if args.loss == "cross_entropy":
criterion = losses.seg_cross_entropy
else:
criterion = losses.seg_ale_cross_entropy
# construct and load model
if args.swa_resume is not None:
checkpoint = torch.load(args.swa_resume)
model = SWAG(
model_cfg.base,
no_cov_mat=False,
max_num_models=20,
num_classes=num_classes,
use_aleatoric=args.loss == "aleatoric",
)
model.cuda()
model.load_state_dict(checkpoint["state_dict"])
model.sample(0.0)
bn_update(loaders["fine_tune"], model)
else:
model = model_cfg.base(num_classes=num_classes,
use_aleatoric=args.loss == "aleatoric").cuda()
checkpoint = torch.load(args.resume)
start_epoch = checkpoint["epoch"]
print(start_epoch)
model.load_state_dict(checkpoint["state_dict"])
print(len(loaders["test"]))
if args.use_test:
print("Using test dataset")
class RegressionRunner(RegressionModel):
def __init__(self,
base,
epochs,
criterion,
batch_size=50,
lr_init=1e-2,
momentum=0.9,
wd=1e-4,
swag_lr=1e-3,
swag_freq=1,
swag_start=50,
subspace_type='pca',
subspace_kwargs={'max_rank': 20},
use_cuda=False,
use_swag=False,
double_bias_lr=False,
model_variance=True,
num_samples=30,
scale=0.5,
const_lr=False,
*args,
**kwargs):
self.base = base
self.model = base(*args, **kwargs)
num_pars = 0
for p in self.model.parameters():
num_pars += p.numel()
print('number of parameters: ', num_pars)
if use_cuda:
self.model.cuda()
if use_swag:
self.swag_model = SWAG(base,
subspace_type=subspace_type,
subspace_kwargs=subspace_kwargs,
*args,
**kwargs)
if use_cuda:
self.swag_model.cuda()
else:
self.swag_model = None
self.use_cuda = use_cuda
if not double_bias_lr:
pars = self.model.parameters()
else:
pars = []
for name, module in self.model.named_parameters():
if 'bias' in str(name):
print('Doubling lr of ', name)
pars.append({'params': module, 'lr': 2.0 * lr_init})
else:
pars.append({'params': module, 'lr': lr_init})
self.optimizer = torch.optim.SGD(pars,
lr=lr_init,
momentum=momentum,
weight_decay=wd)
self.const_lr = const_lr
self.batch_size = batch_size
# TODO: set up criterions better for classification
if model_variance:
self.criterion = criterion(noise_var=None)
else:
self.criterion = criterion(noise_var=1.0)
if self.criterion.noise_var is not None:
self.var = self.criterion.noise_var
self.epochs = epochs
self.lr_init = lr_init
self.use_swag = use_swag
self.swag_start = swag_start
self.swag_lr = swag_lr
self.swag_freq = swag_freq
self.num_samples = num_samples
self.scale = scale
def train(self,
model,
loader,
optimizer,
criterion,
lr_init=1e-2,
epochs=3000,
swag_model=None,
swag=False,
swag_start=2000,
swag_freq=50,
swag_lr=1e-3,
print_freq=100,
use_cuda=False,
const_lr=False):
# copied from pavels regression notebook
if const_lr:
lr = lr_init
train_res_list = []
for epoch in range(epochs):
if not const_lr:
t = (epoch + 1) / swag_start if swag else (epoch + 1) / epochs
lr_ratio = swag_lr / lr_init if swag else 0.05
if t <= 0.5:
factor = 1.0
elif t <= 0.9:
factor = 1.0 - (1.0 - lr_ratio) * (t - 0.5) / 0.4
else:
factor = lr_ratio
lr = factor * lr_init
adjust_learning_rate(optimizer, factor)
train_res = utils.train_epoch(loader,
model,
criterion,
optimizer,
cuda=use_cuda,
regression=True)
train_res_list.append(train_res)
if swag and epoch > swag_start:
swag_model.collect_model(model)
if (epoch % print_freq == 0 or epoch == epochs - 1):
print('Epoch %d. LR: %g. Loss: %.4f' %
(epoch, lr, train_res['loss']))
return train_res_list
def fit(self, features, labels):
self.features, self.labels = torch.FloatTensor(
features), torch.FloatTensor(labels)
# construct data loader
self.data_loader = DataLoader(TensorDataset(self.features,
self.labels),
batch_size=self.batch_size)
# now train with pre-specified options
result = self.train(model=self.model,
loader=self.data_loader,
optimizer=self.optimizer,
criterion=self.criterion,
lr_init=self.lr_init,
swag_model=self.swag_model,
swag=self.use_swag,
swag_start=self.swag_start,
swag_freq=self.swag_freq,
swag_lr=self.swag_lr,
use_cuda=self.use_cuda,
epochs=self.epochs,
const_lr=self.const_lr)
if self.criterion.noise_var is not None:
# another forwards pass through network to estimate noise variance
preds, targets = utils.predictions(model=self.model,
test_loader=self.data_loader,
regression=True,
cuda=self.use_cuda)
self.var = np.power(np.linalg.norm(preds - targets),
2.0) / targets.shape[0]
print(self.var)
return result
def predict(self, features, swag_model=None):
"""
default prediction method is to use built in Low rank Gaussian
SWA: scale = 0.0, num_samples = 1
"""
swag_model = swag_model if swag_model is not None else self.swag_model
if self.use_cuda:
device = torch.device('cuda')
else:
device = torch.device('cpu')
with torch.no_grad():
if swag_model is None:
self.model.eval()
preds = self.model(
torch.FloatTensor(features).to(device)).data.cpu()
if preds.size(1) == 1:
var = torch.ones_like(preds[:, 0]).unsqueeze(1) * self.var
else:
var = preds[:, 1].view(-1, 1)
preds = preds[:, 0].view(-1, 1)
print(var.mean())
else:
prediction = 0
sq_prediction = 0
for _ in range(self.num_samples):
swag_model.sample(scale=self.scale)
current_prediction = swag_model(
torch.FloatTensor(features).to(device)).data.cpu()
prediction += current_prediction
if current_prediction.size(1) == 2:
#convert to standard deviation
current_prediction[:, 1] = current_prediction[:,
1]**0.5
sq_prediction += current_prediction**2.0
# preds = bma/(self.num_samples)
# compute mean of prediction
# \mu^*
preds = (prediction[:, 0] / self.num_samples).view(-1, 1)
# 1/M \sum(\sigma^2(x) + \mu^2(x)) - \mu*^2
var = torch.sum(sq_prediction, 1, keepdim=True
) / self.num_samples - preds.pow(2.0)
# add variance if not heteroscedastic
if prediction.size(1) == 1:
var = var + self.var
return preds.numpy(), var.numpy()