def reset_running_statistics(self, net=None, val_mode=None): if net is None: net = self.network if not val_mode: print('-' * 30, 'Reset Running Statistics', '-' * 30) sub_train_loader = self.run_config.random_sub_train_loader(2000, 250) set_running_statistics(net, sub_train_loader) calibrate(net, sub_train_loader)
def reset_running_statistics(self, net=None): from elastic_nn.utils import set_running_statistics if net is None: net = self.net num_gpu = hvd.size() n_images = 2000 batch_size = (math.ceil(n_images / num_gpu) // 8 + 1) * 8 n_images = batch_size * num_gpu sub_train_loader = self.run_config.random_sub_train_loader( n_images, batch_size, num_replicas=num_gpu, rank=hvd.rank()) set_running_statistics(net, sub_train_loader, distributed=True)
def reset_running_statistics(self, net=None): from elastic_nn.utils import set_running_statistics if net is None: net = self.network sub_train_loader = self.run_config.random_sub_train_loader(2000, 100) subnet = set_running_statistics(net, sub_train_loader) return subnet