def test_svi_multi():
args = make_args()
args.assignment_grad = True
detections = generate_data(args)
pyro.clear_param_store()
pyro.param('noise_scale',
torch.tensor(args.init_noise_scale),
constraint=constraints.positive)
pyro.param('objects_loc', torch.randn(args.max_num_objects, 1))
# Learn object_loc via Newton and noise_scale via Adam.
elbo = TraceEnum_ELBO(max_plate_nesting=2)
adam = Adam({'lr': 0.1})
newton = Newton(trust_radii={'objects_loc': 1.0})
optim = MixedMultiOptimizer([(['noise_scale'], adam),
(['objects_loc'], newton)])
for svi_step in range(50):
with poutine.trace(param_only=True) as param_capture:
loss = elbo.differentiable_loss(model, guide, detections, args)
params = {
name: pyro.param(name).unconstrained()
for name in param_capture.trace.nodes.keys()
}
optim.step(loss, params)
logger.debug(
'step {: >2d}, loss = {:0.6f}, noise_scale = {:0.6f}'.format(
svi_step, loss.item(),
pyro.param('noise_scale').item()))
def test_multi_optimizer_overlap_error():
parts = [
(["x", "y"], pyro.optim.Adam({"lr": 0.1})),
(["y", "z"], pyro.optim.Adam({"lr": 0.01})),
]
with pytest.raises(ValueError):
MixedMultiOptimizer(parts)
def test_multi_optimizer_overlap_error():
parts = [(['x', 'y'], pyro.optim.Adam({'lr': 0.1})),
(['y', 'z'], pyro.optim.Adam({'lr': 0.01}))]
with pytest.raises(ValueError):
MixedMultiOptimizer(parts)
def test_multi_optimizer_disjoint_ok():
parts = [(['w', 'x'], pyro.optim.Adam({'lr': 0.1})),
(['y', 'z'], pyro.optim.Adam({'lr': 0.01}))]
MixedMultiOptimizer(parts)
import torch
from torch.distributions import constraints
import pyro
import pyro.distributions as dist
import pyro.optim
import pyro.poutine as poutine
from pyro.optim.multi import MixedMultiOptimizer, Newton, PyroMultiOptimizer, TorchMultiOptimizer
from tests.common import assert_equal
FACTORIES = [
lambda: PyroMultiOptimizer(pyro.optim.Adam({'lr': 0.05})),
lambda: TorchMultiOptimizer(torch.optim.Adam, {'lr': 0.05}),
lambda: Newton(trust_radii={'z': 0.2}),
lambda: MixedMultiOptimizer(
[(['y'], PyroMultiOptimizer(pyro.optim.Adam({'lr': 0.05}))),
(['x', 'z'], Newton())]),
lambda: MixedMultiOptimizer([(['y'], pyro.optim.Adam({'lr': 0.05})),
(['x', 'z'], Newton())]),
]
@pytest.mark.parametrize('factory', FACTORIES)
def test_optimizers(factory):
optim = factory()
def model(loc, cov):
x = pyro.param("x", torch.randn(2))
y = pyro.param("y", torch.randn(3, 2))
z = pyro.param("z",
torch.randn(4, 2).abs(),
def test_multi_optimizer_disjoint_ok():
parts = [
(["w", "x"], pyro.optim.Adam({"lr": 0.1})),
(["y", "z"], pyro.optim.Adam({"lr": 0.01})),
]
MixedMultiOptimizer(parts)
import pyro.optim
import pyro.poutine as poutine
from pyro.optim.multi import (
MixedMultiOptimizer,
Newton,
PyroMultiOptimizer,
TorchMultiOptimizer,
)
from tests.common import assert_equal
FACTORIES = [
lambda: PyroMultiOptimizer(pyro.optim.Adam({"lr": 0.05})),
lambda: TorchMultiOptimizer(torch.optim.Adam, {"lr": 0.05}),
lambda: Newton(trust_radii={"z": 0.2}),
lambda: MixedMultiOptimizer([
(["y"], PyroMultiOptimizer(pyro.optim.Adam({"lr": 0.05}))),
(["x", "z"], Newton()),
]),
lambda: MixedMultiOptimizer([(["y"], pyro.optim.Adam({"lr": 0.05})),
(["x", "z"], Newton())]),
]
@pytest.mark.parametrize("factory", FACTORIES)
def test_optimizers(factory):
optim = factory()
def model(loc, cov):
x = pyro.param("x", torch.randn(2))
y = pyro.param("y", torch.randn(3, 2))
z = pyro.param("z",
torch.randn(4, 2).abs(),
