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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,41 @@ | ||
| r""" | ||
| Replicates the experiment from `"Deep Graph Infomax" | ||
| <https://arxiv.org/abs/1809.10341>`_ to try and teach | ||
| `EquilibriumAggregation` to learn to take the median of | ||
| a set of numbers | ||
| This example converges slowly to being able to predict the | ||
| median similar to what is observed in the paper. | ||
| """ | ||
|
|
||
| import numpy as np | ||
| import torch | ||
|
|
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| from torch_geometric.nn.aggr import EquilibriumAggregation | ||
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| input_size = 100 | ||
| steps = 10000000 | ||
| embedding_size = 10 | ||
| eval_each = 1000 | ||
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| model = EquilibriumAggregation(1, 10, [256, 256], 1) | ||
| optimizer = torch.optim.Adam(model.parameters(), lr=0.001) | ||
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| norm = torch.distributions.normal.Normal(0.5, 0.4) | ||
| gamma = torch.distributions.gamma.Gamma(0.2, 0.5) | ||
| uniform = torch.distributions.uniform.Uniform(0, 1) | ||
| total_loss = 0 | ||
| n_loss = 0 | ||
|
|
||
| for i in range(steps): | ||
| optimizer.zero_grad() | ||
| dist = np.random.choice([norm, gamma, uniform]) | ||
| x = dist.sample((input_size, 1)) | ||
| y = model(x) | ||
| loss = (y - x.median()).norm(2) / input_size | ||
| loss.backward() | ||
| optimizer.step() | ||
| total_loss += loss | ||
| n_loss += 1 | ||
| if i % eval_each == (eval_each - 1): | ||
| print(f"Average loss at epoc {i} is {total_loss / n_loss}") |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,53 @@ | ||
| import pytest | ||
| import torch | ||
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| from torch_geometric.nn.aggr import EquilibriumAggregation | ||
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| @pytest.mark.parametrize('iter', [0, 1, 5]) | ||
| @pytest.mark.parametrize('alpha', [0, .1, 5]) | ||
| def test_equilibrium(iter, alpha): | ||
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| batch_size = 10 | ||
| feature_channels = 3 | ||
| output_channels = 2 | ||
| x = torch.randn(batch_size, feature_channels) | ||
| model = EquilibriumAggregation(feature_channels, output_channels, | ||
| num_layers=[10, 10], grad_iter=iter) | ||
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| assert model.__repr__() == 'EquilibriumAggregation()' | ||
| out = model(x) | ||
| assert out.size() == (1, 2) | ||
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| with pytest.raises(ValueError): | ||
| model(x, dim_size=0) | ||
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| out = model(x, dim_size=3) | ||
| assert out.size() == (3, 2) | ||
| assert torch.all(out[1:, :] == 0) | ||
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| @pytest.mark.parametrize('iter', [0, 1, 5]) | ||
| @pytest.mark.parametrize('alpha', [0, .1, 5]) | ||
| def test_equilibrium_batch(iter, alpha): | ||
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| batch_1, batch_2 = 4, 6 | ||
| feature_channels = 3 | ||
| output_channels = 2 | ||
| x = torch.randn(batch_1 + batch_2, feature_channels) | ||
| batch = torch.tensor([0 for _ in range(batch_1)] + | ||
| [1 for _ in range(batch_2)]) | ||
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| model = EquilibriumAggregation(feature_channels, output_channels, | ||
| num_layers=[10, 10], grad_iter=iter) | ||
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| assert model.__repr__() == 'EquilibriumAggregation()' | ||
| out = model(x, batch) | ||
| assert out.size() == (2, 2) | ||
|
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| with pytest.raises(ValueError): | ||
| model(x, dim_size=0) | ||
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| out = model(x, dim_size=3) | ||
| assert out.size() == (3, 2) | ||
| assert torch.all(out[1:, :] == 0) |
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