Fix GeMOSA's bugs
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@ -1,8 +1,9 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
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#####################################################
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# python exps/GeMOSA/basic-same.py --env_version v1 --hidden_dim 16 --epochs 500 --init_lr 0.1
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# python exps/GeMOSA/basic-same.py --env_version v2 --hidden_dim 16 --epochs 1000 --init_lr 0.05
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# python exps/GeMOSA/basic-same.py --env_version v1 --hidden_dim 16 --epochs 500 --init_lr 0.1 --device cuda
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# python exps/GeMOSA/basic-same.py --env_version v2 --hidden_dim 16 --epochs 500 --init_lr 0.1 --device cuda
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# python exps/GeMOSA/basic-same.py --env_version v3 --hidden_dim 32 --epochs 1000 --init_lr 0.05 --device cuda
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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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@ -31,8 +32,6 @@ from xautodl.procedures.metric_utils import SaveMetric, MSEMetric, ComposeMetric
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from xautodl.datasets.synthetic_core import get_synthetic_env
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from xautodl.models.xcore import get_model
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from lfna_utils import lfna_setup
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def subsample(historical_x, historical_y, maxn=10000):
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total = historical_x.size(0)
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@ -44,9 +43,17 @@ def subsample(historical_x, historical_y, maxn=10000):
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def main(args):
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logger, model_kwargs = lfna_setup(args)
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prepare_seed(args.rand_seed)
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logger = prepare_logger(args)
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env = get_synthetic_env(mode=None, version=args.env_version)
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model_kwargs = dict(
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config=dict(model_type="norm_mlp"),
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input_dim=env.meta_info["input_dim"],
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output_dim=env.meta_info["output_dim"],
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hidden_dims=[args.hidden_dim] * 2,
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act_cls="relu",
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norm_cls="layer_norm_1d",
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)
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logger.log("The total enviornment: {:}".format(env))
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w_containers = dict()
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@ -149,7 +156,7 @@ if __name__ == "__main__":
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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-same-timestamp",
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default="./outputs/GeMOSA-synthetic/use-same-timestamp",
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help="The checkpoint directory.",
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)
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parser.add_argument(
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@ -2,8 +2,9 @@
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# Learning to Generate Model One Step Ahead #
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#####################################################
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# python exps/GeMOSA/main.py --env_version v1 --workers 0
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# python exps/GeMOSA/main.py --env_version v1 --device cuda --lr 0.002 --seq_length 8 --meta_batch 256
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# python exps/GeMOSA/main.py --env_version v1 --device cuda --lr 0.002 --seq_length 24 --time_dim 32 --meta_batch 128
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# python exps/GeMOSA/main.py --env_version v1 --device cuda --lr 0.002 --hidden_dim 16 --meta_batch 256
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# python exps/GeMOSA/main.py --env_version v2 --device cuda --lr 0.002 --hidden_dim 16 --meta_batch 256
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# python exps/GeMOSA/main.py --env_version v3 --device cuda --lr 0.002 --hidden_dim 32 --meta_batch 256
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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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@ -269,7 +270,7 @@ if __name__ == "__main__":
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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/lfna-battle",
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default="./outputs/GeMOSA-synthetic/GeMOSA",
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help="The checkpoint directory.",
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)
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parser.add_argument(
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@ -21,7 +21,7 @@ def get_synthetic_env(total_timestamp=1600, num_per_task=1000, mode=None, versio
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mean_generator = ConstantFunc(0)
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std_generator = ConstantFunc(1)
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data_generator = GaussianDGenerator(
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[mean_generator], [[std_generator]], (-2, 2)
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[mean_generator], [[std_generator]], (-3, 3)
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)
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time_generator = TimeStamp(
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min_timestamp=0, max_timestamp=max_time, num=total_timestamp, mode=mode
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@ -40,7 +40,7 @@ def get_synthetic_env(total_timestamp=1600, num_per_task=1000, mode=None, versio
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mean_generator = ConstantFunc(0)
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std_generator = ConstantFunc(1)
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data_generator = GaussianDGenerator(
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[mean_generator], [[std_generator]], (-2, 2)
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[mean_generator], [[std_generator]], (-3, 3)
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)
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time_generator = TimeStamp(
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min_timestamp=0, max_timestamp=max_time, num=total_timestamp, mode=mode
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@ -60,7 +60,7 @@ def get_synthetic_env(total_timestamp=1600, num_per_task=1000, mode=None, versio
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mean_generator = SinFunc(params={0: 1, 1: 1, 2: 0}) # sin(t)
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std_generator = CosFunc(params={0: 0.5, 1: 1, 2: 1}) # 0.5 cos(t) + 1
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data_generator = GaussianDGenerator(
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[mean_generator], [[std_generator]], (-2, 2)
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[mean_generator], [[std_generator]], (-3, 3)
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)
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time_generator = TimeStamp(
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min_timestamp=0, max_timestamp=max_time, num=total_timestamp, mode=mode
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@ -77,6 +77,9 @@ def get_synthetic_env(total_timestamp=1600, num_per_task=1000, mode=None, versio
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)
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dynamic_env.set_regression()
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elif version.lower() == "v4":
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dynamic_env = SyntheticDEnv(
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data_generator, oracle_map, time_generator, num_per_task, noise=0.05
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)
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dynamic_env.set_classification(2)
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else:
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raise ValueError("Unknown version: {:}".format(version))
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@ -46,15 +46,19 @@ class SyntheticDEnv(data.Dataset):
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def set_regression(self):
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self._meta_info["task"] = "regression"
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self._meta_info["input_dim"] = self._data_generator.ndim
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self._meta_info["output_shape"] = self._oracle_map.output_shape(self._data_generator.output_shape())
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self._meta_info['output_dim'] = int(np.prod(self._meta_info["output_shape"]))
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self._meta_info["output_shape"] = self._oracle_map.output_shape(
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self._data_generator.output_shape()
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)
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self._meta_info["output_dim"] = int(np.prod(self._meta_info["output_shape"]))
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def set_classification(self, num_classes):
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self._meta_info["task"] = "classification"
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self._meta_info["input_dim"] = self._data_generator.ndim
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self._meta_info["num_classes"] = int(num_classes)
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self._meta_info["output_shape"] = self._oracle_map.output_shape(self._data_generator.output_shape())
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self._meta_info['output_dim'] = int(np.prod(self._meta_info["output_shape"]))
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self._meta_info["output_shape"] = self._oracle_map.output_shape(
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self._data_generator.output_shape()
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)
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self._meta_info["output_dim"] = int(np.prod(self._meta_info["output_shape"]))
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@property
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def oracle_map(self):
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