wehub-resource-sync
241 lines
7.4 KiB
Python
241 lines
7.4 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Notice that the following codes are modified from KerasTuner to implement our own tuner.
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# Please refer to https://github.com/keras-team/keras-tuner/blob/master/keras_tuner/engine/hyperparameters.py.
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import numpy as np
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class TunableVariable:
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"""
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TunableVariable base class.
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"""
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def __init__(self, name, default=None):
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self.name = name
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self._default = default
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@property
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def default(self):
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return self._default
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def get_state(self):
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return {"name": self.name, "default": self.default}
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@classmethod
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def from_state(cls, state):
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return cls(**state)
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class Fixed(TunableVariable):
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"""
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Fixed variable which cannot be changed.
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"""
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def __init__(self, name, default):
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super().__init__(name=name, default=default)
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self.name = name
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if not isinstance(default, (str, int, float, bool)):
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raise ValueError(
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f"Fixed must be an str, int, float or bool, but found {default}"
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)
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self._default = default
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def random(self, seed=None):
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return self._default
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def __repr__(self):
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return f"Fixed(name: {self.name}, value: {self.default})"
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class Boolean(TunableVariable):
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"""
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Choice between True and False.
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"""
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def __init__(self, name, default=False):
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super().__init__(name=name, default=default)
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if default not in {True, False}:
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raise ValueError(
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f"default must be a Python boolean, but got {default}"
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)
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def random(self, seed=None):
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rng = np.random.default_rng(seed)
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return rng.choice((True, False))
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def __repr__(self):
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return f'Boolean(name: "{self.name}", default: {self.default})'
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class Choice(TunableVariable):
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def __init__(self, name, values, default=None):
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super().__init__(name=name, default=default)
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types = {type(v) for v in values}
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if len(types) > 1:
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raise TypeError(
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f"Choice can contain only one type of value, but found values: {values} with types: {types}."
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)
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self._is_unknown_type = False
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if isinstance(values[0], str):
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values = [str(v) for v in values]
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if default is not None:
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default = str(default)
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elif isinstance(values[0], int):
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values = [int(v) for v in values]
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if default is not None:
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default = int(default)
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elif isinstance(values[0], float):
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values = [float(v) for v in values]
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if default is not None:
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default = float(default)
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elif isinstance(values[0], bool):
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values = [bool(v) for v in values]
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if default is not None:
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default = bool(default)
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else:
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self._is_unknown_type = True
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self._indices = list(range(len(values)))
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self.values = values
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if default is not None and default not in values:
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raise ValueError(
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f"The default value should be one of the choices {values}, but found {default}"
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)
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self._default = default
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@property
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def default(self):
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if self._default is None:
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if None in self.values:
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return None
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return self.values[0]
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return self._default
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def random(self, seed=None):
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rng = np.random.default_rng(seed)
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if self._is_unknown_type:
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indice = rng.choice(self._indices)
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return self.values[indice]
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else:
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return rng.choice(self.values)
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def get_state(self):
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state = super().get_state()
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state["values"] = self.values
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return state
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def __repr__(self):
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return f'Choice(name: "{self.name}", values: {self.values}, default: {self.default})'
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class IntRange(TunableVariable):
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"""
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Integer range.
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"""
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def __init__(self, name, start, stop, step=1, default=None, endpoint=False):
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super().__init__(name=name, default=default)
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self.start = self._check_int(start)
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self.stop = self._check_int(stop)
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self.step = self._check_int(step)
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self._default = default
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self.endpoint = endpoint
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@property
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def default(self):
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if self._default is not None:
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return self._default
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return self.start
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def random(self, seed=None):
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rng = np.random.default_rng(seed)
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value = (self.stop - self.start) * rng.random() + self.start
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if self.step is not None:
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if self.endpoint:
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values = np.arange(self.start, self.stop + 1e-7, step=self.step)
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else:
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values = np.arange(self.start, self.stop, step=self.step)
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closest_index = np.abs(values - value).argmin()
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value = values[closest_index]
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return int(value)
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def get_state(self):
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state = super().get_state()
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state["start"] = self.start
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state["stop"] = self.stop
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state["step"] = self.step
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state["default"] = self._default
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return state
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def _check_int(self, val):
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int_val = int(val)
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if int_val != val:
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raise ValueError(f"Expects val is an int, but found: {val}.")
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return int_val
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def __repr__(self):
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return f"IntRange(name: {self.name}, start: {self.start}, stop: {self.stop}, step: {self.step}, default: {self.default})"
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class FloatRange(TunableVariable):
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"""
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Float range.
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"""
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def __init__(
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self, name, start, stop, step=None, default=None, endpoint=False
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):
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super().__init__(name=name, default=default)
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self.stop = float(stop)
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self.start = float(start)
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if step is not None:
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self.step = float(step)
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else:
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self.step = None
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self._default = default
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self.endpoint = endpoint
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@property
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def default(self):
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if self._default is not None:
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return self._default
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return self.start
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def random(self, seed=None):
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rng = np.random.default_rng(seed)
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value = (self.stop - self.start) * rng.random() + self.start
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if self.step is not None:
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if self.endpoint:
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values = np.arange(self.start, self.stop + 1e-7, step=self.step)
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else:
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values = np.arange(self.start, self.stop, step=self.step)
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closest_index = np.abs(values - value).argmin()
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value = values[closest_index]
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return value
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def get_state(self):
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state = super().get_state()
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state["start"] = self.start
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state["stop"] = self.stop
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state["step"] = self.step
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state["endpoint"] = self.endpoint
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return state
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def __repr__(self):
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return f"FloatRange(name: {self.name}, start: {self.start}, stop: {self.stop}, step: {self.step}, default: {self.default}, endpoint: {self.endpoint})"
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