Examples use GPUs if available.
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@ -37,6 +37,7 @@ if __name__ == "__main__":
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# Setup trainer
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trainer = pl.Trainer(
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gpus=-1,
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max_epochs=200,
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callbacks=[
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dvis,
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@ -30,10 +30,11 @@ if __name__ == "__main__":
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prototype_initializer=pt.components.SMI(train_ds))
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# Callbacks
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vis = pt.models.VisGLVQ2D(data=(x_train, y_train))
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vis = pt.models.VisGLVQ2D(data=(x_train, y_train), block=False)
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# Setup trainer
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trainer = pl.Trainer(
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gpus=-1,
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max_epochs=50,
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callbacks=[vis],
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)
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@ -3,17 +3,7 @@
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import prototorch as pt
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import pytorch_lightning as pl
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import torch
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class StopOnNaN(pl.Callback):
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def __init__(self, param):
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super().__init__()
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self.param = param
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def on_epoch_end(self, trainer, pl_module, logs={}):
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if torch.isnan(self.param).any():
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raise ValueError("NaN encountered. Stopping.")
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from prototorch.models.callbacks import StopOnNaN
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if __name__ == "__main__":
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# Dataset
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@ -40,11 +30,12 @@ if __name__ == "__main__":
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noise=1e-1))
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# Callbacks
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vis = pt.models.VisGLVQ2D(train_ds, show_last_only=True, block=True)
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vis = pt.models.VisGLVQ2D(train_ds, show_last_only=False, block=True)
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snan = StopOnNaN(model.proto_layer.components)
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# Setup trainer
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trainer = pl.Trainer(
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gpus=-1,
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max_epochs=200,
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callbacks=[vis, snan],
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)
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@ -29,7 +29,7 @@ if __name__ == "__main__":
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prototype_initializer=pt.components.SMI(train_ds))
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# Setup trainer
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trainer = pl.Trainer(max_epochs=100)
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trainer = pl.Trainer(max_epochs=100, gpus=-1)
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# Training loop
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trainer.fit(model, train_loader)
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@ -53,13 +53,15 @@ if __name__ == "__main__":
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# Callbacks
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vis = pt.models.VisImgComp(data=train_ds,
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nrow=5,
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show=False,
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tensorboard=True)
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show=True,
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tensorboard=True,
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pause_time=0.5)
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# Setup trainer
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trainer = pl.Trainer(
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max_epochs=50,
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callbacks=[vis],
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gpus=-1,
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# overfit_batches=1,
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# fast_dev_run=3,
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)
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@ -26,7 +26,7 @@ if __name__ == "__main__":
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vis = pt.models.VisGLVQ2D(data=(x_train, y_train))
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# Setup trainer
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trainer = pl.Trainer(max_epochs=1, callbacks=[vis])
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trainer = pl.Trainer(max_epochs=1, callbacks=[vis], gpus=-1)
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# Training loop
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# This is only for visualization. k-NN has no training phase.
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@ -34,7 +34,7 @@ if __name__ == "__main__":
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vis = pt.models.VisSiameseGLVQ2D(train_ds, border=0.1)
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# Setup trainer
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trainer = pl.Trainer(max_epochs=200, callbacks=[vis])
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trainer = pl.Trainer(max_epochs=200, callbacks=[vis], gpus=-1)
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# Training loop
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trainer.fit(model, train_loader)
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@ -34,7 +34,7 @@ if __name__ == "__main__":
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vis = pt.models.VisNG2D(data=train_ds)
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# Setup trainer
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trainer = pl.Trainer(max_epochs=200, callbacks=[vis])
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trainer = pl.Trainer(gpus=-1, max_epochs=200, callbacks=[vis])
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# Training loop
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trainer.fit(model, train_loader)
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@ -55,7 +55,7 @@ if __name__ == "__main__":
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vis = pt.models.VisSiameseGLVQ2D(data=train_ds, border=0.1)
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# Setup trainer
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trainer = pl.Trainer(max_epochs=100, callbacks=[vis])
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trainer = pl.Trainer(max_epochs=100, callbacks=[vis], gpus=-1)
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# Training loop
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trainer.fit(model, train_loader)
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14
prototorch/models/callbacks.py
Normal file
14
prototorch/models/callbacks.py
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@ -0,0 +1,14 @@
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"""Callbacks for Pytorch Lighning Modules"""
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import pytorch_lightning as pl
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import torch
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class StopOnNaN(pl.Callback):
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def __init__(self, param):
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super().__init__()
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self.param = param
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def on_epoch_end(self, trainer, pl_module, logs={}):
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if torch.isnan(self.param).any():
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raise ValueError("NaN encountered. Stopping.")
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@ -153,7 +153,7 @@ class CBC(pl.LightningModule):
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with torch.no_grad():
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y_pred = self(x)
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y_pred = torch.argmax(y_pred, dim=1)
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return y_pred.numpy()
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return y_pred
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class ImageCBC(CBC):
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@ -84,7 +84,7 @@ class GLVQ(AbstractPrototypeModel):
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d = self(x)
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plabels = self.proto_layer.component_labels
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y_pred = wtac(d, plabels)
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return y_pred.numpy()
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return y_pred
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class LVQ1(GLVQ):
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@ -33,7 +33,7 @@ class KNN(AbstractPrototypeModel):
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@property
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def prototype_labels(self):
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return self.proto_layer.component_labels.detach().cpu()
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return self.proto_layer.component_labels.detach()
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def forward(self, x):
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protos, _ = self.proto_layer()
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@ -46,7 +46,7 @@ class KNN(AbstractPrototypeModel):
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d = self(x)
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plabels = self.proto_layer.component_labels
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y_pred = knnc(d, plabels, k=self.hparams.k)
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return y_pred.numpy()
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return y_pred
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def training_step(self, train_batch, batch_idx, optimizer_idx=None):
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return 1
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@ -379,8 +379,10 @@ class VisGLVQ2D(Vis2DAbstract):
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self.plot_protos(ax, protos, plabels)
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x = np.vstack((x_train, protos))
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mesh_input, xx, yy = self.get_mesh_input(x)
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y_pred = pl_module.predict(torch.Tensor(mesh_input))
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y_pred = y_pred.reshape(xx.shape)
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_components = pl_module.proto_layer._components
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y_pred = pl_module.predict(
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torch.Tensor(mesh_input).type_as(_components))
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y_pred = y_pred.cpu().reshape(xx.shape)
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ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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self.log_and_display(trainer, pl_module)
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@ -398,20 +400,24 @@ class VisSiameseGLVQ2D(Vis2DAbstract):
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protos = pl_module.prototypes
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plabels = pl_module.prototype_labels
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x_train, y_train = self.x_train, self.y_train
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x_train = pl_module.backbone(torch.Tensor(x_train)).detach()
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x_train = pl_module.backbone(
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torch.Tensor(x_train).to(pl_module.device)).cpu().detach()
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if self.map_protos:
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protos = pl_module.backbone(torch.Tensor(protos)).detach()
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protos = pl_module.backbone(
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torch.Tensor(protos).to(pl_module.device)).cpu().detach()
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ax = self.setup_ax()
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self.plot_data(ax, x_train, y_train)
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if self.show_protos:
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self.plot_protos(ax, protos, plabels)
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x = np.vstack((x_train, protos))
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mesh_input, xx, yy = self.get_mesh_input(x)
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else:
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mesh_input, xx, yy = self.get_mesh_input(x_train)
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y_pred = pl_module.predict_latent(torch.Tensor(mesh_input))
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y_pred = y_pred.reshape(xx.shape)
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ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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#if self.show_protos:
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# self.plot_protos(ax, protos, plabels)
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# x = np.vstack((x_train, protos))
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# mesh_input, xx, yy = self.get_mesh_input(x)
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#else:
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# mesh_input, xx, yy = self.get_mesh_input(x_train)
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#_components = pl_module.proto_layer._components
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#y_pred = pl_module.predict(
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# torch.Tensor(mesh_input).type_as(_components))
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#y_pred = y_pred.cpu().reshape(xx.shape)
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#ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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self.log_and_display(trainer, pl_module)
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@ -429,8 +435,10 @@ class VisCBC2D(Vis2DAbstract):
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self.plot_protos(ax, protos, "w")
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x = np.vstack((x_train, protos))
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mesh_input, xx, yy = self.get_mesh_input(x)
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y_pred = pl_module.predict(torch.Tensor(mesh_input))
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y_pred = y_pred.reshape(xx.shape)
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_components = pl_module.component_layer._components
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y_pred = pl_module.predict(
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torch.Tensor(mesh_input).type_as(_components))
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y_pred = y_pred.cpu().reshape(xx.shape)
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ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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