Updates
This commit is contained in:
parent
c8e95b0ddc
commit
ec241e4d69
476
exps/LFNA/lfna-debug.py
Normal file
476
exps/LFNA/lfna-debug.py
Normal file
@ -0,0 +1,476 @@
|
||||
#####################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
|
||||
#####################################################
|
||||
# python exps/LFNA/lfna-debug.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
|
||||
#####################################################
|
||||
import sys, time, copy, torch, random, argparse
|
||||
from tqdm import tqdm
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
|
||||
lib_dir = (Path(__file__).parent / ".." / "..").resolve()
|
||||
print("LIB-DIR: {:}".format(lib_dir))
|
||||
if str(lib_dir) not in sys.path:
|
||||
sys.path.insert(0, str(lib_dir))
|
||||
|
||||
from xautodl.procedures import (
|
||||
prepare_seed,
|
||||
prepare_logger,
|
||||
save_checkpoint,
|
||||
copy_checkpoint,
|
||||
)
|
||||
from xautodl.log_utils import time_string
|
||||
from xautodl.log_utils import AverageMeter, convert_secs2time
|
||||
|
||||
from xautodl.utils import split_str2indexes
|
||||
|
||||
from xautodl.procedures.advanced_main import basic_train_fn, basic_eval_fn
|
||||
from xautodl.procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
|
||||
from xautodl.datasets.synthetic_core import get_synthetic_env, EnvSampler
|
||||
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
|
||||
|
||||
|
||||
def epoch_train(loader, meta_model, base_model, optimizer, criterion, device, logger):
|
||||
base_model.train()
|
||||
meta_model.train()
|
||||
loss_meter = AverageMeter()
|
||||
for ibatch, batch_data in enumerate(loader):
|
||||
timestamps, (batch_seq_inputs, batch_seq_targets) = batch_data
|
||||
timestamps = timestamps.squeeze(dim=-1).to(device)
|
||||
batch_seq_inputs = batch_seq_inputs.to(device)
|
||||
batch_seq_targets = batch_seq_targets.to(device)
|
||||
|
||||
optimizer.zero_grad()
|
||||
|
||||
batch_seq_containers = meta_model(timestamps)
|
||||
losses = []
|
||||
for seq_containers, seq_inputs, seq_targets in zip(
|
||||
batch_seq_containers, batch_seq_inputs, batch_seq_targets
|
||||
):
|
||||
for container, inputs, targets in zip(
|
||||
seq_containers, seq_inputs, seq_targets
|
||||
):
|
||||
predictions = base_model.forward_with_container(inputs, container)
|
||||
loss = criterion(predictions, targets)
|
||||
losses.append(loss)
|
||||
final_loss = torch.stack(losses).mean()
|
||||
final_loss.backward()
|
||||
optimizer.step()
|
||||
loss_meter.update(final_loss.item())
|
||||
return loss_meter
|
||||
|
||||
|
||||
def epoch_evaluate(loader, meta_model, base_model, criterion, device, logger):
|
||||
with torch.no_grad():
|
||||
base_model.eval()
|
||||
meta_model.eval()
|
||||
loss_meter = AverageMeter()
|
||||
for ibatch, batch_data in enumerate(loader):
|
||||
timestamps, (batch_seq_inputs, batch_seq_targets) = batch_data
|
||||
timestamps = timestamps.squeeze(dim=-1).to(device)
|
||||
batch_seq_inputs = batch_seq_inputs.to(device)
|
||||
batch_seq_targets = batch_seq_targets.to(device)
|
||||
|
||||
batch_seq_containers = meta_model(timestamps)
|
||||
losses = []
|
||||
for seq_containers, seq_inputs, seq_targets in zip(
|
||||
batch_seq_containers, batch_seq_inputs, batch_seq_targets
|
||||
):
|
||||
for container, inputs, targets in zip(
|
||||
seq_containers, seq_inputs, seq_targets
|
||||
):
|
||||
predictions = base_model.forward_with_container(inputs, container)
|
||||
loss = criterion(predictions, targets)
|
||||
losses.append(loss)
|
||||
final_loss = torch.stack(losses).mean()
|
||||
loss_meter.update(final_loss.item())
|
||||
return loss_meter
|
||||
|
||||
|
||||
def pretrain(base_model, meta_model, criterion, xenv, args, logger):
|
||||
base_model.train()
|
||||
meta_model.train()
|
||||
|
||||
optimizer = torch.optim.Adam(
|
||||
meta_model.parameters(),
|
||||
lr=args.lr,
|
||||
weight_decay=args.weight_decay,
|
||||
amsgrad=True,
|
||||
)
|
||||
logger.log("Pre-train the meta-model")
|
||||
logger.log("Using the optimizer: {:}".format(optimizer))
|
||||
|
||||
meta_model.set_best_dir(logger.path(None) / "ckps-basic-pretrain")
|
||||
rand_index = random.randint(0, meta_model.meta_length - xenv.seq_length - 1)
|
||||
for iepoch in range(args.epochs):
|
||||
left_time = "Time Left: {:}".format(
|
||||
convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
|
||||
)
|
||||
losses = []
|
||||
for ibatch in range(args.meta_batch):
|
||||
timestamps = meta_model.meta_timestamps[
|
||||
rand_index : rand_index + xenv.seq_length
|
||||
]
|
||||
seq_timestamps, (seq_inputs, seq_targets) = xenv.seq_call(timestamps)
|
||||
time_embeds = meta_model.super_meta_embed[
|
||||
rand_index : rand_index + xenv.seq_length
|
||||
]
|
||||
[seq_containers], time_embeds = meta_model(
|
||||
None, torch.unsqueeze(time_embeds, dim=0)
|
||||
)
|
||||
seq_inputs, seq_targets = seq_inputs.to(args.device), seq_targets.to(
|
||||
args.device
|
||||
)
|
||||
for container, inputs, targets in zip(
|
||||
seq_containers, seq_inputs, seq_targets
|
||||
):
|
||||
predictions = base_model.forward_with_container(inputs, container)
|
||||
loss = criterion(predictions, targets)
|
||||
losses.append(loss)
|
||||
final_loss = torch.stack(losses).mean()
|
||||
final_loss.backward()
|
||||
optimizer.step()
|
||||
# success
|
||||
success, best_score = meta_model.save_best(-final_loss.item())
|
||||
logger.log(
|
||||
"{:} [{:04d}/{:}] loss : {:.5f}".format(
|
||||
time_string(),
|
||||
iepoch,
|
||||
args.epochs,
|
||||
final_loss.item(),
|
||||
)
|
||||
+ ", batch={:}".format(len(losses))
|
||||
+ ", success={:}, best_score={:.4f}".format(success, -best_score)
|
||||
+ " {:}".format(left_time)
|
||||
)
|
||||
per_epoch_time.update(time.time() - start_time)
|
||||
start_time = time.time()
|
||||
|
||||
|
||||
def main(args):
|
||||
logger, env_info, model_kwargs = lfna_setup(args)
|
||||
train_env = get_synthetic_env(mode="train", version=args.env_version)
|
||||
valid_env = get_synthetic_env(mode="valid", version=args.env_version)
|
||||
logger.log("training enviornment: {:}".format(train_env))
|
||||
logger.log("validation enviornment: {:}".format(valid_env))
|
||||
|
||||
base_model = get_model(**model_kwargs)
|
||||
base_model = base_model.to(args.device)
|
||||
criterion = torch.nn.MSELoss()
|
||||
|
||||
shape_container = base_model.get_w_container().to_shape_container()
|
||||
|
||||
# pre-train the hypernetwork
|
||||
timestamps = train_env.get_timestamp(None)
|
||||
meta_model = LFNA_Meta(shape_container, args.layer_dim, args.time_dim, timestamps)
|
||||
meta_model = meta_model.to(args.device)
|
||||
|
||||
logger.log("The base-model has {:} weights.".format(base_model.numel()))
|
||||
logger.log("The meta-model has {:} weights.".format(meta_model.numel()))
|
||||
|
||||
batch_sampler = EnvSampler(train_env, args.meta_batch, args.sampler_enlarge)
|
||||
train_env.reset_max_seq_length(args.seq_length)
|
||||
valid_env.reset_max_seq_length(args.seq_length)
|
||||
valid_env_loader = torch.utils.data.DataLoader(
|
||||
valid_env,
|
||||
batch_size=args.meta_batch,
|
||||
shuffle=True,
|
||||
num_workers=args.workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
train_env_loader = torch.utils.data.DataLoader(
|
||||
train_env,
|
||||
batch_sampler=batch_sampler,
|
||||
num_workers=args.workers,
|
||||
pin_memory=True,
|
||||
)
|
||||
|
||||
optimizer = torch.optim.Adam(
|
||||
meta_model.parameters(),
|
||||
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 base-model is\n{:}".format(base_model))
|
||||
logger.log("The meta-model is\n{:}".format(meta_model))
|
||||
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)))
|
||||
|
||||
pretrain(base_model, meta_model, criterion, train_env, args, logger)
|
||||
|
||||
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"]
|
||||
w_container_per_epoch = dict()
|
||||
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.timestamp_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.timestamp_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()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(".")
|
||||
parser.add_argument(
|
||||
"--save_dir",
|
||||
type=str,
|
||||
default="./outputs/lfna-synthetic/lfna-battle",
|
||||
help="The checkpoint directory.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--env_version",
|
||||
type=str,
|
||||
required=True,
|
||||
help="The synthetic enviornment version.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--hidden_dim",
|
||||
type=int,
|
||||
default=16,
|
||||
help="The hidden dimension.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--layer_dim",
|
||||
type=int,
|
||||
default=16,
|
||||
help="The layer chunk dimension.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--time_dim",
|
||||
type=int,
|
||||
default=16,
|
||||
help="The timestamp dimension.",
|
||||
)
|
||||
#####
|
||||
parser.add_argument(
|
||||
"--lr",
|
||||
type=float,
|
||||
default=0.002,
|
||||
help="The initial learning rate for the optimizer (default is Adam)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--weight_decay",
|
||||
type=float,
|
||||
default=0.00001,
|
||||
help="The weight decay for the optimizer (default is Adam)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--meta_batch",
|
||||
type=int,
|
||||
default=64,
|
||||
help="The batch size for the meta-model",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--sampler_enlarge",
|
||||
type=int,
|
||||
default=5,
|
||||
help="Enlarge the #iterations for an epoch",
|
||||
)
|
||||
parser.add_argument("--epochs", type=int, default=10000, help="The total #epochs.")
|
||||
parser.add_argument(
|
||||
"--refine_lr",
|
||||
type=float,
|
||||
default=0.005,
|
||||
help="The learning rate for the optimizer, during refine",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--refine_epochs", type=int, default=1000, help="The final refine #epochs."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--early_stop_thresh",
|
||||
type=int,
|
||||
default=20,
|
||||
help="The #epochs for early stop.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--seq_length", type=int, default=10, help="The sequence length."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--workers", type=int, default=4, help="The number of workers in parallel."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default="cpu",
|
||||
help="",
|
||||
)
|
||||
# Random Seed
|
||||
parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
|
||||
args = parser.parse_args()
|
||||
if args.rand_seed is None or args.rand_seed < 0:
|
||||
args.rand_seed = random.randint(1, 100000)
|
||||
assert args.save_dir is not None, "The save dir argument can not be None"
|
||||
args.save_dir = "{:}-d{:}_{:}_{:}-s{:}-lr{:}-wd{:}-e{:}-env{:}".format(
|
||||
args.save_dir,
|
||||
args.hidden_dim,
|
||||
args.layer_dim,
|
||||
args.time_dim,
|
||||
args.seq_length,
|
||||
args.lr,
|
||||
args.weight_decay,
|
||||
args.epochs,
|
||||
args.env_version,
|
||||
)
|
||||
main(args)
|
@ -93,7 +93,7 @@ def epoch_evaluate(loader, meta_model, base_model, criterion, device, logger):
|
||||
return loss_meter
|
||||
|
||||
|
||||
def pretrain(base_model, meta_model, criterion, xenv, args, logger):
|
||||
def pretrain_v2(base_model, meta_model, criterion, xenv, args, logger):
|
||||
optimizer = torch.optim.Adam(
|
||||
meta_model.parameters(),
|
||||
lr=args.lr,
|
||||
@ -164,6 +164,69 @@ def pretrain(base_model, meta_model, criterion, xenv, args, logger):
|
||||
start_time = time.time()
|
||||
|
||||
|
||||
def pretrain(base_model, meta_model, criterion, xenv, args, logger):
|
||||
base_model.train()
|
||||
meta_model.train()
|
||||
optimizer = torch.optim.Adam(
|
||||
meta_model.parameters(),
|
||||
lr=args.lr,
|
||||
weight_decay=args.weight_decay,
|
||||
amsgrad=True,
|
||||
)
|
||||
logger.log("Pre-train the meta-model")
|
||||
logger.log("Using the optimizer: {:}".format(optimizer))
|
||||
|
||||
meta_model.set_best_dir(logger.path(None) / "ckps-basic-pretrain")
|
||||
meta_model.set_best_name("pretrain-{:}.pth".format(args.rand_seed))
|
||||
per_epoch_time, start_time = AverageMeter(), time.time()
|
||||
for iepoch in range(args.epochs):
|
||||
left_time = "Time Left: {:}".format(
|
||||
convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
|
||||
)
|
||||
losses = []
|
||||
optimizer.zero_grad()
|
||||
for ibatch in range(args.meta_batch):
|
||||
rand_index = random.randint(0, meta_model.meta_length - xenv.seq_length - 1)
|
||||
timestamps = meta_model.meta_timestamps[
|
||||
rand_index : rand_index + xenv.seq_length
|
||||
]
|
||||
|
||||
seq_timestamps, (seq_inputs, seq_targets) = xenv.seq_call(timestamps)
|
||||
time_embeds = meta_model.super_meta_embed[
|
||||
rand_index : rand_index + xenv.seq_length
|
||||
]
|
||||
[seq_containers], time_embeds = meta_model(
|
||||
None, torch.unsqueeze(time_embeds, dim=0)
|
||||
)
|
||||
seq_inputs, seq_targets = seq_inputs.to(args.device), seq_targets.to(
|
||||
args.device
|
||||
)
|
||||
for container, inputs, targets in zip(
|
||||
seq_containers, seq_inputs, seq_targets
|
||||
):
|
||||
predictions = base_model.forward_with_container(inputs, container)
|
||||
loss = criterion(predictions, targets)
|
||||
losses.append(loss)
|
||||
final_loss = torch.stack(losses).mean()
|
||||
final_loss.backward()
|
||||
optimizer.step()
|
||||
# success
|
||||
success, best_score = meta_model.save_best(-final_loss.item())
|
||||
logger.log(
|
||||
"{:} [{:04d}/{:}] loss : {:.5f}".format(
|
||||
time_string(),
|
||||
iepoch,
|
||||
args.epochs,
|
||||
final_loss.item(),
|
||||
)
|
||||
+ ", batch={:}".format(len(losses))
|
||||
+ ", success={:}, best_score={:.4f}".format(success, -best_score)
|
||||
+ " {:}".format(left_time)
|
||||
)
|
||||
per_epoch_time.update(time.time() - start_time)
|
||||
start_time = time.time()
|
||||
|
||||
|
||||
def main(args):
|
||||
logger, env_info, model_kwargs = lfna_setup(args)
|
||||
train_env = get_synthetic_env(mode="train", version=args.env_version)
|
||||
|
@ -85,7 +85,6 @@ class LFNA_Meta(super_core.SuperModule):
|
||||
dropout=dropout,
|
||||
)
|
||||
self._generator = get_model(**model_kwargs)
|
||||
# print("generator: {:}".format(self._generator))
|
||||
|
||||
# initialization
|
||||
trunc_normal_(
|
||||
@ -163,9 +162,12 @@ class LFNA_Meta(super_core.SuperModule):
|
||||
corrected_embeds = self.meta_corrector(init_timestamp_embeds)
|
||||
return corrected_embeds
|
||||
|
||||
def forward_raw(self, timestamps):
|
||||
batch, seq = timestamps.shape
|
||||
time_embed = self._obtain_time_embed(timestamps)
|
||||
def forward_raw(self, timestamps, time_embed):
|
||||
if time_embed is None:
|
||||
batch, seq = timestamps.shape
|
||||
time_embed = self._obtain_time_embed(timestamps)
|
||||
else:
|
||||
batch, seq, _ = time_embed.shape
|
||||
# create joint embed
|
||||
num_layer, _ = self._super_layer_embed.shape
|
||||
meta_embed = time_embed.view(batch, seq, 1, -1).expand(-1, -1, num_layer, -1)
|
||||
|
@ -19,6 +19,7 @@ from .super_utils import TensorContainer
|
||||
from .super_utils import ShapeContainer
|
||||
|
||||
BEST_DIR_KEY = "best_model_dir"
|
||||
BEST_NAME_KEY = "best_model_name"
|
||||
BEST_SCORE_KEY = "best_model_score"
|
||||
|
||||
|
||||
@ -94,6 +95,9 @@ class SuperModule(abc.ABC, nn.Module):
|
||||
self._meta_info[BEST_DIR_KEY] = str(xdir)
|
||||
Path(xdir).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
def set_best_name(self, xname):
|
||||
self._meta_info[BEST_NAME_KEY] = str(xname)
|
||||
|
||||
def save_best(self, score):
|
||||
if BEST_DIR_KEY not in self._meta_info:
|
||||
tempdir = tempfile.mkdtemp("-xlayers")
|
||||
@ -102,10 +106,11 @@ class SuperModule(abc.ABC, nn.Module):
|
||||
self._meta_info[BEST_SCORE_KEY] = None
|
||||
best_score = self._meta_info[BEST_SCORE_KEY]
|
||||
if best_score is None or best_score <= score:
|
||||
best_save_path = os.path.join(
|
||||
self._meta_info[BEST_DIR_KEY],
|
||||
"best-{:}.pth".format(self.__class__.__name__),
|
||||
best_save_name = self._meta_info.get(
|
||||
BEST_NAME_KEY, "best-{:}.pth".format(self.__class__.__name__)
|
||||
)
|
||||
|
||||
best_save_path = os.path.join(self._meta_info[BEST_DIR_KEY], best_save_name)
|
||||
self._meta_info[BEST_SCORE_KEY] = score
|
||||
torch.save(self.state_dict(), best_save_path)
|
||||
return True, self._meta_info[BEST_SCORE_KEY]
|
||||
|
Loading…
Reference in New Issue
Block a user