utils.py

import inspect
import torch
import numpy as np
import random

class LinearSchedule(object):
    def __init__(self, schedule_timesteps, final_p, initial_p=1.0):
        """Linear interpolation between initial_p and final_p over
        schedule_timesteps. After this many timesteps pass final_p is
        returned.

        Parameters
        ----------
        schedule_timesteps: int
            Number of timesteps for which to linearly anneal initial_p
            to final_p
        initial_p: float
            initial output value
        final_p: float
            final output value
        """
        self.schedule_timesteps = schedule_timesteps
        self.final_p = final_p
        self.initial_p = initial_p

    def __call__(self, t):
        """See Schedule.value"""
        dt = min(float(t) / self.schedule_timesteps, 1.0)
        return self.initial_p + dt * (self.final_p - self.initial_p)
    
def set_global_seeds(seed):
    """
    set the seed for python random, tensorflow, numpy and gym spaces

    :param seed: (int) the seed
    """
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)
    
class Parameters:
    def save_parameters(self, ignore=[]):
        """Save function arguments into class attributes"""
        frame = inspect.currentframe().f_back
        _, _, _, local_vars = inspect.getargvalues(frame)
        self.hparams = {k:v for k, v in local_vars.items()
                        if k not in set(ignore+['self']) and not k.startswith('_')}
        for k, v in self.hparams.items():
            setattr(self, k, v)