mirror of
https://github.com/wassname/attentive-neural-processes.git
synced 2026-08-01 12:20:26 +08:00
67 lines
2.5 KiB
Python
67 lines
2.5 KiB
Python
import neural_processes.utils
|
|
import pickle, json
|
|
import torch
|
|
import tempfile
|
|
|
|
def test_obectdict(tmpdir):
|
|
o = neural_processes.utils.ObjectDict(z=1, b=4, test="g", w=0)
|
|
pickle.dump(o, open(tmpdir+'/test.pkl', 'wb'))
|
|
o2 = pickle.load(open(tmpdir+'/test.pkl', 'rb'))
|
|
|
|
o3 = json.loads(json.dumps(o))
|
|
print(o, o2, o3)
|
|
|
|
|
|
def test_agg_logs():
|
|
outputs = [
|
|
{'val_loss': torch.tensor(0.7206),
|
|
'log': {'val_loss': torch.tensor(0.7206), 'val_loss_p': torch.tensor(0.7206), 'val_loss_kl': torch.tensor(2.3812e-06), 'val_loss_mse': torch.tensor(0.1838)}},
|
|
{'val_loss': torch.tensor(0.7047),
|
|
'log': {'val_loss': torch.tensor(0.7047), 'val_loss_p': torch.tensor(0.7047), 'val_loss_kl': torch.tensor(2.8391e-06), 'val_loss_mse': torch.tensor(0.1696)}},
|
|
]
|
|
r = neural_processes.utils.agg_logs(outputs)
|
|
assert isinstance(r, dict)
|
|
assert 'agg_val_loss' in r.keys()
|
|
assert 'agg_val_loss_kl' in r['log'].keys()
|
|
assert isinstance(r['agg_val_loss'], float)
|
|
|
|
outputs = {'val_loss': torch.tensor(0.7206),
|
|
'log': {'val_loss': torch.tensor(0.7206), 'val_loss_p': torch.tensor(0.7206), 'val_loss_kl': torch.tensor(2.3812e-06), 'val_loss_mse': torch.tensor(0.1838)}}
|
|
r = neural_processes.utils.agg_logs(outputs)
|
|
assert isinstance(r, dict)
|
|
assert 'agg_val_loss' in r.keys()
|
|
assert 'agg_val_loss_kl' in r['log'].keys()
|
|
assert isinstance(r['agg_val_loss'], float)
|
|
|
|
|
|
def test_round_values():
|
|
r = neural_processes.utils.round_values({'a': 0.00004, 'd': {'b': 124455.45, 'c': 0.004}, 'l': 500})
|
|
|
|
|
|
def test_hparams_power():
|
|
r = neural_processes.utils.hparams_power({'test_power': 2, 'test2': 2})
|
|
assert r['test'] == 2 ** 2
|
|
assert r['test2'] == 2
|
|
|
|
|
|
def test_log_prob_sigma():
|
|
mean = torch.zeros(4, 5)
|
|
log_scale = torch.ones(4, 5)
|
|
value = torch.zeros(4, 5)
|
|
y_dist = torch.distributions.Normal(mean, log_scale.exp())
|
|
r1 = y_dist.log_prob(values)
|
|
r2 = neural_processes.utils.log_prob_sigma(value, loc, log_scale)
|
|
assert (r1==r2).all()
|
|
|
|
def test_kl_loss_var():
|
|
prior_mu = torch.zeros(4, 5)
|
|
post_mu = torch.zeros(4, 5) + 1
|
|
log_var_prior = torch.ones(4, 5)
|
|
log_var_post = torch.ones(4, 5) + 1
|
|
dist_prior = torch.distributions.Normal(prior_mu, log_var_prior.exp())
|
|
dist_post = torch.distributions.Normal(post_mu, log_var_post.exp())
|
|
r1 = torch.distributions.kl_divergence(
|
|
dist_post, dist_prior).mean(-1)
|
|
r2 = neural_processes.utils.kl_loss_var(prior_mu, log_var_prior, post_mu, log_var_post)
|
|
assert (r1==r2).all()
|