safetensor file

cifar_net.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:bf87afeab91ee70d644d0f22733711e058dfa3d20edb740b01cb1abe0ddd2b4e
+oid sha256:0c6818fe3fd14557084d0ddaf577fcdaf2ada6d32356000dae47022f9b0d2b01
 size 251167

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3e248d017220b608973ba016c97fde2e0bd63f76e28bc852d588693c5afd651a
+size 248760

pytorch_classifier_gen.py

@@ -4,6 +4,8 @@ import torchvision.transforms as transforms
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
+from safetensors import safe_open
+from safetensors.torch import save_file
 
 # 데이터셋 불러오기
 transform = transforms.Compose(
@@ -77,6 +79,14 @@ for epoch in range(2):   # 데이터셋을 수차례 반복합니다.
 
 print('Finished Training')
 
-# 모델 저장하기
+# 모델 저장하기 
 PATH = './cifar_net.pth'
-torch.save(net.state_dict(), PATH)
+torch.save(net.state_dict(), PATH) # Not safe way
+
+save_file(net.state_dict(), "model.safetensors")
+
+# 모델 불러오기
+tensors = {}
+with safe_open("model.safetensors", framework="pt", device="cpu") as f:
+   for key in f.keys():
+       tensors[key] = f.get_tensor(key)