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| from __future__ import annotations | |
| import numpy as np | |
| import torch | |
| from torch.utils.data import DataLoader, TensorDataset | |
| from .metrics import ( | |
| ClassificationMetrics, | |
| compute_classification_metrics, | |
| ) | |
| def evaluate_classifier( | |
| model: torch.nn.Module, | |
| embeddings: np.ndarray, | |
| labels: np.ndarray, | |
| device: str = "cuda:0", | |
| batch_size: int = 256, | |
| ) -> ClassificationMetrics: | |
| model.eval() | |
| x = torch.from_numpy( | |
| embeddings | |
| ).float() | |
| y = torch.from_numpy( | |
| labels | |
| ).long() | |
| dataset = TensorDataset( | |
| x, | |
| y, | |
| ) | |
| loader = DataLoader( | |
| dataset, | |
| batch_size=batch_size, | |
| shuffle=False, | |
| ) | |
| all_probabilities = [] | |
| all_labels = [] | |
| with torch.inference_mode(): | |
| for batch_x, batch_y in loader: | |
| batch_x = batch_x.to( | |
| device | |
| ) | |
| logits = model( | |
| batch_x | |
| ) | |
| probabilities = torch.softmax( | |
| logits, | |
| dim=1, | |
| )[:, 1] | |
| all_probabilities.append( | |
| probabilities.cpu().numpy() | |
| ) | |
| all_labels.append( | |
| batch_y.numpy() | |
| ) | |
| probabilities = np.concatenate( | |
| all_probabilities | |
| ) | |
| labels = np.concatenate( | |
| all_labels | |
| ) | |
| return compute_classification_metrics( | |
| labels=labels, | |
| probabilities=probabilities, | |
| ) |