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https://github.com/wassname/attentive-neural-processes.git
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wassname
35 lines
1.4 KiB
Python
35 lines
1.4 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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{'val_loss': torch.tensor(0.7206),
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'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)}},
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{'val_loss': torch.tensor(0.7047),
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'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)}},
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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 = {'val_loss': torch.tensor(0.7206),
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'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)}}
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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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