julius/prototorch_models
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feat: add visualize method to visualization callbacks

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All visualization callbacks now contain a `visualize` method that takes an
appropriate PyTorchLightning Module and visualizes it without the need for a
Trainer. This is to encourage users to perform one-off visualizations after
training.
This commit is contained in:
Jensun Ravichandran 2022-02-02 21:45:44 +01:00
parent 98892afee0
commit 197b728c63
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GPG Key ID: 7612C0CAB643D921
1 changed files with 12 additions and 36 deletions
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48
prototorch/models/vis.py
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@ -114,16 +114,19 @@ class Vis2DAbstract(pl.Callback):
else:
plt.show(block=self.block)
def on_epoch_end(self, trainer, pl_module):
if not self.precheck(trainer):
return True
self.visualize(pl_module)
self.log_and_display(trainer, pl_module)
def on_train_end(self, trainer, pl_module):
plt.close()
class VisGLVQ2D(Vis2DAbstract):
def on_epoch_end(self, trainer, pl_module):
if not self.precheck(trainer):
return True
def visualize(self, pl_module):
protos = pl_module.prototypes
plabels = pl_module.prototype_labels
x_train, y_train = self.x_train, self.y_train
@ -139,8 +142,6 @@ class VisGLVQ2D(Vis2DAbstract):
y_pred = y_pred.cpu().reshape(xx.shape)
ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
self.log_and_display(trainer, pl_module)
class VisSiameseGLVQ2D(Vis2DAbstract):
@ -148,10 +149,7 @@ class VisSiameseGLVQ2D(Vis2DAbstract):
super().__init__(*args, **kwargs)
self.map_protos = map_protos
def on_epoch_end(self, trainer, pl_module):
if not self.precheck(trainer):
return True
def visualize(self, pl_module):
protos = pl_module.prototypes
plabels = pl_module.prototype_labels
x_train, y_train = self.x_train, self.y_train
@ -178,8 +176,6 @@ class VisSiameseGLVQ2D(Vis2DAbstract):
y_pred = y_pred.cpu().reshape(xx.shape)
ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
self.log_and_display(trainer, pl_module)
class VisGMLVQ2D(Vis2DAbstract):
@ -187,10 +183,7 @@ class VisGMLVQ2D(Vis2DAbstract):
super().__init__(*args, **kwargs)
self.ev_proj = ev_proj
def on_epoch_end(self, trainer, pl_module):
if not self.precheck(trainer):
return True
def visualize(self, pl_module):
protos = pl_module.prototypes
plabels = pl_module.prototype_labels
x_train, y_train = self.x_train, self.y_train
@ -212,15 +205,10 @@ class VisGMLVQ2D(Vis2DAbstract):
if self.show_protos:
self.plot_protos(ax, protos, plabels)
self.log_and_display(trainer, pl_module)
class VisCBC2D(Vis2DAbstract):
def on_epoch_end(self, trainer, pl_module):
if not self.precheck(trainer):
return True
def visualize(self, pl_module):
x_train, y_train = self.x_train, self.y_train
protos = pl_module.components
ax = self.setup_ax(xlabel="Data dimension 1",
@ -236,15 +224,10 @@ class VisCBC2D(Vis2DAbstract):
ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
self.log_and_display(trainer, pl_module)
class VisNG2D(Vis2DAbstract):
def on_epoch_end(self, trainer, pl_module):
if not self.precheck(trainer):
return True
def visualize(self, pl_module):
x_train, y_train = self.x_train, self.y_train
protos = pl_module.prototypes
cmat = pl_module.topology_layer.cmat.cpu().numpy()
@ -264,8 +247,6 @@ class VisNG2D(Vis2DAbstract):
"k-",
)
self.log_and_display(trainer, pl_module)
class VisImgComp(Vis2DAbstract):
@ -321,14 +302,9 @@ class VisImgComp(Vis2DAbstract):
dataformats=self.dataformats,
)
def on_epoch_end(self, trainer, pl_module):
if not self.precheck(trainer):
return True
def visualize(self, pl_module):
if self.show:
components = pl_module.components
grid = torchvision.utils.make_grid(components,
nrow=self.num_columns)
plt.imshow(grid.permute((1, 2, 0)).cpu(), cmap=self.cmap)
self.log_and_display(trainer, pl_module)