top-k accuracies

train.py

@@ -2,6 +2,7 @@
 import torch
 
 from utils.delta import compute_delta
+from utils.helpers import accuracy
 
 
 def training(epochs, loaders, model, optimizer, criterion, prev_list,
@@ -23,6 +24,7 @@ def training(epochs, loaders, model, optimizer, criterion, prev_list,
  test_correct = 0.0
  test_total = 0.0
  test_loss = 0.0
+ train_top1, train_top5 = [], []
 
  # train the model
  model.train()
@@ -36,6 +38,12 @@ def training(epochs, loaders, model, optimizer, criterion, prev_list,
  output = model(data)
  # calculate the loss
  loss = criterion(output, labels)
+
+ # measure top-k accuracy for training.
+ top1, top5 = accuracy(output, labels, topk=(1, 5))
+ train_top1.append(top1)
+ train_top5.append(top5)
+
  # backprop
  loss.backward()
  # optimize the weights
@@ -54,13 +62,22 @@ def training(epochs, loaders, model, optimizer, criterion, prev_list,
  train_loss = round(train_loss/len(loaders['train'].dataset), 4)
  train_acc = round(((train_correct/train_total) * 100.0), 4)
 
+ # epoch top k
+ epoch_train_t1 = torch.mean(torch.stack(train_top1)).cpu()
+ epoch_train_t5 = torch.mean(torch.stack(train_top5)).cpu()
+
  configs.experiment.log_metric("accuracy", train_acc, step=epoch)
+ configs.experiment.log_metric("top-1", epoch_train_t1, step=epoch)
+ configs.experiment.log_metric("top-5", epoch_train_t5, step=epoch)
  configs.experiment.log_metric("loss", train_loss, step=epoch)
 
  # compute layer deltas after epoch.
  rmae_delta_dict, prev_list = compute_delta(
  model, prev_list, rmae_delta_dict)
 
+
+ test_top1, test_top5 = [], []
+
  with configs.experiment.test():
  model.eval()
  with torch.no_grad():
@@ -71,6 +88,11 @@ def training(epochs, loaders, model, optimizer, criterion, prev_list,
  output = model(data)
  loss = criterion(output, labels)
 
+ # measure top-k accuracy for test.
+ top1, top5 = accuracy(output, labels, topk=(1, 5))
+ test_top1.append(top1)
+ test_top5.append(top5)
+
  test_loss += loss.item()*data.size(0)
 
  # get the predictions for each image in the batch
@@ -89,14 +111,22 @@ def training(epochs, loaders, model, optimizer, criterion, prev_list,
  test_loss = round(test_loss/len(loaders['test'].dataset), 4)
  test_acc = round(((test_correct/test_total) * 100), 4)
 
+ # epoch top k
+ epoch_test_t1 = torch.mean(torch.stack(test_top1)).cpu()
+ epoch_test_t5 = torch.mean(torch.stack(test_top5)).cpu()
+
  configs.experiment.log_metric("accuracy", test_acc, step=epoch)
+ configs.experiment.log_metric("top-1", epoch_test_t1, step=epoch)
+ configs.experiment.log_metric("top-5", epoch_test_t5, step=epoch)
  configs.experiment.log_metric("loss", test_loss, step=epoch)
 
  train_acc_arr.append(train_acc)
  test_acc_arr.append(test_acc)
 
  print(
- f"Epoch: {epoch} \tTrain Loss: {train_loss} \tTrain Acc: {train_acc}% \tTest Loss: {test_loss} \tTest Acc: {test_acc}%")
+ f"Epoch: {epoch} \tTrain Loss: {train_loss} \tTrain Top-1: {epoch_train_t1} \tTrain Top-5: {epoch_train_t5}% 
+ \tTest Loss: {test_loss} \tTest Top-1: {epoch_test_t1} \tTest Top-5: {epoch_test_t5}%")
+
  if float(test_acc) >= configs.target_val_acc:
  break
 

utils/helpers.py

@@ -6,6 +6,23 @@
 from models.vgg import vgg19_bn
 
 
+def accuracy(output, target, topk=(1,)):
+ """Computes the accuracy over the k top predictions for the specified values of k"""
+ with torch.no_grad():
+ maxk = max(topk)
+ batch_size = target.size(0)
+
+ _, pred = output.topk(maxk, 1, True, True)
+ pred = pred.t()
+ correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+ res = []
+ for k in topk:
+ correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+ accuracy = correct_k.mul_(100.0 / batch_size)
+ res.append(accuracy)
+ return res
+
 def load_model(model_name, training_type, configs):
  """
  Loads model.