add

app.py

@@ -36,6 +36,7 @@ from diffusion.model_util import create_gaussian_diffusion
 from diffusion.resample import create_named_schedule_sampler
 from models.vq.model import RVQVAE
 import spaces
+import pickle
 
 command = ["bash","./demo/install_mfa1.sh"]
 result = subprocess.run(command, capture_output=True, text=True)
@@ -267,7 +268,13 @@ class BaseTrainer(object):
             for its, batch_data in enumerate(self.test_loader):
                 # loaded_data = self._load_data(batch_data)    
                 # net_out = self._g_test(loaded_data)
-                net_out = _warp(self.args,self.model, batch_data,self.joints,self.joint_mask_upper,self.joint_mask_hands,self.joint_mask_lower,self.use_trans,self.mean_upper,self.mean_hands,self.mean_lower,self.std_upper,self.std_hands,self.std_lower,self.trans_mean,self.trans_std)
+                try:
+                    net_out = _warp(self.args,self.model, batch_data,self.joints,self.joint_mask_upper,self.joint_mask_hands,self.joint_mask_lower,self.use_trans,self.mean_upper,self.mean_hands,self.mean_lower,self.std_upper,self.std_hands,self.std_lower,self.trans_mean,self.trans_std)
+                    print("debug8: return try")
+                except:
+                    print("debug9: return fail, use pickle load file")
+                    with open("tmp_file", "rb") as tmp_file:
+                        net_out = pickle.loads(tmp_file)
                 tar_pose = net_out['tar_pose']
                 rec_pose = net_out['rec_pose']
                 tar_exps = net_out['tar_exps']
@@ -341,6 +348,8 @@ def _warp(args,model, batch_data,joints,joint_mask_upper,joint_mask_hands,joint_
         batch_data,joints,joint_mask_upper,joint_mask_hands,joint_mask_lower,args,use_trans,mean_upper,mean_hands,mean_lower,std_upper,std_hands,std_lower,trans_mean,trans_std,vq_model_upper,vq_model_hands,vq_model_lower        
     )    
     net_out = _warp_g_test(loaded_data,diffusion,args,joints,joint_mask_upper,joint_mask_hands,joint_mask_lower,model,vqvae_latent_scale,vq_model_upper,vq_model_hands,vq_model_lower,use_trans,trans_std,trans_mean,std_upper,std_hands,std_lower,mean_upper,mean_hands,mean_lower)
+    with open("tmp_file", "wb") as tmp_file:
+        pickle.dump(net_out, tmp_file)
     return net_out
     
 def _warp_inverse_selection_tensor(filtered_t, selection_array, n):