Update app.py

app.py

@@ -20,17 +20,24 @@ warnings.filterwarnings('ignore')
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# Create an args object for initializing the HumanVQVAE
-class Args:
-    def __init__(self):
-        self.dataname = 't2m'  # example property, adjust as needed
-        self.quantizer = 'ema'  # Set the type of quantizer used in VQVAE_251
-        # Add other properties required by HumanVQVAE initialization
+args = option_trans.get_args_parser()
 
-args = Args()
+args.dataname = 't2m'
+args.resume_pth = './output/VQVAE_imp_resnet_100k_hml3d/net_last.pth'
+args.resume_trans = './output/net_best_fid.pth'
+args.down_t = 2
+args.depth = 3
+args.block_size = 51
 
-
-vqvae_model = vqvae.HumanVQVAE().to(device)
+vqvae_model = vqvae.HumanVQVAE(args,
+                       args.nb_code,
+                       args.code_dim,
+                       args.output_emb_width,
+                       args.down_t,
+                       args.stride_t,
+                       args.width,
+                       args.depth,
+                       args.dilation_growth_rate).to(device)
 transformer_model = trans.Text2Motion_Transformer().to(device)
 vqvae_model.load_state_dict(torch.load("output/VQVAE_imp_resnet_100k_hml3d/net_last.pth", map_location=device))
 transformer_model.load_state_dict(torch.load("output/net_best_fid.pth", map_location=device))