def test_swag_cov(self, **kwargs):
model = torch.nn.Linear(300, 3, bias=True)
swag_model = SWAG(torch.nn.Linear, in_features=300, out_features=3, bias=True, subspace_type = 'covariance',
subspace_kwargs = {'max_rank':140})
optimizer = torch.optim.SGD(model.parameters(), lr = 1e-3)
# construct swag model via training
torch.manual_seed(0)
for _ in range(101):
model.zero_grad()
input = torch.randn(100, 300)
output = model(input)
loss = ((torch.randn(100, 3) - output)**2.0).sum()
loss.backward()
optimizer.step()
swag_model.collect_model(model)
# check to ensure parameters have the correct sizes
mean, var = swag_model._get_mean_and_variance()
cov_mat_sqrt = swag_model.subspace.get_space()
true_cov_mat = cov_mat_sqrt.t().matmul(cov_mat_sqrt) + torch.diag(var)
test_cutoff = chi2(df = mean.numel()).ppf(0.95) #95% quantile of p dimensional chi-square distribution
for scale in [0.01, 0.1, 0.5, 1.0, 2.0, 5.0]:
scaled_cov_mat = true_cov_mat * scale
scaled_cov_inv = torch.inverse(scaled_cov_mat)
# now test to ensure that sampling has the correct covariance matrix probabilistically
all_qforms = []
for _ in range(3000):
swag_model.sample(scale=scale)
curr_pars = []
for (module, name, _) in swag_model.base_params:
curr_pars.append(getattr(module, name))
dev = flatten(curr_pars) - mean
#(x - mu)sigma^{-1}(x - mu)
qform = dev.matmul(scaled_cov_inv).matmul(dev)
all_qforms.append(qform.item())
samples_in_cr = (np.array(all_qforms) < test_cutoff).sum()
print(samples_in_cr)
#between 94 and 96% of the samples should fall within the threshold
#this should be very loose
self.assertTrue(0.94*3000 <= samples_in_cr <= 0.96*3000)
if torch.cuda.is_available():
args.device = torch.device('cuda')
else:
args.device = torch.device('cpu')
print('Using model %s' % args.model)
model_cfg = getattr(models, args.model)
print('Preparing model')
print(*model_cfg.args)
model = model_cfg.base(*model_cfg.args,
num_classes=args.num_classes,
**model_cfg.kwargs)
model.to(args.device)
swag_model = SWAG(model_cfg.base,
subspace_type=args.subspace,
subspace_kwargs={'max_rank': args.max_num_models},
*model_cfg.args,
num_classes=args.num_classes,
**model_cfg.kwargs)
swag_model.to(args.device)
for path in args.checkpoint:
print(path)
ckpt = torch.load(path)
model.load_state_dict(ckpt['state_dict'])
swag_model.collect_model(model)
torch.save({'state_dict': swag_model.state_dict()}, args.path)
momentum=0.9,
weight_decay=1e-4)
loader = generate_dataloaders(N=10)
state_dict = None
for epoch in range(num_epochs):
model.train()
for x, y in loader:
model.zero_grad()
pred = model(x)
loss = ((pred - y)**2.0).sum()
loss.backward()
optimizer.step()
small_swag_model.collect_model(model)
if epoch == 4:
state_dict = small_swag_model.state_dict()
small_swag_model.fit()
with torch.no_grad():
x = torch.arange(-6., 6., 1.0).unsqueeze(1)
for i in range(10):
small_swag_model.sample(0.5)
small_swag_model(x)
_, _ = small_swag_model.get_space(export_cov_factor=False)
_, _, _ = small_swag_model.get_space(export_cov_factor=True)
small_swag_model.load_state_dict(state_dict)
def test_swag_diag(self, **kwargs):
model = torch.nn.Linear(300, 3, bias=True)
swag_model = SWAG(
torch.nn.Linear,
in_features=300,
out_features=3,
bias=True,
no_cov_mat=True,
max_num_models=100,
loading=False,
)
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
# construct swag model via training
torch.manual_seed(0)
for _ in range(101):
model.zero_grad()
input = torch.randn(100, 300)
output = model(input)
loss = ((torch.randn(100, 3) - output) ** 2.0).sum()
loss.backward()
optimizer.step()
swag_model.collect_model(model)
# check to ensure parameters have the correct sizes
mean_list = []
sq_mean_list = []
for (module, name), param in zip(swag_model.params, model.parameters()):
mean = module.__getattr__("%s_mean" % name)
sq_mean = module.__getattr__("%s_sq_mean" % name)
self.assertEqual(param.size(), mean.size())
self.assertEqual(param.size(), sq_mean.size())
mean_list.append(mean)
sq_mean_list.append(sq_mean)
mean = flatten(mean_list).cuda()
sq_mean = flatten(sq_mean_list).cuda()
for scale in [0.01, 0.1, 0.5, 1.0, 2.0, 5.0]:
var = scale * (sq_mean - mean ** 2)
std = torch.sqrt(var)
dist = torch.distributions.Normal(mean, std)
# now test to ensure that sampling has the correct covariance matrix probabilistically
all_qforms = 0
for _ in range(20):
swag_model.sample(scale=scale, cov=False)
curr_pars = []
for (module, name) in swag_model.params:
curr_pars.append(getattr(module, name))
curr_probs = dist.cdf(flatten(curr_pars))
# check if within 95% CI
num_in_cr = ((curr_probs > 0.025) & (curr_probs < 0.975)).float().sum()
# all_qforms.append( num_in_cr )
all_qforms += num_in_cr
# print(all_qforms/(20 * mean.numel()))
# now compute average
avg_prob_in_cr = all_qforms / (20 * mean.numel())
# CLT should hold a bit tighter here
self.assertTrue(0.945 <= avg_prob_in_cr <= 0.955)
def test_swag_cov(self, **kwargs):
model = torch.nn.Linear(300, 3, bias=True)
swag_model = SWAG(
torch.nn.Linear,
in_features=300,
out_features=3,
bias=True,
no_cov_mat=False,
max_num_models=100,
loading=False,
)
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
# construct swag model via training
torch.manual_seed(0)
for _ in range(101):
model.zero_grad()
input = torch.randn(100, 300)
output = model(input)
loss = ((torch.randn(100, 3) - output) ** 2.0).sum()
loss.backward()
optimizer.step()
swag_model.collect_model(model)
# check to ensure parameters have the correct sizes
mean_list = []
sq_mean_list = []
cov_mat_sqrt_list = []
for (module, name), param in zip(swag_model.params, model.parameters()):
mean = module.__getattr__("%s_mean" % name)
sq_mean = module.__getattr__("%s_sq_mean" % name)
cov_mat_sqrt = module.__getattr__("%s_cov_mat_sqrt" % name)
self.assertEqual(param.size(), mean.size())
self.assertEqual(param.size(), sq_mean.size())
self.assertEqual(
[swag_model.max_num_models, param.numel()], list(cov_mat_sqrt.size())
)
mean_list.append(mean)
sq_mean_list.append(sq_mean)
cov_mat_sqrt_list.append(cov_mat_sqrt)
mean = flatten(mean_list).cuda()
sq_mean = flatten(sq_mean_list).cuda()
cov_mat_sqrt = torch.cat(cov_mat_sqrt_list, dim=1).cuda()
true_cov_mat = (
1.0 / (swag_model.max_num_models - 1)
) * cov_mat_sqrt.t().matmul(cov_mat_sqrt) + torch.diag(sq_mean - mean ** 2)
test_cutoff = chi2(df=mean.numel()).ppf(
0.95
) # 95% quantile of p dimensional chi-square distribution
for scale in [0.01, 0.1, 0.5, 1.0, 2.0, 5.0]:
scaled_cov_mat = true_cov_mat * scale
scaled_cov_inv = torch.inverse(scaled_cov_mat)
# now test to ensure that sampling has the correct covariance matrix probabilistically
all_qforms = []
for _ in range(2000):
swag_model.sample(scale=scale, cov=True)
curr_pars = []
for (module, name) in swag_model.params:
curr_pars.append(getattr(module, name))
dev = flatten(curr_pars) - mean
# (x - mu)sigma^{-1}(x - mu)
qform = dev.matmul(scaled_cov_inv).matmul(dev)
all_qforms.append(qform.item())
samples_in_cr = (np.array(all_qforms) < test_cutoff).sum()
print(samples_in_cr)
# between 94 and 96% of the samples should fall within the threshold
# this should be very loose
self.assertTrue(1880 <= samples_in_cr <= 1920)