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1import torch
2from informer_models import InformerConfig, InformerForSequenceClassification
3
4model = InformerForSequenceClassification.from_pretrained("BrachioLab/supernova-classification")
5
6model.to(device)
7model.eval()
8y_true = []
9y_pred = []
10for i, batch in enumerate(test_dataloader):
11 print(f"processing batch {i}")
12 batch = {k: v.to(device) for k, v in batch.items() if k != "objid"}
13 with torch.no_grad():
14 outputs = model(**batch)
15 y_true.extend(batch['labels'].cpu().numpy())
16 y_pred.extend(torch.argmax(outputs.logits, dim=2).squeeze().cpu().numpy())
17print(f"accuracy: {sum([1 for i, j in zip(y_true, y_pred) if i == j]) / len(y_true)}")