add fix for inference

training/inference_3d.py

@@ -61,21 +61,22 @@ def inference(
     conv2d_gradfix.enabled = True  # Improves training speed.
     grid_sample_gradfix.enabled = True  # Avoids errors with the augmentation pipe.
 
+
     common_kwargs = dict(
-        c_dim=0, img_resolution=1024, img_channels=3)
+        c_dim=0, img_resolution=training_set_kwargs['resolution'] if 'resolution' in training_set_kwargs else 1024, img_channels=3)
     G_kwargs['device'] = device
 
     G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs).train().requires_grad_(False).to(
         device)  # subclass of torch.nn.Module
-    D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train().requires_grad_(False).to(
-        device)  # subclass of torch.nn.Module
+    # D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train().requires_grad_(False).to(
+    #     device)  # subclass of torch.nn.Module
     G_ema = copy.deepcopy(G).eval()  # deepcopy can make sure they are correct.
     if resume_pretrain is not None and (rank == 0):
         print('==> resume from pretrained path %s' % (resume_pretrain))
         model_state_dict = torch.load(resume_pretrain, map_location=device)
         G.load_state_dict(model_state_dict['G'], strict=True)
         G_ema.load_state_dict(model_state_dict['G_ema'], strict=True)
-        D.load_state_dict(model_state_dict['D'], strict=True)
+        # D.load_state_dict(model_state_dict['D'], strict=True)
     grid_size = (5, 5)
     n_shape = grid_size[0] * grid_size[1]
     grid_z = torch.randn([n_shape, G.z_dim], device=device).split(1)  # random code for geometry