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Update DEBUG INFO

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D-X-Y 2021-05-10 14:14:06 +08:00
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exps/LFNA/lfna-debug.py Normal file
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#####################################################
# 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
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
network = get_model(dict(model_type="simple_mlp"), **model_kwargs)
criterion = torch.nn.MSELoss()
logger.log("There are {:} weights.".format(network.get_w_container().numel()))
adaptor = LFNAmlp(args.meta_seq, (200, 200), "leaky_relu", criterion)
# pre-train the model
init_dataset = TimeData(0, env_info["0-x"], env_info["0-y"])
init_loss = train_model(network, init_dataset, args.init_lr, args.epochs)
logger.log("The pre-training loss is {:.4f}".format(init_loss))
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)

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exps/LFNA/lfna-fix-init.py Normal file
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
# python exps/LFNA/lfna-fix-init.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
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=0.001, amsgrad=True
)
self.criterion = criterion
def adapt(self, model, seq_datasets):
delta_inputs = []
container = model.get_w_container()
for iseq, dataset in enumerate(seq_datasets):
y_hat = model.forward_with_container(dataset.x, container)
loss = self.criterion(y_hat, dataset.y)
gradients = torch.autograd.grad(loss, container.parameters())
with torch.no_grad():
flatten_g = container.flatten(gradients)
delta_inputs.append(flatten_g)
flatten_w = container.no_grad_clone().flatten()
delta_inputs.append(flatten_w)
delta_inputs = torch.stack(delta_inputs, dim=-1)
delta = self.delta_net(delta_inputs)
delta = torch.clamp(delta, -0.8, 0.8)
unflatten_delta = container.unflatten(delta)
future_container = container.no_grad_clone().additive(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
network = get_model(dict(model_type="simple_mlp"), **model_kwargs)
criterion = torch.nn.MSELoss()
logger.log("There are {:} weights.".format(network.get_w_container().numel()))
adaptor = LFNAmlp(1 + args.meta_seq, (20, 20), "leaky_relu", criterion)
# pre-train the model
init_dataset = TimeData(0, env_info["0-x"], env_info["0-y"])
init_loss = train_model(network, init_dataset, args.init_lr, args.epochs)
logger.log("The pre-training loss is {:.4f}".format(init_loss))
# 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()
batch_indexes, meta_losses = [], []
for ibatch in range(args.meta_batch):
sampled_timestamp = random.random() * train_time_bar
batch_indexes.append("{:.3f}".format(sampled_timestamp))
seq_datasets = []
for iseq in range(args.meta_seq + 1):
cur_time = sampled_timestamp + iseq * dynamic_env.timestamp_interval
cur_time, (x, y) = dynamic_env(cur_time)
seq_datasets.append(TimeData(cur_time, x, y))
history_datasets, future_dataset = seq_datasets[:-1], seq_datasets[-1]
future_container = adaptor.adapt(network, history_datasets)
future_y_hat = network.forward_with_container(
future_dataset.x, future_container
)
future_loss = adaptor.criterion(future_y_hat, future_dataset.y)
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}) batch: {:}".format(
meta_loss_meter.avg, meta_loss_meter.val, ",".join(batch_indexes[:5])
)
)
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()
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_time - iseq * dynamic_env.timestamp_interval
cur_time, (x, y) = dynamic_env(cur_time)
seq_datasets.append(TimeData(cur_time, x, y))
seq_datasets.reverse()
future_container = adaptor.adapt(network, seq_datasets)
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-fix-init",
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=1000,
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)

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exps/LFNA/lfna-v0.py
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
# python exps/LFNA/lfna-v0.py
#####################################################
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
class LFNAmlp:
"""A LFNA meta-model that uses the MLP as delta-net."""
def __init__(self, obs_dim, hidden_sizes, act_name):
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
)
def adapt(self, model, criterion, w_container, seq_datasets):
w_container.requires_grad_(True)
containers = [w_container]
for idx, dataset in enumerate(seq_datasets):
x, y = dataset.x, dataset.y
y_hat = model.forward_with_container(x, containers[-1])
loss = criterion(y_hat, y)
gradients = torch.autograd.grad(loss, containers[-1].tensors)
with torch.no_grad():
flatten_w = containers[-1].flatten().view(-1, 1)
flatten_g = containers[-1].flatten(gradients).view(-1, 1)
input_statistics = torch.tensor([x.mean(), x.std()]).view(1, 2)
input_statistics = input_statistics.expand(flatten_w.numel(), -1)
delta_inputs = torch.cat((flatten_w, flatten_g, input_statistics), dim=-1)
delta = self.delta_net(delta_inputs).view(-1)
delta = torch.clamp(delta, -0.5, 0.5)
unflatten_delta = containers[-1].unflatten(delta)
future_container = containers[-1].no_grad_clone().additive(unflatten_delta)
# future_container = containers[-1].additive(unflatten_delta)
containers.append(future_container)
# containers = containers[1:]
meta_loss = []
temp_containers = []
for idx, dataset in enumerate(seq_datasets):
if idx == 0:
continue
current_container = containers[idx]
y_hat = model.forward_with_container(dataset.x, current_container)
loss = criterion(y_hat, dataset.y)
meta_loss.append(loss)
temp_containers.append((dataset.timestamp, current_container, -loss.item()))
meta_loss = sum(meta_loss)
w_container.requires_grad_(False)
# meta_loss.backward()
# self.meta_optimizer.step()
return meta_loss, temp_containers
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()
class TimeData:
def __init__(self, timestamp, xs, ys):
self._timestamp = timestamp
self._xs = xs
self._ys = ys
@property
def x(self):
return self._xs
@property
def y(self):
return self._ys
@property
def timestamp(self):
return self._timestamp
class Population:
"""A population used to maintain models at different timestamps."""
def __init__(self):
self._time2model = dict()
self._time2score = dict() # higher is better
def append(self, timestamp, model, score):
if timestamp in self._time2model:
if self._time2score[timestamp] > score:
return
self._time2model[timestamp] = model.no_grad_clone()
self._time2score[timestamp] = score
def query(self, timestamp):
closet_timestamp = None
for xtime, model in self._time2model.items():
if closet_timestamp is None or (
xtime < timestamp and timestamp - closet_timestamp >= timestamp - xtime
):
closet_timestamp = xtime
return self._time2model[closet_timestamp], closet_timestamp
def debug_info(self, timestamps):
xstrs = []
for timestamp in timestamps:
if timestamp in self._time2score:
xstrs.append(
"{:04d}: {:.4f}".format(timestamp, self._time2score[timestamp])
)
return ", ".join(xstrs)
def main(args):
prepare_seed(args.rand_seed)
logger = prepare_logger(args)
cache_path = (logger.path(None) / ".." / "env-info.pth").resolve()
if cache_path.exists():
env_info = torch.load(cache_path)
else:
env_info = dict()
dynamic_env = get_synthetic_env()
env_info["total"] = len(dynamic_env)
for idx, (timestamp, (_allx, _ally)) in enumerate(tqdm(dynamic_env)):
env_info["{:}-timestamp".format(idx)] = timestamp
env_info["{:}-x".format(idx)] = _allx
env_info["{:}-y".format(idx)] = _ally
env_info["dynamic_env"] = dynamic_env
torch.save(env_info, cache_path)
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
base_model = get_model(
dict(model_type="simple_mlp"),
act_cls="leaky_relu",
norm_cls="identity",
input_dim=1,
output_dim=1,
)
w_container = base_model.get_w_container()
criterion = torch.nn.MSELoss()
print("There are {:} weights.".format(w_container.numel()))
adaptor = LFNAmlp(4, (50, 20), "leaky_relu")
pool = Population()
pool.append(0, w_container, -100)
# LFNA meta-training
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()
debug_timestamp = set()
all_meta_losses = []
for ibatch in range(args.meta_batch):
sampled_timestamp = random.randint(0, train_time_bar)
query_w_container, query_timestamp = pool.query(sampled_timestamp)
# def adapt(self, model, w_container, xs, ys):
seq_datasets = []
# xs, ys = [], []
for it in range(sampled_timestamp, sampled_timestamp + args.max_seq):
xs = env_info["{:}-x".format(it)]
ys = env_info["{:}-y".format(it)]
seq_datasets.append(TimeData(it, xs, ys))
temp_meta_loss, temp_containers = adaptor.adapt(
base_model, criterion, query_w_container, seq_datasets
)
all_meta_losses.append(temp_meta_loss)
for temp_time, temp_container, temp_score in temp_containers:
pool.append(temp_time, temp_container, temp_score)
debug_timestamp.add(temp_time)
meta_loss = torch.stack(all_meta_losses).mean()
meta_loss.backward()
adaptor.step()
debug_str = pool.debug_info(debug_timestamp)
logger.log("meta-loss: {:.4f}".format(meta_loss.item()))
per_epoch_time.update(time.time() - start_time)
start_time = time.time()
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-v1",
help="The checkpoint directory.",
)
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=5,
help="The batch size for the meta-model",
)
parser.add_argument(
"--epochs",
type=int,
default=1000,
help="The total number of epochs.",
)
parser.add_argument(
"--max_seq",
type=int,
default=5,
help="The maximum length of the sequence.",
)
parser.add_argument(
"--workers",
type=int,
default=4,
help="The number of data loading workers (default: 4)",
)
# 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"
main(args)

21
exps/LFNA/lfna_utils.py
View File

@ -1,6 +1,7 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
import copy
import torch
from tqdm import tqdm
from procedures import prepare_seed, prepare_logger
@ -37,6 +38,24 @@ def lfna_setup(args):
return logger, env_info, model_kwargs
def train_model(model, dataset, lr, epochs):
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr, amsgrad=True)
best_loss, best_param = None, None
for _iepoch in range(epochs):
preds = model(dataset.x)
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())
model.load_state_dict(best_param)
return best_loss
class TimeData:
def __init__(self, timestamp, xs, ys):
self._timestamp = timestamp
@ -56,6 +75,6 @@ class TimeData:
return self._timestamp
def __repr__(self):
return "{name}(timestamp={:}, with {num} samples)".format(
return "{name}(timestamp={timestamp}, with {num} samples)".format(
name=self.__class__.__name__, timestamp=self._timestamp, num=len(self._xs)
)

4
exps/LFNA/vis-synthetic.py
View File

@ -237,6 +237,8 @@ def compare_algs(save_dir, version, alg_dir="./outputs/lfna-synthetic"):
alg_name2dir["Optimal"] = "use-same-timestamp"
alg_name2dir["Supervised Learning (History Data)"] = "use-all-past-data"
alg_name2dir["MAML"] = "use-maml-s1"
alg_name2dir["LFNA (fix init)"] = "lfna-fix-init"
alg_name2dir["LFNA (debug)"] = "lfna-debug"
alg_name2all_containers = OrderedDict()
if version == "v1":
poststr = "v1-d16"
@ -256,7 +258,7 @@ def compare_algs(save_dir, version, alg_dir="./outputs/lfna-synthetic"):
)
alg2xs, alg2ys = defaultdict(list), defaultdict(list)
colors = ["r", "g", "b"]
colors = ["r", "g", "b", "m", "y"]
dynamic_env = env_info["dynamic_env"]
min_t, max_t = dynamic_env.min_timestamp, dynamic_env.max_timestamp

7
lib/datasets/synthetic_env.py
View File

@ -51,6 +51,10 @@ class SyntheticDEnv(data.Dataset):
def max_timestamp(self):
return self._timestamp_generator.max_timestamp
@property
def timestamp_interval(self):
return self._timestamp_generator.interval
def set_oracle_map(self, functor):
self._oracle_map = functor
@ -67,6 +71,9 @@ class SyntheticDEnv(data.Dataset):
def __getitem__(self, index):
assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
index, timestamp = self._timestamp_generator[index]
return self.__call__(timestamp)
def __call__(self, timestamp):
mean_list = [functor(timestamp) for functor in self._mean_functors]
cov_matrix = [
[abs(cov_gen(timestamp)) for cov_gen in cov_functor]

4
lib/datasets/synthetic_utils.py
View File

@ -60,6 +60,10 @@ class TimeStamp(UnifiedSplit, data.Dataset):
@property
def max_timestamp(self):
return self._max_timestamp
@property
def interval(self):
return self._interval
def __iter__(self):
self._iter_num = 0

7
lib/xlayers/super_module.py
View File

@ -46,6 +46,13 @@ class TensorContainer:
result.append(name, new_tensor, self._param_or_buffers[index])
return result
def create_container(self, tensors):
result = TensorContainer()
for index, name in enumerate(self._names):
new_tensor = tensors[index]
result.append(name, new_tensor, self._param_or_buffers[index])
return result
def no_grad_clone(self):
result = TensorContainer()
with torch.no_grad():