321 lines
11 KiB
Python
321 lines
11 KiB
Python
import torch
|
|
import pytest
|
|
from pina.data import PinaDataModule
|
|
from pina.data.dataset import PinaDataset
|
|
from pina.problem.zoo import SupervisedProblem
|
|
from pina.graph import RadiusGraph
|
|
|
|
from pina.data.dataloader import DummyDataloader, PinaDataLoader
|
|
from pina import Trainer
|
|
from pina.solver import SupervisedSolver
|
|
from torch_geometric.data import Batch
|
|
from torch.utils.data import DataLoader
|
|
|
|
input_tensor = torch.rand((100, 10))
|
|
output_tensor = torch.rand((100, 2))
|
|
|
|
x = torch.rand((100, 50, 10))
|
|
pos = torch.rand((100, 50, 2))
|
|
input_graph = [
|
|
RadiusGraph(x=x_, pos=pos_, radius=0.2) for x_, pos_, in zip(x, pos)
|
|
]
|
|
output_graph = torch.rand((100, 50, 10))
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"input_, output_",
|
|
[(input_tensor, output_tensor), (input_graph, output_graph)],
|
|
)
|
|
def test_constructor(input_, output_):
|
|
problem = SupervisedProblem(input_=input_, output_=output_)
|
|
PinaDataModule(problem)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"input_, output_",
|
|
[(input_tensor, output_tensor), (input_graph, output_graph)],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"train_size, val_size, test_size", [(0.7, 0.2, 0.1), (0.7, 0.3, 0)]
|
|
)
|
|
def test_setup_train(input_, output_, train_size, val_size, test_size):
|
|
problem = SupervisedProblem(input_=input_, output_=output_)
|
|
dm = PinaDataModule(
|
|
problem, train_size=train_size, val_size=val_size, test_size=test_size
|
|
)
|
|
dm.setup()
|
|
assert hasattr(dm, "train_dataset")
|
|
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")
|
|
|
|
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(
|
|
"input_, output_",
|
|
[(input_tensor, output_tensor), (input_graph, output_graph)],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"train_size, val_size, test_size", [(0.7, 0.2, 0.1), (0.0, 0.0, 1.0)]
|
|
)
|
|
def test_setup_test(input_, output_, train_size, val_size, test_size):
|
|
problem = SupervisedProblem(input_=input_, output_=output_)
|
|
dm = PinaDataModule(
|
|
problem, train_size=train_size, val_size=val_size, test_size=test_size
|
|
)
|
|
dm.setup(stage="test")
|
|
if train_size > 0:
|
|
assert hasattr(dm, "train_dataset")
|
|
assert dm.train_dataset is None
|
|
else:
|
|
assert not hasattr(dm, "train_dataset")
|
|
if val_size > 0:
|
|
assert hasattr(dm, "val_dataset")
|
|
assert dm.val_dataset is None
|
|
else:
|
|
assert not hasattr(dm, "val_dataset")
|
|
|
|
assert hasattr(dm, "test_dataset")
|
|
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
|
|
)
|
|
|
|
|
|
# @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(
|
|
"input_, output_",
|
|
[(input_tensor, output_tensor), (input_graph, output_graph)],
|
|
)
|
|
@pytest.mark.parametrize("automatic_batching", [True, False])
|
|
def test_dataloader(input_, output_, automatic_batching):
|
|
problem = SupervisedProblem(input_=input_, output_=output_)
|
|
solver = SupervisedSolver(problem=problem, model=torch.nn.Linear(10, 10))
|
|
trainer = Trainer(
|
|
solver,
|
|
batch_size=10,
|
|
train_size=0.7,
|
|
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, PinaDataLoader)
|
|
assert len(dataloader) == 7
|
|
data = next(iter(dataloader))
|
|
assert isinstance(data, dict)
|
|
if isinstance(input_, list):
|
|
assert isinstance(data["data"]["input"], Batch)
|
|
else:
|
|
assert isinstance(data["data"]["input"], torch.Tensor)
|
|
assert isinstance(data["data"]["target"], torch.Tensor)
|
|
|
|
dataloader = dm.val_dataloader()
|
|
assert isinstance(dataloader, PinaDataLoader)
|
|
assert len(dataloader) == 3
|
|
data = next(iter(dataloader))
|
|
assert isinstance(data, dict)
|
|
if isinstance(input_, list):
|
|
assert isinstance(data["data"]["input"], Batch)
|
|
else:
|
|
assert isinstance(data["data"]["input"], torch.Tensor)
|
|
assert isinstance(data["data"]["target"], torch.Tensor)
|
|
|
|
|
|
from pina import LabelTensor
|
|
|
|
input_tensor = LabelTensor(torch.rand((100, 3)), ["u", "v", "w"])
|
|
output_tensor = LabelTensor(torch.rand((100, 3)), ["u", "v", "w"])
|
|
|
|
x = LabelTensor(torch.rand((100, 50, 3)), ["u", "v", "w"])
|
|
pos = LabelTensor(torch.rand((100, 50, 2)), ["x", "y"])
|
|
input_graph = [
|
|
RadiusGraph(x=x[i], pos=pos[i], radius=0.1) for i in range(len(x))
|
|
]
|
|
output_graph = LabelTensor(torch.rand((100, 50, 3)), ["u", "v", "w"])
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"input_, output_",
|
|
[(input_tensor, output_tensor), (input_graph, output_graph)],
|
|
)
|
|
@pytest.mark.parametrize("automatic_batching", [True, False])
|
|
def test_dataloader_labels(input_, output_, automatic_batching):
|
|
problem = SupervisedProblem(input_=input_, output_=output_)
|
|
solver = SupervisedSolver(problem=problem, model=torch.nn.Linear(10, 10))
|
|
trainer = Trainer(
|
|
solver,
|
|
batch_size=10,
|
|
train_size=0.7,
|
|
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, PinaDataLoader)
|
|
assert len(dataloader) == 7
|
|
data = next(iter(dataloader))
|
|
assert isinstance(data, dict)
|
|
if isinstance(input_, list):
|
|
assert isinstance(data["data"]["input"], Batch)
|
|
assert isinstance(data["data"]["input"].x, LabelTensor)
|
|
assert data["data"]["input"].x.labels == ["u", "v", "w"]
|
|
assert data["data"]["input"].pos.labels == ["x", "y"]
|
|
else:
|
|
assert isinstance(data["data"]["input"], LabelTensor)
|
|
assert data["data"]["input"].labels == ["u", "v", "w"]
|
|
assert isinstance(data["data"]["target"], LabelTensor)
|
|
assert data["data"]["target"].labels == ["u", "v", "w"]
|
|
|
|
dataloader = dm.val_dataloader()
|
|
assert isinstance(dataloader, PinaDataLoader)
|
|
assert len(dataloader) == 3
|
|
data = next(iter(dataloader))
|
|
assert isinstance(data, dict)
|
|
if isinstance(input_, list):
|
|
assert isinstance(data["data"]["input"], Batch)
|
|
assert isinstance(data["data"]["input"].x, LabelTensor)
|
|
assert data["data"]["input"].x.labels == ["u", "v", "w"]
|
|
assert data["data"]["input"].pos.labels == ["x", "y"]
|
|
else:
|
|
assert isinstance(data["data"]["input"], torch.Tensor)
|
|
assert isinstance(data["data"]["input"], LabelTensor)
|
|
assert data["data"]["input"].labels == ["u", "v", "w"]
|
|
assert isinstance(data["data"]["target"], torch.Tensor)
|
|
assert data["data"]["target"].labels == ["u", "v", "w"]
|
|
|
|
|
|
def test_input_propery_tensor():
|
|
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,
|
|
automatic_batching=None,
|
|
num_workers=0,
|
|
pin_memory=False,
|
|
)
|
|
datamodule.setup("fit")
|
|
datamodule.setup("test")
|
|
input_ = datamodule.input
|
|
assert isinstance(input_, dict)
|
|
assert isinstance(input_["train"], dict)
|
|
assert isinstance(input_["val"], dict)
|
|
assert isinstance(input_["test"], dict)
|
|
assert torch.isclose(input_["train"]["data"], input[:700]).all()
|
|
assert torch.isclose(input_["val"]["data"], input[900:]).all()
|
|
assert torch.isclose(input_["test"]["data"], input[700:900]).all()
|
|
|
|
|
|
def test_input_propery_graph():
|
|
problem = SupervisedProblem(input_graph, output_graph)
|
|
datamodule = PinaDataModule(
|
|
problem,
|
|
train_size=0.7,
|
|
test_size=0.2,
|
|
val_size=0.1,
|
|
batch_size=64,
|
|
shuffle=False,
|
|
automatic_batching=None,
|
|
num_workers=0,
|
|
pin_memory=False,
|
|
)
|
|
datamodule.setup("fit")
|
|
datamodule.setup("test")
|
|
input_ = datamodule.input
|
|
assert isinstance(input_, dict)
|
|
assert isinstance(input_["train"], dict)
|
|
assert isinstance(input_["val"], dict)
|
|
assert isinstance(input_["test"], dict)
|
|
assert isinstance(input_["train"]["data"], list)
|
|
assert isinstance(input_["val"]["data"], list)
|
|
assert isinstance(input_["test"]["data"], list)
|
|
assert len(input_["train"]["data"]) == 70
|
|
assert len(input_["val"]["data"]) == 10
|
|
assert len(input_["test"]["data"]) == 20
|