def makeTabularTrainer(**config):
cfg = {'dataset':HEPMASS,'network':SmallNN,'net_config': {},
'loader_config': {'amnt_labeled':20+5000,'amnt_dev':5000,'lab_BS':20},
'opt_config': {'lr':1e-4},#{'lr':.1, 'momentum':.9, 'weight_decay':1e-4, 'nesterov':True},
'num_epochs':200,
'unlab_loader_config':{'batch_size':2000,'num_workers':4,'pin_memory':True},
'trainer_config':{'log_dir':os.path.expanduser('~/tb-experiments/UCI/'),'log_args':{'minPeriod':.1, 'timeFrac':3/10}},
}
recursively_update(cfg,config)
trainset = cfg['dataset'](train=True)
testset = cfg['dataset'](train=False)
print(f"Trainset: {len(trainset)}, Testset: {len(testset)}")
device = torch.device('cuda')
model = cfg['network'](num_classes=trainset.num_classes,dim_in=trainset.dim,**cfg['net_config']).to(device)
dataloaders = {}
dataloaders['lab'], dataloaders['dev'] = getLabLoader(trainset,**cfg['loader_config'])
dataloaders['train'] = dataloaders['Train'] = dataloaders['lab']
full_data_loader = DataLoader(trainset,shuffle=True,**cfg['unlab_loader_config'])
dataloaders['_unlab'] = imap(lambda z: z[0], full_data_loader)
dataloaders['test'] = DataLoader(testset,batch_size=cfg['loader_config']['lab_BS'],shuffle=False)
dataloaders = {k:LoaderTo(v,device) for k,v in dataloaders.items()}
opt_constr = lambda params: torch.optim.Adam(params, **cfg['opt_config'])
lr_sched = lambda e: 1.#cosLr(cfg['num_epochs'])
return cfg['trainer'](model,dataloaders,opt_constr,lr_sched,**cfg['trainer_config'])
def logStuff(self, step, minibatch=None):
bpd_func = lambda mb: (self.model.nll(mb).mean().cpu().data.numpy() /
mb.shape[-1] + np.log(256)) / np.log(2)
acc_func = lambda mb: self.model.prior.classify(self.model(mb[
0])).type_as(mb[1]).eq(mb[1]).cpu().data.numpy().mean()
metrics = {}
with Eval(self.model), torch.no_grad():
#metrics['Train_bpd'] = self.evalAverageMetrics(self.dataloaders['unlab'],bpd_func)
metrics['val_bpd'] = self.evalAverageMetrics(
imap(lambda z: z[0], self.dataloaders['val']), bpd_func)
metrics['Train_Acc'] = self.evalAverageMetrics(
self.dataloaders['Train'], acc_func)
metrics['val_Acc'] = self.evalAverageMetrics(
self.dataloaders['val'], acc_func)
metrics['test_Acc'] = self.evalAverageMetrics(
self.dataloaders['test'], acc_func)
if minibatch:
metrics['Unlab_loss(mb)'] = self.model.nll(
minibatch[1]).mean().cpu().data.numpy()
self.logger.add_scalars('metrics', metrics, step)
super().logStuff(step, minibatch)
def LoaderTo(loader):
return imap(functools.partial(minibatch_to), loader)