def test_optimizer(self):
buffer = torch.ones(1)
weight = torch.ones(1, requires_grad=True)
try:
optimizer = torch.optim.Optimizer(weight, {})
except TypeError:
pass
try:
optimizer = torch.optim.Optimizer([], {})
except ValueError:
pass
optimizer = torch.optim.Optimizer([weight], {})
try:
optimizer.add_param_group([weight])
except TypeError:
pass
try:
optimizer.add_param_group({'params': {'param': weight}})
except TypeError:
pass
try:
optimizer.add_param_group({'params': buffer})
except ValueError:
pass
try:
optimizer.add_param_group({'params': weight})
except ValueError:
pass
_ = repr(optimizer)
def test_sgd(self):
weight = torch.ones(1, requires_grad=True)
entries = [(-0.1, 0., False), (0.1, -0.1, False), (0.1, 0., True)]
for lr, momentum, nesterov in entries:
try:
_ = torch.optim.SGD([weight], lr=lr, momentum=momentum, nesterov=nesterov)
except ValueError:
pass
def test_step(self):
weight1 = torch.ones(1, requires_grad=True)
weight2 = torch.ones(1, requires_grad=True)
optimizer = torch.optim.SGD([weight1, weight2], 0.1)
y = weight1 + 1
y.backward(y)
optimizer.step()
self.assertLessEqual(float(weight1) - 0.8, 1e-5)
optimizer.zero_grad()
self.assertLessEqual(float(weight1.grad) - 0., 1e-5)
optimizer.zero_grad(reset=True)
self.assertEqual(weight1.grad.numel(), 0)
for i in range(2):
y = weight1 + 1
y.backward(y)
optimizer.accumulate(0 if i == 0 else 1)
optimizer.step()
self.assertLessEqual(float(weight1) - 0.6, 1e-5)
def test_step(self):
weight1 = torch.ones(1, requires_grad=True)
weight2 = torch.ones(1, requires_grad=True)
optimizer = torch.optim.SGD([weight1, weight2], 0.1)
y = weight1 + 1
y.backward(y)
optimizer.step()
self.assertLessEqual(float(weight1) - 0.8, 1e-5)
optimizer.zero_grad()
self.assertLessEqual(float(weight1.grad) - 0., 1e-5)
optimizer.zero_grad(set_to_none=True)
self.assertEqual(weight1.grad, None)
for i in range(3):
y = weight1 + 1
y.backward(y)
optimizer.sum_grad()
optimizer.step()
self.assertLessEqual(float(weight1) - 0.6, 1e-5)
def test_lars(self):
weight = torch.ones(1, requires_grad=True)
entries = [(-0.1, 0, 0.001), (0.1, -0.1, 0.001), (0.1, 0, -0.001),
(0.1, 0.9, 0.001)]
for lr, momentum, trust_coef in entries:
try:
_ = torch.optim.LARS([weight],
lr=lr,
momentum=momentum,
trust_coef=trust_coef)
except ValueError:
pass
def test_adam(self):
weight = torch.ones(1, requires_grad=True)
entries = [(-0.1, (0., 0.), 1e-8, False),
(0.1, (0., 0.), -1e-8, False),
(0.1, (-0.9, 0.), 1e-8, False),
(0.1, (0.9, -0.999), 1e-8, False),
(0.1, (0.9, 0.999), 1e-8, False),
(0.1, (0.9, 0.999), 1e-8, True)]
for lr, betas, eps, amsgrad in entries:
try:
_ = torch.optim.Adam([weight], lr=lr, betas=betas, eps=eps, amsgrad=amsgrad)
except (ValueError, NotImplementedError):
pass
def test_channel_normalize(self):
entries = [((2, 3, 4), [(1., 2., 3.), (3., 2., 1.), 1], {
'dims': (0, 1, 2)
}), ((2, 3, 4), [(1., 2., 3.), (3., 2., 1.), 2], {
'dims': (0, 2, 1)
})]
for shape, args, kwargs in entries:
perm = kwargs['dims']
data = np.ones(shape, dtype='uint8').transpose(perm)
mean = np.array(args[0]).reshape((1, 3, 1)).transpose(perm)
std = np.array(args[1]).reshape((1, 3, 1)).transpose(perm)
x = torch.ones(shape, dtype='uint8')
y = torch.channel_normalize(x, *args, **kwargs)
self.assertEqual(y, (data - mean) / std)
def test_rmsprop(self):
weight = torch.ones(1, requires_grad=True)
entries = [(-0.1, (0., 0.), 1e-8, False),
(0.1, (0., 0.), -1e-8, False),
(0.1, (-0.99, 0.), 1e-8, False),
(0.1, (0.99, -0.9), 1e-8, False),
(0.1, (0.99, 0.9), 1e-8, False),
(0.1, (0.99, 0.9), 1e-8, True)]
for lr, (alpha, momentum), eps, centered in entries:
try:
_ = torch.optim.RMSprop(
[weight], lr=lr, alpha=alpha, eps=eps,
momentum=momentum, centered=centered)
except ValueError:
pass
