Update LFNA ablation codes
This commit is contained in:
D-X-Y
parent
4da19d6efe
commit
d51e5fdc7f
@ -1,280 +0,0 @@
|
||||
#####################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
|
||||
#####################################################
|
||||
# python exps/LFNA/lfna-debug.py --env_version v1 --hidden_dim 16
|
||||
#####################################################
|
||||
import sys, time, copy, torch, random, argparse
|
||||
from tqdm import tqdm
|
||||
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 procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
|
||||
from log_utils import time_string
|
||||
from log_utils import AverageMeter, convert_secs2time
|
||||
|
||||
from utils import split_str2indexes
|
||||
|
||||
from procedures.advanced_main import basic_train_fn, basic_eval_fn
|
||||
from procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
|
||||
from datasets.synthetic_core import get_synthetic_env
|
||||
from models.xcore import get_model
|
||||
from xlayers import super_core
|
||||
|
||||
|
||||
from lfna_utils import lfna_setup, train_model, TimeData
|
||||
from lfna_models import HyperNet
|
||||
|
||||
|
||||
class LFNAmlp:
|
||||
"""A LFNA meta-model that uses the MLP as delta-net."""
|
||||
|
||||
def __init__(self, obs_dim, hidden_sizes, act_name, criterion):
|
||||
self.delta_net = super_core.SuperSequential(
|
||||
super_core.SuperLinear(obs_dim, hidden_sizes[0]),
|
||||
super_core.super_name2activation[act_name](),
|
||||
super_core.SuperLinear(hidden_sizes[0], hidden_sizes[1]),
|
||||
super_core.super_name2activation[act_name](),
|
||||
super_core.SuperLinear(hidden_sizes[1], 1),
|
||||
)
|
||||
self.meta_optimizer = torch.optim.Adam(
|
||||
self.delta_net.parameters(), lr=0.01, amsgrad=True
|
||||
)
|
||||
self.criterion = criterion
|
||||
|
||||
def adapt(self, model, seq_flatten_w):
|
||||
delta_inputs = torch.stack(seq_flatten_w, dim=-1)
|
||||
delta = self.delta_net(delta_inputs)
|
||||
container = model.get_w_container()
|
||||
unflatten_delta = container.unflatten(delta)
|
||||
future_container = container.create_container(unflatten_delta)
|
||||
return future_container
|
||||
|
||||
def step(self):
|
||||
torch.nn.utils.clip_grad_norm_(self.delta_net.parameters(), 1.0)
|
||||
self.meta_optimizer.step()
|
||||
|
||||
def zero_grad(self):
|
||||
self.meta_optimizer.zero_grad()
|
||||
self.delta_net.zero_grad()
|
||||
|
||||
def state_dict(self):
|
||||
return dict(
|
||||
delta_net=self.delta_net.state_dict(),
|
||||
meta_optimizer=self.meta_optimizer.state_dict(),
|
||||
)
|
||||
|
||||
|
||||
def main(args):
|
||||
logger, env_info, model_kwargs = lfna_setup(args)
|
||||
dynamic_env = env_info["dynamic_env"]
|
||||
model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
|
||||
|
||||
total_time = env_info["total"]
|
||||
for i in range(total_time):
|
||||
for xkey in ("timestamp", "x", "y"):
|
||||
nkey = "{:}-{:}".format(i, xkey)
|
||||
assert nkey in env_info, "{:} no in {:}".format(nkey, list(env_info.keys()))
|
||||
train_time_bar = total_time // 2
|
||||
|
||||
criterion = torch.nn.MSELoss()
|
||||
logger.log("There are {:} weights.".format(model.get_w_container().numel()))
|
||||
|
||||
adaptor = LFNAmlp(args.meta_seq, (200, 200), "leaky_relu", criterion)
|
||||
|
||||
# pre-train the model
|
||||
dataset = init_dataset = TimeData(0, env_info["0-x"], env_info["0-y"])
|
||||
|
||||
shape_container = model.get_w_container().to_shape_container()
|
||||
hypernet = HyperNet(shape_container, 16)
|
||||
|
||||
optimizer = torch.optim.Adam(hypernet.parameters(), lr=args.init_lr, amsgrad=True)
|
||||
|
||||
best_loss, best_param = None, None
|
||||
for _iepoch in range(args.epochs):
|
||||
container = hypernet(None)
|
||||
|
||||
preds = model.forward_with_container(dataset.x, container)
|
||||
optimizer.zero_grad()
|
||||
loss = criterion(preds, dataset.y)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
# save best
|
||||
if best_loss is None or best_loss > loss.item():
|
||||
best_loss = loss.item()
|
||||
best_param = copy.deepcopy(model.state_dict())
|
||||
print("hyper-net : best={:.4f}".format(best_loss))
|
||||
|
||||
init_loss = train_model(model, init_dataset, args.init_lr, args.epochs)
|
||||
logger.log("The pre-training loss is {:.4f}".format(init_loss))
|
||||
import pdb
|
||||
|
||||
pdb.set_trace()
|
||||
|
||||
all_past_containers = []
|
||||
ground_truth_path = (
|
||||
logger.path(None) / ".." / "use-same-timestamp-v1-d16" / "final-ckp.pth"
|
||||
)
|
||||
ground_truth_data = torch.load(ground_truth_path)
|
||||
all_gt_containers = ground_truth_data["w_container_per_epoch"]
|
||||
all_gt_flattens = dict()
|
||||
for idx, container in all_gt_containers.items():
|
||||
all_gt_flattens[idx] = container.no_grad_clone().flatten()
|
||||
|
||||
# LFNA meta-training
|
||||
meta_loss_meter = AverageMeter()
|
||||
per_epoch_time, start_time = AverageMeter(), time.time()
|
||||
for iepoch in range(args.epochs):
|
||||
|
||||
need_time = "Time Left: {:}".format(
|
||||
convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
|
||||
)
|
||||
logger.log(
|
||||
"[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
|
||||
+ need_time
|
||||
)
|
||||
|
||||
adaptor.zero_grad()
|
||||
|
||||
meta_losses = []
|
||||
for ibatch in range(args.meta_batch):
|
||||
future_timestamp = random.randint(args.meta_seq, train_time_bar)
|
||||
future_dataset = TimeData(
|
||||
future_timestamp,
|
||||
env_info["{:}-x".format(future_timestamp)],
|
||||
env_info["{:}-y".format(future_timestamp)],
|
||||
)
|
||||
seq_datasets = []
|
||||
for iseq in range(args.meta_seq):
|
||||
cur_time = future_timestamp - iseq - 1
|
||||
cur_x = env_info["{:}-x".format(cur_time)]
|
||||
cur_y = env_info["{:}-y".format(cur_time)]
|
||||
seq_datasets.append(TimeData(cur_time, cur_x, cur_y))
|
||||
seq_datasets.reverse()
|
||||
seq_flatten_w = [
|
||||
all_gt_flattens[dataset.timestamp] for dataset in seq_datasets
|
||||
]
|
||||
future_container = adaptor.adapt(network, seq_flatten_w)
|
||||
"""
|
||||
future_y_hat = network.forward_with_container(
|
||||
future_dataset.x, future_container
|
||||
)
|
||||
future_loss = adaptor.criterion(future_y_hat, future_dataset.y)
|
||||
"""
|
||||
future_loss = adaptor.criterion(
|
||||
future_container.flatten(), all_gt_flattens[future_timestamp]
|
||||
)
|
||||
# import pdb; pdb.set_trace()
|
||||
meta_losses.append(future_loss)
|
||||
meta_loss = torch.stack(meta_losses).mean()
|
||||
meta_loss.backward()
|
||||
adaptor.step()
|
||||
|
||||
meta_loss_meter.update(meta_loss.item())
|
||||
|
||||
logger.log(
|
||||
"meta-loss: {:.4f} ({:.4f}) ".format(
|
||||
meta_loss_meter.avg, meta_loss_meter.val
|
||||
)
|
||||
)
|
||||
if iepoch % 200 == 0:
|
||||
save_checkpoint(
|
||||
{"adaptor": adaptor.state_dict(), "iepoch": iepoch},
|
||||
logger.path("model"),
|
||||
logger,
|
||||
)
|
||||
per_epoch_time.update(time.time() - start_time)
|
||||
start_time = time.time()
|
||||
|
||||
w_container_per_epoch = dict()
|
||||
# import pdb; pdb.set_trace()
|
||||
for idx in range(1, env_info["total"]):
|
||||
future_time = env_info["{:}-timestamp".format(idx)]
|
||||
future_x = env_info["{:}-x".format(idx)]
|
||||
future_y = env_info["{:}-y".format(idx)]
|
||||
seq_datasets = []
|
||||
for iseq in range(1, args.meta_seq + 1):
|
||||
cur_time = future_timestamp - iseq - 1
|
||||
if cur_time < 0:
|
||||
cur_time = 0
|
||||
cur_x = env_info["{:}-x".format(cur_time)]
|
||||
cur_y = env_info["{:}-y".format(cur_time)]
|
||||
seq_datasets.append(TimeData(cur_time, cur_x, cur_y))
|
||||
seq_datasets.reverse()
|
||||
seq_flatten_w = [all_gt_flattens[dataset.timestamp] for dataset in seq_datasets]
|
||||
future_container = adaptor.adapt(network, seq_flatten_w)
|
||||
w_container_per_epoch[idx] = future_container.no_grad_clone()
|
||||
with torch.no_grad():
|
||||
future_y_hat = network.forward_with_container(
|
||||
future_x, w_container_per_epoch[idx]
|
||||
)
|
||||
future_loss = adaptor.criterion(future_y_hat, future_y)
|
||||
logger.log("meta-test: [{:03d}] -> loss={:.4f}".format(idx, future_loss.item()))
|
||||
|
||||
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("Use the data in the past.")
|
||||
parser.add_argument(
|
||||
"--save_dir",
|
||||
type=str,
|
||||
default="./outputs/lfna-synthetic/lfna-debug",
|
||||
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,
|
||||
required=True,
|
||||
help="The hidden dimension.",
|
||||
)
|
||||
#####
|
||||
parser.add_argument(
|
||||
"--init_lr",
|
||||
type=float,
|
||||
default=0.1,
|
||||
help="The initial learning rate for the optimizer (default is Adam)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--meta_batch",
|
||||
type=int,
|
||||
default=32,
|
||||
help="The batch size for the meta-model",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--meta_seq",
|
||||
type=int,
|
||||
default=10,
|
||||
help="The length of the sequence for meta-model.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--epochs",
|
||||
type=int,
|
||||
default=2000,
|
||||
help="The total number of epochs.",
|
||||
)
|
||||
# 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{:}".format(
|
||||
args.save_dir, args.env_version, args.hidden_dim
|
||||
)
|
||||
main(args)
|
||||
176
exps/LFNA/lfna-test-hpnet.py
Normal file
176
exps/LFNA/lfna-test-hpnet.py
Normal file
@ -0,0 +1,176 @@
|
||||
#####################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
|
||||
#####################################################
|
||||
# python exps/LFNA/lfna-test-hpnet.py --env_version v1 --hidden_dim 16
|
||||
#####################################################
|
||||
import sys, time, copy, torch, random, argparse
|
||||
from tqdm import tqdm
|
||||
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 procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
|
||||
from log_utils import time_string
|
||||
from log_utils import AverageMeter, convert_secs2time
|
||||
|
||||
from utils import split_str2indexes
|
||||
|
||||
from procedures.advanced_main import basic_train_fn, basic_eval_fn
|
||||
from procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
|
||||
from datasets.synthetic_core import get_synthetic_env
|
||||
from models.xcore import get_model
|
||||
from xlayers import super_core, trunc_normal_
|
||||
|
||||
|
||||
from lfna_utils import lfna_setup, train_model, TimeData
|
||||
|
||||
# from lfna_models import HyperNet_VX as HyperNet
|
||||
from lfna_models import HyperNet
|
||||
|
||||
|
||||
def main(args):
|
||||
logger, env_info, model_kwargs = lfna_setup(args)
|
||||
dynamic_env = env_info["dynamic_env"]
|
||||
model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
|
||||
criterion = torch.nn.MSELoss()
|
||||
|
||||
logger.log("There are {:} weights.".format(model.get_w_container().numel()))
|
||||
|
||||
shape_container = model.get_w_container().to_shape_container()
|
||||
hypernet = HyperNet(shape_container, args.hidden_dim, args.task_dim)
|
||||
# task_embed = torch.nn.Parameter(torch.Tensor(env_info["total"], args.task_dim))
|
||||
task_embed = torch.nn.Parameter(torch.Tensor(1, args.task_dim))
|
||||
trunc_normal_(task_embed, std=0.02)
|
||||
|
||||
parameters = list(hypernet.parameters()) + [task_embed]
|
||||
optimizer = torch.optim.Adam(parameters, lr=args.init_lr, amsgrad=True)
|
||||
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
|
||||
optimizer,
|
||||
milestones=[
|
||||
int(args.epochs * 0.8),
|
||||
int(args.epochs * 0.9),
|
||||
],
|
||||
gamma=0.1,
|
||||
)
|
||||
|
||||
# total_bar = env_info["total"] - 1
|
||||
total_bar = 1
|
||||
# LFNA meta-training
|
||||
loss_meter = AverageMeter()
|
||||
per_epoch_time, start_time = AverageMeter(), time.time()
|
||||
for iepoch in range(args.epochs):
|
||||
|
||||
need_time = "Time Left: {:}".format(
|
||||
convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
|
||||
)
|
||||
head_str = (
|
||||
"[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
|
||||
+ need_time
|
||||
)
|
||||
|
||||
losses = []
|
||||
# for ibatch in range(args.meta_batch):
|
||||
for cur_time in range(total_bar):
|
||||
# cur_time = random.randint(0, total_bar)
|
||||
cur_task_embed = task_embed
|
||||
cur_container = hypernet(cur_task_embed)
|
||||
cur_x = env_info["{:}-x".format(cur_time)]
|
||||
cur_y = env_info["{:}-y".format(cur_time)]
|
||||
cur_dataset = TimeData(cur_time, cur_x, cur_y)
|
||||
|
||||
preds = model.forward_with_container(cur_dataset.x, cur_container)
|
||||
optimizer.zero_grad()
|
||||
loss = criterion(preds, cur_dataset.y)
|
||||
|
||||
losses.append(loss)
|
||||
|
||||
final_loss = torch.stack(losses).mean()
|
||||
final_loss.backward()
|
||||
optimizer.step()
|
||||
lr_scheduler.step()
|
||||
|
||||
loss_meter.update(final_loss.item())
|
||||
if iepoch % 200 == 0:
|
||||
logger.log(
|
||||
head_str
|
||||
+ "meta-loss: {:.4f} ({:.4f}) :: lr={:.5f}, batch={:}".format(
|
||||
loss_meter.avg,
|
||||
loss_meter.val,
|
||||
min(lr_scheduler.get_lr()),
|
||||
len(losses),
|
||||
)
|
||||
)
|
||||
|
||||
save_checkpoint(
|
||||
{
|
||||
"hypernet": hypernet.state_dict(),
|
||||
"task_embed": task_embed,
|
||||
"lr_scheduler": lr_scheduler.state_dict(),
|
||||
"iepoch": iepoch,
|
||||
},
|
||||
logger.path("model"),
|
||||
logger,
|
||||
)
|
||||
loss_meter.reset()
|
||||
per_epoch_time.update(time.time() - start_time)
|
||||
start_time = time.time()
|
||||
|
||||
print(model)
|
||||
print(hypernet)
|
||||
|
||||
logger.log("-" * 200 + "\n")
|
||||
logger.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser("Use the data in the past.")
|
||||
parser.add_argument(
|
||||
"--save_dir",
|
||||
type=str,
|
||||
default="./outputs/lfna-synthetic/lfna-test-hpnet",
|
||||
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,
|
||||
required=True,
|
||||
help="The hidden dimension.",
|
||||
)
|
||||
#####
|
||||
parser.add_argument(
|
||||
"--init_lr",
|
||||
type=float,
|
||||
default=0.1,
|
||||
help="The initial learning rate 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(
|
||||
"--epochs",
|
||||
type=int,
|
||||
default=2000,
|
||||
help="The total number of epochs.",
|
||||
)
|
||||
# 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.task_dim = args.hidden_dim
|
||||
args.save_dir = "{:}-{:}-d{:}".format(
|
||||
args.save_dir, args.env_version, args.hidden_dim
|
||||
)
|
||||
main(args)
|
||||
134
exps/LFNA/lfna-ttss-hpnet.py
Normal file
134
exps/LFNA/lfna-ttss-hpnet.py
Normal file
@ -0,0 +1,134 @@
|
||||
#####################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
|
||||
#####################################################
|
||||
# python exps/LFNA/lfna-ttss-hpnet.py --env_version v1 --hidden_dim 16
|
||||
#####################################################
|
||||
import sys, time, copy, torch, random, argparse
|
||||
from tqdm import tqdm
|
||||
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 procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
|
||||
from log_utils import time_string
|
||||
from log_utils import AverageMeter, convert_secs2time
|
||||
|
||||
from utils import split_str2indexes
|
||||
|
||||
from procedures.advanced_main import basic_train_fn, basic_eval_fn
|
||||
from procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
|
||||
from datasets.synthetic_core import get_synthetic_env
|
||||
from models.xcore import get_model
|
||||
from xlayers import super_core
|
||||
|
||||
|
||||
from lfna_utils import lfna_setup, train_model, TimeData
|
||||
from lfna_models import HyperNet_VX as HyperNet
|
||||
|
||||
|
||||
def main(args):
|
||||
logger, env_info, model_kwargs = lfna_setup(args)
|
||||
dynamic_env = env_info["dynamic_env"]
|
||||
model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
|
||||
|
||||
total_time = env_info["total"]
|
||||
for i in range(total_time):
|
||||
for xkey in ("timestamp", "x", "y"):
|
||||
nkey = "{:}-{:}".format(i, xkey)
|
||||
assert nkey in env_info, "{:} no in {:}".format(nkey, list(env_info.keys()))
|
||||
train_time_bar = total_time // 2
|
||||
|
||||
criterion = torch.nn.MSELoss()
|
||||
logger.log("There are {:} weights.".format(model.get_w_container().numel()))
|
||||
|
||||
# pre-train the model
|
||||
dataset = init_dataset = TimeData(0, env_info["0-x"], env_info["0-y"])
|
||||
|
||||
shape_container = model.get_w_container().to_shape_container()
|
||||
hypernet = HyperNet(shape_container, 16)
|
||||
print(hypernet)
|
||||
|
||||
optimizer = torch.optim.Adam(hypernet.parameters(), lr=args.init_lr, amsgrad=True)
|
||||
|
||||
best_loss, best_param = None, None
|
||||
for _iepoch in range(args.epochs):
|
||||
container = hypernet(None)
|
||||
|
||||
preds = model.forward_with_container(dataset.x, container)
|
||||
optimizer.zero_grad()
|
||||
loss = criterion(preds, dataset.y)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
# save best
|
||||
if best_loss is None or best_loss > loss.item():
|
||||
best_loss = loss.item()
|
||||
best_param = copy.deepcopy(model.state_dict())
|
||||
print("hyper-net : best={:.4f}".format(best_loss))
|
||||
|
||||
init_loss = train_model(model, init_dataset, args.init_lr, args.epochs)
|
||||
logger.log("The pre-training loss is {:.4f}".format(init_loss))
|
||||
|
||||
print(model)
|
||||
print(hypernet)
|
||||
|
||||
logger.log("-" * 200 + "\n")
|
||||
logger.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser("Use the data in the past.")
|
||||
parser.add_argument(
|
||||
"--save_dir",
|
||||
type=str,
|
||||
default="./outputs/lfna-synthetic/lfna-debug",
|
||||
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,
|
||||
required=True,
|
||||
help="The hidden dimension.",
|
||||
)
|
||||
#####
|
||||
parser.add_argument(
|
||||
"--init_lr",
|
||||
type=float,
|
||||
default=0.1,
|
||||
help="The initial learning rate for the optimizer (default is Adam)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--meta_batch",
|
||||
type=int,
|
||||
default=32,
|
||||
help="The batch size for the meta-model",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--meta_seq",
|
||||
type=int,
|
||||
default=10,
|
||||
help="The length of the sequence for meta-model.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--epochs",
|
||||
type=int,
|
||||
default=2000,
|
||||
help="The total number of epochs.",
|
||||
)
|
||||
# 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{:}".format(
|
||||
args.save_dir, args.env_version, args.hidden_dim
|
||||
)
|
||||
main(args)
|
||||
97
exps/LFNA/lfna_models.py
Normal file
97
exps/LFNA/lfna_models.py
Normal file
@ -0,0 +1,97 @@
|
||||
#####################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
|
||||
#####################################################
|
||||
import copy
|
||||
import torch
|
||||
|
||||
from xlayers import super_core
|
||||
from xlayers import trunc_normal_
|
||||
from models.xcore import get_model
|
||||
|
||||
|
||||
class HyperNet(super_core.SuperModule):
|
||||
"""The hyper-network."""
|
||||
|
||||
def __init__(
|
||||
self, shape_container, layer_embeding, task_embedding, return_container=True
|
||||
):
|
||||
super(HyperNet, self).__init__()
|
||||
self._shape_container = shape_container
|
||||
self._num_layers = len(shape_container)
|
||||
self._numel_per_layer = []
|
||||
for ilayer in range(self._num_layers):
|
||||
self._numel_per_layer.append(shape_container[ilayer].numel())
|
||||
|
||||
self.register_parameter(
|
||||
"_super_layer_embed",
|
||||
torch.nn.Parameter(torch.Tensor(self._num_layers, layer_embeding)),
|
||||
)
|
||||
trunc_normal_(self._super_layer_embed, std=0.02)
|
||||
|
||||
model_kwargs = dict(
|
||||
input_dim=layer_embeding + task_embedding,
|
||||
output_dim=max(self._numel_per_layer),
|
||||
hidden_dim=layer_embeding * 4,
|
||||
act_cls="sigmoid",
|
||||
norm_cls="identity",
|
||||
)
|
||||
self._generator = get_model(dict(model_type="simple_mlp"), **model_kwargs)
|
||||
self._return_container = return_container
|
||||
print("generator: {:}".format(self._generator))
|
||||
|
||||
def forward_raw(self, task_embed):
|
||||
task_embed = task_embed.view(1, -1).expand(self._num_layers, -1)
|
||||
joint_embed = torch.cat((task_embed, self._super_layer_embed), dim=-1)
|
||||
weights = self._generator(joint_embed)
|
||||
if self._return_container:
|
||||
weights = torch.split(weights, 1)
|
||||
return self._shape_container.translate(weights)
|
||||
else:
|
||||
return weights
|
||||
|
||||
def forward_candidate(self, input):
|
||||
raise NotImplementedError
|
||||
|
||||
def extra_repr(self) -> str:
|
||||
return "(_super_layer_embed): {:}".format(list(self._super_layer_embed.shape))
|
||||
|
||||
|
||||
class HyperNet_VX(super_core.SuperModule):
|
||||
def __init__(self, shape_container, input_embeding, return_container=True):
|
||||
super(HyperNet_VX, self).__init__()
|
||||
self._shape_container = shape_container
|
||||
self._num_layers = len(shape_container)
|
||||
self._numel_per_layer = []
|
||||
for ilayer in range(self._num_layers):
|
||||
self._numel_per_layer.append(shape_container[ilayer].numel())
|
||||
|
||||
self.register_parameter(
|
||||
"_super_layer_embed",
|
||||
torch.nn.Parameter(torch.Tensor(self._num_layers, input_embeding)),
|
||||
)
|
||||
trunc_normal_(self._super_layer_embed, std=0.02)
|
||||
|
||||
model_kwargs = dict(
|
||||
input_dim=input_embeding,
|
||||
output_dim=max(self._numel_per_layer),
|
||||
hidden_dim=input_embeding * 4,
|
||||
act_cls="sigmoid",
|
||||
norm_cls="identity",
|
||||
)
|
||||
self._generator = get_model(dict(model_type="simple_mlp"), **model_kwargs)
|
||||
self._return_container = return_container
|
||||
print("generator: {:}".format(self._generator))
|
||||
|
||||
def forward_raw(self, input):
|
||||
weights = self._generator(self._super_layer_embed)
|
||||
if self._return_container:
|
||||
weights = torch.split(weights, 1)
|
||||
return self._shape_container.translate(weights)
|
||||
else:
|
||||
return weights
|
||||
|
||||
def forward_candidate(self, input):
|
||||
raise NotImplementedError
|
||||
|
||||
def extra_repr(self) -> str:
|
||||
return "(_super_layer_embed): {:}".format(list(self._super_layer_embed.shape))
|
||||
@ -41,4 +41,5 @@ super_name2activation = {
|
||||
|
||||
|
||||
from .super_trade_stem import SuperAlphaEBDv1
|
||||
from .super_positional_embedding import SuperDynamicPositionE
|
||||
from .super_positional_embedding import SuperPositionalEncoder
|
||||
|
||||
@ -10,6 +10,41 @@ from .super_module import SuperModule
|
||||
from .super_module import IntSpaceType
|
||||
|
||||
|
||||
class SuperDynamicPositionE(SuperModule):
|
||||
"""Applies a positional encoding to the input positions."""
|
||||
|
||||
def __init__(self, dimension: int, scale: float = 1.0) -> None:
|
||||
super(SuperDynamicPositionE, self).__init__()
|
||||
|
||||
self._scale = scale
|
||||
self._dimension = dimension
|
||||
# weights to be optimized
|
||||
self.register_buffer(
|
||||
"_div_term",
|
||||
torch.exp(
|
||||
torch.arange(0, dimension, 2).float() * (-math.log(10000.0) / dimension)
|
||||
),
|
||||
)
|
||||
|
||||
@property
|
||||
def abstract_search_space(self):
|
||||
root_node = spaces.VirtualNode(id(self))
|
||||
return root_node
|
||||
|
||||
def forward_candidate(self, input: torch.Tensor) -> torch.Tensor:
|
||||
return self.forward_raw(input)
|
||||
|
||||
def forward_raw(self, input: torch.Tensor) -> torch.Tensor:
|
||||
import pdb
|
||||
|
||||
pdb.set_trace()
|
||||
print("---")
|
||||
return F.linear(input, self._super_weight, self._super_bias)
|
||||
|
||||
def extra_repr(self) -> str:
|
||||
return "scale={:}, dim={:}".format(self._scale, self._dimension)
|
||||
|
||||
|
||||
class SuperPositionalEncoder(SuperModule):
|
||||
"""Attention Is All You Need: https://arxiv.org/pdf/1706.03762.pdf
|
||||
https://github.com/pytorch/examples/blob/master/word_language_model/model.py#L65
|
||||
|
||||
Loading…
Reference in New Issue
Block a user