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 makeTrainer():
device = torch.device('cuda')
CNN = smallCNN(**net_config).to(device)
fullCNN = nn.Sequential(C10augLayers(),CNN)
trainset, testset = CIFAR10(False, '~/datasets/cifar10/')
dataloaders = {}
dataloaders['train'], dataloaders['dev'] = getLabLoader(trainset,**loader_config)
dataloaders = {k: loader_to(device)(v) for k,v in dataloaders.items()}
opt_constr = lambda params: optim.SGD(params, **opt_config)
lr_sched = cosLr(**sched_config)
return Classifier(fullCNN,dataloaders,opt_constr,lr_sched,**trainer_config,tracked_hypers=all_hypers)
def makeTrainer(config):
cfg = {
'dataset': CIFAR10,
'network': iCNN,
'net_config': {},
'loader_config': {
'amnt_dev': 5000,
'lab_BS': 32,
'pin_memory': True,
'num_workers': 3
},
'opt_config': {
'lr': .0003,
}, # 'momentum':.9, 'weight_decay':1e-4,'nesterov':True},
'num_epochs': 100,
'trainer_config': {},
'parallel': False,
}
recursively_update(cfg, config)
train_transforms = transforms.Compose(
[transforms.ToTensor(),
transforms.RandomHorizontalFlip()])
trainset = cfg['dataset'](
'~/datasets/{}/'.format(cfg['dataset']),
flow=True,
)
device = torch.device('cuda')
fullCNN = cfg['network'](num_classes=trainset.num_classes,
**cfg['net_config']).to(device)
if cfg['parallel']: fullCNN = multigpu_parallelize(fullCNN, cfg)
dataloaders = {}
dataloaders['train'], dataloaders['dev'] = getLabLoader(
trainset, **cfg['loader_config'])
dataloaders['Train'] = islice(dataloaders['train'],
10000 // cfg['loader_config']['lab_BS'])
if len(dataloaders['dev']) == 0:
testset = cfg['dataset']('~/datasets/{}/'.format(cfg['dataset']),
train=False,
flow=True)
dataloaders['test'] = DataLoader(
testset,
batch_size=cfg['loader_config']['lab_BS'],
shuffle=False)
dataloaders = {k: LoaderTo(v, device)
for k, v in dataloaders.items()} #LoaderTo(v,device)
opt_constr = lambda params: torch.optim.Adam(params, **cfg['opt_config'
])
lr_sched = cosLr(cfg['num_epochs'])
return Flow(fullCNN, dataloaders, opt_constr, lr_sched,
**cfg['trainer_config'])
def makeTrainer(config):
cfg = {
'dataset': CIFAR10,
'network': layer13s,
'net_config': {},
'loader_config': {
'amnt_dev': 5000,
'lab_BS': 20,
'pin_memory': True,
'num_workers': 2
},
'opt_config': {
'lr': .3e-4
}, #, 'momentum':.9, 'weight_decay':1e-4,'nesterov':True},
'num_epochs': 100,
'trainer_config': {},
}
recursively_update(cfg, config)
trainset = cfg['dataset']('~/datasets/{}/'.format(cfg['dataset']),
flow=True)
device = torch.device('cuda')
fullCNN = torch.nn.Sequential(
trainset.default_aug_layers(),
cfg['network'](num_classes=trainset.num_classes,
**cfg['net_config']).to(device))
dataloaders = {}
dataloaders['train'], dataloaders['dev'] = getLabLoader(
trainset, **cfg['loader_config'])
dataloaders['Train'] = islice(dataloaders['train'],
10000 // cfg['loader_config']['lab_BS'])
if len(dataloaders['dev']) == 0:
testset = cfg['dataset']('~/datasets/{}/'.format(cfg['dataset']),
train=False)
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']) #torch.optim.SGD(params, **cfg['opt_config'])
lr_sched = cosLr(cfg['num_epochs'])
return iClassifier(fullCNN, dataloaders, opt_constr, lr_sched,
**cfg['trainer_config'])