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