Use 'num_' in all variable names

This commit is contained in:
Alexander Engelsberger
2021-05-25 15:41:10 +02:00
parent e7e6bf9173
commit 72e064338c
8 changed files with 24 additions and 23 deletions

View File

@@ -31,9 +31,9 @@ class PrototypeImageModel(pl.LightningModule):
def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
self.proto_layer.components.data.clamp_(0.0, 1.0)
def get_prototype_grid(self, nrow=2, return_channels_last=True):
def get_prototype_grid(self, num_columns=2, return_channels_last=True):
from torchvision.utils import make_grid
grid = make_grid(self.components, nrow=nrow)
grid = make_grid(self.components, nrow=num_columns)
if return_channels_last:
grid = grid.permute((1, 2, 0))
return grid.cpu()

View File

@@ -58,9 +58,9 @@ class MarginLoss(torch.nn.modules.loss._Loss):
class ReasoningLayer(torch.nn.Module):
def __init__(self, num_components, num_classes, n_replicas=1):
def __init__(self, num_components, num_classes, num_replicas=1):
super().__init__()
self.n_replicas = n_replicas
self.num_replicas = num_replicas
self.num_classes = num_classes
probabilities_init = torch.zeros(2, 1, num_components,
self.num_classes)
@@ -122,8 +122,8 @@ class CBC(SiameseGLVQ):
x, y = batch
# x = x.view(x.size(0), -1)
y_pred = self(x)
nclasses = self.reasoning_layer.num_classes
y_true = torch.nn.functional.one_hot(y.long(), num_classes=nclasses)
num_classes = self.reasoning_layer.num_classes
y_true = torch.nn.functional.one_hot(y.long(), num_classes=num_classes)
loss = MarginLoss(self.margin)(y_pred, y_true).mean(dim=0)
return y_pred, loss

View File

@@ -246,14 +246,14 @@ class VisImgComp(Vis2DAbstract):
*args,
random_data=0,
dataformats="CHW",
nrow=2,
num_columns=2,
add_embedding=False,
embedding_data=100,
**kwargs):
super().__init__(*args, **kwargs)
self.random_data = random_data
self.dataformats = dataformats
self.nrow = nrow
self.num_columns = num_columns
self.add_embedding = add_embedding
self.embedding_data = embedding_data
@@ -278,7 +278,7 @@ class VisImgComp(Vis2DAbstract):
size=self.random_data,
replace=False)
data = self.x_train[ind]
grid = torchvision.utils.make_grid(data, nrow=self.nrow)
grid = torchvision.utils.make_grid(data, nrow=self.num_columns)
tb.add_image(tag="Data",
img_tensor=grid,
global_step=None,
@@ -288,7 +288,7 @@ class VisImgComp(Vis2DAbstract):
tb = pl_module.logger.experiment
components = pl_module.components
grid = torchvision.utils.make_grid(components, nrow=self.nrow)
grid = torchvision.utils.make_grid(components, nrow=self.num_columns)
tb.add_image(
tag="Components",
img_tensor=grid,
@@ -302,7 +302,8 @@ class VisImgComp(Vis2DAbstract):
if self.show:
components = pl_module.components
grid = torchvision.utils.make_grid(components, nrow=self.nrow)
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)