update

app.py

@@ -10,7 +10,8 @@ channels = [64, 128, 256, 512]
 layers = [2, 2, 4, 2]
 num_subnet = 4
 net_i = FullNet_NLP(channels, layers, num_subnet, 4,num_classes=1000, drop_path=0,save_memory=True, inter_supv=True, head_init_scale=None,kernel_size=3).cpu()
-net_i=net_i.half()
+for param in net_i.parameters():
+    param.data = param.data.to(torch.float16)
 net_i.load_state_dict(torch.load('./fp16_check.pt')['icnn'])
 net_c = PretrainedConvNext("convnext_small_in22k").cpu()
 net_c.load_state_dict(torch.load('./classifier_32.pt')['icnn'])