92 lines
2.6 KiB
Python
92 lines
2.6 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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#####################################################
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import copy
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import torch
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from tqdm import tqdm
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from procedures import prepare_seed, prepare_logger
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from datasets.synthetic_core import get_synthetic_env
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def lfna_setup(args):
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prepare_seed(args.rand_seed)
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logger = prepare_logger(args)
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cache_path = (
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logger.path(None) / ".." / "env-{:}-info.pth".format(args.env_version)
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).resolve()
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if cache_path.exists():
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env_info = torch.load(cache_path)
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else:
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env_info = dict()
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dynamic_env = get_synthetic_env(version=args.env_version)
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env_info["total"] = len(dynamic_env)
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for idx, (timestamp, (_allx, _ally)) in enumerate(tqdm(dynamic_env)):
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env_info["{:}-timestamp".format(idx)] = timestamp
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env_info["{:}-x".format(idx)] = _allx
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env_info["{:}-y".format(idx)] = _ally
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env_info["dynamic_env"] = dynamic_env
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torch.save(env_info, cache_path)
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"""
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model_kwargs = dict(
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config=dict(model_type="simple_mlp"),
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input_dim=1,
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output_dim=1,
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hidden_dim=args.hidden_dim,
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act_cls="leaky_relu",
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norm_cls="identity",
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)
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"""
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model_kwargs = dict(
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config=dict(model_type="norm_mlp"),
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input_dim=1,
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output_dim=1,
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hidden_dims=[args.hidden_dim] * 2,
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act_cls="gelu",
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norm_cls="layer_norm_1d",
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)
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return logger, env_info, model_kwargs
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def train_model(model, dataset, lr, epochs):
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criterion = torch.nn.MSELoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=lr, amsgrad=True)
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best_loss, best_param = None, None
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for _iepoch in range(epochs):
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preds = model(dataset.x)
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optimizer.zero_grad()
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loss = criterion(preds, dataset.y)
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loss.backward()
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optimizer.step()
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# save best
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if best_loss is None or best_loss > loss.item():
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best_loss = loss.item()
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best_param = copy.deepcopy(model.state_dict())
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model.load_state_dict(best_param)
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return best_loss
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class TimeData:
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def __init__(self, timestamp, xs, ys):
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self._timestamp = timestamp
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self._xs = xs
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self._ys = ys
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@property
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def x(self):
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return self._xs
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@property
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def y(self):
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return self._ys
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@property
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def timestamp(self):
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return self._timestamp
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def __repr__(self):
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return "{name}(timestamp={timestamp}, with {num} samples)".format(
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name=self.__class__.__name__, timestamp=self._timestamp, num=len(self._xs)
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)
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