Add crop
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1 changed files with 20 additions and 1 deletions
21
run_nerf.py
21
run_nerf.py
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@ -482,6 +482,12 @@ def config_parser():
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parser.add_argument("--render_factor", type=int, default=0,
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help='downsampling factor to speed up rendering, set 4 or 8 for fast preview')
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# training options
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parser.add_argument("--precrop_iters", type=int, default=0,
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help='number of steps to train on central crops')
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parser.add_argument("--precrop_frac", type=float,
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default=.5, help='fraction of img taken for central crops')
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# dataset options
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parser.add_argument("--dataset_type", type=str, default='llff',
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help='options: llff / blender / deepvoxels')
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@ -707,7 +713,20 @@ def train():
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if N_rand is not None:
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rays_o, rays_d = get_rays(H, W, focal, torch.Tensor(pose)) # (H, W, 3), (H, W, 3)
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coords = torch.stack(torch.meshgrid(torch.linspace(0, H-1, H), torch.linspace(0, W-1, W)), -1) # (H, W, 2)
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if i < args.precrop_iters:
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dH = int(H//2 * args.precrop_frac)
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dW = int(W//2 * args.precrop_frac)
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coords = torch.stack(
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torch.meshgrid(
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torch.linspace(H//2 - dH, H//2 + dH - 1, 2*dH),
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torch.linspace(W//2 - dW, W//2 + dW - 1, 2*dW)
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), -1)
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if i == start:
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print(f"[Config] Center cropping of size {2*dH} x {2*dW} is enabled until iter {args.precrop_iters}")
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else:
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coords = torch.stack(torch.meshgrid(torch.linspace(0, H-1, H), torch.linspace(0, W-1, W)), -1) # (H, W, 2)
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coords = torch.reshape(coords, [-1,2]) # (H * W, 2)
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select_inds = np.random.choice(coords.shape[0], size=[N_rand], replace=False) # (N_rand,)
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select_coords = coords[select_inds].long() # (N_rand, 2)
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