mirror of
https://github.com/wassname/attentive-neural-processes.git
synced 2026-06-27 19:14:00 +08:00
wassname
93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
import neural_processes.utils
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import pickle, json
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import torch
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import tempfile
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def test_obectdict(tmpdir):
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o = neural_processes.utils.ObjectDict(z=1, b=4, test="g", w=0)
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pickle.dump(o, open(tmpdir + "/test.pkl", "wb"))
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o2 = pickle.load(open(tmpdir + "/test.pkl", "rb"))
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o3 = json.loads(json.dumps(o))
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print(o, o2, o3)
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def test_agg_logs():
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outputs = [
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{
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"val_loss": torch.tensor(0.7206),
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"log": {
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"val_loss": torch.tensor(0.7206),
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"val_loss_p": torch.tensor(0.7206),
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"val_loss_kl": torch.tensor(2.3812e-06),
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"val_loss_mse": torch.tensor(0.1838),
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},
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},
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{
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"val_loss": torch.tensor(0.7047),
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"log": {
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"val_loss": torch.tensor(0.7047),
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"val_loss_p": torch.tensor(0.7047),
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"val_loss_kl": torch.tensor(2.8391e-06),
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"val_loss_mse": torch.tensor(0.1696),
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},
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},
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]
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r = neural_processes.utils.agg_logs(outputs)
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assert isinstance(r, dict)
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assert "agg_val_loss" in r.keys()
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assert "agg_val_loss_kl" in r["log"].keys()
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assert isinstance(r["agg_val_loss"], float)
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outputs = {
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"val_loss": torch.tensor(0.7206),
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"log": {
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"val_loss": torch.tensor(0.7206),
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"val_loss_p": torch.tensor(0.7206),
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"val_loss_kl": torch.tensor(2.3812e-06),
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"val_loss_mse": torch.tensor(0.1838),
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},
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}
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r = neural_processes.utils.agg_logs(outputs)
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assert isinstance(r, dict)
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assert "agg_val_loss" in r.keys()
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assert "agg_val_loss_kl" in r["log"].keys()
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assert isinstance(r["agg_val_loss"], float)
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def test_round_values():
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r = neural_processes.utils.round_values(
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{"a": 0.00004, "d": {"b": 124455.45, "c": 0.004}, "l": 500}
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)
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def test_hparams_power():
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r = neural_processes.utils.hparams_power({"test_power": 2, "test2": 2})
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assert r["test"] == 2 ** 2
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assert r["test2"] == 2
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def test_log_prob_sigma():
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mean = torch.zeros(4, 5)
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log_scale = torch.ones(4, 5)
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value = torch.zeros(4, 5)
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y_dist = torch.distributions.Normal(mean, log_scale.exp())
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r1 = y_dist.log_prob(value)
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r2 = neural_processes.utils.log_prob_sigma(value, mean, log_scale)
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assert (r1 == r2).all()
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def test_kl_loss_var():
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prior_mu = torch.zeros(4, 5)
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post_mu = torch.zeros(4, 5) + 1
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log_var_prior = torch.ones(4, 5)
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log_var_post = torch.ones(4, 5) + 1
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dist_prior = torch.distributions.Normal(prior_mu, torch.exp(0.5 * log_var_prior))
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dist_post = torch.distributions.Normal(post_mu, torch.exp(0.5 * log_var_post))
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r1 = torch.distributions.kl_divergence(dist_post, dist_prior)
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r2 = neural_processes.utils.kl_loss_var(
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prior_mu, log_var_prior, post_mu, log_var_post
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)
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assert (r1 == r2).all()
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