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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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# Network Pruning via Transformable Architecture Search, NeurIPS 2019 #
#######################################################################
import sys, time, torch, random, argparse
from PIL import ImageFile
from os import path as osp
ImageFile.LOAD_TRUNCATED_IMAGES = True
import numpy as np
from copy import deepcopy
from pathlib import Path
lib_dir = (Path(__file__).parent / ".." / "lib").resolve()
if str(lib_dir) not in sys.path:
sys.path.insert(0, str(lib_dir))
from config_utils import load_config, configure2str, obtain_search_args as obtain_args
from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
from procedures import get_optim_scheduler, get_procedures
from datasets import get_datasets, SearchDataset
from models import obtain_search_model, obtain_model, change_key
from utils import get_model_infos
from log_utils import AverageMeter, time_string, convert_secs2time
def main(args):
assert torch.cuda.is_available(), "CUDA is not available."
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
# torch.backends.cudnn.deterministic = True
torch.set_num_threads(args.workers)
prepare_seed(args.rand_seed)
logger = prepare_logger(args)
# prepare dataset
train_data, valid_data, xshape, class_num = get_datasets(
args.dataset, args.data_path, args.cutout_length
)
# train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=True , num_workers=args.workers, pin_memory=True)
valid_loader = torch.utils.data.DataLoader(
valid_data,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
)
split_file_path = Path(args.split_path)
assert split_file_path.exists(), "{:} does not exist".format(split_file_path)
split_info = torch.load(split_file_path)
train_split, valid_split = split_info["train"], split_info["valid"]
assert (
len(set(train_split).intersection(set(valid_split))) == 0
), "There should be 0 element that belongs to both train and valid"
assert len(train_split) + len(valid_split) == len(
train_data
), "{:} + {:} vs {:}".format(len(train_split), len(valid_split), len(train_data))
search_dataset = SearchDataset(args.dataset, train_data, train_split, valid_split)
search_train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
pin_memory=True,
num_workers=args.workers,
)
search_valid_loader = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
pin_memory=True,
num_workers=args.workers,
)
search_loader = torch.utils.data.DataLoader(
search_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
sampler=None,
)
# get configures
if args.ablation_num_select is None or args.ablation_num_select <= 0:
model_config = load_config(
args.model_config, {"class_num": class_num, "search_mode": "shape"}, logger
)
else:
model_config = load_config(
args.model_config,
{
"class_num": class_num,
"search_mode": "ablation",
"num_random_select": args.ablation_num_select,
},
logger,
)
# obtain the model
search_model = obtain_search_model(model_config)
MAX_FLOP, param = get_model_infos(search_model, xshape)
optim_config = load_config(
args.optim_config, {"class_num": class_num, "FLOP": MAX_FLOP}, logger
)
logger.log("Model Information : {:}".format(search_model.get_message()))
logger.log("MAX_FLOP = {:} M".format(MAX_FLOP))
logger.log("Params = {:} M".format(param))
logger.log("train_data : {:}".format(train_data))
logger.log("search-data: {:}".format(search_dataset))
logger.log("search_train_loader : {:} samples".format(len(train_split)))
logger.log("search_valid_loader : {:} samples".format(len(valid_split)))
base_optimizer, scheduler, criterion = get_optim_scheduler(
search_model.base_parameters(), optim_config
)
arch_optimizer = torch.optim.Adam(
search_model.arch_parameters(optim_config.arch_LR),
lr=optim_config.arch_LR,
betas=(0.5, 0.999),
weight_decay=optim_config.arch_decay,
)
logger.log("base-optimizer : {:}".format(base_optimizer))
logger.log("arch-optimizer : {:}".format(arch_optimizer))
logger.log("scheduler : {:}".format(scheduler))
logger.log("criterion : {:}".format(criterion))
last_info, model_base_path, model_best_path = (
logger.path("info"),
logger.path("model"),
logger.path("best"),
)
network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda()
# load checkpoint
if last_info.exists() or (
args.resume is not None and osp.isfile(args.resume)
): # automatically resume from previous checkpoint
if args.resume is not None and osp.isfile(args.resume):
resume_path = Path(args.resume)
elif last_info.exists():
resume_path = last_info
else:
raise ValueError("Something is wrong.")
logger.log(
"=> loading checkpoint of the last-info '{:}' start".format(resume_path)
)
checkpoint = torch.load(resume_path)
if "last_checkpoint" in checkpoint:
last_checkpoint_path = checkpoint["last_checkpoint"]
if not last_checkpoint_path.exists():
logger.log(
"Does not find {:}, try another path".format(last_checkpoint_path)
)
last_checkpoint_path = (
resume_path.parent
/ last_checkpoint_path.parent.name
/ last_checkpoint_path.name
)
assert (
last_checkpoint_path.exists()
), "can not find the checkpoint from {:}".format(last_checkpoint_path)
checkpoint = torch.load(last_checkpoint_path)
start_epoch = checkpoint["epoch"] + 1
# for key, value in checkpoint['search_model'].items():
# print('K {:} = Shape={:}'.format(key, value.shape))
search_model.load_state_dict(checkpoint["search_model"])
scheduler.load_state_dict(checkpoint["scheduler"])
base_optimizer.load_state_dict(checkpoint["base_optimizer"])
arch_optimizer.load_state_dict(checkpoint["arch_optimizer"])
valid_accuracies = checkpoint["valid_accuracies"]
arch_genotypes = checkpoint["arch_genotypes"]
discrepancies = checkpoint["discrepancies"]
max_bytes = checkpoint["max_bytes"]
logger.log(
"=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(
resume_path, start_epoch
)
)
else:
logger.log(
"=> do not find the last-info file : {:} or resume : {:}".format(
last_info, args.resume
)
)
start_epoch, valid_accuracies, arch_genotypes, discrepancies, max_bytes = (
0,
{"best": -1},
{},
{},
{},
)
# main procedure
train_func, valid_func = get_procedures(args.procedure)
total_epoch = optim_config.epochs + optim_config.warmup
start_time, epoch_time = time.time(), AverageMeter()
for epoch in range(start_epoch, total_epoch):
scheduler.update(epoch, 0.0)
search_model.set_tau(
args.gumbel_tau_max, args.gumbel_tau_min, epoch * 1.0 / total_epoch
)
need_time = "Time Left: {:}".format(
convert_secs2time(epoch_time.avg * (total_epoch - epoch), True)
)
epoch_str = "epoch={:03d}/{:03d}".format(epoch, total_epoch)
LRs = scheduler.get_lr()
find_best = False
logger.log(
"\n***{:s}*** start {:s} {:s}, LR=[{:.6f} ~ {:.6f}], scheduler={:}, tau={:}, FLOP={:.2f}".format(
time_string(),
epoch_str,
need_time,
min(LRs),
max(LRs),
scheduler,
search_model.tau,
MAX_FLOP,
)
)
# train for one epoch
train_base_loss, train_arch_loss, train_acc1, train_acc5 = train_func(
search_loader,
network,
criterion,
scheduler,
base_optimizer,
arch_optimizer,
optim_config,
{
"epoch-str": epoch_str,
"FLOP-exp": MAX_FLOP * args.FLOP_ratio,
"FLOP-weight": args.FLOP_weight,
"FLOP-tolerant": MAX_FLOP * args.FLOP_tolerant,
},
args.print_freq,
logger,
)
# log the results
logger.log(
"***{:s}*** TRAIN [{:}] base-loss = {:.6f}, arch-loss = {:.6f}, accuracy-1 = {:.2f}, accuracy-5 = {:.2f}".format(
time_string(),
epoch_str,
train_base_loss,
train_arch_loss,
train_acc1,
train_acc5,
)
)
cur_FLOP, genotype = search_model.get_flop(
"genotype", model_config._asdict(), None
)
arch_genotypes[epoch] = genotype
arch_genotypes["last"] = genotype
logger.log("[{:}] genotype : {:}".format(epoch_str, genotype))
# save the configuration
configure2str(
genotype,
str(logger.path("log") / "seed-{:}-temp.config".format(args.rand_seed)),
)
arch_info, discrepancy = search_model.get_arch_info()
logger.log(arch_info)
discrepancies[epoch] = discrepancy
logger.log(
"[{:}] FLOP : {:.2f} MB, ratio : {:.4f}, Expected-ratio : {:.4f}, Discrepancy : {:.3f}".format(
epoch_str,
cur_FLOP,
cur_FLOP / MAX_FLOP,
args.FLOP_ratio,
np.mean(discrepancy),
)
)
# if cur_FLOP/MAX_FLOP > args.FLOP_ratio:
# init_flop_weight = init_flop_weight * args.FLOP_decay
# else:
# init_flop_weight = init_flop_weight / args.FLOP_decay
# evaluate the performance
if (epoch % args.eval_frequency == 0) or (epoch + 1 == total_epoch):
logger.log("-" * 150)
valid_loss, valid_acc1, valid_acc5 = valid_func(
search_valid_loader,
network,
criterion,
epoch_str,
args.print_freq_eval,
logger,
)
valid_accuracies[epoch] = valid_acc1
logger.log(
"***{:s}*** VALID [{:}] loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f} | Best-Valid-Acc@1={:.2f}, Error@1={:.2f}".format(
time_string(),
epoch_str,
valid_loss,
valid_acc1,
valid_acc5,
valid_accuracies["best"],
100 - valid_accuracies["best"],
)
)
if valid_acc1 > valid_accuracies["best"]:
valid_accuracies["best"] = valid_acc1
arch_genotypes["best"] = genotype
find_best = True
logger.log(
"Currently, the best validation accuracy found at {:03d}-epoch :: acc@1={:.2f}, acc@5={:.2f}, error@1={:.2f}, error@5={:.2f}, save into {:}.".format(
epoch,
valid_acc1,
valid_acc5,
100 - valid_acc1,
100 - valid_acc5,
model_best_path,
)
)
# log the GPU memory usage
# num_bytes = torch.cuda.max_memory_allocated( next(network.parameters()).device ) * 1.0
num_bytes = (
torch.cuda.max_memory_cached(next(network.parameters()).device) * 1.0
)
logger.log(
"[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]".format(
next(network.parameters()).device,
int(num_bytes),
num_bytes / 1e3,
num_bytes / 1e6,
num_bytes / 1e9,
)
)
max_bytes[epoch] = num_bytes
# save checkpoint
save_path = save_checkpoint(
{
"epoch": epoch,
"args": deepcopy(args),
"max_bytes": deepcopy(max_bytes),
"valid_accuracies": deepcopy(valid_accuracies),
"model-config": model_config._asdict(),
"optim-config": optim_config._asdict(),
"search_model": search_model.state_dict(),
"scheduler": scheduler.state_dict(),
"base_optimizer": base_optimizer.state_dict(),
"arch_optimizer": arch_optimizer.state_dict(),
"arch_genotypes": arch_genotypes,
"discrepancies": discrepancies,
},
model_base_path,
logger,
)
if find_best:
copy_checkpoint(model_base_path, model_best_path, logger)
last_info = save_checkpoint(
{
"epoch": epoch,
"args": deepcopy(args),
"last_checkpoint": save_path,
},
logger.path("info"),
logger,
)
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
logger.log("")
logger.log("-" * 100)
last_config_path = logger.path("log") / "seed-{:}-last.config".format(
args.rand_seed
)
configure2str(arch_genotypes["last"], str(last_config_path))
logger.log(
"save the last config int {:} :\n{:}".format(
last_config_path, arch_genotypes["last"]
)
)
best_arch, valid_acc = arch_genotypes["best"], valid_accuracies["best"]
for key, config in arch_genotypes.items():
if key == "last":
continue
FLOP_ratio = config["estimated_FLOP"] / MAX_FLOP
if abs(FLOP_ratio - args.FLOP_ratio) <= args.FLOP_tolerant:
if valid_acc <= valid_accuracies[key]:
best_arch, valid_acc = config, valid_accuracies[key]
print(
"Best-Arch : {:}\nRatio={:}, Valid-ACC={:}".format(
best_arch, best_arch["estimated_FLOP"] / MAX_FLOP, valid_acc
)
)
best_config_path = logger.path("log") / "seed-{:}-best.config".format(
args.rand_seed
)
configure2str(best_arch, str(best_config_path))
logger.log(
"save the last config int {:} :\n{:}".format(best_config_path, best_arch)
)
logger.log("\n" + "-" * 200)
logger.log(
"Finish training/validation in {:} with Max-GPU-Memory of {:.2f} GB, and save final checkpoint into {:}".format(
convert_secs2time(epoch_time.sum, True),
max(v for k, v in max_bytes.items()) / 1e9,
logger.path("info"),
)
)
logger.close()
if __name__ == "__main__":
args = obtain_args()
main(args)