File size: 18,229 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 |
# 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
import os
import functools
import math
from tqdm import tqdm, trange
import argparse
import time
import subprocess
import re
import sys
sys.path.insert(1, os.path.join(sys.path[0], ".."))
import numpy as np
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.optim as optim
# Import my stuff
import data_utils.inception_utils as inception_utils
import utils
import train_fns
from sync_batchnorm import patch_replication_callback
from data_utils import utils as data_utils
def run(config, ddp_setup="slurm", master_node=""):
config["n_classes"] = 1000 # utils.nclass_dict[self.config['dataset']]
config["G_activation"] = utils.activation_dict[config["G_nl"]]
config["D_activation"] = utils.activation_dict[config["D_nl"]]
config = utils.update_config_roots(config)
# Prepare root folders if necessary
utils.prepare_root(config)
if config["ddp_train"]:
if ddp_setup == "slurm":
n_nodes = int(os.environ.get("SLURM_JOB_NUM_NODES"))
n_gpus_per_node = int(os.environ.get("SLURM_TASKS_PER_NODE").split("(")[0])
world_size = n_gpus_per_node * n_nodes
print(
"Master node is ",
master_node,
" World size is ",
world_size,
" with ",
n_gpus_per_node,
"gpus per node.",
)
dist_url = "tcp://"
dist_url += master_node
port = 40000
dist_url += ":" + str(port)
print("Dist url ", dist_url)
train(-1, world_size, config, dist_url)
else:
world_size = torch.cuda.device_count()
dist_url = "env://"
mp.spawn(
train, args=(world_size, config, dist_url), nprocs=world_size, join=True
)
else:
train(0, -1, config, None)
def train(rank, world_size, config, dist_url):
print("Rank of this job is ", rank)
copy_locally = False
tmp_dir = ""
if config["ddp_train"]:
if dist_url == "env://":
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "12355"
local_rank = rank
else:
rank = int(os.environ.get("SLURM_PROCID"))
local_rank = int(os.environ.get("SLURM_LOCALID"))
copy_locally = True
tmp_dir = "/scratch/slurm_tmpdir/" + str(os.environ.get("SLURM_JOB_ID"))
print("Before setting process group")
print(dist_url, rank)
dist.init_process_group(
backend="nccl", init_method=dist_url, rank=rank, world_size=world_size
)
print("After setting process group")
device = "cuda:{}".format(local_rank) # rank % 8)
print(dist_url, rank, " /Device is ", device)
else:
device = "cuda"
local_rank = "cuda"
# Update the config dict as necessary
# This is for convenience, to add settings derived from the user-specified
# configuration into the config-dict (e.g. inferring the number of classes
# and size of the images from the dataset, passing in a pytorch object
# for the activation specified as a string)'
# Seed RNG
utils.seed_rng(config["seed"] + rank)
# Setup cudnn.benchmark for free speed
torch.backends.cudnn.benchmark = True
if config["deterministic_run"]:
torch.backends.cudnn.deterministic = True
# Import the model--this line allows us to dynamically select different files.
model = __import__(config["model"])
experiment_name = (
config["experiment_name"]
if config["experiment_name"]
else utils.name_from_config(config)
)
print("Experiment name is %s" % experiment_name)
if config["ddp_train"]:
torch.cuda.set_device(device)
# Next, build the model
G = model.Generator(**{**config, "embedded_optimizers": False}).to(device)
D = model.Discriminator(**{**config, "embedded_optimizers": False}).to(device)
# If using EMA, prepare it
if config["ema"]:
print("Preparing EMA for G with decay of {}".format(config["ema_decay"]))
G_ema = model.Generator(**{**config, "skip_init": True, "no_optim": True}).to(
device
)
ema = utils.ema(G, G_ema, config["ema_decay"], config["ema_start"])
else:
G_ema, ema = None, None
print(
"Number of params in G: {} D: {}".format(
*[sum([p.data.nelement() for p in net.parameters()]) for net in [G, D]]
)
)
# Setup the optimizers
if config["D_fp16"]:
print("Using fp16 adam ")
optim_type = utils.Adam16
else:
optim_type = optim.Adam
optimizer_D = optim_type(
params=D.parameters(),
lr=config["D_lr"],
betas=(config["D_B1"], config["D_B2"]),
weight_decay=0,
eps=config["adam_eps"],
)
optimizer_G = optim_type(
params=G.parameters(),
lr=config["G_lr"],
betas=(config["G_B1"], config["G_B2"]),
weight_decay=0,
eps=config["adam_eps"],
)
# Prepare state dict, which holds things like epoch # and itr #
state_dict = {
"itr": 0,
"epoch": 0,
"save_num": 0,
"save_best_num": 0,
"best_IS": 0,
"best_FID": 999999,
"es_epoch": 0,
"config": config,
}
# FP16?
if config["G_fp16"]:
print("Casting G to float16...")
G = G.half()
if config["ema"]:
G_ema = G_ema.half()
if config["D_fp16"]:
print("Casting D to fp16...")
D = D.half()
## DDP the models
if config["ddp_train"]:
print("before G DDP ")
G = DDP(
G,
device_ids=[local_rank],
output_device=local_rank,
find_unused_parameters=True,
)
print("After G DDP ")
D = DDP(
D,
device_ids=[local_rank],
output_device=local_rank,
find_unused_parameters=True,
)
# If loading from a pre-trained model, load weights
print("Loading weights...")
if config["ddp_train"]:
dist.barrier()
map_location = device
else:
map_location = None
utils.load_weights(
G,
D,
state_dict,
config["weights_root"],
experiment_name,
config["load_weights"] if config["load_weights"] else None,
G_ema if config["ema"] else None,
map_location=map_location,
embedded_optimizers=False,
G_optim=optimizer_G,
D_optim=optimizer_D,
)
# wrapper class
GD = model.G_D(G, D, optimizer_G=optimizer_G, optimizer_D=optimizer_D)
if config["parallel"] and world_size > -1:
GD = nn.DataParallel(GD)
if config["cross_replica"]:
patch_replication_callback(GD)
# Prepare loggers for stats; metrics holds test metrics,
# lmetrics holds any desired training metrics.
if rank == 0:
test_metrics_fname = "%s/%s_log.jsonl" % (config["logs_root"], experiment_name)
train_metrics_fname = "%s/%s" % (config["logs_root"], experiment_name)
print("Inception Metrics will be saved to {}".format(test_metrics_fname))
test_log = utils.MetricsLogger(test_metrics_fname, reinitialize=False)
print("Training Metrics will be saved to {}".format(train_metrics_fname))
train_log = utils.MyLogger(
train_metrics_fname, reinitialize=False, logstyle=config["logstyle"]
)
# Write metadata
utils.write_metadata(config["logs_root"], experiment_name, config, state_dict)
else:
test_log = None
train_log = None
D_batch_size = (
config["batch_size"] * config["num_D_steps"] * config["num_D_accumulations"]
)
if config["longtail"]:
samples_per_class = np.load(
"imagenet_lt/imagenet_lt_samples_per_class.npy", allow_pickle=True
)
class_probabilities = np.load(
"imagenet_lt/imagenet_lt_class_prob.npy", allow_pickle=True
)
else:
samples_per_class, class_probabilities = None, None
train_dataset = data_utils.get_dataset_hdf5(
**{
**config,
"data_path": config["data_root"],
"batch_size": D_batch_size,
"augment": config["hflips"],
"local_rank": local_rank,
"copy_locally": copy_locally,
"tmp_dir": tmp_dir,
"ddp": config["ddp_train"],
}
)
train_loader = data_utils.get_dataloader(
**{
**config,
"dataset": train_dataset,
"batch_size": config["batch_size"],
"start_epoch": state_dict["epoch"],
"start_itr": state_dict["itr"],
"longtail_temperature": config["longtail_temperature"],
"samples_per_class": samples_per_class,
"class_probabilities": class_probabilities,
"rank": rank,
"world_size": world_size,
"shuffle": True,
"drop_last": True,
}
)
# Prepare inception metrics: FID and IS
is_moments_prefix = "I" if config["which_dataset"] == "imagenet" else "COCO"
im_filename = "%s%i_%s" % (
is_moments_prefix,
config["resolution"],
"" if not config["longtail"] else "longtail",
)
print("Using ", im_filename, "for Inception metrics.")
get_inception_metrics = inception_utils.prepare_inception_metrics(
im_filename,
samples_per_class,
config["parallel"],
config["no_fid"],
config["data_root"],
device=device,
)
G_batch_size = config["G_batch_size"]
z_, y_ = data_utils.prepare_z_y(
G_batch_size,
G.module.dim_z if config["ddp_train"] else G.dim_z,
config["n_classes"],
device=device,
fp16=config["G_fp16"],
longtail_gen=config["longtail_gen"],
custom_distrib=config["custom_distrib_gen"],
longtail_temperature=config["longtail_temperature"],
class_probabilities=class_probabilities,
)
# Balance instance sampling for ImageNet-LT
weights_sampling = None
if (
config["longtail"]
and config["use_balanced_sampler"]
and config["instance_cond"]
):
if config["which_knn_balance"] == "center_balance":
print(
"Balancing the instance features." "Using custom temperature distrib?",
config["custom_distrib_gen"],
" with temperature",
config["longtail_temperature"],
)
weights_sampling = data_utils.make_weights_for_balanced_classes(
samples_per_class,
train_loader.dataset.labels,
1000,
config["custom_distrib_gen"],
config["longtail_temperature"],
class_probabilities=class_probabilities,
)
# Balancing the NN classes (p(y))
elif config["which_knn_balance"] == "nnclass_balance":
print(
"Balancing the class distribution (classes drawn from the neighbors)."
" Using custom temperature distrib?",
config["custom_distrib_gen"],
" with temperature",
config["longtail_temperature"],
)
weights_sampling = torch.exp(
class_probabilities / config["longtail_temperature"]
) / torch.sum(
torch.exp(class_probabilities / config["longtail_temperature"])
)
# Configure conditioning sampling function to train G
sample_conditioning = functools.partial(
data_utils.sample_conditioning_values,
z_=z_,
y_=y_,
dataset=train_dataset,
batch_size=G_batch_size,
weights_sampling=weights_sampling,
ddp=config["ddp_train"],
constant_conditioning=config["constant_conditioning"],
class_cond=config["class_cond"],
instance_cond=config["instance_cond"],
nn_sampling_strategy=config["which_knn_balance"],
)
print("G batch size ", G_batch_size)
# Loaders are loaded, prepare the training function
train = train_fns.GAN_training_function(
G,
D,
GD,
ema,
state_dict,
config,
sample_conditioning,
embedded_optimizers=False,
device=device,
batch_size=G_batch_size,
)
# Prepare Sample function for use with inception metrics
sample = functools.partial(
utils.sample,
G=(G_ema if config["ema"] and config["use_ema"] else G),
sample_conditioning_func=sample_conditioning,
config=config,
class_cond=config["class_cond"],
instance_cond=config["instance_cond"],
)
print("Beginning training at epoch %d..." % state_dict["epoch"])
# Train for specified number of epochs, although we mostly track G iterations.
best_FID_run = state_dict["best_FID"]
FID = state_dict["best_FID"]
for epoch in range(state_dict["epoch"], config["num_epochs"]):
# Set epoch for distributed loader
if config["ddp_train"]:
train_loader.sampler.set_epoch(epoch)
# Initialize seeds at every epoch (useful for conditioning and
# noise sampling, as well as data order in the sampler)
if config["deterministic_run"]:
utils.seed_rng(config["seed"] + rank + state_dict["epoch"])
# Which progressbar to use? TQDM or my own?
if config["pbar"] == "mine":
pbar = utils.progress(
train_loader,
displaytype="s1k" if config["use_multiepoch_sampler"] else "eta",
)
else:
pbar = tqdm(train_loader)
s = time.time()
print("Before iteration, dataloader length", len(train_loader))
for i, batch in enumerate(pbar):
# if i> 5:
# break
in_label, in_feat = None, None
if config["instance_cond"] and config["class_cond"]:
x, in_label, in_feat, _ = batch
elif config["instance_cond"]:
x, in_feat, _ = batch
elif config["class_cond"]:
x, in_label = batch
if config["constant_conditioning"]:
in_label = torch.zeros_like(in_label)
else:
x = batch
x = x.to(device, non_blocking=True)
if in_label is not None:
in_label = in_label.to(device, non_blocking=True)
if in_feat is not None:
in_feat = in_feat.float().to(device, non_blocking=True)
# Increment the iteration counter
state_dict["itr"] += 1
# Make sure G and D are in training mode, just in case they got set to eval
# For D, which typically doesn't have BN, this shouldn't matter much.
G.train()
D.train()
if config["ema"]:
G_ema.train()
metrics = train(x, in_label, in_feat)
# print('After training step ', time.time() - s_stratified)
# s_stratified = time.time()
if rank == 0:
train_log.log(itr=int(state_dict["itr"]), **metrics)
# If using my progbar, print metrics.
if config["pbar"] == "mine" and rank == 0:
print(
", ".join(
["itr: %d" % state_dict["itr"]]
+ ["%s : %+4.3f" % (key, metrics[key]) for key in metrics]
),
end=" ",
)
# Test every specified interval
print("Iteration time ", time.time() - s)
s = time.time()
# Increment epoch counter at end of epoch
state_dict["epoch"] += 1
if not (state_dict["epoch"] % config["test_every"]):
if config["G_eval_mode"]:
print("Switching G to eval mode...")
G.eval()
D.eval()
# Compute IS and FID using training dataset as reference
test_time = time.time()
IS, FID = train_fns.test(
G,
D,
G_ema,
z_,
y_,
state_dict,
config,
sample,
get_inception_metrics,
experiment_name,
test_log,
loader=None,
embedded_optimizers=False,
G_optim=optimizer_G,
D_optim=optimizer_D,
rank=rank,
)
print("Testing took ", time.time() - test_time)
if 2 * IS < state_dict["best_IS"] and config["stop_when_diverge"]:
print("Experiment diverged!")
break
else:
print("IS is ", IS, " and 2x best is ", 2 * state_dict["best_IS"])
if not (state_dict["epoch"] % config["save_every"]) and rank == 0:
train_fns.save_weights(
G,
D,
G_ema,
state_dict,
config,
experiment_name,
embedded_optimizers=False,
G_optim=optimizer_G,
D_optim=optimizer_D,
)
if rank == 0:
if FID < best_FID_run:
best_FID_run = FID
state_dict["es_epoch"] = 0
else:
state_dict["es_epoch"] += 1
if state_dict["es_epoch"] >= config["es_patience"]:
print("reached Early stopping!")
return FID
return FID
|