100"] else 16384, 512
gen_c_dim = DATA.num_classes if MODEL.g_cond_mtd == "cAdaIN" else 0
dis_c_dim = DATA.num_classes if MODEL.d_cond_mtd in STYLEGAN.cond_type else 0
if RUN.mixed_precision:
num_fp16_res = 4
conv_clamp = 256
else:
num_fp16_res = 0
conv_clamp = None
if MODEL.backbone == "stylegan2":
mapping_kwargs = {"num_layers": STYLEGAN.mapping_network}
if getattr(STYLEGAN, "use_rtm_mapper", False):
mapping_kwargs.update({
"use_rtm_mapper": True,
"rtm_num_tokens": STYLEGAN.rtm_num_tokens,
"rtm_H_cycles": STYLEGAN.rtm_H_cycles,
"rtm_L_cycles": STYLEGAN.rtm_L_cycles,
"rtm_H_layers": STYLEGAN.rtm_H_layers,
"rtm_L_layers": STYLEGAN.rtm_L_layers,
"rtm_hidden_size": STYLEGAN.rtm_hidden_size,
"rtm_expansion": STYLEGAN.rtm_expansion,
"rtm_refinement_steps": STYLEGAN.rtm_refinement_steps,
"rtm_with_grad": STYLEGAN.rtm_with_grad,
"rtm_cycle_noise_std": STYLEGAN.rtm_cycle_noise_std,
"use_rtm_equalized": getattr(STYLEGAN, "use_rtm_equalized", False),
"rtm_lr_multiplier": getattr(STYLEGAN, "rtm_lr_multiplier", 1.0),
})
Gen = module.Generator(z_dim=MODEL.z_dim,
c_dim=gen_c_dim,
w_dim=MODEL.w_dim,
img_resolution=DATA.img_size,
img_channels=DATA.img_channels,
MODEL=MODEL,
mapping_kwargs=mapping_kwargs,
synthesis_kwargs={"channel_base": channel_base, "channel_max": channel_max, \
"num_fp16_res": num_fp16_res, "conv_clamp": conv_clamp}).to(device)
else:
magnitude_ema_beta = 0.5 ** (OPTIMIZATION.batch_size * OPTIMIZATION.acml_steps / (20 * 1e3))
g_channel_base, g_channel_max, conv_kernel, use_radial_filters = channel_base, channel_max, 3, False
if STYLEGAN.stylegan3_cfg == "stylegan3-r":
g_channel_base, g_channel_max, conv_kernel, use_radial_filters = channel_base * 2, channel_max * 2, 1, True
Gen = module.Generator(z_dim=MODEL.z_dim,
c_dim=gen_c_dim,
w_dim=MODEL.w_dim,
img_resolution=DATA.img_size,
img_channels=DATA.img_channels,
MODEL=MODEL,
mapping_kwargs={"num_layers": STYLEGAN.mapping_network},
synthesis_kwargs={"channel_base": g_channel_base, "channel_max": g_channel_max, \
"num_fp16_res": num_fp16_res, "conv_clamp": conv_clamp, "conv_kernel": conv_kernel, \
"use_radial_filters": use_radial_filters, "magnitude_ema_beta": magnitude_ema_beta}).to(device)
Gen_mapping, Gen_synthesis = Gen.mapping, Gen.synthesis
module = __import__("models.stylegan2", fromlist=["something"]) # always use StyleGAN2 discriminator
Dis = module.Discriminator(c_dim=dis_c_dim,
img_resolution=DATA.img_size,
img_channels=DATA.img_channels,
architecture=STYLEGAN.d_architecture,
channel_base=channel_base,
channel_max=channel_max,
num_fp16_res=num_fp16_res,
conv_clamp=conv_clamp,
cmap_dim=None,
d_cond_mtd=MODEL.d_cond_mtd,
aux_cls_type=MODEL.aux_cls_type,
d_embed_dim=MODEL.d_embed_dim,
num_classes=DATA.num_classes,
normalize_d_embed=MODEL.normalize_d_embed,
block_kwargs={},
mapping_kwargs={},
epilogue_kwargs={
"mbstd_group_size": STYLEGAN.d_epilogue_mbstd_group_size
},
MODEL=MODEL).to(device)
if MODEL.apply_g_ema:
if device == 0:
logger.info("Prepare exponential moving average generator with decay rate of {decay}."\
.format(decay=MODEL.g_ema_decay))
Gen_ema = copy.deepcopy(Gen)
Gen_ema_mapping, Gen_ema_synthesis = Gen_ema.mapping, Gen_ema.synthesis
ema = EmaStylegan2(source=Gen,
target=Gen_ema,
ema_kimg=STYLEGAN.g_ema_kimg,
ema_rampup=STYLEGAN.g_ema_rampup,
effective_batch_size=OPTIMIZATION.batch_size * OPTIMIZATION.acml_steps)
else:
Gen_ema, Gen_ema_mapping, Gen_ema_synthesis, ema = None, None, None, None
else:
Gen = module.Generator(z_dim=MODEL.z_dim,
g_shared_dim=MODEL.g_shared_dim,
img_size=DATA.img_size,
g_conv_dim=MODEL.g_conv_dim,
apply_attn=MODEL.apply_attn,
attn_g_loc=MODEL.attn_g_loc,
g_cond_mtd=MODEL.g_cond_mtd,
num_classes=DATA.num_classes,
g_init=MODEL.g_init,
g_depth=MODEL.g_depth,
mixed_precision=RUN.mixed_precision,
MODULES=MODULES,
MODEL=MODEL).to(device)
Gen_mapping, Gen_synthesis = None, None
Dis = module.Discriminator(img_size=DATA.img_size,
d_conv_dim=MODEL.d_conv_dim,
apply_d_sn=MODEL.apply_d_sn,
apply_attn=MODEL.apply_attn,
attn_d_loc=MODEL.attn_d_loc,
d_cond_mtd=MODEL.d_cond_mtd,
aux_cls_type=MODEL.aux_cls_type,
d_embed_dim=MODEL.d_embed_dim,
num_classes=DATA.num_classes,
normalize_d_embed=MODEL.normalize_d_embed,
d_init=MODEL.d_init,
d_depth=MODEL.d_depth,
mixed_precision=RUN.mixed_precision,
MODULES=MODULES,
MODEL=MODEL).to(device)
if MODEL.apply_g_ema:
if device == 0:
logger.info("Prepare exponential moving average generator with decay rate of {decay}."\
.format(decay=MODEL.g_ema_decay))
Gen_ema = copy.deepcopy(Gen)
Gen_ema_mapping, Gen_ema_synthesis = None, None
ema = Ema(source=Gen, target=Gen_ema, decay=MODEL.g_ema_decay, start_iter=MODEL.g_ema_start)
else:
Gen_ema, Gen_ema_mapping, Gen_ema_synthesis, ema = None, None, None, None
if device == 0:
logger.info(misc.count_parameters(Gen))
if device == 0:
logger.info(Gen)
if device == 0:
logger.info(misc.count_parameters(Dis))
if device == 0:
logger.info(Dis)
return Gen, Gen_mapping, Gen_synthesis, Dis, Gen_ema, Gen_ema_mapping, Gen_ema_synthesis, ema
def prepare_parallel_training(Gen, Gen_mapping, Gen_synthesis, Dis, Gen_ema, Gen_ema_mapping, Gen_ema_synthesis,
MODEL, world_size, distributed_data_parallel, synchronized_bn, apply_g_ema, device,
STYLEGAN=None):
if distributed_data_parallel:
if synchronized_bn:
process_group = torch.distributed.new_group([w for w in range(world_size)])
Gen = torch.nn.SyncBatchNorm.convert_sync_batchnorm(Gen, process_group)
Dis = torch.nn.SyncBatchNorm.convert_sync_batchnorm(Dis, process_group)
if apply_g_ema:
Gen_ema = torch.nn.SyncBatchNorm.convert_sync_batchnorm(Gen_ema, process_group)
# The RTM mapper runs (H_cycles - 1) of its inner H/L iterations under
# torch.no_grad() when rtm_with_grad is False, so the H_init / L_init
# initial-state Parameters do not always receive gradients. DDP rejects
# that unless we explicitly allow unused parameters on the mapping.
gen_mapping_find_unused = bool(getattr(STYLEGAN, "use_rtm_mapper", False))
if MODEL.backbone in ["stylegan2", "stylegan3"]:
Gen_mapping = DDP(Gen.mapping, device_ids=[device], broadcast_buffers=False,
find_unused_parameters=gen_mapping_find_unused)
Gen_synthesis = DDP(Gen.synthesis, device_ids=[device], broadcast_buffers=False)
else:
Gen = DDP(Gen, device_ids=[device], broadcast_buffers=synchronized_bn)
Dis = DDP(Dis, device_ids=[device],
broadcast_buffers=False if MODEL.backbone in ["stylegan2", "stylegan3"] else synchronized_bn,
find_unused_parameters=True if MODEL.info_type in ["discrete", "continuous", "both"] else False)
if apply_g_ema:
if MODEL.backbone in ["stylegan2", "stylegan3"]:
Gen_ema_mapping = DDP(Gen_ema.mapping, device_ids=[device], broadcast_buffers=False,
find_unused_parameters=gen_mapping_find_unused)
Gen_ema_synthesis = DDP(Gen_ema.synthesis, device_ids=[device], broadcast_buffers=False)
else:
Gen_ema = DDP(Gen_ema, device_ids=[device], broadcast_buffers=synchronized_bn)
else:
if MODEL.backbone in ["stylegan2", "stylegan3"]:
Gen_mapping = DataParallel(Gen.mapping, output_device=device)
Gen_synthesis = DataParallel(Gen.synthesis, output_device=device)
else:
Gen = DataParallel(Gen, output_device=device)
Dis = DataParallel(Dis, output_device=device)
if apply_g_ema:
if MODEL.backbone in ["stylegan2", "stylegan3"]:
Gen_ema_mapping = DataParallel(Gen_ema.mapping, output_device=device)
Gen_ema_synthesis = DataParallel(Gen_ema.synthesis, output_device=device)
else:
Gen_ema = DataParallel(Gen_ema, output_device=device)
if synchronized_bn:
Gen = convert_model(Gen).to(device)
Dis = convert_model(Dis).to(device)
if apply_g_ema:
Gen_ema = convert_model(Gen_ema).to(device)
return Gen, Gen_mapping, Gen_synthesis, Dis, Gen_ema, Gen_ema_mapping, Gen_ema_synthesis
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