Refactor visualization callbacks
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Jensun Ravichandran
parent
dd75fbfff8
commit
ff7a1e93d2
@ -269,6 +269,7 @@ class Vis2DAbstract(pl.Callback):
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cmap="viridis",
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border=1,
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resolution=50,
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show_protos=True,
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tensorboard=False,
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show_last_only=False,
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pause_time=0.1,
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@ -288,11 +289,17 @@ class Vis2DAbstract(pl.Callback):
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self.cmap = cmap
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self.border = border
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self.resolution = resolution
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self.show_protos = show_protos
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self.tensorboard = tensorboard
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self.show_last_only = show_last_only
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self.pause_time = pause_time
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self.block = block
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def precheck(self, trainer):
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if self.show_last_only:
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if trainer.current_epoch != trainer.max_epochs - 1:
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return
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def setup_ax(self, xlabel=None, ylabel=None):
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ax = self.fig.gca()
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ax.cla()
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@ -312,6 +319,28 @@ class Vis2DAbstract(pl.Callback):
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mesh_input = np.c_[xx.ravel(), yy.ravel()]
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return mesh_input, xx, yy
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def plot_data(self, ax, x, y):
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ax.scatter(
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x[:, 0],
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x[:, 1],
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c=y,
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cmap=self.cmap,
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edgecolor="k",
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marker="o",
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s=30,
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)
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def plot_protos(self, ax, protos, plabels):
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ax.scatter(
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protos[:, 0],
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protos[:, 1],
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c=plabels,
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cmap=self.cmap,
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edgecolor="k",
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marker="D",
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s=50,
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)
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def add_to_tensorboard(self, trainer, pl_module):
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tb = pl_module.logger.experiment
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tb.add_figure(tag=f"{self.title}",
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@ -330,115 +359,89 @@ class Vis2DAbstract(pl.Callback):
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class VisGLVQ2D(Vis2DAbstract):
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def on_epoch_end(self, trainer, pl_module):
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if self.show_last_only:
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if trainer.current_epoch != trainer.max_epochs - 1:
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return
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self.precheck(trainer)
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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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ax = self.setup_ax(xlabel="Data dimension 1",
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ylabel="Data dimension 2")
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ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
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ax.scatter(
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protos[:, 0],
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protos[:, 1],
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c=plabels,
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cmap=self.cmap,
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edgecolor="k",
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marker="D",
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s=50,
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)
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self.plot_data(ax, x_train, y_train)
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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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ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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# ax.set_xlim(left=x_min + 0, right=x_max - 0)
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# ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
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self.log_and_display(trainer, pl_module)
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class VisSiameseGLVQ2D(Vis2DAbstract):
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def __init__(self, *args, map_protos=True, **kwargs):
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super().__init__(*args, **kwargs)
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self.map_protos = map_protos
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def on_epoch_end(self, trainer, pl_module):
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self.precheck(trainer)
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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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protos = pl_module.backbone(torch.Tensor(protos)).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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ax = self.setup_ax()
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ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
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ax.scatter(
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protos[:, 0],
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protos[:, 1],
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c=plabels,
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cmap=self.cmap,
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edgecolor="k",
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marker="D",
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s=50,
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)
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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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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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# ax.set_xlim(left=x_min + 0, right=x_max - 0)
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# ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
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self.log_and_display(trainer, pl_module)
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class VisCBC2D(Vis2DAbstract):
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def on_epoch_end(self, trainer, pl_module):
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self.precheck(trainer)
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x_train, y_train = self.x_train, self.y_train
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protos = pl_module.components
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ax = self.setup_ax(xlabel="Data dimension 1",
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ylabel="Data dimension 2")
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ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
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ax.scatter(
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protos[:, 0],
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protos[:, 1],
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c="w",
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cmap=self.cmap,
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edgecolor="k",
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marker="D",
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s=50,
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)
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self.plot_data(ax, x_train, y_train)
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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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ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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# ax.set_xlim(left=x_min + 0, right=x_max - 0)
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# ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
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self.log_and_display(trainer, pl_module)
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class VisNG2D(Vis2DAbstract):
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def on_epoch_end(self, trainer, pl_module):
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self.precheck(trainer)
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x_train, y_train = self.x_train, self.y_train
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protos = pl_module.prototypes
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cmat = pl_module.topology_layer.cmat.cpu().numpy()
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ax = self.setup_ax(xlabel="Data dimension 1",
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ylabel="Data dimension 2")
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ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
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ax.scatter(
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protos[:, 0],
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protos[:, 1],
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c="k",
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edgecolor="k",
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marker="D",
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s=50,
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)
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self.plot_data(ax, x_train, y_train)
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self.plot_protos(ax, protos, "w")
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# Draw connections
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for i in range(len(protos)):
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for j in range(len(protos)):
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for j in range(i, len(protos)):
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if cmat[i][j]:
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ax.plot(
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[protos[i, 0], protos[j, 0]],
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