def test_jvp_zeros(self):
def foo(x):
def bar(y):
return np.sin(x * y)
return jvp(bar, (3 * x,), (2 * x,))
jtu.check_eq(jit(foo)(0.5), foo(0.5))
def test_update_params():
params = {"a": {"b": {"c": {"d": 1}, "e": np.array(2)}, "f": np.ones(4)}}
prior = {"a.b.c.d": dist.Delta(4), "a.f": dist.Delta(5)}
new_params = deepcopy(params)
with handlers.seed(rng_seed=0):
_update_params(params, new_params, prior)
assert params == {
"a": {
"b": {
"c": {
"d": ParamShape(())
},
"e": 2
},
"f": ParamShape((4, ))
}
}
test_util.check_eq(
new_params,
{
"a": {
"b": {
"c": {
"d": np.array(4.0)
},
"e": np.array(2)
},
"f": np.full((4, ), 5.0),
}
},
)
def test_save_restore_checkpoints_w_float_steps(self):
tmp_dir = self.create_tempdir().full_path
test_object0 = {'a': np.array([0, 0, 0], np.int32),
'b': np.array([0, 0, 0], np.int32)}
test_object1 = {'a': np.array([1, 2, 3], np.int32),
'b': np.array([1, 1, 1], np.int32)}
test_object2 = {'a': np.array([4, 5, 6], np.int32),
'b': np.array([2, 2, 2], np.int32)}
# Create leftover temporary checkpoint, which should be ignored.
gfile.GFile(os.path.join(tmp_dir, 'test_tmp'), 'w')
checkpoints.save_checkpoint(
tmp_dir, test_object1, 0.0, prefix='test_', keep=1)
self.assertIn('test_0.0', os.listdir(tmp_dir))
new_object = checkpoints.restore_checkpoint(
tmp_dir, test_object0, prefix='test_')
jtu.check_eq(new_object, test_object1)
checkpoints.save_checkpoint(
tmp_dir, test_object1, 2.0, prefix='test_', keep=1)
with self.assertRaises(errors.InvalidCheckpointError):
checkpoints.save_checkpoint(
tmp_dir, test_object2, 1.0, prefix='test_', keep=1)
checkpoints.save_checkpoint(
tmp_dir, test_object2, 3.0, prefix='test_', keep=2)
self.assertIn('test_3.0', os.listdir(tmp_dir))
self.assertIn('test_2.0', os.listdir(tmp_dir))
jtu.check_eq(new_object, test_object1)
def test_optimized_lstm_cell_matches_regular(self):
# Create regular LSTMCell.
rng = random.PRNGKey(0)
key1, key2 = random.split(rng)
x = random.normal(key1, (2, 3))
c0, h0 = nn.LSTMCell.initialize_carry(rng, (2, ), 4)
self.assertEqual(c0.shape, (2, 4))
self.assertEqual(h0.shape, (2, 4))
lstm = nn.LSTMCell()
(_, y), lstm_params = lstm.init_with_output(key2, (c0, h0), x)
# Create OptimizedLSTMCell.
rng = random.PRNGKey(0)
key1, key2 = random.split(rng)
x = random.normal(key1, (2, 3))
c0, h0 = nn.OptimizedLSTMCell.initialize_carry(rng, (2, ), 4)
self.assertEqual(c0.shape, (2, 4))
self.assertEqual(h0.shape, (2, 4))
lstm_opt = nn.OptimizedLSTMCell()
(_, y_opt), lstm_opt_params = lstm_opt.init_with_output(
key2, (c0, h0), x)
np.testing.assert_allclose(y, y_opt, rtol=1e-6)
jtu.check_eq(lstm_params, lstm_opt_params)
def test_update_params():
params = {'a': {'b': {'c': {'d': 1}, 'e': np.array(2)}, 'f': np.ones(4)}}
prior = {'a.b.c.d': dist.Delta(4), 'a.f': dist.Delta(5)}
new_params = deepcopy(params)
with handlers.seed(rng_seed=0):
_update_params(params, new_params, prior)
assert params == {
'a': {
'b': {
'c': {
'd': ParamShape(())
},
'e': 2
},
'f': ParamShape((4, ))
}
}
test_util.check_eq(
new_params, {
'a': {
'b': {
'c': {
'd': np.array(4.)
},
'e': np.array(2)
},
'f': np.full((4, ), 5.)
}
})
def test_optimized_lstm_cell_matches_regular(self):
# Create regular LSTMCell.
rng = random.PRNGKey(0)
key1, key2 = random.split(rng)
x = random.normal(key1, (2, 3))
c0, h0 = nn.LSTMCell.initialize_carry(rng, (2, ), 4)
self.assertEqual(c0.shape, (2, 4))
self.assertEqual(h0.shape, (2, 4))
(carry, y), initial_params = nn.LSTMCell.init(key2, (c0, h0), x)
lstm = nn.Model(nn.LSTMCell, initial_params)
# Create OptimizedLSTMCell.
rng = random.PRNGKey(0)
key1, key2 = random.split(rng)
x = random.normal(key1, (2, 3))
c0, h0 = nn.OptimizedLSTMCell.initialize_carry(rng, (2, ), 4)
self.assertEqual(c0.shape, (2, 4))
self.assertEqual(h0.shape, (2, 4))
(carry, y_opt), initial_params = nn.OptimizedLSTMCell.partial(
name='LSTMCell').init(key2, (c0, h0), x)
lstm_opt = nn.Model(nn.OptimizedLSTMCell.partial(name='LSTMCell'),
initial_params)
onp.testing.assert_allclose(y, y_opt, rtol=1e-6)
jtu.check_eq(lstm.params, lstm_opt.params)
def test_fori_collect():
def f(x):
return {'i': x['i'] + x['j'], 'j': x['i'] - x['j']}
a = {'i': jnp.array([0.]), 'j': jnp.array([1.])}
expected_tree = {'i': jnp.array([[0.], [2.]])}
actual_tree = fori_collect(1, 3, f, a, transform=lambda a: {'i': a['i']})
check_eq(actual_tree, expected_tree)
def test_jvp_zeros(self):
def foo(x):
def bar(y):
x1, y1 = core.pack((x, y))
return np.sin(x1 * y1)
return jvp(bar, (3 * x,), (2 * x,))
jtu.check_eq(jit(foo)(0.5), foo(0.5))
def test_fori_collect():
def f(x):
return {"i": x["i"] + x["j"], "j": x["i"] - x["j"]}
a = {"i": jnp.array([0.0]), "j": jnp.array([1.0])}
expected_tree = {"i": jnp.array([[0.0], [2.0]])}
actual_tree = fori_collect(1, 3, f, a, transform=lambda a: {"i": a["i"]})
check_eq(actual_tree, expected_tree)
def test_iaf():
# test for substitute logic for exposed methods `sample_posterior` and `get_transforms`
N, dim = 3000, 3
data = random.normal(random.PRNGKey(0), (N, dim))
true_coefs = jnp.arange(1.0, dim + 1.0)
logits = jnp.sum(true_coefs * data, axis=-1)
labels = dist.Bernoulli(logits=logits).sample(random.PRNGKey(1))
def model(data, labels):
coefs = numpyro.sample("coefs",
dist.Normal(jnp.zeros(dim), jnp.ones(dim)))
offset = numpyro.sample("offset", dist.Uniform(-1, 1))
logits = offset + jnp.sum(coefs * data, axis=-1)
return numpyro.sample("obs", dist.Bernoulli(logits=logits), obs=labels)
adam = optim.Adam(0.01)
rng_key_init = random.PRNGKey(1)
guide = AutoIAFNormal(model)
svi = SVI(model, guide, adam, Trace_ELBO())
svi_state = svi.init(rng_key_init, data, labels)
params = svi.get_params(svi_state)
x = random.normal(random.PRNGKey(0), (dim + 1, ))
rng_key = random.PRNGKey(1)
actual_sample = guide.sample_posterior(rng_key, params)
actual_output = guide._unpack_latent(guide.get_transform(params)(x))
flows = []
for i in range(guide.num_flows):
if i > 0:
flows.append(transforms.PermuteTransform(
jnp.arange(dim + 1)[::-1]))
arn_init, arn_apply = AutoregressiveNN(
dim + 1,
[dim + 1, dim + 1],
permutation=jnp.arange(dim + 1),
skip_connections=guide._skip_connections,
nonlinearity=guide._nonlinearity,
)
arn = partial(arn_apply, params["auto_arn__{}$params".format(i)])
flows.append(InverseAutoregressiveTransform(arn))
flows.append(guide._unpack_latent)
transform = transforms.ComposeTransform(flows)
_, rng_key_sample = random.split(rng_key)
expected_sample = transform(
dist.Normal(jnp.zeros(dim + 1), 1).sample(rng_key_sample))
expected_output = transform(x)
assert_allclose(actual_sample["coefs"], expected_sample["coefs"])
assert_allclose(
actual_sample["offset"],
transforms.biject_to(constraints.interval(-1, 1))(
expected_sample["offset"]),
)
check_eq(actual_output, expected_output)
def test_fori_collect_return_last(progbar):
def f(x):
x['i'] = x['i'] + 1
return x
tree, init_state = fori_collect(2, 4, f, {'i': 0},
transform=lambda a: {'i': a['i']},
return_last_val=True,
progbar=progbar)
expected_tree = {'i': jnp.array([3, 4])}
expected_last_state = {'i': jnp.array(4)}
check_eq(init_state, expected_last_state)
check_eq(tree, expected_tree)
def test_save_restore_checkpoints_target_none(self):
tmp_dir = self.create_tempdir().full_path
test_object0 = {'a': np.array([0, 0, 0], np.int32),
'b': np.array([0, 0, 0], np.int32)}
# Target pytree is a dictionary, so it's equal to a restored state_dict.
checkpoints.save_checkpoint(tmp_dir, test_object0, 0)
new_object = checkpoints.restore_checkpoint(tmp_dir, target=None)
jtu.check_eq(new_object, test_object0)
# Target pytree it's a tuple, check the expected state_dict is recovered.
test_object1 = (np.array([0, 0, 0], np.int32),
np.array([1, 1, 1], np.int32))
checkpoints.save_checkpoint(tmp_dir, test_object1, 1)
new_object = checkpoints.restore_checkpoint(tmp_dir, target=None)
expected_new_object = {str(k): v for k, v in enumerate(test_object1)}
jtu.check_eq(new_object, expected_new_object)
def test_dynamic_supports():
true_coef = 0.9
data = true_coef + random.normal(random.PRNGKey(0), (1000,))
def actual_model(data):
alpha = numpyro.sample("alpha", dist.Uniform(0, 1))
with numpyro.handlers.reparam(config={"loc": TransformReparam()}):
loc = numpyro.sample(
"loc",
dist.TransformedDistribution(
dist.Uniform(0, 1), transforms.AffineTransform(0, alpha)
),
)
with numpyro.plate("N", len(data)):
numpyro.sample("obs", dist.Normal(loc, 0.1), obs=data)
def expected_model(data):
alpha = numpyro.sample("alpha", dist.Uniform(0, 1))
loc = numpyro.sample("loc", dist.Uniform(0, 1)) * alpha
with numpyro.plate("N", len(data)):
numpyro.sample("obs", dist.Normal(loc, 0.1), obs=data)
adam = optim.Adam(0.01)
rng_key_init = random.PRNGKey(1)
guide = AutoDiagonalNormal(actual_model)
svi = SVI(actual_model, guide, adam, Trace_ELBO())
svi_state = svi.init(rng_key_init, data)
actual_opt_params = adam.get_params(svi_state.optim_state)
actual_params = svi.get_params(svi_state)
actual_values = guide.median(actual_params)
actual_loss = svi.evaluate(svi_state, data)
guide = AutoDiagonalNormal(expected_model)
svi = SVI(expected_model, guide, adam, Trace_ELBO())
svi_state = svi.init(rng_key_init, data)
expected_opt_params = adam.get_params(svi_state.optim_state)
expected_params = svi.get_params(svi_state)
expected_values = guide.median(expected_params)
expected_loss = svi.evaluate(svi_state, data)
# test auto_loc, auto_scale
check_eq(actual_opt_params, expected_opt_params)
check_eq(actual_params, expected_params)
# test latent values
assert_allclose(actual_values["alpha"], expected_values["alpha"])
assert_allclose(actual_values["loc_base"], expected_values["loc"])
assert_allclose(actual_loss, expected_loss)
def test_overwrite_checkpoints(self):
tmp_dir = self.create_tempdir().full_path
test_object0 = {'a': np.array([0, 0, 0], np.int32)}
test_object = {'a': np.array([1, 2, 3], np.int32)}
checkpoints.save_checkpoint(tmp_dir, test_object0, 0, keep=1)
with self.assertRaises(errors.InvalidCheckpointError):
checkpoints.save_checkpoint(tmp_dir, test_object, 0, keep=1)
checkpoints.save_checkpoint(tmp_dir,
test_object,
0,
keep=1,
overwrite=True)
new_object = checkpoints.restore_checkpoint(tmp_dir, test_object0)
jtu.check_eq(new_object, test_object)
checkpoints.save_checkpoint(tmp_dir,
test_object0,
2,
keep=1,
overwrite=True)
new_object = checkpoints.restore_checkpoint(tmp_dir, test_object)
jtu.check_eq(new_object, test_object0)
with self.assertRaises(errors.InvalidCheckpointError):
checkpoints.save_checkpoint(tmp_dir, test_object, 1, keep=1)
checkpoints.save_checkpoint(tmp_dir,
test_object,
1,
keep=1,
overwrite=True)
new_object = checkpoints.restore_checkpoint(tmp_dir, test_object0)
jtu.check_eq(new_object, test_object)
os.chdir(os.path.dirname(tmp_dir))
rel_tmp_dir = './' + os.path.basename(tmp_dir)
checkpoints.save_checkpoint(rel_tmp_dir, test_object, 3, keep=1)
new_object = checkpoints.restore_checkpoint(rel_tmp_dir, test_object0)
jtu.check_eq(new_object, test_object)
non_norm_dir_path = tmp_dir + '//'
checkpoints.save_checkpoint(non_norm_dir_path, test_object, 4, keep=1)
new_object = checkpoints.restore_checkpoint(non_norm_dir_path,
test_object0)
jtu.check_eq(new_object, test_object)
def test_dynamic_supports():
true_coef = 0.9
data = true_coef + random.normal(random.PRNGKey(0), (1000, ))
def actual_model(data):
alpha = numpyro.sample('alpha', dist.Uniform(0, 1))
loc = numpyro.sample('loc', dist.Uniform(0, alpha))
numpyro.sample('obs', dist.Normal(loc, 0.1), obs=data)
def expected_model(data):
alpha = numpyro.sample('alpha', dist.Uniform(0, 1))
loc = numpyro.sample('loc', dist.Uniform(0, 1)) * alpha
numpyro.sample('obs', dist.Normal(loc, 0.1), obs=data)
adam = optim.Adam(0.01)
rng_key_init = random.PRNGKey(1)
guide = AutoDiagonalNormal(actual_model)
svi = SVI(actual_model, guide, adam, AutoContinuousELBO())
svi_state = svi.init(rng_key_init, data)
actual_opt_params = adam.get_params(svi_state.optim_state)
actual_params = svi.get_params(svi_state)
actual_values = guide.median(actual_params)
actual_loss = svi.evaluate(svi_state, data)
guide = AutoDiagonalNormal(expected_model)
svi = SVI(expected_model, guide, adam, AutoContinuousELBO())
svi_state = svi.init(rng_key_init, data)
expected_opt_params = adam.get_params(svi_state.optim_state)
expected_params = svi.get_params(svi_state)
expected_values = guide.median(expected_params)
expected_loss = svi.evaluate(svi_state, data)
# test auto_loc, auto_scale
check_eq(actual_opt_params, expected_opt_params)
check_eq(actual_params, expected_params)
# test latent values
assert_allclose(actual_values['alpha'], expected_values['alpha'])
assert_allclose(actual_values['loc'],
expected_values['alpha'] * expected_values['loc'])
assert_allclose(actual_loss, expected_loss)
def test_dynamic_supports():
true_coef = 0.9
data = true_coef + random.normal(random.PRNGKey(0), (1000, ))
def actual_model(data):
alpha = sample('alpha', dist.Uniform(0, 1))
loc = sample('loc', dist.Uniform(0, alpha))
sample('obs', dist.Normal(loc, 0.1), obs=data)
def expected_model(data):
alpha = sample('alpha', dist.Uniform(0, 1))
loc = sample('loc', dist.Uniform(0, 1)) * alpha
sample('obs', dist.Normal(loc, 0.1), obs=data)
opt_init, opt_update, get_params = optimizers.adam(0.01)
rng_guide, rng_init, rng_train = random.split(random.PRNGKey(1), 3)
guide = AutoDiagonalNormal(rng_guide, actual_model, get_params)
svi_init, _, svi_eval = svi(actual_model, guide, elbo, opt_init,
opt_update, get_params)
opt_state, constrain_fn = svi_init(rng_init, (data, ), (data, ))
actual_params = get_params(opt_state)
actual_base_values = constrain_fn(actual_params)
actual_values = guide.median(opt_state)
actual_loss = svi_eval(random.PRNGKey(1), opt_state, (data, ), (data, ))
guide = AutoDiagonalNormal(rng_guide, expected_model, get_params)
svi_init, _, svi_eval = svi(expected_model, guide, elbo, opt_init,
opt_update, get_params)
opt_state, constrain_fn = svi_init(rng_init, (data, ), (data, ))
expected_params = get_params(opt_state)
expected_base_values = constrain_fn(expected_params)
expected_values = guide.median(opt_state)
expected_loss = svi_eval(random.PRNGKey(1), opt_state, (data, ), (data, ))
check_eq(actual_params, expected_params)
check_eq(actual_base_values, expected_base_values)
assert_allclose(actual_values['alpha'], expected_values['alpha'])
assert_allclose(actual_values['loc'],
expected_values['alpha'] * expected_values['loc'])
assert_allclose(actual_loss, expected_loss)
def test_jitted_update_fn():
data = np.array([1.0] * 8 + [0.0] * 2)
def model(data):
f = numpyro.sample("beta", dist.Beta(1., 1.))
numpyro.sample("obs", dist.Bernoulli(f), obs=data)
def guide(data):
alpha_q = numpyro.param("alpha_q", 1.0,
constraint=constraints.positive)
beta_q = numpyro.param("beta_q", 1.0,
constraint=constraints.positive)
numpyro.sample("beta", dist.Beta(alpha_q, beta_q))
adam = optim.Adam(0.05)
svi = SVI(model, guide, adam, ELBO())
svi_state = svi.init(random.PRNGKey(1), data)
expected = svi.get_params(svi.update(svi_state, data)[0])
actual = svi.get_params(jit(svi.update)(svi_state, data=data)[0])
check_eq(actual, expected)
def test_overwrite_checkpoints(self):
tmp_dir = self.create_tempdir().full_path
test_object0 = {'a': np.array([0, 0, 0], np.int32)}
test_object = {'a': np.array([1, 2, 3], np.int32)}
checkpoints.save_checkpoint(
tmp_dir, test_object0, 0, keep=1)
with self.assertRaises(errors.InvalidCheckpointError):
checkpoints.save_checkpoint(
tmp_dir, test_object, 0, keep=1)
checkpoints.save_checkpoint(
tmp_dir, test_object, 0, keep=1, overwrite=True)
new_object = checkpoints.restore_checkpoint(tmp_dir, test_object0)
jtu.check_eq(new_object, test_object)
checkpoints.save_checkpoint(
tmp_dir, test_object0, 2, keep=1, overwrite=True)
new_object = checkpoints.restore_checkpoint(tmp_dir, test_object)
jtu.check_eq(new_object, test_object0)
with self.assertRaises(errors.InvalidCheckpointError):
checkpoints.save_checkpoint(
tmp_dir, test_object, 1, keep=1)
checkpoints.save_checkpoint(
tmp_dir, test_object, 1, keep=1, overwrite=True)
new_object = checkpoints.restore_checkpoint(tmp_dir, test_object0)
jtu.check_eq(new_object, test_object)
def test_fori_collect_thinning():
def f(x):
return x + 1.0
actual2 = fori_collect(0, 9, f, jnp.array([-1]), thinning=2)
expected2 = jnp.array([[2], [4], [6], [8]])
check_eq(actual2, expected2)
actual3 = fori_collect(0, 9, f, jnp.array([-1]), thinning=3)
expected3 = jnp.array([[2], [5], [8]])
check_eq(actual3, expected3)
actual4 = fori_collect(0, 9, f, jnp.array([-1]), thinning=4)
expected4 = jnp.array([[4], [8]])
check_eq(actual4, expected4)
actual5 = fori_collect(12, 37, f, jnp.array([-1]), thinning=5)
expected5 = jnp.array([[16], [21], [26], [31], [36]])
check_eq(actual5, expected5)
def test_save_restore_checkpoints(self):
tmp_dir = pathlib.Path(self.create_tempdir().full_path)
test_object0 = {
'a': np.array([0, 0, 0], np.int32),
'b': np.array([0, 0, 0], np.int32)
}
test_object1 = {
'a': np.array([1, 2, 3], np.int32),
'b': np.array([1, 1, 1], np.int32)
}
test_object2 = {
'a': np.array([4, 5, 6], np.int32),
'b': np.array([2, 2, 2], np.int32)
}
new_object = checkpoints.restore_checkpoint(tmp_dir,
test_object0,
prefix='test_')
jtu.check_eq(new_object, test_object0)
# Create leftover temporary checkpoint, which should be ignored.
gfile.GFile(os.path.join(tmp_dir, 'test_tmp'), 'w')
checkpoints.save_checkpoint(tmp_dir,
test_object1,
0,
prefix='test_',
keep=1)
self.assertIn('test_0', os.listdir(tmp_dir))
new_object = checkpoints.restore_checkpoint(tmp_dir,
test_object0,
prefix='test_')
jtu.check_eq(new_object, test_object1)
checkpoints.save_checkpoint(tmp_dir,
test_object1,
1,
prefix='test_',
keep=1)
checkpoints.save_checkpoint(tmp_dir,
test_object2,
2,
prefix='test_',
keep=1)
new_object = checkpoints.restore_checkpoint(tmp_dir,
test_object0,
prefix='test_')
jtu.check_eq(new_object, test_object2)
checkpoints.save_checkpoint(tmp_dir,
test_object2,
3,
prefix='test_',
keep=2)
checkpoints.save_checkpoint(tmp_dir,
test_object1,
4,
prefix='test_',
keep=2)
new_object = checkpoints.restore_checkpoint(tmp_dir,
test_object0,
prefix='test_')
jtu.check_eq(new_object, test_object1)
new_object = checkpoints.restore_checkpoint(tmp_dir,
test_object0,
step=3,
prefix='test_')
jtu.check_eq(new_object, test_object2)
# Restore a specific path.
new_object = checkpoints.restore_checkpoint(
os.path.join(tmp_dir, 'test_3'), test_object0)
jtu.check_eq(new_object, test_object2)
# If a specific path is specified, but it does not exist, the same behavior
# as when a directory is empty should apply: the target is returned
# unchanged.
new_object = checkpoints.restore_checkpoint(
os.path.join(tmp_dir, 'test_not_there'), test_object0)
jtu.check_eq(new_object, test_object0)
with self.assertRaises(ValueError):
checkpoints.restore_checkpoint(tmp_dir,
test_object0,
step=5,
prefix='test_')
def test_jit(self, f, args):
jtu.check_eq(jit(f)(*args), f(*args))
def test_save_restore_checkpoints(self):
tmp_dir = self.create_tempdir().full_path
test_object0 = {
'a': np.array([0, 0, 0], np.int32),
'b': np.array([0, 0, 0], np.int32)
}
test_object1 = {
'a': np.array([1, 2, 3], np.int32),
'b': np.array([1, 1, 1], np.int32)
}
test_object2 = {
'a': np.array([4, 5, 6], np.int32),
'b': np.array([2, 2, 2], np.int32)
}
new_object = checkpoints.restore_checkpoint(tmp_dir,
test_object0,
prefix='test_')
jtu.check_eq(new_object, test_object0)
# Create leftover temporary checkpoint, which should be ignored.
gfile.GFile(os.path.join(tmp_dir, 'test_tmp'), 'w')
checkpoints.save_checkpoint(tmp_dir,
test_object1,
0,
prefix='test_',
keep=1)
self.assertIn('test_0', os.listdir(tmp_dir))
new_object = checkpoints.restore_checkpoint(tmp_dir,
test_object0,
prefix='test_')
jtu.check_eq(new_object, test_object1)
checkpoints.save_checkpoint(tmp_dir,
test_object1,
1,
prefix='test_',
keep=1)
checkpoints.save_checkpoint(tmp_dir,
test_object2,
2,
prefix='test_',
keep=1)
new_object = checkpoints.restore_checkpoint(tmp_dir,
test_object0,
prefix='test_')
jtu.check_eq(new_object, test_object2)
checkpoints.save_checkpoint(tmp_dir,
test_object2,
3,
prefix='test_',
keep=2)
checkpoints.save_checkpoint(tmp_dir,
test_object1,
4,
prefix='test_',
keep=2)
new_object = checkpoints.restore_checkpoint(tmp_dir,
test_object0,
prefix='test_')
jtu.check_eq(new_object, test_object1)
new_object = checkpoints.restore_checkpoint(tmp_dir,
test_object0,
step=3,
prefix='test_')
jtu.check_eq(new_object, test_object2)
with self.assertRaises(ValueError):
checkpoints.restore_checkpoint(tmp_dir,
test_object0,
step=5,
prefix='test_')
