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fix tests

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This commit is contained in:
FilippoOlivo
2025-11-13 17:03:31 +01:00
parent 0ee63686dd
commit 8440a672a7
5 changed files with 289 additions and 300 deletions
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173
tests/test_data/test_data_module.py
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@@ -1,10 +1,11 @@
import torch
import pytest
from pina.data import PinaDataModule
from pina.data.dataset import PinaTensorDataset, PinaGraphDataset
from pina.data.dataset import PinaDataset
from pina.problem.zoo import SupervisedProblem
from pina.graph import RadiusGraph
from pina.data.data_module import DummyDataloader
from pina.data.dataloader import DummyDataloader, PinaDataLoader
from pina import Trainer
from pina.solver import SupervisedSolver
from torch_geometric.data import Batch
@@ -44,22 +45,33 @@ def test_setup_train(input_, output_, train_size, val_size, test_size):
)
dm.setup()
assert hasattr(dm, "train_dataset")
if isinstance(input_, torch.Tensor):
assert isinstance(dm.train_dataset, PinaTensorDataset)
else:
assert isinstance(dm.train_dataset, PinaGraphDataset)
# assert len(dm.train_dataset) == int(len(input_) * train_size)
assert isinstance(dm.train_dataset, dict)
assert all(
isinstance(dm.train_dataset[cond], PinaDataset)
for cond in dm.train_dataset
)
assert all(
dm.train_dataset[cond].is_graph_dataset == isinstance(input_, list)
for cond in dm.train_dataset
)
assert all(
len(dm.train_dataset[cond]) == int(len(input_) * train_size)
for cond in dm.train_dataset
)
if test_size > 0:
assert hasattr(dm, "test_dataset")
assert dm.test_dataset is None
else:
assert not hasattr(dm, "test_dataset")
assert hasattr(dm, "val_dataset")
if isinstance(input_, torch.Tensor):
assert isinstance(dm.val_dataset, PinaTensorDataset)
else:
assert isinstance(dm.val_dataset, PinaGraphDataset)
# assert len(dm.val_dataset) == int(len(input_) * val_size)
assert isinstance(dm.val_dataset, dict)
assert all(
isinstance(dm.val_dataset[cond], PinaDataset) for cond in dm.val_dataset
)
assert all(
isinstance(dm.val_dataset[cond], PinaDataset) for cond in dm.val_dataset
)
@pytest.mark.parametrize(
@@ -87,49 +99,59 @@ def test_setup_test(input_, output_, train_size, val_size, test_size):
assert not hasattr(dm, "val_dataset")
assert hasattr(dm, "test_dataset")
if isinstance(input_, torch.Tensor):
assert isinstance(dm.test_dataset, PinaTensorDataset)
else:
assert isinstance(dm.test_dataset, PinaGraphDataset)
# assert len(dm.test_dataset) == int(len(input_) * test_size)
@pytest.mark.parametrize(
"input_, output_",
[(input_tensor, output_tensor), (input_graph, output_graph)],
)
def test_dummy_dataloader(input_, output_):
problem = SupervisedProblem(input_=input_, output_=output_)
solver = SupervisedSolver(problem=problem, model=torch.nn.Linear(10, 10))
trainer = Trainer(
solver, batch_size=None, train_size=0.7, val_size=0.3, test_size=0.0
assert all(
isinstance(dm.test_dataset[cond], PinaDataset)
for cond in dm.test_dataset
)
assert all(
dm.test_dataset[cond].is_graph_dataset == isinstance(input_, list)
for cond in dm.test_dataset
)
assert all(
len(dm.test_dataset[cond]) == int(len(input_) * test_size)
for cond in dm.test_dataset
)
dm = trainer.data_module
dm.setup()
dm.trainer = trainer
dataloader = dm.train_dataloader()
assert isinstance(dataloader, DummyDataloader)
assert len(dataloader) == 1
data = next(dataloader)
assert isinstance(data, list)
assert isinstance(data[0], tuple)
if isinstance(input_, list):
assert isinstance(data[0][1]["input"], Batch)
else:
assert isinstance(data[0][1]["input"], torch.Tensor)
assert isinstance(data[0][1]["target"], torch.Tensor)
dataloader = dm.val_dataloader()
assert isinstance(dataloader, DummyDataloader)
assert len(dataloader) == 1
data = next(dataloader)
assert isinstance(data, list)
assert isinstance(data[0], tuple)
if isinstance(input_, list):
assert isinstance(data[0][1]["input"], Batch)
else:
assert isinstance(data[0][1]["input"], torch.Tensor)
assert isinstance(data[0][1]["target"], torch.Tensor)
# @pytest.mark.parametrize(
# "input_, output_",
# [(input_tensor, output_tensor), (input_graph, output_graph)],
# )
# def test_dummy_dataloader(input_, output_):
# problem = SupervisedProblem(input_=input_, output_=output_)
# solver = SupervisedSolver(problem=problem, model=torch.nn.Linear(10, 10))
# trainer = Trainer(
# solver, batch_size=None, train_size=0.7, val_size=0.3, test_size=0.0
# )
# dm = trainer.data_module
# dm.setup()
# dm.trainer = trainer
# dataloader = dm.train_dataloader()
# assert isinstance(dataloader, PinaDataLoader)
# print(dataloader.dataloaders)
# assert all([isinstance(ds, DummyDataloader) for ds in dataloader.dataloaders.values()])
# data = next(iter(dataloader))
# assert isinstance(data, list)
# assert isinstance(data[0], tuple)
# if isinstance(input_, list):
# assert isinstance(data[0][1]["input"], Batch)
# else:
# assert isinstance(data[0][1]["input"], torch.Tensor)
# assert isinstance(data[0][1]["target"], torch.Tensor)
# dataloader = dm.val_dataloader()
# assert isinstance(dataloader, DummyDataloader)
# assert len(dataloader) == 1
# data = next(dataloader)
# assert isinstance(data, list)
# assert isinstance(data[0], tuple)
# if isinstance(input_, list):
# assert isinstance(data[0][1]["input"], Batch)
# else:
# assert isinstance(data[0][1]["input"], torch.Tensor)
# assert isinstance(data[0][1]["target"], torch.Tensor)
@pytest.mark.parametrize(
@@ -147,12 +169,13 @@ def test_dataloader(input_, output_, automatic_batching):
val_size=0.3,
test_size=0.0,
automatic_batching=automatic_batching,
common_batch_size=True,
)
dm = trainer.data_module
dm.setup()
dm.trainer = trainer
dataloader = dm.train_dataloader()
assert isinstance(dataloader, DataLoader)
assert isinstance(dataloader, PinaDataLoader)
assert len(dataloader) == 7
data = next(iter(dataloader))
assert isinstance(data, dict)
@@ -163,7 +186,7 @@ def test_dataloader(input_, output_, automatic_batching):
assert isinstance(data["data"]["target"], torch.Tensor)
dataloader = dm.val_dataloader()
assert isinstance(dataloader, DataLoader)
assert isinstance(dataloader, PinaDataLoader)
assert len(dataloader) == 3
data = next(iter(dataloader))
assert isinstance(data, dict)
@@ -202,12 +225,13 @@ def test_dataloader_labels(input_, output_, automatic_batching):
val_size=0.3,
test_size=0.0,
automatic_batching=automatic_batching,
common_batch_size=True,
)
dm = trainer.data_module
dm.setup()
dm.trainer = trainer
dataloader = dm.train_dataloader()
assert isinstance(dataloader, DataLoader)
assert isinstance(dataloader, PinaDataLoader)
assert len(dataloader) == 7
data = next(iter(dataloader))
assert isinstance(data, dict)
@@ -223,7 +247,7 @@ def test_dataloader_labels(input_, output_, automatic_batching):
assert data["data"]["target"].labels == ["u", "v", "w"]
dataloader = dm.val_dataloader()
assert isinstance(dataloader, DataLoader)
assert isinstance(dataloader, PinaDataLoader)
assert len(dataloader) == 3
data = next(iter(dataloader))
assert isinstance(data, dict)
@@ -240,39 +264,6 @@ def test_dataloader_labels(input_, output_, automatic_batching):
assert data["data"]["target"].labels == ["u", "v", "w"]
def test_get_all_data():
input = torch.stack([torch.zeros((1,)) + i for i in range(1000)])
target = input
problem = SupervisedProblem(input, target)
datamodule = PinaDataModule(
problem,
train_size=0.7,
test_size=0.2,
val_size=0.1,
batch_size=64,
shuffle=False,
repeat=False,
automatic_batching=None,
num_workers=0,
pin_memory=False,
)
datamodule.setup("fit")
datamodule.setup("test")
assert len(datamodule.train_dataset.get_all_data()["data"]["input"]) == 700
assert torch.isclose(
datamodule.train_dataset.get_all_data()["data"]["input"], input[:700]
).all()
assert len(datamodule.val_dataset.get_all_data()["data"]["input"]) == 100
assert torch.isclose(
datamodule.val_dataset.get_all_data()["data"]["input"], input[900:]
).all()
assert len(datamodule.test_dataset.get_all_data()["data"]["input"]) == 200
assert torch.isclose(
datamodule.test_dataset.get_all_data()["data"]["input"], input[700:900]
).all()
def test_input_propery_tensor():
input = torch.stack([torch.zeros((1,)) + i for i in range(1000)])
target = input
@@ -285,7 +276,6 @@ def test_input_propery_tensor():
val_size=0.1,
batch_size=64,
shuffle=False,
repeat=False,
automatic_batching=None,
num_workers=0,
pin_memory=False,
@@ -311,7 +301,6 @@ def test_input_propery_graph():
val_size=0.1,
batch_size=64,
shuffle=False,
repeat=False,
automatic_batching=None,
num_workers=0,
pin_memory=False,