206 lines
7.4 KiB
Python
206 lines
7.4 KiB
Python
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
|
|
|
|
|
|
def rescaled_cosine_similarity(x, y):
|
|
"""Cosine Similarity rescaled to [0, 1]."""
|
|
similarities = cosine_similarity(x, y)
|
|
return (similarities + 1.0) / 2.0
|
|
|
|
|
|
def shift_activation(x):
|
|
return (x + 1.0) / 2.0
|
|
|
|
|
|
def euclidean_similarity(x, y):
|
|
d = euclidean_distance(x, y)
|
|
return torch.exp(-d * 3)
|
|
|
|
|
|
class CosineSimilarity(torch.nn.Module):
|
|
def __init__(self, activation=shift_activation):
|
|
super().__init__()
|
|
self.activation = activation
|
|
|
|
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 / torch.linalg.norm(x, dim=1))
|
|
diss = torch.inner(normed_x, normed_y)
|
|
return self.activation(diss)
|
|
|
|
|
|
class MarginLoss(torch.nn.modules.loss._Loss):
|
|
def __init__(self,
|
|
margin=0.3,
|
|
size_average=None,
|
|
reduce=None,
|
|
reduction="mean"):
|
|
super().__init__(size_average, reduce, reduction)
|
|
self.margin = margin
|
|
|
|
def forward(self, input_, target):
|
|
dp = torch.sum(target * input_, dim=-1)
|
|
dm = torch.max(input_ - target, dim=-1).values
|
|
return torch.nn.functional.relu(dm - dp + self.margin)
|
|
|
|
|
|
class ReasoningLayer(torch.nn.Module):
|
|
def __init__(self, n_components, n_classes, n_replicas=1):
|
|
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)
|
|
|
|
# @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)
|
|
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):
|
|
super().__init__()
|
|
self.save_hyperparameters(hparams)
|
|
self.margin = margin
|
|
self.proto_layer = Prototypes1D(
|
|
input_dim=self.hparams.input_dim,
|
|
nclasses=self.hparams.nclasses,
|
|
prototypes_per_class=self.hparams.prototypes_per_class,
|
|
prototype_initializer=self.hparams.prototype_initializer,
|
|
**kwargs)
|
|
# self.similarity = CosineSimilarity()
|
|
self.similarity = similarity
|
|
self.backbone = backbone_class()
|
|
self.backbone_dependent = backbone_class().requires_grad_(False)
|
|
n_components = self.components.shape[0]
|
|
self.reasoning_layer = ReasoningLayer(n_components=n_components,
|
|
n_classes=self.hparams.nclasses)
|
|
self.train_acc = torchmetrics.Accuracy()
|
|
|
|
@property
|
|
def components(self):
|
|
return self.proto_layer.prototypes.detach().numpy()
|
|
|
|
def configure_optimizers(self):
|
|
optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
|
|
return optimizer
|
|
|
|
def sync_backbones(self):
|
|
master_state = self.backbone.state_dict()
|
|
self.backbone_dependent.load_state_dict(master_state, strict=True)
|
|
|
|
def forward(self, x):
|
|
self.sync_backbones()
|
|
# protos = self.proto_layer.prototypes
|
|
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
|
|
|
|
def training_step(self, train_batch, batch_idx):
|
|
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).sum(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",
|
|
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 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()
|
|
|
|
|
|
class ImageCBC(CBC):
|
|
"""CBC model that constrains the components to the range [0, 1] by
|
|
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.)
|