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import tqdm
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import nerfacc
import comfy.utils
from pytorch_msssim import SSIM, MS_SSIM
from kiui.op import safe_normalize
from kiui.cam import orbit_camera
from kiui.nn import MLP, trunc_exp
from shared_utils.image_utils import prepare_torch_img
class InstantNGP(nn.Module):
def __init__(self, resolution=128, device="cuda"):
super().__init__()
from kiui.gridencoder import GridEncoder
self.device = torch.device(device)
self.ref_size_H = resolution
self.ref_size_W = resolution
self.render_step_size = 5e-3
self.aabb = torch.tensor([-1.0, -1.0, -1.0, 1.0, 1.0, 1.0], device=self.device)
self.estimator = nerfacc.OccGridEstimator(roi_aabb=self.aabb, resolution=64, levels=1)
self.encoder_density = GridEncoder(num_levels=12) # VMEncoder(output_dim=16, mode='sum')
self.encoder = GridEncoder(num_levels=12)
self.mlp_density = MLP(self.encoder_density.output_dim, 1, 32, 2, bias=False)
self.mlp = MLP(self.encoder.output_dim, 3, 32, 2, bias=False)
def get_rays(self, pose, h, w, fovy, opengl=True):
x, y = torch.meshgrid(
torch.arange(w, device=pose.device),
torch.arange(h, device=pose.device),
indexing="xy",
)
x = x.flatten()
y = y.flatten()
cx = w * 0.5
cy = h * 0.5
focal = h * 0.5 / np.tan(0.5 * np.deg2rad(fovy))
camera_dirs = F.pad(
torch.stack(
[
(x - cx + 0.5) / focal,
(y - cy + 0.5) / focal * (-1.0 if opengl else 1.0),
],
dim=-1,
),
(0, 1),
value=(-1.0 if opengl else 1.0),
) # [hw, 3]
rays_d = camera_dirs @ pose[:3, :3].transpose(0, 1) # [hw, 3]
rays_o = pose[:3, 3].unsqueeze(0).expand_as(rays_d) # [hw, 3]
rays_o = rays_o.view(h, w, 3)
rays_d = safe_normalize(rays_d).view(h, w, 3)
return rays_o, rays_d
def get_color(self, xs):
return torch.sigmoid(self.mlp(self.encoder(xs.to(self.device))))
def get_density(self, xs):
# xs: [..., 3]
xs = xs.to(self.device)
prefix = xs.shape[:-1]
xs = xs.view(-1, 3)
feats = self.encoder_density(xs)
density = trunc_exp(self.mlp_density(feats))
density = density.view(*prefix, 1)
return density
def prepare_training(self, reference_images, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy):
self.ref_imgs_num = len(reference_images)
self.all_ref_cam_poses = reference_orbit_camera_poses
self.ref_cam_fovy = reference_orbit_camera_fovy
# prepare reference images and masks
ref_imgs_torch_list = []
ref_masks_torch_list = []
for i in range(self.ref_imgs_num):
ref_imgs_torch_list.append(prepare_torch_img(reference_images[i].unsqueeze(0), self.ref_size_H, self.ref_size_W, self.device))
ref_masks_torch_list.append(prepare_torch_img(reference_masks[i].unsqueeze(2).unsqueeze(0), self.ref_size_H, self.ref_size_W, self.device))
self.ref_imgs_torch = torch.cat(ref_imgs_torch_list, dim=0) # [N, 3, H, W]
self.ref_masks_torch = torch.cat(ref_masks_torch_list, dim=0).squeeze(1) # [N, H, W]
def render_nerf(self, pose, bg_color=1):
pose = torch.from_numpy(pose.astype(np.float32)).to(self.device)
# get rays
rays_o, rays_d = self.get_rays(pose, self.ref_size_H, self.ref_size_W, self.ref_cam_fovy)
hw = rays_o.shape[0] * rays_o.shape[1]
rays_o = rays_o.view(hw, 3)
rays_d = rays_d.view(hw, 3)
# update occ grid
if self.training:
def occ_eval_fn(xs):
sigmas = self.get_density(xs)
return self.render_step_size * sigmas
self.estimator.update_every_n_steps(self.render_step, occ_eval_fn=occ_eval_fn, occ_thre=0.01, n=8)
self.render_step += 1
# render
def sigma_fn(t_starts, t_ends, ray_indices):
t_origins = rays_o[ray_indices]
t_dirs = rays_d[ray_indices]
xs = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0
sigmas = self.get_density(xs)
return sigmas.squeeze(-1)
with torch.no_grad():
ray_indices, t_starts, t_ends = self.estimator.sampling(
rays_o,
rays_d,
sigma_fn=sigma_fn,
near_plane=0.01,
far_plane=100,
render_step_size=self.render_step_size,
stratified=self.training,
cone_angle=0,
)
t_origins = rays_o[ray_indices]
t_dirs = rays_d[ray_indices]
xs = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0
sigmas = self.get_density(xs).squeeze(-1)
rgbs = torch.sigmoid(self.mlp(self.encoder(xs)))
n_rays=rays_o.shape[0]
weights, t, alphas = nerfacc.render_weight_from_density(t_starts, t_ends, sigmas, ray_indices=ray_indices, n_rays=n_rays)
color = nerfacc.accumulate_along_rays(weights, values=rgbs, ray_indices=ray_indices, n_rays=n_rays)
alpha = nerfacc.accumulate_along_rays(weights, values=None, ray_indices=ray_indices, n_rays=n_rays)
color = color + (1.0 - alpha) * bg_color
color = color.view(self.ref_size_H, self.ref_size_W, 3).clamp(0, 1).permute(2, 0, 1).contiguous()
alpha = alpha.view(self.ref_size_H, self.ref_size_W).clamp(0, 1).contiguous()
return color, alpha
def fit_nerf(self, iters=512, bg_color=1):
optimizer = torch.optim.Adam([
{'params': self.encoder_density.parameters(), 'lr': 1e-2},
{'params': self.encoder.parameters(), 'lr': 1e-2},
{'params': self.mlp_density.parameters(), 'lr': 1e-3},
{'params': self.mlp.parameters(), 'lr': 1e-3},
])
print(f"[INFO] fitting nerf...")
self.render_step = 0
ref_imgs_num_minus_1 = self.ref_imgs_num-1
comfy_pbar = comfy.utils.ProgressBar(iters)
pbar = tqdm.trange(iters)
for step in pbar:
i = random.randint(0, ref_imgs_num_minus_1)
radius, elevation, azimuth, center_X, center_Y, center_Z = self.all_ref_cam_poses[i]
orbit_target = np.array([center_X, center_Y, center_Z], dtype=np.float32)
pose = orbit_camera(elevation, azimuth, radius, target=orbit_target)
image_gt = self.ref_imgs_torch[i] # [3, H, W]
alpha_gt = self.ref_masks_torch[i] # [H, W]
image_pred, alpha_pred = self.render_nerf(pose, bg_color)
# if i % 200 == 0:
# kiui.vis.plot_image(image_gt, alpha_gt, image_pred, alpha_pred)
loss_mse = F.mse_loss(image_pred, image_gt) + 0.1 * F.mse_loss(alpha_pred, alpha_gt)
loss = loss_mse #+ 0.1 * self.encoder_density.tv_loss() #+ 0.0001 * self.encoder_density.density_loss()
#loss += self.lambda_ssim * (1 - self.ms_ssim_loss(image_gt, image_pred))
loss.backward()
self.encoder_density.grad_total_variation(1e-8)
optimizer.step()
optimizer.zero_grad()
pbar.set_description(f"NeRF Fitting Loss = {loss_mse.item():.6f}")
comfy_pbar.update_absolute(step + 1)
torch.cuda.synchronize()
print(f"[INFO] finished fitting nerf!") |