autodl-projects/lib/datasets/synthetic_adaptive_environment.py

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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
#####################################################
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import math
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import abc
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import numpy as np
from typing import Optional
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import torch
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import torch.utils.data as data
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class FitFunc(abc.ABC):
"""The fit function that outputs f(x) = a * x^2 + b * x + c."""
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def __init__(self, freedom: int, list_of_points=None):
self._params = dict()
for i in range(freedom):
self._params[i] = None
self._freedom = freedom
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if list_of_points is not None:
self.fit(list_of_points)
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def set(self, _params):
self._params = copy.deepcopy(_params)
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def check_valid(self):
for key, value in self._params.items():
if value is None:
raise ValueError("The {:} is None".format(key))
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@abc.abstractmethod
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def __getitem__(self, x):
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raise NotImplementedError
@abc.abstractmethod
def _getitem(self, x):
raise NotImplementedError
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def fit(
self,
list_of_points,
max_iter=900,
lr_max=1.0,
verbose=False,
):
with torch.no_grad():
data = torch.Tensor(list_of_points).type(torch.float32)
assert data.ndim == 2 and data.size(1) == 2, "Invalid shape : {:}".format(
data.shape
)
x, y = data[:, 0], data[:, 1]
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weights = torch.nn.Parameter(torch.Tensor(self._freedom))
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torch.nn.init.normal_(weights, mean=0.0, std=1.0)
optimizer = torch.optim.Adam([weights], lr=lr_max, amsgrad=True)
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lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[
int(max_iter * 0.25),
int(max_iter * 0.5),
int(max_iter * 0.75),
],
gamma=0.1,
)
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if verbose:
print("The optimizer: {:}".format(optimizer))
best_loss = None
for _iter in range(max_iter):
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y_hat = self._getitem(x, weights)
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loss = torch.mean(torch.abs(y - y_hat))
optimizer.zero_grad()
loss.backward()
optimizer.step()
lr_scheduler.step()
if verbose:
print(
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"In the fit, loss at the {:02d}/{:02d}-th iter is {:}".format(
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_iter, max_iter, loss.item()
)
)
# Update the params
if best_loss is None or best_loss > loss.item():
best_loss = loss.item()
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for i in range(self._freedom):
self._params[i] = weights[i].item()
def __repr__(self):
return "{name}(freedom={freedom})".format(
name=self.__class__.__name__, freedom=freedom
)
class QuadraticFunc(FitFunc):
"""The quadratic function that outputs f(x) = a * x^2 + b * x + c."""
def __init__(self, list_of_points=None):
super(QuadraticFunc, self).__init__(3, list_of_points)
def __getitem__(self, x):
self.check_valid()
return self._params[0] * x * x + self._params[1] * x + self._params[2]
def _getitem(self, x, weights):
return weights[0] * x * x + weights[1] * x + weights[2]
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def __repr__(self):
return "{name}(y = {a} * x^2 + {b} * x + {c})".format(
name=self.__class__.__name__,
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a=self._params[0],
b=self._params[1],
c=self._params[2],
)
class CubicFunc(FitFunc):
"""The cubic function that outputs f(x) = a * x^3 + b * x^2 + c * x + d."""
def __init__(self, list_of_points=None):
super(CubicFunc, self).__init__(4, list_of_points)
def __getitem__(self, x):
self.check_valid()
return (
self._params[0] * x ** 3
+ self._params[1] * x ** 2
+ self._params[2] * x
+ self._params[3]
)
def _getitem(self, x, weights):
return weights[0] * x ** 3 + weights[1] * x ** 2 + weights[2] * x + weights[3]
def __repr__(self):
return "{name}(y = {a} * x^3 + {b} * x^2 + {c} * x + {d})".format(
name=self.__class__.__name__,
a=self._params[0],
b=self._params[1],
c=self._params[2],
d=self._params[3],
)
class QuarticFunc(FitFunc):
"""The quartic function that outputs f(x) = a * x^4 + b * x^3 + c * x^2 + d * x + e."""
def __init__(self, list_of_points=None):
super(QuarticFunc, self).__init__(5, list_of_points)
def __getitem__(self, x):
self.check_valid()
return (
self._params[0] * x ** 4
+ self._params[1] * x ** 3
+ self._params[2] * x ** 2
+ self._params[3] * x
+ self._params[4]
)
def _getitem(self, x, weights):
return (
weights[0] * x ** 4
+ weights[1] * x ** 3
+ weights[2] * x ** 2
+ weights[3] * x
+ weights[4]
)
def __repr__(self):
return "{name}(y = {a} * x^4 + {b} * x^3 + {c} * x^2 + {d} * x + {e})".format(
name=self.__class__.__name__,
a=self._params[0],
b=self._params[1],
c=self._params[2],
d=self._params[3],
e=self._params[3],
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)
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class SynAdaptiveEnv(data.Dataset):
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"""The synethtic dataset for adaptive 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
"""
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def __init__(
self,
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num: int = 100,
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num_sin_phase: int = 7,
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min_amplitude: float = 1,
max_amplitude: float = 4,
phase_shift: float = 0,
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mode: Optional[str] = None,
):
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self._amplitude_scale = QuadraticFunc(
[(0, min_amplitude), (0.5, max_amplitude), (1, min_amplitude)]
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)
self._num_sin_phase = num_sin_phase
self._interval = 1.0 / (float(num) - 1)
self._total_num = num
fitting_data = []
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temp_max_scalar = 2 ** (num_sin_phase - 1)
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for i in range(num_sin_phase):
value = (2 ** i) / temp_max_scalar
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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)
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# Training Set 60%
num_of_train = int(self._total_num * 0.6)
# Validation Set 20%
num_of_valid = int(self._total_num * 0.2)
# Test Set 20%
num_of_set = self._total_num - num_of_train - num_of_valid
all_indexes = list(range(self._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))
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]
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position = self._interval * index
value = self._amplitude_scale[position] * math.sin(
self._period_phase_shift[position]
)
return index, position, value
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def __len__(self):
return len(self._indexes)
def __repr__(self):
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return (
"{name}({cur_num:}/{total} elements,\n"
"amplitude={amplitude},\n"
"period_phase_shift={period_phase_shift})".format(
name=self.__class__.__name__,
cur_num=self._total_num,
total=len(self),
amplitude=self._amplitude_scale,
period_phase_shift=self._period_phase_shift,
)
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)