autodl-projects/lib/datasets/synthetic_utils.py

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2021-04-22 17:08:43 +02:00
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#####################################################
import math
import abc
import numpy as np
from typing import Optional
import torch
import torch.utils.data as data
from .math_base_funcs import QuadraticFunc, QuarticFunc
class UnifiedSplit:
"""A class to unify the split strategy."""
def __init__(self, total_num, mode):
# Training Set 60%
num_of_train = int(total_num * 0.6)
# Validation Set 20%
num_of_valid = int(total_num * 0.2)
# Test Set 20%
num_of_set = total_num - num_of_train - num_of_valid
all_indexes = list(range(total_num))
if mode is None:
self._indexes = all_indexes
elif mode.lower() in ("train", "training"):
self._indexes = all_indexes[:num_of_train]
elif mode.lower() in ("valid", "validation"):
self._indexes = all_indexes[num_of_train : num_of_train + num_of_valid]
elif mode.lower() in ("test", "testing"):
self._indexes = all_indexes[num_of_train + num_of_valid :]
else:
raise ValueError("Unkonwn mode of {:}".format(mode))
self._mode = mode
@property
def mode(self):
return self._mode
class SinGenerator(UnifiedSplit, data.Dataset):
"""The synethtic generator for the dynamically changing environment.
- x in [0, 1]
- y = amplitude-scale-of(x) * sin( period-phase-shift-of(x) )
- where
- the amplitude scale is a quadratic function of x
- the period-phase-shift is another quadratic function of x
"""
def __init__(
self,
num: int = 100,
num_sin_phase: int = 7,
min_amplitude: float = 1,
max_amplitude: float = 4,
phase_shift: float = 0,
mode: Optional[str] = None,
):
self._amplitude_scale = QuadraticFunc(
[(0, min_amplitude), (0.5, max_amplitude), (1, min_amplitude)]
)
self._num_sin_phase = num_sin_phase
self._interval = 1.0 / (float(num) - 1)
self._total_num = num
fitting_data = []
temp_max_scalar = 2 ** (num_sin_phase - 1)
for i in range(num_sin_phase):
value = (2 ** i) / temp_max_scalar
next_value = (2 ** (i + 1)) / temp_max_scalar
for _phase in (0, 0.25, 0.5, 0.75):
inter_value = value + (next_value - value) * _phase
fitting_data.append((inter_value, math.pi * (2 * i + _phase)))
self._period_phase_shift = QuarticFunc(fitting_data)
UnifiedSplit.__init__(self, self._total_num, mode)
self._transform = lambda x: x
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 set_transform(self, transform):
self._transform = transform
def __getitem__(self, index):
assert 0 <= index < len(self), "{:} is not in [0, {:})".format(index, len(self))
index = self._indexes[index]
position = self._interval * index
value = self._amplitude_scale[position] * math.sin(
self._period_phase_shift[position]
)
return index, position, self._transform(value)
def __len__(self):
return len(self._indexes)
def __repr__(self):
return (
"{name}({cur_num:}/{total} elements,\n"
"amplitude={amplitude},\n"
"period_phase_shift={period_phase_shift})".format(
name=self.__class__.__name__,
cur_num=len(self),
total=self._total_num,
amplitude=self._amplitude_scale,
period_phase_shift=self._period_phase_shift,
)
)
class ConstantGenerator(UnifiedSplit, data.Dataset):
"""The constant generator."""
def __init__(
self,
num: int = 100,
constant: float = 0.1,
mode: Optional[str] = None,
):
self._total_num = num
self._constant = constant
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]
return index, index, self._constant
def __len__(self):
return len(self._indexes)
def __repr__(self):
return "{name}({cur_num:}/{total} elements)".format(
name=self.__class__.__name__,
cur_num=len(self),
total=self._total_num,
)