Delete Parameter.py
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David Rotermund
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Parameter.py
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Parameter.py
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# MIT License
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# Copyright 2022 University of Bremen
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#
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# Permission is hereby granted, free of charge, to any person obtaining
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# a copy of this software and associated documentation files (the "Software"),
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# to deal in the Software without restriction, including without limitation
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# the rights to use, copy, modify, merge, publish, distribute, sublicense,
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# and/or sell copies of the Software, and to permit persons to whom the
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# Software is furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included
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# in all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
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# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
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# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
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# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR
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# THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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#
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#
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# David Rotermund ( davrot@uni-bremen.de )
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#
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#
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# Release history:
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# ================
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# 1.0.0 -- 01.05.2022: first release
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#
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#
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# %%
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from dataclasses import dataclass, field
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import numpy as np
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import torch
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import os
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@dataclass
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class Network:
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"""Parameters of the network. The details about
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its layers and the number of output neurons."""
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number_of_output_neurons: int = field(default=0)
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forward_neuron_numbers: list[list[int]] = field(default_factory=list)
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is_pooling_layer: list[bool] = field(default_factory=list)
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forward_kernel_size: list[list[int]] = field(default_factory=list)
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strides: list[list[int]] = field(default_factory=list)
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dilation: list[list[int]] = field(default_factory=list)
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padding: list[list[int]] = field(default_factory=list)
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w_trainable: list[bool] = field(default_factory=list)
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eps_xy_trainable: list[bool] = field(default_factory=list)
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eps_xy_mean: list[bool] = field(default_factory=list)
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@dataclass
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class LearningParameters:
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"""Parameter required for training"""
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learning_active: bool = field(default=True)
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loss_coeffs_mse: float = field(default=0.5)
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loss_coeffs_kldiv: float = field(default=1.0)
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optimizer_name: str = field(default="Adam")
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learning_rate_gamma_w: float = field(default=-1.0)
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learning_rate_gamma_eps_xy: float = field(default=-1.0)
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learning_rate_threshold_w: float = field(default=0.00001)
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learning_rate_threshold_eps_xy: float = field(default=0.00001)
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lr_schedule_name: str = field(default="ReduceLROnPlateau")
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lr_scheduler_use_performance: bool = field(default=True)
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lr_scheduler_factor_w: float = field(default=0.75)
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lr_scheduler_patience_w: int = field(default=-1)
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lr_scheduler_tau_w: int = field(default=10)
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lr_scheduler_factor_eps_xy: float = field(default=0.75)
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lr_scheduler_patience_eps_xy: int = field(default=-1)
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lr_scheduler_tau_eps_xy: int = field(default=10)
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number_of_batches_for_one_update: int = field(default=1)
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overload_path: str = field(default="./Previous")
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weight_noise_amplitude: float = field(default=0.01)
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eps_xy_intitial: float = field(default=0.1)
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test_every_x_learning_steps: int = field(default=50)
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test_during_learning: bool = field(default=True)
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alpha_number_of_iterations: int = field(default=0)
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@dataclass
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class Augmentation:
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"""Parameters used for data augmentation."""
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crop_width_in_pixel: int = field(default=2)
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flip_p: float = field(default=0.5)
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jitter_brightness: float = field(default=0.5)
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jitter_contrast: float = field(default=0.1)
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jitter_saturation: float = field(default=0.1)
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jitter_hue: float = field(default=0.15)
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use_on_off_filter: bool = field(default=True)
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@dataclass
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class ImageStatistics:
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"""(Statistical) information about the input. i.e.
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mean values and the x and y size of the input"""
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mean: list[float] = field(default_factory=list)
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the_size: list[int] = field(default_factory=list)
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@dataclass
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class Config:
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"""Master config class."""
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# Sub classes
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network_structure: Network = field(default_factory=Network)
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learning_parameters: LearningParameters = field(default_factory=LearningParameters)
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augmentation: Augmentation = field(default_factory=Augmentation)
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image_statistics: ImageStatistics = field(default_factory=ImageStatistics)
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batch_size: int = field(default=500)
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data_mode: str = field(default="")
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learning_step: int = field(default=0)
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learning_step_max: int = field(default=10000)
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number_of_cpu_processes: int = field(default=-1)
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number_of_spikes: int = field(default=0)
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cooldown_after_number_of_spikes: int = field(default=-1)
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weight_path: str = field(default="./Weights/")
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eps_xy_path: str = field(default="./EpsXY/")
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data_path: str = field(default="./")
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results_path: str = field(default="./Results")
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reduction_cooldown: float = field(default=25.0)
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epsilon_0: float = field(default=1.0)
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def __post_init__(self) -> None:
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"""Post init determines the number of cores.
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Creates the required directory and gives us an optimized
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(for the amount of cores) batch size."""
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number_of_cpu_processes_temp = os.cpu_count()
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if self.number_of_cpu_processes < 1:
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if number_of_cpu_processes_temp is None:
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self.number_of_cpu_processes = 1
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else:
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self.number_of_cpu_processes = number_of_cpu_processes_temp
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os.makedirs(self.weight_path, exist_ok=True)
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os.makedirs(self.eps_xy_path, exist_ok=True)
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os.makedirs(self.data_path, exist_ok=True)
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os.makedirs(self.results_path, exist_ok=True)
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self.batch_size = (
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self.batch_size // self.number_of_cpu_processes
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) * self.number_of_cpu_processes
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self.batch_size = np.max((self.batch_size, self.number_of_cpu_processes))
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self.batch_size = int(self.batch_size)
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def get_epsilon_t(self):
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"""Generates the time series of the basic epsilon."""
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np_epsilon_t: np.ndarray = np.ones((self.number_of_spikes), dtype=np.float32)
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if (self.cooldown_after_number_of_spikes < self.number_of_spikes) and (
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self.cooldown_after_number_of_spikes >= 0
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):
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np_epsilon_t[
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self.cooldown_after_number_of_spikes : self.number_of_spikes
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] /= self.reduction_cooldown
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return torch.tensor(np_epsilon_t)
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def get_update_after_x_pattern(self):
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"""Tells us after how many pattern we need to update the weights."""
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return (
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self.batch_size * self.learning_parameters.number_of_batches_for_one_update
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)
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