thanks for reply, well i got this
torch.Size([1, 64, 256, 256])
torch.Size([1, 128, 128, 128])
torch.Size([1, 256, 64, 64])
torch.Size([1, 512, 32, 32])
torch.Size([1, 512, 16, 16])
i tried to save as eval and cpu:
# model.eval().cpu()          
# torch.save({"optimizer":copy.deepcopy(optimizer.state_dict()), 
#             "model":copy.deepcopy(model.state_dict())
#             },"saved_model_for_styletransfer_cpu.pth")

with torch.no_grad(): 
  checkpoint = torch.load("saved_model_for_styletransfer_cpu.pth")
  for k in list(checkpoint.keys()):
    if re.search(r'in\d+\.running_(mean|var)$', k):
        del checkpoint[k]
  model = VGG().cpu()
  model.load_state_dict(checkpoint['model'])
  ad = load_image("and.png").cpu()
  new_img = model(ad)
  for i in new_img:
    print(i.shape)
  save_image(new_img, 'output.png')

well training works, error is getting lower when i load model, but if i want only to do forward pass  i get that error… i do not understand why …