127 lines
3.8 KiB
Python
127 lines
3.8 KiB
Python
import torch
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import torch.nn as nn
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from torch_geometric.nn import MessagePassing
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from torch.nn.utils import spectral_norm
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from matplotlib.tri import Triangulation
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from matplotlib import pyplot as plt
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def _plot_mesh(y_pred, batch, iteration=None):
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idx = batch.batch == 0
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y = batch.y[idx].detach().cpu()
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y_pred = y_pred[idx].detach().cpu()
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pos = batch.pos[idx].detach().cpu()
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pos = pos.detach().cpu()
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tria = Triangulation(pos[:, 0], pos[:, 1])
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plt.figure(figsize=(18, 5))
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plt.subplot(1, 3, 1)
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plt.tricontourf(tria, y.squeeze().numpy(), levels=14)
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plt.colorbar()
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plt.title("True temperature")
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plt.subplot(1, 3, 2)
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plt.tricontourf(tria, y_pred.squeeze().numpy(), levels=14)
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plt.colorbar()
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plt.title("Predicted temperature")
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plt.subplot(1, 3, 3)
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plt.tricontourf(tria, torch.abs(y_pred - y).squeeze().numpy(), levels=14)
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plt.colorbar()
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plt.title("Error")
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plt.suptitle("GNO", fontsize=16)
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name = (
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f"images/gno_iter_{iteration:04d}.png"
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if iteration is not None
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else "gno.png"
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)
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plt.savefig(name, dpi=72)
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plt.close()
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class FiniteDifferenceStep(MessagePassing):
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"""
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TODO: add docstring.
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"""
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def __init__(self, edge_ch=5, hidden_dim=16, aggr: str = "add"):
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super().__init__(aggr=aggr)
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self.x_embedding = nn.Sequential(
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spectral_norm(nn.Linear(1, hidden_dim // 2)),
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nn.GELU(),
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spectral_norm(nn.Linear(hidden_dim // 2, hidden_dim)),
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)
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self.edge_embedding = nn.Sequential(
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spectral_norm(nn.Linear(edge_ch, hidden_dim // 2)),
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nn.GELU(),
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spectral_norm(nn.Linear(hidden_dim // 2, hidden_dim)),
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)
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self.update_net = nn.Sequential(
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spectral_norm(nn.Linear(2 * hidden_dim, hidden_dim)),
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nn.GELU(),
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spectral_norm(nn.Linear(hidden_dim, hidden_dim)),
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nn.GELU(),
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# spectral_norm(nn.Linear(hidden_dim // 2, 1)),
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)
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# self.message_net = nn.Sequential(
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# spectral_norm(nn.Linear(2 * hidden_dim, hidden_dim)),
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# nn.GELU(),
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# spectral_norm(nn.Linear(hidden_dim, hidden_dim // 2)),
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# nn.GELU(),
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# spectral_norm(nn.Linear(hidden_dim // 2, hidden_dim)),
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# )
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self.out_net = nn.Sequential(
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spectral_norm(nn.Linear(hidden_dim, hidden_dim // 2)),
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nn.GELU(),
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spectral_norm(nn.Linear(hidden_dim // 2, 1)),
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)
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def forward(self, x, edge_index, edge_attr, deg):
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"""
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TODO: add docstring.
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"""
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x_ = self.x_embedding(x)
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edge_attr_ = self.edge_embedding(edge_attr)
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out = self.propagate(edge_index, x=x_, edge_attr=edge_attr_, deg=deg)
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return self.out_net(x_ + out)
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def message(self, x_j, edge_attr):
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"""
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TODO: add docstring.
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"""
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# msg_input = torch.cat([x_j, edge_attr], dim=-1)
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# return self.message_net(msg_input) * edge_attr[:, 3].view(-1, 1)
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return x_j * edge_attr
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def update(self, aggr_out, x):
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"""
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TODO: add docstring.
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"""
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update_input = torch.cat([x, aggr_out], dim=-1)
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return self.update_net(update_input)
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# return self.update_net(aggr_out)
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# return aggr_out
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# h = self.update_net(aggr_out, x)
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# return h
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def aggregate(self, inputs, index, deg):
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"""
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TODO: add docstring.
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"""
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out = super().aggregate(inputs, index)
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deg = deg + 1e-7
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return out / deg.view(-1, 1)
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# # Da fare:
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# # - Finire calcolo della loss su ogni step e poi media
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# # - Test con vari modelli
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# # - Se non dovesse funzionare, provare ad adeguare il criterio di uscita
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# # PINN batching:
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# # - Provare singola condizione
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# # - Ottimizzatore del secondo ordine (LBFGS)
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