otthorn/nerf-pytorch
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Add crop

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This commit is contained in:
Yen-Chen Lin 2020-04-17 22:34:48 -04:00
parent 7158181ee4
commit c9074d8b60
1 changed files with 20 additions and 1 deletions
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21
run_nerf.py
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@ -482,6 +482,12 @@ def config_parser():
parser.add_argument("--render_factor", type=int, default=0, parser.add_argument("--render_factor", type=int, default=0,
help='downsampling factor to speed up rendering, set 4 or 8 for fast preview') 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 # dataset options
parser.add_argument("--dataset_type", type=str, default='llff', parser.add_argument("--dataset_type", type=str, default='llff',
help='options: llff / blender / deepvoxels') help='options: llff / blender / deepvoxels')
@ -707,7 +713,20 @@ def train():
if N_rand is not None: if N_rand is not None:
rays_o, rays_d = get_rays(H, W, focal, torch.Tensor(pose)) # (H, W, 3), (H, W, 3) rays_o, rays_d = get_rays(H, W, focal, torch.Tensor(pose)) # (H, W, 3), (H, W, 3)
coords = torch.stack(torch.meshgrid(torch.linspace(0, H-1, H), torch.linspace(0, W-1, W)), -1) # (H, W, 2)
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) 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_inds = np.random.choice(coords.shape[0], size=[N_rand], replace=False) # (N_rand,)
select_coords = coords[select_inds].long() # (N_rand, 2) select_coords = coords[select_inds].long() # (N_rand, 2)