222 lines
7.3 KiB
Python
222 lines
7.3 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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#####################################################
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# python exps/LFNA/basic-maml.py --env_version v1 --hidden_dim 16 --inner_step 5
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# python exps/LFNA/basic-maml.py --env_version v2
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#####################################################
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import sys, time, copy, torch, random, argparse
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from tqdm import tqdm
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from copy import deepcopy
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from pathlib import Path
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lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
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from log_utils import time_string
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from log_utils import AverageMeter, convert_secs2time
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from utils import split_str2indexes
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from procedures.advanced_main import basic_train_fn, basic_eval_fn
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from procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
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from datasets.synthetic_core import get_synthetic_env
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from models.xcore import get_model
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from xlayers import super_core
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from lfna_utils import lfna_setup, TimeData
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class MAML:
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"""A LFNA meta-model that uses the MLP as delta-net."""
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def __init__(
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self, network, criterion, epochs, meta_lr, inner_lr=0.01, inner_step=1
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):
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self.criterion = criterion
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# self.container = container
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self.network = network
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self.meta_optimizer = torch.optim.Adam(
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self.network.parameters(), lr=meta_lr, amsgrad=True
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)
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self.meta_lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
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optimizer,
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milestones=[
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int(epochs * 0.25),
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int(epochs * 0.5),
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int(epochs * 0.75),
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],
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gamma=0.3,
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)
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self.inner_lr = inner_lr
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self.inner_step = inner_step
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self._best_info = dict(state_dict=None, score=None)
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print("There are {:} weights.".format(w_container.numel()))
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def adapt(self, dataset):
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# create a container for the future timestamp
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container = self.network.get_w_container()
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for k in range(0, self.inner_step):
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y_hat = self.network.forward_with_container(dataset.x, container)
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loss = self.criterion(y_hat, dataset.y)
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grads = torch.autograd.grad(loss, container.parameters())
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container = container.additive([-self.inner_lr * grad for grad in grads])
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return container
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def predict(self, x, container=None):
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if container is not None:
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y_hat = self.network.forward_with_container(x, container)
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else:
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y_hat = self.network(x)
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return y_hat
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def step(self):
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torch.nn.utils.clip_grad_norm_(self.container.parameters(), 1.0)
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self.meta_optimizer.step()
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self.meta_lr_scheduler.step()
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def zero_grad(self):
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self.meta_optimizer.zero_grad()
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def save_best(self, network, score):
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if self._best_info["score"] is None or self._best_info["score"] < score:
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state_dict = dict(
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criterion=criterion,
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network=network.state_dict(),
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meta_optimizer=self.meta_optimizer.state_dict(),
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meta_lr_scheduler=self.meta_lr_scheduler.state_dict(),
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)
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self._best_info["state_dict"] = state_dict
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self._best_info["score"] = score
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def main(args):
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logger, env_info, model_kwargs = lfna_setup(args)
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model = get_model(dict(model_type="simple_mlp"), **model_kwargs)
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total_time = env_info["total"]
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for i in range(total_time):
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for xkey in ("timestamp", "x", "y"):
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nkey = "{:}-{:}".format(i, xkey)
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assert nkey in env_info, "{:} no in {:}".format(nkey, list(env_info.keys()))
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train_time_bar = total_time // 2
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criterion = torch.nn.MSELoss()
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maml = MAML(
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model, criterion, args.epochs, args.meta_lr, args.inner_lr, args.inner_step
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)
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# meta-training
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per_epoch_time, start_time = AverageMeter(), time.time()
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for iepoch in range(args.epochs):
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need_time = "Time Left: {:}".format(
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convert_secs2time(per_epoch_time.avg * (args.epochs - iepoch), True)
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)
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logger.log(
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"[{:}] [{:04d}/{:04d}] ".format(time_string(), iepoch, args.epochs)
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+ need_time
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)
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maml.zero_grad()
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meta_losses = []
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for ibatch in range(args.meta_batch):
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sampled_timestamp = random.randint(0, train_time_bar)
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past_dataset = TimeData(
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sampled_timestamp,
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env_info["{:}-x".format(sampled_timestamp)],
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env_info["{:}-y".format(sampled_timestamp)],
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)
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future_dataset = TimeData(
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sampled_timestamp + 1,
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env_info["{:}-x".format(sampled_timestamp + 1)],
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env_info["{:}-y".format(sampled_timestamp + 1)],
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)
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future_container = maml.adapt(model, past_dataset)
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future_y_hat = maml.predict(future_dataset.x, future_container)
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future_loss = maml.criterion(future_y_hat, future_dataset.y)
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meta_losses.append(future_loss)
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meta_loss = torch.stack(meta_losses).mean()
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meta_loss.backward()
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maml.step()
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logger.log("meta-loss: {:.4f}".format(meta_loss.item()))
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per_epoch_time.update(time.time() - start_time)
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start_time = time.time()
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import pdb
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pdb.set_trace()
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logger.log("-" * 200 + "\n")
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logger.close()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("Use the data in the past.")
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parser.add_argument(
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"--save_dir",
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type=str,
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default="./outputs/lfna-synthetic/use-maml",
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help="The checkpoint directory.",
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)
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parser.add_argument(
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"--env_version",
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type=str,
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required=True,
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help="The synthetic enviornment version.",
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)
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parser.add_argument(
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"--hidden_dim",
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type=int,
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required=True,
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help="The hidden dimension.",
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)
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parser.add_argument(
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"--meta_lr",
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type=float,
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default=0.1,
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help="The learning rate for the MAML optimizer (default is Adam)",
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)
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parser.add_argument(
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"--inner_lr",
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type=float,
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default=0.01,
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help="The learning rate for the inner optimization",
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)
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parser.add_argument(
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"--inner_step", type=int, default=1, help="The inner loop steps for MAML."
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)
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parser.add_argument(
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"--meta_batch",
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type=int,
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default=10,
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help="The batch size for the meta-model",
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)
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parser.add_argument(
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"--epochs",
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type=int,
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default=1000,
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help="The total number of epochs.",
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)
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parser.add_argument(
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"--workers",
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type=int,
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default=4,
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help="The number of data loading workers (default: 4)",
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)
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# Random Seed
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parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
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args = parser.parse_args()
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if args.rand_seed is None or args.rand_seed < 0:
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args.rand_seed = random.randint(1, 100000)
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assert args.save_dir is not None, "The save dir argument can not be None"
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args.save_dir = "{:}-s{:}-{:}-d{:}".format(
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args.save_dir, args.inner_step, args.env_version, args.hidden_dim
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)
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main(args)
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