def __init__(self, config, install_dir): AgentLogicBase.__init__(self, config) self.dr = WinDataRetriver() self.commandHandler = CommandHandlerWin() hooks_dir = os.path.join(install_dir, 'hooks') self.hooks = Hooks(logging.getLogger('Hooks'), hooks_dir) set_bcd_useplatformclock()
def __init__(self, config, install_dir): AgentLogicBase.__init__(self, config) self.dr = WinDataRetriver() self.commandHandler = CommandHandlerWin(self.dr) hooks_dir = os.path.join(install_dir, 'hooks') self.hooks = Hooks(logging.getLogger('Hooks'), hooks_dir) if config.getboolean('general', 'apply_timer_configuration'): apply_clock_tuning()
def __init__(self, config): AgentLogicBase.__init__(self, config) self.dr = LinuxDataRetriver() self.dr.app_list = config.get("general", "applications_list") self.dr.ignored_fs = set(config.get("general", "ignored_fs").split()) self.dr.ignore_zero_size_fs = config.get("general", "ignore_zero_size_fs") self.commandHandler = CommandHandlerLinux(self) self.cred_server = CredServer() self.hooks = Hooks(logging.getLogger('Hooks'), _GUEST_HOOKS_CONFIG_PATH)
def main(): train_dataset = ImagenetteDataset(training=True) val_dataset = ImagenetteDataset(training=False) bs = 64 train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=bs) val_dataloader = DataLoader(val_dataset, shuffle=False, batch_size=bs) net = ConvNet(in_ch=3).cuda() hooks = Hooks(net) optim = torch.optim.SGD(net.parameters(), lr=0.5) lossfxn = nn.CrossEntropyLoss() for epoch in range(5): pbar = tqdm(train_dataloader) for batch in pbar: optim.zero_grad() imgs = batch['image'].cuda() labels = batch['label'].cuda() preds = net(imgs) loss = lossfxn(preds, labels) pbar.set_postfix({'Loss': float(loss)}) loss.backward() optim.step() hooks.show_me() pbar = tqdm(val_dataloader) total = 0 correct = 0 for batch in pbar: imgs = batch['image'].cuda() labels = batch['label'].cuda() with torch.no_grad(): preds = net(imgs) assert_shapes(labels, ['bs'], preds, ['bs', 10]) total += labels.numel() correct += (labels == torch.argmax(preds, dim=1)).sum().item() print(f'Top 1 accuracy is {correct/total}')