156 lines
5.3 KiB
Python
156 lines
5.3 KiB
Python
import pytorch_lightning as pl
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import torch
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import torchmetrics
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from prototorch.components.components import Components
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from prototorch.functions.distances import euclidean_distance
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from prototorch.functions.similarities import cosine_similarity
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from .abstract import AbstractPrototypeModel, PrototypeImageModel
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from .glvq import SiameseGLVQ
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def rescaled_cosine_similarity(x, y):
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"""Cosine Similarity rescaled to [0, 1]."""
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similarities = cosine_similarity(x, y)
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return (similarities + 1.0) / 2.0
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def shift_activation(x):
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return (x + 1.0) / 2.0
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def euclidean_similarity(x, y, beta=3):
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d = euclidean_distance(x, y)
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return torch.exp(-d * beta)
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class CosineSimilarity(torch.nn.Module):
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def __init__(self, activation=shift_activation):
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super().__init__()
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self.activation = activation
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def forward(self, x, y):
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epsilon = torch.finfo(x.dtype).eps
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normed_x = (x / x.pow(2).sum(dim=tuple(range(
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1, x.ndim)), keepdim=True).clamp(min=epsilon).sqrt()).flatten(
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start_dim=1)
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normed_y = (y / y.pow(2).sum(dim=tuple(range(
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1, y.ndim)), keepdim=True).clamp(min=epsilon).sqrt()).flatten(
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start_dim=1)
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# normed_x = (x / torch.linalg.norm(x, dim=1))
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diss = torch.inner(normed_x, normed_y)
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return self.activation(diss)
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class MarginLoss(torch.nn.modules.loss._Loss):
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def __init__(self,
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margin=0.3,
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size_average=None,
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reduce=None,
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reduction="mean"):
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super().__init__(size_average, reduce, reduction)
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self.margin = margin
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def forward(self, input_, target):
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dp = torch.sum(target * input_, dim=-1)
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dm = torch.max(input_ - target, dim=-1).values
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return torch.nn.functional.relu(dm - dp + self.margin)
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class ReasoningLayer(torch.nn.Module):
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def __init__(self, num_components, num_classes, n_replicas=1):
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super().__init__()
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self.n_replicas = n_replicas
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self.num_classes = num_classes
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probabilities_init = torch.zeros(2, 1, num_components,
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self.num_classes)
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probabilities_init.uniform_(0.4, 0.6)
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self.reasoning_probabilities = torch.nn.Parameter(probabilities_init)
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@property
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def reasonings(self):
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pk = self.reasoning_probabilities[0]
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nk = (1 - pk) * self.reasoning_probabilities[1]
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ik = 1 - pk - nk
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img = torch.cat([pk, nk, ik], dim=0).permute(1, 0, 2)
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return img.unsqueeze(1)
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def forward(self, detections):
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pk = self.reasoning_probabilities[0].clamp(0, 1)
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nk = (1 - pk) * self.reasoning_probabilities[1].clamp(0, 1)
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epsilon = torch.finfo(pk.dtype).eps
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numerator = (detections @ (pk - nk)) + nk.sum(1)
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probs = numerator / (pk + nk).sum(1)
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probs = probs.squeeze(0)
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return probs
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class CBC(SiameseGLVQ):
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"""Classification-By-Components."""
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def __init__(self,
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hparams,
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margin=0.1,
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similarity=euclidean_similarity,
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**kwargs):
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super().__init__(hparams, **kwargs)
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self.margin = margin
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self.similarity_fn = kwargs.get("similarity_fn", euclidean_similarity)
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num_components = self.components.shape[0]
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self.reasoning_layer = ReasoningLayer(num_components=num_components,
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num_classes=self.num_classes)
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self.component_layer = self.proto_layer
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@property
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def components(self):
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return self.prototypes
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@property
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def reasonings(self):
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return self.reasoning_layer.reasonings.cpu()
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def forward(self, x):
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components, _ = self.component_layer()
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latent_x = self.backbone(x)
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self.backbone.requires_grad_(self.both_path_gradients)
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latent_components = self.backbone(components)
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self.backbone.requires_grad_(True)
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detections = self.similarity_fn(latent_x, latent_components)
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probs = self.reasoning_layer(detections)
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return probs
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def shared_step(self, batch, batch_idx, optimizer_idx=None):
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x, y = batch
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# x = x.view(x.size(0), -1)
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y_pred = self(x)
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nclasses = self.reasoning_layer.num_classes
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y_true = torch.nn.functional.one_hot(y.long(), num_classes=nclasses)
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loss = MarginLoss(self.margin)(y_pred, y_true).mean(dim=0)
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return y_pred, loss
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def training_step(self, batch, batch_idx, optimizer_idx=None):
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y_pred, train_loss = self.shared_step(batch, batch_idx, optimizer_idx)
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preds = torch.argmax(y_pred, dim=1)
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self.acc_metric(preds.int(), batch[1].int())
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self.log("train_acc",
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self.acc_metric,
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on_step=False,
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on_epoch=True,
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prog_bar=True,
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logger=True)
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return train_loss
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def predict(self, x):
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with torch.no_grad():
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y_pred = self(x)
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y_pred = torch.argmax(y_pred, dim=1)
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return y_pred
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class ImageCBC(CBC):
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"""CBC model that constrains the components to the range [0, 1] by
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clamping after updates.
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"""
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def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
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# super().on_train_batch_end(outputs, batch, batch_idx, dataloader_idx)
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self.component_layer.components.data.clamp_(0.0, 1.0)
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