コード例 #1
0
    def training_step(self, batch, _batch_nb):
        # TODO: plot
        y, x, uids = (emiss, laser_params, uids) = batch

        x_pred = self(y)
        with torch.no_grad():
            x_loss = rmse(x_pred, x)
            self.log("backward/train/x/loss", x_loss, prog_bar=True)

        if self.forward_model is not None:
            y_pred = self.forward_model(x_pred)
            y_loss = rmse(y_pred, y)

            self.log(
                "backward/train/y/loss",
                y_loss,
                prog_bar=True,
            )

            loss = y_loss

        if self.current_epoch == self.config["backward_num_epochs"] - 5:
            nngraph.save_integral_emiss_point(
                y_pred,
                y,
                "/data-new/alok/laser/backwards_train_points.txt",
                all_points=True,
            )
        self.log(f"backward/train/loss", loss, prog_bar=True)

        return loss
コード例 #2
0
    def test_step(self, batch, batch_nb):
        # TODO: plot
        y, x, uids = (emiss, laser_params, uids) = batch

        x_pred = self(y)
        with torch.no_grad():
            x_loss = rmse(x_pred, x)
            self.log("backward/test/x/loss", x_loss, prog_bar=True)
        if self.forward_model is not None:
            y_pred = self.forward_model(x_pred)
            y_loss = rmse(y_pred, y)

            self.log(
                "backward/test/y/loss",
                y_loss,
                prog_bar=True,
            )
            loss = y_loss

            torch.save(x, "/data-new/alok/laser/params_true_back.pt")
            torch.save(y, "/data-new/alok/laser/emiss_true_back.pt")
            torch.save(y_pred, "/data-new/alok/laser/emiss_pred.pt")
            torch.save(x_pred, "/data-new/alok/laser/param_pred.pt")

        nngraph.save_integral_emiss_point(
            y_pred,
            y,
            "/data-new/alok/laser/backwards_test_points.txt",
            all_points=True)
        return loss
コード例 #3
0
 def test_step(self, batch, batch_nb):
     x, y, uids = batch
     y_pred = self(x)
     loss = rmse(y_pred, y)
     self.log(f"forward/test/loss", loss, prog_bar=True)
     nngraph.save_integral_emiss_point(
         y_pred, y, "/data-new/alok/laser/forwards_val_points.txt", all_points=True
     )
     return loss
コード例 #4
0
    def validation_step(self, batch, batch_nb):
        x, y, uids = batch
        y_pred = self(x)
        loss = rmse(y_pred, y)
        randcheck = np.random.uniform()
        self.log(f"forward/val/loss", loss, prog_bar=True)

        if self.current_epoch > self.config["forward_num_epochs"] - 5:
            nngraph.save_integral_emiss_point(
                y_pred, y, "/data-new/alok/laser/forwards_val_points.txt", all_points=True
            )
        return loss
コード例 #5
0
    def training_step(self, batch, batch_nb):
        x, y, uids = batch
        y_pred = self(x)
        loss = rmse(y_pred, y)
        # nngraph.emiss_error_graph(y_pred, y, "train_step.png")
        # self.log_image(key="train_forwards_error_graphs", images=["train_step.png"])
        if self.current_epoch == self.config["forward_num_epochs"] - 5:
            nngraph.save_integral_emiss_point(
                y_pred, y, "/data-new/alok/laser/forwards_train_points.txt", all_points=True
            )

        self.log(f"forward/train/loss", loss, prog_bar=True)
        return loss