Delete Parameter.py

Parameter.py

@@ -1,188 +0,0 @@
-# MIT License
-# Copyright 2022 University of Bremen
-#
-# Permission is hereby granted, free of charge, to any person obtaining
-# a copy of this software and associated documentation files (the "Software"),
-# to deal in the Software without restriction, including without limitation
-# the rights to use, copy, modify, merge, publish, distribute, sublicense,
-# and/or sell copies of the Software, and to permit persons to whom the
-# Software is furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included
-# in all copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
-# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
-# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR
-# THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-#
-#
-# David Rotermund ( davrot@uni-bremen.de )
-#
-#
-# Release history:
-# ================
-# 1.0.0 -- 01.05.2022: first release
-#
-#
-
-# %%
-from dataclasses import dataclass, field
-import numpy as np
-import torch
-import os
-
-
-@dataclass
-class Network:
-    """Parameters of the network. The details about
-    its layers and the number of output neurons."""
-
-    number_of_output_neurons: int = field(default=0)
-    forward_neuron_numbers: list[list[int]] = field(default_factory=list)
-    is_pooling_layer: list[bool] = field(default_factory=list)
-
-    forward_kernel_size: list[list[int]] = field(default_factory=list)
-    strides: list[list[int]] = field(default_factory=list)
-    dilation: list[list[int]] = field(default_factory=list)
-    padding: list[list[int]] = field(default_factory=list)
-
-    w_trainable: list[bool] = field(default_factory=list)
-    eps_xy_trainable: list[bool] = field(default_factory=list)
-    eps_xy_mean: list[bool] = field(default_factory=list)
-
-
-@dataclass
-class LearningParameters:
-    """Parameter required for training"""
-
-    learning_active: bool = field(default=True)
-
-    loss_coeffs_mse: float = field(default=0.5)
-    loss_coeffs_kldiv: float = field(default=1.0)
-
-    optimizer_name: str = field(default="Adam")
-    learning_rate_gamma_w: float = field(default=-1.0)
-    learning_rate_gamma_eps_xy: float = field(default=-1.0)
-    learning_rate_threshold_w: float = field(default=0.00001)
-    learning_rate_threshold_eps_xy: float = field(default=0.00001)
-
-    lr_schedule_name: str = field(default="ReduceLROnPlateau")
-    lr_scheduler_use_performance: bool = field(default=True)
-    lr_scheduler_factor_w: float = field(default=0.75)
-    lr_scheduler_patience_w: int = field(default=-1)
-    lr_scheduler_tau_w: int = field(default=10)
-
-    lr_scheduler_factor_eps_xy: float = field(default=0.75)
-    lr_scheduler_patience_eps_xy: int = field(default=-1)
-    lr_scheduler_tau_eps_xy: int = field(default=10)
-
-    number_of_batches_for_one_update: int = field(default=1)
-    overload_path: str = field(default="./Previous")
-
-    weight_noise_amplitude: float = field(default=0.01)
-    eps_xy_intitial: float = field(default=0.1)
-
-    test_every_x_learning_steps: int = field(default=50)
-    test_during_learning: bool = field(default=True)
-
-    alpha_number_of_iterations: int = field(default=0)
-
-
-@dataclass
-class Augmentation:
-    """Parameters used for data augmentation."""
-
-    crop_width_in_pixel: int = field(default=2)
-
-    flip_p: float = field(default=0.5)
-
-    jitter_brightness: float = field(default=0.5)
-    jitter_contrast: float = field(default=0.1)
-    jitter_saturation: float = field(default=0.1)
-    jitter_hue: float = field(default=0.15)
-
-    use_on_off_filter: bool = field(default=True)
-
-
-@dataclass
-class ImageStatistics:
-    """(Statistical) information about the input. i.e.
-    mean values and the x and y size of the input"""
-
-    mean: list[float] = field(default_factory=list)
-    the_size: list[int] = field(default_factory=list)
-
-
-@dataclass
-class Config:
-    """Master config class."""
-
-    # Sub classes
-    network_structure: Network = field(default_factory=Network)
-    learning_parameters: LearningParameters = field(default_factory=LearningParameters)
-    augmentation: Augmentation = field(default_factory=Augmentation)
-    image_statistics: ImageStatistics = field(default_factory=ImageStatistics)
-
-    batch_size: int = field(default=500)
-    data_mode: str = field(default="")
-
-    learning_step: int = field(default=0)
-    learning_step_max: int = field(default=10000)
-
-    number_of_cpu_processes: int = field(default=-1)
-
-    number_of_spikes: int = field(default=0)
-    cooldown_after_number_of_spikes: int = field(default=-1)
-
-    weight_path: str = field(default="./Weights/")
-    eps_xy_path: str = field(default="./EpsXY/")
-    data_path: str = field(default="./")
-    results_path: str = field(default="./Results")
-
-    reduction_cooldown: float = field(default=25.0)
-    epsilon_0: float = field(default=1.0)
-
-    def __post_init__(self) -> None:
-        """Post init determines the number of cores.
-        Creates the required directory and gives us an optimized
-        (for the amount of cores) batch size."""
-        number_of_cpu_processes_temp = os.cpu_count()
-
-        if self.number_of_cpu_processes < 1:
-            if number_of_cpu_processes_temp is None:
-                self.number_of_cpu_processes = 1
-            else:
-                self.number_of_cpu_processes = number_of_cpu_processes_temp
-
-        os.makedirs(self.weight_path, exist_ok=True)
-        os.makedirs(self.eps_xy_path, exist_ok=True)
-        os.makedirs(self.data_path, exist_ok=True)
-        os.makedirs(self.results_path, exist_ok=True)
-
-        self.batch_size = (
-            self.batch_size // self.number_of_cpu_processes
-        ) * self.number_of_cpu_processes
-
-        self.batch_size = np.max((self.batch_size, self.number_of_cpu_processes))
-        self.batch_size = int(self.batch_size)
-
-    def get_epsilon_t(self):
-        """Generates the time series of the basic epsilon."""
-        np_epsilon_t: np.ndarray = np.ones((self.number_of_spikes), dtype=np.float32)
-        if (self.cooldown_after_number_of_spikes < self.number_of_spikes) and (
-            self.cooldown_after_number_of_spikes >= 0
-        ):
-            np_epsilon_t[
-                self.cooldown_after_number_of_spikes : self.number_of_spikes
-            ] /= self.reduction_cooldown
-        return torch.tensor(np_epsilon_t)
-
-    def get_update_after_x_pattern(self):
-        """Tells us after how many pattern we need to update the weights."""
-        return (
-            self.batch_size * self.learning_parameters.number_of_batches_for_one_update
-        )