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Reformulate via black

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D-X-Y
2021-03-17 09:25:58 +00:00
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commit f98edea22a
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exps/search-transformable.py
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@@ -4,214 +4,314 @@
# Network Pruning via Transformable Architecture Search, NeurIPS 2019 #
#######################################################################
import sys, time, torch, random, argparse
from PIL import ImageFile
from os import path as osp
from PIL import ImageFile
from os import path as osp
ImageFile.LOAD_TRUNCATED_IMAGES = True
import numpy as np
from copy import deepcopy
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))
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
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)
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)
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)
prepare_seed(args.rand_seed)
logger = prepare_logger(args)
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)
# 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
)
# 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()
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)
# 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}, {}, {}, {}
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)
# 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))
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,
)
# 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)))
# 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))
#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
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()
# 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)
# 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}, {}, {}, {}
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
# 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('')
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']))
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
)
)
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()
# 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)
if __name__ == "__main__":
args = obtain_args()
main(args)