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This commit is contained in:
Alexander Engelsberger
2021-04-23 17:27:47 +02:00
parent db4499a103
commit c4c51a16fe
12 changed files with 404 additions and 159 deletions
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4
prototorch/models/__init__.py
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@@ -1,8 +1,8 @@
from importlib.metadata import version, PackageNotFoundError
from importlib.metadata import PackageNotFoundError, version
VERSION_FALLBACK = "uninstalled_version"
try:
__version__ = version(__name__.replace(".", "-"))
except PackageNotFoundError:
__version__ = VERSION_FALLBACK
pass
pass

61
prototorch/models/cbc.py
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@@ -1,13 +1,9 @@
import argparse
import pytorch_lightning as pl
import torch
import torchmetrics
from prototorch.functions.competitions import wtac
from prototorch.functions.distances import euclidean_distance
from prototorch.functions.similarities import cosine_similarity
from prototorch.functions.initializers import get_initializer
from prototorch.functions.losses import glvq_loss
from prototorch.modules.prototypes import Prototypes1D
@@ -64,9 +60,6 @@ class ReasoningLayer(torch.nn.Module):
super().__init__()
self.n_replicas = n_replicas
self.n_classes = n_classes
# probabilities_init = torch.zeros(2, self.n_replicas, n_components,
# self.n_classes)
# probabilities_init = torch.zeros(2, n_components, self.n_classes)
probabilities_init = torch.zeros(2, 1, n_components, self.n_classes)
probabilities_init.uniform_(0.4, 0.6)
self.reasoning_probabilities = torch.nn.Parameter(probabilities_init)
@@ -75,37 +68,28 @@ class ReasoningLayer(torch.nn.Module):
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)
ik = 1 - pk - nk
img = torch.cat([pk, nk, ik], dim=0).permute(1, 0, 2)
return img.unsqueeze(1) # (n_components, 1, 3, n_classes)
return img.unsqueeze(1)
def forward(self, detections):
pk = self.reasoning_probabilities[0].clamp(0, 1)
nk = (1 - pk) * self.reasoning_probabilities[1].clamp(0, 1)
epsilon = torch.finfo(pk.dtype).eps
# print(f"{detections.shape=}")
# print(f"{pk.shape=}")
# print(f"{detections.min()=}")
# print(f"{detections.max()=}")
numerator = (detections @ (pk - nk)) + nk.sum(1)
# probs = numerator / (pk + nk).sum(1).clamp(min=epsilon)
probs = numerator / (pk + nk).sum(1)
# probs = probs.squeeze(0)
probs = probs.squeeze(0)
return probs
class CBC(pl.LightningModule):
"""Classification-By-Components."""
def __init__(
self,
hparams,
margin=0.1,
backbone_class=torch.nn.Identity,
# similarity=rescaled_cosine_similarity,
similarity=euclidean_similarity,
**kwargs):
def __init__(self,
hparams,
margin=0.1,
backbone_class=torch.nn.Identity,
similarity=euclidean_similarity,
**kwargs):
super().__init__()
self.save_hyperparameters(hparams)
self.margin = margin
@@ -142,15 +126,11 @@ class CBC(pl.LightningModule):
def forward(self, x):
self.sync_backbones()
protos = self.proto_layer.prototypes
# protos, _ = self.proto_layer()
protos, _ = self.proto_layer()
latent_x = self.backbone(x)
latent_protos = self.backbone_dependent(protos)
# print(f"{latent_x.dtype=}")
# print(f"{latent_protos.dtype=}")
detections = self.similarity(latent_x, latent_protos)
probs = self.reasoning_layer(detections)
return probs
@@ -159,20 +139,10 @@ class CBC(pl.LightningModule):
x, y = train_batch
x = x.view(x.size(0), -1)
y_pred = self(x)
# print(f"{y_pred.min()=}")
# print(f"{y_pred.max()=}")
nclasses = self.reasoning_layer.n_classes
# 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).mean(dim=0)
self.log("train_loss", loss)
# with torch.no_grad():
# preds = torch.argmax(y_pred, dim=1)
# # self.train_acc.update(preds.int(), y.int())
# self.train_acc(
# preds.int(),
# y.int()) # FloatTensors are assumed to be class probabilities
self.train_acc(y_pred, y_true)
self.log(
"acc",
@@ -184,17 +154,8 @@ class CBC(pl.LightningModule):
)
return loss
#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
# # automatically done by setting `on_epoch=True` when logging in `self.training_step(...)`
# self.log("train_acc_epoch", self.train_acc.compute())
def predict(self, x):
with torch.no_grad():
# model.eval() # ?!
y_pred = self(x)
y_pred = torch.argmax(y_pred, dim=1)
return y_pred.numpy()
@@ -205,5 +166,5 @@ 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, dataloader_idx)
# super().on_train_batch_end(outputs, batch, batch_idx, dataloader_idx)
self.proto_layer.prototypes.data.clamp_(0.0, 1.0)

20
prototorch/models/glvq.py
View File

@@ -1,11 +1,9 @@
import argparse
import pytorch_lightning as pl
import torch
import torchmetrics
from prototorch.functions.competitions import wtac
from prototorch.functions.distances import euclidean_distance
from prototorch.functions.initializers import get_initializer
from prototorch.functions.losses import glvq_loss
from prototorch.modules.prototypes import Prototypes1D
@@ -54,12 +52,14 @@ class GLVQ(pl.LightningModule):
self.train_acc(
preds.int(),
y.int()) # FloatTensors are assumed to be class probabilities
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 training_epoch_end(self, outs):
@@ -81,4 +81,4 @@ class ImageGLVQ(GLVQ):
clamping after updates.
"""
def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
self.proto_layer.prototypes.data.clamp_(0., 1.)
self.proto_layer.prototypes.data.clamp_(0.0, 1.0)