fix model and datamodule
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@@ -17,7 +17,7 @@ def import_class(class_path: str):
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def _plot_mesh(pos_, y_, y_pred_, y_true_, batch, i, batch_idx):
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# print(pos_.shape, y_.shape, y_pred_.shape, y_true_.shape)
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for j in [0]:
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for j in [0, 5, 10, 20]:
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idx = (batch == j).nonzero(as_tuple=True)[0]
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y = y_[idx].detach().cpu()
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y_pred = y_pred_[idx].detach().cpu()
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@@ -38,39 +38,37 @@ def _plot_mesh(pos_, y_, y_pred_, y_true_, batch, i, batch_idx):
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# plt.savefig("test_scatter_step_before.png", dpi=72)
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# x = z
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plt.subplot(1, 3, 1)
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# plt.tricontourf(tria, y_pred.squeeze().numpy(), levels=100)
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plt.scatter(
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pos[:, 0],
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pos[:, 1],
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c=y_pred.squeeze().numpy(),
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s=20,
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cmap="viridis",
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)
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plt.tricontourf(tria, y_pred.squeeze().numpy(), levels=100)
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# plt.scatter(
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# pos[:, 0],
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# pos[:, 1],
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# c=y_pred.squeeze().numpy(),
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# s=20,
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# cmap="viridis",
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# )
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plt.colorbar()
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plt.title("Step t Predicted")
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plt.subplot(1, 3, 2)
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# plt.tricontourf(tria, y_true.squeeze().numpy(), levels=100)
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plt.scatter(
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pos[:, 0],
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pos[:, 1],
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c=y_true.squeeze().numpy(),
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s=20,
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cmap="viridis",
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)
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plt.tricontourf(tria, y_true.squeeze().numpy(), levels=100)
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# plt.scatter(
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# pos[:, 0],
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# pos[:, 1],
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# c=y_true.squeeze().numpy(),
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# s=20,
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# cmap="viridis",
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# )
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plt.colorbar()
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plt.title("t True")
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plt.subplot(1, 3, 3)
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per_element_relative_error = torch.abs(y_pred - y_true) / torch.clamp(
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torch.abs(y_true), min=1e-6
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)
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# plt.tricontourf(tria, per_element_relative_error.squeeze(), levels=100)
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plt.scatter(
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pos[:, 0],
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pos[:, 1],
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c=per_element_relative_error.squeeze().numpy(),
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s=20,
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cmap="viridis",
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)
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per_element_relative_error = torch.abs(y_pred - y_true)
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plt.tricontourf(tria, per_element_relative_error.squeeze(), levels=100)
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# plt.scatter(
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# pos[:, 0],
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# pos[:, 1],
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# c=per_element_relative_error.squeeze().numpy(),
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# s=20,
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# cmap="viridis",
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# )
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plt.colorbar()
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plt.title("Relative Error")
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plt.suptitle("GNO", fontsize=16)
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@@ -216,20 +214,20 @@ class GraphSolver(LightningModule):
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batch.boundary_values,
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conductivity,
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)
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if (
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batch_idx == 0
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and self.current_epoch % 10 == 0
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and self.current_epoch > 0
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):
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_plot_mesh(
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batch.pos,
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x,
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out,
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y[:, i, :],
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batch.batch,
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i,
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self.current_epoch,
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)
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# if (
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# batch_idx == 0
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# and self.current_epoch % 10 == 0
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# and self.current_epoch > 0
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# ):
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# _plot_mesh(
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# batch.pos,
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# x,
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# out,
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# y[:, i, :],
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# batch.batch,
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# i,
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# self.current_epoch,
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# )
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x = out
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losses.append(self.loss(out, y[:, i, :]))
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