working changes

configs.json

@@ -1,17 +1,17 @@
 {
- "epochs": 150,
- "exp_name": "ResNet-101-ImageNet",
+ "epochs": 25,
+ "exp_name": "ResNet50-CIFAR100",
  "seed_list": [
  420
  ],
- "model_name": "resnet101",
- "dataset": "ImageNet",
+ "model_name": "Resnet50",
+ "dataset": "cifar-100",
  "lr": 0.001,
- "batch_size": 512,
+ "batch_size": 128,
  "target_val_acc": 94.0,
  "save_path": "./",
  "tolerance": 5,
- "arr_save_path": "/home/shared/results/",
+ "arr_save_path": "/content/",
  "num_workers": 4,
- "data_path": "/home/shared/imagenet"
+ "data_path": "./"
 }

multi_train.py

@@ -11,7 +11,7 @@
 
 def multi_train(configs_path="./configs.json"):
 
- configs_path = "/home/shared/Layer-Wise-Learning-Trends-PyTorch/configs.json"
+ # configs_path = "/home/shared/Layer-Wise-Learning-Trends-PyTorch/configs.json"
  print(f"Configs Path: {configs_path}")
  configs = SimpleNamespace(**json.load(open(configs_path)))
  print(configs)

train.py

@@ -126,8 +126,7 @@ def training(epochs, loaders, model, optimizer, criterion, prev_list,
  test_acc_arr.append(test_acc)
 
  print(
- 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}%")
+ 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}%")
 
  # early stopping
  # accumulate consecutive counters

utils/helpers.py

@@ -14,11 +14,11 @@ def accuracy(output, target, topk=(1,)):
 
  _, pred = output.topk(maxk, 1, True, True)
  pred = pred.t()
- correct = pred.eq(target.view(1, -1).expand_as(pred))
+ correct = pred.eq(target.reshape(1, -1).expand_as(pred))
 
  res = []
  for k in topk:
- correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+ correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
  accuracy = correct_k.mul_(100.0 / batch_size)
  res.append(accuracy)
  return res