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Improve conditions and refactor dataset classes (#475)

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* Reimplement conditions

* Refactor datasets and implement LabelBatch

---------

Co-authored-by: Dario Coscia <dariocos99@gmail.com>
This commit is contained in:
Filippo Olivo
2025-03-07 11:24:09 +01:00
committed by Nicola Demo
parent bdad144461
commit a0cbf1c44a
40 changed files with 943 additions and 550 deletions
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36
tests/test_data/test_graph_dataset.py
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@@ -24,8 +24,8 @@ output_2_ = torch.rand((50, 20, 10))
# Problem with a single condition
conditions_dict_single = {
"data": {
"input_points": input_,
"output_points": output_,
"input": input_,
"target": output_,
}
}
max_conditions_lengths_single = {"data": 100}
@@ -33,12 +33,12 @@ max_conditions_lengths_single = {"data": 100}
# Problem with multiple conditions
conditions_dict_single_multi = {
"data_1": {
"input_points": input_,
"output_points": output_,
"input": input_,
"target": output_,
},
"data_2": {
"input_points": input_2_,
"output_points": output_2_,
"input": input_2_,
"target": output_2_,
},
}
@@ -77,56 +77,56 @@ def test_getitem(conditions_dict, max_conditions_lengths):
)
data = dataset[50]
assert isinstance(data, dict)
assert all([isinstance(d["input_points"], Data) for d in data.values()])
assert all([isinstance(d["input"], Data) for d in data.values()])
assert all(
[isinstance(d["output_points"], torch.Tensor) for d in data.values()]
[isinstance(d["target"], torch.Tensor) for d in data.values()]
)
assert all(
[
d["input_points"].x.shape == torch.Size((20, 10))
d["input"].x.shape == torch.Size((20, 10))
for d in data.values()
]
)
assert all(
[
d["output_points"].shape == torch.Size((20, 10))
d["target"].shape == torch.Size((20, 10))
for d in data.values()
]
)
assert all(
[
d["input_points"].edge_index.shape == torch.Size((2, 60))
d["input"].edge_index.shape == torch.Size((2, 60))
for d in data.values()
]
)
assert all(
[d["input_points"].edge_attr.shape[0] == 60 for d in data.values()]
[d["input"].edge_attr.shape[0] == 60 for d in data.values()]
)
data = dataset.fetch_from_idx_list([i for i in range(20)])
assert isinstance(data, dict)
assert all([isinstance(d["input_points"], Data) for d in data.values()])
assert all([isinstance(d["input"], Data) for d in data.values()])
assert all(
[isinstance(d["output_points"], torch.Tensor) for d in data.values()]
[isinstance(d["target"], torch.Tensor) for d in data.values()]
)
assert all(
[
d["input_points"].x.shape == torch.Size((400, 10))
d["input"].x.shape == torch.Size((400, 10))
for d in data.values()
]
)
assert all(
[
d["output_points"].shape == torch.Size((400, 10))
d["target"].shape == torch.Size((400, 10))
for d in data.values()
]
)
assert all(
[
d["input_points"].edge_index.shape == torch.Size((2, 1200))
d["input"].edge_index.shape == torch.Size((2, 1200))
for d in data.values()
]
)
assert all(
[d["input_points"].edge_attr.shape[0] == 1200 for d in data.values()]
[d["input"].edge_attr.shape[0] == 1200 for d in data.values()]
)