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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 | # 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/utils/ckpt.py
from os.path import join
import os
import glob
import torch
import numpy as np
import utils.log as log
try:
import utils.misc as misc
except AttributeError:
pass
blacklist = ["CCMGAN2048-train-2021_06_22_06_11_37"]
def make_ckpt_dir(ckpt_dir):
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
return ckpt_dir
def load_ckpt(model, optimizer, ckpt_path, load_model=False, load_opt=False, load_misc=False, is_freezeD=False):
ckpt = torch.load(ckpt_path, map_location=lambda storage, loc: storage, weights_only=False)
if load_model:
if is_freezeD:
mismatch_names = misc.load_parameters(src=ckpt["state_dict"],
dst=model.state_dict(),
strict=False)
print("The following parameters/buffers do not match with the ones of the pre-trained model:", mismatch_names)
else:
# Inference helpers (paper viz dumpers) set STUDIOGAN_LOAD_STRICT=0 so that
# state_dicts saved by an older code revision (e.g. extra mapping.rtm_*
# buffers) still load. Default behaviour is unchanged.
_strict = os.environ.get("STUDIOGAN_LOAD_STRICT", "1") != "0"
if _strict:
model.load_state_dict(ckpt["state_dict"], strict=True)
else:
missing, unexpected = model.load_state_dict(ckpt["state_dict"], strict=False)
if missing or unexpected:
print("[ckpt] non-strict load: missing={n_m} unexpected={n_u}".format(
n_m=len(missing), n_u=len(unexpected)))
if load_opt:
optimizer.load_state_dict(ckpt["optimizer"])
for state in optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
if load_misc:
seed = ckpt["seed"]
run_name = ckpt["run_name"]
step = ckpt["step"]
try:
aa_p = ckpt["aa_p"]
except:
aa_p = ckpt["ada_p"]
best_step = ckpt["best_step"]
best_fid = ckpt["best_fid"]
try:
epoch = ckpt["epoch"]
except:
epoch = 0
try:
topk = ckpt["topk"]
except:
topk = "initialize"
try:
best_ckpt_path = ckpt["best_fid_checkpoint_path"]
except:
best_ckpt_path = ckpt["best_fid_ckpt"]
try:
lecam_emas = ckpt["lecam_emas"]
except:
lecam_emas = None
return seed, run_name, step, epoch, topk, aa_p, best_step, best_fid, best_ckpt_path, lecam_emas
def load_StudioGAN_ckpts(ckpt_dir, load_best, Gen, Dis, g_optimizer, d_optimizer, run_name, apply_g_ema, Gen_ema, ema,
is_train, RUN, logger, global_rank, device, cfg_file):
when = "best" if load_best is True else "current"
x = join(ckpt_dir, "model=G-{when}-weights-step=".format(when=when))
y = join(ckpt_dir, "model=D-{when}-weights-step=".format(when=when))
Gen_glob = glob.glob(glob.escape(x) + '*.pth')
Dis_glob = glob.glob(glob.escape(y) + '*.pth')
# Inference-only fallback for shipped checkpoint dirs that contain
# *only* G_ema-* (e.g. StudioGAN's pretrained baseline tarballs).
# Triggered only when both the Gen and Dis files are absent and the
# caller is not training. Returns dummy step/epoch metadata.
if not Gen_glob and not Dis_glob and not is_train and apply_g_ema:
z_pat = join(ckpt_dir, "model=G_ema-{when}-weights-step=".format(when=when))
z_glob = glob.glob(glob.escape(z_pat) + '*.pth')
if not z_glob:
z_glob = glob.glob(join(ckpt_dir, "model=G_ema-current-weights-step=*.pth"))
if z_glob:
Gen_ema_ckpt_path = sorted(z_glob)[-1]
print("[ckpt] G/D ckpts missing -> G_ema-only inference load:", Gen_ema_ckpt_path)
os.environ.setdefault("STUDIOGAN_LOAD_STRICT", "0")
load_ckpt(model=Gen_ema, optimizer=None, ckpt_path=Gen_ema_ckpt_path,
load_model=True, load_opt=False, load_misc=False)
try:
load_ckpt(model=Gen, optimizer=None, ckpt_path=Gen_ema_ckpt_path,
load_model=True, load_opt=False, load_misc=False)
except Exception as _e:
print("[ckpt] (skipping Gen mirror; not needed for sampling):", _e)
ema.source, ema.target = Gen, Gen_ema
return run_name, 0, 0, "initialize", 0, 0, 0.0, "", None, logger
Gen_ckpt_path = Gen_glob[0]
Dis_ckpt_path = Dis_glob[0]
prev_run_name = torch.load(Dis_ckpt_path, map_location=lambda storage, loc: storage, weights_only=False)["run_name"]
is_freezeD = True if RUN.freezeD > -1 else False
load_ckpt(model=Gen,
optimizer=g_optimizer,
ckpt_path=Gen_ckpt_path,
load_model=True,
load_opt=False if prev_run_name in blacklist or is_freezeD or not is_train else True,
load_misc=False,
is_freezeD=is_freezeD)
seed, prev_run_name, step, epoch, topk, aa_p, best_step, best_fid, best_ckpt_path, lecam_emas =\
load_ckpt(model=Dis,
optimizer=d_optimizer,
ckpt_path=Dis_ckpt_path,
load_model=True,
load_opt=False if prev_run_name in blacklist or is_freezeD or not is_train else True,
load_misc=True,
is_freezeD=is_freezeD)
if apply_g_ema:
z = join(ckpt_dir, "model=G_ema-{when}-weights-step=".format(when=when))
Gen_ema_ckpt_path = glob.glob(glob.escape(z) + '*.pth')[0]
load_ckpt(model=Gen_ema,
optimizer=None,
ckpt_path=Gen_ema_ckpt_path,
load_model=True,
load_opt=False,
load_misc=False,
is_freezeD=is_freezeD)
ema.source, ema.target = Gen, Gen_ema
if is_train and RUN.seed != seed:
RUN.seed = seed + global_rank
misc.fix_seed(RUN.seed)
if device == 0:
if not is_freezeD:
logger = log.make_logger(RUN.save_dir, prev_run_name, None)
logger.info("Generator checkpoint is {}".format(Gen_ckpt_path))
if apply_g_ema:
logger.info("EMA_Generator checkpoint is {}".format(Gen_ema_ckpt_path))
logger.info("Discriminator checkpoint is {}".format(Dis_ckpt_path))
if is_freezeD:
prev_run_name, step, epoch, topk, aa_p, best_step, best_fid, best_ckpt_path =\
run_name, 0, 0, "initialize", None, 0, None, None
return prev_run_name, step, epoch, topk, aa_p, best_step, best_fid, best_ckpt_path, lecam_emas, logger
def load_best_model(ckpt_dir, Gen, Dis, apply_g_ema, Gen_ema, ema):
Gen, Dis, Gen_ema = misc.peel_models(Gen, Dis, Gen_ema)
Gen_glob = glob.glob(join(ckpt_dir, "model=G-best-weights-step*.pth"))
Dis_glob = glob.glob(join(ckpt_dir, "model=D-best-weights-step*.pth"))
if not Gen_glob and not Dis_glob and apply_g_ema:
z_glob = glob.glob(join(ckpt_dir, "model=G_ema-best-weights-step*.pth"))
if not z_glob:
z_glob = glob.glob(join(ckpt_dir, "model=G_ema-current-weights-step*.pth"))
if z_glob:
Gen_ema_ckpt_path = sorted(z_glob)[-1]
print("[ckpt] G/D best ckpts missing -> G_ema-only inference load:", Gen_ema_ckpt_path)
os.environ.setdefault("STUDIOGAN_LOAD_STRICT", "0")
load_ckpt(model=Gen_ema, optimizer=None, ckpt_path=Gen_ema_ckpt_path,
load_model=True, load_opt=False, load_misc=False)
try:
load_ckpt(model=Gen, optimizer=None, ckpt_path=Gen_ema_ckpt_path,
load_model=True, load_opt=False, load_misc=False)
except Exception as _e:
print("[ckpt] (skipping Gen mirror; not needed for sampling):", _e)
ema.source, ema.target = Gen, Gen_ema
try:
step_str = os.path.basename(Gen_ema_ckpt_path).split("step=")[1].split(".")[0]
return int(step_str)
except Exception:
return 0
Gen_ckpt_path = Gen_glob[0]
Dis_ckpt_path = Dis_glob[0]
load_ckpt(model=Gen,
optimizer=None,
ckpt_path=Gen_ckpt_path,
load_model=True,
load_opt=False,
load_misc=False,
is_freezeD=False)
_, _, _, _, _, _, best_step, _, _, _ = load_ckpt(model=Dis,
optimizer=None,
ckpt_path=Dis_ckpt_path,
load_model=True,
load_opt=False,
load_misc=True,
is_freezeD=False)
if apply_g_ema:
Gen_ema_ckpt_path = glob.glob(join(ckpt_dir, "model=G_ema-best-weights-step*.pth"))[0]
load_ckpt(model=Gen_ema,
optimizer=None,
ckpt_path=Gen_ema_ckpt_path,
load_model=True,
load_opt=False,
load_misc=False,
is_freezeD=False)
ema.source, ema.target = Gen, Gen_ema
return best_step
def load_prev_dict(directory, file_name):
return np.load(join(directory, file_name), allow_pickle=True).item()
def check_is_pre_trained_model(ckpt_dir, GAN_train, GAN_test):
assert GAN_train*GAN_test == 0, "cannot conduct GAN_train and GAN_test togather."
if GAN_train:
mode = "fake_trained"
else:
mode = "real_trained"
ckpt_list = glob.glob(join(ckpt_dir, "model=C-{mode}-best-weights.pth".format(mode=mode)))
if len(ckpt_list) == 0:
is_pre_train_model = False
else:
is_pre_train_model = True
return is_pre_train_model, mode
def load_GAN_train_test_model(model, mode, optimizer, RUN):
ckpt_path = join(RUN.ckpt_dir, "model=C-{mode}-best-weights.pth".format(mode=mode))
ckpt = torch.load(ckpt_path, map_location=lambda storage, loc: storage, weights_only=False)
model.load_state_dict(ckpt["state_dict"])
optimizer.load_state_dict(ckpt["optimizer"])
epoch_trained = ckpt["epoch"]
best_top1 = ckpt["best_top1"]
best_top5 = ckpt["best_top5"]
best_epoch = ckpt["best_epoch"]
return epoch_trained, best_top1, best_top5, best_epoch
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