feat: 최종 업로드

@@ -36,10 +36,6 @@ parser.add_argument("--weight-decay", "--wd", default=1e-4, type=float, help="We
 parser.add_argument('--pretrained', default='', type=str, help='path to pretrained model (default: none)')
 parser.add_argument("--gpus", default="0", type=str, help="gpu ids (default: 0)")
 
-
-total_loss_for_plot = list()
-total_pnsr = list()
-
 def main():
     global opt, model
     opt = parser.parse_args()
@@ -141,17 +137,14 @@ def train(training_data_loader, optimizer, model, criterion, epoch):
         optimizer.step()
 
     epoch_loss = total_loss / len(training_data_loader)
-    total_loss_for_plot.append(epoch_loss)
     psnr = PSNR(epoch_loss)
-    total_pnsr.append(psnr)
     print("===> Epoch[{}]: loss : {:.10f} ,PSNR : {:.10f}".format(epoch, epoch_loss, psnr))
         # if iteration%100 == 0:
         #     print("===> Epoch[{}]({}/{}): Loss: {:.10f}".format(epoch, iteration, len(training_data_loader), loss.item()))
 
 def save_checkpoint(model, epoch, optimizer):
     model_out_path = "checkpoint/" + "model_epoch_{}_{}.pth".format(epoch, opt.featureType)
-    state = {"epoch": epoch ,"model": model, "model_state_dict":model.state_dict(), "optimizer_state_dict":optimizer.state_dict(),
-        "loss": total_loss_for_plot, "psnr":total_pnsr}
+    state = {"epoch": epoch ,"model": model, "model_state_dict":model.state_dict(), "optimizer_state_dict":optimizer.state_dict()}
     if not os.path.exists("checkpoint/"):
         os.makedirs("checkpoint/")