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run_tiny_nerf.py
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import matplotlib.pyplot as plt
import numpy as np
import torch
from torch import nn, optim
def get_coarse_query_points(ds, N_c, t_i_c_bin_edges, t_i_c_gap, os):
u_is_c = torch.rand(*list(ds.shape[:2]) + [N_c]).to(ds)
t_is_c = t_i_c_bin_edges + u_is_c * t_i_c_gap
r_ts_c = os[..., None, :] + t_is_c[..., :, None] * ds[..., None, :]
return (r_ts_c, t_is_c)
def render_radiance_volume(r_ts, ds, chunk_size, F, t_is):
r_ts_flat = r_ts.reshape((-1, 3))
ds_rep = ds.unsqueeze(2).repeat(1, 1, r_ts.shape[-2], 1)
ds_flat = ds_rep.reshape((-1, 3))
c_is = []
sigma_is = []
for chunk_start in range(0, r_ts_flat.shape[0], chunk_size):
r_ts_batch = r_ts_flat[chunk_start : chunk_start + chunk_size]
ds_batch = ds_flat[chunk_start : chunk_start + chunk_size]
preds = F(r_ts_batch, ds_batch)
c_is.append(preds["c_is"])
sigma_is.append(preds["sigma_is"])
c_is = torch.cat(c_is).reshape(r_ts.shape)
sigma_is = torch.cat(sigma_is).reshape(r_ts.shape[:-1])
delta_is = t_is[..., 1:] - t_is[..., :-1]
one_e_10 = torch.Tensor([1e10]).expand(delta_is[..., :1].shape)
delta_is = torch.cat([delta_is, one_e_10.to(delta_is)], dim=-1)
delta_is = delta_is * ds.norm(dim=-1).unsqueeze(-1)
alpha_is = 1.0 - torch.exp(-sigma_is * delta_is)
T_is = torch.cumprod(1.0 - alpha_is + 1e-10, -1)
T_is = torch.roll(T_is, 1, -1)
T_is[..., 0] = 1.0
w_is = T_is * alpha_is
C_rs = (w_is[..., None] * c_is).sum(dim=-2)
return C_rs
def run_one_iter_of_tiny_nerf(ds, N_c, t_i_c_bin_edges, t_i_c_gap, os, chunk_size, F_c):
(r_ts_c, t_is_c) = get_coarse_query_points(ds, N_c, t_i_c_bin_edges, t_i_c_gap, os)
C_rs_c = render_radiance_volume(r_ts_c, ds, chunk_size, F_c, t_is_c)
return C_rs_c
class VeryTinyNeRFMLP(nn.Module):
def __init__(self):
super().__init__()
self.L_pos = 6
self.L_dir = 4
pos_enc_feats = 3 + 3 * 2 * self.L_pos
dir_enc_feats = 3 + 3 * 2 * self.L_dir
net_width = 256
self.early_mlp = nn.Sequential(
nn.Linear(pos_enc_feats, net_width),
nn.ReLU(),
nn.Linear(net_width, net_width + 1),
nn.ReLU(),
)
self.late_mlp = nn.Sequential(
nn.Linear(net_width + dir_enc_feats, net_width),
nn.ReLU(),
nn.Linear(net_width, 3),
nn.Sigmoid(),
)
def forward(self, xs, ds):
xs_encoded = [xs]
for l_pos in range(self.L_pos):
xs_encoded.append(torch.sin(2**l_pos * torch.pi * xs))
xs_encoded.append(torch.cos(2**l_pos * torch.pi * xs))
xs_encoded = torch.cat(xs_encoded, dim=-1)
ds = ds / ds.norm(p=2, dim=-1).unsqueeze(-1)
ds_encoded = [ds]
for l_dir in range(self.L_dir):
ds_encoded.append(torch.sin(2**l_dir * torch.pi * ds))
ds_encoded.append(torch.cos(2**l_dir * torch.pi * ds))
ds_encoded = torch.cat(ds_encoded, dim=-1)
outputs = self.early_mlp(xs_encoded)
sigma_is = outputs[:, 0]
c_is = self.late_mlp(torch.cat([outputs[:, 1:], ds_encoded], dim=-1))
return {"c_is": c_is, "sigma_is": sigma_is}
def main():
seed = 9458
torch.manual_seed(seed)
np.random.seed(seed)
device = "cuda:0"
F_c = VeryTinyNeRFMLP().to(device)
chunk_size = 16384
lr = 5e-3
optimizer = optim.Adam(F_c.parameters(), lr=lr)
criterion = nn.MSELoss()
data_f = "66bdbc812bd0a196e194052f3f12cb2e.npz"
data = np.load(data_f)
images = data["images"] / 255
img_size = images.shape[1]
xs = torch.arange(img_size) - (img_size / 2 - 0.5)
ys = torch.arange(img_size) - (img_size / 2 - 0.5)
(xs, ys) = torch.meshgrid(xs, -ys, indexing="xy")
focal = float(data["focal"])
pixel_coords = torch.stack([xs, ys, torch.full_like(xs, -focal)], dim=-1)
camera_coords = pixel_coords / focal
init_ds = camera_coords.to(device)
init_o = torch.Tensor(np.array([0, 0, float(data["camera_distance"])])).to(device)
test_idx = 150
plt.imshow(images[test_idx])
plt.show()
test_img = torch.Tensor(images[test_idx]).to(device)
poses = data["poses"]
test_R = torch.Tensor(poses[test_idx, :3, :3]).to(device)
test_ds = torch.einsum("ij,hwj->hwi", test_R, init_ds)
test_os = (test_R @ init_o).expand(test_ds.shape)
t_n = 1.0
t_f = 4.0
N_c = 32
t_i_c_gap = (t_f - t_n) / N_c
t_i_c_bin_edges = (t_n + torch.arange(N_c) * t_i_c_gap).to(device)
train_idxs = np.arange(len(images)) != test_idx
images = torch.Tensor(images[train_idxs])
poses = torch.Tensor(poses[train_idxs])
psnrs = []
iternums = []
num_iters = 20000
display_every = 100
F_c.train()
for i in range(num_iters):
target_img_idx = np.random.randint(images.shape[0])
target_pose = poses[target_img_idx].to(device)
R = target_pose[:3, :3]
ds = torch.einsum("ij,hwj->hwi", R, init_ds)
os = (R @ init_o).expand(ds.shape)
C_rs_c = run_one_iter_of_tiny_nerf(
ds, N_c, t_i_c_bin_edges, t_i_c_gap, os, chunk_size, F_c
)
loss = criterion(C_rs_c, images[target_img_idx].to(device))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i % display_every == 0:
F_c.eval()
with torch.no_grad():
C_rs_c = run_one_iter_of_tiny_nerf(
test_ds, N_c, t_i_c_bin_edges, t_i_c_gap, test_os, chunk_size, F_c
)
loss = criterion(C_rs_c, test_img)
print(f"Loss: {loss.item()}")
psnr = -10.0 * torch.log10(loss)
psnrs.append(psnr.item())
iternums.append(i)
plt.figure(figsize=(10, 4))
plt.subplot(121)
plt.imshow(C_rs_c.detach().cpu().numpy())
plt.title(f"Iteration {i}")
plt.subplot(122)
plt.plot(iternums, psnrs)
plt.title("PSNR")
plt.show()
F_c.train()
print("Done!")
if __name__ == "__main__":
main()