Implementation of DataLoader and DataModule (#383)
Refactoring for 0.2 * Data module, data loader and dataset * Refactor LabelTensor * Refactor solvers Co-authored-by: dario-coscia <dariocos99@gmail.com>
This commit is contained in:
Filippo Olivo
committed by
Nicola Demo
Nicola Demo
parent
dd43c8304c
commit
a27bd35443
@@ -1,5 +1,4 @@
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import torch
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from pina.problem import SpatialProblem, InverseProblem
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from pina.operators import laplacian
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from pina.domain import CartesianDomain
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@@ -9,7 +8,7 @@ from pina.trainer import Trainer
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from pina.model import FeedForward
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from pina.equation.equation import Equation
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from pina.equation.equation_factory import FixedValue
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from pina.loss.loss_interface import LpLoss
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from pina.loss import LpLoss
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def laplace_equation(input_, output_):
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@@ -54,22 +53,22 @@ class InversePoisson(SpatialProblem, InverseProblem):
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# define the conditions for the loss (boundary conditions, equation, data)
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conditions = {
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'gamma1': Condition(location=CartesianDomain({'x': [x_min, x_max],
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'gamma1': Condition(domain=CartesianDomain({'x': [x_min, x_max],
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'y': y_max}),
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equation=FixedValue(0.0, components=['u'])),
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'gamma2': Condition(location=CartesianDomain(
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'gamma2': Condition(domain=CartesianDomain(
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{'x': [x_min, x_max], 'y': y_min
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}),
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equation=FixedValue(0.0, components=['u'])),
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'gamma3': Condition(location=CartesianDomain(
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'gamma3': Condition(domain=CartesianDomain(
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{'x': x_max, 'y': [y_min, y_max]
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}),
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equation=FixedValue(0.0, components=['u'])),
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'gamma4': Condition(location=CartesianDomain(
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'gamma4': Condition(domain=CartesianDomain(
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{'x': x_min, 'y': [y_min, y_max]
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}),
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equation=FixedValue(0.0, components=['u'])),
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'D': Condition(location=CartesianDomain(
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'D': Condition(domain=CartesianDomain(
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{'x': [x_min, x_max], 'y': [y_min, y_max]
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}),
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equation=Equation(laplace_equation)),
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@@ -84,16 +83,16 @@ class Poisson(SpatialProblem):
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conditions = {
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'gamma1': Condition(
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location=CartesianDomain({'x': [0, 1], 'y': 1}),
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domain=CartesianDomain({'x': [0, 1], 'y': 1}),
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equation=FixedValue(0.0)),
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'gamma2': Condition(
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location=CartesianDomain({'x': [0, 1], 'y': 0}),
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domain=CartesianDomain({'x': [0, 1], 'y': 0}),
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equation=FixedValue(0.0)),
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'gamma3': Condition(
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location=CartesianDomain({'x': 1, 'y': [0, 1]}),
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domain=CartesianDomain({'x': 1, 'y': [0, 1]}),
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equation=FixedValue(0.0)),
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'gamma4': Condition(
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location=CartesianDomain({'x': 0, 'y': [0, 1]}),
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domain=CartesianDomain({'x': 0, 'y': [0, 1]}),
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equation=FixedValue(0.0)),
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'D': Condition(
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input_points=LabelTensor(torch.rand(size=(100, 2)), ['x', 'y']),
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@@ -112,7 +111,6 @@ class Poisson(SpatialProblem):
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truth_solution = poisson_sol
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class myFeature(torch.nn.Module):
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"""
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Feature: sin(x)
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@@ -158,21 +156,35 @@ def test_train_cpu():
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pinn = PINN(problem = poisson_problem, model=model,
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extra_features=None, loss=LpLoss())
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trainer = Trainer(solver=pinn, max_epochs=1,
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accelerator='cpu', batch_size=20)
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trainer.train()
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accelerator='cpu', batch_size=20, val_size=0., train_size=1., test_size=0.)
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def test_log():
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poisson_problem.discretise_domain(100)
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solver = PINN(problem = poisson_problem, model=model,
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extra_features=None, loss=LpLoss())
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trainer = Trainer(solver, max_epochs=2, accelerator='cpu')
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def test_train_load():
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tmpdir = "tests/tmp_load"
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poisson_problem = Poisson()
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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pinn = PINN(problem=poisson_problem,
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model=model,
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extra_features=None,
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loss=LpLoss())
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trainer = Trainer(solver=pinn,
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max_epochs=15,
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accelerator='cpu',
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default_root_dir=tmpdir)
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trainer.train()
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# assert the logged metrics are correct
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logged_metrics = sorted(list(trainer.logged_metrics.keys()))
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total_metrics = sorted(
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list([key + '_loss' for key in poisson_problem.conditions.keys()])
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+ ['mean_loss'])
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assert logged_metrics == total_metrics
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new_pinn = PINN.load_from_checkpoint(
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f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=15.ckpt',
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problem = poisson_problem, model=model)
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test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10)
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assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1)
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assert new_pinn.forward(test_pts).extract(
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['u']).shape == pinn.forward(test_pts).extract(['u']).shape
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torch.testing.assert_close(
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new_pinn.forward(test_pts).extract(['u']),
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pinn.forward(test_pts).extract(['u']))
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import shutil
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shutil.rmtree(tmpdir)
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def test_train_restore():
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tmpdir = "tests/tmp_restore"
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@@ -192,36 +204,7 @@ def test_train_restore():
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ntrainer = Trainer(solver=pinn, max_epochs=15, accelerator='cpu')
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t = ntrainer.train(
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ckpt_path=f'{tmpdir}/lightning_logs/version_0/'
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'checkpoints/epoch=4-step=10.ckpt')
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import shutil
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shutil.rmtree(tmpdir)
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def test_train_load():
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tmpdir = "tests/tmp_load"
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poisson_problem = Poisson()
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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pinn = PINN(problem=poisson_problem,
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model=model,
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extra_features=None,
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loss=LpLoss())
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trainer = Trainer(solver=pinn,
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max_epochs=15,
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accelerator='cpu',
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default_root_dir=tmpdir)
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trainer.train()
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new_pinn = PINN.load_from_checkpoint(
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f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=30.ckpt',
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problem = poisson_problem, model=model)
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test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10)
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assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1)
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assert new_pinn.forward(test_pts).extract(
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['u']).shape == pinn.forward(test_pts).extract(['u']).shape
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torch.testing.assert_close(
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new_pinn.forward(test_pts).extract(['u']),
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pinn.forward(test_pts).extract(['u']))
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'checkpoints/epoch=4-step=5.ckpt')
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import shutil
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shutil.rmtree(tmpdir)
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@@ -229,36 +212,24 @@ def test_train_inverse_problem_cpu():
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poisson_problem = InversePoisson()
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D']
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n = 100
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poisson_problem.discretise_domain(n, 'random', locations=boundaries)
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poisson_problem.discretise_domain(n, 'random', locations=boundaries,
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variables=['x', 'y'])
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pinn = PINN(problem = poisson_problem, model=model,
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extra_features=None, loss=LpLoss())
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trainer = Trainer(solver=pinn, max_epochs=1,
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accelerator='cpu', batch_size=20)
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trainer.train()
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# # TODO does not currently work
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# def test_train_inverse_problem_restore():
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# tmpdir = "tests/tmp_restore_inv"
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# poisson_problem = InversePoisson()
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4', 'D']
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# n = 100
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# poisson_problem.discretise_domain(n, 'random', locations=boundaries)
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# pinn = PINN(problem=poisson_problem,
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# model=model,
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# extra_features=None,
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# loss=LpLoss())
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# trainer = Trainer(solver=pinn,
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# max_epochs=5,
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# accelerator='cpu',
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# default_root_dir=tmpdir)
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# trainer.train()
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# ntrainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu')
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# t = ntrainer.train(
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# ckpt_path=f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=4-step=10.ckpt')
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# import shutil
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# shutil.rmtree(tmpdir)
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def test_train_extra_feats_cpu():
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poisson_problem = Poisson()
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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pinn = PINN(problem=poisson_problem,
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model=model_extra_feats,
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extra_features=extra_feats)
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trainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu')
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trainer.train()
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def test_train_inverse_problem_load():
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tmpdir = "tests/tmp_load_inv"
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@@ -276,7 +247,7 @@ def test_train_inverse_problem_load():
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default_root_dir=tmpdir)
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trainer.train()
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new_pinn = PINN.load_from_checkpoint(
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f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=30.ckpt',
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f'{tmpdir}/lightning_logs/version_0/checkpoints/epoch=14-step=15.ckpt',
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problem = poisson_problem, model=model)
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test_pts = CartesianDomain({'x': [0, 1], 'y': [0, 1]}).sample(10)
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assert new_pinn.forward(test_pts).extract(['u']).shape == (10, 1)
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@@ -286,160 +257,4 @@ def test_train_inverse_problem_load():
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new_pinn.forward(test_pts).extract(['u']),
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pinn.forward(test_pts).extract(['u']))
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import shutil
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shutil.rmtree(tmpdir)
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# # TODO fix asap. Basically sampling few variables
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# # works only if both variables are in a range.
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# # if one is fixed and the other not, this will
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# # not work. This test also needs to be fixed and
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# # insert in test problem not in test pinn.
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# def test_train_cpu_sampling_few_vars():
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# poisson_problem = Poisson()
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# boundaries = ['gamma1', 'gamma2', 'gamma3']
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# n = 10
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# poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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# poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['x'])
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# poisson_problem.discretise_domain(n, 'random', locations=['gamma4'], variables=['y'])
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# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss())
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# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'cpu'})
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# trainer.train()
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def test_train_extra_feats_cpu():
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poisson_problem = Poisson()
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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pinn = PINN(problem=poisson_problem,
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model=model_extra_feats,
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extra_features=extra_feats)
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trainer = Trainer(solver=pinn, max_epochs=5, accelerator='cpu')
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trainer.train()
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# TODO, fix GitHub actions to run also on GPU
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# def test_train_gpu():
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# poisson_problem = Poisson()
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 10
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# poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss())
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# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'})
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# trainer.train()
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# def test_train_gpu(): #TODO fix ASAP
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# poisson_problem = Poisson()
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 10
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# poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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# poisson_problem.conditions.pop('data') # The input/output pts are allocated on cpu
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# pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss())
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# trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'gpu'})
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# trainer.train()
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# def test_train_2():
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 10
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# expected_keys = [[], list(range(0, 50, 3))]
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# param = [0, 3]
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# for i, truth_key in zip(param, expected_keys):
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# pinn = PINN(problem, model)
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# pinn.discretise_domain(n, 'grid', locations=boundaries)
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# pinn.discretise_domain(n, 'grid', locations=['D'])
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# pinn.train(50, save_loss=i)
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# assert list(pinn.history_loss.keys()) == truth_key
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# def test_train_extra_feats():
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# pinn = PINN(problem, model_extra_feat, [myFeature()])
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 10
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# pinn.discretise_domain(n, 'grid', locations=boundaries)
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# pinn.discretise_domain(n, 'grid', locations=['D'])
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# pinn.train(5)
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# def test_train_2_extra_feats():
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 10
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# expected_keys = [[], list(range(0, 50, 3))]
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# param = [0, 3]
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# for i, truth_key in zip(param, expected_keys):
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# pinn = PINN(problem, model_extra_feat, [myFeature()])
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# pinn.discretise_domain(n, 'grid', locations=boundaries)
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# pinn.discretise_domain(n, 'grid', locations=['D'])
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# pinn.train(50, save_loss=i)
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# assert list(pinn.history_loss.keys()) == truth_key
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# def test_train_with_optimizer_kwargs():
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 10
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# expected_keys = [[], list(range(0, 50, 3))]
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# param = [0, 3]
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# for i, truth_key in zip(param, expected_keys):
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# pinn = PINN(problem, model, optimizer_kwargs={'lr' : 0.3})
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# pinn.discretise_domain(n, 'grid', locations=boundaries)
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# pinn.discretise_domain(n, 'grid', locations=['D'])
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# pinn.train(50, save_loss=i)
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# assert list(pinn.history_loss.keys()) == truth_key
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# def test_train_with_lr_scheduler():
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 10
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# expected_keys = [[], list(range(0, 50, 3))]
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# param = [0, 3]
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# for i, truth_key in zip(param, expected_keys):
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# pinn = PINN(
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# problem,
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# model,
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# lr_scheduler_type=torch.optim.lr_scheduler.CyclicLR,
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# lr_scheduler_kwargs={'base_lr' : 0.1, 'max_lr' : 0.3, 'cycle_momentum': False}
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# )
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# pinn.discretise_domain(n, 'grid', locations=boundaries)
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# pinn.discretise_domain(n, 'grid', locations=['D'])
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# pinn.train(50, save_loss=i)
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# assert list(pinn.history_loss.keys()) == truth_key
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# # def test_train_batch():
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# # pinn = PINN(problem, model, batch_size=6)
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# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# # n = 10
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# # pinn.discretise_domain(n, 'grid', locations=boundaries)
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# # pinn.discretise_domain(n, 'grid', locations=['D'])
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# # pinn.train(5)
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# # def test_train_batch_2():
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# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# # n = 10
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# # expected_keys = [[], list(range(0, 50, 3))]
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# # param = [0, 3]
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# # for i, truth_key in zip(param, expected_keys):
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# # pinn = PINN(problem, model, batch_size=6)
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# # pinn.discretise_domain(n, 'grid', locations=boundaries)
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# # pinn.discretise_domain(n, 'grid', locations=['D'])
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# # pinn.train(50, save_loss=i)
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# # assert list(pinn.history_loss.keys()) == truth_key
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# if torch.cuda.is_available():
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# # def test_gpu_train():
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# # pinn = PINN(problem, model, batch_size=20, device='cuda')
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# # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# # n = 100
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# # pinn.discretise_domain(n, 'grid', locations=boundaries)
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# # pinn.discretise_domain(n, 'grid', locations=['D'])
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# # pinn.train(5)
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# def test_gpu_train_nobatch():
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# pinn = PINN(problem, model, batch_size=None, device='cuda')
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 100
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# pinn.discretise_domain(n, 'grid', locations=boundaries)
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# pinn.discretise_domain(n, 'grid', locations=['D'])
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# pinn.train(5)
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shutil.rmtree(tmpdir)
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@@ -121,7 +121,7 @@ def test_train_cpu():
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batch_size=5,
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train_size=1,
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test_size=0.,
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eval_size=0.)
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val_size=0.)
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trainer.train()
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test_train_cpu()
|
||||
|
||||
|
||||
Reference in New Issue
Block a user