davide
b8301a4329
- pytest.ini: configura testpaths, marker slow, output verboso - tests/conftest.py: fixture condivise (device, small_data, pinn_model) - tests/test_config.py: sanità parametri fisici e numerici, CFL, _pde_scale - tests/test_model.py: HeatPINN.forward e heat_pinn_loss (shape, finiti, zero-weight analytici per IC e BC, scaling dei pesi) - tests/test_engine_data.py: set_seed, _get_device, prepare_data (shape, bounds, device consistency, determinismo) - tests/test_integration_pinn.py: pipeline dati→modello→loss→backward - tests/test_e2e.py: FDM completo, visualizer FDM/PINN con tmp_path, training breve (2 test @slow) - requirements.txt: aggiunge pytest>=7.0.0 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
103 lines
3.9 KiB
Python
103 lines
3.9 KiB
Python
import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import torch
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import config
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from model import HeatPINN, heat_pinn_loss
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# ── HeatPINN.forward ─────────────────────────────────────────────────────────
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def test_forward_output_shape(pinn_model, device):
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xt = torch.zeros(64, 2, device=device)
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xt[:, 0] = torch.rand(64) * config.L
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xt[:, 1] = torch.rand(64) * config.T_END
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assert pinn_model(xt).shape == (64, 1)
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def test_forward_finite(pinn_model, device):
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xt = torch.zeros(100, 2, device=device)
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xt[:, 0] = torch.rand(100) * config.L
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xt[:, 1] = torch.rand(100) * config.T_END
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assert torch.isfinite(pinn_model(xt)).all()
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def test_forward_zero_weights_returns_t_amb(device):
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"""Con pesi nulli net(x,t)=0 ⇒ forward restituisce T_AMB per ogni input."""
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model = HeatPINN().to(device)
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for p in model.parameters():
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p.data.zero_()
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xt = torch.zeros(20, 2, device=device)
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xt[:, 0] = torch.rand(20) * config.L
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xt[:, 1] = torch.rand(20) * config.T_END
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out = model(xt)
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torch.testing.assert_close(out, torch.full_like(out, config.T_AMB), atol=1e-5, rtol=0.0)
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def test_forward_t_normalization(device):
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"""t viene normalizzato a [0,1]: il modello deve restituire output finiti
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anche a t=T_END senza saturazione di Tanh."""
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model = HeatPINN().to(device)
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torch.nn.init.xavier_uniform_(model.net[0].weight)
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xt = torch.tensor([[0.5, 0.0], [0.5, config.T_END]], device=device)
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out = model(xt)
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assert out.shape == (2, 1)
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assert torch.isfinite(out).all()
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# ── heat_pinn_loss ────────────────────────────────────────────────────────────
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def _dummy_inputs(device, n_f=100, n_ic=50, n_bc=50):
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x_f = torch.rand(n_f, device=device) * config.L
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t_f = torch.rand(n_f, device=device) * config.T_END
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x_ic = torch.rand(n_ic, device=device) * config.L
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t_bc = torch.rand(n_bc, device=device) * config.T_END
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return x_f, t_f, x_ic, t_bc
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def test_loss_returns_four_values(pinn_model, device):
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result = heat_pinn_loss(pinn_model, *_dummy_inputs(device))
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assert len(result) == 4
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def test_loss_components_non_negative(pinn_model, device):
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total, L_pde, L_ic, L_bc = heat_pinn_loss(pinn_model, *_dummy_inputs(device))
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assert total.item() >= 0.0
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assert L_pde.item() >= 0.0
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assert L_ic.item() >= 0.0
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assert L_bc.item() >= 0.0
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def test_loss_finite(pinn_model, device):
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for v in heat_pinn_loss(pinn_model, *_dummy_inputs(device)):
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assert torch.isfinite(v), f"loss non finita: {v}"
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def test_loss_weight_doubles_pde_contribution(pinn_model, device):
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"""Raddoppiare w_pde con w_ic=w_bc=0 deve raddoppiare il totale."""
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inputs = _dummy_inputs(device)
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total1, L_pde1, _, _ = heat_pinn_loss(pinn_model, *inputs, w_pde=1.0, w_ic=0.0, w_bc=0.0)
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total2, L_pde2, _, _ = heat_pinn_loss(pinn_model, *inputs, w_pde=2.0, w_ic=0.0, w_bc=0.0)
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# L_pde deve essere identico tra le due chiamate (stesso modello, stessi dati)
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torch.testing.assert_close(L_pde1, L_pde2, atol=1e-5, rtol=1e-4)
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torch.testing.assert_close(total2, 2.0 * total1, atol=1e-5, rtol=1e-4)
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def test_ic_loss_zero_when_net_is_zero(device):
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"""Con net=0 ⇒ T = T_AMB = T0 ⇒ L_ic = 0."""
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model = HeatPINN().to(device)
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for p in model.parameters():
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p.data.zero_()
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_, _, L_ic, _ = heat_pinn_loss(model, *_dummy_inputs(device))
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assert L_ic.item() < 1e-8
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def test_bc_loss_zero_when_net_is_zero(device):
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"""Con net=0 ⇒ T = T_AMB e dT/dx = 0 ⇒ Robin BC soddisfatta ⇒ L_bc = 0."""
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model = HeatPINN().to(device)
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for p in model.parameters():
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p.data.zero_()
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_, _, _, L_bc = heat_pinn_loss(model, *_dummy_inputs(device))
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assert L_bc.item() < 1e-8
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