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main_4d.py
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import os
import cv2
import time
import tqdm
import numpy as np
import torch
import torch.nn.functional as F
import rembg
from cam_utils import orbit_camera, OrbitCamera
from gs_renderer_4d import Renderer, MiniCam
from grid_put import mipmap_linear_grid_put_2d
from mesh import Mesh, safe_normalize
import imageio
import copy
class GUI:
def __init__(self, opt):
self.opt = opt # shared with the trainer's opt to support in-place modification of rendering parameters.
self.gui = opt.gui # enable gui
self.W = opt.W
self.H = opt.H
self.cam = OrbitCamera(opt.W, opt.H, r=opt.radius, fovy=opt.fovy)
self.mode = "image"
# self.seed = "random"
self.seed = 888
self.buffer_image = np.ones((self.W, self.H, 3), dtype=np.float32)
self.need_update = True # update buffer_image
# models
self.device = torch.device("cuda")
self.bg_remover = None
self.guidance_sd = None
self.guidance_zero123 = None
self.guidance_svd = None
self.enable_sd = False
self.enable_zero123 = False
self.enable_svd = False
# renderer
self.renderer = Renderer(self.opt, sh_degree=self.opt.sh_degree)
self.gaussain_scale_factor = 1
# input image
self.input_img = None
self.input_mask = None
self.input_img_torch = None
self.input_mask_torch = None
self.overlay_input_img = False
self.overlay_input_img_ratio = 0.5
self.input_img_list = None
self.input_mask_list = None
self.input_img_torch_list = None
self.input_mask_torch_list = None
# input text
self.prompt = ""
self.negative_prompt = ""
# training stuff
self.training = False
self.optimizer = None
self.step = 0
self.train_steps = 1 # steps per rendering loop
# load input data from cmdline
if self.opt.input is not None: # True
self.load_input(self.opt.input) # load imgs, if has bg, then rm bg; or just load imgs
# override prompt from cmdline
if self.opt.prompt is not None: # None
self.prompt = self.opt.prompt
# override if provide a checkpoint
if self.opt.load is not None: # not None
self.renderer.initialize(self.opt.load)
# self.renderer.gaussians.load_model(opt.outdir, opt.save_path)
else:
# initialize gaussians to a blob
self.renderer.initialize(num_pts=self.opt.num_pts)
self.seed_everything()
def seed_everything(self):
try:
seed = int(self.seed)
except:
seed = np.random.randint(0, 1000000)
print(f'Seed: {seed:d}')
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
self.last_seed = seed
def prepare_train(self):
self.step = 0
# setup training
self.renderer.gaussians.training_setup(self.opt)
# # do not do progressive sh-level
self.renderer.gaussians.active_sh_degree = self.renderer.gaussians.max_sh_degree
self.optimizer = self.renderer.gaussians.optimizer
# default camera
if self.opt.mvdream or self.opt.imagedream:
# the second view is the front view for mvdream/imagedream.
pose = orbit_camera(self.opt.elevation, 90, self.opt.radius)
else:
pose = orbit_camera(self.opt.elevation, 0, self.opt.radius)
self.fixed_cam = MiniCam(
pose,
self.opt.ref_size,
self.opt.ref_size,
self.cam.fovy,
self.cam.fovx,
self.cam.near,
self.cam.far,
)
self.enable_sd = self.opt.lambda_sd > 0
self.enable_zero123 = self.opt.lambda_zero123 > 0
self.enable_svd = self.opt.lambda_svd > 0 and self.input_img is not None
# lazy load guidance model
if self.guidance_sd is None and self.enable_sd:
if self.opt.mvdream:
print(f"[INFO] loading MVDream...")
from guidance.mvdream_utils import MVDream
self.guidance_sd = MVDream(self.device)
print(f"[INFO] loaded MVDream!")
elif self.opt.imagedream:
print(f"[INFO] loading ImageDream...")
from guidance.imagedream_utils import ImageDream
self.guidance_sd = ImageDream(self.device)
print(f"[INFO] loaded ImageDream!")
else:
print(f"[INFO] loading SD...")
from guidance.sd_utils import StableDiffusion
self.guidance_sd = StableDiffusion(self.device)
print(f"[INFO] loaded SD!")
if self.guidance_zero123 is None and self.enable_zero123:
print(f"[INFO] loading zero123...")
from guidance.zero123_utils import Zero123
if self.opt.stable_zero123:
self.guidance_zero123 = Zero123(self.device, model_key='ashawkey/stable-zero123-diffusers')
else:
self.guidance_zero123 = Zero123(self.device, model_key='ashawkey/zero123-xl-diffusers')
print(f"[INFO] loaded zero123!")
if self.guidance_svd is None and self.enable_svd: # False
print(f"[INFO] loading SVD...")
from guidance.svd_utils import StableVideoDiffusion
self.guidance_svd = StableVideoDiffusion(self.device)
print(f"[INFO] loaded SVD!")
# input image
if self.input_img is not None:
self.input_img_torch = torch.from_numpy(self.input_img).permute(2, 0, 1).unsqueeze(0).to(self.device)
self.input_img_torch = F.interpolate(self.input_img_torch, (self.opt.ref_size, self.opt.ref_size), mode="bilinear", align_corners=False)
self.input_mask_torch = torch.from_numpy(self.input_mask).permute(2, 0, 1).unsqueeze(0).to(self.device)
self.input_mask_torch = F.interpolate(self.input_mask_torch, (self.opt.ref_size, self.opt.ref_size), mode="bilinear", align_corners=False)
if self.input_img_list is not None:
self.input_img_torch_list = [torch.from_numpy(input_img).permute(2, 0, 1).unsqueeze(0).to(self.device) for input_img in self.input_img_list]
self.input_img_torch_list = [F.interpolate(input_img_torch, (self.opt.ref_size, self.opt.ref_size), mode="bilinear", align_corners=False) for input_img_torch in self.input_img_torch_list]
self.input_mask_torch_list = [torch.from_numpy(input_mask).permute(2, 0, 1).unsqueeze(0).to(self.device) for input_mask in self.input_mask_list]
self.input_mask_torch_list = [F.interpolate(input_mask_torch, (self.opt.ref_size, self.opt.ref_size), mode="bilinear", align_corners=False) for input_mask_torch in self.input_mask_torch_list]
# prepare embeddings
with torch.no_grad():
if self.enable_sd:
if self.opt.imagedream:
img_pos_list, img_neg_list, ip_pos_list, ip_neg_list, emb_pos_list, emb_neg_list = [], [], [], [], [], []
for _ in range(self.opt.n_views):
for input_img_torch in self.input_img_torch_list:
img_pos, img_neg, ip_pos, ip_neg, emb_pos, emb_neg = self.guidance_sd.get_image_text_embeds(input_img_torch, [self.prompt], [self.negative_prompt])
img_pos_list.append(img_pos)
img_neg_list.append(img_neg)
ip_pos_list.append(ip_pos)
ip_neg_list.append(ip_neg)
emb_pos_list.append(emb_pos)
emb_neg_list.append(emb_neg)
self.guidance_sd.image_embeddings['pos'] = torch.cat(img_pos_list, 0)
self.guidance_sd.image_embeddings['neg'] = torch.cat(img_pos_list, 0)
self.guidance_sd.image_embeddings['ip_img'] = torch.cat(ip_pos_list, 0)
self.guidance_sd.image_embeddings['neg_ip_img'] = torch.cat(ip_neg_list, 0)
self.guidance_sd.embeddings['pos'] = torch.cat(emb_pos_list, 0)
self.guidance_sd.embeddings['neg'] = torch.cat(emb_neg_list, 0)
else:
self.guidance_sd.get_text_embeds([self.prompt], [self.negative_prompt])
if self.enable_zero123:
c_list, v_list = [], []
for _ in range(self.opt.n_views):
for input_img_torch in self.input_img_torch_list:
c, v = self.guidance_zero123.get_img_embeds(input_img_torch)
c_list.append(c)
v_list.append(v)
self.guidance_zero123.embeddings = [torch.cat(c_list, 0), torch.cat(v_list, 0)]
if self.enable_svd:
self.guidance_svd.get_img_embeds(self.input_img)
def train_step(self):
starter = torch.cuda.Event(enable_timing=True)
ender = torch.cuda.Event(enable_timing=True)
starter.record()
for _ in range(self.train_steps): # 1
self.step += 1 # self.step starts from 0
step_ratio = min(1, self.step / self.opt.iters) # 1, step / 500
# update lr
self.renderer.gaussians.update_learning_rate(self.step)
loss = 0
self.renderer.prepare_render()
### known view
if not self.opt.imagedream:
for b_idx in range(self.opt.batch_size):
cur_cam = copy.deepcopy(self.fixed_cam)
cur_cam.time = b_idx
out = self.renderer.render(cur_cam)
# rgb loss
image = out["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1]
loss = loss + 10000 * step_ratio * F.mse_loss(image, self.input_img_torch_list[b_idx]) / self.opt.batch_size
# mask loss
mask = out["alpha"].unsqueeze(0) # [1, 1, H, W] in [0, 1]
loss = loss + 1000 * step_ratio * F.mse_loss(mask, self.input_mask_torch_list[b_idx]) / self.opt.batch_size
### novel view (manual batch)
render_resolution = 128 if step_ratio < 0.3 else (256 if step_ratio < 0.6 else 512)
# render_resolution = 512
images = []
poses = []
vers, hors, radii = [], [], []
# avoid too large elevation (> 80 or < -80), and make sure it always cover [-30, 30]
min_ver = max(min(self.opt.min_ver, self.opt.min_ver - self.opt.elevation), -80 - self.opt.elevation)
max_ver = min(max(self.opt.max_ver, self.opt.max_ver - self.opt.elevation), 80 - self.opt.elevation)
for _ in range(self.opt.n_views):
for b_idx in range(self.opt.batch_size):
# render random view
ver = np.random.randint(min_ver, max_ver)
hor = np.random.randint(-180, 180)
radius = 0
vers.append(ver)
hors.append(hor)
radii.append(radius)
pose = orbit_camera(self.opt.elevation + ver, hor, self.opt.radius + radius)
poses.append(pose)
cur_cam = MiniCam(pose, render_resolution, render_resolution, self.cam.fovy, self.cam.fovx, self.cam.near, self.cam.far, time=b_idx)
bg_color = torch.tensor([1, 1, 1] if np.random.rand() > self.opt.invert_bg_prob else [0, 0, 0], dtype=torch.float32, device="cuda")
out = self.renderer.render(cur_cam, bg_color=bg_color)
image = out["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1]
images.append(image)
# enable mvdream training
if self.opt.mvdream or self.opt.imagedream: # False
for view_i in range(1, 4):
pose_i = orbit_camera(self.opt.elevation + ver, hor + 90 * view_i, self.opt.radius + radius)
poses.append(pose_i)
cur_cam_i = MiniCam(pose_i, render_resolution, render_resolution, self.cam.fovy, self.cam.fovx, self.cam.near, self.cam.far)
# bg_color = torch.tensor([0.5, 0.5, 0.5], dtype=torch.float32, device="cuda")
out_i = self.renderer.render(cur_cam_i, bg_color=bg_color)
image = out_i["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1]
images.append(image)
images = torch.cat(images, dim=0)
poses = torch.from_numpy(np.stack(poses, axis=0)).to(self.device)
# guidance loss
if self.enable_sd:
if self.opt.mvdream or self.opt.imagedream:
loss = loss + self.opt.lambda_sd * self.guidance_sd.train_step(images, poses, step_ratio)
else:
loss = loss + self.opt.lambda_sd * self.guidance_sd.train_step(images, step_ratio)
if self.enable_zero123:
loss = loss + self.opt.lambda_zero123 * self.guidance_zero123.train_step(images, vers, hors, radii, step_ratio) / (self.opt.batch_size * self.opt.n_views)
if self.enable_svd:
loss = loss + self.opt.lambda_svd * self.guidance_svd.train_step(images, step_ratio)
# optimize step
loss.backward()
self.optimizer.step()
self.optimizer.zero_grad()
# densify and prune
if self.step >= self.opt.density_start_iter and self.step <= self.opt.density_end_iter:
viewspace_point_tensor, visibility_filter, radii = out["viewspace_points"], out["visibility_filter"], out["radii"]
self.renderer.gaussians.max_radii2D[visibility_filter] = torch.max(self.renderer.gaussians.max_radii2D[visibility_filter], radii[visibility_filter])
self.renderer.gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter)
if self.step % self.opt.densification_interval == 0:
# size_threshold = 20 if self.step > self.opt.opacity_reset_interval else None
self.renderer.gaussians.densify_and_prune(self.opt.densify_grad_threshold, min_opacity=0.01, extent=0.5, max_screen_size=1)
if self.step % self.opt.opacity_reset_interval == 0:
self.renderer.gaussians.reset_opacity()
ender.record()
torch.cuda.synchronize()
t = starter.elapsed_time(ender)
self.need_update = True
def load_input(self, file):
if self.opt.data_mode == 'c4d':
file_list = [os.path.join(file, f'{x * self.opt.downsample_rate}.png') for x in range(self.opt.batch_size)]
elif self.opt.data_mode == 'svd':
# file_list = [file.replace('.png', f'_frames/{x* self.opt.downsample_rate:03d}_rgba.png') for x in range(self.opt.batch_size)]
# file_list = [x if os.path.exists(x) else (x.replace('_rgba.png', '.png')) for x in file_list]
file_list = [file.replace('.png', f'_frames/{x* self.opt.downsample_rate:03d}.png') for x in range(self.opt.batch_size)]
else:
raise NotImplementedError
self.input_img_list, self.input_mask_list = [], []
for file in file_list:
# load image
print(f'[INFO] load image from {file}...')
img = cv2.imread(file, cv2.IMREAD_UNCHANGED)
if img.shape[-1] == 3:
if self.bg_remover is None:
self.bg_remover = rembg.new_session()
img = rembg.remove(img, session=self.bg_remover)
# cv2.imwrite(file.replace('.png', '_rgba.png'), img)
img = cv2.resize(img, (self.W, self.H), interpolation=cv2.INTER_AREA)
img = img.astype(np.float32) / 255.0
input_mask = img[..., 3:]
# white bg
input_img = img[..., :3] * input_mask + (1 - input_mask)
# bgr to rgb
input_img = input_img[..., ::-1].copy()
self.input_img_list.append(input_img)
self.input_mask_list.append(input_mask)
@torch.no_grad()
def save_model(self, mode='geo', texture_size=1024, interp=1):
os.makedirs(self.opt.outdir, exist_ok=True)
if mode == 'geo':
path = f'logs/{opt.save_path}_mesh_{t:03d}.ply'
mesh = self.renderer.gaussians.extract_mesh_t(path, self.opt.density_thresh, t=t)
mesh.write_ply(path)
elif mode == 'geo+tex':
os.makedirs(os.path.join(self.opt.outdir, self.opt.save_path+'_meshes'), exist_ok=True)
for t in range(self.opt.batch_size):
path = os.path.join(self.opt.outdir, self.opt.save_path+'_meshes', f'{t:03d}.obj')
mesh = self.renderer.gaussians.extract_mesh_t(path, self.opt.density_thresh, t=t)
# perform texture extraction
print(f"[INFO] unwrap uv...")
h = w = texture_size
mesh.auto_uv()
mesh.auto_normal()
albedo = torch.zeros((h, w, 3), device=self.device, dtype=torch.float32)
cnt = torch.zeros((h, w, 1), device=self.device, dtype=torch.float32)
vers = [0] * 8 + [-45] * 8 + [45] * 8 + [-89.9, 89.9]
hors = [0, 45, -45, 90, -90, 135, -135, 180] * 3 + [0, 0]
render_resolution = 512
import nvdiffrast.torch as dr
if not self.opt.force_cuda_rast and (not self.opt.gui or os.name == 'nt'):
glctx = dr.RasterizeGLContext()
else:
glctx = dr.RasterizeCudaContext()
for ver, hor in zip(vers, hors):
# render image
pose = orbit_camera(ver, hor, self.cam.radius)
cur_cam = MiniCam(
pose,
render_resolution,
render_resolution,
self.cam.fovy,
self.cam.fovx,
self.cam.near,
self.cam.far,
time=t
)
cur_out = self.renderer.render(cur_cam)
rgbs = cur_out["image"].unsqueeze(0) # [1, 3, H, W] in [0, 1]
# get coordinate in texture image
pose = torch.from_numpy(pose.astype(np.float32)).to(self.device)
proj = torch.from_numpy(self.cam.perspective.astype(np.float32)).to(self.device)
v_cam = torch.matmul(F.pad(mesh.v, pad=(0, 1), mode='constant', value=1.0), torch.inverse(pose).T).float().unsqueeze(0)
v_clip = v_cam @ proj.T
rast, rast_db = dr.rasterize(glctx, v_clip, mesh.f, (render_resolution, render_resolution))
depth, _ = dr.interpolate(-v_cam[..., [2]], rast, mesh.f) # [1, H, W, 1]
depth = depth.squeeze(0) # [H, W, 1]
alpha = (rast[0, ..., 3:] > 0).float()
uvs, _ = dr.interpolate(mesh.vt.unsqueeze(0), rast, mesh.ft) # [1, 512, 512, 2] in [0, 1]
# use normal to produce a back-project mask
normal, _ = dr.interpolate(mesh.vn.unsqueeze(0).contiguous(), rast, mesh.fn)
normal = safe_normalize(normal[0])
# rotated normal (where [0, 0, 1] always faces camera)
rot_normal = normal @ pose[:3, :3]
viewcos = rot_normal[..., [2]]
mask = (alpha > 0) & (viewcos > 0.5) # [H, W, 1]
mask = mask.view(-1)
uvs = uvs.view(-1, 2).clamp(0, 1)[mask]
rgbs = rgbs.view(3, -1).permute(1, 0)[mask].contiguous()
# update texture image
cur_albedo, cur_cnt = mipmap_linear_grid_put_2d(
h, w,
uvs[..., [1, 0]] * 2 - 1,
rgbs,
min_resolution=256,
return_count=True,
)
mask = cnt.squeeze(-1) < 0.1
albedo[mask] += cur_albedo[mask]
cnt[mask] += cur_cnt[mask]
mask = cnt.squeeze(-1) > 0
albedo[mask] = albedo[mask] / cnt[mask].repeat(1, 3)
mask = mask.view(h, w)
albedo = albedo.detach().cpu().numpy()
mask = mask.detach().cpu().numpy()
# dilate texture
from sklearn.neighbors import NearestNeighbors
from scipy.ndimage import binary_dilation, binary_erosion
inpaint_region = binary_dilation(mask, iterations=32)
inpaint_region[mask] = 0
search_region = mask.copy()
not_search_region = binary_erosion(search_region, iterations=3)
search_region[not_search_region] = 0
search_coords = np.stack(np.nonzero(search_region), axis=-1)
inpaint_coords = np.stack(np.nonzero(inpaint_region), axis=-1)
knn = NearestNeighbors(n_neighbors=1, algorithm="kd_tree").fit(
search_coords
)
_, indices = knn.kneighbors(inpaint_coords)
albedo[tuple(inpaint_coords.T)] = albedo[tuple(search_coords[indices[:, 0]].T)]
mesh.albedo = torch.from_numpy(albedo).to(self.device)
mesh.write(path)
elif mode == 'frames':
os.makedirs(os.path.join(self.opt.outdir, self.opt.save_path+'_frames'), exist_ok=True)
for t in range(self.opt.batch_size * interp):
tt = t / interp
path = os.path.join(self.opt.outdir, self.opt.save_path+'_frames', f'{t:03d}.ply')
self.renderer.gaussians.save_frame_ply(path, tt)
else:
path = os.path.join(self.opt.outdir, self.opt.save_path + '_4d_model.ply')
self.renderer.gaussians.save_ply(path)
self.renderer.gaussians.save_deformation(self.opt.outdir, self.opt.save_path)
print(f"[INFO] save model to {path}.")
# no gui mode
def train(self, iters=500, ui=False):
if self.gui:
from visualizer.visergui import ViserViewer
self.viser_gui = ViserViewer(device="cuda", viewer_port=8080)
if iters > 0:
self.prepare_train()
if self.gui:
self.viser_gui.set_renderer(self.renderer, self.fixed_cam)
for i in tqdm.trange(iters):
self.train_step()
if self.gui:
self.viser_gui.update()
if self.opt.mesh_format == 'frames':
self.save_model(mode='frames', interp=4)
elif self.opt.mesh_format == 'obj':
self.save_model(mode='geo+tex')
if self.opt.save_model:
self.save_model(mode='model')
# render eval
image_list =[]
nframes = self.opt.batch_size * 7 + 15 * 7
hor = 180
delta_hor = 45 / 15
delta_time = 1
for i in range(8):
time = 0
for j in range(self.opt.batch_size + 15):
pose = orbit_camera(self.opt.elevation, hor-180, self.opt.radius)
cur_cam = MiniCam(
pose,
512,
512,
self.cam.fovy,
self.cam.fovx,
self.cam.near,
self.cam.far,
time=time
)
with torch.no_grad():
outputs = self.renderer.render(cur_cam)
out = outputs["image"].cpu().detach().numpy().astype(np.float32)
out = np.transpose(out, (1, 2, 0))
out = np.uint8(out*255)
image_list.append(out)
time = (time + delta_time) % self.opt.batch_size
if j >= self.opt.batch_size:
hor = (hor+delta_hor) % 360
imageio.mimwrite(f'vis_data/{opt.save_path}.mp4', image_list, fps=7)
if self.gui:
while True:
self.viser_gui.update()
if __name__ == "__main__":
import argparse
from omegaconf import OmegaConf
parser = argparse.ArgumentParser()
parser.add_argument("--config", required=True, help="path to the yaml config file")
args, extras = parser.parse_known_args()
# override default config from cli
opt = OmegaConf.merge(OmegaConf.load(args.config), OmegaConf.from_cli(extras))
opt.save_path = os.path.splitext(os.path.basename(opt.input))[0] if opt.save_path == '' else opt.save_path
# auto find mesh from stage 1
opt.load = os.path.join(opt.outdir, opt.save_path + '_model.ply')
gui = GUI(opt)
gui.train(opt.iters)
# python main_4d.py --config configs/4d_low.yaml input=data/CONSISTENT4D_DATA/in-the-wild/blooming_rose