/examples/pytorch/hardgat/utils.py

"""
Graph Representation Learning via Hard Attention Networks in DGL using Adam optimization.
References
----------
Paper: https://arxiv.org/abs/1907.04652
"""

import numpy as np
import torch
import torch.nn as nn

class EarlyStopping:
    def __init__(self, patience=10):
        self.patience = patience
        self.counter = 0
        self.best_score = None
        self.early_stop = False

    def step(self, acc, model):
        score = acc
        if self.best_score is None:
            self.best_score = score
            self.save_checkpoint(model)
        elif score < self.best_score:
            self.counter += 1
            print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
            if self.counter >= self.patience:
                self.early_stop = True
        else:
            self.best_score = score
            self.save_checkpoint(model)
            self.counter = 0
        return self.early_stop

    def save_checkpoint(self, model):
        '''Saves model when validation loss decrease.'''
        torch.save(model.state_dict(), 'es_checkpoint.pt')