130 lines
4.2 KiB
Python
130 lines
4.2 KiB
Python
import pytest
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import torch
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from pina.graph import KNNGraph
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from pina.model import GraphNeuralOperator
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from torch_geometric.data import Batch
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x = [torch.rand(100, 6) for _ in range(10)]
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pos = [torch.rand(100, 3) for _ in range(10)]
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graph = KNNGraph(x=x, pos=pos, build_edge_attr=True, k=6)
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input_ = Batch.from_data_list(graph.data)
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@pytest.mark.parametrize(
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"shared_weights",
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[
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True,
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False
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]
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)
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def test_constructor(shared_weights):
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lifting_operator = torch.nn.Linear(6, 16)
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projection_operator = torch.nn.Linear(16, 3)
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GraphNeuralOperator(lifting_operator=lifting_operator,
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projection_operator=projection_operator,
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edge_features=3,
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internal_layers=[16, 16],
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shared_weights=shared_weights)
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GraphNeuralOperator(lifting_operator=lifting_operator,
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projection_operator=projection_operator,
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edge_features=3,
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inner_size=16,
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internal_n_layers=10,
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shared_weights=shared_weights)
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int_func = torch.nn.Softplus
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ext_func = torch.nn.ReLU
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GraphNeuralOperator(lifting_operator=lifting_operator,
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projection_operator=projection_operator,
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edge_features=3,
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internal_n_layers=10,
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shared_weights=shared_weights,
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internal_func=int_func,
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external_func=ext_func)
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@pytest.mark.parametrize(
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"shared_weights",
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[
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True,
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False
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]
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)
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def test_forward_1(shared_weights):
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lifting_operator = torch.nn.Linear(6, 16)
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projection_operator = torch.nn.Linear(16, 3)
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model = GraphNeuralOperator(lifting_operator=lifting_operator,
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projection_operator=projection_operator,
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edge_features=3,
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internal_layers=[16, 16],
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shared_weights=shared_weights)
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output_ = model(input_)
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assert output_.shape == torch.Size([1000, 3])
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@pytest.mark.parametrize(
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"shared_weights",
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[
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True,
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False
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]
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)
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def test_forward_2(shared_weights):
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lifting_operator = torch.nn.Linear(6, 16)
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projection_operator = torch.nn.Linear(16, 3)
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model = GraphNeuralOperator(lifting_operator=lifting_operator,
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projection_operator=projection_operator,
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edge_features=3,
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inner_size=32,
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internal_n_layers=2,
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shared_weights=shared_weights)
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output_ = model(input_)
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assert output_.shape == torch.Size([1000, 3])
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@pytest.mark.parametrize(
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"shared_weights",
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[
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True,
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False
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]
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)
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def test_backward(shared_weights):
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lifting_operator = torch.nn.Linear(6, 16)
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projection_operator = torch.nn.Linear(16, 3)
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model = GraphNeuralOperator(lifting_operator=lifting_operator,
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projection_operator=projection_operator,
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edge_features=3,
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internal_layers=[16, 16],
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shared_weights=shared_weights)
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input_.x.requires_grad = True
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output_ = model(input_)
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l = torch.mean(output_)
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l.backward()
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assert input_.x.grad.shape == torch.Size([1000, 6])
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@pytest.mark.parametrize(
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"shared_weights",
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[
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True,
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False
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]
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)
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def test_backward_2(shared_weights):
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lifting_operator = torch.nn.Linear(6, 16)
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projection_operator = torch.nn.Linear(16, 3)
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model = GraphNeuralOperator(lifting_operator=lifting_operator,
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projection_operator=projection_operator,
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edge_features=3,
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inner_size=32,
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internal_n_layers=2,
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shared_weights=shared_weights)
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input_.x.requires_grad = True
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output_ = model(input_)
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l = torch.mean(output_)
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l.backward()
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assert input_.x.grad.shape == torch.Size([1000, 6])
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