add

app.py

@@ -154,8 +154,8 @@ class BaseTrainer(object):
         self.args.vae_layer = 2
         self.args.vae_length = 256
         self.args.vae_test_dim = 106
-        self.vq_model_face = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank)
-        other_tools.load_checkpoints(self.vq_model_face, "./datasets/hub/pretrained_vq/face_vertex_1layer_790.bin", args.e_name)
+        # self.vq_model_face = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank)
+        # other_tools.load_checkpoints(self.vq_model_face, "./datasets/hub/pretrained_vq/face_vertex_1layer_790.bin", args.e_name)
         
         
         vq_type = self.args.vqvae_type
@@ -260,7 +260,7 @@ class BaseTrainer(object):
         self.args.vae_length = 240
 
 
-        self.vq_model_face.eval()
+        # self.vq_model_face.eval()
         self.vq_model_upper.eval()
         self.vq_model_hands.eval()
         self.vq_model_lower.eval()
@@ -375,7 +375,7 @@ class BaseTrainer(object):
             tar_trans_v = (tar_trans_v - self.trans_mean)/self.trans_std
             tar_pose_lower = torch.cat([tar_pose_lower,tar_trans_v], dim=-1)
       
-        latent_face_top = self.vq_model_face.map2latent(tar_pose_face) # bs*n/4
+        latent_face_top = None#self.vq_model_face.map2latent(tar_pose_face) # bs*n/4
         latent_upper_top = self.vq_model_upper.map2latent(tar_pose_upper)
         latent_hands_top = self.vq_model_hands.map2latent(tar_pose_hands)
         latent_lower_top = self.vq_model_lower.map2latent(tar_pose_lower)