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Add more CBC examples. MNIST is broken.

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This commit is contained in:
Alexander Engelsberger
2021-04-22 17:37:20 +02:00
parent 2e2f6707f6
commit db4499a103
3 changed files with 283 additions and 30 deletions
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64
prototorch/models/cbc.py
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@@ -33,14 +33,12 @@ class CosineSimilarity(torch.nn.Module):
def forward(self, x, y):
epsilon = torch.finfo(x.dtype).eps
normed_x = (x/ x.pow(2) \
.sum(dim=tuple(range(1, x.ndim)), keepdim=True) \
.clamp(min=epsilon) \
.sqrt()).flatten(start_dim=1)
normed_y = (y / y.pow(2) \
.sum(dim=tuple(range(1, y.ndim)), keepdim=True) \
.clamp(min=epsilon) \
.sqrt()).flatten(start_dim=1)
normed_x = (x / x.pow(2).sum(dim=tuple(range(
1, x.ndim)), keepdim=True).clamp(min=epsilon).sqrt()).flatten(
start_dim=1)
normed_y = (y / y.pow(2).sum(dim=tuple(range(
1, y.ndim)), keepdim=True).clamp(min=epsilon).sqrt()).flatten(
start_dim=1)
# normed_x = (x / torch.linalg.norm(x, dim=1))
diss = torch.inner(normed_x, normed_y)
return self.activation(diss)
@@ -73,14 +71,14 @@ class ReasoningLayer(torch.nn.Module):
probabilities_init.uniform_(0.4, 0.6)
self.reasoning_probabilities = torch.nn.Parameter(probabilities_init)
# @property
# def reasonings(self):
# pk = self.reasoning_probabilities[0]
# nk = (1 - pk) * self.reasoning_probabilities[1]
# ik = (1 - pk - nk)
# # pk is of shape (1, n_components, n_classes)
# img = torch.cat([pk, nk, ik], dim=0).permute(1, 0, 2)
# return img.unsqueeze(1) # (n_components, 1, 3, n_classes)
@property
def reasonings(self):
pk = self.reasoning_probabilities[0]
nk = (1 - pk) * self.reasoning_probabilities[1]
ik = (1 - pk - nk)
# pk is of shape (1, n_components, n_classes)
img = torch.cat([pk, nk, ik], dim=0).permute(1, 0, 2)
return img.unsqueeze(1) # (n_components, 1, 3, n_classes)
def forward(self, detections):
pk = self.reasoning_probabilities[0].clamp(0, 1)
@@ -128,7 +126,11 @@ class CBC(pl.LightningModule):
@property
def components(self):
return self.proto_layer.prototypes.detach().numpy()
return self.proto_layer.prototypes.detach().cpu()
@property
def reasonings(self):
return self.reasoning_layer.reasonings.cpu()
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
@@ -140,8 +142,8 @@ class CBC(pl.LightningModule):
def forward(self, x):
self.sync_backbones()
# protos = self.proto_layer.prototypes
protos, _ = self.proto_layer()
protos = self.proto_layer.prototypes
# protos, _ = self.proto_layer()
latent_x = self.backbone(x)
latent_protos = self.backbone_dependent(protos)
@@ -163,7 +165,7 @@ class CBC(pl.LightningModule):
# y_true = torch.nn.functional.one_hot(y, num_classes=nclasses)
# y_true = torch.eye(nclasses)[y.long()]
y_true = torch.nn.functional.one_hot(y.long(), num_classes=nclasses)
loss = MarginLoss(self.margin)(y_pred, y_true).sum(dim=0)
loss = MarginLoss(self.margin)(y_pred, y_true).mean(dim=0)
self.log("train_loss", loss)
# with torch.no_grad():
# preds = torch.argmax(y_pred, dim=1)
@@ -172,16 +174,18 @@ class CBC(pl.LightningModule):
# preds.int(),
# y.int()) # FloatTensors are assumed to be class probabilities
self.train_acc(y_pred, y_true)
self.log("acc",
self.train_acc,
on_step=False,
on_epoch=True,
prog_bar=True,
logger=True)
self.log(
"acc",
self.train_acc,
on_step=False,
on_epoch=True,
prog_bar=True,
logger=True,
)
return loss
# def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
# self.reasoning_layer.reasoning_probabilities.data.clamp_(0., 1.)
#def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
# self.reasoning_layer.reasoning_probabilities.data.clamp_(0., 1.)
# def training_epoch_end(self, outs):
# # Calling `self.train_acc.compute()` is
@@ -201,5 +205,5 @@ class ImageCBC(CBC):
clamping after updates.
"""
def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
super().on_train_batch_end(outputs, batch, batch_idx, dataload_idx)
self.proto_layer.prototypes.data.clamp_(0., 1.)
#super().on_train_batch_end(outputs, batch, batch_idx, dataloader_idx)
self.proto_layer.prototypes.data.clamp_(0.0, 1.0)