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