def save(self, model, iter_nb, train_metrics_values, test_metrics_values, tasks_weights=[], optimizer=None):
    self.logs_dict['train'][str(iter_nb)] = {}
    self.logs_dict['val'][str(iter_nb)] = {}
    for k in range(len(self.metrics)):
        self.logs_dict['train'][str(iter_nb)][self.metrics[k]] = float(train_metrics_values[k])
        self.logs_dict['val'][str(iter_nb)][self.metrics[k]] = float(test_metrics_values[k])

    if len(tasks_weights) > 0:
        for k in range(len(tasks_weights)):
            self.logs_dict['val'][str(iter_nb)]['weight_' + str(k)] = tasks_weights[k]

    with open(self.logs_file, 'w') as f:
        json.dump(self.logs_dict, f)

    ckpt = {
        'model_state_dict': model.state_dict(),
        'iter_nb': iter_nb,
    }
    if optimizer:
        ckpt['optimizer_state_dict'] = optimizer.state_dict()

    # Saves best miou score if reached
    if 'MEAN_IOU' in self.metrics:
        miou = float(test_metrics_values[self.metrics.index('MEAN_IOU')])
        if miou > self.best_miou and iter_nb > 0:
            print('Best miou. Saving it.')
            torch.save(ckpt, self.best_miou_weights_file)
            self.best_miou = miou
            self.config_dict['best_miou'] = self.best_miou
    # Saves best relative error if reached
    if 'REL_ERR' in self.metrics:
        rel_error = float(test_metrics_values[self.metrics.index('REL_ERR')])
        if rel_error < self.best_rel_error and iter_nb > 0:
            print('Best rel error. Saving it.')
            torch.save(ckpt, self.best_rel_error_weights_file)
            self.best_rel_error = rel_error
            self.config_dict['best_rel_error'] = self.best_rel_error

    # Saves last checkpoint
    torch.save(ckpt, self.last_checkpoint_weights_file)
    self.iter_nb = iter_nb
    self.config_dict['iter'] = self.iter_nb
    with open(self.config_file, 'w') as f:
        json.dump(self.config_dict, f)