calculate scores from eval set, small fix

oc_classifier.py

@@ -168,18 +168,17 @@ def score_eval_set(extractor, encoder, dataloader, device, reference_embedding,
  # total_labels.append(target)
 
  # calculate the distance between the reference embedding and all embeddings
- for emb in total_embeddings:
- distance = F.pairwise_distance(reference_embedding, emb, p=2)
- total_distances.append(distance)
  # write the scores to a file
  # each line contains a score and a label
  with open("scores.txt", "w") as f:
- for distance in total_distances:
+ for emb in total_embeddings:
+ distance = F.pairwise_distance(reference_embedding, emb, p=2)
+ total_distances.append(distance)
  if float(distance) > threshold:
  f.write(f"{float(distance)}, 1 \n")
  else:
  f.write(f"{float(distance)}, 0 \n")
- 
+
  # calculate the EER
  # total_distances = torch.stack(total_distances)
  # total_labels = torch.stack(total_labels)
@@ -210,8 +209,8 @@ def score_eval_set(extractor, encoder, dataloader, device, reference_embedding,
  senet = se_resnet34().to(device)
 
  # load pretrained weights
- ssl.load_state_dict(torch.load("/datac/longnv/occm/ssl_1.pt"))
- senet.load_state_dict(torch.load("/datac/longnv/occm/senet34_1.pt"))
+ ssl.load_state_dict(torch.load("/datac/longnv/occm/ssl_4.pt"))
+ senet.load_state_dict(torch.load("/datac/longnv/occm/senet34_4.pt"))
  senet = DataParallel(senet)
  ssl = DataParallel(ssl)
  print("Pretrained weights loaded")