File size: 11,731 Bytes
a00ee36 |
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 |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
# All contributions by Andy Brock:
# Copyright (c) 2019 Andy Brock
# MIT License
""" train_fns.py
Functions for the main loop of training different conditional image models
"""
import torch
import utils
import losses
# Dummy training function for debugging
def dummy_training_function():
def train(x, y):
return {}
return train
def GAN_training_function(
G,
D,
GD,
ema,
state_dict,
config,
sample_conditionings,
embedded_optimizers=True,
device="cuda",
batch_size=0,
):
def train(x, y=None, features=None):
if embedded_optimizers:
G.optim.zero_grad()
D.optim.zero_grad()
else:
GD.optimizer_D.zero_grad()
GD.optimizer_G.zero_grad()
# How many chunks to split x and y into?
x = torch.split(x, batch_size)
if y is not None:
y = torch.split(y, batch_size)
if features is not None:
f_ = torch.split(features, batch_size)
else:
f_ = None
counter = 0
# Optionally toggle D and G's "require_grad"
if config["toggle_grads"]:
utils.toggle_grad(D, True)
utils.toggle_grad(G, False)
for step_index in range(config["num_D_steps"]):
# If accumulating gradients, loop multiple times before an optimizer step
if embedded_optimizers:
D.optim.zero_grad()
else:
GD.optimizer_D.zero_grad()
for accumulation_index in range(config["num_D_accumulations"]):
# Sample conditioning for G
sampled_cond = sample_conditionings()
labels_g, f_g = None, None
if features is not None and y is not None:
z_, labels_g, f_g = sampled_cond
elif y is not None:
z_, labels_g = sampled_cond
elif features is not None:
z_, f_g = sampled_cond
# Tensors to device
if labels_g is not None:
labels_g = (
labels_g[:batch_size].to(device, non_blocking=True).long()
)
if f_g is not None:
f_g = f_g[:batch_size].to(device, non_blocking=True)
z_ = z_[:batch_size].to(device, non_blocking=True)
# Obtain discriminator scores
D_fake, D_real = GD(
z_,
labels_g,
f_g,
x[counter],
y[counter] if y is not None else None,
f_[counter] if f_ is not None else None,
train_G=False,
split_D=config["split_D"],
policy=config["DiffAugment"],
DA=config["DA"],
)
# Compute components of D's loss, average them, and divide by
# the number of gradient accumulations
D_loss_real, D_loss_fake = losses.discriminator_loss(D_fake, D_real)
D_loss = (D_loss_real + D_loss_fake) / float(
config["num_D_accumulations"]
)
D_loss.backward()
counter += 1
# Optionally apply ortho reg in D
if config["D_ortho"] > 0.0:
# Debug print to indicate we're using ortho reg in D.
print("using modified ortho reg in D")
utils.ortho(D, config["D_ortho"])
if embedded_optimizers:
D.optim.step()
else:
GD.optimizer_D.step()
# Optionally toggle "requires_grad"
if config["toggle_grads"]:
utils.toggle_grad(D, False)
utils.toggle_grad(G, True)
# Zero G's gradients by default before training G, for safety
if embedded_optimizers:
G.optim.zero_grad()
else:
GD.optimizer_G.zero_grad()
counter = 0
# If accumulating gradients, loop multiple times
for accumulation_index in range(config["num_G_accumulations"]):
# Sample conditioning for G
sampled_cond = sample_conditionings()
labels_g, f_g = None, None
if features is not None and y is not None:
z_, labels_g, f_g = sampled_cond
elif y is not None:
z_, labels_g = sampled_cond
elif features is not None:
z_, f_g = sampled_cond
# Tensors to device
if labels_g is not None:
labels_g = labels_g.to(device, non_blocking=True).long()
if f_g is not None:
f_g = f_g.to(device, non_blocking=True)
z_ = z_.to(device, non_blocking=True)
# Obtain discriminator scores
D_fake = GD(
z_,
labels_g,
f_g,
train_G=True,
split_D=config["split_D"],
policy=config["DiffAugment"],
DA=config["DA"],
)
G_loss = losses.generator_loss(D_fake) / float(
config["num_G_accumulations"]
)
G_loss.backward()
counter += 1
# Optionally apply modified ortho reg in G
if config["G_ortho"] > 0.0:
print(
"using modified ortho reg in G"
) # Debug print to indicate we're using ortho reg in G
# Don't ortho reg shared, it makes no sense. Really we should blacklist any embeddings for this
utils.ortho(
G,
config["G_ortho"],
blacklist=[param for param in G.shared.parameters()],
)
if embedded_optimizers:
G.optim.step()
else:
GD.optimizer_G.step()
# If we have an ema, update it, regardless of if we test with it or not
if config["ema"]:
ema.update(state_dict["itr"])
out = {
"G_loss": float(G_loss.item()),
"D_loss_real": float(D_loss_real.item()),
"D_loss_fake": float(D_loss_fake.item()),
}
# Return G's loss and the components of D's loss.
return out
return train
def save_weights(
G,
D,
G_ema,
state_dict,
config,
experiment_name,
embedded_optimizers=True,
G_optim=None,
D_optim=None,
):
utils.save_weights(
G,
D,
state_dict,
config["weights_root"],
experiment_name,
None,
G_ema if config["ema"] else None,
embedded_optimizers=embedded_optimizers,
G_optim=G_optim,
D_optim=D_optim,
)
# Save an additional copy to mitigate accidental corruption if process
# is killed during a save (it's happened to me before -.-)
if config["num_save_copies"] > 0:
utils.save_weights(
G,
D,
state_dict,
config["weights_root"],
experiment_name,
"copy%d" % state_dict["save_num"],
G_ema if config["ema"] else None,
embedded_optimizers=embedded_optimizers,
G_optim=G_optim,
D_optim=D_optim,
)
state_dict["save_num"] = (state_dict["save_num"] + 1) % config[
"num_save_copies"
]
""" This function takes in the model, saves the weights (multiple copies if
requested), and prepares sample sheets: one consisting of samples given
a fixed noise seed (to show how the model evolves throughout training),
a set of full conditional sample sheets, and a set of interp sheets. """
def save_and_sample(
G, D, G_ema, z_, y_, fixed_z, fixed_y, state_dict, config, experiment_name
):
utils.save_weights(
G,
D,
state_dict,
config["weights_root"],
experiment_name,
None,
G_ema if config["ema"] else None,
)
# Save an additional copy to mitigate accidental corruption if process
# is killed during a save (it's happened to me before -.-)
if config["num_save_copies"] > 0:
utils.save_weights(
G,
D,
state_dict,
config["weights_root"],
experiment_name,
"copy%d" % state_dict["save_num"],
G_ema if config["ema"] else None,
)
state_dict["save_num"] = (state_dict["save_num"] + 1) % config[
"num_save_copies"
]
# Accumulate standing statistics?
if config["accumulate_stats"]:
utils.accumulate_standing_stats(
G_ema if config["ema"] and config["use_ema"] else G,
z_,
y_,
config["n_classes"],
config["num_standing_accumulations"],
)
""" This function runs the inception metrics code, checks if the results
are an improvement over the previous best (either in IS or FID,
user-specified), logs the results, and saves a best_ copy if it's an
improvement. """
def test(
G,
D,
G_ema,
z_,
y_,
state_dict,
config,
sample,
get_inception_metrics,
experiment_name,
test_log,
loader=None,
embedded_optimizers=True,
G_optim=None,
D_optim=None,
rank=0,
):
print("Gathering inception metrics...")
if config["accumulate_stats"]:
utils.accumulate_standing_stats(
G_ema if config["ema"] and config["use_ema"] else G,
z_,
y_,
config["n_classes"],
config["num_standing_accumulations"],
)
if loader is not None:
IS_mean, IS_std, FID, stratified_FID, prdc_metrics = get_inception_metrics(
sample, config["num_inception_images"], num_splits=10, loader_ref=loader
)
else:
IS_mean, IS_std, FID, stratified_FID = get_inception_metrics(
sample, config["num_inception_images"], num_splits=10
)
print(
"Itr %d: PYTORCH UNOFFICIAL Inception Score is %3.3f +/- %3.3f, PYTORCH UNOFFICIAL FID is %5.4f"
% (state_dict["itr"], IS_mean, IS_std, FID)
)
# If improved over previous best metric, save approrpiate copy
if rank == 0:
if (config["which_best"] == "IS" and IS_mean > state_dict["best_IS"]) or (
config["which_best"] == "FID" and FID < state_dict["best_FID"]
):
print(
"%s improved over previous best, saving checkpoint..."
% config["which_best"]
)
utils.save_weights(
G,
D,
state_dict,
config["weights_root"],
experiment_name,
"best%d" % state_dict["save_best_num"],
G_ema if config["ema"] else None,
embedded_optimizers=embedded_optimizers,
G_optim=G_optim,
D_optim=D_optim,
)
state_dict["save_best_num"] = (state_dict["save_best_num"] + 1) % config[
"num_best_copies"
]
state_dict["best_IS"] = max(state_dict["best_IS"], IS_mean)
state_dict["best_FID"] = min(state_dict["best_FID"], FID)
# Log results to file
test_log.log(
itr=int(state_dict["itr"]),
IS_mean=float(IS_mean),
IS_std=float(IS_std),
FID=float(FID),
)
return IS_mean, FID
|