87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
# import torch
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# import pytest
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# from pina.data.dataset import PinaDatasetFactory, PinaTensorDataset
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# input_tensor = torch.rand((100, 10))
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# output_tensor = torch.rand((100, 2))
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# input_tensor_2 = torch.rand((50, 10))
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# output_tensor_2 = torch.rand((50, 2))
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# conditions_dict_single = {
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# "data": {
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# "input": input_tensor,
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# "target": output_tensor,
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# }
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# }
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# conditions_dict_single_multi = {
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# "data_1": {
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# "input": input_tensor,
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# "target": output_tensor,
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# },
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# "data_2": {
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# "input": input_tensor_2,
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# "target": output_tensor_2,
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# },
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# }
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# max_conditions_lengths_single = {"data": 100}
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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_tensor(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, PinaTensorDataset)
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# def test_getitem_single():
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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=False,
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# )
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# tensors = dataset.fetch_from_idx_list([i for i in range(70)])
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# assert isinstance(tensors, dict)
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# assert list(tensors.keys()) == ["data"]
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# assert sorted(list(tensors["data"].keys())) == ["input", "target"]
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# assert isinstance(tensors["data"]["input"], torch.Tensor)
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# assert tensors["data"]["input"].shape == torch.Size((70, 10))
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# assert isinstance(tensors["data"]["target"], torch.Tensor)
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# assert tensors["data"]["target"].shape == torch.Size((70, 2))
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# def test_getitem_multi():
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# dataset = PinaDatasetFactory(
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# conditions_dict_single_multi,
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# max_conditions_lengths=max_conditions_lengths_multi,
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# automatic_batching=False,
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# )
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# tensors = dataset.fetch_from_idx_list([i for i in range(70)])
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# assert isinstance(tensors, dict)
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# assert list(tensors.keys()) == ["data_1", "data_2"]
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# assert sorted(list(tensors["data_1"].keys())) == ["input", "target"]
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# assert isinstance(tensors["data_1"]["input"], torch.Tensor)
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# assert tensors["data_1"]["input"].shape == torch.Size((70, 10))
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# assert isinstance(tensors["data_1"]["target"], torch.Tensor)
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# assert tensors["data_1"]["target"].shape == torch.Size((70, 2))
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# assert sorted(list(tensors["data_2"].keys())) == ["input", "target"]
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# assert isinstance(tensors["data_2"]["input"], torch.Tensor)
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# assert tensors["data_2"]["input"].shape == torch.Size((50, 10))
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# assert isinstance(tensors["data_2"]["target"], torch.Tensor)
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# assert tensors["data_2"]["target"].shape == torch.Size((50, 2))
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