ab6ca78d85
* Simplifying Graph class and adjust tests --------- Co-authored-by: Dario Coscia <dariocos99@gmail.com>
133 lines
3.5 KiB
Python
133 lines
3.5 KiB
Python
import torch
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import pytest
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from pina.data.dataset import PinaDatasetFactory, PinaGraphDataset
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from pina.graph import KNNGraph
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from torch_geometric.data import Data
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x = torch.rand((100, 20, 10))
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pos = torch.rand((100, 20, 2))
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input_ = [
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KNNGraph(x=x_, pos=pos_, neighbours=3, edge_attr=True)
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for x_, pos_ in zip(x, pos)
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]
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output_ = torch.rand((100, 20, 10))
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x_2 = torch.rand((50, 20, 10))
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pos_2 = torch.rand((50, 20, 2))
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input_2_ = [
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KNNGraph(x=x_, pos=pos_, neighbours=3, edge_attr=True)
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for x_, pos_ in zip(x_2, pos_2)
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]
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output_2_ = torch.rand((50, 20, 10))
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# Problem with a single condition
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conditions_dict_single = {
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"data": {
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"input_points": input_,
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"output_points": output_,
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}
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}
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max_conditions_lengths_single = {"data": 100}
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# Problem with multiple conditions
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conditions_dict_single_multi = {
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"data_1": {
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"input_points": input_,
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"output_points": output_,
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},
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"data_2": {
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"input_points": input_2_,
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"output_points": output_2_,
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},
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}
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max_conditions_lengths_multi = {"data_1": 100, "data_2": 50}
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@pytest.mark.parametrize(
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"conditions_dict, max_conditions_lengths",
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[
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(conditions_dict_single, max_conditions_lengths_single),
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(conditions_dict_single_multi, max_conditions_lengths_multi),
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],
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)
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def test_constructor(conditions_dict, max_conditions_lengths):
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dataset = PinaDatasetFactory(
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conditions_dict,
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max_conditions_lengths=max_conditions_lengths,
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automatic_batching=True,
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)
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assert isinstance(dataset, PinaGraphDataset)
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assert len(dataset) == 100
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@pytest.mark.parametrize(
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"conditions_dict, max_conditions_lengths",
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[
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(conditions_dict_single, max_conditions_lengths_single),
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(conditions_dict_single_multi, max_conditions_lengths_multi),
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],
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)
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def test_getitem(conditions_dict, max_conditions_lengths):
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dataset = PinaDatasetFactory(
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conditions_dict,
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max_conditions_lengths=max_conditions_lengths,
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automatic_batching=True,
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)
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data = dataset[50]
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assert isinstance(data, dict)
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assert all([isinstance(d["input_points"], Data) for d in data.values()])
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assert all(
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[isinstance(d["output_points"], torch.Tensor) for d in data.values()]
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)
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assert all(
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[
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d["input_points"].x.shape == torch.Size((20, 10))
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for d in data.values()
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]
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)
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assert all(
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[
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d["output_points"].shape == torch.Size((20, 10))
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for d in data.values()
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]
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)
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assert all(
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[
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d["input_points"].edge_index.shape == torch.Size((2, 60))
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for d in data.values()
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]
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)
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assert all(
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[d["input_points"].edge_attr.shape[0] == 60 for d in data.values()]
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)
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data = dataset.fetch_from_idx_list([i for i in range(20)])
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assert isinstance(data, dict)
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assert all([isinstance(d["input_points"], Data) for d in data.values()])
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assert all(
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[isinstance(d["output_points"], torch.Tensor) for d in data.values()]
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)
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assert all(
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[
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d["input_points"].x.shape == torch.Size((400, 10))
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for d in data.values()
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]
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)
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assert all(
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[
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d["output_points"].shape == torch.Size((400, 10))
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for d in data.values()
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]
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)
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assert all(
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[
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d["input_points"].edge_index.shape == torch.Size((2, 1200))
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for d in data.values()
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]
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)
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assert all(
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[d["input_points"].edge_attr.shape[0] == 1200 for d in data.values()]
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)
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