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Add int search space

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This commit is contained in:
D-X-Y
2021-03-18 16:02:55 +08:00
parent ece6ac5f41
commit 63c8bb9bc8
67 changed files with 5150 additions and 1474 deletions
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165
exps/search-transformable.py
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@@ -35,10 +35,16 @@ def main(args):
logger = prepare_logger(args)
# prepare dataset
train_data, valid_data, xshape, class_num = get_datasets(args.dataset, args.data_path, args.cutout_length)
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
valid_data,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
)
split_file_path = Path(args.split_path)
@@ -49,9 +55,9 @@ def main(args):
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)
)
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(
@@ -78,18 +84,26 @@ def main(args):
)
# 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)
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},
{
"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)
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))
@@ -97,7 +111,9 @@ def main(args):
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)
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,
@@ -109,7 +125,11 @@ def main(args):
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")
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
@@ -122,14 +142,24 @@ def main(args):
resume_path = last_info
else:
raise ValueError("Something is wrong.")
logger.log("=> loading checkpoint of the last-info '{:}' start".format(resume_path))
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)
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():
@@ -143,11 +173,23 @@ def main(args):
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)
"=> 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}, {}, {}, {}
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)
@@ -155,15 +197,26 @@ def main(args):
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))
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
time_string(),
epoch_str,
need_time,
min(LRs),
max(LRs),
scheduler,
search_model.tau,
MAX_FLOP,
)
)
@@ -188,21 +241,35 @@ def main(args):
# 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
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)
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)))
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)
epoch_str,
cur_FLOP,
cur_FLOP / MAX_FLOP,
args.FLOP_ratio,
np.mean(discrepancy),
)
)
@@ -215,7 +282,12 @@ def main(args):
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
search_valid_loader,
network,
criterion,
epoch_str,
args.print_freq_eval,
logger,
)
valid_accuracies[epoch] = valid_acc1
logger.log(
@@ -235,15 +307,26 @@ def main(args):
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
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
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
next(network.parameters()).device,
int(num_bytes),
num_bytes / 1e3,
num_bytes / 1e6,
num_bytes / 1e9,
)
)
max_bytes[epoch] = num_bytes
@@ -285,9 +368,15 @@ def main(args):
logger.log("")
logger.log("-" * 100)
last_config_path = logger.path("log") / "seed-{:}-last.config".format(args.rand_seed)
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(
"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():
@@ -298,15 +387,23 @@ def main(args):
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-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
)
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(
"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")
convert_secs2time(epoch_time.sum, True),
max(v for k, v in max_bytes.items()) / 1e9,
logger.path("info"),
)
)
logger.close()