143 lines
4.1 KiB
Python
143 lines
4.1 KiB
Python
import torch
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import pytest
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from pina import Condition, LabelTensor
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from pina.condition import InputTargetCondition
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from pina.problem import AbstractProblem
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from pina.solver import SupervisedSolver
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from pina.model import FeedForward
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from pina.trainer import Trainer
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from torch._dynamo.eval_frame import OptimizedModule
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class LabelTensorProblem(AbstractProblem):
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input_variables = ["u_0", "u_1"]
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output_variables = ["u"]
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conditions = {
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"data": Condition(
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input=LabelTensor(torch.randn(20, 2), ["u_0", "u_1"]),
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target=LabelTensor(torch.randn(20, 1), ["u"]),
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),
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}
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class TensorProblem(AbstractProblem):
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input_variables = ["u_0", "u_1"]
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output_variables = ["u"]
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conditions = {
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"data": Condition(input=torch.randn(20, 2), target=torch.randn(20, 1))
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}
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model = FeedForward(2, 1)
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def test_constructor():
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SupervisedSolver(problem=TensorProblem(), model=model)
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SupervisedSolver(problem=LabelTensorProblem(), model=model)
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assert SupervisedSolver.accepted_conditions_types == (InputTargetCondition)
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@pytest.mark.parametrize("batch_size", [None, 1, 5, 20])
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@pytest.mark.parametrize("use_lt", [True, False])
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@pytest.mark.parametrize("compile", [True, False])
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def test_solver_train(use_lt, batch_size, compile):
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problem = LabelTensorProblem() if use_lt else TensorProblem()
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solver = SupervisedSolver(problem=problem, model=model, use_lt=use_lt)
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trainer = Trainer(
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solver=solver,
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max_epochs=2,
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accelerator="cpu",
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batch_size=batch_size,
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train_size=1.0,
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test_size=0.0,
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val_size=0.0,
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compile=compile,
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)
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trainer.train()
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if trainer.compile:
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assert isinstance(solver.model, OptimizedModule)
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@pytest.mark.parametrize("use_lt", [True, False])
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@pytest.mark.parametrize("compile", [True, False])
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def test_solver_validation(use_lt, compile):
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problem = LabelTensorProblem() if use_lt else TensorProblem()
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solver = SupervisedSolver(problem=problem, model=model, use_lt=use_lt)
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trainer = Trainer(
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solver=solver,
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max_epochs=2,
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accelerator="cpu",
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batch_size=None,
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train_size=0.9,
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val_size=0.1,
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test_size=0.0,
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compile=compile,
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)
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trainer.train()
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if trainer.compile:
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assert isinstance(solver.model, OptimizedModule)
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@pytest.mark.parametrize("use_lt", [True, False])
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@pytest.mark.parametrize("compile", [True, False])
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def test_solver_test(use_lt, compile):
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problem = LabelTensorProblem() if use_lt else TensorProblem()
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solver = SupervisedSolver(problem=problem, model=model, use_lt=use_lt)
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trainer = Trainer(
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solver=solver,
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max_epochs=2,
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accelerator="cpu",
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batch_size=None,
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train_size=0.8,
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val_size=0.1,
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test_size=0.1,
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compile=compile,
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)
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trainer.test()
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if trainer.compile:
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assert isinstance(solver.model, OptimizedModule)
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def test_train_load_restore():
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dir = "tests/test_solver/tmp/"
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problem = LabelTensorProblem()
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solver = SupervisedSolver(problem=problem, model=model)
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trainer = Trainer(
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solver=solver,
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max_epochs=5,
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accelerator="cpu",
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batch_size=None,
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train_size=0.9,
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test_size=0.1,
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val_size=0.0,
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default_root_dir=dir,
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)
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trainer.train()
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# restore
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new_trainer = Trainer(solver=solver, max_epochs=5, accelerator="cpu")
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new_trainer.train(
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ckpt_path=f"{dir}/lightning_logs/version_0/checkpoints/"
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+ "epoch=4-step=5.ckpt"
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)
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# loading
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new_solver = SupervisedSolver.load_from_checkpoint(
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f"{dir}/lightning_logs/version_0/checkpoints/epoch=4-step=5.ckpt",
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problem=problem,
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model=model,
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)
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test_pts = LabelTensor(torch.rand(20, 2), problem.input_variables)
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assert new_solver.forward(test_pts).shape == (20, 1)
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assert new_solver.forward(test_pts).shape == solver.forward(test_pts).shape
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torch.testing.assert_close(
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new_solver.forward(test_pts), solver.forward(test_pts)
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)
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# rm directories
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import shutil
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shutil.rmtree("tests/test_solver/tmp")
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