autodl-projects/lib/datasets/synthetic_env.py

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2021-04-22 17:08:43 +02:00
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.04 #
#####################################################
import math
import abc
import numpy as np
from typing import List, Optional
import torch
import torch.utils.data as data
from .synthetic_utils import UnifiedSplit
class SyntheticDEnv(UnifiedSplit, data.Dataset):
"""The synethtic dynamic environment."""
def __init__(
self,
mean_generators: List[data.Dataset],
cov_generators: List[List[data.Dataset]],
num_per_task: int = 5000,
mode: Optional[str] = None,
):
self._ndim = len(mean_generators)
assert self._ndim == len(
cov_generators
), "length does not match {:} vs. {:}".format(self._ndim, len(cov_generators))
for cov_generator in cov_generators:
assert self._ndim == len(
cov_generator
), "length does not match {:} vs. {:}".format(
self._ndim, len(cov_generator)
)
self._num_per_task = num_per_task
self._total_num = len(mean_generators[0])
for mean_generator in mean_generators:
assert self._total_num == len(mean_generator)
for cov_generator in cov_generators:
for cov_g in cov_generator:
assert self._total_num == len(cov_g)
self._mean_generators = mean_generators
self._cov_generators = cov_generators
UnifiedSplit.__init__(self, self._total_num, mode)
def __iter__(self):
self._iter_num = 0
return self
def __next__(self):
if self._iter_num >= len(self):
raise StopIteration
self._iter_num += 1
return self.__getitem__(self._iter_num - 1)
def __getitem__(self, index):
assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
index = self._indexes[index]
mean_list = [generator[index][-1] for generator in self._mean_generators]
cov_matrix = [
[cov_gen[index][-1] for cov_gen in cov_generator]
for cov_generator in self._cov_generators
]
dataset = np.random.multivariate_normal(
mean_list, cov_matrix, size=self._num_per_task
)
return index, torch.Tensor(dataset)
def __len__(self):
return len(self._indexes)
def __repr__(self):
return "{name}({cur_num:}/{total} elements, ndim={ndim}, num_per_task={num_per_task})".format(
name=self.__class__.__name__,
cur_num=len(self),
total=self._total_num,
ndim=self._ndim,
num_per_task=self._num_per_task,
)