139 lines
4.3 KiB
Python
139 lines
4.3 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": input_,
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# "target": 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_multi = {
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# "data_1": {
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# "input": input_,
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# "target": output_,
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# },
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# "data_2": {
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# "input": input_2_,
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# "target": 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_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_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"], Data) for d in data.values()])
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# assert all([isinstance(d["target"], torch.Tensor) for d in data.values()])
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# assert all(
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# [d["input"].x.shape == torch.Size((20, 10)) for d in data.values()]
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# )
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# assert all(
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# [d["target"].shape == torch.Size((20, 10)) 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"].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([d["input"].edge_attr.shape[0] == 60 for d in data.values()])
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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"], Data) for d in data.values()])
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# assert all([isinstance(d["target"], torch.Tensor) for d in data.values()])
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# assert all(
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# [d["input"].x.shape == torch.Size((400, 10)) for d in data.values()]
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# )
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# assert all(
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# [d["target"].shape == torch.Size((20, 20, 10)) 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"].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([d["input"].edge_attr.shape[0] == 1200 for d in data.values()])
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# def test_input_single_condition():
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# dataset = PinaDatasetFactory(
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# conditions_dict_single,
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# max_conditions_lengths=max_conditions_lengths_single,
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# automatic_batching=True,
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# )
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# input_ = dataset.input
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# assert isinstance(input_, dict)
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# assert isinstance(input_["data"], list)
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# assert all([isinstance(d, Data) for d in input_["data"]])
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# def test_input_multi_condition():
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# dataset = PinaDatasetFactory(
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# conditions_dict_multi,
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# max_conditions_lengths=max_conditions_lengths_multi,
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# automatic_batching=True,
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# )
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# input_ = dataset.input
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# assert isinstance(input_, dict)
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# assert isinstance(input_["data_1"], list)
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# assert all([isinstance(d, Data) for d in input_["data_1"]])
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# assert isinstance(input_["data_2"], list)
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# assert all([isinstance(d, Data) for d in input_["data_2"]])
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