LFNA -> GMOA

This commit is contained in:
D-X-Y 2021-05-24 08:04:27 +00:00
parent 53b63d3924
commit cd33f8f72f
11 changed files with 9 additions and 181 deletions

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@ -1,9 +1,10 @@
#####################################################
# Learning to Generate Model One Step Ahead #
#####################################################
# python exps/LFNA/lfna.py --env_version v1 --workers 0
# python exps/LFNA/lfna.py --env_version v1 --device cuda --lr 0.001
# python exps/LFNA/lfna.py --env_version v1 --device cuda --lr 0.002 --meta_batch 128
# python exps/GMOA/lfna.py --env_version v1 --workers 0
# python exps/GMOA/lfna.py --env_version v1 --device cuda --lr 0.001
# python exps/GMOA/lfna.py --env_version v1 --device cuda --lr 0.002 --seq_length 16 --meta_batch 128
# python exps/GMOA/lfna.py --env_version v1 --device cuda --lr 0.002 --seq_length 24 --time_dim 32 --meta_batch 128
#####################################################
import pdb, sys, time, copy, torch, random, argparse
from tqdm import tqdm
@ -33,7 +34,7 @@ from xautodl.models.xcore import get_model
from xautodl.xlayers import super_core, trunc_normal_
from lfna_utils import lfna_setup, train_model, TimeData
from lfna_meta_model import LFNA_Meta
from lfna_meta_model import MetaModelV1
def epoch_train(loader, meta_model, base_model, optimizer, criterion, device, logger):
@ -240,7 +241,7 @@ def main(args):
# pre-train the hypernetwork
timestamps = trainval_env.get_timestamp(None)
meta_model = LFNA_Meta(
meta_model = MetaModelV1(
shape_container,
args.layer_dim,
args.time_dim,
@ -270,179 +271,6 @@ def main(args):
logger.path(None) / "final-ckp.pth",
logger,
)
return
"""
optimizer = torch.optim.Adam(
meta_model.get_parameters(True, True, False), # fix hypernet
lr=args.lr,
weight_decay=args.weight_decay,
amsgrad=True,
)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[1, 2, 3, 4, 5],
gamma=0.2,
)
logger.log("The optimizer is\n{:}".format(optimizer))
logger.log("The scheduler is\n{:}".format(lr_scheduler))
logger.log("Per epoch iterations = {:}".format(len(train_env_loader)))
if logger.path("model").exists():
ckp_data = torch.load(logger.path("model"))
base_model.load_state_dict(ckp_data["base_model"])
meta_model.load_state_dict(ckp_data["meta_model"])
optimizer.load_state_dict(ckp_data["optimizer"])
lr_scheduler.load_state_dict(ckp_data["lr_scheduler"])
last_success_epoch = ckp_data["last_success_epoch"]
start_epoch = ckp_data["iepoch"] + 1
check_strs = [
"epochs",
"env_version",
"hidden_dim",
"lr",
"layer_dim",
"time_dim",
"seq_length",
]
for xstr in check_strs:
cx = getattr(args, xstr)
px = getattr(ckp_data["args"], xstr)
assert cx == px, "[{:}] {:} vs {:}".format(xstr, cx, ps)
success, _ = meta_model.save_best(ckp_data["cur_score"])
logger.log("Load ckp from {:}".format(logger.path("model")))
if success:
logger.log(
"Re-save the best model with score={:}".format(ckp_data["cur_score"])
)
else:
start_epoch, last_success_epoch = 0, 0
# LFNA meta-train
meta_model.set_best_dir(logger.path(None) / "checkpoint")
per_epoch_time, start_time = AverageMeter(), time.time()
for iepoch in range(start_epoch, args.epochs):
head_str = "[{:}] [{:04d}/{:04d}] ".format(
time_string(), iepoch, args.epochs
) + "Time Left: {:}".format(
convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
)
loss_meter = epoch_train(
train_env_loader,
meta_model,
base_model,
optimizer,
criterion,
args.device,
logger,
)
valid_loss_meter = epoch_evaluate(
valid_env_loader, meta_model, base_model, criterion, args.device, logger
)
logger.log(
head_str
+ " meta-train-loss: {meter.avg:.4f} ({meter.count:.0f})".format(
meter=loss_meter
)
+ " meta-valid-loss: {meter.val:.4f}".format(meter=valid_loss_meter)
+ " :: lr={:.5f}".format(min(lr_scheduler.get_last_lr()))
+ " :: last-success={:}".format(last_success_epoch)
)
success, best_score = meta_model.save_best(-loss_meter.avg)
if success:
logger.log("Achieve the best with best-score = {:.5f}".format(best_score))
last_success_epoch = iepoch
save_checkpoint(
{
"meta_model": meta_model.state_dict(),
"base_model": base_model.state_dict(),
"optimizer": optimizer.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
"last_success_epoch": last_success_epoch,
"cur_score": -loss_meter.avg,
"iepoch": iepoch,
"args": args,
},
logger.path("model"),
logger,
)
if iepoch - last_success_epoch >= args.early_stop_thresh:
if lr_scheduler.last_epoch > 4:
logger.log("Early stop at {:}".format(iepoch))
break
else:
last_success_epoch = iepoch
lr_scheduler.step()
logger.log("Decay the lr [{:}]".format(lr_scheduler.last_epoch))
per_epoch_time.update(time.time() - start_time)
start_time = time.time()
# meta-test
meta_model.load_best()
eval_env = env_info["dynamic_env"]
for idx in range(args.seq_length, len(eval_env)):
# build-timestamp
future_time = env_info["{:}-timestamp".format(idx)].item()
time_seqs = []
for iseq in range(args.seq_length):
time_seqs.append(future_time - iseq * eval_env.time_interval)
time_seqs.reverse()
with torch.no_grad():
meta_model.eval()
base_model.eval()
time_seqs = torch.Tensor(time_seqs).view(1, -1).to(args.device)
[seq_containers] = meta_model(time_seqs)
future_container = seq_containers[-1]
w_container_per_epoch[idx] = future_container.no_grad_clone()
# evaluation
future_x = env_info["{:}-x".format(idx)].to(args.device)
future_y = env_info["{:}-y".format(idx)].to(args.device)
future_y_hat = base_model.forward_with_container(
future_x, w_container_per_epoch[idx]
)
future_loss = criterion(future_y_hat, future_y)
logger.log(
"meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item())
)
# creating the new meta-time-embedding
distance = meta_model.get_closest_meta_distance(future_time)
if distance < eval_env.time_interval:
continue
#
new_param = meta_model.create_meta_embed()
optimizer = torch.optim.Adam(
[new_param], lr=args.refine_lr, weight_decay=1e-5, amsgrad=True
)
meta_model.replace_append_learnt(
torch.Tensor([future_time]).to(args.device), new_param
)
meta_model.eval()
base_model.train()
for iepoch in range(args.refine_epochs):
optimizer.zero_grad()
[seq_containers] = meta_model(time_seqs)
future_container = seq_containers[-1]
future_y_hat = base_model.forward_with_container(future_x, future_container)
future_loss = criterion(future_y_hat, future_y)
future_loss.backward()
optimizer.step()
logger.log(
"post-meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item())
)
with torch.no_grad():
meta_model.replace_append_learnt(None, None)
meta_model.append_fixed(torch.Tensor([future_time]), new_param)
save_checkpoint(
{"w_container_per_epoch": w_container_per_epoch},
logger.path(None) / "final-ckp.pth",
logger,
)
"""
logger.log("-" * 200 + "\n")
logger.close()
@ -513,7 +341,7 @@ if __name__ == "__main__":
help="The learning rate for the optimizer, during refine",
)
parser.add_argument(
"--refine_epochs", type=int, default=50, help="The final refine #epochs."
"--refine_epochs", type=int, default=100, help="The final refine #epochs."
)
parser.add_argument(
"--early_stop_thresh",

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@ -10,8 +10,8 @@ from xautodl.xlayers import trunc_normal_
from xautodl.models.xcore import get_model
class LFNA_Meta(super_core.SuperModule):
"""Learning to Forecast Neural Adaptation (Meta Model Design)."""
class MetaModelV1(super_core.SuperModule):
"""Learning to Generate Models One Step Ahead (Meta Model Design)."""
def __init__(
self,