Add crop

run_nerf.py

@@ -482,6 +482,12 @@ def config_parser():
     parser.add_argument("--render_factor", type=int, default=0, 
                         help='downsampling factor to speed up rendering, set 4 or 8 for fast preview')
 
+    # training options
+    parser.add_argument("--precrop_iters", type=int, default=0,
+                        help='number of steps to train on central crops')
+    parser.add_argument("--precrop_frac", type=float,
+                        default=.5, help='fraction of img taken for central crops') 
+
     # dataset options
     parser.add_argument("--dataset_type", type=str, default='llff', 
                         help='options: llff / blender / deepvoxels')
@@ -707,7 +713,20 @@ def train():
 
             if N_rand is not None:
                 rays_o, rays_d = get_rays(H, W, focal, torch.Tensor(pose))  # (H, W, 3), (H, W, 3)
+
+                if i < args.precrop_iters:
+                    dH = int(H//2 * args.precrop_frac)
+                    dW = int(W//2 * args.precrop_frac)
+                    coords = torch.stack(
+                        torch.meshgrid(
+                            torch.linspace(H//2 - dH, H//2 + dH - 1, 2*dH), 
+                            torch.linspace(W//2 - dW, W//2 + dW - 1, 2*dW)
+                        ), -1)
+                    if i == start:
+                        print(f"[Config] Center cropping of size {2*dH} x {2*dW} is enabled until iter {args.precrop_iters}")                
+                else:
                     coords = torch.stack(torch.meshgrid(torch.linspace(0, H-1, H), torch.linspace(0, W-1, W)), -1)  # (H, W, 2)
+
                 coords = torch.reshape(coords, [-1,2])  # (H * W, 2)
                 select_inds = np.random.choice(coords.shape[0], size=[N_rand], replace=False)  # (N_rand,)
                 select_coords = coords[select_inds].long()  # (N_rand, 2)